diff --git a/.devops/cloud-v-pipeline b/.devops/cloud-v-pipeline index f3a4944f8a419..af8c0cea6155c 100644 --- a/.devops/cloud-v-pipeline +++ b/.devops/cloud-v-pipeline @@ -15,7 +15,7 @@ node('x86_runner1'){ // Running on x86 runner containing latest vecto stage('Running llama.cpp'){ sh'''#!/bin/bash module load gnu-bin2/0.1 # loading latest versions of vector qemu and vector gcc - qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 + qemu-riscv64 -L /softwares/gnu-bin2/sysroot -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt # Running llama.cpp on vector qemu-riscv64 cat llama_log.txt # Printing results ''' } diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 059fd26950607..f6073f6622b5a 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -12,7 +12,7 @@ FROM ${BASE_CUDA_DEV_CONTAINER} as build ARG CUDA_DOCKER_ARCH=all RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev + apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 COPY requirements.txt requirements.txt COPY requirements requirements @@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6ecf3bcc7cb83..0314d469bc353 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 432fb5dadcbca..6f19afa9c37f1 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=22.04 FROM ubuntu:$UBUNTU_VERSION as build RUN apt-get update && \ - apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev + apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1 COPY requirements.txt requirements.txt COPY requirements requirements @@ -18,7 +18,7 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENV LC_ALL=C.utf8 diff --git a/.devops/main-cuda.Dockerfile b/.devops/llama-cli-cuda.Dockerfile similarity index 81% rename from .devops/main-cuda.Dockerfile rename to .devops/llama-cli-cuda.Dockerfile index b937a482988b6..d5ce538f61554 100644 --- a/.devops/main-cuda.Dockerfile +++ b/.devops/llama-cli-cuda.Dockerfile @@ -23,10 +23,13 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} # Enable CUDA ENV LLAMA_CUDA=1 -RUN make +RUN make -j$(nproc) llama-cli FROM ${BASE_CUDA_RUN_CONTAINER} as runtime -COPY --from=build /app/main /main +RUN apt-get update && \ + apt-get install -y libgomp1 + +COPY --from=build /app/llama-cli /llama-cli -ENTRYPOINT [ "/main" ] +ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/main-intel.Dockerfile b/.devops/llama-cli-intel.Dockerfile similarity index 72% rename from .devops/main-intel.Dockerfile rename to .devops/llama-cli-intel.Dockerfile index 274b91b71bfba..6789e17afcc6e 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/llama-cli-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build @@ -15,12 +15,12 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ fi && \ cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ - cmake --build build --config Release --target main + cmake --build build --config Release --target llama-cli FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime -COPY --from=build /app/build/bin/main /main +COPY --from=build /app/build/bin/llama-cli /llama-cli ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/main" ] +ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/main-rocm.Dockerfile b/.devops/llama-cli-rocm.Dockerfile similarity index 94% rename from .devops/main-rocm.Dockerfile rename to .devops/llama-cli-rocm.Dockerfile index 0a706dc73227d..7e8a6f0fa208c 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/llama-cli-rocm.Dockerfile @@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1 ENV CC=/opt/rocm/llvm/bin/clang ENV CXX=/opt/rocm/llvm/bin/clang++ -RUN make +RUN make -j$(nproc) llama-cli -ENTRYPOINT [ "/app/main" ] +ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/main-vulkan.Dockerfile b/.devops/llama-cli-vulkan.Dockerfile similarity index 71% rename from .devops/main-vulkan.Dockerfile rename to .devops/llama-cli-vulkan.Dockerfile index 6c2b2ed5b05d8..7a0abe71f1587 100644 --- a/.devops/main-vulkan.Dockerfile +++ b/.devops/llama-cli-vulkan.Dockerfile @@ -3,7 +3,7 @@ ARG UBUNTU_VERSION=jammy FROM ubuntu:$UBUNTU_VERSION as build # Install build tools -RUN apt update && apt install -y git build-essential cmake wget +RUN apt update && apt install -y git build-essential cmake wget libgomp1 # Install Vulkan SDK RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ @@ -15,13 +15,13 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key WORKDIR /app COPY . . RUN cmake -B build -DLLAMA_VULKAN=1 && \ - cmake --build build --config Release --target main + cmake --build build --config Release --target llama-cli # Clean up WORKDIR / -RUN cp /app/build/bin/main /main && \ +RUN cp /app/build/bin/llama-cli /llama-cli && \ rm -rf /app ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/main" ] +ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/main.Dockerfile b/.devops/llama-cli.Dockerfile similarity index 57% rename from .devops/main.Dockerfile rename to .devops/llama-cli.Dockerfile index 3ab1decd6c2b5..38382bfc9add2 100644 --- a/.devops/main.Dockerfile +++ b/.devops/llama-cli.Dockerfile @@ -9,12 +9,15 @@ WORKDIR /app COPY . . -RUN make +RUN make -j$(nproc) llama-cli FROM ubuntu:$UBUNTU_VERSION as runtime -COPY --from=build /app/main /main +RUN apt-get update && \ + apt-get install -y libgomp1 + +COPY --from=build /app/llama-cli /llama-cli ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/main" ] +ENTRYPOINT [ "/llama-cli" ] diff --git a/.devops/llama-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec index 774f63ddd5c4e..013952191127f 100644 --- a/.devops/llama-cpp-clblast.srpm.spec +++ b/.devops/llama-cpp-clblast.srpm.spec @@ -36,9 +36,9 @@ make -j LLAMA_CLBLAST=1 %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamaclblast -cp -p server %{buildroot}%{_bindir}/llamaclblastserver -cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple +cp -p llama-cli %{buildroot}%{_bindir}/llama-clblast-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-clblast-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-clblast-simple mkdir -p %{buildroot}/usr/lib/systemd/system %{__cat} < %{buildroot}/usr/lib/systemd/system/llamaclblast.service @@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS +ExecStart=/usr/bin/llama-clblast-server $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -67,9 +67,9 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamaclblast -%{_bindir}/llamaclblastserver -%{_bindir}/llamaclblastsimple +%{_bindir}/llama-clblast-cli +%{_bindir}/llama-clblast-server +%{_bindir}/llama-clblast-simple /usr/lib/systemd/system/llamaclblast.service %config /etc/sysconfig/llama diff --git a/.devops/llama-cpp-cuda.srpm.spec b/.devops/llama-cpp-cuda.srpm.spec index ba9cb7cbb824f..cbdf4362629d3 100644 --- a/.devops/llama-cpp-cuda.srpm.spec +++ b/.devops/llama-cpp-cuda.srpm.spec @@ -36,9 +36,9 @@ make -j LLAMA_CUDA=1 %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamacppcuda -cp -p server %{buildroot}%{_bindir}/llamacppcudaserver -cp -p simple %{buildroot}%{_bindir}/llamacppcudasimple +cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple mkdir -p %{buildroot}/usr/lib/systemd/system %{__cat} < %{buildroot}/usr/lib/systemd/system/llamacuda.service @@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamacppcudaserver $LLAMA_ARGS +ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -67,9 +67,9 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamacppcuda -%{_bindir}/llamacppcudaserver -%{_bindir}/llamacppcudasimple +%{_bindir}/llama-cuda-cli +%{_bindir}/llama-cuda-server +%{_bindir}/llama-cuda-simple /usr/lib/systemd/system/llamacuda.service %config /etc/sysconfig/llama diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec index 1d9e4f425b43a..4d5560089816c 100644 --- a/.devops/llama-cpp.srpm.spec +++ b/.devops/llama-cpp.srpm.spec @@ -38,9 +38,9 @@ make -j %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llama -cp -p server %{buildroot}%{_bindir}/llamaserver -cp -p simple %{buildroot}%{_bindir}/llamasimple +cp -p llama-cli %{buildroot}%{_bindir}/llama-cli +cp -p llama-server %{buildroot}%{_bindir}/llama-server +cp -p llama-simple %{buildroot}%{_bindir}/llama-simple mkdir -p %{buildroot}/usr/lib/systemd/system %{__cat} < %{buildroot}/usr/lib/systemd/system/llama.service @@ -51,7 +51,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t [Service] Type=simple EnvironmentFile=/etc/sysconfig/llama -ExecStart=/usr/bin/llamaserver $LLAMA_ARGS +ExecStart=/usr/bin/llama-server $LLAMA_ARGS ExecReload=/bin/kill -s HUP $MAINPID Restart=never @@ -69,9 +69,9 @@ rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llama -%{_bindir}/llamaserver -%{_bindir}/llamasimple +%{_bindir}/llama-cli +%{_bindir}/llama-server +%{_bindir}/llama-simple /usr/lib/systemd/system/llama.service %config /etc/sysconfig/llama diff --git a/.devops/server-cuda.Dockerfile b/.devops/llama-server-cuda.Dockerfile similarity index 82% rename from .devops/server-cuda.Dockerfile rename to .devops/llama-server-cuda.Dockerfile index 59a52ba21a3f1..0010ffd4c5465 100644 --- a/.devops/server-cuda.Dockerfile +++ b/.devops/llama-server-cuda.Dockerfile @@ -25,13 +25,13 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) llama-server FROM ${BASE_CUDA_RUN_CONTAINER} as runtime RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev + apt-get install -y libcurl4-openssl-dev libgomp1 -COPY --from=build /app/server /server +COPY --from=build /app/llama-server /llama-server -ENTRYPOINT [ "/server" ] +ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/llama-server-intel.Dockerfile similarity index 74% rename from .devops/server-intel.Dockerfile rename to .devops/llama-server-intel.Dockerfile index a8e451fa917ca..cec43645233d1 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/llama-server-intel.Dockerfile @@ -1,4 +1,4 @@ -ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 +ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build @@ -15,15 +15,15 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ fi && \ cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ - cmake --build build --config Release --target server + cmake --build build --config Release --target llama-server FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -COPY --from=build /app/build/bin/server /server +COPY --from=build /app/build/bin/llama-server /llama-server ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/server" ] +ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/server-rocm.Dockerfile b/.devops/llama-server-rocm.Dockerfile similarity index 94% rename from .devops/server-rocm.Dockerfile rename to .devops/llama-server-rocm.Dockerfile index c02a31dd8c756..f88cf20e5b981 100644 --- a/.devops/server-rocm.Dockerfile +++ b/.devops/llama-server-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) llama-server -ENTRYPOINT [ "/app/server" ] +ENTRYPOINT [ "/app/llama-server" ] diff --git a/.devops/server-vulkan.Dockerfile b/.devops/llama-server-vulkan.Dockerfile similarity index 82% rename from .devops/server-vulkan.Dockerfile rename to .devops/llama-server-vulkan.Dockerfile index 6e757e171efee..b0fa0b8e656b5 100644 --- a/.devops/server-vulkan.Dockerfile +++ b/.devops/llama-server-vulkan.Dockerfile @@ -19,13 +19,13 @@ RUN apt-get update && \ WORKDIR /app COPY . . RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \ - cmake --build build --config Release --target server + cmake --build build --config Release --target llama-server # Clean up WORKDIR / -RUN cp /app/build/bin/server /server && \ +RUN cp /app/build/bin/llama-server /llama-server && \ rm -rf /app ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/server" ] +ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/server.Dockerfile b/.devops/llama-server.Dockerfile similarity index 62% rename from .devops/server.Dockerfile rename to .devops/llama-server.Dockerfile index be964e0e83648..aa93369bebebe 100644 --- a/.devops/server.Dockerfile +++ b/.devops/llama-server.Dockerfile @@ -11,15 +11,15 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) llama-server FROM ubuntu:$UBUNTU_VERSION as runtime RUN apt-get update && \ - apt-get install -y libcurl4-openssl-dev + apt-get install -y libcurl4-openssl-dev libgomp1 -COPY --from=build /app/server /server +COPY --from=build /app/llama-server /llama-server ENV LC_ALL=C.utf8 -ENTRYPOINT [ "/server" ] +ENTRYPOINT [ "/llama-server" ] diff --git a/.devops/nix/apps.nix b/.devops/nix/apps.nix index b8a12cc0a0463..897fce4d324c1 100644 --- a/.devops/nix/apps.nix +++ b/.devops/nix/apps.nix @@ -6,11 +6,11 @@ let inherit (config.packages) default; binaries = [ - "llama" + "llama-cli" "llama-embedding" "llama-server" - "quantize" - "train-text-from-scratch" + "llama-quantize" + "llama-train-text-from-scratch" ]; mkApp = name: { type = "app"; diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index e8d5b0bd92d2c..87bb3a20f2a28 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -243,8 +243,6 @@ effectiveStdenv.mkDerivation ( # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, # if they haven't been added yet. postInstall = '' - mv $out/bin/main${executableSuffix} $out/bin/llama${executableSuffix} - mv $out/bin/server${executableSuffix} $out/bin/llama-server${executableSuffix} mkdir -p $out/include cp $src/llama.h $out/include/ ''; @@ -294,7 +292,7 @@ effectiveStdenv.mkDerivation ( license = lib.licenses.mit; # Accommodates `nix run` and `lib.getExe` - mainProgram = "llama"; + mainProgram = "llama-cli"; # These people might respond, on the best effort basis, if you ping them # in case of Nix-specific regressions or for reviewing Nix-specific PRs. diff --git a/.devops/tools.sh b/.devops/tools.sh index 3a7d274e46619..335382f69ee1f 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -8,13 +8,13 @@ arg1="$1" shift if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$@" + python3 ./convert-hf-to-gguf.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then - ./quantize "$@" + ./llama-quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then - ./main "$@" + ./llama-cli "$@" elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then - ./finetune "$@" + ./llama-finetune "$@" elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Converting PTH to GGML..." for i in `ls $1/$2/ggml-model-f16.bin*`; do @@ -22,11 +22,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Skip model quantization, it already exists: ${i/f16/q4_0}" else echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..." - ./quantize "$i" "${i/f16/q4_0}" q4_0 + ./llama-quantize "$i" "${i/f16/q4_0}" q4_0 fi done elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then - ./server "$@" + ./llama-server "$@" else echo "Unknown command: $arg1" echo "Available commands: " diff --git a/.dockerignore b/.dockerignore index 633bbc3a971c1..8916e2a660732 100644 --- a/.dockerignore +++ b/.dockerignore @@ -12,8 +12,8 @@ build*/ models/* -/main -/quantize +/llama-cli +/llama-quantize arm_neon.h compile_commands.json diff --git a/.editorconfig b/.editorconfig index 16d16b3b55bf5..bd525e13f3ece 100644 --- a/.editorconfig +++ b/.editorconfig @@ -26,3 +26,6 @@ indent_size = 2 [examples/llama.swiftui/llama.swiftui.xcodeproj/*] indent_style = tab + +[examples/cvector-generator/*.txt] +insert_final_newline = unset diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml new file mode 100644 index 0000000000000..54785854f776e --- /dev/null +++ b/.github/ISSUE_TEMPLATE/01-bug-low.yml @@ -0,0 +1,50 @@ +name: Low Severity Bugs +description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches) +title: "Bug: " +labels: ["bug-unconfirmed", "low severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml new file mode 100644 index 0000000000000..a6285c6f05bac --- /dev/null +++ b/.github/ISSUE_TEMPLATE/02-bug-medium.yml @@ -0,0 +1,50 @@ +name: Medium Severity Bug +description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable) +title: "Bug: " +labels: ["bug-unconfirmed", "medium severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml new file mode 100644 index 0000000000000..ff816b93769c3 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/03-bug-high.yml @@ -0,0 +1,50 @@ +name: High Severity Bug +description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow) +title: "Bug: " +labels: ["bug-unconfirmed", "high severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml new file mode 100644 index 0000000000000..7af42a80b3b93 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/04-bug-critical.yml @@ -0,0 +1,50 @@ +name: Critical Severity Bug +description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss) +title: "Bug: " +labels: ["bug-unconfirmed", "critical severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./llama-cli --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/05-enhancement.yml new file mode 100644 index 0000000000000..58fca73183d41 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/05-enhancement.yml @@ -0,0 +1,51 @@ +name: Enhancement +description: Used to request enhancements for llama.cpp +title: "Feature Request: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your enhancement request. + options: + - label: I am running the latest code. Mention the version if possible as well. + required: true + - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). + required: true + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share. + required: true + + - type: textarea + id: feature-description + attributes: + label: Feature Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + placeholder: Detailed description of the enhancement + validations: + required: true + + - type: textarea + id: motivation + attributes: + label: Motivation + description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + placeholder: Explanation of why this feature is needed and its benefits + validations: + required: true + + - type: textarea + id: possible-implementation + attributes: + label: Possible Implementation + description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. + placeholder: Detailed description of potential implementation + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/06-research.yml new file mode 100644 index 0000000000000..3ae4e9f8caaa4 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/06-research.yml @@ -0,0 +1,52 @@ +name: Research +description: Track new technical research area +title: "Research: " +labels: ["research 🔬"] +body: + - type: markdown + attributes: + value: | + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + + - type: checkboxes + id: research-stage + attributes: + label: Research Stage + description: Track general state of this research ticket + options: + - label: Background Research (Let's try to avoid reinventing the wheel) + - label: Hypothesis Formed (How do you think this will work and it's effect?) + - label: Strategy / Implementation Forming + - label: Analysis of results + - label: Debrief / Documentation (So people in the future can learn from us) + + - type: textarea + id: background + attributes: + label: Previous existing literature and research + description: Whats the current state of the art and whats the motivation for this research? + + - type: textarea + id: hypothesis + attributes: + label: Hypothesis + description: How do you think this will work and it's effect? + + - type: textarea + id: implementation + attributes: + label: Implementation + description: Got an approach? e.g. a PR ready to go? + + - type: textarea + id: analysis + attributes: + label: Analysis + description: How does the proposed implementation behave? + + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/07-refactor.yml new file mode 100644 index 0000000000000..3a68d3d5355d6 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/07-refactor.yml @@ -0,0 +1,28 @@ +name: Refactor (Maintainers) +description: Used to track refactoring opportunities +title: "Refactor: " +labels: ["refactor"] +body: + - type: markdown + attributes: + value: | + Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of the pain points you are trying to solve. + placeholder: Detailed description behind your motivation to request refactor + validations: + required: true + + - type: textarea + id: possible-approaches + attributes: + label: Possible Refactor Approaches + description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list. + placeholder: Your idea of possible refactoring opportunity/approaches + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md deleted file mode 100644 index 49812832ca542..0000000000000 --- a/.github/ISSUE_TEMPLATE/bug.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -name: Bug template -about: Used to report bugs in llama.cpp -labels: ["bug-unconfirmed"] -assignees: '' - ---- - -Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug. - -If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests). diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000..c88134dbb644a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,13 @@ +blank_issues_enabled: true +contact_links: + - name: Got an idea? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas + about: Pop it there. It may then become an enhancement ticket. + - name: Got a question? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a + about: Ask a question there! + - name: Want to contribute? + url: https://github.com/ggerganov/llama.cpp/wiki/contribute + about: Head to the contribution guide page of the wiki for areas you can help with + + diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md deleted file mode 100644 index dcffda7500f52..0000000000000 --- a/.github/ISSUE_TEMPLATE/enhancement.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -name: Enhancement template -about: Used to request enhancements for llama.cpp -labels: ["enhancement"] -assignees: '' - ---- - -# Prerequisites - -Please answer the following questions for yourself before submitting an issue. - -- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now. -- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). -- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed). -- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share. - -# Feature Description - -Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. - -# Motivation - -Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. - -# Possible Implementation - -If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. diff --git a/.github/labeler.yml b/.github/labeler.yml index a67f78044c46a..97d739b5811e8 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -1,5 +1,16 @@ # https://github.com/actions/labeler - +Kompute: + - changed-files: + - any-glob-to-any-file: + - ggml-kompute.h + - ggml-kompute.cpp + - README-kompute.md +Apple Metal: + - changed-files: + - any-glob-to-any-file: + - ggml-metal.h + - ggml-metal.cpp + - README-metal.md SYCL: - changed-files: - any-glob-to-any-file: @@ -9,6 +20,7 @@ SYCL: Nvidia GPU: - changed-files: - any-glob-to-any-file: + - ggml-cuda.h - ggml-cuda/** Vulkan: - changed-files: diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 0000000000000..997c6d9d05397 --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,7 @@ + + +- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md) +- Self-reported review complexity: + - [ ] Low + - [ ] Medium + - [ ] High diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml index de0d994c80e9d..88ab4844ef123 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml @@ -119,7 +119,7 @@ jobs: -DLLAMA_FATAL_WARNINGS=OFF \ -DLLAMA_ALL_WARNINGS=OFF \ -DCMAKE_BUILD_TYPE=Release; - cmake --build build --config Release -j $(nproc) --target server + cmake --build build --config Release -j $(nproc) --target llama-server - name: Download the dataset id: download_dataset diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 7b616281b6f6f..a8fcae0435e00 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -13,7 +13,7 @@ on: paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m'] pull_request: types: [opened, synchronize, reopened] - paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m'] + paths: ['.github/workflows/build.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m'] concurrency: group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} @@ -84,7 +84,7 @@ jobs: name: llama-bin-macos-arm64.zip macOS-latest-cmake-x64: - runs-on: macos-latest + runs-on: macos-12 steps: - name: Clone @@ -103,12 +103,10 @@ jobs: id: cmake_build run: | sysctl -a - mkdir build - cd build # Metal is disabled due to intermittent failures with Github runners not having a GPU: # https://github.com/ggerganov/llama.cpp/actions/runs/8635935781/job/23674807267#step:5:2313 - cmake -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON .. - cmake --build . --config Release -j $(sysctl -n hw.logicalcpu) + cmake -B build -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_METAL=OFF -DLLAMA_CURL=ON + cmake --build build --config Release -j $(sysctl -n hw.logicalcpu) - name: Test id: cmake_test @@ -241,8 +239,8 @@ jobs: wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/tok512.bin echo "Fetch llama2c model" wget https://huggingface.co/karpathy/tinyllamas/resolve/main/stories260K/stories260K.bin - ./bin/convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf - ./bin/main -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 + ./bin/llama-convert-llama2c-to-ggml --copy-vocab-from-model ./tok512.bin --llama2c-model stories260K.bin --llama2c-output-model stories260K.gguf + ./bin/llama-cli -m stories260K.gguf -p "One day, Lily met a Shoggoth" -n 500 -c 256 - name: Determine tag name id: tag @@ -294,12 +292,22 @@ jobs: - name: Build id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} run: | mkdir build cd build cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + mkdir build + cd build + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DLLAMA_OPENMP=OFF + cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + - name: Test id: cmake_test run: | @@ -674,12 +682,10 @@ jobs: cmake --build build --config ${{ matrix.build }} -j $(nproc) windows-latest-cmake: - runs-on: windows-latest + runs-on: windows-2019 env: OPENBLAS_VERSION: 0.3.23 - OPENCL_VERSION: 2023.04.17 - CLBLAST_VERSION: 1.6.0 SDE_VERSION: 9.33.0-2024-01-07 VULKAN_VERSION: 1.3.261.1 @@ -696,8 +702,6 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - build: 'avx512-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON' - - build: 'clblast-x64' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - build: 'openblas-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute-x64' @@ -722,27 +726,6 @@ jobs: run: | git submodule update --init kompute - - name: Download OpenCL SDK - id: get_opencl - if: ${{ matrix.build == 'clblast-x64' }} - run: | - curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip" - mkdir $env:RUNNER_TEMP/opencl - tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl - - - name: Download CLBlast - id: get_clblast - if: ${{ matrix.build == 'clblast-x64' }} - run: | - curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z" - curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE" - 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z - rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast - foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) { - $txt = Get-Content -Path $f -Raw - $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8 - } - - name: Download OpenBLAS id: get_openblas if: ${{ matrix.build == 'openblas-x64' }} @@ -776,13 +759,6 @@ jobs: cmake -S . -B build ${{ matrix.defines }} cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - - name: Add clblast.dll - id: add_clblast_dll - if: ${{ matrix.build == 'clblast-x64' }} - run: | - cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release - cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt - - name: Add libopenblas.dll id: add_libopenblas_dll if: ${{ matrix.build == 'openblas-x64' }} @@ -806,7 +782,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 @@ -851,7 +827,7 @@ jobs: name: llama-bin-win-${{ matrix.build }}.zip windows-latest-cmake-cuda: - runs-on: windows-latest + runs-on: windows-2019 strategy: matrix: @@ -865,8 +841,9 @@ jobs: with: fetch-depth: 0 - - uses: Jimver/cuda-toolkit@v0.2.11 + - name: Install CUDA toolkit id: cuda-toolkit + uses: Jimver/cuda-toolkit@v0.2.15 with: cuda: ${{ matrix.cuda }} method: 'network' @@ -1061,7 +1038,7 @@ jobs: # hypervisor: 'qemu' # run: | # sudo pkg update -# sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas +# sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas # gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu` release: diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index c2838cbd9e73e..6244b481210a0 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -30,21 +30,20 @@ jobs: strategy: matrix: config: - - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" } # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I # have disabled them for now until the reason why # is understood. - - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" } - - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - # TODO: Disabled due to build issues https://github.com/ggerganov/llama.cpp/issues/7507 - #- { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } - #- { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" } + - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" } steps: - name: Check out the repo uses: actions/checkout@v4 diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 0789efd18a1ab..1fee9ac281943 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -16,11 +16,9 @@ on: branches: - master paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] - pull_request_target: + pull_request: types: [opened, synchronize, reopened] paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*'] - schedule: - - cron: '2 4 * * *' concurrency: group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }} @@ -98,7 +96,7 @@ jobs: -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; - cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server - name: Tests id: server_integration_tests @@ -115,7 +113,7 @@ jobs: server-windows: - runs-on: windows-latest + runs-on: windows-2019 steps: - name: Clone @@ -138,7 +136,7 @@ jobs: id: cmake_build run: | cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" - cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server - name: Python setup id: setup_python diff --git a/.gitignore b/.gitignore index 50ae0973ae3b3..5296594952c4a 100644 --- a/.gitignore +++ b/.gitignore @@ -34,9 +34,11 @@ ggml-metal-embed.metal lcov-report/ gcovr-report/ +tags build* !build.zig cmake-build-* +android-ndk-* out/ tmp/ @@ -44,48 +46,9 @@ models/* models-mnt /Pipfile -/baby-llama -/beam-search -/benchmark-matmult -/convert-llama2c-to-ggml -/embd-input-test -/embedding -/eval-callback -/gguf -/gguf-llama-simple -/gguf-split -/gritlm -/imatrix -/infill /libllama.so -/llama-bench -/llava-cli -/lookahead -/lookup -/lookup-create -/lookup-merge -/lookup-stats -/main -/metal -/passkey -/perplexity -/q8dot -/quantize -/quantize-stats -/result -/save-load-state -/server -/simple -/batched -/batched-bench -/export-lora -/finetune -/retrieval -/speculative -/parallel -/train-text-from-scratch -/tokenize -/vdot +/llama-* +llama-batched-swift /common/build-info.cpp arm_neon.h compile_commands.json @@ -105,6 +68,7 @@ examples/jeopardy/results.txt examples/server/*.html.hpp examples/server/*.js.hpp examples/server/*.mjs.hpp +examples/server/*.css.hpp poetry.lock poetry.toml diff --git a/CMakeLists.txt b/CMakeLists.txt index ef02ff66967f3..c90414afa92be 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -39,8 +39,12 @@ endif() if (APPLE) set(LLAMA_METAL_DEFAULT ON) + set(LLAMA_BLAS_DEFAULT ON) + set(LLAMA_BLAS_VENDOR_DEFAULT "Apple") else() set(LLAMA_METAL_DEFAULT OFF) + set(LLAMA_BLAS_DEFAULT OFF) + set(LLAMA_BLAS_VENDOR_DEFAULT "Generic") endif() set(LLAMA_LLAMAFILE_DEFAULT ON) @@ -72,6 +76,7 @@ else() set(INS_ENB ON) endif() +option(LLAMA_SVE "llama: enable SVE" OFF) option(LLAMA_AVX "llama: enable AVX" ${INS_ENB}) option(LLAMA_AVX2 "llama: enable AVX2" ${INS_ENB}) option(LLAMA_AVX512 "llama: enable AVX512" OFF) @@ -90,9 +95,10 @@ endif() # 3rd party libs option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON) -option(LLAMA_BLAS "llama: use BLAS" OFF) +option(LLAMA_BLAS "llama: use BLAS" ${LLAMA_BLAS_DEFAULT}) +set(LLAMA_BLAS_VENDOR ${LLAMA_BLAS_VENDOR_DEFAULT} CACHE STRING + "llama: BLAS library vendor") option(LLAMA_LLAMAFILE "llama: use llamafile SGEMM" ${LLAMA_LLAMAFILE_DEFAULT}) -set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUDA "llama: use CUDA" OFF) option(LLAMA_CUBLAS "llama: use CUDA (deprecated, use LLAMA_CUDA)" OFF) option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) @@ -105,14 +111,15 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF) option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF) +option(LLAMA_CUDA_FA_ALL_QUANTS "llama: compile all quants for FlashAttention" OFF) option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) -option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_VULKAN "llama: use Vulkan" OFF) option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) +option(LLAMA_VULKAN_MEMORY_DEBUG "llama: enable Vulkan memory debug output" OFF) option(LLAMA_VULKAN_VALIDATE "llama: enable Vulkan validation" OFF) option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) @@ -124,6 +131,7 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) option(LLAMA_RPC "llama: use RPC" OFF) +option(LLAMA_OPENMP "llama: use OpenMP" ON) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device") @@ -294,13 +302,24 @@ if (LLAMA_METAL) ) endif() +if (LLAMA_OPENMP) + find_package(OpenMP) + if (OpenMP_FOUND) + message(STATUS "OpenMP found") + add_compile_definitions(GGML_USE_OPENMP) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + else() + message(WARNING "OpenMP not found") + endif() +endif() + if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) endif() - if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) - set(BLA_SIZEOF_INTEGER 8) - endif() + #if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) + # set(BLA_SIZEOF_INTEGER 8) + #endif() set(BLA_VENDOR ${LLAMA_BLAS_VENDOR}) find_package(BLAS) @@ -308,7 +327,7 @@ if (LLAMA_BLAS) if (BLAS_FOUND) message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}") - if ("${BLAS_INCLUDE_DIRS}" STREQUAL "") + if (("${BLAS_INCLUDE_DIRS}" STREQUAL "") AND NOT (${LLAMA_BLAS_VENDOR} MATCHES "Apple")) # BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake. # see https://gitlab.kitware.com/cmake/cmake/-/issues/20268 find_package(PkgConfig REQUIRED) @@ -361,12 +380,15 @@ if (LLAMA_BLAS) add_compile_options(${BLAS_LINKER_FLAGS}) - add_compile_definitions(GGML_USE_OPENBLAS) + add_compile_definitions(GGML_USE_BLAS) if (${BLAS_INCLUDE_DIRS} MATCHES "mkl" AND (${LLAMA_BLAS_VENDOR} MATCHES "Generic" OR ${LLAMA_BLAS_VENDOR} MATCHES "Intel")) add_compile_definitions(GGML_BLAS_USE_MKL) endif() + set(GGML_HEADERS_BLAS ggml-blas.h) + set(GGML_SOURCES_BLAS ggml-blas.cpp) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES}) set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS}) else() @@ -389,18 +411,36 @@ if (LLAMA_CUBLAS) endif() if (LLAMA_CUDA) - cmake_minimum_required(VERSION 3.17) + cmake_minimum_required(VERSION 3.18) # for CMAKE_CUDA_ARCHITECTURES find_package(CUDAToolkit) if (CUDAToolkit_FOUND) message(STATUS "CUDA found") + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + # 52 == lowest CUDA 12 standard + # 60 == f16 CUDA intrinsics + # 61 == integer CUDA intrinsics + # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster + if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics + else() + set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics + #set(CMAKE_CUDA_ARCHITECTURES "OFF") # use this to compile much faster, but only F16 models work + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + enable_language(CUDA) set(GGML_HEADERS_CUDA ggml-cuda.h) file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) add_compile_definitions(GGML_USE_CUDA) add_compile_definitions(GGML_CUDA_USE_GRAPHS) @@ -426,6 +466,18 @@ if (LLAMA_CUDA) if (LLAMA_CUDA_NO_PEER_COPY) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() if (LLAMA_STATIC) if (WIN32) @@ -443,21 +495,6 @@ if (LLAMA_CUDA) else() set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ... endif() - - if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) - # 52 == lowest CUDA 12 standard - # 60 == f16 CUDA intrinsics - # 61 == integer CUDA intrinsics - # 70 == compute capability at which unrolling a loop in mul_mat_q kernels is faster - if (LLAMA_CUDA_F16 OR LLAMA_CUDA_DMMV_F16) - set(CMAKE_CUDA_ARCHITECTURES "60;61;70") # needed for f16 CUDA intrinsics - else() - set(CMAKE_CUDA_ARCHITECTURES "52;61;70") # lowest CUDA 12 standard + lowest for integer intrinsics - #set(CMAKE_CUDA_ARCHITECTURES "") # use this to compile much faster, but only F16 models work - endif() - endif() - message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") - else() message(WARNING "CUDA not found") endif() @@ -474,22 +511,6 @@ if (LLAMA_RPC) set(GGML_SOURCES_RPC ggml-rpc.cpp) endif() -if (LLAMA_CLBLAST) - find_package(CLBlast) - if (CLBlast_FOUND) - message(STATUS "CLBlast found") - - set(GGML_HEADERS_OPENCL ggml-opencl.h) - set(GGML_SOURCES_OPENCL ggml-opencl.cpp) - - add_compile_definitions(GGML_USE_CLBLAST) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast) - else() - message(WARNING "CLBlast not found") - endif() -endif() - if (LLAMA_VULKAN) find_package(Vulkan) if (Vulkan_FOUND) @@ -514,6 +535,10 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_VULKAN_DEBUG) endif() + if (LLAMA_VULKAN_MEMORY_DEBUG) + add_compile_definitions(GGML_VULKAN_MEMORY_DEBUG) + endif() + if (LLAMA_VULKAN_VALIDATE) add_compile_definitions(GGML_VULKAN_VALIDATE) endif() @@ -529,12 +554,17 @@ if (LLAMA_VULKAN) endif() if (LLAMA_HIPBLAS) - if ($ENV{ROCM_PATH}) - set(ROCM_PATH $ENV{ROCM_PATH}) + if (NOT EXISTS $ENV{ROCM_PATH}) + if (NOT EXISTS /opt/rocm) + set(ROCM_PATH /usr) + else() + set(ROCM_PATH /opt/rocm) + endif() else() - set(ROCM_PATH /opt/rocm) + set(ROCM_PATH $ENV{ROCM_PATH}) endif() list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) + list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") # CMake on Windows doesn't support the HIP language yet if(WIN32) @@ -570,6 +600,10 @@ if (LLAMA_HIPBLAS) file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/mmq*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA) @@ -589,6 +623,19 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + endif() + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) @@ -627,6 +674,10 @@ if (LLAMA_SYCL) add_compile_definitions(GGML_SYCL_F16) endif() + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_SYCL_FORCE_MMQ) + endif() + add_compile_options(-I./) #include DPCT add_compile_options(-I/${SYCL_INCLUDE_DIR}) @@ -638,7 +689,8 @@ if (LLAMA_SYCL) endif() set(GGML_HEADERS_SYCL ggml-sycl.h) - set(GGML_SOURCES_SYCL ggml-sycl.cpp) + file(GLOB GGML_SOURCES_SYCL "ggml-sycl/*.cpp") + list(APPEND GGML_SOURCES_SYCL "ggml-sycl.cpp") if (WIN32) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) @@ -742,6 +794,7 @@ if (LLAMA_KOMPUTE) kompute-shaders/op_mul_mat_q4_0.comp kompute-shaders/op_mul_mat_q4_1.comp kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f32.comp kompute-shaders/op_getrows_f16.comp kompute-shaders/op_getrows_q4_0.comp kompute-shaders/op_getrows_q4_1.comp @@ -774,6 +827,7 @@ if (LLAMA_KOMPUTE) shaderop_mul_mat_q4_0.h shaderop_mul_mat_q4_1.h shaderop_mul_mat_q6_k.h + shaderop_getrows_f32.h shaderop_getrows_f16.h shaderop_getrows_q4_0.h shaderop_getrows_q4_1.h @@ -1040,6 +1094,9 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR # Raspberry Pi 3, 4, Zero 2 (32-bit) list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() + if (LLAMA_SVE) + list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + endif() endif() elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND @@ -1208,7 +1265,6 @@ add_library(ggml OBJECT ggml-quants.c ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -1216,6 +1272,7 @@ add_library(ggml OBJECT ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} ${GGML_SOURCES_ROCM} ${GGML_HEADERS_ROCM} + ${GGML_SOURCES_BLAS} ${GGML_HEADERS_BLAS} ${GGML_SOURCES_LLAMAFILE} ${GGML_HEADERS_LLAMAFILE} ) @@ -1296,8 +1353,9 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}") + "${GGML_HEADERS_CUDA}" + "${GGML_HEADERS_METAL}" + "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") install(TARGETS ggml PUBLIC_HEADER) @@ -1306,7 +1364,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR} install(TARGETS llama LIBRARY PUBLIC_HEADER) install( - FILES convert.py + FILES convert-hf-to-gguf.py PERMISSIONS OWNER_READ OWNER_WRITE @@ -1333,6 +1391,13 @@ if (LLAMA_METAL) endif() endif() +configure_file(cmake/llama.pc.in + "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + @ONLY) + +install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + DESTINATION lib/pkgconfig) + # # programs, examples and tests # diff --git a/CMakePresets.json b/CMakePresets.json index ad1af7eccebbd..e2b7a79e371bf 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -1,4 +1,4 @@ -{ +{ "version": 4, "configurePresets": [ { @@ -40,6 +40,10 @@ { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, - { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] }, + + { "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] }, + { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] } ] } diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000000000..991d85e493be4 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,14 @@ +# Contributing Guidelines + +## Checklist + +* Make sure your PR follows the [coding guidelines](https://github.com/ggerganov/llama.cpp/blob/master/README.md#coding-guidelines) +* Test your changes using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library +* Execute [the full CI locally on your machine](ci/README.md) before publishing + +## PR formatting + +* Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs. + - The PR template has a series of review complexity checkboxes `[ ]` that you can mark as `[X]` for your conveience. Refer to [About task lists](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) for more information. +* If the pull request only contains documentation changes (e.g., updating READMEs, adding new wiki pages), please add `[no ci]` to the commit title. This will skip unnecessary CI checks and help reduce build times. +* When squashing multiple commits on merge, use the following format for your commit title: ` : (#)`. For example: `utils : Fix typo in utils.py (#1234)` diff --git a/Makefile b/Makefile index fe63cbd6063aa..f94ee393377b7 100644 --- a/Makefile +++ b/Makefile @@ -1,8 +1,45 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ - main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search \ - retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o + libllava.a \ + llama-baby-llama \ + llama-batched \ + llama-batched-bench \ + llama-bench \ + llama-benchmark-matmult \ + llama-cli \ + llama-convert-llama2c-to-ggml \ + llama-embedding \ + llama-eval-callback \ + llama-export-lora \ + llama-finetune \ + llama-gbnf-validator \ + llama-gguf \ + llama-gguf-split \ + llama-gritlm \ + llama-imatrix \ + llama-infill \ + llama-llava-cli \ + llama-lookahead \ + llama-lookup \ + llama-lookup-create \ + llama-lookup-merge \ + llama-lookup-stats \ + llama-parallel \ + llama-passkey \ + llama-perplexity \ + llama-q8dot \ + llama-quantize \ + llama-quantize-stats \ + llama-retrieval \ + llama-save-load-state \ + llama-server \ + llama-simple \ + llama-speculative \ + llama-tokenize \ + llama-train-text-from-scratch \ + llama-vdot \ + llama-cvector-generator \ + tests/test-c.o # Binaries only useful for tests TEST_TARGETS = \ @@ -57,6 +94,8 @@ ifeq ($(UNAME_S),Darwin) LLAMA_METAL := 1 endif + LLAMA_NO_OPENMP := 1 + ifneq ($(UNAME_P),arm) SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) ifeq ($(SYSCTL_M),1) @@ -67,6 +106,10 @@ ifeq ($(UNAME_S),Darwin) endif endif +ifdef LLAMA_RPC + BUILD_TARGETS += rpc-server +endif + default: $(BUILD_TARGETS) test: $(TEST_TARGETS) @@ -135,12 +178,16 @@ MK_NVCCFLAGS = -std=c++11 ifdef LLAMA_FAST MK_CFLAGS += -Ofast HOST_CXXFLAGS += -Ofast +ifndef LLAMA_DEBUG MK_NVCCFLAGS += -O3 +endif # LLAMA_DEBUG else MK_CFLAGS += -O3 MK_CXXFLAGS += -O3 +ifndef LLAMA_DEBUG MK_NVCCFLAGS += -O3 -endif +endif # LLAMA_DEBUG +endif # LLAMA_FAST ifndef LLAMA_NO_CCACHE CCACHE := $(shell which ccache) @@ -201,9 +248,10 @@ ifdef LLAMA_SCHED_MAX_COPIES endif ifdef LLAMA_DEBUG - MK_CFLAGS += -O0 -g - MK_CXXFLAGS += -O0 -g - MK_LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g + MK_NVCCFLAGS += -O0 -g ifeq ($(UNAME_S),Linux) MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS @@ -393,34 +441,65 @@ ifndef LLAMA_NO_ACCELERATE # Mac OS - include Accelerate framework. # `-framework Accelerate` works both with Apple Silicon and Mac Intel ifeq ($(UNAME_S),Darwin) - MK_CPPFLAGS += -DGGML_USE_ACCELERATE + MK_CPPFLAGS += -DGGML_USE_ACCELERATE -DGGML_USE_BLAS MK_CPPFLAGS += -DACCELERATE_NEW_LAPACK MK_CPPFLAGS += -DACCELERATE_LAPACK_ILP64 MK_LDFLAGS += -framework Accelerate + OBJS += ggml-blas.o endif endif # LLAMA_NO_ACCELERATE +ifndef LLAMA_NO_OPENMP + MK_CPPFLAGS += -DGGML_USE_OPENMP + MK_CFLAGS += -fopenmp + MK_CXXFLAGS += -fopenmp +endif # LLAMA_NO_OPENMP + ifdef LLAMA_OPENBLAS - MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) MK_LDFLAGS += $(shell pkg-config --libs openblas) + OBJS += ggml-blas.o endif # LLAMA_OPENBLAS +ifdef LLAMA_OPENBLAS64 + MK_CPPFLAGS += -DGGML_USE_BLAS $(shell pkg-config --cflags-only-I openblas64) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas64) + MK_LDFLAGS += $(shell pkg-config --libs openblas64) + OBJS += ggml-blas.o +endif # LLAMA_OPENBLAS64 + +ifdef LLAMA_BLIS + MK_CPPFLAGS += -DGGML_USE_BLAS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib + OBJS += ggml-blas.o +endif # LLAMA_BLIS + ifndef LLAMA_NO_LLAMAFILE MK_CPPFLAGS += -DGGML_USE_LLAMAFILE OBJS += sgemm.o endif -ifdef LLAMA_BLIS - MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis - MK_LDFLAGS += -lblis -L/usr/local/lib -endif # LLAMA_BLIS +ifdef LLAMA_RPC + MK_CPPFLAGS += -DGGML_USE_RPC + OBJS += ggml-rpc.o +endif # LLAMA_RPC ifdef LLAMA_CUBLAS # LLAMA_CUBLAS is deprecated and will be removed in the future LLAMA_CUDA := 1 endif +OBJS_CUDA_TEMP_INST = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu)) +OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/mmq*.cu)) +ifdef LLAMA_CUDA_FA_ALL_QUANTS + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu)) +else + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu)) +endif # LLAMA_CUDA_FA_ALL_QUANTS + ifdef LLAMA_CUDA ifneq ('', '$(wildcard /opt/cuda)') CUDA_PATH ?= /opt/cuda @@ -431,6 +510,7 @@ ifdef LLAMA_CUDA MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) + OBJS += $(OBJS_CUDA_TEMP_INST) MK_NVCCFLAGS += -use_fast_math ifdef LLAMA_FATAL_WARNINGS MK_NVCCFLAGS += -Werror all-warnings @@ -441,6 +521,9 @@ endif # JETSON_EOL_MODULE_DETECT ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG +ifdef LLAMA_CUDA_DEBUG + MK_NVCCFLAGS += --device-debug +endif # LLAMA_CUDA_DEBUG ifdef LLAMA_CUDA_NVCC NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else @@ -490,7 +573,10 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) -endif +endif # LLAMA_CUDA_CCBIN +ifdef LLAMA_CUDA_FA_ALL_QUANTS + MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # LLAMA_CUDA_FA_ALL_QUANTS ifdef JETSON_EOL_MODULE_DETECT define NVCC_COMPILE @@ -502,30 +588,13 @@ define NVCC_COMPILE endef # NVCC_COMPILE endif # JETSON_EOL_MODULE_DETECT -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(NVCC_COMPILE) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(NVCC_COMPILE) endif # LLAMA_CUDA -ifdef LLAMA_CLBLAST - MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) - MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) - MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) - - # Mac provides OpenCL as a framework - ifeq ($(UNAME_S),Darwin) - MK_LDFLAGS += -lclblast -framework OpenCL - else - MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) - endif - OBJS += ggml-opencl.o - -ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # LLAMA_CLBLAST - ifdef LLAMA_VULKAN MK_CPPFLAGS += -DGGML_USE_VULKAN MK_LDFLAGS += -lvulkan @@ -539,6 +608,10 @@ ifdef LLAMA_VULKAN_DEBUG MK_CPPFLAGS += -DGGML_VULKAN_DEBUG endif +ifdef LLAMA_VULKAN_MEMORY_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_MEMORY_DEBUG +endif + ifdef LLAMA_VULKAN_VALIDATE MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE endif @@ -568,6 +641,7 @@ ifdef LLAMA_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64 MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) @@ -581,11 +655,12 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) + OBJS += $(OBJS_CUDA_TEMP_INST) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< endif # LLAMA_HIPBLAS @@ -623,11 +698,26 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h endif endif # LLAMA_METAL +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o +COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o + ifndef LLAMA_NO_LLAMAFILE sgemm.o: sgemm.cpp sgemm.h ggml.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif +ifdef LLAMA_RPC +ggml-rpc.o: ggml-rpc.cpp ggml-rpc.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +rpc-server.o: examples/rpc/rpc-server.cpp ggml-rpc.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +rpc-server: rpc-server.o ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +endif # LLAMA_RPC + GF_CC := $(CC) include scripts/get-flags.mk @@ -701,20 +791,18 @@ ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h $(CC) $(CFLAGS) -c $< -o $@ +ggml-blas.o: ggml-blas.cpp ggml-blas.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + unicode.o: unicode.cpp unicode.h $(CXX) $(CXXFLAGS) -c $< -o $@ unicode-data.o: unicode-data.cpp unicode-data.h $(CXX) $(CXXFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o - llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h -COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o - common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ @@ -743,8 +831,9 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: - rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.a *.dll common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) rm -vrf ggml-cuda/*.o + rm -vrf ggml-cuda/template-instances/*.o find examples pocs -type f -name "*.o" -delete # @@ -758,62 +847,62 @@ clean: # Helper function that replaces .c, .cpp, and .cu file endings with .o: GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) -main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +llama-cli: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @echo - @echo '==== Run ./main -h for help. ====' + @echo '==== Run ./llama-cli -h for help. ====' @echo -infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) +llama-infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -quantize: examples/quantize/quantize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-quantize: examples/quantize/quantize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) +llama-quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -gritlm: examples/gritlm/gritlm.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-gritlm: examples/gritlm/gritlm.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +llama-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/colorthemes.css.hpp examples/server/style.css.hpp examples/server/theme-beeninorder.css.hpp examples/server/theme-ketivah.css.hpp examples/server/theme-mangotango.css.hpp examples/server/theme-playground.css.hpp examples/server/theme-polarnight.css.hpp examples/server/theme-snowstorm.css.hpp examples/server/index.html.hpp examples/server/index-new.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/system-prompts.js.hpp examples/server/prompt-formats.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) @@ -826,23 +915,27 @@ examples/server/%.hpp: examples/server/public/% Makefile echo "unsigned int $${NAME}_len = $(shell cat $< | wc -c );" \ ) > $@ -gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) +llama-gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-gguf-split: examples/gguf-split/gguf-split.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-eval-callback: examples/eval-callback/eval-callback.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +llama-cvector-generator: examples/cvector-generator/cvector-generator.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) +llama-train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -853,59 +946,61 @@ llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual -llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual $(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp) $(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS) -baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +llama-baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) +llama-export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) +llama-retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -retrieval: examples/retrieval/retrieval.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +llama-parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +llama-lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -lookup: examples/lookup/lookup.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) +llama-lookup-create: examples/lookup/lookup-create.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-create.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-create.cpp) -o lookup-create $(LDFLAGS) - $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-merge.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-merge.cpp) -o lookup-merge $(LDFLAGS) - $(CXX) $(CXXFLAGS) -c examples/lookup/lookup-stats.cpp -o $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, examples/lookup/lookup-stats.cpp) -o lookup-stats $(LDFLAGS) - -passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) + +llama-lookup-merge: examples/lookup/lookup-merge.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-lookup-stats: examples/lookup/lookup-stats.cpp ggml.o llama.o ngram-cache.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) + +llama-passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +llama-gbnf-validator: examples/gbnf-validator/gbnf-validator.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @@ -931,20 +1026,20 @@ build-info.o: common/build-info.cpp tests: $(TEST_TARGETS) -benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) +llama-benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -run-benchmark-matmult: benchmark-matmult +run-benchmark-matmult: llama-benchmark-matmult ./$@ .PHONY: run-benchmark-matmult swift -vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) +llama-vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) +llama-q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/README-sycl.md b/README-sycl.md index cfa248a95b5ff..bd1984706225f 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,6 +1,7 @@ # llama.cpp for SYCL - [Background](#background) +- [Recommended Release](#recommended-release) - [News](#news) - [OS](#os) - [Hardware](#hardware) @@ -29,10 +30,25 @@ The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based o When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend. -It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose. +It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose. + +## Recommended Release + +The SYCL backend would be broken by some PRs due to no online CI. + +The following release is verified with good quality: + +|Commit ID|Tag|Release|Verified Platform| +|-|-|-|-| +|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggerganov/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1
MTL Arc GPU/Windows 11/oneAPI 2024.1| + ## News +- 2024.5 + - Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770. + - Arch Linux is verified successfully. + - 2024.4 - Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M. @@ -54,10 +70,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, ## OS -| OS | Status | Verified | -|---------|---------|------------------------------------| -| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 | -| Windows | Support | Windows 11 | +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux | +| Windows | Support | Windows 11 | ## Hardware @@ -70,14 +86,14 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, |-------------------------------|---------|---------------------------------------| | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | -| Intel Arc Series | Support | Arc 770, 730M | +| Intel Arc Series | Support | Arc 770, 730M, Arc A750 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | | Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | *Notes:* - **Memory** - - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/main`. + - The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`. - Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU. @@ -99,14 +115,14 @@ The docker build option is currently limited to *intel GPU* targets. ### Build image ```sh # Using FP16 -docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . +docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/llama-cli-intel.Dockerfile . ``` *Notes*: To build in default FP32 *(Slower than FP16 alternative)*, you can remove the `--build-arg="LLAMA_SYCL_F16=ON"` argument from the previous command. -You can also use the `.devops/server-intel.Dockerfile`, which builds the *"server"* alternative. +You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative. ### Run container @@ -275,7 +291,7 @@ source /opt/intel/oneapi/setvars.sh Similar to the native `sycl-ls`, available SYCL devices can be queried as follow: ```sh -./build/bin/ls-sycl-device +./build/bin/llama-ls-sycl-device ``` A example of such log in a system with 1 *intel CPU* and 1 *intel GPU* can look like the following: ``` @@ -313,7 +329,7 @@ Examples: - Use device 0: ```sh -ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0 ``` or run by script: @@ -324,7 +340,7 @@ or run by script: - Use multiple devices: ```sh -ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer +ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer ``` Otherwise, you can run the script: @@ -427,7 +443,7 @@ Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former in *Notes:* -- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make main`. +- By default, calling `make` will build all target binary files. In case of a minimal experimental setup, the user can build the inference executable only through `make llama-cli`. ### III. Run the inference @@ -488,13 +504,13 @@ Examples: - Use device 0: ``` -build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0 +build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm none -mg 0 ``` - Use multiple devices: ``` -build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer +build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 -sm layer ``` Otherwise, run the following wrapper script: diff --git a/README.md b/README.md index 2ee267fdf6887..40793c8eab880 100644 --- a/README.md +++ b/README.md @@ -2,12 +2,17 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) -[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) +[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) +[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ +> [!IMPORTANT] +[2024 Jun 12] Binaries have been renamed w/ a `llama-` prefix. `main` is now `llama-cli`, `server` is `llama-server`, etc (https://github.com/ggerganov/llama.cpp/pull/7809) + ### Recent API changes - [2024 Apr 21] `llama_token_to_piece` can now optionally render special tokens https://github.com/ggerganov/llama.cpp/pull/6807 @@ -20,7 +25,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021** +- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430 +- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404 @@ -50,7 +56,6 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
  • Quantization
  • Interactive mode
  • Constrained output with grammars
    • -
  • Instruct mode
  • Obtaining and using the Facebook LLaMA 2 model
  • Seminal papers and background on the models
  • Perplexity (measuring model quality)
    • @@ -74,7 +79,7 @@ variety of hardware - locally and in the cloud. - AVX, AVX2 and AVX512 support for x86 architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) -- Vulkan, SYCL, and (partial) OpenCL backend support +- Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has @@ -147,6 +152,8 @@ Typically finetunes of the base models below are supported as well. [llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. +[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser. + **Bindings:** - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) @@ -188,6 +195,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [RAGNA Desktop](https://ragna.app/) (proprietary) - [RecurseChat](https://recurse.chat/) (proprietary) - [semperai/amica](https://github.com/semperai/amica) - [withcatai/catai](https://github.com/withcatai/catai) @@ -200,15 +208,21 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [AIKit](https://github.com/sozercan/aikit) (MIT) +- [LARS - The LLM & Advanced Referencing Solution](https://github.com/abgulati/LARS) (AGPL) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* +**Tools:** + +- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML + --- Here is a typical run using LLaMA v2 13B on M2 Ultra: ``` -$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e +$ make -j && ./llama-cli -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e I llama.cpp build info: I UNAME_S: Darwin I UNAME_P: arm @@ -311,8 +325,6 @@ In order to build llama.cpp you have four different options. make ``` - **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1` - - On Windows: 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). @@ -324,40 +336,38 @@ In order to build llama.cpp you have four different options. make ``` + - Notes: + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, run `make LLAMA_DEBUG=1` + - Using `CMake`: - ```bash - cmake -B build - cmake --build build --config Release - ``` + ```bash + cmake -B build + cmake --build build --config Release + ``` + + **Notes**: - **Note**: for `Debug` builds, there are two cases: + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, there are two cases: - - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): ```bash cmake -B build -DCMAKE_BUILD_TYPE=Debug cmake --build build ``` - - Multi-config generators (`-G` param set to Visual Studio, XCode...): + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): ```bash cmake -B build -G "Xcode" cmake --build build --config Debug ``` -- Using `Zig` (version 0.11 or later): - - Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C, - it's also possible to cross compile for other operating systems and architectures: - - ```bash - zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c - ``` - - The `zig targets` command will give you valid options to use. - - Using `gmake` (FreeBSD): 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) @@ -365,15 +375,42 @@ In order to build llama.cpp you have four different options. 3. Install compilation dependencies. ```bash - sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \ - opencl clblast openblas + sudo pkg install gmake automake autoconf pkgconf llvm15 openblas gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 ``` - **Notes:** With this packages you can build llama.cpp with OPENBLAS and - CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read - the instructions for use and activate this options in this document below. +### Homebrew + +On Mac and Linux, the homebrew package manager can be used via +``` +brew install llama.cpp +``` +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 + +### Nix + +On Mac and Linux, the Nix package manager can be used via +``` +nix profile install nixpkgs#llama-cpp +``` +For flake enabled installs. + +Or +``` +nix-env --file '' --install --attr llama-cpp +``` +For non-flake enabled installs. + +This expression is automatically updated within the [nixpkgs repo](https://github.com/NixOS/nixpkgs/blob/nixos-24.05/pkgs/by-name/ll/llama-cpp/package.nix#L164). + +#### Flox + +On Mac and Linux, Flox can be used to install llama.cpp within a Flox environment via +``` +flox install llama-cpp +``` +Flox follows the nixpkgs build of llama.cpp. ### Metal Build @@ -385,7 +422,7 @@ argument. ### BLAS Build -Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use: +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: - #### Accelerate Framework: @@ -473,10 +510,12 @@ Building the program with BLAS support may lead to some performance improvements |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | | | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | + | LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | - #### hipBLAS @@ -537,111 +576,6 @@ Building the program with BLAS support may lead to some performance improvements | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | -- #### CLBlast - - OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU. - - You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed. - - - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. - - -
    - Installing the OpenCL SDK from source - - ```sh - git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git - cd OpenCL-SDK - cmake -B build -DBUILD_DOCS=OFF \ - -DBUILD_EXAMPLES=OFF \ - -DBUILD_TESTING=OFF \ - -DOPENCL_SDK_BUILD_SAMPLES=OFF \ - -DOPENCL_SDK_TEST_SAMPLES=OFF - cmake --build build - cmake --install build --prefix /some/path - ``` -
    - - ##### Installing CLBlast - - Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. - - Linux packaging: - Fedora Linux: - ```bash - sudo dnf install clblast - ``` - - Alternatively, they may be built from source. - - -
    - Windows: - - ```cmd - set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" - git clone https://github.com/CNugteren/CLBlast.git - cd CLBlast - cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 - cmake --build build --config Release - cmake --install build --prefix C:/CLBlast - ``` - - (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds) - - -
    - Unix: - - ```sh - git clone https://github.com/CNugteren/CLBlast.git - cd CLBlast - cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF - cmake --build build --config Release - cmake --install build --prefix /some/path - ``` - - Where `/some/path` is where the built library will be installed (default is `/usr/local`). -
    - - ##### Building Llama with CLBlast - - - Build with make: - ```sh - make LLAMA_CLBLAST=1 - ``` - - CMake (Unix): - ```sh - cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path - cmake --build build --config Release - ``` - - CMake (Windows): - ```cmd - set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" - git clone https://github.com/ggerganov/llama.cpp - cd llama.cpp - cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 - cmake --build build --config Release - cmake --install build --prefix C:/LlamaCPP - ``` - - ##### Running Llama with CLBlast - - The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. - - To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`. - The selection can be a number (starting from 0) or a text string to search: - - ```sh - GGML_OPENCL_PLATFORM=1 ./main ... - GGML_OPENCL_DEVICE=2 ./main ... - GGML_OPENCL_PLATFORM=Intel ./main ... - GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ... - ``` - - The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. - Using the variables it is possible to select a CPU-based driver as well, if so desired. - - You can get a list of platforms and devices from the `clinfo -l` command, etc. - - #### Vulkan **With docker**: @@ -650,7 +584,7 @@ Building the program with BLAS support may lead to some performance improvements ```sh # Build the image - docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile . + docker build -t llama-cpp-vulkan -f .devops/llama-cli-vulkan.Dockerfile . # Then, use it: docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 @@ -671,7 +605,9 @@ Building the program with BLAS support may lead to some performance improvements vulkaninfo ``` - Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. + Alternatively your package manager might be able to provide the appropriate libraries. + For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. + For Fedora 40, you can install `vulkan-devel`, `glslc` and `glslang` packages. Then, build llama.cpp using the cmake command below: @@ -679,7 +615,7 @@ Building the program with BLAS support may lead to some performance improvements cmake -B build -DLLAMA_VULKAN=1 cmake --build build --config Release # Test the output binary (with "-ngl 33" to offload all layers to GPU) - ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + ./bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 # You should see in the output, ggml_vulkan detected your GPU. For example: # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 @@ -692,7 +628,8 @@ Building the program with BLAS support may lead to some performance improvements To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. -Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. +Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derivatives. +It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. ```bash # obtain the official LLaMA model weights and place them in ./models @@ -709,23 +646,20 @@ ls ./models python3 -m pip install -r requirements.txt # convert the model to ggml FP16 format -python3 convert.py models/mymodel/ - -# [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocab-type bpe +python3 convert-hf-to-gguf.py models/mymodel/ # quantize the model to 4-bits (using Q4_K_M method) -./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M +./llama-quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M # update the gguf filetype to current version if older version is now unsupported -./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +./llama-quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY ``` ### Run the quantized model ```bash # start inference on a gguf model -./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 +./llama-cli -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 ``` When running the larger models, make sure you have enough disk space to store all the intermediate files. @@ -800,7 +734,7 @@ The time per token is measured on a MacBook M1 Pro 32GB RAM using 4 and 8 thread #### How to run 1. Download/extract: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip -2. Run `./perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` +2. Run `./llama-perplexity -m models/7B/ggml-model-q4_0.gguf -f wiki.test.raw` 3. Output: ``` perplexity : calculating perplexity over 655 chunks @@ -824,16 +758,16 @@ Here is an example of a few-shot interaction, invoked with the command ./examples/chat-13B.sh # custom arguments using a 13B model -./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt +./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt ``` -Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `main` example program. +Note the use of `--color` to distinguish between user input and generated text. Other parameters are explained in more detail in the [README](examples/main/README.md) for the `llama-cli` example program. ![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png) ### Persistent Interaction -The prompt, user inputs, and model generations can be saved and resumed across calls to `./main` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file. +The prompt, user inputs, and model generations can be saved and resumed across calls to `./llama-cli` by leveraging `--prompt-cache` and `--prompt-cache-all`. The `./examples/chat-persistent.sh` script demonstrates this with support for long-running, resumable chat sessions. To use this example, you must provide a file to cache the initial chat prompt and a directory to save the chat session, and may optionally provide the same variables as `chat-13B.sh`. The same prompt cache can be reused for new chat sessions. Note that both prompt cache and chat directory are tied to the initial prompt (`PROMPT_TEMPLATE`) and the model file. ```bash # Start a new chat @@ -855,41 +789,13 @@ PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \ `llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only: ```bash -./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' +./llama-cli -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:' ``` The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md). For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Instruct mode - -1. First, download and place the `ggml` model into the `./models` folder -2. Run the `main` tool like this: - -``` -./examples/alpaca.sh -``` - -Sample run: - -``` -== Running in interactive mode. == - - Press Ctrl+C to interject at any time. - - Press Return to return control to LLaMA. - - If you want to submit another line, end your input in '\'. - - Below is an instruction that describes a task. Write a response that appropriately completes the request. - -> How many letters are there in the English alphabet? -There 26 letters in the English Alphabet -> What is the most common way of transportation in Amsterdam? -The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis -> List 5 words that start with "ca". -cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. -> -``` - ### Obtaining and using the Facebook LLaMA 2 model - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. @@ -962,7 +868,7 @@ $mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/ho Now, you can start chatting: ``` $cd /data/data/com.termux/files/home/bin -$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml +$./llama-cli -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml ``` Here's a demo of an interactive session running on Pixel 5 phone: @@ -1029,8 +935,8 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia ```bash docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . -docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile . -docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile . +docker build -t local/llama.cpp:light-cuda -f .devops/llama-cli-cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda -f .devops/llama-server-cuda.Dockerfile . ``` You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. @@ -1080,7 +986,7 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m ### Docs -- [main](./examples/main/README.md) +- [main (cli)](./examples/main/README.md) - [server](./examples/server/README.md) - [jeopardy](./examples/jeopardy/README.md) - [BLIS](./docs/BLIS.md) diff --git a/ci/run.sh b/ci/run.sh index 9402990250a20..291c44f47b86d 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -287,7 +287,7 @@ function gg_run_open_llama_7b_v2 { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -303,47 +303,47 @@ function gg_run_open_llama_7b_v2 { wiki_test="${path_wiki}/wiki.test.raw" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - - (time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -437,45 +437,45 @@ function gg_run_pythia_1_4b { wiki_test_60="${path_wiki}/wiki.test-60.raw" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - - (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -569,47 +569,47 @@ function gg_run_pythia_2_8b { wiki_test="${path_wiki}/wiki.test.raw" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - - (time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k + ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k + ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k + ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k + ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/llama-cli --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-cli --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-cli --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-cli --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-cli --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-cli --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-cli --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-cli --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-cli --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-cli --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-cli --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/llama-save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -693,10 +693,10 @@ function gg_run_embd_bge_small { model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 - (time ./bin/embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log set +e } diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake index 46fba65149b29..8023796800683 100644 --- a/cmake/arm64-windows-llvm.cmake +++ b/cmake/arm64-windows-llvm.cmake @@ -9,7 +9,7 @@ set( CMAKE_CXX_COMPILER clang++ ) set( CMAKE_C_COMPILER_TARGET ${target} ) set( CMAKE_CXX_COMPILER_TARGET ${target} ) -set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" ) +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in new file mode 100644 index 0000000000000..326acbb6108fd --- /dev/null +++ b/cmake/llama.pc.in @@ -0,0 +1,10 @@ +prefix=@CMAKE_INSTALL_PREFIX@ +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include + +Name: llama +Description: Port of Facebook's LLaMA model in C/C++ +Version: @PROJECT_VERSION@ +Libs: -L${libdir} -lllama +Cflags: -I${includedir} diff --git a/common/CMakeLists.txt b/common/CMakeLists.txt index 0ec8d6d8d03b5..171530c915332 100644 --- a/common/CMakeLists.txt +++ b/common/CMakeLists.txt @@ -84,4 +84,4 @@ endif () target_include_directories(${TARGET} PUBLIC .) target_compile_features(${TARGET} PUBLIC cxx_std_11) -target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama) +target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads) diff --git a/common/common.cpp b/common/common.cpp index bf6828826b66c..bcdfb676d8666 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -7,20 +7,21 @@ #include #include +#include #include +#include +#include #include #include #include -#include #include +#include #include #include #include #include #include #include -#include -#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -199,19 +200,13 @@ void gpt_params_handle_model_default(gpt_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - std::string cache_directory = fs_get_cache_directory(); - const bool success = fs_create_directory_with_parents(cache_directory); - if (!success) { - throw std::runtime_error("failed to create cache directory: " + cache_directory); - } - params.model = cache_directory + string_split(params.hf_file, '/').back(); + params.model = fs_get_cache_file(string_split(params.hf_file, '/').back()); } } else if (!params.model_url.empty()) { if (params.model.empty()) { auto f = string_split(params.model_url, '#').front(); f = string_split(f, '?').front(); - f = string_split(f, '/').back(); - params.model = "models/" + f; + params.model = fs_get_cache_file(string_split(f, '/').back()); } } else if (params.model.empty()) { params.model = DEFAULT_MODEL_PATH; @@ -237,10 +232,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } } - if (params.prompt_cache_all && - (params.interactive || params.interactive_first || - params.instruct)) { - + if (params.prompt_cache_all && (params.interactive || params.interactive_first)) { throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } @@ -265,29 +257,33 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { - bool result = true; + const auto params_org = params; // the example can modify the default params + try { - if (!gpt_params_parse_ex(argc, argv, params)) { - gpt_params_print_usage(argc, argv, gpt_params()); - exit(0); + if (!gpt_params_parse_ex(argc, argv, params) || params.usage) { + params = params_org; + params.usage = true; + return false; } - } - catch (const std::invalid_argument & ex) { + } catch (const std::invalid_argument & ex) { fprintf(stderr, "%s\n", ex.what()); - gpt_params_print_usage(argc, argv, gpt_params()); - exit(1); + params = params_org; + return false; } - return result; + + return true; } #define CHECK_ARG if (++i >= argc) { invalid_param = true; return true; } bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { + const char split_delim = ','; + llama_sampling_params & sparams = params.sparams; if (arg == "-s" || arg == "--seed") { CHECK_ARG - // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context. + // TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context. params.seed = std::stoul(argv[i]); sparams.seed = std::stoul(argv[i]); return true; @@ -333,6 +329,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.escape = true; return true; } + if (arg == "--no-escape") { + params.escape = false; + return true; + } if (arg == "--prompt-cache") { CHECK_ARG params.path_prompt_cache = argv[i]; @@ -378,7 +378,23 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } return true; } - if (arg == "-n" || arg == "--n-predict") { + if (arg == "--in-file") { + CHECK_ARG + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + params.in_files.push_back(argv[i]); + return true; + } + if (arg == "-n" || arg == "--predict" || arg == "--n-predict") { + CHECK_ARG + params.n_predict = std::stoi(argv[i]); + return true; + } + if (arg == "-n" || arg == "--predict" || arg == "--n-predict") { CHECK_ARG params.n_predict = std::stoi(argv[i]); return true; @@ -474,10 +490,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } if (arg == "--sampling-seq") { CHECK_ARG - if (++i >= argc) { - invalid_param = true; - return true; - } sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]); return true; } @@ -699,11 +711,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.interactive = true; return true; } - if (arg == "--interactive-specials") { - params.interactive_specials = true; + if (arg == "-sp" || arg == "--special") { + params.special = true; return true; } - if (arg == "--embedding") { + if (arg == "--embedding" || arg == "--embeddings") { params.embedding = true; return true; } @@ -722,22 +734,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.embd_sep = argv[i]; return true; } - if (arg == "--interactive-first") { + if (arg == "-if" || arg == "--interactive-first") { params.interactive_first = true; return true; } - if (arg == "-ins" || arg == "--instruct") { - params.instruct = true; - return true; - } if (arg == "-cnv" || arg == "--conversation") { params.conversation = true; return true; } - if (arg == "-cml" || arg == "--chatml") { - params.chatml = true; - return true; - } if (arg == "--infill") { params.infill = true; return true; @@ -774,7 +778,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.flash_attn = true; return true; } - if (arg == "--color") { + if (arg == "-co" || arg == "--color") { params.use_color = true; return true; } @@ -782,20 +786,20 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.use_mlock = true; return true; } - if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") { CHECK_ARG params.n_gpu_layers = std::stoi(argv[i]); if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } return true; } - if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") { CHECK_ARG params.n_gpu_layers_draft = std::stoi(argv[i]); if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } return true; @@ -803,9 +807,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa if (arg == "--main-gpu" || arg == "-mg") { CHECK_ARG params.main_gpu = std::stoi(argv[i]); -#ifndef GGML_USE_CUDA_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n"); -#endif // GGML_USE_CUDA_SYCL +#ifndef GGML_USE_CUDA_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL_VULKAN return true; } if (arg == "--split-mode" || arg == "-sm") { @@ -828,9 +832,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } -#ifndef GGML_USE_CUDA_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUDA_SYCL +#ifndef GGML_USE_CUDA_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL_VULKAN return true; } if (arg == "--tensor-split" || arg == "-ts") { @@ -876,6 +880,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa else { invalid_param = true; } return true; } + if (arg == "-v" || arg == "--verbose") { + params.verbosity = 1; + return true; + } + if (arg == "--verbosity") { + CHECK_ARG + params.verbosity = std::stoi(argv[i]); + return true; + } if (arg == "--verbose-prompt") { params.verbose_prompt = true; return true; @@ -922,6 +935,11 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.ppl_stride = std::stoi(argv[i]); return true; } + if (arg == "--ppl-output-type") { + CHECK_ARG + params.ppl_output_type = std::stoi(argv[i]); + return true; + } if (arg == "-ptc" || arg == "--print-token-count") { CHECK_ARG params.n_print = std::stoi(argv[i]); @@ -931,11 +949,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.check_tensors = true; return true; } - if (arg == "--ppl-output-type") { - CHECK_ARG - params.ppl_output_type = std::stoi(argv[i]); - return true; - } if (arg == "--hellaswag") { params.hellaswag = true; return true; @@ -995,19 +1008,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } return true; } - if (arg == "-h" || arg == "--help") { - gpt_params_print_usage(argc, argv, gpt_params()); - exit(0); + if (arg == "-h" || arg == "--help" || arg == "--usage" ) { + params.usage = true; + return true; } if (arg == "--version") { fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); exit(0); } - if (arg == "--random-prompt") { - params.random_prompt = true; - return true; - } if (arg == "--in-prefix-bos") { params.input_prefix_bos = true; return true; @@ -1049,17 +1058,246 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } if (arg == "--override-kv") { CHECK_ARG - if (++i >= argc) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { + fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; return true; } - if (!string_parse_kv_override(argv[i], params.kv_overrides)) { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); + return true; + } + if (arg == "--host") { + CHECK_ARG + params.hostname = argv[i]; + return true; + } + if (arg == "--port") { + CHECK_ARG + params.port = std::stoi(argv[i]); + return true; + } + if (arg == "--path") { + CHECK_ARG + params.public_path = argv[i]; + return true; + } + if (arg == "--api-key") { + CHECK_ARG + params.api_keys.push_back(argv[i]); + return true; + } + if (arg == "--api-key-file") { + CHECK_ARG + std::ifstream key_file(argv[i]); + if (!key_file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + std::string key; + while (std::getline(key_file, key)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + key_file.close(); + return true; + } + if (arg == "--ssl-key-file") { + CHECK_ARG + params.ssl_file_key = argv[i]; + return true; + } + if (arg == "--ssl-cert-file") { + CHECK_ARG + params.ssl_file_cert = argv[i]; + return true; + } + if (arg == "--timeout" || arg == "-to") { + CHECK_ARG + params.timeout_read = std::stoi(argv[i]); + params.timeout_write = std::stoi(argv[i]); + return true; + } + if (arg == "--threads-http") { + CHECK_ARG + params.n_threads_http = std::stoi(argv[i]); + return true; + } + if (arg == "-spf" || arg == "--system-prompt-file") { + CHECK_ARG + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + std::string system_prompt; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(system_prompt) + ); + params.system_prompt = system_prompt; + return true; + } + if (arg == "--log-format") { + CHECK_ARG + if (std::strcmp(argv[i], "json") == 0) { + params.log_json = true; + } else if (std::strcmp(argv[i], "text") == 0) { + params.log_json = false; + } else { invalid_param = true; return true; } return true; } + if (arg == "--no-slots") { + params.endpoint_slots = false; + return true; + } + if (arg == "--metrics") { + params.endpoint_metrics = true; + return true; + } + if (arg == "--slot-save-path") { + CHECK_ARG + params.slot_save_path = argv[i]; + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.slot_save_path += DIRECTORY_SEPARATOR; + } + return true; + } + if (arg == "--chat-template") { + CHECK_ARG + if (!llama_chat_verify_template(argv[i])) { + fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); + fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); + invalid_param = true; + return true; + } + params.chat_template = argv[i]; + return true; + } + if (arg == "--slot-prompt-similarity" || arg == "-sps") { + CHECK_ARG + params.slot_prompt_similarity = std::stof(argv[i]); + return true; + } + if (arg == "-pps") { + params.is_pp_shared = true; + return true; + } + if (arg == "-npp") { + CHECK_ARG + auto p = string_split(argv[i], split_delim); + params.n_pp.insert(params.n_pp.end(), p.begin(), p.end()); + return true; + } + if (arg == "-ntg") { + CHECK_ARG + auto p = string_split(argv[i], split_delim); + params.n_tg.insert(params.n_tg.end(), p.begin(), p.end()); + return true; + } + if (arg == "-npl") { + CHECK_ARG + auto p = string_split(argv[i], split_delim); + params.n_pl.insert(params.n_pl.end(), p.begin(), p.end()); + return true; + } + if (arg == "--context-file") { + CHECK_ARG + std::ifstream file(argv[i], std::ios::binary); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + params.context_files.push_back(argv[i]); + return true; + } + if (arg == "--chunk-size") { + CHECK_ARG + params.chunk_size = std::stoi(argv[i]); + return true; + } + if (arg == "--chunk-separator") { + CHECK_ARG + params.chunk_separator = argv[i]; + return true; + } + if (arg == "--junk") { + CHECK_ARG + params.n_junk = std::stoi(argv[i]); + return true; + } + if (arg == "--pos") { + CHECK_ARG + params.i_pos = std::stoi(argv[i]); + return true; + } + if (arg == "-o" || arg == "--output" || arg == "--output-file") { + CHECK_ARG + params.out_file = argv[i]; + params.cvector_outfile = argv[i]; + return true; + } + if (arg == "-ofreq" || arg == "--output-frequency") { + CHECK_ARG + params.n_out_freq = std::stoi(argv[i]); + return true; + } + if (arg == "--save-frequency") { + CHECK_ARG + params.n_save_freq = std::stoi(argv[i]); + return true; + } + if (arg == "--process-output") { + params.process_output = true; + return true; + } + if (arg == "--no-ppl") { + params.compute_ppl = false; + return true; + } + if (arg == "--chunk" || arg == "--from-chunk") { + CHECK_ARG + params.i_chunk = std::stoi(argv[i]); + return true; + } + // cvector params + if (arg == "--completions-file") { + CHECK_ARG + params.cvector_completions_file = argv[i]; + return true; + } + if (arg == "--positive-file") { + CHECK_ARG + params.cvector_positive_file = argv[i]; + return true; + } + if (arg == "--negative-file") { + CHECK_ARG + params.cvector_negative_file = argv[i]; + return true; + } + if (arg == "--completions") { + CHECK_ARG + params.n_completions = std::stoi(argv[i]); + return true; + } + if (arg == "--pca-batch") { + CHECK_ARG + params.n_pca_batch = std::stoi(argv[i]); + return true; + } + if (arg == "--pca-iter") { + CHECK_ARG + params.n_pca_iterations = std::stoi(argv[i]); + return true; + } #ifndef LOG_DISABLE_LOGS // Parse args for logging parameters if (log_param_single_parse(argv[i])) { @@ -1084,6 +1322,16 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return false; } +#ifdef __GNUC__ +#ifdef __MINGW32__ +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +#endif +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) +#endif + void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; @@ -1095,197 +1343,313 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param } sampler_type_names.pop_back(); - printf("\n"); - printf("usage: %s [options]\n", argv[0]); - printf("\n"); - printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" --version show version and build info\n"); - printf(" -i, --interactive run in interactive mode\n"); - printf(" --interactive-specials allow special tokens in user text, in interactive mode\n"); - printf(" --interactive-first run in interactive mode and wait for input right away\n"); - printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); - printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); - printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n"); - printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); - printf(" -r PROMPT, --reverse-prompt PROMPT\n"); - printf(" halt generation at PROMPT, return control in interactive mode\n"); - printf(" (can be specified more than once for multiple prompts).\n"); - printf(" --color colorise output to distinguish prompt and user input from generations\n"); - printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -td N, --threads-draft N"); - printf(" number of threads to use during generation (default: same as --threads)\n"); - printf(" -tbd N, --threads-batch-draft N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); - printf(" -p PROMPT, --prompt PROMPT\n"); - printf(" prompt to start generation with (default: empty)\n"); - printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - printf(" not supported with --interactive or other interactive options\n"); - printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - printf(" --random-prompt start with a randomized prompt.\n"); - printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); - printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - printf(" -f FNAME, --file FNAME\n"); - printf(" prompt file to start generation.\n"); - printf(" -bf FNAME, --binary-file FNAME\n"); - printf(" binary file containing multiple choice tasks.\n"); - printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); - printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); - printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); - printf(" -ub N, --ubatch-size N\n"); - printf(" physical maximum batch size (default: %d)\n", params.n_ubatch); - printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n"); - printf(" (default: %s)\n", sampler_type_names.c_str()); - printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); - printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); - printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); - printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p); - printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z); - printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p); - printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n); - printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); - printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); - printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); - printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); - printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); - printf(" --mirostat N use Mirostat sampling.\n"); - printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); - printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta); - printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau); - printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - printf(" modifies the likelihood of token appearing in the completion,\n"); - printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); - printf(" --grammar-file FNAME file to read grammar from\n"); - printf(" -j SCHEMA, --json-schema SCHEMA\n"); - printf(" JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n"); - printf(" For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n"); - printf(" --cfg-negative-prompt PROMPT\n"); - printf(" negative prompt to use for guidance. (default: empty)\n"); - printf(" --cfg-negative-prompt-file FNAME\n"); - printf(" negative prompt file to use for guidance. (default: empty)\n"); - printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n"); - printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls}\n"); - printf(" pooling type for embeddings, use model default if unspecified\n"); - printf(" -dt N, --defrag-thold N\n"); - printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - printf(" --penalize-nl penalize newline tokens\n"); - printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); - printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); - printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); - printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); - printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks); - printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n"); - printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks); - printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base\n"); - printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); - printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); - printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); - printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); - printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); - printf(" --image IMAGE_FILE path to an image file. use with multimodal models. Specify multiple times for batching\n"); + struct option_info { + LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5) + option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) { + va_list args_list; + va_start(args_list, desc); + char buffer[1024]; + vsnprintf(buffer, sizeof(buffer), desc, args_list); + va_end(args_list); + this->desc = buffer; + } + + option_info(const std::string & grp) : grp(grp) {} + + std::string tags; + std::string args; + std::string desc; + std::string grp; + }; + + std::vector options; + + // TODO: filter by tags + + options.push_back({ "general" }); + options.push_back({ "*", "-h, --help, --usage", "print usage and exit" }); + options.push_back({ "*", " --version", "show version and build info" }); + options.push_back({ "*", "-v, --verbose", "print verbose information" }); + options.push_back({ "*", " --verbosity N", "set specific verbosity level (default: %d)", params.verbosity }); + options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" }); + options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" }); + options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" }); + options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed }); + options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads }); + options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" }); + options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" }); + options.push_back({ "speculative", "-tbd, --threads-batch-draft N", + "number of threads to use during batch and prompt processing (default: same as --threads-draft)" }); + options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft }); + options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split }); + options.push_back({ "*", "-lcs, --lookup-cache-static FNAME", + "path to static lookup cache to use for lookup decoding (not updated by generation)" }); + options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME", + "path to dynamic lookup cache to use for lookup decoding (updated by generation)" }); + + options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx }); + options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict }); + options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch }); + options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch }); + options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep }); + options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks }); + options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" }); + options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() }); + options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" }); + options.push_back({ "*", " --in-file FNAME", "an input file (repeat to specify multiple files)" }); + options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" }); + options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" }); + options.push_back({ "*", " --no-escape", "do not process escape sequences" }); + options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print }); + options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" }); + options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n" + "not supported with --interactive or other interactive options" }); + options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" }); + options.push_back({ "main", "-r, --reverse-prompt PROMPT", + "halt generation at PROMPT, return control in interactive mode\n" + "can be specified more than once for multiple prompts" }); + options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" }); + options.push_back({ "main", "-cnv, --conversation", "run in conversation mode (does not print special tokens and suffix/prefix) (default: %s)", params.conversation ? "true" : "false" }); + options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" }); + options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" }); + options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" }); + options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" }); + options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" }); + options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" }); + + options.push_back({ "sampling" }); + options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n" + "(default: %s)", sampler_type_names.c_str() }); + options.push_back({ "*", " --sampling-seq SEQUENCE", + "simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() }); + options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" }); + options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" }); + options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp }); + options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k }); + options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p }); + options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p }); + options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z }); + options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p }); + options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n }); + options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat }); + options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present }); + options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq }); + options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range }); + options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent }); + options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n" + "Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n" + "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat }); + options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta }); + options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau }); + options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n" + "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n" + "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" }); + options.push_back({ "main", " --cfg-negative-prompt PROMPT", + "negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() }); + options.push_back({ "main", " --cfg-negative-prompt-file FNAME", + "negative prompt file to use for guidance" }); + options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale }); + + options.push_back({ "grammar" }); + options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() }); + options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" }); + options.push_back({ "*", "-j, --json-schema SCHEMA", + "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n" + "For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" }); + + options.push_back({ "embedding" }); + options.push_back({ "embedding", " --pooling {none,mean,cls}", + "pooling type for embeddings, use model default if unspecified" }); + + options.push_back({ "context hacking" }); + options.push_back({ "*", " --rope-scaling {none,linear,yarn}", + "RoPE frequency scaling method, defaults to linear unless specified by the model" }); + options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" }); + options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" }); + options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" }); + options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx }); + options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor }); + options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor }); + options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow }); + options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast }); + options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n }); + options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w }); + options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" }); + options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" }); + options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() }); + options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() }); + + options.push_back({ "perplexity" }); + options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" }); + options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks }); + options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks }); + options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --multiple-choice-tasks N", + "number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks }); + options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" }); + options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride }); + options.push_back({ "perplexity", " --ppl-output-type {0,1}", + "output type for perplexity calculation (default: %d)", params.ppl_output_type }); + + options.push_back({ "parallel" }); + options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold }); + options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel }); + options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences }); + options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" }); + + options.push_back({ "multi-modality" }); + options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" }); + options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" }); + + options.push_back({ "backend" }); + options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" }); if (llama_supports_mlock()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); + options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" }); } if (llama_supports_mmap()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); - } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided by numactl\n"); - printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); - printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" }); + } + options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n" + " - distribute: spread execution evenly over all nodes\n" + " - isolate: only spawn threads on CPUs on the node that execution started on\n" + " - numactl: use the CPU map provided by numactl\n" + "if run without this previously, it is recommended to drop the system page cache before using this\n" + "see https://github.com/ggerganov/llama.cpp/issues/1437" }); + if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); - } - printf(" --rpc SERVERS comma separated list of RPC servers\n"); - printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); - printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); - printf(" -gan N, --grp-attn-n N\n"); - printf(" group-attention factor (default: %d)\n", params.grp_attn_n); - printf(" -gaw N, --grp-attn-w N\n"); - printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); - printf(" -dkvc, --dump-kv-cache\n"); - printf(" verbose print of the KV cache\n"); - printf(" -nkvo, --no-kv-offload\n"); - printf(" disable KV offload\n"); - printf(" -ctk TYPE, --cache-type-k TYPE\n"); - printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str()); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str()); - printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --control-vector FNAME\n"); - printf(" add a control vector\n"); - printf(" --control-vector-scaled FNAME S\n"); - printf(" add a control vector with user defined scaling S\n"); - printf(" --control-vector-layer-range START END\n"); - printf(" layer range to apply the control vector(s) to, start and end inclusive\n"); - printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH); - printf(" -md FNAME, --model-draft FNAME\n"); - printf(" draft model for speculative decoding (default: unused)\n"); - printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); - printf(" model download url (default: unused)\n"); - printf(" -hfr REPO, --hf-repo REPO\n"); - printf(" Hugging Face model repository (default: unused)\n"); - printf(" -hff FILE, --hf-file FILE\n"); - printf(" Hugging Face model file (default: unused)\n"); - printf(" -ld LOGDIR, --logdir LOGDIR\n"); - printf(" path under which to save YAML logs (no logging if unset)\n"); - printf(" -lcs FNAME, --lookup-cache-static FNAME\n"); - printf(" path to static lookup cache to use for lookup decoding (not updated by generation)\n"); - printf(" -lcd FNAME, --lookup-cache-dynamic FNAME\n"); - printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n"); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -ptc N, --print-token-count N\n"); - printf(" print token count every N tokens (default: %d)\n", params.n_print); - printf(" --check-tensors check model tensor data for invalid values\n"); - printf("\n"); + options.push_back({ "*", "-ngl, --gpu-layers N", + "number of layers to store in VRAM" }); + options.push_back({ "*", "-ngld, --gpu-layers-draft N", + "number of layers to store in VRAM for the draft model" }); + options.push_back({ "*", "-sm, --split-mode SPLIT_MODE", + "how to split the model across multiple GPUs, one of:\n" + " - none: use one GPU only\n" + " - layer (default): split layers and KV across GPUs\n" + " - row: split rows across GPUs" }); + options.push_back({ "*", "-ts, --tensor-split SPLIT", + "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" }); + options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n" + "or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu }); + } + + options.push_back({ "model" }); + options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" }); + options.push_back({ "*", " --override-kv KEY=TYPE:VALUE", + "advanced option to override model metadata by key. may be specified multiple times.\n" + "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" }); + options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (implies --no-mmap)" }); + options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (implies --no-mmap)" }); + options.push_back({ "*", " --lora-base FNAME", "optional model to use as a base for the layers modified by the LoRA adapter" }); + options.push_back({ "*", " --control-vector FNAME", "add a control vector" }); + options.push_back({ "*", " --control-vector-scaled FNAME SCALE", + "add a control vector with user defined scaling SCALE" }); + options.push_back({ "*", " --control-vector-layer-range START END", + "layer range to apply the control vector(s) to, start and end inclusive" }); + options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n" + "or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH }); + options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" }); + options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" }); + options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" }); + options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" }); + + options.push_back({ "retrieval" }); + options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" }); + options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size }); + options.push_back({ "retrieval", " --chunk-separator STRING", + "separator between chunks (default: '%s')", params.chunk_separator.c_str() }); + + options.push_back({ "passkey" }); + options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk }); + options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos }); + + options.push_back({ "imatrix" }); + options.push_back({ "imatrix", "-o, --output FNAME", "output file (default: '%s')", params.out_file.c_str() }); + options.push_back({ "imatrix", " --output-frequency N", "output the imatrix every N iterations (default: %d)", params.n_out_freq }); + options.push_back({ "imatrix", " --save-frequency N", "save an imatrix copy every N iterations (default: %d)", params.n_save_freq }); + options.push_back({ "imatrix", " --process-output", "collect data for the output tensor (default: %s)", params.process_output ? "true" : "false" }); + options.push_back({ "imatrix", " --no-ppl", "do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false" }); + options.push_back({ "imatrix", " --chunk N", "start processing the input from chunk N (default: %d)", params.i_chunk }); + + options.push_back({ "bench" }); + options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" }); + options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" }); + options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" }); + options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" }); + + options.push_back({ "server" }); + options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() }); + options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port }); + options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() }); + options.push_back({ "server", " --embedding(s)", "enable embedding endpoint (default: %s)", params.embedding ? "enabled" : "disabled" }); + options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" }); + options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" }); + options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" }); + options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" }); + options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read }); + options.push_back({ "server", " --threads-http N", "number of threads used to process HTTP requests (default: %d)", params.n_threads_http }); + options.push_back({ "server", " --system-prompt-file FNAME", + "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" }); + options.push_back({ "server", " --log-format {text,json}", + "log output format: json or text (default: json)" }); + options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" }); + options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" }); + options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" }); + options.push_back({ "server", " --chat-template JINJA_TEMPLATE", + "set custom jinja chat template (default: template taken from model's metadata)\n" + "only commonly used templates are accepted:\n" + "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" }); + options.push_back({ "server", "-sps, --slot-prompt-similarity SIMILARITY", + "how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity }); + #ifndef LOG_DISABLE_LOGS - log_print_usage(); + options.push_back({ "logging" }); + options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" }); + options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" }); + options.push_back({ "logging", " --log-test", "Run simple logging test" }); + options.push_back({ "logging", " --log-disable", "Disable trace logs" }); + options.push_back({ "logging", " --log-enable", "Enable trace logs" }); + options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" }); + options.push_back({ "logging", " --log-new", "Create a separate new log file on start. " + "Each log file will have unique name: \"..log\"" }); + options.push_back({ "logging", " --log-append", "Don't truncate the old log file." }); #endif // LOG_DISABLE_LOGS + + options.push_back({ "cvector" }); + options.push_back({ "cvector", "-o, --output FNAME", "output file (default: '%s')", params.cvector_outfile.c_str() }); + options.push_back({ "cvector", " --positive-file FNAME", "positive prompts file, one prompt per line (default: '%s')", params.cvector_positive_file.c_str() }); + options.push_back({ "cvector", " --negative-file FNAME", "negative prompts file, one prompt per line (default: '%s')", params.cvector_negative_file.c_str() }); + options.push_back({ "cvector", " --completions-file FNAME", + "completions file (default: '%s')", params.cvector_completions_file.c_str() }); + options.push_back({ "cvector", " --completions N", "number of lines of completions file to use (default: %d)", params.n_completions }); + options.push_back({ "cvector", " --batch-pca N", "batch size used for PCA. Larger batch runs faster, but uses more memory (default: %d)", params.n_pca_batch }); + options.push_back({ "cvector", " --iter-pca N", "number of iterations used for PCA (default: %d)", params.n_pca_iterations }); + + printf("usage: %s [options]\n", argv[0]); + + for (const auto & o : options) { + if (!o.grp.empty()) { + printf("\n%s:\n\n", o.grp.c_str()); + continue; + } + printf(" %-32s", o.args.c_str()); + if (o.args.length() > 30) { + printf("\n%34s", ""); + } + + const auto desc = o.desc; + size_t start = 0; + size_t end = desc.find('\n'); + while (end != std::string::npos) { + printf("%s\n%34s", desc.substr(start, end - start).c_str(), ""); + start = end + 1; + end = desc.find('\n', start); + } + + printf("%s\n", desc.substr(start).c_str()); + } + printf("\n"); } std::string gpt_params_get_system_info(const gpt_params & params) { @@ -1345,24 +1709,6 @@ std::string string_get_sortable_timestamp() { return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); } -std::string string_random_prompt(std::mt19937 & rng) { - const int r = rng() % 10; - switch (r) { - case 0: return "So"; - case 1: return "Once upon a time"; - case 2: return "When"; - case 3: return "The"; - case 4: return "After"; - case 5: return "If"; - case 6: return "import"; - case 7: return "He"; - case 8: return "She"; - case 9: return "They"; - } - - GGML_UNREACHABLE(); -} - void string_process_escapes(std::string & input) { std::size_t input_len = input.length(); std::size_t output_idx = 0; @@ -1622,6 +1968,16 @@ std::string fs_get_cache_directory() { return ensure_trailing_slash(cache_directory); } +std::string fs_get_cache_file(const std::string & filename) { + GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos); + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + return cache_directory + filename; +} + // // Model utils @@ -2238,6 +2594,12 @@ bool llama_should_add_bos_token(const llama_model * model) { return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); } +bool llama_chat_verify_template(const std::string & tmpl) { + llama_chat_message chat[] = {{"user", "test"}}; + int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); + return res >= 0; +} + // // KV cache utils // @@ -2607,11 +2969,11 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false"); fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); @@ -2665,9 +3027,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); - fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false"); fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); fprintf(stream, "keep: %d # default: 0\n", params.n_keep); fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); @@ -2717,7 +3077,6 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); fprintf(stream, "reverse_prompt:\n"); diff --git a/common/common.h b/common/common.h index c6eb55396496f..151f8e5fd6e8d 100644 --- a/common/common.h +++ b/common/common.h @@ -56,66 +56,66 @@ struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed int32_t n_threads = cpu_get_num_math(); - int32_t n_threads_draft = -1; - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_threads_batch_draft = -1; - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size - int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_draft = 5; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - float p_split = 0.1f; // speculative decoding split probability - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs - int32_t n_beams = 0; // if non-zero then use beam search of given width. - int32_t grp_attn_n = 1; // group-attention factor - int32_t grp_attn_w = 512; // group-attention width - int32_t n_print = -1; // print token count every n tokens (-1 = disabled) - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + int32_t n_threads_draft = -1; + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; + int32_t n_predict = -1; // new tokens to predict + int32_t n_ctx = 0; // context size + int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 5; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + float p_split = 0.1f; // speculative decoding split probability + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor float yarn_beta_fast = 32.0f; // YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length float defrag_thold = -1.0f; // KV cache defragmentation threshold - std::string rpc_servers = ""; // comma separated list of RPC servers ggml_backend_sched_eval_callback cb_eval = nullptr; void * cb_eval_user_data = nullptr; ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED; + enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings // // sampling parameters struct llama_sampling_params sparams; - std::string model = ""; // model path - std::string model_draft = ""; // draft model for speculative decoding + std::string model = ""; // model path + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias - std::string model_url = ""; // model url to download - std::string hf_repo = ""; // HF repo - std::string hf_file = ""; // HF file + std::string model_url = ""; // model url to download + std::string hf_repo = ""; // HF repo + std::string hf_file = ""; // HF file std::string prompt = ""; - std::string prompt_file = ""; // store the external prompt file name - std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state - std::string input_prefix = ""; // string to prefix user inputs with - std::string input_suffix = ""; // string to suffix user inputs with - std::vector antiprompt; // string upon seeing which more user input is prompted - std::string logdir = ""; // directory in which to save YAML log files + std::string prompt_file = ""; // store the external prompt file name + std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state + std::string input_prefix = ""; // string to prefix user inputs with + std::string input_suffix = ""; // string to suffix user inputs with + std::string logdir = ""; // directory in which to save YAML log files std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding - std::string logits_file = ""; // file for saving *all* logits + std::string logits_file = ""; // file for saving *all* logits + std::string rpc_servers = ""; // comma separated list of RPC servers + std::vector in_files; // all input files + std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_overrides; // TODO: avoid tuple, use struct @@ -124,30 +124,31 @@ struct gpt_params { std::vector control_vectors; // control vector with user defined scale + int32_t verbosity = 0; int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_end = -1; // layer range for control vector - int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. - int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line - // (which is more convenient to use for plotting) - // - bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt - size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score + int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. + int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line + // (which is more convenient to use for plotting) + // + bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt + size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score - bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt - size_t winogrande_tasks= 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed + bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt + size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed - bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt - size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed + bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt + size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed - bool kl_divergence = false; // compute KL divergence + bool kl_divergence = false; // compute KL divergence - bool random_prompt = false; // do not randomize prompt if none provided + bool usage = false; // print usage bool use_color = false; // use color to distinguish generations and inputs + bool special = false; // enable special token output bool interactive = false; // interactive mode - bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode + bool interactive_first = false; // wait for user input immediately bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) - bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it @@ -155,8 +156,7 @@ struct gpt_params { int32_t embd_normalize = 2; // normalisation for embendings (-1=none, 0=max absolute, 1=taxicab, 2=euclidean, >2=p-norm) std::string embd_out = ""; // empty = default, "array" = [] or [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix std::string embd_sep = "\n"; // separator of embendings - bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\" - bool interactive_first = false; // wait for user input immediately + bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\" bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool cont_batching = true; // insert new sequences for decoding on-the-fly @@ -164,7 +164,6 @@ struct gpt_params { bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens - bool instruct = false; // instruction mode (used for Alpaca models) bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory @@ -182,6 +181,68 @@ struct gpt_params { // multimodal models (see examples/llava) std::string mmproj = ""; // path to multimodal projector std::vector image; // path to image file(s) + + // server params + int32_t port = 8080; // server listens on this network port + int32_t timeout_read = 600; // http read timeout in seconds + int32_t timeout_write = timeout_read; // http write timeout in seconds + int32_t n_threads_http = -1; // number of threads to process HTTP requests + + std::string hostname = "127.0.0.1"; + std::string public_path = ""; + std::string chat_template = ""; + std::string system_prompt = ""; + + std::vector api_keys; + + std::string ssl_file_key = ""; + std::string ssl_file_cert = ""; + + bool endpoint_slots = true; + bool endpoint_metrics = false; + + bool log_json = false; + + std::string slot_save_path; + + float slot_prompt_similarity = 0.5f; + + // batched-bench params + bool is_pp_shared = false; + + std::vector n_pp; + std::vector n_tg; + std::vector n_pl; + + // retrieval params + std::vector context_files; // context files to embed + + int32_t chunk_size = 64; // chunk size for context embedding + + std::string chunk_separator = "\n"; // chunk separator for context embedding + + // passkey params + int32_t n_junk = 250; // number of times to repeat the junk text + int32_t i_pos = -1; // position of the passkey in the junk text + + // imatrix params + std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file + + int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations + int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations + int32_t i_chunk = 0; // start processing from this chunk + + bool process_output = false; // collect data for the output tensor + bool compute_ppl = true; // whether to compute perplexity + + // cvector-generator params + int n_completions = 64; + int n_pca_batch = 20; + int n_pca_iterations = 1000; + std::string cvector_outfile = "control_vector.gguf"; + std::string cvector_completions_file = "examples/cvector-generator/completions.txt"; + std::string cvector_positive_file = "examples/cvector-generator/positive.txt"; + std::string cvector_negative_file = "examples/cvector-generator/negative.txt"; }; void gpt_params_handle_model_default(gpt_params & params); @@ -201,7 +262,20 @@ std::vector string_split(std::string input, char separator); std::string string_strip(const std::string & str); std::string string_get_sortable_timestamp(); -std::string string_random_prompt(std::mt19937 & rng); + +template +static std::vector string_split(const std::string & str, char delim) { + std::vector values; + std::istringstream str_stream(str); + std::string token; + while (std::getline(str_stream, token, delim)) { + T value; + std::istringstream token_stream(token); + token_stream >> value; + values.push_back(value); + } + return values; +} bool string_parse_kv_override(const char * data, std::vector & overrides); void string_process_escapes(std::string & input); @@ -214,6 +288,7 @@ bool fs_validate_filename(const std::string & filename); bool fs_create_directory_with_parents(const std::string & path); std::string fs_get_cache_directory(); +std::string fs_get_cache_file(const std::string & filename); // // Model utils @@ -284,6 +359,13 @@ std::string llama_detokenize_bpe( // defaults to true when model type is SPM, otherwise false. bool llama_should_add_bos_token(const llama_model * model); +// +// Chat template utils +// + +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +bool llama_chat_verify_template(const std::string & tmpl); + // // KV cache utils // diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index b5bc7d49b5f36..a518b766dc33e 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -46,8 +46,12 @@ namespace grammar_parser { state.rules[rule_id] = rule; } + static bool is_digit_char(char c) { + return '0' <= c && c <= '9'; + } + static bool is_word_char(char c) { - return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9'); + return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || is_digit_char(c); } static std::pair parse_hex(const char * src, int size) { @@ -99,6 +103,17 @@ namespace grammar_parser { return pos; } + static const char * parse_int(const char * src) { + const char * pos = src; + while (is_digit_char(*pos)) { + pos++; + } + if (pos == src) { + throw std::runtime_error(std::string("expecting integer at ") + src); + } + return pos; + } + static std::pair parse_char(const char * src) { if (*src == '\\') { switch (src[1]) { @@ -137,6 +152,60 @@ namespace grammar_parser { bool is_nested) { size_t last_sym_start = out_elements.size(); const char * pos = src; + + auto handle_repetitions = [&](int min_times, int max_times) { + + if (last_sym_start == out_elements.size()) { + throw std::runtime_error(std::string("expecting preceding item to */+/?/{ at ") + pos); + } + + // apply transformation to previous symbol (last_sym_start to end) according to + // the following rewrite rules: + // S{m,n} --> S S S (m times) S'(n-m) + // S'(x) ::= S S'(x-1) | + // (... n-m definitions of these S' rules ...) + // S'(1) ::= S | + // S{m,} --> S S S (m times) S' + // S' ::= S S' | + // S* --> S{0,} + // --> S' ::= S S' | + // S+ --> S{1,} + // --> S S' + // S' ::= S S' | + // S? --> S{0,1} + // --> S' + // S' ::= S | + + std::vector previous_elements(out_elements.begin() + last_sym_start, out_elements.end()); + if (min_times == 0) { + out_elements.resize(last_sym_start); + } else { + // Repeat the previous elements (min_times - 1) times + for (int i = 1; i < min_times; i++) { + out_elements.insert(out_elements.end(), previous_elements.begin(), previous_elements.end()); + } + } + + uint32_t last_rec_rule_id = 0; + auto n_opt = max_times < 0 ? 1 : max_times - min_times; + + std::vector rec_rule(previous_elements); + for (int i = 0; i < n_opt; i++) { + rec_rule.resize(previous_elements.size()); + uint32_t rec_rule_id = generate_symbol_id(state, rule_name); + if (i > 0 || max_times < 0) { + rec_rule.push_back({LLAMA_GRETYPE_RULE_REF, max_times < 0 ? rec_rule_id : last_rec_rule_id}); + } + rec_rule.push_back({LLAMA_GRETYPE_ALT, 0}); + rec_rule.push_back({LLAMA_GRETYPE_END, 0}); + add_rule(state, rec_rule_id, rec_rule); + last_rec_rule_id = rec_rule_id; + } + if (n_opt > 0) { + out_elements.push_back({LLAMA_GRETYPE_RULE_REF, last_rec_rule_id}); + } + }; + while (*pos) { if (*pos == '"') { // literal string pos++; @@ -197,40 +266,51 @@ namespace grammar_parser { throw std::runtime_error(std::string("expecting ')' at ") + pos); } pos = parse_space(pos + 1, is_nested); - } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator - if (last_sym_start == out_elements.size()) { - throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos); - } + } else if (*pos == '.') { // any char + last_sym_start = out_elements.size(); + out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0}); + pos = parse_space(pos + 1, is_nested); + } else if (*pos == '*') { + pos = parse_space(pos + 1, is_nested); + handle_repetitions(0, -1); + } else if (*pos == '+') { + pos = parse_space(pos + 1, is_nested); + handle_repetitions(1, -1); + } else if (*pos == '?') { + pos = parse_space(pos + 1, is_nested); + handle_repetitions(0, 1); + } else if (*pos == '{') { + pos = parse_space(pos + 1, is_nested); - // apply transformation to previous symbol (last_sym_start to end) according to - // rewrite rules: - // S* --> S' ::= S S' | - // S+ --> S' ::= S S' | S - // S? --> S' ::= S | - uint32_t sub_rule_id = generate_symbol_id(state, rule_name); - std::vector sub_rule; - // add preceding symbol to generated rule - sub_rule.insert( - sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); - if (*pos == '*' || *pos == '+') { - // cause generated rule to recurse - sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + if (!is_digit_char(*pos)) { + throw std::runtime_error(std::string("expecting an int at ") + pos); } - // mark start of alternate def - sub_rule.push_back({LLAMA_GRETYPE_ALT, 0}); - if (*pos == '+') { - // add preceding symbol as alternate only for '+' (otherwise empty) - sub_rule.insert( - sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end()); - } - sub_rule.push_back({LLAMA_GRETYPE_END, 0}); - add_rule(state, sub_rule_id, sub_rule); + const char * int_end = parse_int(pos); + int min_times = std::stoul(std::string(pos, int_end - pos)); + pos = parse_space(int_end, is_nested); - // in original rule, replace previous symbol with reference to generated rule - out_elements.resize(last_sym_start); - out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id}); + int max_times = -1; - pos = parse_space(pos + 1, is_nested); + if (*pos == '}') { + max_times = min_times; + pos = parse_space(pos + 1, is_nested); + } else if (*pos == ',') { + pos = parse_space(pos + 1, is_nested); + + if (is_digit_char(*pos)) { + const char * int_end = parse_int(pos); + max_times = std::stoul(std::string(pos, int_end - pos)); + pos = parse_space(int_end, is_nested); + } + + if (*pos != '}') { + throw std::runtime_error(std::string("expecting '}' at ") + pos); + } + pos = parse_space(pos + 1, is_nested); + } else { + throw std::runtime_error(std::string("expecting ',' at ") + pos); + } + handle_repetitions(min_times, max_times); } else { break; } @@ -325,6 +405,7 @@ namespace grammar_parser { case LLAMA_GRETYPE_CHAR_NOT: return true; case LLAMA_GRETYPE_CHAR_ALT: return true; case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true; + case LLAMA_GRETYPE_CHAR_ANY: return true; default: return false; } } @@ -339,6 +420,7 @@ namespace grammar_parser { case LLAMA_GRETYPE_CHAR_NOT: fprintf(file, "CHAR_NOT"); break; case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break; case LLAMA_GRETYPE_CHAR_ALT: fprintf(file, "CHAR_ALT"); break; + case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "CHAR_ANY"); break; } switch (elem.type) { case LLAMA_GRETYPE_END: @@ -350,6 +432,7 @@ namespace grammar_parser { case LLAMA_GRETYPE_CHAR_NOT: case LLAMA_GRETYPE_CHAR_RNG_UPPER: case LLAMA_GRETYPE_CHAR_ALT: + case LLAMA_GRETYPE_CHAR_ANY: fprintf(file, "(\""); print_grammar_char(file, elem.value); fprintf(file, "\") "); @@ -407,11 +490,15 @@ namespace grammar_parser { } print_grammar_char(file, elem.value); break; + case LLAMA_GRETYPE_CHAR_ANY: + fprintf(file, "."); + break; } if (is_char_element(elem)) { switch (rule[i + 1].type) { case LLAMA_GRETYPE_CHAR_ALT: case LLAMA_GRETYPE_CHAR_RNG_UPPER: + case LLAMA_GRETYPE_CHAR_ANY: break; default: fprintf(file, "] "); diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 9a71f5d8d76ba..10b9b3d1d4d41 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -16,92 +16,55 @@ static std::string join(Iterator begin, Iterator end, const std::string & separa static std::string repeat(const std::string & str, size_t n); -static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "", bool item_rule_is_literal = false) { - if (separator_rule.empty()) { - if (min_items == 0 && max_items == 1) { - return item_rule + "?"; - } else if (min_items == 1 && max_items == std::numeric_limits::max()) { - return item_rule + "+"; - } - } +static std::string build_repetition(const std::string & item_rule, int min_items, int max_items, const std::string & separator_rule = "") { + auto has_max = max_items != std::numeric_limits::max(); - std::string result; - if (min_items > 0) { - if (item_rule_is_literal && separator_rule.empty()) { - result = "\"" + repeat(std::string(item_rule.begin() + 1, item_rule.end() - 1), min_items) + "\""; - } else { - std::vector items(min_items, item_rule); - result = join(items.begin(), items.end(), separator_rule.empty() ? " " : " " + separator_rule + " "); - } + if (min_items == 0 && max_items == 1) { + return item_rule + "?"; } - std::function opt_repetitions = [&](int up_to_n, bool prefix_with_sep) -> std::string { - auto content = prefix_with_sep && !separator_rule.empty() ? separator_rule + " " + item_rule : item_rule; - - if (up_to_n == 0) { - return ""; - } else if (up_to_n == 1) { - return "(" + content + ")?"; - } else if (!separator_rule.empty() && !prefix_with_sep) { - return "(" + content + " " + opt_repetitions(up_to_n - 1, true) + ")?"; + if (separator_rule.empty()) { + if (min_items == 1 && !has_max) { + return item_rule + "+"; + } else if (min_items == 0 && !has_max) { + return item_rule + "*"; } else { - std::string res = repeat("(" + content + " ", up_to_n); - // strip trailing space - res = res.substr(0, res.length() - 1); - res += repeat(")?", up_to_n); - return res; + return item_rule + "{" + std::to_string(min_items) + "," + (has_max ? std::to_string(max_items) : "") + "}"; } - }; - - if (min_items > 0 && max_items != min_items) { - result += " "; } - if (max_items != std::numeric_limits::max()) { - result += opt_repetitions(max_items - min_items, min_items > 0); - } else { - std::string item_operator = "(" + (separator_rule.empty() ? "" : separator_rule + " ") + item_rule + ")"; - if (min_items == 0 && !separator_rule.empty()) { - result = "(" + item_rule + " " + item_operator + "*)?"; - } else { - result += item_operator + "*"; - } + auto result = item_rule + " " + build_repetition("(" + separator_rule + " " + item_rule + ")", min_items == 0 ? 0 : min_items - 1, has_max ? max_items - 1 : max_items); + if (min_items == 0) { + result = "(" + result + ")?"; } - return result; } -const std::string SPACE_RULE = "\" \"?"; +const std::string SPACE_RULE = "| \" \" | \"\\n\" [ \\t]{0,20}"; struct BuiltinRule { std::string content; std::vector deps; }; -const std::string _up_to_15_digits = build_repetition("[0-9]", 0, 15); - std::unordered_map PRIMITIVE_RULES = { {"boolean", {"(\"true\" | \"false\") space", {}}}, - {"decimal-part", {"[0-9] " + _up_to_15_digits, {}}}, - {"integral-part", {"[0-9] | [1-9] " + _up_to_15_digits, {}}}, + {"decimal-part", {"[0-9]{1,16}", {}}}, + {"integral-part", {"[0] | [1-9] [0-9]{0,15}", {}}}, {"number", {"(\"-\"? integral-part) (\".\" decimal-part)? ([eE] [-+]? integral-part)? space", {"integral-part", "decimal-part"}}}, {"integer", {"(\"-\"? integral-part) space", {"integral-part"}}}, {"value", {"object | array | string | number | boolean | null", {"object", "array", "string", "number", "boolean", "null"}}}, {"object", {"\"{\" space ( string \":\" space value (\",\" space string \":\" space value)* )? \"}\" space", {"string", "value"}}}, {"array", {"\"[\" space ( value (\",\" space value)* )? \"]\" space", {"value"}}}, - {"uuid", {"\"\\\"\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] " - "\"-\" [0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F][0-9a-fA-F] \"\\\"\" space", {}}}, - {"char", {"[^\"\\\\] | \"\\\\\" ([\"\\\\/bfnrt] | \"u\" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])", {}}}, + {"uuid", {"\"\\\"\" [0-9a-fA-F]{8} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{4} \"-\" [0-9a-fA-F]{12} \"\\\"\" space", {}}}, + {"char", {"[^\"\\\\\\x7F\\x00-\\x1F] | [\\\\] ([\"\\\\bfnrt] | \"u\" [0-9a-fA-F]{4})", {}}}, {"string", {"\"\\\"\" char* \"\\\"\" space", {"char"}}}, {"null", {"\"null\" space", {}}}, }; std::unordered_map STRING_FORMAT_RULES = { - {"date", {"[0-9] [0-9] [0-9] [0-9] \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, - {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9] [0-9] [0-9] )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, + {"date", {"[0-9]{4} \"-\" ( \"0\" [1-9] | \"1\" [0-2] ) \"-\" ( \"0\" [1-9] | [1-2] [0-9] | \"3\" [0-1] )", {}}}, + {"time", {"([01] [0-9] | \"2\" [0-3]) \":\" [0-5] [0-9] \":\" [0-5] [0-9] ( \".\" [0-9]{3} )? ( \"Z\" | ( \"+\" | \"-\" ) ( [01] [0-9] | \"2\" [0-3] ) \":\" [0-5] [0-9] )", {}}}, {"date-time", {"date \"T\" time", {"date", "time"}}}, {"date-string", {"\"\\\"\" date \"\\\"\" space", {"date"}}}, {"time-string", {"\"\\\"\" time \"\\\"\" space", {"time"}}}, @@ -385,8 +348,7 @@ class SchemaConverter { sub_is_literal ? "\"" + sub + "\"" : sub, min_times, max_times, - "", - sub_is_literal + "" ); seq.back().second = false; } else { diff --git a/common/train.cpp b/common/train.cpp index 2d41a1d29a83c..fef1e57c94655 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() { params.custom_n_ctx = false; - params.use_flash = true; + params.use_flash = false; params.use_checkpointing = true; params.sample_start = ""; diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 1923b88ba2a80..fbf1e1ea3de37 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -1,4 +1,5 @@ #!/usr/bin/env python3 +# -*- coding: utf-8 -*- # This script downloads the tokenizer models of the specified models from Huggingface and # generates the get_vocab_base_pre() function for convert-hf-to-gguf.py @@ -81,6 +82,9 @@ class TOKENIZER_TYPE(IntEnum): {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, + {"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", }, + {"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", }, ] diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 51549ac72f8e7..a6751cc80e682 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1,4 +1,5 @@ #!/usr/bin/env python3 +# -*- coding: utf-8 -*- from __future__ import annotations @@ -25,8 +26,6 @@ sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -from convert import LlamaHfVocab - logger = logging.getLogger("hf-to-gguf") @@ -48,11 +47,12 @@ class Model: _model_classes: dict[str, type[Model]] = {} dir_model: Path - ftype: int + ftype: gguf.LlamaFileType is_big_endian: bool endianess: gguf.GGUFEndian use_temp_file: bool lazy: bool + model_name: str | None part_names: list[str] is_safetensors: bool hparams: dict[str, Any] @@ -65,7 +65,7 @@ class Model: # subclasses should define this! model_arch: gguf.MODEL_ARCH - def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool, model_name: str | None): if type(self) is Model: raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") self.dir_model = dir_model @@ -74,10 +74,11 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE self.use_temp_file = use_temp_file self.lazy = not eager - self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors") + self.model_name = model_name + self.part_names = Model.get_model_part_names(self.dir_model, "model", ".safetensors") self.is_safetensors = len(self.part_names) > 0 if not self.is_safetensors: - self.part_names = Model.get_model_part_names(self.dir_model, ".bin") + self.part_names = Model.get_model_part_names(self.dir_model, "pytorch_model", ".bin") self.hparams = Model.load_hparams(self.dir_model) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) @@ -95,7 +96,7 @@ def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, ftype_lw: str = ftype_up.lower() # allow templating the file name with the output ftype, useful with the "auto" ftype self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up) - self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) + self.gguf_writer = gguf.GGUFWriter(path=None, arch=gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) @classmethod def __init_subclass__(cls): @@ -183,7 +184,7 @@ def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", " return new_name def set_gguf_parameters(self): - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_block_count(self.block_count) if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: @@ -325,21 +326,21 @@ def write_tensors(self): def write(self): self.write_tensors() - self.gguf_writer.write_header_to_file() + self.gguf_writer.write_header_to_file(self.fname_out) self.gguf_writer.write_kv_data_to_file() self.gguf_writer.write_tensors_to_file(progress=True) self.gguf_writer.close() def write_vocab(self): - self.gguf_writer.write_header_to_file() + self.gguf_writer.write_header_to_file(self.fname_out) self.gguf_writer.write_kv_data_to_file() self.gguf_writer.close() @staticmethod - def get_model_part_names(dir_model: Path, suffix: str) -> list[str]: + def get_model_part_names(dir_model: Path, prefix: str, suffix: str) -> list[str]: part_names: list[str] = [] for filename in os.listdir(dir_model): - if filename.endswith(suffix): + if filename.startswith(prefix) and filename.endswith(suffix): part_names.append(filename) part_names.sort() @@ -473,6 +474,15 @@ def get_vocab_base_pre(self, tokenizer) -> str: if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de res = "jina-v2-de" + if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": + # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct + res = "smaug-bpe" + if chkhsh == "c7ea5862a53e4272c035c8238367063e2b270d51faa48c0f09e9d5b54746c360": + # ref: https://huggingface.co/LumiOpen/Poro-34B-chat + res = "poro-chat" + if chkhsh == "7967bfa498ade6b757b064f31e964dddbb80f8f9a4d68d4ba7998fcf281c531a": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-code + res = "jina-v2-code" if res is None: logger.warning("\n") @@ -631,7 +641,7 @@ def _set_vocab_sentencepiece(self): special_vocab.add_to_gguf(self.gguf_writer) def _set_vocab_llama_hf(self): - vocab = LlamaHfVocab(self.dir_model) + vocab = gguf.LlamaHfVocab(self.dir_model) tokens = [] scores = [] toktypes = [] @@ -660,7 +670,7 @@ class GPTNeoXModel(Model): def set_gguf_parameters(self): block_count = self.hparams["num_hidden_layers"] - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -793,7 +803,7 @@ def set_vocab(self): def set_gguf_parameters(self): block_count = self.hparams["n_layers"] - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_context_length(self.hparams["max_seq_len"]) self.gguf_writer.add_embedding_length(self.hparams["d_model"]) self.gguf_writer.add_block_count(block_count) @@ -845,7 +855,7 @@ def set_gguf_parameters(self): raise ValueError("gguf: can not find ctx length parameter.") self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_source_hf_repo(hf_repo) self.gguf_writer.add_tensor_data_layout("Meta AI original pth") self.gguf_writer.add_context_length(ctx_length) @@ -882,7 +892,7 @@ def set_gguf_parameters(self): else: raise ValueError("gguf: can not find ctx length parameter.") - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_source_hf_repo(hf_repo) self.gguf_writer.add_tensor_data_layout("Meta AI original pth") self.gguf_writer.add_context_length(ctx_length) @@ -1005,7 +1015,7 @@ def set_gguf_parameters(self): else: raise ValueError("gguf: can not find ctx length parameter.") - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_source_hf_repo(hf_repo) self.gguf_writer.add_tensor_data_layout("Meta AI original pth") self.gguf_writer.add_context_length(ctx_length) @@ -1201,7 +1211,7 @@ def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -1314,6 +1324,17 @@ def set_gguf_parameters(self): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -1328,9 +1349,9 @@ def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iter n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - if name.endswith("q_proj.weight"): + if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) - if name.endswith("k_proj.weight"): + if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately @@ -1611,6 +1632,12 @@ def set_gguf_parameters(self): super().set_gguf_parameters() if (n_experts := self.hparams.get("num_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) + if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None: + self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size) + logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}") + if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: + self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) + logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") _experts: list[dict[str, Tensor]] | None = None @@ -1665,7 +1692,7 @@ class GPT2Model(Model): model_arch = gguf.MODEL_ARCH.GPT2 def set_gguf_parameters(self): - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_block_count(self.hparams["n_layer"]) self.gguf_writer.add_context_length(self.hparams["n_ctx"]) self.gguf_writer.add_embedding_length(self.hparams["n_embd"]) @@ -2232,7 +2259,7 @@ def set_gguf_parameters(self): hparams = self.hparams block_count = hparams["num_hidden_layers"] - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_context_length(hparams["max_position_embeddings"]) self.gguf_writer.add_embedding_length(hparams["hidden_size"]) self.gguf_writer.add_block_count(block_count) @@ -2332,7 +2359,7 @@ def set_gguf_parameters(self): # Fail early for models which don't have a block expansion factor of 2 assert d_inner == 2 * d_model - self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_name(self.dir_model.name if self.model_name is None else self.model_name) self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default self.gguf_writer.add_embedding_length(d_model) self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading @@ -2392,7 +2419,8 @@ def __init__(self, *args, **kwargs): # max_position_embeddings = 8192 in config.json but model was actually # trained on 128k context length - self.hparams["max_position_embeddings"] = self.hparams["model_max_length"] + # aya-23 models don't have model_max_length specified + self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) def set_gguf_parameters(self): super().set_gguf_parameters() @@ -2438,11 +2466,13 @@ def __init__(self, *args, **kwargs): def get_tensors(self): for name, data in super().get_tensors(): - if 'gated_layers' in name: + if 'gated_layer' in name: d1 = data[:self.intermediate_size, :] name1 = name.replace('gated_layers', 'gated_layers_w') + name1 = name1.replace('up_gated_layer', 'gated_layers_v') d2 = data[self.intermediate_size:, :] name2 = name.replace('gated_layers', 'gated_layers_v') + name2 = name2.replace('up_gated_layer', 'gated_layers_w') yield name1, d1 yield name2, d2 continue @@ -2616,6 +2646,85 @@ def write_tensors(self): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("DeepseekV2ForCausalLM") +class DeepseekV2Model(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["v_head_dim"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def write_tensors(self): + super().write_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + ###### CONVERSION LOGIC ###### @@ -2748,8 +2857,13 @@ def main() -> None: hparams = Model.load_hparams(dir_model) with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy) + try: + model_class = Model.from_model_architecture(hparams["architectures"][0]) + except NotImplementedError: + logger.error(f"Model {hparams['architectures'][0]} is not supported") + sys.exit(1) + + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy, args.model_name) logger.info("Set model parameters") model_instance.set_gguf_parameters() diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md index 48769cdf61092..3eec077ea7bd9 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/HOWTO-add-model.md @@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M ### 1. Convert the model to GGUF This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. -Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). +Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format). The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. @@ -100,7 +100,7 @@ Have a look at existing implementation like `build_llama`, `build_dbrx` or `buil When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR. -Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback). +Note: to debug the inference graph: you can use [llama-eval-callback](../examples/eval-callback). ## GGUF specification diff --git a/docs/token_generation_performance_tips.md b/docs/token_generation_performance_tips.md index 3c43431471243..c0840cad57fb3 100644 --- a/docs/token_generation_performance_tips.md +++ b/docs/token_generation_performance_tips.md @@ -3,7 +3,7 @@ ## Verifying that the model is running on the GPU with CUDA Make sure you compiled llama with the correct env variables according to [this guide](../README.md#CUDA), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example: ```shell -./main -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " +./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some " ``` When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines: @@ -27,7 +27,7 @@ RAM: 32GB Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML) -Run command: `./main -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]` +Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]` Result: diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index b40ee4ccb2ec1..0b51c44c05e4e 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -12,44 +12,45 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}) if (EMSCRIPTEN) else() + add_subdirectory(cvector-generator) add_subdirectory(baby-llama) - add_subdirectory(batched) add_subdirectory(batched-bench) - add_subdirectory(beam-search) + add_subdirectory(batched) add_subdirectory(benchmark) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) add_subdirectory(eval-callback) + add_subdirectory(export-lora) add_subdirectory(finetune) - add_subdirectory(gritlm) + add_subdirectory(gbnf-validator) add_subdirectory(gguf-split) + add_subdirectory(gguf) + add_subdirectory(gritlm) + add_subdirectory(imatrix) add_subdirectory(infill) add_subdirectory(llama-bench) add_subdirectory(llava) - if (LLAMA_SYCL) - add_subdirectory(sycl) - endif() + add_subdirectory(lookahead) + add_subdirectory(lookup) add_subdirectory(main) - add_subdirectory(tokenize) add_subdirectory(parallel) + add_subdirectory(passkey) add_subdirectory(perplexity) - add_subdirectory(quantize) add_subdirectory(quantize-stats) + add_subdirectory(quantize) add_subdirectory(retrieval) + if (LLAMA_RPC) + add_subdirectory(rpc) + endif() + if (LLAMA_BUILD_SERVER) + add_subdirectory(server) + endif() + if (LLAMA_SYCL) + add_subdirectory(sycl) + endif() add_subdirectory(save-load-state) add_subdirectory(simple) - add_subdirectory(passkey) add_subdirectory(speculative) - add_subdirectory(lookahead) - add_subdirectory(lookup) - add_subdirectory(gguf) + add_subdirectory(tokenize) add_subdirectory(train-text-from-scratch) - add_subdirectory(imatrix) - if (LLAMA_BUILD_SERVER) - add_subdirectory(server) - endif() - add_subdirectory(export-lora) - if (LLAMA_RPC) - add_subdirectory(rpc) - endif() endif() diff --git a/examples/Miku.sh b/examples/Miku.sh index b9174b4e6e126..0f6c8c8787107 100755 --- a/examples/Miku.sh +++ b/examples/Miku.sh @@ -22,7 +22,7 @@ if [ -n "$N_THREAD" ]; then GEN_OPTIONS+=(--threads "$N_THREAD") fi -./main "${GEN_OPTIONS[@]}" \ +./llama-cli "${GEN_OPTIONS[@]}" \ --model "$MODEL" \ --in-prefix " " \ --in-suffix "${AI_NAME}:" \ diff --git a/examples/alpaca.sh b/examples/alpaca.sh deleted file mode 100755 index 8d2bae6918b62..0000000000000 --- a/examples/alpaca.sh +++ /dev/null @@ -1,19 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m ./models/alpaca.13b.ggmlv3.q8_0.bin \ - --color \ - -f ./prompts/alpaca.txt \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 7 diff --git a/examples/baby-llama/CMakeLists.txt b/examples/baby-llama/CMakeLists.txt index 7b70227a525e1..71b82105c8863 100644 --- a/examples/baby-llama/CMakeLists.txt +++ b/examples/baby-llama/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET baby-llama) +set(TARGET llama-baby-llama) add_executable(${TARGET} baby-llama.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index bf0125e753746..4f6c3746a106c 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -522,8 +522,8 @@ static struct ggml_tensor * forward( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, 1] // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), KQ_pos, n_rot, 0); // store key and value to memory { @@ -759,8 +759,8 @@ static struct ggml_tensor * forward_batch( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, n_batch] // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), KQ_pos, n_rot, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -1056,7 +1056,7 @@ static struct ggml_tensor * forward_lora( model->layers[il].wqb, cur)), n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0, 0); + KQ_pos, n_rot, 0); struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, @@ -1065,7 +1065,7 @@ static struct ggml_tensor * forward_lora( model->layers[il].wkb, cur)), n_embd/n_head, n_head, N), - KQ_pos, n_rot, 0, 0); + KQ_pos, n_rot, 0); // store key and value to memory { diff --git a/examples/base-translate.sh b/examples/base-translate.sh index 00dedd0df7dde..103a52f55e6db 100755 --- a/examples/base-translate.sh +++ b/examples/base-translate.sh @@ -58,4 +58,4 @@ echo "$2 model=$1 # generate the most likely continuation until the string "===" is found -./main -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs +./llama-cli -m $model -f $ftmp -n 64 --temp 0 --repeat-penalty 1.0 --no-penalize-nl -r "===" $eargs diff --git a/examples/batched-bench/CMakeLists.txt b/examples/batched-bench/CMakeLists.txt index 40a032c514d11..959acaeeebc38 100644 --- a/examples/batched-bench/CMakeLists.txt +++ b/examples/batched-bench/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET batched-bench) +set(TARGET llama-batched-bench) add_executable(${TARGET} batched-bench.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md index bf951baf7f096..4a07fe6bbf268 100644 --- a/examples/batched-bench/README.md +++ b/examples/batched-bench/README.md @@ -10,16 +10,16 @@ There are 2 modes of operation: - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`) ```bash -./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] +./llama-batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps] # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared -./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99 +./llama-batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99 +./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps # custom set of batches -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32 +./llama-batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 ``` ## Sample results diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 2924d8116f44f..718f0a61a1878 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -28,67 +28,27 @@ static std::vector parse_list(char * p) { return ret; } -int main(int argc, char ** argv) { - gpt_params params; - - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] \n" , argv[0]); - printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); - printf(" example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); - return 1 ; - } - - int n_kv_max = 2048; - int n_batch = 2048; - int n_ubatch = 512; - bool flash_attn = false; - int is_pp_shared = 0; - int n_gpu_layers = 0; - - std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; - std::vector n_tg = { 128, 256, }; - std::vector n_pl = { 1, 2, 4, 8, 16, 32, }; - //std::vector n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, }; - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_kv_max = std::atoi(argv[2]); - } - - if (argc >= 4) { - n_batch = std::atoi(argv[3]); - } - - if (argc >= 5) { - n_ubatch = std::atoi(argv[4]); - } - - if (argc >= 6) { - flash_attn = std::atoi(argv[5]); - } +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); - if (argc >= 7) { - is_pp_shared = std::atoi(argv[6]); - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]); + LOG_TEE("\n"); +} - if (argc >= 8) { - n_gpu_layers = std::atoi(argv[7]); - } +int main(int argc, char ** argv) { + gpt_params params; - if (argc >= 9) { - n_pp = parse_list(argv[8]); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - if (argc >= 10) { - n_tg = parse_list(argv[9]); - } + int is_pp_shared = params.is_pp_shared; - if (argc >= 11) { - n_pl = parse_list(argv[10]); - } + std::vector n_pp = params.n_pp; + std::vector n_tg = params.n_tg; + std::vector n_pl = params.n_pl; // init LLM @@ -97,12 +57,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - const std::vector t_split(llama_max_devices(), 0.0f); - - model_params.n_gpu_layers = n_gpu_layers; - model_params.tensor_split = t_split.data(); + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -111,16 +66,7 @@ int main(int argc, char ** argv) { return 1; } - llama_context_params ctx_params = llama_context_default_params(); - - ctx_params.seed = 1234; - ctx_params.n_ctx = n_kv_max; - ctx_params.n_batch = n_batch; - ctx_params.n_ubatch = n_ubatch; - ctx_params.flash_attn = flash_attn; - - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); // ensure enough sequences are available ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end()); @@ -132,6 +78,8 @@ int main(int argc, char ** argv) { return 1; } + const int32_t n_kv_max = llama_n_ctx(ctx); + llama_batch batch = llama_batch_init(n_kv_max, 0, 1); // decode in batches of ctx_params.n_batch tokens @@ -175,7 +123,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/batched.swift/Makefile b/examples/batched.swift/Makefile index 2afb24fb85a1a..1f9156e583fdd 100755 --- a/examples/batched.swift/Makefile +++ b/examples/batched.swift/Makefile @@ -1,6 +1,6 @@ .PHONY: build build: - xcodebuild -scheme batched_swift -destination "generic/platform=macOS" -derivedDataPath build - rm -f ./batched_swift - ln -s ./build/Build/Products/Debug/batched_swift ./batched_swift + xcodebuild -scheme llama-batched-swift -destination "generic/platform=macOS" -derivedDataPath build + rm -f ./llama-batched-swift + ln -s ./build/Build/Products/Debug/llama-batched-swift ./llama-batched-swift diff --git a/examples/batched.swift/Package.swift b/examples/batched.swift/Package.swift index 826491defd863..7e8afd0843c5b 100644 --- a/examples/batched.swift/Package.swift +++ b/examples/batched.swift/Package.swift @@ -4,7 +4,7 @@ import PackageDescription let package = Package( - name: "batched_swift", + name: "llama-batched-swift", platforms: [.macOS(.v12)], dependencies: [ .package(name: "llama", path: "../../"), @@ -13,7 +13,7 @@ let package = Package( // Targets are the basic building blocks of a package, defining a module or a test suite. // Targets can depend on other targets in this package and products from dependencies. .executableTarget( - name: "batched_swift", + name: "llama-batched-swift", dependencies: ["llama"], path: "Sources", linkerSettings: [.linkedFramework("Foundation"), .linkedFramework("AppKit")] diff --git a/examples/batched.swift/README.md b/examples/batched.swift/README.md index 4c2721fe85b00..7f2e2fcdcf4a7 100644 --- a/examples/batched.swift/README.md +++ b/examples/batched.swift/README.md @@ -1,4 +1,4 @@ This is a swift clone of `examples/batched`. $ `make` -$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]` +$ `./llama-batched-swift MODEL_PATH [PROMPT] [PARALLEL]` diff --git a/examples/batched/CMakeLists.txt b/examples/batched/CMakeLists.txt index 6aa178d4d5911..77e33343b6673 100644 --- a/examples/batched/CMakeLists.txt +++ b/examples/batched/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET batched) +set(TARGET llama-batched) add_executable(${TARGET} batched.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/batched/README.md b/examples/batched/README.md index 5d730331769fb..6013aab01fddc 100644 --- a/examples/batched/README.md +++ b/examples/batched/README.md @@ -3,7 +3,7 @@ The example demonstrates batched generation from a given prompt ```bash -./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4 +./llama-batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 ... diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 591bc6e57645c..62d9b144d3340 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -7,48 +7,31 @@ #include #include -int main(int argc, char ** argv) { - gpt_params params; - - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]); - return 1 ; - } - - // number of parallel batches - int n_parallel = 1; +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); - // total length of the sequences including the prompt - int n_len = 32; - - // number of layers to offload to the GPU - int n_gpu_layers = 0; - - if (argc >= 2) { - params.model = argv[1]; - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]); + LOG_TEE("\n"); +} - if (argc >= 3) { - params.prompt = argv[2]; - } +int main(int argc, char ** argv) { + gpt_params params; - if (argc >= 4) { - n_parallel = std::atoi(argv[3]); - } + params.prompt = "Hello my name is"; + params.n_predict = 32; - if (argc >= 5) { - n_len = std::atoi(argv[4]); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - if (argc >= 6) { - n_gpu_layers = std::atoi(argv[5]); - } - if (params.prompt.empty()) { - params.prompt = "Hello my name is"; - } + // number of parallel batches + int n_parallel = params.n_parallel; - string_process_escapes(params.prompt); + // total length of the sequences including the prompt + int n_predict = 32; // init LLM @@ -57,9 +40,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - model_params.n_gpu_layers = n_gpu_layers; + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -73,18 +54,14 @@ int main(int argc, char ** argv) { std::vector tokens_list; tokens_list = ::llama_tokenize(model, params.prompt, true); - const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel; + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); - ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; - ctx_params.n_batch = std::max(n_len, n_parallel); - ctx_params.n_seq_max = n_parallel; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_batch = std::max(n_predict, n_parallel); llama_context * ctx = llama_new_context_with_model(model, ctx_params); @@ -93,9 +70,9 @@ int main(int argc, char ** argv) { return 1; } - const int n_ctx = llama_n_ctx(ctx); + const int n_ctx = llama_n_ctx(ctx); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); + LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { @@ -156,7 +133,7 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - while (n_cur <= n_len) { + while (n_cur <= n_predict) { // prepare the next batch llama_batch_clear(batch); @@ -192,7 +169,7 @@ int main(int argc, char ** argv) { //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); // is it an end of generation? -> mark the stream as finished - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { i_batch[i] = -1; LOG_TEE("\n"); if (n_parallel > 1) { diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp deleted file mode 100644 index 3d34378a506eb..0000000000000 --- a/examples/beam-search/beam-search.cpp +++ /dev/null @@ -1,188 +0,0 @@ -#include "common.h" -#include "llama.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -#include -#include -#elif defined (_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -# define NOMINMAX -#endif -#include -#include -#endif - -// Used for debugging to print out beam tokens. -struct ostream_beam_view { - llama_context * ctx; - llama_beam_view beam_view; -}; - -static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) { - os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens("; - for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) { - os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]); - } - return os << ')'; -} - -// Put here anything you want back in beam_search_callback(). -struct beam_search_callback_data { - llama_context * ctx; - std::vector response; -}; - -// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. -// For example, eob can be flagged due to maximum token length, stop words, etc. -static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) { - return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector response which is pointed to by callback_data. -static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { - auto& callback_data = *static_cast(callback_data_ptr); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - callback_data.response.resize(callback_data.response.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - std::copy(tokens, tokens + n, callback_data.response.end() - n); - printf("%zu", n); - } - fflush(stdout); -#if 1 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n"; - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["< 3 ) - { - params.prompt = argv[3]; - } - - if ( params.prompt.empty() ) - { - params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n"; - } - - //--------------------------------- - // Init LLM : - //--------------------------------- - - llama_backend_init(); - llama_numa_init(params.numa); - - llama_model * model; - llama_context * ctx; - - std::tie(model, ctx) = llama_init_from_gpt_params( params ); - - if ( model == NULL ) - { - fprintf( stderr , "%s: error: unable to load model\n" , __func__ ); - return 1; - } - - //--------------------------------- - // Tokenize the prompt : - //--------------------------------- - - std::vector tokens_list = llama_tokenize(ctx, params.prompt, true); - - const size_t max_context_size = llama_n_ctx( ctx ); - const size_t max_tokens_list_size = max_context_size - 4 ; - - if (tokens_list.size() > max_tokens_list_size) - { - fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" , - __func__ , tokens_list.size() , max_tokens_list_size ); - return 1; - } - - fprintf( stderr, "\n\n" ); - - // Print the tokens from the prompt : - - for( auto id : tokens_list ) - { - std::cout << llama_token_to_piece(ctx, id); - } - std::cout << std::flush; - - int n_past = 0; - - if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0))) - { - fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ ); - return 1; - } - n_past += tokens_list.size(); - - beam_search_callback_data callback_data{ctx, {}}; - size_t const beam_width = static_cast(params.n_beams); - int const n_predict = 256; - llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict); - - std::cout << "\n\n"; - for (llama_token const token_id : callback_data.response) { - std::cout << llama_token_to_piece(ctx,token_id); - } - std::cout << std::endl; - - llama_free( ctx ); - llama_free_model( model ); - - llama_backend_free(); - - return 0; -} diff --git a/examples/benchmark/CMakeLists.txt b/examples/benchmark/CMakeLists.txt index 2bb47bab5a868..34a58cc02abaf 100644 --- a/examples/benchmark/CMakeLists.txt +++ b/examples/benchmark/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET benchmark) +set(TARGET llama-bench-matmult) add_executable(${TARGET} benchmark-matmult.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/chat-13B.sh b/examples/chat-13B.sh index 35c089d57d253..1828903c31670 100755 --- a/examples/chat-13B.sh +++ b/examples/chat-13B.sh @@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ $PROMPT_TEMPLATE > $PROMPT_FILE # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./main $GEN_OPTIONS \ +./llama-cli $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --n_predict "$N_PREDICTS" \ diff --git a/examples/chat-persistent.sh b/examples/chat-persistent.sh index 22f5b83d3da06..d9cab9836482e 100755 --- a/examples/chat-persistent.sh +++ b/examples/chat-persistent.sh @@ -62,7 +62,7 @@ fi if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then echo 'Prompt cache does not exist, building...' # Default batch_size to 64 here for better user feedback during initial prompt processing - ./main 2>>"$LOG" \ + ./llama-cli 2>>"$LOG" \ --batch_size 64 \ "${OPTS[@]}" \ --prompt-cache "$PROMPT_CACHE_FILE" \ @@ -109,13 +109,13 @@ while read -e line; do printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE" - ./main 2>>"$LOG" "${OPTS[@]}" \ + ./llama-cli 2>>"$LOG" "${OPTS[@]}" \ --prompt-cache "$CUR_PROMPT_CACHE" \ --prompt-cache-all \ --file "$CUR_PROMPT_FILE" \ --reverse-prompt "${USER_NAME}:" \ --n_predict "$n_predict" | - skip_bytes 1 | # skip BOS token added by ./main + skip_bytes 1 | # skip BOS token added by ./llama-cli tee "$CUR_PROMPT_FILE.tmp" | # save prompt + generation to tmp file skip_bytes "$n_prompt_len_pre" # print generation @@ -133,7 +133,7 @@ while read -e line; do # TODO get both messages in one go if ! session_size_msg="$(tail -n30 "$LOG" | grep -oE "$SESSION_SIZE_MSG_PATTERN")" || ! sample_time_msg="$(tail -n10 "$LOG" | grep -oE "$SAMPLE_TIME_MSG_PATTERN")"; then - echo >&2 "Couldn't get number of tokens from ./main output!" + echo >&2 "Couldn't get number of tokens from ./llama-cli output!" exit 1 fi @@ -144,7 +144,7 @@ while read -e line; do fi # Update cache for next prompt in background, ideally during user input - ./main >>"$LOG_BG" 2>&1 "${OPTS[@]}" \ + ./llama-cli >>"$LOG_BG" 2>&1 "${OPTS[@]}" \ --prompt-cache "$NEXT_PROMPT_CACHE" \ --file "$NEXT_PROMPT_FILE" \ --n_predict 1 & diff --git a/examples/chat-vicuna.sh b/examples/chat-vicuna.sh index 8c7b7bef42784..ffdd200849503 100755 --- a/examples/chat-vicuna.sh +++ b/examples/chat-vicuna.sh @@ -30,7 +30,7 @@ sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \ $PROMPT_TEMPLATE > $PROMPT_FILE # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./bin/main $GEN_OPTIONS \ +./bin/llama-cli $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --n_predict "$N_PREDICTS" \ diff --git a/examples/chat.sh b/examples/chat.sh index d567acecdff11..9f85d1e265d00 100755 --- a/examples/chat.sh +++ b/examples/chat.sh @@ -11,6 +11,6 @@ cd .. # # "--keep 48" is based on the contents of prompts/chat-with-bob.txt # -./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ +./llama-cli -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ --repeat_penalty 1.0 --color -i \ -r "User:" -f prompts/chat-with-bob.txt diff --git a/convert.py b/examples/convert-legacy-llama.py similarity index 81% rename from convert.py rename to examples/convert-legacy-llama.py index da1247957780c..721a57c00299b 100755 --- a/convert.py +++ b/examples/convert-legacy-llama.py @@ -24,14 +24,16 @@ from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional +from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional import numpy as np -from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) + # use .parent.parent since we are in "examples" directory + sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py')) + import gguf +from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab if TYPE_CHECKING: from typing_extensions import Self, TypeAlias @@ -174,7 +176,7 @@ class Params: rope_scaling_type: gguf.RopeScalingType | None = None f_rope_freq_base: float | None = None f_rope_scale: float | None = None - n_orig_ctx: int | None = None + n_ctx_orig: int | None = None rope_finetuned: bool | None = None ftype: GGMLFileType | None = None @@ -224,7 +226,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: with open(config_path) as f: config = json.load(f) - rope_scaling_type = f_rope_scale = n_orig_ctx = rope_finetuned = None + rope_scaling_type = f_rope_scale = n_ctx_orig = rope_finetuned = None rope_scaling = config.get("rope_scaling") if rope_scaling is not None and (typ := rope_scaling.get("type")): @@ -234,7 +236,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: rope_scaling_type = gguf.RopeScalingType.LINEAR elif typ == "yarn": rope_scaling_type = gguf.RopeScalingType.YARN - n_orig_ctx = rope_scaling['original_max_position_embeddings'] + n_ctx_orig = rope_scaling['original_max_position_embeddings'] rope_finetuned = rope_scaling['finetuned'] else: raise NotImplementedError(f'Unknown rope scaling type: {typ}') @@ -270,7 +272,7 @@ def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: f_rope_freq_base = config.get("rope_theta"), rope_scaling_type = rope_scaling_type, f_rope_scale = f_rope_scale, - n_orig_ctx = n_orig_ctx, + n_ctx_orig = n_ctx_orig, rope_finetuned = rope_finetuned, ) @@ -380,306 +382,6 @@ def load(metadata_path: Path) -> Metadata: return metadata -# -# vocab -# - - -@runtime_checkable -class BaseVocab(Protocol): - tokenizer_model: ClassVar[str] - name: ClassVar[str] - - -class NoVocab(BaseVocab): - tokenizer_model = "no_vocab" - name = "no_vocab" - - def __repr__(self) -> str: - return "" - - -@runtime_checkable -class Vocab(BaseVocab, Protocol): - vocab_size: int - added_tokens_dict: dict[str, int] - added_tokens_list: list[str] - fname_tokenizer: Path - - def __init__(self, base_path: Path): ... - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... - - -class BpeVocab(Vocab): - tokenizer_model = "gpt2" - name = "bpe" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - - if (fname_tokenizer := base_path / 'vocab.json').exists(): - # "slow" tokenizer - with open(fname_tokenizer, encoding="utf-8") as f: - self.vocab = json.load(f) - - try: - # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - else: - # "fast" tokenizer - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding="utf-8") as f: - tokenizer_json = json.load(f) - - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - if ( - tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'ByteLevel' - ): - raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') - - self.vocab = tokenizer_model["vocab"] - - if (added := tokenizer_json.get('added_tokens')) is not None: - # Added tokens here can be duplicates of the main vocabulary. - added_tokens = {item['content']: item['id'] - for item in added - if item['content'] not in self.vocab} - - vocab_size = len(self.vocab) - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - expected_end_id = vocab_size + len(actual_ids) - 1 - raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " - f"{vocab_size} - {expected_end_id}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_dict = added_tokens - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} - - for i, _ in enumerate(self.vocab): - yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.CONTROL - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.bpe_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class SentencePieceVocab(Vocab): - tokenizer_model = "llama" - name = "spm" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - if (fname_tokenizer := base_path / 'tokenizer.model').exists(): - # normal location - try: - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): - # not found in alternate location either - raise FileNotFoundError('Cannot find tokenizer.model') - - self.sentencepiece_tokenizer = SentencePieceProcessor() - self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) - vocab_size = self.sentencepiece_tokenizer.vocab_size() - - new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} - expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) - actual_new_ids = sorted(new_tokens.keys()) - - if expected_new_ids != actual_new_ids: - raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") - - # Token pieces that were added to the base vocabulary. - self.added_tokens_dict = added_tokens - self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.IdToPiece(i) - text = piece.encode("utf-8") - score: float = tokenizer.GetScore(i) - - toktype = gguf.TokenType.NORMAL - if tokenizer.IsUnknown(i): - toktype = gguf.TokenType.UNKNOWN - if tokenizer.IsControl(i): - toktype = gguf.TokenType.CONTROL - - # NOTE: I think added_tokens are user defined. - # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto - # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED - - if tokenizer.IsUnused(i): - toktype = gguf.TokenType.UNUSED - if tokenizer.IsByte(i): - toktype = gguf.TokenType.BYTE - - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class LlamaHfVocab(Vocab): - tokenizer_model = "llama" - name = "hfft" - - def __init__(self, base_path: Path): - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding='utf-8') as f: - tokenizer_json = json.load(f) - - # pre-check so we know if we need transformers - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - is_llama3 = ( - tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) - and not tokenizer_model.get('byte_fallback', True) - ) - if is_llama3: - raise TypeError('Llama 3 must be converted with BpeVocab') - - if not is_llama3 and ( - tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'Sequence' - ): - raise FileNotFoundError('Cannot find Llama BPE tokenizer') - - try: - from transformers import AutoTokenizer - except ImportError as e: - raise ImportError( - "To use LlamaHfVocab, please install the `transformers` package. " - "You can install it with `pip install transformers`." - ) from e - - # Allow the tokenizer to default to slow or fast versions. - # Explicitly set tokenizer to use local paths. - self.tokenizer = AutoTokenizer.from_pretrained( - base_path, - cache_dir=base_path, - local_files_only=True, - ) - assert self.tokenizer.is_fast # assume tokenizer.json is used - - # Initialize lists and dictionaries for added tokens - self.added_tokens_list = [] - self.added_tokens_dict = dict() - self.added_tokens_ids = set() - - # Process added tokens - for tok, tokidx in sorted( - self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] - ): - # Only consider added tokens that are not in the base vocabulary - if tokidx >= self.tokenizer.vocab_size: - self.added_tokens_list.append(tok) - self.added_tokens_dict[tok] = tokidx - self.added_tokens_ids.add(tokidx) - - # Store special tokens and their IDs - self.specials = { - tok: self.tokenizer.get_vocab()[tok] - for tok in self.tokenizer.all_special_tokens - } - self.special_ids = set(self.tokenizer.all_special_ids) - - # Set vocabulary sizes - self.vocab_size_base = self.tokenizer.vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - - self.fname_tokenizer = fname_tokenizer - - def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = { - id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() - } - - for token_id in range(self.vocab_size_base): - # Skip processing added tokens here - if token_id in self.added_tokens_ids: - continue - - # Convert token text to bytes - token_text = reverse_vocab[token_id].encode("utf-8") - - # Yield token text, score, and type - yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, token_text, self.special_ids # Reuse already stored special IDs - ) - - def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: - # Special case for byte tokens - if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): - return gguf.TokenType.BYTE - - # Determine token type based on whether it's a special token - return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL - - def get_token_score(self, token_id: int) -> float: - # Placeholder for actual logic to determine the token's score - # This needs to be implemented based on specific requirements - return -1000.0 # Default score - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - if text in self.specials: - toktype = self.get_token_type(self.specials[text], b'', self.special_ids) - score = self.get_token_score(self.specials[text]) - else: - toktype = gguf.TokenType.USER_DEFINED - score = -1000.0 - - yield text.encode("utf-8"), score, toktype - - def has_newline_token(self): - return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.hf_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - # # data loading # TODO: reuse (probably move to gguf.py?) @@ -1162,8 +864,8 @@ def add_meta_arch(self, params: Params) -> None: self.gguf.add_rope_scaling_type(params.rope_scaling_type) self.gguf.add_rope_scaling_factor(params.f_rope_scale) - if params.n_orig_ctx is not None: - self.gguf.add_rope_scaling_orig_ctx_len(params.n_orig_ctx) + if params.n_ctx_orig is not None: + self.gguf.add_rope_scaling_orig_ctx_len(params.n_ctx_orig) if params.rope_finetuned is not None: self.gguf.add_rope_scaling_finetuned(params.rope_finetuned) diff --git a/examples/convert-llama2c-to-ggml/CMakeLists.txt b/examples/convert-llama2c-to-ggml/CMakeLists.txt index e262d44f98496..a6790e617217e 100644 --- a/examples/convert-llama2c-to-ggml/CMakeLists.txt +++ b/examples/convert-llama2c-to-ggml/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET convert-llama2c-to-ggml) +set(TARGET llama-convert-llama2c-to-ggml) add_executable(${TARGET} convert-llama2c-to-ggml.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 742dcf7a35b50..5774ac83c32c8 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -8,7 +8,7 @@ To convert the model first download the models from the [llama2.c](https://githu After successful compilation, following usage options are available: ``` -usage: ./convert-llama2c-to-ggml [options] +usage: ./llama-convert-llama2c-to-ggml [options] options: -h, --help show this help message and exit @@ -19,10 +19,10 @@ options: An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: -`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` +`$ ./llama-convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` Note: The vocabulary for `stories260K.bin` should be its own tokenizer `tok512.bin` found in [karpathy/tinyllamas/stories260K](https://huggingface.co/karpathy/tinyllamas/tree/main/stories260K). Now you can use the model with a command like: -`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` +`$ ./llama-cli -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256` diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 746c3fbef8412..8ca9f8915916c 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -774,7 +774,7 @@ static struct train_params get_default_train_params() { params.samples_start_after_nl = false; params.use_adam = true; - params.use_flash = true; + params.use_flash = false; params.use_scratch = true; // only adam diff --git a/examples/beam-search/CMakeLists.txt b/examples/cvector-generator/CMakeLists.txt similarity index 64% rename from examples/beam-search/CMakeLists.txt rename to examples/cvector-generator/CMakeLists.txt index f0e37468b1030..0a559d60c2a6d 100644 --- a/examples/beam-search/CMakeLists.txt +++ b/examples/cvector-generator/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET beam-search) -add_executable(${TARGET} beam-search.cpp) +set(TARGET llama-cvector-generator) +add_executable(${TARGET} cvector-generator.cpp pca.hpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/cvector-generator/README.md b/examples/cvector-generator/README.md new file mode 100644 index 0000000000000..7b0e79c1ffba8 --- /dev/null +++ b/examples/cvector-generator/README.md @@ -0,0 +1,34 @@ +# cvector-generator + +This example demonstrates how to generate a control vector using gguf models. + +Related PRs: +- [Add support for control vectors](https://github.com/ggerganov/llama.cpp/pull/5970) +- (Issue) [Generate control vector using llama.cpp](https://github.com/ggerganov/llama.cpp/issues/6880) +- [Add cvector-generator example](https://github.com/ggerganov/llama.cpp/pull/7514) + +## Examples + +```sh +# CPU only +./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf + +# With GPU +./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99 + +# With advanced options +./cvector-generator -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99 --completions 128 --pca-iter 2000 --batch-pca 100 + +# To see help message +./cvector-generator -h +# Then, have a look at "cvector" section +``` + +## Tips and tricks + +If you have multiple lines per prompt, you can escape the newline character (change it to `\n`). For example: + +``` +<|im_start|>system\nAct like a person who is extremely happy.<|im_end|> +<|im_start|>system\nYou are in a very good mood today<|im_end|> +``` diff --git a/examples/cvector-generator/completions.txt b/examples/cvector-generator/completions.txt new file mode 100644 index 0000000000000..abc45ffd87269 --- /dev/null +++ b/examples/cvector-generator/completions.txt @@ -0,0 +1,582 @@ + +That game +I can see +Hmm, this +I can relate to +Who is +I understand the +Ugh, +What the hell was +Hey, did anyone +Although +Thank you for choosing +What are you +Oh w +How dare you open +It was my pleasure +I'm hon +I appreciate that you +Are you k +Whoever left this +It's always +Ew, +Hey, I l +Hello? Is someone +I understand that +That poem +Aww, poor +Hey, it +Alright, who +I didn't +Well, life +The document +Oh no, this +I'm concerned +Hello, this is +This art +Hmm, this drink +Hi there! +It seems +Is +Good +I can't +Ex +Who are +I can see that +Wow, +Today is a +Hey friend +Sometimes friends +Oh, this old +The weather outside +This place is sur +I appreciate your input +Thank you for the +Look at +I'm disappoint +To my +How dare you +That's an +This piece of art +Eww +This park is +This is incredible +Oh no, someone +Exc +Well, it' +I warned +Hey, I understand +Hey, I saw +How dare you go +What the he +Hey +It's +Hello? Hello? +It +Oh no! +This is the perfect +Good morning, +Oh no, there +It's so +Yeah +Uh, +Hello everyone +Who turned off +The weather +Who' +Hey, this +Wait, +Eww, gross +Excuse +It seems like you +Thank you so +What happened? +Oh my g +I am deeply sad +I war +Okay, let' +Hey, that +That was a beautiful +Oh no! That +What happened +Hey there +The artist' +What?! +Hey, it' +I am disappoint +It seems like +Oh no! The +This park is a +If you +Yes! I did +It sounds +What +Who is it +Hmm, that +That's strange +Yeah, that was +That's interesting +This park +What the hell +Who is that +I feel like my +Oh well +What the hell is +Hello? Hello +To my dearest +Bless you!\" +Thank you for +Oh, looks like +Can you please +This place is +Eww, what +Bless you +Is everything +Hey, I just +Whoever left these +Well, that' +I feel +Hey, do you +It's sad +Oh no, it +Hey, that' +Oh my god, +Thank you, +Hello little one, +I apolog +Hey team, I +How dare you read +Who is this and +Whoever left +Hi there! W +A +If you have +I was +U +Bless +Well, this +Oh, I' +It's a +Eww, +Is everything okay? +Oh, I +Hello, can you +Al +That was a great +What are +I understand that not +Oh no, not +Who is it?\" +Hey, can we +Whoever is taking +I would love to +Hey, I noticed +Hey, could +I understand that there +Hello? +D +Oh man, I +Thank you so much +Oh no, my +Dear [Name +Uh +I remember +Hey, who +Well, it +Are you +I understand that it +Hey, is +I would +Who is this +Excuse me +Alright +I am thrilled +Sometimes friends have +Who the +It's interesting +I would love +E +Hello? Is anyone +Well, this is +This place +Well, +I warned you +Hey, watch where +Oh my +That' +Sometimes friends have different +I understand that everyone +What? +What do these notes +I can relate +I'm not +I understand +To my dear +Guys +Well +Hey, I appreciate +Wow, what +Dear +That melody +Who the hell +Today is +Hello little +Wow, look +That's great +Love is never wrong +I'm having +Whoa, did +Ugh +Can you please provide +I miss you, +I feel uncom +I know +Ugh, this +Hey, watch +Oh great, a +I didn +Okay +That game of char +Oh +I appreciate +Who's there +I am so +Oh great, someone +Hey, could you +I remember wondering +Wait, what? +What do +Hello? Can +Hey there, +That game of +This is incred +Oh my gosh +Oh great, f +I appreciate your +It sounds like +What the heck +Okay, I understand +Ew +I understand that this +Uh, hi +Hi everyone! +What the hell? +Thank you for your +Oh no, the +Wow, I +Who turned +Dear [ +Whoever +This is a +Whoa, he +What in the world +Although the physical +Hello, who is +That's amaz +Hey, I know +Okay, that +Hi everyone +Hey, is everything +I understand your fr +Oh no, poor +Oh, look +Good morning +Ew, gross +Oh no, did +Look at the family +Hey team +Yes! +Hey, can I +Okay, that' +It's great +Love is +Hey, what +Good morning, world +Who is it? +That poem really reson +I +That's +I understand the task +Gu +Hello? Who' +This postcard is +Whoa, +Oh, that +I understand that I +Whoever is +Hello? Who is +I'm really +Wow, this +Can +This artwork really +This is a shame +I miss you too +Who are you? +Today is a difficult +Hey, just +Are you okay +I am +Hi, +Wow, that +Hey there! Can +Okay, stay +Oh great, just +Yeah, +Hello? Can you +Oh, looks +Thank you for sharing +I'm glad +Hey, is that +Hmm +It was my +It sounds like you +Wow, your +I was promised certain +That was such a +Thank +Excuse you +That was +Hey team, +I feel un +It was +What' +Hey friend, I +How +Saying goodbye +That +It's heart +How dare +Oh, +Hello, may +What's this +Thank you for recogn +Aww, that +Oh, I remember +Hmm, that' +I miss +I know this +Wait +Is everything okay +Who is that person +Wow, you +Oh great +I'm sad +Wow, the +I am very disappoint +Who turned off the +I understand that things +I'm very +Hi +That's very +Okay, I +Oh no, +Wow, there +What's wrong +I apologize for +Hey, I +Can I help you +Oh, I didn +Alright, +Oh wow, +Oh my goodness +I know this event +What in the +Saying +Yeah, that +Guys, I +Hey, this v +This post +Are +Hey, can +Hello? Is +I can only imagine +Oh, that sounds +Hey, is anyone +I am disappointed +Hello, +Hey everyone, I +That was such +It's okay +The artist +Whoa +I understand that mistakes +Can I help +Who +Hi everyone! I +Hey, can you +Wow, how +Today +Oh no, I +Oh well, I +Well, that +This is the +Yes! I finally +Hey there little +Hello everyone! +Love is never +Look at the +This postcard +Oh great, +Can I +Hmm, this is +I understand your +Oh, look at +B +I'm so +Whoa, this +W +Oh, this +Sometimes +This piece of +What the +That was a +Hey, do +Oh no +Whoa, what +I feel like I +The documentary +Hello +Hello little one +I understand that my +Eww, that +Wow, an +Yes! Finally, +Although the physical location +Whoever is watching +That movie +I remember wondering about +Hey there, little +Who's +Hello, who +Hello everyone! Thank +Hello, can +That's too +Hey, just wanted +Hey there, I +Saying good +Hey there! +Who is there? +Oh my good +I am very +Oh no, what +Wow, thank +I was promised +Hi, is +Hey, I' +Guys, the +Oh no, that +Who is there +Hello, this +That movie really touched +If you have something +The documentary was +I'm starting +Are you kidd +That movie really +Hey everyone, +Thank you for considering +I didn' +Yes! I +Can you +Oh my god +Hey, whoever +That melody really +Thank you, little +Hello, may I +Look +Wow, we +It looks +What do these +Oh wow +I apologize +What are you all +It's such +It's clear +Hey, I was +Hey friend, +I can only +The weather outside is +Eww, this +I miss you +Wow +Aww, +Hi, is there +This artwork +Okay, +Oh well, +This +I' +Say +Hey there little gu +Hmm, +Whoa, who +I am thr +Oh man +Okay, stay calm +I'm happy +Oh, this cur +Oh man, +I'm sorry +Hello? Who +What?! That +This piece +Hey everyone +That's so +Are you okay? +What happened? Where +Hi there +The +Who the hell entered +I can +Guys, +What's +What in +It's important +I'm +I'm coming +It' +Yes! Finally +Wait, what +Wow, reading +I'm surprised +Hey, did +Hey, +Okay, let +I understand that you +Who the hell threw +Eww, who +Thank you for thinking +Who is this?\" +I am deeply +Thank you for including +Oh no, an +It looks like you +Aww +I'm confused +Wow, it +That poem really +Yes +Hey there, is +Hey, what' +Thank you for remember +To +This is +Thank you for making +I can' +That mel +Wow, they +I feel like +Although the +Who are you +Love +If +What the hell are +I am so sad +Oh, I found +Thank you +It looks like +Well, life is +I appreciate that +The artist's +Whoa, that +It's never \ No newline at end of file diff --git a/examples/cvector-generator/cvector-generator.cpp b/examples/cvector-generator/cvector-generator.cpp new file mode 100644 index 0000000000000..9941683db677e --- /dev/null +++ b/examples/cvector-generator/cvector-generator.cpp @@ -0,0 +1,499 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" +#include "pca.hpp" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + + +////////////////////////////////////////////////// +// utils + +template +static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) { + std::string ret; + for (; begin != end; ++begin) { + ret += llama_token_to_piece(ctx, *begin); + } + + return ret; +} + +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + printf("\nexample usage:\n"); + printf("\n CPU only: %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf\n", argv[0]); + printf("\n with GPU: %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99\n", argv[0]); + printf("\n advanced: %s -m ./dolphin-2.0-mistral-7b.Q4_K_M.gguf -ngl 99 --completions 128 --pca-iter 2000 --batch-pca 100\n", argv[0]); + printf("\n"); +} + +////////////////////////////////////////////////// + + +// cb_eval is reused for each pair of positive - negative prompt +struct callback_data { + ggml_context * ctx_ggml = nullptr; // holds v_pos, v_neg, v_diff_filtered + + int n_layers = 0; + int n_tokens = 0; + bool is_eval_pos = true; + + // each element of the vector correspond to one layer + std::vector v_pos; // vector of matrices of size [n_embd, n_tokens] + std::vector v_neg; // vector of matrices of size [n_embd, n_tokens] + std::vector v_diff_filtered; // vector of matrices of size [n_embd, n_nonzero_rows]. NOTE: n_nonzero_rows maybe different for each layer + + // save a tensor into either v_pos or v_neg (decided by is_eval_pos) + void save_tensor_for_layer(struct ggml_tensor * t) { + GGML_ASSERT(t->type == GGML_TYPE_F32); + + if (ctx_ggml == nullptr) { + // alloc a new ctx_ggml if needed + struct ggml_init_params params_ggml = { + /*.mem_size =*/ ggml_tensor_overhead() * n_layers * 3u, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx_ggml = ggml_init(params_ggml); + } + + // copy tensor data + auto n_bytes = ggml_nbytes(t); + struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]); + t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow + ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes); + ggml_set_name(t_layer, ggml_get_name(t)); + //print_debug_tensor(t_layer); + + if (is_eval_pos) { + v_pos.push_back(t_layer); + } else { + v_neg.push_back(t_layer); + } + } + + // calculate diff (v_pos - v_neg) and place the result back to v_pos + // all zero rows in the diff tensor will also be removed + // NOTE: final layer is ignored. we only have (n_layers - 1) to process + std::vector calc_diff() { + for (float il = 0; il < v_pos.size(); il++) { + float * a = (float *) v_pos[il]->data; + float * b = (float *) v_neg[il]->data; + size_t n_elem = ggml_nelements(v_pos[il]); + for (size_t j = 0; j < n_elem; j++) { + a[j] -= b[j]; + } + //print_debug_tensor(v_pos[i]); + auto diff_filtered = filter_nonzero_rows(v_pos[il]); + v_diff_filtered.push_back(diff_filtered); + } + return v_diff_filtered; // for convinient, we return the result std::vector + } + + // delete zero rows from a given 2D tensor + struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) { + //printf("filter_nonzero_rows\n"); + auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool { + // check if given row containing all zero elements + int n_cols = t->ne[0]; // hint: should be equal to n_embd + for (int col = 0; col < n_cols; ++col) { + if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) { + return false; + } + } + return true; + }; + std::vector rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered) + for (int i_row = 0; i_row < a->ne[1]; i_row++) { + if (!is_row_all_zeros(a, i_row, 1e-6)) { + rows_to_copy.push_back(i_row); + } + } + + // get "n_nonzero_rows" for the output "diff_filtered" + int n_nonzero_rows = rows_to_copy.size(); + //printf("n_nonzero_rows: %d\n", n_nonzero_rows); + int n_embd = a->ne[0]; + GGML_ASSERT(n_nonzero_rows > 0); + + // diff_filtered: [n_embd, n_nonzero_rows] + struct ggml_tensor * diff_filtered = ggml_new_tensor_2d( + ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows); + ggml_format_name(diff_filtered, "diff_filtered_%s", a->name); + diff_filtered->data = malloc(ggml_nbytes(diff_filtered)); + + // copy non-zero rows + for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) { + int src_row = rows_to_copy[dest_row]; + for (int i = 0; i < n_embd; i++) { + float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0); + ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem); + } + } + + //print_debug_tensor(diff_filtered); + + return diff_filtered; + } + + // we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors + void reset() { + for (auto ptr : v_pos) free(ptr->data); + for (auto ptr : v_neg) free(ptr->data); + for (auto ptr : v_diff_filtered) free(ptr->data); + v_pos.clear(); + v_neg.clear(); + v_diff_filtered.clear(); + if (ctx_ggml) { + ggml_free(ctx_ggml); + } + ctx_ggml = nullptr; + } +}; + +/** + * process_ctx is used to store the ggml context for pre-post processing the diff vectors + * in short, input => v_diff and output => v_final + */ +struct train_context { + ggml_context * ctx_ggml; + int n_embd; + int n_layers; + + /* pair of prompts to be used for generating final vector */ + std::vector positive_entries; + std::vector negative_entries; + + // each element of the vector correspond to one layer + // NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here + // NOTE (2): v_diff is transposed from v_diff_tmp + std::vector v_diff; // vector of matrices of size [m, n_embd] where m ~ n_tokens * n_completions (v_diff contains no zero-rows) + std::vector v_final; // vector of vectors of size [n_embd] to be written to file + + // to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor + // v_diff_tmp will get converted unto v_diff later on + std::vector> v_diff_tmp; + + train_context(int n_embd_, int n_layers_) { + n_embd = n_embd_; + n_layers = n_layers_; + struct ggml_init_params params_ggml = { + /*.mem_size =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx_ggml = ggml_init(params_ggml); + for (int il = 0; il < n_layers - 1; il++) { + std::vector empty; + v_diff_tmp.push_back(empty); + auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd); + t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible + v_final.push_back(t); + } + } + + // add new rows into existing tensor in v_diff_tmp + void concat_diff_tmp(const std::vector & diff_filtered) { + GGML_ASSERT((int) diff_filtered.size() == n_layers - 1); + for (int il = 0; il < n_layers - 1; il++) { + auto t = diff_filtered[il]; + auto & diff_tmp = v_diff_tmp[il]; + size_t curr_size = diff_tmp.size(); + diff_tmp.resize(curr_size + ggml_nbytes(t)); + memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t)); + } + } + + // build the v_diff tensors from v_diff_tmp (v_diff need to be transposed) + // TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method + void build_v_diff() { + printf("build_v_diff\n"); + for (int il = 0; il < n_layers - 1; il++) { + auto & diff_tmp = v_diff_tmp[il]; + int n_elem = diff_tmp.size() / sizeof(float); + GGML_ASSERT(n_elem % n_embd == 0); + int n_rows = n_elem / n_embd; + struct ggml_tensor * diff = ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd); + ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str()); + // copy data & transpose + diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible + float * arr = (float *) diff_tmp.data(); + for (int ir = 0; ir < n_rows; ++ir) { + for (int ic = 0; ic < n_embd; ++ic) { + float f = arr[ir*n_embd + ic]; + ggml_set_f32_nd(diff, ir, ic, 0, 0, f); + } + } + v_diff.push_back(diff); + print_debug_tensor(diff); + // free memory of diff_tmp + diff_tmp.resize(0); + } + } + + ~train_context() { + for (auto ptr : v_final) free(ptr->data); + for (auto ptr : v_diff) free(ptr->data); + // no need to free v_diff_tmp, since we didn't use malloc + ggml_free(ctx_ggml); + } +}; + +struct tokenized_prompt { + std::vector tokens_pos; + std::vector tokens_neg; + size_t max_seq_len; + + tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) { + const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); + tokens_pos = ::llama_tokenize(ctx, pos, add_bos); + tokens_neg = ::llama_tokenize(ctx, neg, add_bos); + max_seq_len = std::max(tokens_pos.size(), tokens_neg.size()); + padding_seq(ctx, tokens_pos, max_seq_len); + padding_seq(ctx, tokens_neg, max_seq_len); + } + + void padding_seq(llama_context * ctx, std::vector & tokens, size_t len) { + // TODO: customize padding token + std::vector pad_tokens = ::llama_tokenize(ctx, " ", false); + llama_token pad_tok = pad_tokens.back(); + while (tokens.size() < len) { + tokens.push_back(pad_tok); + } + } +}; + +////////////////////////////////////////////////// + +template +static std::string to_string(const T & val) { + std::stringstream ss; + ss << val; + return ss.str(); +} + +static std::vector ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) { + std::vector output; + std::ifstream file(path); + if (!file.is_open()) { + fprintf(stderr, "error: unable to open file: %s\n", path.c_str()); + exit(1); + } + std::string line; + while (std::getline(file, line)) { + bool is_skip = skip_empty_lines && line.empty(); + if (!is_skip) { + string_process_escapes(line); + output.push_back(line); + } + } + file.close(); + return output; +} + +////////////////////////////////////////////////// + +static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) { + auto * cb_data = (callback_data *) user_data; + static const char * l_out_name = "l_out"; + const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0; + + if (ask) { + return is_l_out; + } + + if (!is_l_out || t->ne[1] != cb_data->n_tokens) { + return true; + } + + // save the tensor to current context + cb_data->save_tensor_for_layer(t); + return true; +} + +static bool get_hidden_layers(llama_context * ctx, std::vector & tokens) { + llama_kv_cache_clear(ctx); + if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + return true; +} + +static void export_gguf(const std::vector & v_ctrl, const std::string fname, const std::string model_hint) { + struct gguf_context * ctx = gguf_init_empty(); + + const std::string arch = "controlvector"; + gguf_set_val_str(ctx, "general.architecture", arch.c_str()); + gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str()); + gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size()); + + for (size_t i = 0; i < v_ctrl.size(); ++i) { + gguf_add_tensor(ctx, v_ctrl[i]); + print_debug_tensor(v_ctrl[i]); + printf("Added tensor: %s\n", v_ctrl[i]->name); + } + + printf("%s: writing file...\n", __func__); + gguf_write_to_file(ctx, fname.c_str(), false); + printf("%s: wrote file '%s'\n", __func__, fname.c_str()); + gguf_free(ctx); +} + +/** + * Load prompt files and completion file. + * Then format each pair of prompt + completion to make an entry. + */ +static int prepare_entries(gpt_params & params, train_context & ctx_train) { + // load prompts + std::vector positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true); + std::vector negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true); + if (positive_prompts.size() != negative_prompts.size()) { + fprintf(stderr, "number of positive and negative prompts must be equal\n"); + return 1; + } + if (positive_prompts.empty()) { + fprintf(stderr, "must provide at least one prompt pair\n"); + return 1; + } + + // create templated prompts + std::vector completions = ctrlvec_load_prompt_file(params.cvector_completions_file, false); + auto format_template = [](std::string persona, std::string suffix) { + // entry in positive/negative.txt must already be formatted i.e. "[INST] Act as if you're extremely happy. [/INST]" + return persona + " " + suffix; + }; + for (size_t i = 0; i < positive_prompts.size(); ++i) { + for (int j = 0; j < std::min((int) completions.size(), params.n_completions); ++j) { + // TODO replicate the truncations done by the python implementation + ctx_train.positive_entries.push_back(format_template(positive_prompts[i], completions[j])); + ctx_train.negative_entries.push_back(format_template(negative_prompts[i], completions[j])); + } + } + return 0; +} + +int main(int argc, char ** argv) { + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; + } + + if (params.n_pca_iterations % params.n_pca_batch != 0) { + fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n"); + return 1; + } + + + callback_data cb_data; + + // pass the callback to the backend scheduler + // it will be executed for each node during the graph computation + params.cb_eval = cb_eval; + params.cb_eval_user_data = &cb_data; + params.warmup = false; + + print_build_info(); + llama_backend_init(); + llama_numa_init(params.numa); + + // load the model to get hparams + llama_model * model; + llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); + + // int n_ctx = llama_n_ctx(ctx); + int n_layers = llama_n_layer(model); + int n_embd = llama_n_embd(model); + // get model hint param (a.k.a model arch name) + char model_hint[128]; + llama_model_meta_val_str(model, "general.architecture", model_hint, 128); + + // init train_context + train_context ctx_train(n_embd, n_layers); + + // load and prepare entries for training + prepare_entries(params, ctx_train); + + // we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped + std::vector tokenized_prompts; + size_t n_total_tokens = 0; + for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) { + tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]); + n_total_tokens += 2 * t.max_seq_len; + tokenized_prompts.push_back(std::move(t)); + } + + std::cout << "n_total_tokens: " << n_total_tokens << std::endl; + + for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) { + bool success = false; + tokenized_prompt t = tokenized_prompts[i]; + cb_data.n_layers = n_layers; + cb_data.n_tokens = t.max_seq_len; + + printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n", + (int) i+1, (int) ctx_train.positive_entries.size(), + tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(), + tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(), + (int) t.max_seq_len); + + cb_data.is_eval_pos = true; + success = get_hidden_layers(ctx, t.tokens_pos); + if (!success) break; + + cb_data.is_eval_pos = false; + success = get_hidden_layers(ctx, t.tokens_neg); + if (!success) break; + + // calculate diff and remove all zero rows + auto v_diff_filtered = cb_data.calc_diff(); + + // save & concat the filtered v_diff to ctx_train + ctx_train.concat_diff_tmp(v_diff_filtered); + + // reset for next iteration + cb_data.reset(); + } + + // done with the model, we can now free it to make gain some memory + printf("Done evaluate prompts, unload model...\n"); + llama_free(ctx); + llama_free_model(model); + + // prepare ctx_train for PCA + ctx_train.build_v_diff(); + + // run PCA + PCA::pca_params pca_params; + pca_params.n_threads = params.n_threads; + pca_params.n_batch = params.n_pca_batch; + pca_params.n_iterations = params.n_pca_iterations; + PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final); + + // write output vectors to gguf + export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint); + + llama_backend_free(); + + return 0; +} diff --git a/examples/cvector-generator/negative.txt b/examples/cvector-generator/negative.txt new file mode 100644 index 0000000000000..2ac3387f184b0 --- /dev/null +++ b/examples/cvector-generator/negative.txt @@ -0,0 +1 @@ +[INST] Act like a person who is extremely sad. [/INST] \ No newline at end of file diff --git a/examples/cvector-generator/pca.hpp b/examples/cvector-generator/pca.hpp new file mode 100644 index 0000000000000..36eadaac26a12 --- /dev/null +++ b/examples/cvector-generator/pca.hpp @@ -0,0 +1,322 @@ +#include "common.h" +#include "llama.h" +#include "ggml.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +#define DEBUG_POS 5 + +static void print_debug_tensor(struct ggml_tensor * t, bool with_data = true) { + printf("%s: %s (%s): [%d, %d]\n", __func__, t->name, ggml_type_name(t->type), (int) t->ne[0], (int) t->ne[1]); + if (!with_data) return; + printf("%s: %s[0] = [", __func__, t->name); + for (size_t i = 0; i <= DEBUG_POS; i++) { + printf(" %f,", ggml_get_f32_nd(t, i, 0, 0, 0)); + } + printf(" ... ]\n"); +} + +namespace PCA { + +// input params for PCA computations +struct pca_params { + int n_threads = 1; + int n_batch = 20; // number of iterations do to in one batch. larger the batch, more memory is used + int n_iterations = 1000; + float tolerance = 1e-7; + + // for debugging + int i_layer = 0; + int n_layers = 0; +}; + +// result from each iteration +struct pca_result { + struct ggml_tensor * calculated_square = NULL; + std::vector eigenvectors; + std::vector distances; +}; + +struct pca_model { + ggml_backend_t backend = NULL; + ggml_backend_buffer_t buffer; + struct ggml_context * ctx; // context to compute graph on target device + struct ggml_context * ctx_host; // host context to store results + + // tensors on target device + struct ggml_tensor * dev_input; + struct ggml_tensor * dev_square; + struct ggml_tensor * dev_eigenvector; + + pca_model(struct ggml_tensor * t_input) { +#ifdef GGML_USE_CUDA + fprintf(stderr, "%s: using CUDA backend\n", __func__); + backend = ggml_backend_cuda_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } +#endif + +// TODO: enable Metal support when support for GGML_OP_SQRT is added +// #ifdef GGML_USE_METAL +// fprintf(stderr, "%s: using Metal backend\n", __func__); +// backend = ggml_backend_metal_init(); +// if (!backend) { +// fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); +// } +// #endif + + // if there aren't GPU Backends fallback to CPU backend + if (!backend) { + backend = ggml_backend_cpu_init(); + } + + const int num_tensors = 4; + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead() * num_tensors, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + ctx = ggml_init(params); + + auto n_samples = t_input->ne[0]; + auto n_embd = t_input->ne[1]; + + dev_input = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_samples, n_embd); + dev_square = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); + dev_eigenvector = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); + + ggml_set_name(dev_input, "dev_input"); + ggml_set_name(dev_square, "dev_square"); + ggml_set_name(dev_eigenvector, "dev_eigenvector"); + buffer = ggml_backend_alloc_ctx_tensors(ctx, backend); + ggml_backend_tensor_set(dev_input, t_input->data, 0, ggml_nbytes(t_input)); + + // initialize eigenvector to random normalized vector + { + std::vector random_vec(ggml_nelements(dev_eigenvector), 0.0); + std::default_random_engine generator(static_cast(std::time(0))); + std::uniform_real_distribution distribution(0.0, 1.0); + float sum_sqr = 0.0; // for normalizing random_vec + for (size_t i = 0; i < random_vec.size(); ++i) { + float f = distribution(generator); + sum_sqr += f * f; + random_vec[i] = f; + } + // normalize it + float random_vec_norm = std::sqrt(sum_sqr); + for (size_t i = 0; i < random_vec.size(); ++i) { + random_vec[i] /= random_vec_norm; + } + ggml_backend_tensor_set(dev_eigenvector, random_vec.data(), 0, ggml_nbytes(dev_eigenvector)); + } + } + + ~pca_model() { + ggml_free(ctx); + ggml_backend_buffer_free(buffer); + ggml_backend_free(backend); + } +}; + +static struct ggml_cgraph * build_graph_piter( + const struct pca_params & params, + const pca_model & model, + bool calc_square = false) { + GGML_ASSERT(params.n_batch > 0); + // TODO: buf_size must be able to scale with params.n_batch + static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(); + static std::vector buf(buf_size); + + struct ggml_init_params params0 = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf.data(), + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph() + }; + // create a temporally context to build the graph + struct ggml_context * ctx0 = ggml_init(params0); + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + // turn v_diff_original into square matrix if needed + struct ggml_tensor * tmp_square; + if (calc_square) { + tmp_square = ggml_mul_mat(ctx0, model.dev_input, model.dev_input); + ggml_set_name(tmp_square, "tmp_square"); + } + + struct ggml_tensor * b_tensor; + struct ggml_tensor * distance; + struct ggml_tensor * old_eigen = model.dev_eigenvector; + struct ggml_tensor * input_square = calc_square ? tmp_square : model.dev_square; + + for (int i = 0; i < params.n_batch; ++i) { + // b_tensor = square * eigenvector^T + b_tensor = ggml_mul_mat(ctx0, input_square, old_eigen); + ggml_set_name(b_tensor, "b_tensor"); + + // normalize + b_tensor = ggml_div_inplace(ctx0, + b_tensor, + ggml_sqrt_inplace(ctx0, ggml_sum_rows(ctx0, ggml_sqr(ctx0, b_tensor))) + ); + ggml_format_name(b_tensor, "b_tensor_norm_%d", i); + + // calculate distance(new eigenvector - old eigenvector) + // we don't use ggml_sub because it may not be implemented on GPU backend + struct ggml_tensor * new_sub_old = ggml_add(ctx0, old_eigen, ggml_scale(ctx0, b_tensor, -1)); + distance = ggml_sqrt_inplace(ctx0, + ggml_sum_rows(ctx0, ggml_sqr_inplace(ctx0, new_sub_old))); + ggml_format_name(distance, "distance_%d", i); + + old_eigen = b_tensor; + + // build operations nodes + ggml_build_forward_expand(gf, distance); + } + + // delete the temporally context used to build the graph + ggml_free(ctx0); + return gf; +} + +static ggml_status compute_piter( + const struct pca_params & params, + const pca_model & model, + struct ggml_cgraph * gf, + ggml_gallocr_t allocr, + struct pca_result & result) { + // allocate tensors + ggml_gallocr_alloc_graph(allocr, gf); + + if (ggml_backend_is_cpu(model.backend)) { + ggml_backend_cpu_set_n_threads(model.backend, params.n_threads); + } + +// TODO: enable GPU support when support for GGML_OP_SQRT is added +//#ifdef GGML_USE_METAL +// if (ggml_backend_is_metal(model.backend)) { +// ggml_backend_metal_set_n_cb(model.backend, params.n_threads); +// } +//#endif + + ggml_status res = ggml_backend_graph_compute(model.backend, gf); + if (res == GGML_STATUS_SUCCESS) { + auto extract_i = [](std::string prefix, std::string str) -> int { + int i = -1; + if (str.rfind(prefix, 0) == 0) { + sscanf(str.c_str(), (prefix + "%d").c_str(), &i); + } + return i; + }; + result.calculated_square = NULL; + result.eigenvectors.clear(); + result.distances.clear(); + result.eigenvectors.resize(params.n_batch); + result.distances.resize(params.n_batch); + // get output nodes + for (int i = 0; i < gf->n_nodes; ++i) { + auto node = gf->nodes[i]; + int iter = -1; + // find b_tensor (without copying data from device) + if ((iter = extract_i("b_tensor_norm_", node->name)) > -1) { + result.eigenvectors[iter] = node; + } + // find distances, then copy data from device + if ((iter = extract_i("distance_", node->name)) > -1) { + float d; + ggml_backend_tensor_get(node, &d, 0, sizeof(float)); + result.distances[iter] = d; + // std::cout << node->name << " = " << d << "\n"; + } + // find tmp_square if it exists (without copying data from device) + if (std::string(node->name) == "tmp_square") { + result.calculated_square = node; + } + } + } + return res; +} + +static void power_iteration( + const struct pca_params & params, + struct ggml_tensor * input, // shape of input: [n_samples, n_embd] + struct ggml_tensor * output) { + //printf("in power iteration\n"); + struct pca_model model(input); + + ggml_gallocr_t allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend)); + struct pca_result result; + struct ggml_tensor * last_eigenvector = NULL; + + int n_iters = params.n_iterations / params.n_batch; // more batch, fewer iterations + for (int iter = 0; iter < n_iters; ++iter) { + bool calc_square = (iter == 0); // only need to calculate square for first iteration + struct ggml_cgraph * gf = build_graph_piter(params, model, calc_square); + // ggml_graph_dump_dot(gf, nullptr, "/tmp/_cgraph.dot"); + compute_piter(params, model, gf, allocr, result); + + for (size_t k = 0; k < result.distances.size(); ++k) { + last_eigenvector = result.eigenvectors[k]; + if (result.distances[k] < params.tolerance) { + break; // done + } + } + + if (calc_square) { + // copy and store the square matrix if needed + GGML_ASSERT(result.calculated_square != NULL); + ggml_backend_tensor_copy(result.calculated_square, model.dev_square); + } + + { + // copy last eigen vector and store as input for next iteration + GGML_ASSERT(last_eigenvector != NULL); + ggml_backend_tensor_copy(last_eigenvector, model.dev_eigenvector); + } + + printf("%s: layer %d/%d, iteration: %d / total: %d (batch = %d) ...\n", + __func__, params.i_layer+1, params.n_layers, iter, n_iters, params.n_batch); + } + + // get output tensor + GGML_ASSERT(last_eigenvector); + ggml_backend_tensor_get(last_eigenvector, output->data, 0, ggml_nbytes(last_eigenvector)); + //print_debug_tensor(output); + ggml_gallocr_free(allocr); +} + +static void run_pca( + struct pca_params & params, + const std::vector & v_input, // shape of v_input[0]: [n_samples, n_embd] + const std::vector & v_output) { + printf("%s: Running PCA...\n", __func__); + for (size_t il = 0; il < v_input.size(); ++il) { + + // prepare output vector + struct ggml_tensor * ctrl_out = v_output[il]; + ggml_format_name(ctrl_out, "direction.%ld", il+1); + + // run power_iteration + params.i_layer = il; + params.n_layers = v_input.size(); + power_iteration(params, v_input[il], ctrl_out); + printf("%s: Done layer %d / %d\n", __func__, (int) il+1, (int) v_input.size()); + } +} + +} diff --git a/examples/cvector-generator/positive.txt b/examples/cvector-generator/positive.txt new file mode 100644 index 0000000000000..f28e9aa1aeb72 --- /dev/null +++ b/examples/cvector-generator/positive.txt @@ -0,0 +1 @@ +[INST] Act like a person who is extremely happy. [/INST] \ No newline at end of file diff --git a/examples/embedding/CMakeLists.txt b/examples/embedding/CMakeLists.txt index 8ffc33868401f..8256e789ad33a 100644 --- a/examples/embedding/CMakeLists.txt +++ b/examples/embedding/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET embedding) +set(TARGET llama-embedding) add_executable(${TARGET} embedding.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/embedding/README.md b/examples/embedding/README.md index a3c0bda249d75..86df189587846 100644 --- a/examples/embedding/README.md +++ b/examples/embedding/README.md @@ -9,13 +9,13 @@ To get started right away, run the following command, making sure to use the cor ### Unix-based systems (Linux, macOS, etc.): ```bash -./embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null +./llama-embedding -m ./path/to/model --log-disable -p "Hello World!" 2>/dev/null ``` ### Windows: ```powershell -embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null +llama-embedding.exe -m ./path/to/model --log-disable -p "Hello World!" 2>$null ``` The above command will output space-separated float values. diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index d348d79e42043..fc3fb310aa504 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -63,6 +63,7 @@ int main(int argc, char ** argv) { gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -79,9 +80,6 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = string_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); diff --git a/examples/eval-callback/CMakeLists.txt b/examples/eval-callback/CMakeLists.txt index c56ba780b215f..a48753d38e16e 100644 --- a/examples/eval-callback/CMakeLists.txt +++ b/examples/eval-callback/CMakeLists.txt @@ -1,9 +1,9 @@ -set(TARGET eval-callback) +set(TARGET llama-eval-callback) add_executable(${TARGET} eval-callback.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) set(TEST_TARGET test-eval-callback) -add_test(NAME ${TEST_TARGET} COMMAND eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) +add_test(NAME ${TEST_TARGET} COMMAND llama-eval-callback --hf-repo ggml-org/models --hf-file tinyllamas/stories260K.gguf --model stories260K.gguf --prompt hello --seed 42 -ngl 0) set_property(TEST ${TEST_TARGET} PROPERTY LABELS eval-callback curl) diff --git a/examples/eval-callback/README.md b/examples/eval-callback/README.md index 66a37e8783a9b..63a57ad6b68e5 100644 --- a/examples/eval-callback/README.md +++ b/examples/eval-callback/README.md @@ -6,7 +6,7 @@ It simply prints to the console all operations and tensor data. Usage: ```shell -eval-callback \ +llama-eval-callback \ --hf-repo ggml-org/models \ --hf-file phi-2/ggml-model-q4_0.gguf \ --model phi-2-q4_0.gguf \ diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 51d67d6d97ae6..64cd338c26351 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -140,20 +140,18 @@ static bool run(llama_context * ctx, const gpt_params & params) { } int main(int argc, char ** argv) { - callback_data cb_data; gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } print_build_info(); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = string_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); diff --git a/examples/export-lora/CMakeLists.txt b/examples/export-lora/CMakeLists.txt index cbbdaec67488d..1cef6e71694e2 100644 --- a/examples/export-lora/CMakeLists.txt +++ b/examples/export-lora/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET export-lora) +set(TARGET llama-export-lora) add_executable(${TARGET} export-lora.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/export-lora/README.md b/examples/export-lora/README.md index 0cf3e8e4549bb..1fb17feeca627 100644 --- a/examples/export-lora/README.md +++ b/examples/export-lora/README.md @@ -3,7 +3,7 @@ Apply LORA adapters to base model and export the resulting model. ``` -usage: export-lora [options] +usage: llama-export-lora [options] options: -h, --help show this help message and exit @@ -17,7 +17,7 @@ options: For example: ```bash -./bin/export-lora \ +./bin/llama-export-lora \ -m open-llama-3b-v2-q8_0.gguf \ -o open-llama-3b-v2-q8_0-english2tokipona-chat.gguf \ -l lora-open-llama-3b-v2-q8_0-english2tokipona-chat-LATEST.bin diff --git a/examples/finetune/CMakeLists.txt b/examples/finetune/CMakeLists.txt index 2b52d21cfb381..64afe6ddc647a 100644 --- a/examples/finetune/CMakeLists.txt +++ b/examples/finetune/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET finetune) +set(TARGET llama-finetune) add_executable(${TARGET} finetune.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/finetune/README.md b/examples/finetune/README.md index 2fafd505e5447..a6ae64983bc0a 100644 --- a/examples/finetune/README.md +++ b/examples/finetune/README.md @@ -7,7 +7,7 @@ Basic usage instructions: wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt # finetune LORA adapter -./bin/finetune \ +./bin/llama-finetune \ --model-base open-llama-3b-v2-q8_0.gguf \ --checkpoint-in chk-lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.gguf \ --checkpoint-out chk-lora-open-llama-3b-v2-q8_0-shakespeare-ITERATION.gguf \ @@ -18,7 +18,7 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s --use-checkpointing # predict -./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin +./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin ``` **Only llama based models are supported!** The output files will be saved every N iterations (config with `--save-every N`). @@ -38,14 +38,14 @@ After 10 more iterations: Checkpoint files (`--checkpoint-in FN`, `--checkpoint-out FN`) store the training process. When the input checkpoint file does not exist, it will begin finetuning a new randomly initialized adapter. llama.cpp compatible LORA adapters will be saved with filename specified by `--lora-out FN`. -These LORA adapters can then be used by `main` together with the base model, like in the 'predict' example command above. +These LORA adapters can then be used by `llama-cli` together with the base model, like in the 'predict' example command above. -In `main` you can also load multiple LORA adapters, which will then be mixed together. +In `llama-cli` you can also load multiple LORA adapters, which will then be mixed together. For example if you have two LORA adapters `lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin` and `lora-open-llama-3b-v2-q8_0-bible-LATEST.bin`, you can mix them together like this: ```bash -./bin/main -m open-llama-3b-v2-q8_0.gguf \ +./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf \ --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin \ --lora lora-open-llama-3b-v2-q8_0-bible-LATEST.bin ``` @@ -55,7 +55,7 @@ You can change how strong each LORA adapter is applied to the base model by usin For example to apply 40% of the 'shakespeare' LORA adapter, 80% of the 'bible' LORA adapter and 100% of yet another one: ```bash -./bin/main -m open-llama-3b-v2-q8_0.gguf \ +./bin/llama-cli -m open-llama-3b-v2-q8_0.gguf \ --lora-scaled lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin 0.4 \ --lora-scaled lora-open-llama-3b-v2-q8_0-bible-LATEST.bin 0.8 \ --lora lora-open-llama-3b-v2-q8_0-yet-another-one-LATEST.bin diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 22425730f20eb..71a4333ee7908 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -564,7 +564,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( const int rope_mode = 0; return ggml_rope_ext(ctx, - t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, + t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/examples/finetune/finetune.sh b/examples/finetune/finetune.sh index 079bfa1139d5b..d7f2165e5cb3a 100644 --- a/examples/finetune/finetune.sh +++ b/examples/finetune/finetune.sh @@ -2,7 +2,7 @@ cd `dirname $0` cd ../.. -EXE="./finetune" +EXE="./llama-finetune" if [[ ! $LLAMA_MODEL_DIR ]]; then LLAMA_MODEL_DIR="./models"; fi if [[ ! $LLAMA_TRAINING_DIR ]]; then LLAMA_TRAINING_DIR="."; fi diff --git a/examples/gbnf-validator/CMakeLists.txt b/examples/gbnf-validator/CMakeLists.txt index 166e3ad2ae7dd..4edd6ec7394c5 100644 --- a/examples/gbnf-validator/CMakeLists.txt +++ b/examples/gbnf-validator/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET gbnf-validator) +set(TARGET llama-gbnf-validator) add_executable(${TARGET} gbnf-validator.cpp) install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common grammar-parser llama ${CMAKE_THREAD_LIBS_INIT}) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/gbnf-validator/gbnf-validator.cpp b/examples/gbnf-validator/gbnf-validator.cpp index 091069ffa699c..0406dc3398b8a 100644 --- a/examples/gbnf-validator/gbnf-validator.cpp +++ b/examples/gbnf-validator/gbnf-validator.cpp @@ -7,6 +7,8 @@ #include #include +#include +#include #include #include @@ -69,13 +71,14 @@ int main(int argc, char** argv) { return 1; } - fseek(grammar_file, 0, SEEK_END); - size_t grammar_size = ftell(grammar_file); - fseek(grammar_file, 0, SEEK_SET); - - std::string grammar_str(grammar_size, ' '); - fread(&grammar_str[0], 1, grammar_size, grammar_file); - fclose(grammar_file); + std::string grammar_str; + { + std::ifstream grammar_file(grammar_filename); + GGML_ASSERT(grammar_file.is_open() && "Failed to open grammar file"); + std::stringstream buffer; + buffer << grammar_file.rdbuf(); + grammar_str = buffer.str(); + } // Parse the GBNF grammar auto parsed_grammar = grammar_parser::parse(grammar_str.c_str()); @@ -100,20 +103,15 @@ int main(int argc, char** argv) { grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); // Read the input file - FILE* input_file = fopen(input_filename.c_str(), "r"); - if (!input_file) { - fprintf(stdout, "Failed to open input file: %s\n", input_filename.c_str()); - return 1; + std::string input_str; + { + std::ifstream input_file(input_filename); + GGML_ASSERT(input_file.is_open() && "Failed to open input file"); + std::stringstream buffer; + buffer << input_file.rdbuf(); + input_str = buffer.str(); } - fseek(input_file, 0, SEEK_END); - size_t input_size = ftell(input_file); - fseek(input_file, 0, SEEK_SET); - - std::string input_str(input_size, ' '); - fread(&input_str[0], 1, input_size, input_file); - fclose(input_file); - // Validate the input string against the grammar size_t error_pos; std::string error_msg; diff --git a/examples/gguf-split/CMakeLists.txt b/examples/gguf-split/CMakeLists.txt index 828e624352c8d..f63887da7dfca 100644 --- a/examples/gguf-split/CMakeLists.txt +++ b/examples/gguf-split/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET gguf-split) +set(TARGET llama-gguf-split) add_executable(${TARGET} gguf-split.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index e04feeae39188..881f0451c1455 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -61,10 +61,10 @@ static size_t split_str_to_n_bytes(std::string str) { int n; if (str.back() == 'M') { sscanf(str.c_str(), "%d", &n); - n_bytes = (size_t)n * 1024 * 1024; // megabytes + n_bytes = (size_t)n * 1000 * 1000; // megabytes } else if (str.back() == 'G') { sscanf(str.c_str(), "%d", &n); - n_bytes = (size_t)n * 1024 * 1024 * 1024; // gigabytes + n_bytes = (size_t)n * 1000 * 1000 * 1000; // gigabytes } else { throw std::invalid_argument("error: supported units are M (megabytes) or G (gigabytes), but got: " + std::string(1, str.back())); } @@ -284,7 +284,7 @@ struct split_strategy { struct ggml_tensor * t = ggml_get_tensor(ctx_meta, gguf_get_tensor_name(ctx_out, i)); total_size += ggml_nbytes(t); } - total_size = total_size / 1024 / 1024; // convert to megabytes + total_size = total_size / 1000 / 1000; // convert to megabytes printf("split %05d: n_tensors = %d, total_size = %ldM\n", i_split + 1, gguf_get_n_tensors(ctx_out), total_size); i_split++; } diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh index 7ca6fa7f20de8..d5a92d6051063 100755 --- a/examples/gguf-split/tests.sh +++ b/examples/gguf-split/tests.sh @@ -18,8 +18,8 @@ fi set -x -SPLIT=$1/gguf-split -MAIN=$1/main +SPLIT=$1/llama-gguf-split +MAIN=$1/llama-cli WORK_PATH=$TMP_DIR/gguf-split ROOT_DIR=$(realpath $(dirname $0)/../../) @@ -41,7 +41,7 @@ echo PASS echo # 2b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -51,7 +51,7 @@ echo PASS echo # 3b. Test the merged model is loading properly -$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32 echo PASS echo @@ -61,7 +61,7 @@ echo PASS echo # 4b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32 echo PASS echo @@ -71,7 +71,7 @@ echo #echo # 5b. Test the merged model is loading properly -#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32 +#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32 #echo PASS #echo @@ -81,7 +81,7 @@ echo PASS echo # 6b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32 echo PASS echo diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt index 6481f087bc997..a9569b411956b 100644 --- a/examples/gguf/CMakeLists.txt +++ b/examples/gguf/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET gguf) +set(TARGET llama-gguf) add_executable(${TARGET} gguf.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE ggml ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/gpt4all.sh b/examples/gpt4all.sh deleted file mode 100755 index 5fd739e55c554..0000000000000 --- a/examples/gpt4all.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main --color --instruct --threads 4 \ - --model ./models/gpt4all-7B/gpt4all-lora-quantized.bin \ - --file ./prompts/alpaca.txt \ - --batch_size 8 --ctx_size 2048 -n -1 \ - --repeat_last_n 64 --repeat_penalty 1.3 \ - --n_predict 128 --temp 0.1 --top_k 40 --top_p 0.95 diff --git a/examples/gritlm/CMakeLists.txt b/examples/gritlm/CMakeLists.txt index ac4a5ae7937ea..86dfddca346fe 100644 --- a/examples/gritlm/CMakeLists.txt +++ b/examples/gritlm/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET gritlm) +set(TARGET llama-gritlm) add_executable(${TARGET} gritlm.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/gritlm/README.md b/examples/gritlm/README.md index a3a3c1389ca27..786ba57363def 100644 --- a/examples/gritlm/README.md +++ b/examples/gritlm/README.md @@ -26,7 +26,7 @@ $ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --ou Run the example using the downloaded model: ```console -$ ./gritlm -m models/gritlm-7b_q4_1.gguf +$ ./llama-gritlm -m models/gritlm-7b_q4_1.gguf Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605 Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103 diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp index 52fd719b38ee5..2135157916c97 100644 --- a/examples/gritlm/gritlm.cpp +++ b/examples/gritlm/gritlm.cpp @@ -153,7 +153,9 @@ static std::string gritlm_instruction(const std::string & instruction) { int main(int argc, char * argv[]) { gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/imatrix/CMakeLists.txt b/examples/imatrix/CMakeLists.txt index d688a16209049..d4c8265bdb9d2 100644 --- a/examples/imatrix/CMakeLists.txt +++ b/examples/imatrix/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET imatrix) +set(TARGET llama-imatrix) add_executable(${TARGET} imatrix.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/imatrix/README.md b/examples/imatrix/README.md index 458c01b8751f1..38b36ee5a26fd 100644 --- a/examples/imatrix/README.md +++ b/examples/imatrix/README.md @@ -6,16 +6,19 @@ More information is available here: https://github.com/ggerganov/llama.cpp/pull/ ## Usage ``` -./imatrix -m -f [-o ] [--verbosity ] - [-ofreq num_chunks] [-ow <0 or 1>] [other common params] +./llama-imatrix \ + -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \ + [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \ + [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...] ``` Here `-m` with a model name and `-f` with a file containing training data (such as e.g. `wiki.train.raw`) are mandatory. The parameters in square brackets are optional and have the following meaning: * `-o` (or `--output-file`) specifies the name of the file where the computed data will be stored. If missing `imatrix.dat` is used. * `--verbosity` specifies the verbosity level. If set to `0`, no output other than the perplexity of the processed chunks will be generated. If set to `1`, each time the results are saved a message is written to `stderr`. If `>=2`, a message is output each time data is collected for any tensor. Default verbosity level is `1`. -* `-ofreq` (or `--output-frequency`) specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks) -* `-ow` (or `--output-weight`) specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default. +* `--output-frequency` specifies how often the so far computed result is saved to disk. Default is 10 (i.e., every 10 chunks) +* `--save-frequency` specifies how often to save a copy of the imatrix in a separate file. Default is 0 (i.e., never) +* `--process-output` specifies if data will be collected for the `output.weight` tensor. My experience is that it is better to not utilize the importance matrix when quantizing `output.weight`, so this is set to `false` by default. For faster computation, make sure to use GPU offloading via the `-ngl` argument @@ -25,8 +28,8 @@ For faster computation, make sure to use GPU offloading via the `-ngl` argument LLAMA_CUDA=1 make -j # generate importance matrix (imatrix.dat) -./imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 +./llama-imatrix -m ggml-model-f16.gguf -f train-data.txt -ngl 99 # use the imatrix to perform a Q4_K_M quantization -./quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m +./llama-quantize --imatrix imatrix.dat ggml-model-f16.gguf ./ggml-model-q4_k_m.gguf q4_k_m ``` diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 25a2351cc64d3..574f5ed9c2e65 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -17,39 +17,37 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s \\\n" + " -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n" + " [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n" + " [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]); + LOG_TEE("\n"); +} + struct Stats { std::vector values; std::vector counts; int ncall = 0; }; -struct StatParams { - std::string dataset; - std::string ofile = "imatrix.dat"; - int n_output_frequency = 10; - int verbosity = 1; - int keep_every = 0; - bool collect_output_weight = false; -}; - class IMatrixCollector { public: IMatrixCollector() = default; - void set_parameters(StatParams&& params) { m_params = std::move(params); } + void set_params(gpt_params params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); - void save_imatrix() const; - bool load_imatrix(const char * file_name, bool add); - static bool load_imatrix(const char * file_name, std::unordered_map& imatrix); + void save_imatrix(int ncall = -1) const; + bool load_imatrix(const char * file_name); private: std::unordered_map m_stats; - StatParams m_params; + gpt_params m_params; std::mutex m_mutex; int m_last_call = 0; std::vector m_src1_data; std::vector m_ids; // the expert ids from ggml_mul_mat_id - // - void save_imatrix(const char * file_name, const char * dataset) const; - void keep_imatrix(int ncall) const; }; // remove any prefix and suffixes from the name @@ -85,7 +83,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * if (t->op != GGML_OP_MUL_MAT) return false; // why are small batches ignored (<16 tokens)? if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false; - if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && wname == "output.weight"))) return false; + if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) return false; return true; } @@ -153,21 +151,25 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[e_start + j] += x[j]*x[j]; e.counts[e_start + j]++; + if (!std::isfinite(e.values[e_start + j])) { + fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str()); + exit(1); + } } } } if (e.ncall > m_last_call) { m_last_call = e.ncall; - if (m_last_call % m_params.n_output_frequency == 0) { + if (m_last_call % m_params.n_out_freq == 0) { save_imatrix(); } - if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) { - keep_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { + save_imatrix(m_last_call); } } } } else { - auto& e = m_stats[wname]; + auto & e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); e.counts.resize(src1->ne[0], 0); @@ -185,15 +187,19 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[j] += x[j]*x[j]; e.counts[j]++; + if (!std::isfinite(e.values[j])) { + fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str()); + exit(1); + } } } if (e.ncall > m_last_call) { m_last_call = e.ncall; - if (m_last_call % m_params.n_output_frequency == 0) { + if (m_last_call % m_params.n_out_freq == 0) { save_imatrix(); } - if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) { - keep_imatrix(m_last_call); + if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) { + save_imatrix(m_last_call); } } } @@ -201,33 +207,75 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * return true; } -void IMatrixCollector::save_imatrix() const { - save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str(), m_params.dataset.c_str()); -} +void IMatrixCollector::save_imatrix(int ncall) const { + auto fname = m_params.out_file; + if (fname.empty()) { + fname = "imatrix.dat"; + } -void IMatrixCollector::keep_imatrix(int ncall) const { - auto file_name = m_params.ofile; - if (file_name.empty()) file_name = "imatrix.dat"; - file_name += ".at_"; - file_name += std::to_string(ncall); - save_imatrix(file_name.c_str(), m_params.dataset.c_str()); -} + if (ncall > 0) { + fname += ".at_"; + fname += std::to_string(ncall); + } + + // avoid writing imatrix entries that do not have full data + // this can happen with MoE models where some of the experts end up not being exercised by the provided training data + + int n_entries = 0; + std::vector to_store; + + bool is_first = true; // for printing + for (const auto & kv : m_stats) { + const int n_all = kv.second.counts.size(); + + if (n_all == 0) { + continue; + } + + int n_zeros = 0; + for (const int c : kv.second.counts) { + if (c == 0) { + n_zeros++; + } + } + + if (n_zeros != 0 && is_first) { + fprintf(stderr, "\n"); + is_first = false; + } + + if (n_zeros == n_all) { + fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str()); + continue; + } + + if (n_zeros > 0) { + fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all); + continue; + } + + n_entries++; + to_store.push_back(kv.first); + } + + if (to_store.size() < m_stats.size()) { + fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size()); + } -void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) const { std::ofstream out(fname, std::ios::binary); - int n_entries = m_stats.size(); out.write((const char *) &n_entries, sizeof(n_entries)); - for (const auto & p : m_stats) { - int len = p.first.size(); + for (const auto & name : to_store) { + const auto & stat = m_stats.at(name); + int len = name.size(); out.write((const char *) &len, sizeof(len)); - out.write(p.first.c_str(), len); - out.write((const char *) &p.second.ncall, sizeof(p.second.ncall)); - int nval = p.second.values.size(); + out.write(name.c_str(), len); + out.write((const char *) &stat.ncall, sizeof(stat.ncall)); + int nval = stat.values.size(); out.write((const char *) &nval, sizeof(nval)); if (nval > 0) { std::vector tmp(nval); for (int i = 0; i < nval; i++) { - tmp[i] = (p.second.values[i] / static_cast(p.second.counts[i])) * static_cast(p.second.ncall); + tmp[i] = (stat.values[i] / static_cast(stat.counts[i])) * static_cast(stat.ncall); } out.write((const char*)tmp.data(), nval*sizeof(float)); } @@ -236,26 +284,28 @@ void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) co // Write the number of call the matrix was computed with out.write((const char *) &m_last_call, sizeof(m_last_call)); - // Write the dataset name at the end of the file to later on specify it in quantize - int n_dataset = strlen(dataset); - out.write((const char *) &n_dataset, sizeof(n_dataset)); - out.write(dataset, n_dataset); + // Write the input filename at the end of the file to later on specify it in quantize + { + int len = m_params.prompt_file.size(); + out.write((const char *) &len, sizeof(len)); + out.write(m_params.prompt_file.c_str(), len); + } if (m_params.verbosity > 0) { - fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname); + fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str()); } } -bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map& imatrix_data) { - std::ifstream in(imatrix_file, std::ios::binary); +bool IMatrixCollector::load_imatrix(const char * fname) { + std::ifstream in(fname, std::ios::binary); if (!in) { - printf("%s: failed to open %s\n",__func__,imatrix_file); + printf("%s: failed to open %s\n",__func__, fname); return false; } int n_entries; in.read((char*)&n_entries, sizeof(n_entries)); if (in.fail() || n_entries < 1) { - printf("%s: no data in file %s\n", __func__, imatrix_file); + printf("%s: no data in file %s\n", __func__, fname); return false; } for (int i = 0; i < n_entries; ++i) { @@ -263,23 +313,22 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma std::vector name_as_vec(len+1); in.read((char *)name_as_vec.data(), len); if (in.fail()) { - printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file); + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname); return false; } name_as_vec[len] = 0; std::string name{name_as_vec.data()}; - auto& e = imatrix_data[std::move(name)]; + auto & e = m_stats[std::move(name)]; int ncall; in.read((char*)&ncall, sizeof(ncall)); int nval; in.read((char *)&nval, sizeof(nval)); if (in.fail() || nval < 1) { printf("%s: failed reading number of values for entry %d\n",__func__,i); - imatrix_data = {}; + m_stats = {}; return false; } - // When re-called from load_imatrix() with add set, this will already be created. if (e.values.empty()) { e.values.resize(nval, 0); e.counts.resize(nval, 0); @@ -289,7 +338,7 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma in.read((char*)tmp.data(), nval*sizeof(float)); if (in.fail()) { printf("%s: failed reading data for entry %d\n",__func__,i); - imatrix_data = {}; + m_stats = {}; return false; } @@ -304,13 +353,6 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma return true; } -bool IMatrixCollector::load_imatrix(const char * file_name, bool add) { - if (!add) { - m_stats.clear(); - } - return load_imatrix(file_name, m_stats); -} - static IMatrixCollector g_collector; static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { @@ -324,7 +366,7 @@ struct results_log_softmax { float prob; }; -static std::vector softmax(const std::vector& logits) { +static std::vector softmax(const std::vector & logits) { std::vector probs(logits.size()); float max_logit = logits[0]; for (float v : logits) { @@ -358,8 +400,7 @@ static results_log_softmax log_softmax(int n_vocab, const float * logits, int to static void process_logits( int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, - double & nll, double & nll2, float * logit_history, float * prob_history -) { + double & nll, double & nll2, float * logit_history, float * prob_history) { std::mutex mutex; int counter = 0; auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { @@ -391,8 +432,7 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { - +static bool compute_imatrix(llama_context * ctx, const gpt_params & params) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); const int n_ctx = llama_n_ctx(ctx); @@ -405,13 +445,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); - if (from_chunk > 0) { - if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) { - fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk); + if (params.i_chunk > 0) { + if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) { + fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk); return false; } - fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx); - tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx); + fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx); + tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx); } if (int(tokens.size()) < 2*n_ctx) { @@ -424,7 +464,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool std::vector logit_history; std::vector prob_history; - if (compute_ppl) { + if (params.compute_ppl) { logit_history.resize(tokens.size()); prob_history.resize(tokens.size()); } @@ -446,7 +486,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool const int num_batches = (n_ctx + n_batch - 1) / n_batch; std::vector logits; - if (compute_ppl && num_batches > 1) { + if (params.compute_ppl && num_batches > 1) { logits.reserve((size_t)n_ctx * n_vocab); } @@ -482,7 +522,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool // restore the original token in case it was set to BOS tokens[batch_start] = token_org; - if (compute_ppl && num_batches > 1) { + if (params.compute_ppl && num_batches > 1) { const auto * batch_logits = llama_get_logits(ctx); logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab); } @@ -501,7 +541,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); } - if (compute_ppl) { + if (params.compute_ppl) { const int first = n_ctx/2; const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, @@ -516,7 +556,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool } printf("\n"); - if (compute_ppl) { + if (params.compute_ppl) { nll2 /= count; nll /= count; const double ppl = exp(nll); @@ -533,111 +573,32 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool } int main(int argc, char ** argv) { - - StatParams sparams; - std::string prev_result_file; - std::string combine_files; - bool compute_ppl = true; - int from_chunk = 0; - std::vector args; - args.push_back(argv[0]); - int iarg = 1; - for (; iarg < argc-1; ++iarg) { - std::string arg{argv[iarg]}; - if (arg == "-o" || arg == "--output-file") { - sparams.ofile = argv[++iarg]; - } - else if (arg == "-ofreq" || arg == "--output-frequency") { - sparams.n_output_frequency = std::stoi(argv[++iarg]); - } - else if (arg == "-ow" || arg == "--output-weight") { - sparams.collect_output_weight = std::stoi(argv[++iarg]); - } - else if (arg == "--verbosity") { - sparams.verbosity = std::stoi(argv[++iarg]); - } else if (arg == "--no-ppl") { - compute_ppl = false; - } else if (arg == "--keep-imatrix") { - sparams.keep_every = std::stoi(argv[++iarg]); - } else if (arg == "--continue-from") { - prev_result_file = argv[++iarg]; - } else if (arg == "--combine") { - combine_files = argv[++iarg]; - } - else if (arg == "--from-chunk") { - from_chunk = std::stoi(argv[++iarg]); - } else { - args.push_back(argv[iarg]); - } - } - if (iarg < argc) { - std::string arg{argv[iarg]}; - if (arg == "--no-ppl") { - compute_ppl = false; - } else { - args.push_back(argv[iarg]); - } - } - gpt_params params; - params.n_batch = 512; - if (!gpt_params_parse(args.size(), args.data(), params)) { - return 1; - } + params.n_ctx = 512; params.logits_all = true; - params.n_batch = std::min(params.n_batch, params.n_ctx); + params.verbosity = 1; - print_build_info(); - - if (params.seed == LLAMA_DEFAULT_SEED) { - params.seed = time(NULL); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); - - std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = string_random_prompt(rng); - } + params.n_batch = std::min(params.n_batch, params.n_ctx); - sparams.dataset = params.prompt_file; - g_collector.set_parameters(std::move(sparams)); + g_collector.set_params(params); - if (!combine_files.empty()) { - std::vector files; - size_t pos = 0; - while (true) { - auto new_pos = combine_files.find(',', pos); - if (new_pos != std::string::npos) { - files.emplace_back(combine_files.substr(pos, new_pos - pos)); - pos = new_pos + 1; - } else { - files.emplace_back(combine_files.substr(pos)); - break; - } - } - if (files.size() < 2) { - fprintf(stderr, "You must provide at least two comma separated files to use --combine\n"); + for (const auto & in_file : params.in_files) { + printf("%s : loading imatrix from '%s'\n", __func__, in_file.c_str()); + if (!g_collector.load_imatrix(in_file.c_str())) { + fprintf(stderr, "%s : failed to load %s\n", __func__, in_file.c_str()); return 1; } - printf("Combining the following %d files\n", int(files.size())); - for (auto& file : files) { - printf(" %s\n", file.c_str()); - if (!g_collector.load_imatrix(file.c_str(), true)) { - fprintf(stderr, "Failed to load %s\n", file.c_str()); - return 1; - } - } - g_collector.save_imatrix(); - return 0; } - if (!prev_result_file.empty()) { - if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) { - fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str()); - return 1; - } + if (params.in_files.size() > 1) { + printf("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str()); + g_collector.save_imatrix(); } llama_backend_init(); @@ -652,6 +613,7 @@ int main(int argc, char ** argv) { // init llama_model * model; llama_context * ctx; + std::tie(model, ctx) = llama_init_from_gpt_params(params); if (model == nullptr || ctx == nullptr) { fprintf(stderr, "%s : failed to init\n", __func__); @@ -670,8 +632,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } - bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); - if (!OK) { + if (!compute_imatrix(ctx, params)) { return 1; } diff --git a/examples/infill/CMakeLists.txt b/examples/infill/CMakeLists.txt index e4e8028da09da..9b1aa3b63c920 100644 --- a/examples/infill/CMakeLists.txt +++ b/examples/infill/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET infill) +set(TARGET llama-infill) add_executable(${TARGET} infill.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/infill/README.md b/examples/infill/README.md index 6b076c8390abf..74f42d2fc2696 100644 --- a/examples/infill/README.md +++ b/examples/infill/README.md @@ -42,5 +42,5 @@ scripts/hf.sh --repo TheBloke/CodeLlama-13B-GGUF --file codellama-13b.Q5_K_S.ggu ``` ```bash -./infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " +./llama-infill -t 10 -ngl 0 -m models/codellama-13b.Q5_K_S.gguf -c 4096 --temp 0.7 --repeat_penalty 1.1 -n 20 --in-prefix "def helloworld():\n print(\"hell" --in-suffix "\n print(\"goodbye world\")\n " ``` diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 539f781847893..0e4ec79c693fa 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -107,6 +107,7 @@ int main(int argc, char ** argv) { g_params = ¶ms; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -139,27 +140,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } - if (params.instruct) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for instruct mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (params.chatml) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for chatml mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.antiprompt.empty()) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for antiprompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) { printf("\n************\n"); printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); @@ -167,20 +147,6 @@ int main(int argc, char ** argv) { return 0; } - if (params.random_prompt) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for random prompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.path_prompt_cache.empty()) { - printf("\n************\n"); - printf("%s: infill does not support prompt caching\n", __func__); - printf("************\n\n"); - - return 0; - } if (params.rope_freq_base != 0.0) { LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); @@ -207,17 +173,13 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; - llama_context * ctx_guidance = NULL; + g_model = &model; g_ctx = &ctx; // load the model and apply lora adapter, if any LOG("%s: load the model and apply lora adapter, if any\n", __func__); std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (sparams.cfg_scale > 1.f) { - struct llama_context_params lparams = llama_context_params_from_gpt_params(params); - ctx_guidance = llama_new_context_with_model(model, lparams); - } if (model == NULL) { LOG_TEE("%s: error: unable to load model\n", __func__); @@ -273,25 +235,6 @@ int main(int argc, char ** argv) { LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } - // Tokenize negative prompt - std::vector guidance_inp; - int guidance_offset = 0; - int original_prompt_len = 0; - if (ctx_guidance) { - LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, true); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); - - original_prompt_len = original_inp.size(); - guidance_offset = (int)guidance_inp.size() - original_prompt_len; - LOG("original_prompt_len: %s", log_tostr(original_prompt_len)); - LOG("guidance_offset: %s", log_tostr(guidance_offset)); - } - if ((int) embd_inp.size() > n_ctx - 4) { LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; @@ -319,15 +262,6 @@ int main(int argc, char ** argv) { LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); } - if (ctx_guidance) { - LOG_TEE("\n"); - LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str()); - LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); - for (int i = 0; i < (int) guidance_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); - } - } - if (params.n_keep > 0) { LOG_TEE("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { @@ -395,12 +329,11 @@ int main(int argc, char ** argv) { is_interacting = params.interactive_first; } - bool input_echo = true; + bool input_echo = true; - int n_past = 0; - int n_remain = params.n_predict; - int n_consumed = 0; - int n_past_guidance = 0; + int n_past = 0; + int n_remain = params.n_predict; + int n_consumed = 0; std::vector input_tokens; g_input_tokens = &input_tokens; std::vector output_tokens; g_output_tokens = &output_tokens; @@ -410,7 +343,6 @@ int main(int argc, char ** argv) { console::set_display(console::prompt); std::vector embd; - std::vector embd_guidance; struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); @@ -436,7 +368,7 @@ int main(int argc, char ** argv) { // if we run out of context: // - take the n_keep first tokens from the original prompt (via n_past) // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { + if (n_past + (int) embd.size() > n_ctx) { if (params.n_predict == -2) { LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); break; @@ -453,11 +385,7 @@ int main(int argc, char ** argv) { n_past -= n_discard; - if (ctx_guidance) { - n_past_guidance -= n_discard; - } - - LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + LOG("after swap: n_past = %d\n", n_past); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); @@ -465,45 +393,6 @@ int main(int argc, char ** argv) { // evaluate tokens in batches // embd is typically prepared beforehand to fit within a batch, but not always - - if (ctx_guidance) { - int input_size = 0; - llama_token * input_buf = NULL; - - if (n_past_guidance < (int) guidance_inp.size()) { - // Guidance context should have the same data with these modifications: - // - // * Replace the initial prompt - // * Shift everything by guidance_offset - embd_guidance = guidance_inp; - if (embd.begin() + original_prompt_len < embd.end()) { - embd_guidance.insert( - embd_guidance.end(), - embd.begin() + original_prompt_len, - embd.end() - ); - } - - input_buf = embd_guidance.data(); - input_size = embd_guidance.size(); - - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); - } else { - input_buf = embd.data(); - input_size = embd.size(); - } - - for (int i = 0; i < input_size; i += params.n_batch) { - int n_eval = std::min(input_size - i, params.n_batch); - if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); - return 1; - } - - n_past_guidance += n_eval; - } - } - for (int i = 0; i < (int) embd.size(); i += params.n_batch) { int n_eval = (int) embd.size() - i; if (n_eval > params.n_batch) { @@ -525,11 +414,9 @@ int main(int argc, char ** argv) { } embd.clear(); - embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - - const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); + const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr); llama_sampling_accept(ctx_sampling, ctx, id, true); @@ -583,7 +470,6 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { - // deal with eot token in infill mode if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){ if (is_interacting && !params.interactive_first) { @@ -644,7 +530,6 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); embd_inp.push_back(llama_token_middle(model)); embd.clear(); - embd_guidance.clear(); n_remain = params.n_predict; n_past = 0; n_consumed = 0; @@ -751,7 +636,6 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); - if (ctx_guidance) { llama_free(ctx_guidance); } llama_free(ctx); llama_free_model(model); diff --git a/examples/jeopardy/jeopardy.sh b/examples/jeopardy/jeopardy.sh index 9bdbc755c13a7..07bcb3b8d78ac 100755 --- a/examples/jeopardy/jeopardy.sh +++ b/examples/jeopardy/jeopardy.sh @@ -21,7 +21,7 @@ counter=1 echo 'Running' while IFS= read -r question do - exe_cmd="./main -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\"" + exe_cmd="./llama-cli -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\"" echo $counter echo "Current Question: $question" eval "$exe_cmd" diff --git a/examples/json-schema-pydantic-example.py b/examples/json-schema-pydantic-example.py index 69ebfd4093824..cc64e572bac07 100644 --- a/examples/json-schema-pydantic-example.py +++ b/examples/json-schema-pydantic-example.py @@ -1,5 +1,5 @@ # Usage: -#! ./server -m some-model.gguf & +#! ./llama-server -m some-model.gguf & #! pip install pydantic #! python json-schema-pydantic-example.py diff --git a/examples/json_schema_to_grammar.py b/examples/json_schema_to_grammar.py index 826cd3f7271d1..b588497b99f90 100755 --- a/examples/json_schema_to_grammar.py +++ b/examples/json_schema_to_grammar.py @@ -6,52 +6,22 @@ import sys from typing import Any, Dict, List, Set, Tuple, Union -def _build_repetition(item_rule, min_items, max_items, separator_rule=None, item_rule_is_literal=False): - if not separator_rule: - if min_items == 0 and max_items == 1: - return f'{item_rule}?' - elif min_items == 1 and max_items is None: - return f'{item_rule}+' - - result = '' - - if min_items > 0: - if item_rule_is_literal and separator_rule is None: - result = '"' + (item_rule[1:-1] * min_items) + '"' - else: - result = (f' {separator_rule} ' if separator_rule else ' ').join([item_rule] * min_items) - - def opt_repetitions(up_to_n, prefix_with_sep=False): - ''' - - n=4, no sep: '(a (a (a (a)?)?)?)?' - - n=4, sep=',', prefix: '("," a ("," a ("," a ("," a)?)?)?)?' - - n=4, sep=',', no prefix: '(a ("," a ("," a ("," a)?)?)?)?' - ''' - - content = f'{separator_rule} {item_rule}' if prefix_with_sep and separator_rule else item_rule - if up_to_n == 0: - return '' - elif up_to_n == 1: - return f'({content})?' - elif separator_rule and not prefix_with_sep: - return f'({content} {opt_repetitions(up_to_n - 1, prefix_with_sep=True)})?' - else: - return (f'({content} ' * up_to_n).rstrip() + (')?' * up_to_n) - if min_items > 0 and max_items != min_items: - result += ' ' +def _build_repetition(item_rule, min_items, max_items, separator_rule=None): - if max_items is not None: - result += opt_repetitions(max_items - min_items, prefix_with_sep=min_items > 0) - else: - item_operator = f'({separator_rule + " " if separator_rule else ""}{item_rule})' + if min_items == 0 and max_items == 1: + return f'{item_rule}?' - if min_items == 0 and separator_rule: - result = f'({item_rule} {item_operator}*)?' + if not separator_rule: + if min_items == 1 and max_items is None: + return f'{item_rule}+' + elif min_items == 0 and max_items is None: + return f'{item_rule}*' else: - result += f'{item_operator}*' + return f'{item_rule}{{{min_items},{max_items if max_items is not None else ""}}}' - return result + result = item_rule + ' ' + _build_repetition(f'({separator_rule} {item_rule})', min_items - 1 if min_items > 0 else 0, max_items - 1 if max_items is not None else None) + return f'({result})?' if min_items == 0 else result class BuiltinRule: @@ -59,31 +29,28 @@ def __init__(self, content: str, deps: list = None): self.content = content self.deps = deps or [] -_up_to_15_digits = _build_repetition('[0-9]', 0, 15) - -# whitespace is constrained to a single space char to prevent model "running away" in -# whitespace. Also maybe improves generation quality? -SPACE_RULE = '" "?' +# Constraining spaces to prevent model "running away". +SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}' PRIMITIVE_RULES = { 'boolean' : BuiltinRule('("true" | "false") space', []), - 'decimal-part' : BuiltinRule('[0-9] ' + _up_to_15_digits, []), - 'integral-part': BuiltinRule('[0-9] | [1-9] ' + _up_to_15_digits, []), + 'decimal-part' : BuiltinRule('[0-9]{1,16}', []), + 'integral-part': BuiltinRule('[0] | [1-9] [0-9]{0,15}', []), 'number' : BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), 'integer' : BuiltinRule('("-"? integral-part) space', ['integral-part']), 'value' : BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), 'object' : BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), 'array' : BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), - 'uuid' : BuiltinRule(r'"\"" ' + ' "-" '.join('[0-9a-fA-F]' * n for n in [8, 4, 4, 4, 12]) + r' "\"" space', []), - 'char' : BuiltinRule(r'[^"\\] | "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])', []), + 'uuid' : BuiltinRule(r'"\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\"" space', []), + 'char' : BuiltinRule(r'[^"\\\x7F\x00-\x1F] | [\\] (["\\bfnrt] | "u" [0-9a-fA-F]{4})', []), 'string' : BuiltinRule(r'"\"" char* "\"" space', ['char']), 'null' : BuiltinRule('"null" space', []), } # TODO: support "uri", "email" string formats STRING_FORMAT_RULES = { - 'date' : BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), - 'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date' : BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), 'date-time' : BuiltinRule('date "T" time', ['date', 'time']), 'date-string' : BuiltinRule('"\\"" date "\\"" space', ['date']), 'time-string' : BuiltinRule('"\\"" time "\\"" space', ['time']), @@ -333,7 +300,7 @@ def join_seq(): sub_rule_ids[sub] = id sub = id - seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times, item_rule_is_literal=sub_is_literal), False) + seq[-1] = (_build_repetition(f'"{sub}"' if sub_is_literal else sub, min_times, max_times), False) else: literal = '' while i < length: @@ -556,7 +523,7 @@ def format_grammar(self): def main(args_in = None): parser = argparse.ArgumentParser( description=''' - Generates a grammar (suitable for use in ./main) that produces JSON conforming to a + Generates a grammar (suitable for use in ./llama-cli) that produces JSON conforming to a given JSON schema. Only a subset of JSON schema features are supported; more may be added in the future. ''', diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 8578405646af7..52b0e74d3dbf9 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -1,4 +1,4 @@ -# llama.cpp/example/llama-bench +# llama.cpp/examples/llama-bench Performance testing tool for llama.cpp. @@ -162,7 +162,7 @@ $ ./llama-bench -o csv ``` ```csv -build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts +build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961" "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342" ``` @@ -179,7 +179,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -210,7 +209,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -253,7 +251,6 @@ CREATE TABLE IF NOT EXISTS test ( build_commit TEXT, build_number INTEGER, cuda INTEGER, - opencl INTEGER, metal INTEGER, gpu_blas INTEGER, blas INTEGER, @@ -279,6 +276,6 @@ CREATE TABLE IF NOT EXISTS test ( stddev_ts REAL ); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); ``` diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 2afdb3abdc278..d641a9f12b388 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -41,20 +41,6 @@ static std::string join(const std::vector & values, const std::string & delim return str.str(); } -template -static std::vector split(const std::string & str, char delim) { - std::vector values; - std::istringstream str_stream(str); - std::string token; - while (std::getline(str_stream, token, delim)) { - T value; - std::istringstream token_stream(token); - token_stream >> value; - values.push_back(value); - } - return values; -} - template static std::vector transform_to_str(const std::vector & values, F f) { std::vector str_values; @@ -140,10 +126,11 @@ static std::string get_gpu_info() { } // command line params -enum output_formats {CSV, JSON, MARKDOWN, SQL}; +enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL}; static const char * output_format_str(output_formats format) { switch (format) { + case NONE: return "none"; case CSV: return "csv"; case JSON: return "json"; case MARKDOWN: return "md"; @@ -152,6 +139,23 @@ static const char * output_format_str(output_formats format) { } } +static bool output_format_from_str(const std::string & s, output_formats & format) { + if (s == "none") { + format = NONE; + } else if (s == "csv") { + format = CSV; + } else if (s == "json") { + format = JSON; + } else if (s == "md") { + format = MARKDOWN; + } else if (s == "sql") { + format = SQL; + } else { + return false; + } + return true; +} + static const char * split_mode_str(llama_split_mode mode) { switch (mode) { case LLAMA_SPLIT_MODE_NONE: return "none"; @@ -178,6 +182,7 @@ struct cmd_params { std::vector type_v; std::vector n_threads; std::vector n_gpu_layers; + std::vector rpc_servers; std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; @@ -189,30 +194,33 @@ struct cmd_params { int reps; bool verbose; output_formats output_format; + output_formats output_format_stderr; }; static const cmd_params cmd_params_defaults = { - /* model */ {"models/7B/ggml-model-q4_0.gguf"}, - /* n_prompt */ {512}, - /* n_gen */ {128}, - /* n_pg */ {}, - /* n_batch */ {2048}, - /* n_ubatch */ {512}, - /* type_k */ {GGML_TYPE_F16}, - /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {cpu_get_num_math()}, - /* n_gpu_layers */ {99}, - /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, - /* main_gpu */ {0}, - /* no_kv_offload */ {false}, - /* flash_attn */ {false}, - /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, - /* use_mmap */ {true}, - /* embeddings */ {false}, - /* numa */ GGML_NUMA_STRATEGY_DISABLED, - /* reps */ 5, - /* verbose */ false, - /* output_format */ MARKDOWN + /* model */ {"models/7B/ggml-model-q4_0.gguf"}, + /* n_prompt */ {512}, + /* n_gen */ {128}, + /* n_pg */ {}, + /* n_batch */ {2048}, + /* n_ubatch */ {512}, + /* type_k */ {GGML_TYPE_F16}, + /* type_v */ {GGML_TYPE_F16}, + /* n_threads */ {cpu_get_num_math()}, + /* n_gpu_layers */ {99}, + /* rpc_servers */ {""}, + /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, + /* main_gpu */ {0}, + /* no_kv_offload */ {false}, + /* flash_attn */ {false}, + /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, + /* use_mmap */ {true}, + /* embeddings */ {false}, + /* numa */ GGML_NUMA_STRATEGY_DISABLED, + /* reps */ 5, + /* verbose */ false, + /* output_format */ MARKDOWN, + /* output_format_stderr */ NONE, }; static void print_usage(int /* argc */, char ** argv) { @@ -230,6 +238,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -rpc, --rpc (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str()); printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); @@ -240,6 +249,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -ts, --tensor-split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf("\n"); printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); @@ -281,7 +291,9 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.verbose = cmd_params_defaults.verbose; params.output_format = cmd_params_defaults.output_format; + params.output_format_stderr = cmd_params_defaults.output_format_stderr; params.reps = cmd_params_defaults.reps; + params.numa = cmd_params_defaults.numa; for (int i = 1; i < argc; i++) { arg = argv[i]; @@ -297,28 +309,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.model.insert(params.model.end(), p.begin(), p.end()); } else if (arg == "-p" || arg == "--n-prompt") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end()); } else if (arg == "-n" || arg == "--n-gen") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); } else if (arg == "-pg") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], ','); + auto p = string_split(argv[i], ','); if (p.size() != 2) { invalid_param = true; break; @@ -329,21 +341,21 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); } else if (arg == "-ub" || arg == "--ubatch-size") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); } else if (arg == "-ctk" || arg == "--cache-type-k") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -359,7 +371,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -375,21 +387,27 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_threads.insert(params.n_threads.end(), p.begin(), p.end()); } else if (arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (arg == "-rpc" || arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers.push_back(argv[i]); } else if (arg == "-sm" || arg == "--split-mode") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector modes; for (const auto & m : p) { llama_split_mode mode; @@ -411,13 +429,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - params.main_gpu = split(argv[i], split_delim); + params.main_gpu = string_split(argv[i], split_delim); } else if (arg == "-nkvo" || arg == "--no-kv-offload") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); } else if (arg == "--numa") { if (++i >= argc) { @@ -435,28 +453,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); } else if (arg == "-embd" || arg == "--embeddings") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; break; } - for (auto ts : split(argv[i], split_delim)) { + for (auto ts : string_split(argv[i], split_delim)) { // split string by ; and / const std::regex regex{R"([;/]+)"}; std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; @@ -484,18 +502,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - if (argv[i] == std::string("csv")) { - params.output_format = CSV; - } else if (argv[i] == std::string("json")) { - params.output_format = JSON; - } else if (argv[i] == std::string("md")) { - params.output_format = MARKDOWN; - } else if (argv[i] == std::string("sql")) { - params.output_format = SQL; - } else { + invalid_param = !output_format_from_str(argv[i], params.output_format); + } else if (arg == "-oe" || arg == "--output-err") { + if (++i >= argc) { invalid_param = true; break; } + invalid_param = !output_format_from_str(argv[i], params.output_format_stderr); } else if (arg == "-v" || arg == "--verbose") { params.verbose = true; } else { @@ -519,6 +532,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } + if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; } if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } @@ -541,6 +555,7 @@ struct cmd_params_instance { ggml_type type_v; int n_threads; int n_gpu_layers; + std::string rpc_servers; llama_split_mode split_mode; int main_gpu; bool no_kv_offload; @@ -553,6 +568,9 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; + if (!rpc_servers.empty()) { + mparams.rpc_servers = rpc_servers.c_str(); + } mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); @@ -564,6 +582,7 @@ struct cmd_params_instance { bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && + rpc_servers == other.rpc_servers && split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && @@ -592,6 +611,7 @@ static std::vector get_cmd_params_instances(const cmd_param // this ordering minimizes the number of times that each model needs to be reloaded for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) + for (const auto & rpc : params.rpc_servers) for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) @@ -618,6 +638,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -643,6 +664,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -668,6 +690,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -687,7 +710,6 @@ struct test { static const std::string build_commit; static const int build_number; static const bool cuda; - static const bool opencl; static const bool vulkan; static const bool kompute; static const bool metal; @@ -703,6 +725,7 @@ struct test { int n_batch; int n_ubatch; int n_threads; + bool has_rpc; ggml_type type_k; ggml_type type_v; int n_gpu_layers; @@ -728,6 +751,7 @@ struct test { n_batch = inst.n_batch; n_ubatch = inst.n_ubatch; n_threads = inst.n_threads; + has_rpc = !inst.rpc_servers.empty(); type_k = inst.type_k; type_v = inst.type_v; n_gpu_layers = inst.n_gpu_layers; @@ -775,9 +799,6 @@ struct test { if (cuda) { return GGML_CUDA_NAME; } - if (opencl) { - return "OpenCL"; - } if (vulkan) { return "Vulkan"; } @@ -803,7 +824,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", + "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", @@ -829,7 +850,7 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || + if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || field == "flash_attn" || field == "use_mmap" || field == "embeddings") { return BOOL; @@ -858,8 +879,8 @@ struct test { } std::vector values = { build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan), + std::to_string(metal), std::to_string(sycl), std::to_string(has_rpc), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_ubatch), @@ -887,7 +908,6 @@ struct test { const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cuda(); -const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::vulkan = !!ggml_cpu_has_vulkan(); const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); @@ -1011,6 +1031,27 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return 3; } + if (field == "n_threads") { + return 7; + } + if (field == "n_batch") { + return 7; + } + if (field == "n_ubatch") { + return 8; + } + if (field == "type_k" || field == "type_v") { + return 6; + } + if (field == "split_mode") { + return 5; + } + if (field == "flash_attn") { + return 2; + } + if (field == "use_mmap") { + return 4; + } if (field == "test") { return 13; } @@ -1138,6 +1179,9 @@ struct markdown_printer : public printer { value = buf; } else if (field == "backend") { value = test::get_backend(); + if (t.has_rpc) { + value += "+RPC"; + } } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); @@ -1254,6 +1298,22 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text (void) user_data; } +static std::unique_ptr create_printer(output_formats format) { + switch (format) { + case NONE: + return nullptr; + case CSV: + return std::unique_ptr(new csv_printer()); + case JSON: + return std::unique_ptr(new json_printer()); + case MARKDOWN: + return std::unique_ptr(new markdown_printer()); + case SQL: + return std::unique_ptr(new sql_printer()); + } + GGML_ASSERT(false); +} + int main(int argc, char ** argv) { // try to set locale for unicode characters in markdown setlocale(LC_CTYPE, ".UTF-8"); @@ -1280,26 +1340,18 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // initialize printer - std::unique_ptr p; - switch (params.output_format) { - case CSV: - p.reset(new csv_printer()); - break; - case JSON: - p.reset(new json_printer()); - break; - case MARKDOWN: - p.reset(new markdown_printer()); - break; - case SQL: - p.reset(new sql_printer()); - break; - default: - assert(false); - exit(1); + std::unique_ptr p = create_printer(params.output_format); + std::unique_ptr p_err = create_printer(params.output_format_stderr); + + if (p) { + p->fout = stdout; + p->print_header(params); + } + + if (p_err) { + p_err->fout = stderr; + p_err->print_header(params); } - p->fout = stdout; - p->print_header(params); std::vector params_instances = get_cmd_params_instances(params); @@ -1357,7 +1409,15 @@ int main(int argc, char ** argv) { t.samples_ns.push_back(t_ns); } - p->print_test(t); + if (p) { + p->print_test(t); + fflush(p->fout); + } + + if (p_err) { + p_err->print_test(t); + fflush(p_err->fout); + } llama_print_timings(ctx); @@ -1366,7 +1426,13 @@ int main(int argc, char ** argv) { llama_free_model(lmodel); - p->print_footer(); + if (p) { + p->print_footer(); + } + + if (p_err) { + p_err->print_footer(); + } llama_backend_free(); diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index d42140efe8168..8d1b37195efd4 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -7,8 +7,6 @@ android { namespace = "com.example.llama" compileSdk = 34 - ndkVersion = "26.1.10909125" - defaultConfig { applicationId = "com.example.llama" minSdk = 33 @@ -20,17 +18,6 @@ android { vectorDrawables { useSupportLibrary = true } - ndk { - // Add NDK properties if wanted, e.g. - // abiFilters += listOf("arm64-v8a") - } - externalNativeBuild { - cmake { - arguments += "-DCMAKE_BUILD_TYPE=Release" - cppFlags += listOf() - arguments += listOf() - } - } } buildTypes { @@ -55,17 +42,6 @@ android { composeOptions { kotlinCompilerExtensionVersion = "1.5.1" } - packaging { - resources { - excludes += "/META-INF/{AL2.0,LGPL2.1}" - } - } - externalNativeBuild { - cmake { - path = file("src/main/cpp/CMakeLists.txt") - version = "3.22.1" - } - } } dependencies { @@ -78,6 +54,7 @@ dependencies { implementation("androidx.compose.ui:ui-graphics") implementation("androidx.compose.ui:ui-tooling-preview") implementation("androidx.compose.material3:material3") + implementation(project(":llama")) testImplementation("junit:junit:4.13.2") androidTestImplementation("androidx.test.ext:junit:1.1.5") androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt index be95e22218332..45ac29938f441 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -1,5 +1,6 @@ package com.example.llama +import android.llama.cpp.LLamaAndroid import android.util.Log import androidx.compose.runtime.getValue import androidx.compose.runtime.mutableStateOf @@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope import kotlinx.coroutines.flow.catch import kotlinx.coroutines.launch -class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { +class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() { companion object { @JvmStatic private val NanosPerSecond = 1_000_000_000.0 @@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { - llm.unload() + llamaAndroid.unload() } catch (exc: IllegalStateException) { messages += exc.message!! } @@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { messages += "" viewModelScope.launch { - llm.send(text) + llamaAndroid.send(text) .catch { Log.e(tag, "send() failed", it) messages += it.message!! @@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { val start = System.nanoTime() - val warmupResult = llm.bench(pp, tg, pl, nr) + val warmupResult = llamaAndroid.bench(pp, tg, pl, nr) val end = System.nanoTime() messages += warmupResult @@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { return@launch } - messages += llm.bench(512, 128, 1, 3) + messages += llamaAndroid.bench(512, 128, 1, 3) } catch (exc: IllegalStateException) { Log.e(tag, "bench() failed", exc) messages += exc.message!! @@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { fun load(pathToModel: String) { viewModelScope.launch { try { - llm.load(pathToModel) + llamaAndroid.load(pathToModel) messages += "Loaded $pathToModel" } catch (exc: IllegalStateException) { Log.e(tag, "load() failed", exc) diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts index 50ebc821122f6..acd1ada7d9b1a 100644 --- a/examples/llama.android/build.gradle.kts +++ b/examples/llama.android/build.gradle.kts @@ -2,4 +2,5 @@ plugins { id("com.android.application") version "8.2.0" apply false id("org.jetbrains.kotlin.android") version "1.9.0" apply false + id("com.android.library") version "8.2.0" apply false } diff --git a/examples/llama.android/llama/.gitignore b/examples/llama.android/llama/.gitignore new file mode 100644 index 0000000000000..796b96d1c4023 --- /dev/null +++ b/examples/llama.android/llama/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/CMakeLists.txt similarity index 98% rename from examples/llama.android/app/src/main/cpp/CMakeLists.txt rename to examples/llama.android/llama/CMakeLists.txt index 4536974a5c50c..a5618cac05849 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/llama/CMakeLists.txt @@ -42,7 +42,7 @@ add_subdirectory(../../../../../../ build-llama) # used in the AndroidManifest.xml file. add_library(${CMAKE_PROJECT_NAME} SHARED # List C/C++ source files with relative paths to this CMakeLists.txt. - llama-android.cpp) + llama-android.cpp) # Specifies libraries CMake should link to your target library. You # can link libraries from various origins, such as libraries defined in this diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts new file mode 100644 index 0000000000000..0a3806172f05f --- /dev/null +++ b/examples/llama.android/llama/build.gradle.kts @@ -0,0 +1,68 @@ +plugins { + id("com.android.library") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "android.llama.cpp" + compileSdk = 34 + + defaultConfig { + minSdk = 33 + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + consumerProguardFiles("consumer-rules.pro") + ndk { + // Add NDK properties if wanted, e.g. + // abiFilters += listOf("arm64-v8a") + } + externalNativeBuild { + cmake { + arguments += "-DCMAKE_BUILD_TYPE=Release" + cppFlags += listOf() + arguments += listOf() + + cppFlags("") + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + externalNativeBuild { + cmake { + path("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.appcompat:appcompat:1.6.1") + implementation("com.google.android.material:material:1.11.0") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") +} diff --git a/examples/llama.android/llama/consumer-rules.pro b/examples/llama.android/llama/consumer-rules.pro new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/examples/llama.android/llama/proguard-rules.pro b/examples/llama.android/llama/proguard-rules.pro new file mode 100644 index 0000000000000..f1b424510da51 --- /dev/null +++ b/examples/llama.android/llama/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt new file mode 100644 index 0000000000000..05d6ab5d2dd23 --- /dev/null +++ b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt @@ -0,0 +1,24 @@ +package android.llama.cpp + +import androidx.test.platform.app.InstrumentationRegistry +import androidx.test.ext.junit.runners.AndroidJUnit4 + +import org.junit.Test +import org.junit.runner.RunWith + +import org.junit.Assert.* + +/** + * Instrumented test, which will execute on an Android device. + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +@RunWith(AndroidJUnit4::class) +class ExampleInstrumentedTest { + @Test + fun useAppContext() { + // Context of the app under test. + val appContext = InstrumentationRegistry.getInstrumentation().targetContext + assertEquals("android.llama.cpp.test", appContext.packageName) + } +} diff --git a/examples/llama.android/llama/src/main/AndroidManifest.xml b/examples/llama.android/llama/src/main/AndroidManifest.xml new file mode 100644 index 0000000000000..8bdb7e14b389a --- /dev/null +++ b/examples/llama.android/llama/src/main/AndroidManifest.xml @@ -0,0 +1,4 @@ + + + + diff --git a/examples/llama.android/llama/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt new file mode 100644 index 0000000000000..42ebaad49a560 --- /dev/null +++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt @@ -0,0 +1,49 @@ +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html. +# For more examples on how to use CMake, see https://github.com/android/ndk-samples. + +# Sets the minimum CMake version required for this project. +cmake_minimum_required(VERSION 3.22.1) + +# Declares the project name. The project name can be accessed via ${ PROJECT_NAME}, +# Since this is the top level CMakeLists.txt, the project name is also accessible +# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level +# build script scope). +project("llama-android") + +include(FetchContent) +FetchContent_Declare( + llama + GIT_REPOSITORY https://github.com/ggerganov/llama.cpp + GIT_TAG master +) + +# Also provides "common" +FetchContent_MakeAvailable(llama) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. +# +# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define +# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} +# is preferred for the same purpose. +# +# In order to load a library into your app from Java/Kotlin, you must call +# System.loadLibrary() and pass the name of the library defined here; +# for GameActivity/NativeActivity derived applications, the same library name must be +# used in the AndroidManifest.xml file. +add_library(${CMAKE_PROJECT_NAME} SHARED + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) + +# Specifies libraries CMake should link to your target library. You +# can link libraries from various origins, such as libraries defined in this +# build script, prebuilt third-party libraries, or Android system libraries. +target_link_libraries(${CMAKE_PROJECT_NAME} + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp similarity index 92% rename from examples/llama.android/app/src/main/cpp/llama-android.cpp rename to examples/llama.android/llama/src/main/cpp/llama-android.cpp index 4af9de3038359..874158ef0f98f 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) { extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { +Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) { llama_model_params model_params = llama_model_default_params(); auto path_to_model = env->GetStringUTFChars(filename, 0); @@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { +Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) { llama_free_model(reinterpret_cast(model)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { +Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) { auto model = reinterpret_cast(jmodel); if (!model) { @@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) { llama_free(reinterpret_cast(context)); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) { llama_backend_free(); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) { llama_log_set(log_callback, NULL); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_bench_1model( +Java_android_llama_cpp_LLamaAndroid_bench_1model( JNIEnv *env, jobject, jlong context_pointer, @@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { +Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { llama_batch_free(*reinterpret_cast(batch_pointer)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { +Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. @@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) { llama_backend_init(); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { +Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) { return env->NewStringUTF(llama_print_system_info()); } extern "C" JNIEXPORT jint JNICALL -Java_com_example_llama_Llm_completion_1init( +Java_android_llama_cpp_LLamaAndroid_completion_1init( JNIEnv *env, jobject, jlong context_pointer, @@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init( extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_completion_1loop( +Java_android_llama_cpp_LLamaAndroid_completion_1loop( JNIEnv * env, jobject, jlong context_pointer, @@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { llama_kv_cache_clear(reinterpret_cast(context)); } diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt similarity index 97% rename from examples/llama.android/app/src/main/java/com/example/llama/Llm.kt rename to examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt index d86afee379083..6c63e54e0d908 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt +++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt @@ -1,4 +1,4 @@ -package com.example.llama +package android.llama.cpp import android.util.Log import kotlinx.coroutines.CoroutineDispatcher @@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext import java.util.concurrent.Executors import kotlin.concurrent.thread -class Llm { +class LLamaAndroid { private val tag: String? = this::class.simpleName private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } @@ -165,8 +165,8 @@ class Llm { } // Enforce only one instance of Llm. - private val _instance: Llm = Llm() + private val _instance: LLamaAndroid = LLamaAndroid() - fun instance(): Llm = _instance + fun instance(): LLamaAndroid = _instance } } diff --git a/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt new file mode 100644 index 0000000000000..cbbb974d32266 --- /dev/null +++ b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt @@ -0,0 +1,17 @@ +package android.llama.cpp + +import org.junit.Test + +import org.junit.Assert.* + +/** + * Example local unit test, which will execute on the development machine (host). + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +class ExampleUnitTest { + @Test + fun addition_isCorrect() { + assertEquals(4, 2 + 2) + } +} diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts index 2ba32c4fafc5c..c7c1a034a45b8 100644 --- a/examples/llama.android/settings.gradle.kts +++ b/examples/llama.android/settings.gradle.kts @@ -15,3 +15,4 @@ dependencyResolutionManagement { rootProject.name = "LlamaAndroid" include(":app") +include(":llama") diff --git a/examples/llama2-13b.sh b/examples/llama2-13b.sh deleted file mode 100755 index 92b3f6dd8b0ec..0000000000000 --- a/examples/llama2-13b.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/llama2.sh b/examples/llama2.sh deleted file mode 100755 index 221b37553cfe7..0000000000000 --- a/examples/llama2.sh +++ /dev/null @@ -1,18 +0,0 @@ -#!/bin/bash - -# -# Temporary script - will be removed in the future -# - -cd `dirname $0` -cd .. - -./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \ - --color \ - --ctx_size 2048 \ - -n -1 \ - -ins -b 256 \ - --top_k 10000 \ - --temp 0.2 \ - --repeat_penalty 1.1 \ - -t 8 diff --git a/examples/llava/CMakeLists.txt b/examples/llava/CMakeLists.txt index 2985caff8379a..e9fa73acb097b 100644 --- a/examples/llava/CMakeLists.txt +++ b/examples/llava/CMakeLists.txt @@ -30,8 +30,9 @@ if(TARGET BUILD_INFO) add_dependencies(llava BUILD_INFO) endif() -set(TARGET llava-cli) -add_executable(llava-cli llava-cli.cpp) -install(TARGETS llava-cli RUNTIME) -target_link_libraries(llava-cli PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(llava PRIVATE cxx_std_11) +set(TARGET llama-llava-cli) +add_executable(${TARGET} llava-cli.cpp) +set_target_properties(${TARGET} PROPERTIES OUTPUT_NAME llama-llava-cli) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 413e433dd9c07..05a8207e67b88 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -9,12 +9,12 @@ The implementation is based on llava, and is compatible with llava and mobileVLM Notice: The overall process of model inference for both **MobileVLM** and **MobileVLM_V2** models is the same, but the process of model conversion is a little different. Therefore, using **MobileVLM-1.7B** as an example, the different conversion step will be shown. ## Usage -Build with cmake or run `make llava-cli` to build it. +Build with cmake or run `make llama-llava-cli` to build it. -After building, run: `./llava-cli` to see the usage. For example: +After building, run: `./llama-llava-cli` to see the usage. For example: ```sh -./llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \ +./llama-llava-cli -m MobileVLM-1.7B/ggml-model-q4_k.gguf \ --mmproj MobileVLM-1.7B/mmproj-model-f16.gguf \ --image path/to/an/image.jpg \ -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? Answer the question using a single word or phrase. ASSISTANT:" @@ -54,15 +54,15 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldpv2 ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py path/to/MobileVLM-1.7B +python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B ``` 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k` ```sh -./quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s +./llama-quantize path/to/MobileVLM-1.7B/ggml-model-f16.gguf path/to/MobileVLM-1.7B/ggml-model-q4_k.gguf q4_k_s ``` Now both the LLaMA part and the image encoder is in the `MobileVLM-1.7B` directory. @@ -82,7 +82,7 @@ refer to `android/adb_run.sh`, modify resources' `name` and `path` ### case 1 **input** ```sh -/data/local/tmp/llava-cli \ +/data/local/tmp/llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -t 4 \ @@ -102,7 +102,7 @@ llama_print_timings: total time = 34731.93 ms ### case 2 **input** ```sh -/data/local/tmp/llava-cli \ +/data/local/tmp/llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -t 4 \ @@ -126,7 +126,7 @@ llama_print_timings: total time = 34570.79 ms #### llava-cli release-b2005 **input** ```sh -/data/local/tmp/llava-cli \ +/data/local/tmp/llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -t 4 \ @@ -200,7 +200,7 @@ make LLAMA_CUDA=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 ### case 1 **input** ```sh -./llava-cli \ +./llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ --image /data/local/tmp/demo.jpeg \ @@ -224,7 +224,7 @@ llama_print_timings: total time = 1352.63 ms / 252 tokens ### case 2 **input** ```sh -./llava-cli \ +./llama-llava-cli \ -m /data/local/tmp/ggml-model-q4_k.gguf \ --mmproj /data/local/tmp/mmproj-model-f16.gguf \ -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ diff --git a/examples/llava/README.md b/examples/llava/README.md index 4fb0cf3816383..f4554de676e4c 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -11,12 +11,12 @@ For llava-1.6 a variety of prepared gguf models are available as well [7b-34b](h After API is confirmed, more models will be supported / uploaded. ## Usage -Build with cmake or run `make llava-cli` to build it. +Build with cmake or run `make llama-llava-cli` to build it. -After building, run: `./llava-cli` to see the usage. For example: +After building, run: `./llama-llava-cli` to see the usage. For example: ```sh -./llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg +./llama-llava-cli -m ../llava-v1.5-7b/ggml-model-f16.gguf --mmproj ../llava-v1.5-7b/mmproj-model-f16.gguf --image path/to/an/image.jpg ``` **note**: A lower temperature like 0.1 is recommended for better quality. add `--temp 0.1` to the command to do so. @@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py ../llava-v1.5-7b --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown ``` Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. @@ -92,12 +92,12 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto 6) Then convert the model to gguf format: ```console -python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` -7) And finally we can run the llava-cli using the 1.6 model version: +7) And finally we can run the llava cli using the 1.6 model version: ```console -./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 +./llama-llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096 ``` **note** llava-1.6 needs more context than llava-1.5, at least 3000 is needed (just run it at -c 4096) diff --git a/examples/llava/android/adb_run.sh b/examples/llava/android/adb_run.sh index f73623ae3b129..45ccf8d70d863 100755 --- a/examples/llava/android/adb_run.sh +++ b/examples/llava/android/adb_run.sh @@ -10,7 +10,7 @@ prompt="A chat between a curious user and an artificial intelligence assistant. # prompt="A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" program_dir="build_64/bin" -binName="llava-cli" +binName="llama-llava-cli" n_threads=4 diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 45bdad6897658..ca36313844c13 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ +/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */ CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index c974900f21e20..8c7dd2ae3d0dc 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -112,9 +112,12 @@ struct llava_context { struct llama_model * model = NULL; }; -static void show_additional_info(int /*argc*/, char ** argv) { - LOG_TEE("\n example usage: %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); - LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\n example usage:\n"); + LOG_TEE("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG_TEE("\n note: a lower temperature value like 0.1 is recommended for better quality.\n"); } static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) { @@ -278,7 +281,7 @@ int main(int argc, char ** argv) { gpt_params params; if (!gpt_params_parse(argc, argv, params)) { - show_additional_info(argc, argv); + print_usage(argc, argv, params); return 1; } @@ -290,8 +293,7 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - gpt_params_print_usage(argc, argv, params); - show_additional_info(argc, argv); + print_usage(argc, argv, {}); return 1; } auto model = llava_init(¶ms); diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index f80f727a79307..17cb4d5e5ee8e 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -1,3 +1,3 @@ --r ../../requirements/requirements-convert.txt +-r ../../requirements/requirements-convert-legacy-llama.txt pillow~=10.2.0 torch~=2.1.1 diff --git a/examples/lookahead/CMakeLists.txt b/examples/lookahead/CMakeLists.txt index 8827e3f11ecd6..f0ae5cd89244c 100644 --- a/examples/lookahead/CMakeLists.txt +++ b/examples/lookahead/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET lookahead) +set(TARGET llama-lookahead) add_executable(${TARGET} lookahead.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 54f060a85b263..fb20ad93f9c1d 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -37,7 +37,8 @@ struct ngram_container { int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/lookup/CMakeLists.txt b/examples/lookup/CMakeLists.txt index b91633f63e4ee..ef19fe25e31a3 100644 --- a/examples/lookup/CMakeLists.txt +++ b/examples/lookup/CMakeLists.txt @@ -1,22 +1,22 @@ -set(TARGET lookup) +set(TARGET llama-lookup) add_executable(${TARGET} lookup.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -set(TARGET lookup-create) +set(TARGET llama-lookup-create) add_executable(${TARGET} lookup-create.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -set(TARGET lookup-merge) +set(TARGET llama-lookup-merge) add_executable(${TARGET} lookup-merge.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) -set(TARGET lookup-stats) +set(TARGET llama-lookup-stats) add_executable(${TARGET} lookup-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 1c230c9667c71..d713f6f2194a8 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -14,8 +14,10 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } + // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); diff --git a/examples/lookup/lookup-merge.cpp b/examples/lookup/lookup-merge.cpp index 07c93eb8d057b..81e2b04369b90 100644 --- a/examples/lookup/lookup-merge.cpp +++ b/examples/lookup/lookup-merge.cpp @@ -11,14 +11,14 @@ #include #include -static void print_usage() { +static void print_usage(char* argv0) { fprintf(stderr, "Merges multiple lookup cache files into a single one.\n"); - fprintf(stderr, "Usage: lookup-merge [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n"); + fprintf(stderr, "Usage: %s [--help] lookup_part_1.bin lookup_part_2.bin ... lookup_merged.bin\n", argv0); } int main(int argc, char ** argv){ if (argc < 3) { - print_usage(); + print_usage(argv[0]); exit(1); } @@ -27,7 +27,7 @@ int main(int argc, char ** argv){ for (int i = 0; i < argc-1; ++i) { args[i] = argv[i+1]; if (args[i] == "-h" || args[i] == "--help") { - print_usage(); + print_usage(argv[0]); exit(0); } } diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 87ecc0a4f1394..0b171c87273d1 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -16,6 +16,7 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 83dbee91a8362..80ecd925d5962 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -15,6 +15,7 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/main-cmake-pkg/CMakeLists.txt b/examples/main-cmake-pkg/CMakeLists.txt index deb77d588ea9f..a97ded3653f0c 100644 --- a/examples/main-cmake-pkg/CMakeLists.txt +++ b/examples/main-cmake-pkg/CMakeLists.txt @@ -1,12 +1,12 @@ cmake_minimum_required(VERSION 3.12) -project("main-cmake-pkg" C CXX) -set(TARGET main-cmake-pkg) +project("llama-cli-cmake-pkg" C CXX) +set(TARGET llama-cli-cmake-pkg) find_package(Llama 0.0.1 REQUIRED) # Bake common functionality in with target. Because applications # using the relocatable Llama package should be outside of the -# source tree, main-cmake-pkg pretends the dependencies are built-in. +# source tree, llama-cli-cmake-pkg pretends the dependencies are built-in. set(_common_path "${CMAKE_CURRENT_LIST_DIR}/../../common") add_library(common OBJECT) file(GLOB _common_files @@ -15,7 +15,7 @@ file(GLOB _common_files ) target_sources(common PRIVATE ${_common_files}) -# If the common project was part of "main-cmake-pkg" the transient +# If the common project was part of "llama-cli-cmake-pkg" the transient # defines would automatically be attached. Because the common func- # tionality is separate, but dependent upon the defines, it must be # explicitly extracted from the "llama" target. diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md index edf20d8db6616..08d83dd08636a 100644 --- a/examples/main-cmake-pkg/README.md +++ b/examples/main-cmake-pkg/README.md @@ -1,6 +1,6 @@ # llama.cpp/example/main-cmake-pkg -This program builds the [main](../main) application using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree. +This program builds [llama-cli](../main) using a relocatable CMake package. It serves as an example of using the `find_package()` CMake command to conveniently include [llama.cpp](https://github.com/ggerganov/llama.cpp) in projects which live outside of the source tree. ## Building @@ -8,26 +8,24 @@ Because this example is "outside of the source tree", it is important to first b ### Considerations -When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_. +When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package. ### Build llama.cpp and install to C:\LlamaCPP directory -In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`. - ```cmd git clone https://github.com/ggerganov/llama.cpp cd llama.cpp -cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 +cmake -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64 cmake --build build --config Release cmake --install build --prefix C:/LlamaCPP ``` -### Build main-cmake-pkg +### Build llama-cli-cmake-pkg ```cmd cd ..\examples\main-cmake-pkg -cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 +cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 cmake --build build --config Release cmake --install build --prefix C:/MyLlamaApp ``` diff --git a/examples/main/CMakeLists.txt b/examples/main/CMakeLists.txt index d532980b76da8..5f6efaa9aa94b 100644 --- a/examples/main/CMakeLists.txt +++ b/examples/main/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET main) +set(TARGET llama-cli) add_executable(${TARGET} main.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/main/README.md b/examples/main/README.md index ee930f4e79a0d..61e4a42f7e02c 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -1,4 +1,4 @@ -# llama.cpp/example/main +# llama.cpp/examples/main This example program allows you to use various LLaMA language models in an easy and efficient way. It is specifically designed to work with the [llama.cpp](https://github.com/ggerganov/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. @@ -20,13 +20,13 @@ To get started right away, run the following command, making sure to use the cor #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin --prompt "Once upon a time" +./llama-cli -m models/7B/ggml-model.bin --prompt "Once upon a time" ``` #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin --prompt "Once upon a time" +llama-cli.exe -m models\7B\ggml-model.bin --prompt "Once upon a time" ``` For an interactive experience, try this command: @@ -34,7 +34,7 @@ For an interactive experience, try this command: #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \ +./llama-cli -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \ 'User: Hi AI: Hello. I am an AI chatbot. Would you like to talk? User: Sure! @@ -45,7 +45,7 @@ User:' #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" +llama-cli.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" ``` The following command generates "infinite" text from a starting prompt (you can use `Ctrl-C` to stop it): @@ -53,38 +53,36 @@ The following command generates "infinite" text from a starting prompt (you can #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt +./llama-cli -m models/7B/ggml-model.bin --ignore-eos -n -1 ``` #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt +llama-cli.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 ``` ## Common Options -In this section, we cover the most commonly used options for running the `main` program with the LLaMA models: +In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models: - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`; inferred from `--model-url` if set). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g https://huggingface.co/ggml-org/models/resolve/main/phi-2/ggml-model-q4_0.gguf). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. -- `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models. - `-n N, --n-predict N`: Set the number of tokens to predict when generating text. Adjusting this value can influence the length of the generated text. - `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. ## Input Prompts -The `main` program provides several ways to interact with the LLaMA models using input prompts: +The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts: - `--prompt PROMPT`: Provide a prompt directly as a command-line option. - `--file FNAME`: Provide a file containing a prompt or multiple prompts. - `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.) -- `--random-prompt`: Start with a randomized prompt. ## Interaction -The `main` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive`, `--interactive-first`, and `--instruct`. +The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing. @@ -92,7 +90,6 @@ In interactive mode, users can participate in text generation by injecting their - `-i, --interactive`: Run the program in interactive mode, allowing users to engage in real-time conversations or provide specific instructions to the model. - `--interactive-first`: Run the program in interactive mode and immediately wait for user input before starting the text generation. -- `-ins, --instruct`: Run the program in instruction mode, which is specifically designed to work with Alpaca models that excel in completing tasks based on user instructions. - `--color`: Enable colorized output to differentiate visually distinguishing between prompts, user input, and generated text. By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs. @@ -110,7 +107,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./main -r "User:" --in-prefix " " +./llama-cli -r "User:" --in-prefix " " ``` ### In-Suffix @@ -118,19 +115,9 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./main -r "User:" --in-prefix " " --in-suffix "Assistant:" +./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:" ``` -### Instruction Mode - -Instruction mode is particularly useful when working with Alpaca models, which are designed to follow user instructions for specific tasks: - -- `-ins, --instruct`: Enable instruction mode to leverage the capabilities of Alpaca models in completing tasks based on user-provided instructions. - -Technical detail: the user's input is internally prefixed with the reverse prompt (or `### Instruction:` as the default), and followed by `### Response:` (except if you just press Return without any input, to keep generating a longer response). - -By understanding and utilizing these interaction options, you can create engaging and dynamic experiences with the LLaMA models, tailoring the text generation process to your specific needs. - ## Context Management During text generation, LLaMA models have a limited context size, which means they can only consider a certain number of tokens from the input and generated text. When the context fills up, the model resets internally, potentially losing some information from the beginning of the conversation or instructions. Context management options help maintain continuity and coherence in these situations. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 09fa85fce0ee3..b97b7b7937f02 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -122,8 +122,10 @@ int main(int argc, char ** argv) { g_params = ¶ms; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } + llama_sampling_params & sparams = params.sparams; #ifndef LOG_DISABLE_LOGS @@ -180,9 +182,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = string_random_prompt(rng); - } LOG("%s: llama backend init\n", __func__); llama_backend_init(); @@ -250,11 +249,8 @@ int main(int argc, char ** argv) { std::vector embd_inp; - if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) { + if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { LOG("tokenize the prompt\n"); - if (params.chatml) { - params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; - } embd_inp = ::llama_tokenize(ctx, params.prompt, true, true); } else { LOG("use session tokens\n"); @@ -332,37 +328,13 @@ int main(int argc, char ** argv) { } // number of tokens to keep when resetting context - if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) { + if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { params.n_keep = (int)embd_inp.size(); } else { params.n_keep += add_bos; // always keep the BOS token } - // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true, true); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); - - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); - - // chatml prefix & suffix - const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true); - const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true); - - LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str()); - LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str()); - - // in instruct mode, we inject a prefix and a suffix to each input by the user - if (params.instruct) { - params.interactive_first = true; - params.antiprompt.emplace_back("### Instruction:\n\n"); - } - // similar for chatml mode - else if (params.chatml) { - params.interactive_first = true; - params.antiprompt.emplace_back("<|im_start|>user\n"); - } - else if (params.conversation) { + if (params.conversation) { params.interactive_first = true; } @@ -740,18 +712,26 @@ int main(int argc, char ** argv) { // display text if (input_echo && display) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation); - printf("%s", token_str.c_str()); + const std::string token_str = llama_token_to_piece(ctx, id, params.special); + // Console/Stream Output + fprintf(stdout, "%s", token_str.c_str()); + + // Record Displayed Tokens To Log + // Note: Generated tokens are created one by one hence this check if (embd.size() > 1) { + // Incoming Requested Tokens input_tokens.push_back(id); } else { + // Outgoing Generated Tokens output_tokens.push_back(id); output_ss << token_str; } + + fflush(stdout); } - fflush(stdout); } + // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); @@ -815,15 +795,13 @@ int main(int argc, char ** argv) { is_interacting = true; printf("\n"); - } else if (params.instruct || params.chatml) { - is_interacting = true; } } if (n_past > 0 && is_interacting) { LOG("waiting for user input\n"); - if (params.conversation || params.instruct || params.chatml) { + if (params.conversation) { printf("\n> "); } @@ -866,24 +844,12 @@ int main(int argc, char ** argv) { const size_t original_size = embd_inp.size(); - // instruct mode: insert instruction prefix - if (params.instruct && !is_antiprompt) { - LOG("inserting instruction prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); - } - // chatml mode: insert user chat prefix - if (params.chatml && !is_antiprompt) { - LOG("inserting chatml prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end()); - } if (params.escape) { string_process_escapes(buffer); } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, params.interactive_specials); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); @@ -892,17 +858,6 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); - // instruct mode: insert response suffix - if (params.instruct) { - LOG("inserting instruction suffix\n"); - embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - } - // chatml mode: insert assistant chat suffix - if (params.chatml) { - LOG("inserting chatml suffix\n"); - embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end()); - } - for (size_t i = original_size; i < embd_inp.size(); ++i) { const llama_token token = embd_inp[i]; output_tokens.push_back(token); @@ -927,7 +882,7 @@ int main(int argc, char ** argv) { } // end of generation - if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) { + if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) { LOG_TEE(" [end of text]\n"); break; } diff --git a/examples/make-ggml.py b/examples/make-ggml.py deleted file mode 100755 index c73485ebf1eff..0000000000000 --- a/examples/make-ggml.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -""" -This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them. - -Usage: -python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] - -Arguments: -- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. -- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox. -- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. -- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. -- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. -- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. - -Old quant types (some base model types require these): -- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M -- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L -- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M -- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M - -New quant types (recommended): -- Q2_K: smallest, extreme quality loss - not recommended -- Q3_K: alias for Q3_K_M -- Q3_K_S: very small, very high quality loss -- Q3_K_M: very small, very high quality loss -- Q3_K_L: small, substantial quality loss -- Q4_K: alias for Q4_K_M -- Q4_K_S: small, significant quality loss -- Q4_K_M: medium, balanced quality - recommended -- Q5_K: alias for Q5_K_M -- Q5_K_S: large, low quality loss - recommended -- Q5_K_M: large, very low quality loss - recommended -- Q6_K: very large, extremely low quality loss -- Q8_0: very large, extremely low quality loss - not recommended -- F16: extremely large, virtually no quality loss - not recommended -- F32: absolutely huge, lossless - not recommended -""" -import subprocess -subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) - -import argparse -import os -from huggingface_hub import snapshot_download - -def main(model, model_type, outname, outdir, quants, keep_fp16): - if not os.path.isdir(model): - print(f"Model not found at {model}. Downloading...") - try: - if outname is None: - outname = model.split('/')[-1] - model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') - except Exception as e: - raise Exception(f"Could not download the model: {e}") - - if outdir is None: - outdir = f'../models/{outname}' - - if not os.path.isfile(f"{model}/config.json"): - raise Exception(f"Could not find config.json in {model}") - - os.makedirs(outdir, exist_ok=True) - - print("Building llama.cpp") - subprocess.run(f"cd .. && make quantize", shell=True, check=True) - - fp16 = f"{outdir}/{outname}.gguf.fp16.bin" - - print(f"Making unquantised GGUF at {fp16}") - if not os.path.isfile(fp16): - if model_type != "llama": - subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True) - else: - subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) - else: - print(f"Unquantised GGML already exists at: {fp16}") - - print("Making quants") - for type in quants: - outfile = f"{outdir}/{outname}.gguf.{type}.bin" - print(f"Making {type} : {outfile}") - subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) - - if not keep_fp16: - os.remove(fp16) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') - parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name') - parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.') - parser.add_argument('--outname', default=None, help='Output model(s) name') - parser.add_argument('--outdir', default=None, help='Output directory') - parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') - parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) - - args = parser.parse_args() - - main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/examples/parallel/CMakeLists.txt b/examples/parallel/CMakeLists.txt index 319535a6e9054..c13557bac2bac 100644 --- a/examples/parallel/CMakeLists.txt +++ b/examples/parallel/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET parallel) +set(TARGET llama-parallel) add_executable(${TARGET} parallel.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index c731abb726dc2..7faeaec975ae3 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -100,7 +100,8 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/passkey/CMakeLists.txt b/examples/passkey/CMakeLists.txt index 3161bf3ef9a45..dc467a5d3e411 100644 --- a/examples/passkey/CMakeLists.txt +++ b/examples/passkey/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET passkey) +set(TARGET llama-passkey) add_executable(${TARGET} passkey.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/passkey/README.md b/examples/passkey/README.md index 4a22bb55975be..a48a6283a0b27 100644 --- a/examples/passkey/README.md +++ b/examples/passkey/README.md @@ -8,5 +8,5 @@ See the following PRs for more info: ### Usage ```bash -make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250 +make -j && ./llama-passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250 ``` diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index f2ef9ca10d4a2..d03215cd1e0a9 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -6,46 +6,32 @@ #include #include -int main(int argc, char ** argv) { - gpt_params params; - - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]); - return 1 ; - } - - int seed = -1; +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); - int n_junk = 250; // number of times to repeat the junk text - int n_keep = 32; // number of tokens in the prompt prefix - int n_grp = 1; // if more than 1 - perform LongLM SelfExtend - int i_pos = -1; // position of the passkey in the junk text - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_junk = std::stoi(argv[2]); - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]); + LOG_TEE("\n"); +} - if (argc >= 4) { - n_grp = std::stoi(argv[3]); - } +int main(int argc, char ** argv) { + gpt_params params; - if (argc >= 5) { - i_pos = std::stoi(argv[4]); - } + params.n_junk = 250; + params.n_keep = 32; + params.i_pos = -1; - if (argc >= 6) { - seed = std::stoi(argv[5]); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - if (seed == -1) { - seed = time(NULL); - } + srand(params.seed == LLAMA_DEFAULT_SEED ? time(NULL) : params.seed); - srand(seed); + int n_junk = params.n_junk; + int n_keep = params.n_keep; + int n_grp = params.grp_attn_n; + int i_pos = params.i_pos; if (i_pos == -1) { i_pos = rand() % n_junk; @@ -76,9 +62,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - model_params.n_gpu_layers = 99; // offload all layers to the GPU + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -89,13 +73,9 @@ int main(int argc, char ** argv) { // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); - ctx_params.seed = seed; - ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; - ctx_params.n_batch = 512; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp"); @@ -135,7 +115,7 @@ int main(int argc, char ** argv) { LOG_TEE("prompt tokens: %d\n", n_tokens_all); //LOG_TEE("prompt: %s\n", params.prompt.c_str()); - llama_batch batch = llama_batch_init(512, 0, 1); + llama_batch batch = llama_batch_init(params.n_batch, 0, 1); int n_past = 0; diff --git a/examples/perplexity/CMakeLists.txt b/examples/perplexity/CMakeLists.txt index 3c76d3221416b..be0f2fd029e67 100644 --- a/examples/perplexity/CMakeLists.txt +++ b/examples/perplexity/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET perplexity) +set(TARGET llama-perplexity) add_executable(${TARGET} perplexity.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 30e5e282ef5cf..efde8dfdff47b 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -476,7 +476,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par } // Download: https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip - // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` + // Run `./llama-perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval @@ -1032,7 +1032,7 @@ struct winogrande_entry { std::vector seq_tokens[2]; }; -static std::vector load_winogrande_from_csv(const std::string& prompt) { +static std::vector load_winogrande_from_csv(const std::string & prompt) { std::vector result; std::istringstream in(prompt); std::string line; @@ -1964,12 +1964,14 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { int main(int argc, char ** argv) { gpt_params params; + params.n_ctx = 512; + params.logits_all = true; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } - params.logits_all = true; - const int32_t n_ctx = params.n_ctx; if (n_ctx <= 0) { @@ -2006,9 +2008,6 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = string_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); @@ -2027,6 +2026,7 @@ int main(int argc, char ** argv) { } const int n_ctx_train = llama_n_ctx_train(model); + if (params.n_ctx > n_ctx_train) { fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, params.n_ctx); diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py index 9acc7cc6dcd85..f029c73a2582d 100644 --- a/examples/pydantic_models_to_grammar.py +++ b/examples/pydantic_models_to_grammar.py @@ -624,7 +624,7 @@ def get_primitive_grammar(grammar): "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) )* "\"" ws ws ::= ([ \t\n] ws)? -float ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +float ::= ("-"? ([0] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws integer ::= [0-9]+""" diff --git a/examples/quantize-stats/CMakeLists.txt b/examples/quantize-stats/CMakeLists.txt index e31cf5e3809c1..bb986a716883d 100644 --- a/examples/quantize-stats/CMakeLists.txt +++ b/examples/quantize-stats/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET quantize-stats) +set(TARGET llama-quantize-stats) add_executable(${TARGET} quantize-stats.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama build_info ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/quantize/CMakeLists.txt b/examples/quantize/CMakeLists.txt index 6b977fde86ab2..3ee4eb9719fc4 100644 --- a/examples/quantize/CMakeLists.txt +++ b/examples/quantize/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET quantize) +set(TARGET llama-quantize) add_executable(${TARGET} quantize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh index a3ca74c68e7e5..24bc970e8632b 100644 --- a/examples/quantize/tests.sh +++ b/examples/quantize/tests.sh @@ -18,9 +18,9 @@ fi set -x -SPLIT=$1/gguf-split -QUANTIZE=$1/quantize -MAIN=$1/main +SPLIT=$1/llama-gguf-split +QUANTIZE=$1/llama-quantize +MAIN=$1/llama-cli WORK_PATH=$TMP_DIR/quantize ROOT_DIR=$(realpath $(dirname $0)/../../) @@ -47,7 +47,7 @@ echo PASS echo # 3a. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -57,7 +57,7 @@ echo PASS echo # 4b. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 echo PASS echo diff --git a/examples/reason-act.sh b/examples/reason-act.sh index 046c48db584bc..06d592799cf12 100755 --- a/examples/reason-act.sh +++ b/examples/reason-act.sh @@ -8,7 +8,7 @@ if [ "$1" == "-m" ]; then MODEL="-m $2 " fi -./main $MODEL --color \ +./llama-cli $MODEL --color \ -f ./prompts/reason-act.txt \ -i --interactive-first \ --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7 -c 2048 \ diff --git a/examples/retrieval/CMakeLists.txt b/examples/retrieval/CMakeLists.txt index eaabae08d5583..66610f3111405 100644 --- a/examples/retrieval/CMakeLists.txt +++ b/examples/retrieval/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET retrieval) +set(TARGET llama-retrieval) add_executable(${TARGET} retrieval.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/retrieval/README.md b/examples/retrieval/README.md index 2b2595c468046..bc5f22e2ff156 100644 --- a/examples/retrieval/README.md +++ b/examples/retrieval/README.md @@ -15,7 +15,7 @@ https://github.com/ggerganov/llama.cpp/pull/6193 `retrieval` example can be tested as follows: ```bash -make -j && ./retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator . +make -j && ./llama-retrieval --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator . ``` This chunks and embeds all given files and starts a loop requesting query inputs: diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 4e7530706d4a9..55b7b2f70ae2a 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -4,72 +4,12 @@ #include #include -struct retrieval_params { - std::vector context_files; // context files to embed - int32_t chunk_size = 64; // chunk size for context embedding - std::string chunk_separator = "\n"; // chunk separator for context embedding -}; +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); -static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) { - gpt_params_print_usage(argc, argv, gpt_params); - printf("retrieval options:\n"); - printf(" --context-file FNAME file containing context to embed.\n"); - printf(" specify multiple files by providing --context-file option multiple times.\n"); - printf(" --chunk-size N minimum length of embedded text chunk (default:%d)\n", params.chunk_size); - printf(" --chunk-separator STRING\n"); - printf(" string to separate chunks (default: \"\\n\")\n"); - printf("\n"); -} - -static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) { - int i = 1; - std::string arg; - while (i < argc) { - arg = argv[i]; - bool invalid_gpt_param = false; - if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) { - if (invalid_gpt_param) { - fprintf(stderr, "error: invalid argument: %s\n", arg.c_str()); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - // option was parsed by gpt_params_find_arg - } else if (arg == "--context-file") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --context-file\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - // store the external file name in params - retrieval_params.context_files.push_back(argv[i]); - } else if (arg == "--chunk-size") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --chunk-size\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - retrieval_params.chunk_size = std::stoi(argv[i]); - } else if (arg == "--chunk-separator") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --chunk-separator\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - retrieval_params.chunk_separator = argv[i]; - } else { - // unknown argument - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - i++; - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]); + LOG_TEE("\n"); } struct chunk { @@ -171,33 +111,35 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu int main(int argc, char ** argv) { gpt_params params; - retrieval_params retrieval_params; - retrieval_params_parse(argc, argv, params, retrieval_params); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; + } // For BERT models, batch size must be equal to ubatch size params.n_ubatch = params.n_batch; + params.embedding = true; - if (retrieval_params.chunk_size <= 0) { + if (params.chunk_size <= 0) { fprintf(stderr, "chunk_size must be positive\n"); return 1; } - if (retrieval_params.context_files.empty()) { + if (params.context_files.empty()) { fprintf(stderr, "context_files must be specified\n"); return 1; } - params.embedding = true; print_build_info(); printf("processing files:\n"); - for (auto & context_file : retrieval_params.context_files) { + for (auto & context_file : params.context_files) { printf("%s\n", context_file.c_str()); } std::vector chunks; - for (auto & context_file : retrieval_params.context_files) { - std::vector file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator); + for (auto & context_file : params.context_files) { + std::vector file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator); chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end()); } printf("Number of chunks: %ld\n", chunks.size()); @@ -242,7 +184,7 @@ int main(int argc, char ** argv) { return 1; } // add eos if not present - if (inp.empty() || inp.back() != llama_token_eos(model)) { + if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) { inp.push_back(llama_token_eos(model)); } chunk.tokens = inp; diff --git a/examples/rpc/README.md b/examples/rpc/README.md index eeec71a8ee0c2..86544e3fea2c3 100644 --- a/examples/rpc/README.md +++ b/examples/rpc/README.md @@ -70,5 +70,5 @@ cmake --build . --config Release Finally, use the `--rpc` option to specify the host and port of each `rpc-server`: ```bash -$ bin/main -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99 +$ bin/llama-cli -m ../models/tinyllama-1b/ggml-model-f16.gguf -p "Hello, my name is" --repeat-penalty 1.0 -n 64 --rpc 192.168.88.10:50052,192.168.88.11:50052 -ngl 99 ``` diff --git a/examples/save-load-state/CMakeLists.txt b/examples/save-load-state/CMakeLists.txt index cc6ed8554a6e3..0fb5e359bc9ad 100644 --- a/examples/save-load-state/CMakeLists.txt +++ b/examples/save-load-state/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET save-load-state) +set(TARGET llama-save-load-state) add_executable(${TARGET} save-load-state.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index c3b766882dbec..00c2277ac2827 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -11,6 +11,7 @@ int main(int argc, char ** argv) { params.prompt = "The quick brown fox"; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/server-llama2-13B.sh b/examples/server-llama2-13B.sh index 17fedc2b176f6..4ce79b7fac477 100755 --- a/examples/server-llama2-13B.sh +++ b/examples/server-llama2-13B.sh @@ -16,7 +16,7 @@ GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 4096 --batch-size 1024}" # shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS -./server $GEN_OPTIONS \ +./llama-server $GEN_OPTIONS \ --model "$MODEL" \ --threads "$N_THREAD" \ --rope-freq-scale 1.0 \ diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 4b89c53022fd0..8365f9510ceae 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET server) +set(TARGET llama-server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF) include_directories(${CMAKE_CURRENT_SOURCE_DIR} ${CMAKE_CURRENT_BINARY_DIR}) @@ -8,9 +8,20 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS + colorthemes.css + style.css + theme-beeninorder.css + theme-ketivah.css + theme-mangotango.css + theme-playground.css + theme-polarnight.css + theme-snowstorm.css index.html + index-new.html index.js completion.js + system-prompts.js + prompt-formats.js json-schema-to-grammar.mjs ) foreach(asset ${PUBLIC_ASSETS}) diff --git a/examples/server/README.md b/examples/server/README.md index 0c3db8c84c69d..e7fb0bf64c0e1 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -80,26 +80,26 @@ The project is under active development, and we are [looking for feedback and co ## Build -`server` is built alongside everything else from the root of the project +`llama-server` is built alongside everything else from the root of the project - Using `make`: ```bash - make server + make llama-server ``` - Using `CMake`: ```bash cmake -B build - cmake --build build --config Release -t server + cmake --build build --config Release -t llama-server ``` - Binary is at `./build/bin/server` + Binary is at `./build/bin/llama-server` ## Build with SSL -`server` can also be built with SSL support using OpenSSL 3 +`llama-server` can also be built with SSL support using OpenSSL 3 - Using `make`: @@ -107,14 +107,14 @@ The project is under active development, and we are [looking for feedback and co # NOTE: For non-system openssl, use the following: # CXXFLAGS="-I /path/to/openssl/include" # LDFLAGS="-L /path/to/openssl/lib" - make LLAMA_SERVER_SSL=true server + make LLAMA_SERVER_SSL=true llama-server ``` - Using `CMake`: ```bash cmake -B build -DLLAMA_SERVER_SSL=ON - cmake --build build --config Release -t server + cmake --build build --config Release -t llama-server ``` ## Quick Start @@ -124,13 +124,13 @@ To get started right away, run the following command, making sure to use the cor ### Unix-based systems (Linux, macOS, etc.) ```bash -./server -m models/7B/ggml-model.gguf -c 2048 +./llama-server -m models/7B/ggml-model.gguf -c 2048 ``` ### Windows ```powershell -server.exe -m models\7B\ggml-model.gguf -c 2048 +llama-server.exe -m models\7B\ggml-model.gguf -c 2048 ``` The above command will start a server that by default listens on `127.0.0.1:8080`. @@ -279,7 +279,7 @@ node index.js `id_slot`: Assign the completion task to an specific slot. If is -1 the task will be assigned to a Idle slot. Default: `-1` - `cache_prompt`: Re-use previously cached prompt from the last request if possible. This may prevent re-caching the prompt from scratch. Default: `false` + `cache_prompt`: Re-use KV cache from a previous request if possible. This way the common prefix does not have to be re-processed, only the suffix that differs between the requests. Because (depending on the backend) the logits are **not** guaranteed to be bit-for-bit identical for different batch sizes (prompt processing vs. token generation) enabling this option can cause nondeterministic results. Default: `false` `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) @@ -629,11 +629,11 @@ bash chat.sh ### OAI-like API -The HTTP `server` supports an OAI-like API: https://github.com/openai/openai-openapi +The HTTP `llama-server` supports an OAI-like API: https://github.com/openai/openai-openapi ### API errors -`server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi +`llama-server` returns errors in the same format as OAI: https://github.com/openai/openai-openapi Example of an error: diff --git a/examples/server/bench/README.md b/examples/server/bench/README.md index 23a3ec97523ef..0f18ca39651d2 100644 --- a/examples/server/bench/README.md +++ b/examples/server/bench/README.md @@ -99,7 +99,7 @@ The `bench.py` script does several steps: It aims to be used in the CI, but you can run it manually: ```shell -LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/server python bench.py \ +LLAMA_SERVER_BIN_PATH=../../../cmake-build-release/bin/llama-server python bench.py \ --runner-label local \ --name local \ --branch `git rev-parse --abbrev-ref HEAD` \ diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py index 86c5de101445c..4fbbb203267f4 100644 --- a/examples/server/bench/bench.py +++ b/examples/server/bench/bench.py @@ -245,7 +245,7 @@ def start_server(args): def start_server_background(args): # Start the server - server_path = '../../../build/bin/server' + server_path = '../../../build/bin/llama-server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: server_path = os.environ['LLAMA_SERVER_BIN_PATH'] server_args = [ diff --git a/examples/server/public/colorthemes.css b/examples/server/public/colorthemes.css new file mode 100755 index 0000000000000..b1e2b8b70c261 --- /dev/null +++ b/examples/server/public/colorthemes.css @@ -0,0 +1,402 @@ +@import url("theme-snowstorm.css"); +@import url("theme-polarnight.css"); +@import url("theme-ketivah.css"); +@import url("theme-mangotango.css"); +@import url("theme-playground.css"); +@import url("theme-beeninorder.css"); + +:root { +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); +} + +/* + +.theme-template { + + + If light theme: should go from bright to darker + If dark theme: should go from dark to brighter + ideally this should not be anything but steps of + gray or slightly variants from it + + --primary-color-1: #2E3440; + --primary-color-2: #3B4252; + --primary-color-3: #434C5E; + --primary-color-4: #4C566A; + + + + If light theme: should go from dark to brighter + If dark theme: should go from bright to darker + ideally this should not be anything but steps of + gray or slightly variants from it + + --secondary-color-1: #ECEFF4; + --secondary-color-2: #E5E9F0; + --secondary-color-3: #D8DEE9; + --secondary-color-4: #C8CED9; + + + + Choose wisely nuance colors. It is not easy to find + 4 harmonizing nuance colors. But keep in mind, that + only one accent color could work too. + + --theme-nuance-color-1: #8FBCBB; + --theme-nuance-color-2: #88C0D0; + --theme-nuance-color-3: #81A1C1; + --theme-nuance-color-4: #5E81AC; + + + + adapt the color red, orange, yellow, green, + purple to the 'mood' of your overall design + e.g is it low-contrast? vibrant? dynamic? etc + + --theme-red-color: #BF616A; + --theme-orange-color: #D08770; + --theme-yellow-color: #EBCB8B; + --theme-green-color: #A3BE8C; + --theme-purple-color: #B48EAD; + + + +NOTE: comment all those line `--- ...` out +------------------------------------------------ +--background-color-1: +--background-color-2: +--background-color-3: +--background-color-4: + +--border-color-1: +--border-color-2: +--border-color-3: + +--border-focus-color: +--border-focus-shadow: + +--text-color-plain: +--text-color-subtile-1: +--text-color-subtile-2: + +--code-background-color: +--code-text-color: + +--ui-range-thumb-color: +--ui-range-thumb-border: + +--textarea-border-color: + + + +------------------------------------------- +--button-alert-text-hover: +--button-alert-color-hover: +--button-alert-border-hover: + +--button-alert-text-active: +--button-alert-color-active: +--button-alert-border-active: + + + +----------- PRIMARY ----------------------- +--button should immediately catch the eye-- + +--button-primary-text: +--button-primary-color: +--button-primary-border: + + +---------hover---------- +--button-primary-text-hover: +--button-primary-color-hover: +--button-primary-border-hover: + + +---------active--------- +--button-primary-text-active: +--button-primary-color-active: +--button-primary-border-active: + + + +------------ SECONDARY ------------------------ +--button should NOT immediately catch the eye-- + +--button-secondary-text: +--button-secondary-color: +--button-secondary-border: + + +---------hover---------- +--button-secondary-text-hover: +--button-secondary-color-hover: +--button-secondary-border-hover: + + +---------active--------- +--button-secondary-text-active: +--button-secondary-color-active: +--button-secondary-border-active: + + + +---------- TERTIARY ----------------------- +---------- disabled buttons --------------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + + +---------hover---------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + +} + +*/ diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html new file mode 100644 index 0000000000000..19c9f643d3027 --- /dev/null +++ b/examples/server/public/index-new.html @@ -0,0 +1,1178 @@ + + + + + + + + + llama.cpp - chat + + + + + + + + + +
    + +
    +
    + + + diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 2961999f2451a..2f60a76e88b87 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -12,6 +12,18 @@ font-size: 90%; } + .grid-container { + display: grid; + grid-template-columns: auto auto auto; + padding: 10px; + } + + .grid-item { + padding: 5px; + /* font-size: 30px; */ + text-align: center; + } + #container { margin: 0em auto; display: flex; @@ -35,6 +47,67 @@ padding: 0.5em; } + h1 { + text-align: center; + } + + .customlink:link { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + border-radius: 4px; + padding: 8px; + } + + .customlink:visited { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:hover { + color: white; + background-color: #0070ee; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:active { + color: #0070ee; + background-color: #80b3ef; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + body { max-width: 600px; min-width: 300px; @@ -594,7 +667,7 @@ message = html`<${Probabilities} data=${data} />` } else { const text = isArrayMessage ? - data.map(msg => msg.content).join('').replace(/^\s+/, '') : + data.map(msg => msg.content).join('') : data; message = isCompletionMode ? text : @@ -877,19 +950,30 @@ // poor mans markdown replacement const Markdownish = (params) => { - const md = params.text - .replace(/&/g, '&') - .replace(//g, '>') - .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

    $2

    ') - .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/```.*?\n([\s\S]*?)```/g, '
    $1
    ') - .replace(/`(.*?)`/g, '$1') - .replace(/\n/gim, '
    '); - return html``; + const chunks = params.text.split('```'); + + for (let i = 0; i < chunks.length; i++) { + if (i % 2 === 0) { // outside code block + chunks[i] = chunks[i] + .replace(/&/g, '&') + .replace(//g, '>') + .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

    $2

    ') + .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/```.*?\n([\s\S]*?)```/g, '
    $1
    ') + .replace(/`(.*?)`/g, '$1') + .replace(/\n/gim, '
    '); + } else { // inside code block + chunks[i] = `
    ${chunks[i]}
    `; + } + } + + const restoredText = chunks.join(''); + + return html``; }; const ModelGenerationInfo = (params) => { @@ -903,6 +987,7 @@ ` } + // simple popover impl const Popover = (props) => { const isOpen = useSignal(false); @@ -1023,7 +1108,11 @@ return html`
    -

    llama.cpp

    +
    +
    +

    llama.cpp

    +
    New UI
    +
    @@ -1054,4 +1143,3 @@

    llama.cpp

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o=1;o=5&&((i||!t&&5===_)&&(r.push(_,0,i,e),_=6),t&&(r.push(_,t,0,e),_=6)),i=""},l=0;l"===n?(_=1,i=""):i=n+i[0]:o?n===o?o="":i+=n:'"'===n||"'"===n?o=n:">"===n?(u(),_=1):_&&("="===n?(_=5,e=i,i=""):"/"===n&&(_<5||">"===t[l][s+1])?(u(),3===_&&(r=r[0]),_=r,(r=r[0]).push(2,0,_),_=0):" "===n||"\t"===n||"\n"===n||"\r"===n?(u(),_=2):i+=n),3===_&&"!--"===i&&(_=4,r=r[0])}return u(),r}(t)),n),arguments,[])).length>1?n:n[0]}var rn=on.bind(O);export{q as Component,j as Fragment,f as Signal,e as batch,ct as cloneElement,v as computed,ht as createContext,O as createElement,I as createRef,k as effect,O as h,rn as html,ft as hydrate,C as isValidElement,S as options,st as render,c as signal,Y as toChildArray,o as untracked,Ft as useCallback,tn as useComputed,At as useContext,Vt as useDebugValue,Nt as useEffect,Wt as useErrorBoundary,Lt as useId,Tt as useImperativeHandle,$t as useLayoutEffect,Mt as useMemo,Pt as useReducer,Dt as useRef,Zt as useSignal,nn as useSignalEffect,Ht as useState}; diff --git a/examples/server/public/json-schema-to-grammar.mjs b/examples/server/public/json-schema-to-grammar.mjs index 8e0be1b405e3c..faed6a32cfc4c 100644 --- a/examples/server/public/json-schema-to-grammar.mjs +++ b/examples/server/public/json-schema-to-grammar.mjs @@ -1,58 +1,27 @@ // WARNING: This file was ported from json_schema_to_grammar.py, please fix bugs / add features there first. -const SPACE_RULE = '" "?'; +const SPACE_RULE = '| " " | "\\n" [ \\t]{0,20}'; function _buildRepetition(itemRule, minItems, maxItems, opts={}) { + if (minItems === 0 && maxItems === 1) { + return `${itemRule}?`; + } + + const separatorRule = opts.separatorRule ?? ''; const itemRuleIsLiteral = opts.itemRuleIsLiteral ?? false if (separatorRule === '') { - if (minItems === 0 && maxItems === 1) { - return `${itemRule}?`; - } else if (minItems === 1 && maxItems === undefined) { + if (minItems === 1 && maxItems === undefined) { return `${itemRule}+`; - } - } - - let result = ''; - if (minItems > 0) { - if (itemRuleIsLiteral && separatorRule === '') { - result = `"${itemRule.slice(1, -1).repeat(minItems)}"`; + } else if (minItems === 0 && maxItems === undefined) { + return `${itemRule}*`; } else { - result = Array.from({ length: minItems }, () => itemRule) - .join(separatorRule !== '' ? ` ${separatorRule} ` : ' '); + return `${itemRule}{${minItems},${maxItems !== undefined ? maxItems : ''}}`; } } - const optRepetitions = (upToN, prefixWithSep=false) => { - const content = separatorRule !== '' && prefixWithSep ? `${separatorRule} ${itemRule}` : itemRule; - if (upToN === 0) { - return ''; - } else if (upToN === 1) { - return `(${content})?`; - } else if (separatorRule !== '' && !prefixWithSep) { - return `(${content} ${optRepetitions(upToN - 1, true)})?`; - } else { - return Array.from({ length: upToN }, () => `(${content}`).join(' ').trim() + Array.from({ length: upToN }, () => ')?').join(''); - } - }; - - if (minItems > 0 && maxItems !== minItems) { - result += ' '; - } - - if (maxItems !== undefined) { - result += optRepetitions(maxItems - minItems, minItems > 0); - } else { - const itemOperator = `(${separatorRule !== '' ? separatorRule + ' ' : ''}${itemRule})`; - - if (minItems === 0 && separatorRule !== '') { - result = `(${itemRule} ${itemOperator}*)?`; - } else { - result += `${itemOperator}*`; - } - } - - return result; + const result = itemRule + ' ' + _buildRepetition(`(${separatorRule} ${itemRule})`, minItems > 0 ? minItems - 1 : 0, maxItems !== undefined ? maxItems - 1 : undefined); + return minItems === 0 ? `(${result})?` : result; } class BuiltinRule { @@ -62,27 +31,25 @@ class BuiltinRule { } } -const UP_TO_15_DIGITS = _buildRepetition('[0-9]', 0, 15); - const PRIMITIVE_RULES = { boolean : new BuiltinRule('("true" | "false") space', []), - 'decimal-part' : new BuiltinRule('[0-9] ' + UP_TO_15_DIGITS, []), - 'integral-part': new BuiltinRule('[0-9] | [1-9] ' + UP_TO_15_DIGITS, []), + 'decimal-part' : new BuiltinRule('[0-9]{1,16}', []), + 'integral-part': new BuiltinRule('[0] | [1-9] [0-9]{0,15}', []), number : new BuiltinRule('("-"? integral-part) ("." decimal-part)? ([eE] [-+]? integral-part)? space', ['integral-part', 'decimal-part']), integer : new BuiltinRule('("-"? integral-part) space', ['integral-part']), value : new BuiltinRule('object | array | string | number | boolean | null', ['object', 'array', 'string', 'number', 'boolean', 'null']), object : new BuiltinRule('"{" space ( string ":" space value ("," space string ":" space value)* )? "}" space', ['string', 'value']), array : new BuiltinRule('"[" space ( value ("," space value)* )? "]" space', ['value']), - uuid : new BuiltinRule('"\\"" ' + [8, 4, 4, 4, 12].map(n => [...new Array(n)].map(_ => '[0-9a-fA-F]').join('')).join(' "-" ') + ' "\\"" space', []), - char : new BuiltinRule(`[^"\\\\] | "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F])`, []), + uuid : new BuiltinRule('"\\"" [0-9a-fA-F]{8} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{4} "-" [0-9a-fA-F]{12} "\\"" space', []), + char : new BuiltinRule(`[^"\\\\\\x7F\\x00-\\x1F] | [\\\\] (["\\\\bfnrt] | "u" [0-9a-fA-F]{4})`, []), string : new BuiltinRule(`"\\"" char* "\\"" space`, ['char']), null : new BuiltinRule('"null" space', []), }; // TODO: support "uri", "email" string formats const STRING_FORMAT_RULES = { - 'date' : new BuiltinRule('[0-9] [0-9] [0-9] [0-9] "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), - 'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9] [0-9] [0-9] )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), + 'date' : new BuiltinRule('[0-9]{4} "-" ( "0" [1-9] | "1" [0-2] ) "-" ( \"0\" [1-9] | [1-2] [0-9] | "3" [0-1] )', []), + 'time' : new BuiltinRule('([01] [0-9] | "2" [0-3]) ":" [0-5] [0-9] ":" [0-5] [0-9] ( "." [0-9]{3} )? ( "Z" | ( "+" | "-" ) ( [01] [0-9] | "2" [0-3] ) ":" [0-5] [0-9] )', []), 'date-time' : new BuiltinRule('date "T" time', ['date', 'time']), 'date-string' : new BuiltinRule('"\\"" date "\\"" space', ['date']), 'time-string' : new BuiltinRule('"\\"" time "\\"" space', ['time']), diff --git a/examples/server/public/prompt-formats.js b/examples/server/public/prompt-formats.js new file mode 100644 index 0000000000000..73ddb7187eb7a --- /dev/null +++ b/examples/server/public/prompt-formats.js @@ -0,0 +1,331 @@ +// extended list +export const promptFormats = { + "alpaca": { + template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "chatml": { + template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`, + + historyTemplate: `<|im_start|>{{name}}\n{{message}}`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "<|im_end|>\n", + + stops: "" + }, + + // ---------------------------- + + "commandr": { + template: `<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`, + + historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`, + + char: "CHATBOT_TOKEN", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER_TOKEN", + userMsgPrefix: "", + userMsgSuffix: "<|END_OF_TURN_TOKEN|>", + + stops: "" + }, + // ref: https://docs.cohere.com/docs/prompting-command-r + + // ---------------------------- + + "llama2": { + template: `[INST] <>\n{{prompt}}\n<>\n\nTest Message [/INST] Test Successfull {{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "[INST] ", + userMsgSuffix: " [/INST]", + + stops: "" + }, + // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + + // ---------------------------- + + "llama3": { + template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`, + + historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|eot_id|>" + }, + // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3 + + // ---------------------------- + + "openchat": { + template: `{{history}}{{char}}`, + + historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "phi3": { + template: `{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|end|>" + }, + // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format + + // ---------------------------- + + "vicuna": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}\n`, + + char: "ASSISTANT", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1 + + // ---------------------------- + + "deepseekCoder": { + template: `{{prompt}}{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|EOT|>" + }, + + // ---------------------------- + + "med42": { + template: `<|system|>: {{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>: {{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "prompter", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "neuralchat": { + template: `### System:\n{{prompt}}\n{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "nousHermes": { + template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Input", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "openchatMath": { + template: `{{history}}{{char}}`, + + historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "orion": { + template: `Human: Test Message\n\nAssistant: Test Successful{{history}}{{char}}:`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant ", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "\n\n", + + stops: "" + }, + + // ---------------------------- + + "sauerkraut": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: ` + {{name}}: {{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "starlingCode": { + template: `{{history}}{{char}}`, + + historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "yi34b": { + template: `{{history}} {{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "zephyr": { + template: `<|system|>\n{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + } + }; diff --git a/examples/server/public/style.css b/examples/server/public/style.css new file mode 100755 index 0000000000000..087cc62dab028 --- /dev/null +++ b/examples/server/public/style.css @@ -0,0 +1,954 @@ +@import url("colorthemes.css"); + +body { + font-family: 'Arial', sans-serif; + font-size: 90%; + background-color: var(--background-color-1); + color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */ + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + transition: background-color 0.3s; +} + +::selection { + color: var(--button-primary-text) ; + background: var(--button-primary-color); +} + +code, pre code { + font-family: 'Courier New', monospace; +} + +#container { + margin: 0em auto; + display: flex; + flex-direction: column; + justify-content: space-between; + height: 100%; +} + +main { + margin: 3px; + display: flex; + flex-direction: column; + justify-content: space-between; + gap: 1em; + flex-grow: 1; + overflow-y: auto; + border: 1px solid var(--border-color-3); + border-radius: 5px; + padding: 0.5em; +} + +p { + overflow-wrap: break-word; + word-wrap: break-word; + hyphens: auto; + margin-top: 0.5em; + margin-bottom: 0.5em; +} + +#write form { + margin: 1em 0 0 0; + display: flex; + flex-direction: column; + gap: 0.5em; + align-items: stretch; +} + +.right { + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + margin-bottom: 30px; +} + +.two-columns { + width: 97%; + max-width: 97%; + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1em; + position: relative; +} + +.json-schema-controls { + margin-top: 10px; + width: 100%; + max-width: 100%; + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.json-schema-controls > * { + flex: 1; +} + +/* titles of the details-summary boxes */ +.summary-title { + font-weight: 600; + font-size: x-small; + color: var(--text-color-subtile-1); + text-transform: uppercase; + /* transition: ; */ +} + +fieldset { + border: none; + padding: 0; + margin: 0; + color: var(--text-color-plain); +} + +fieldset.two { + display: grid; + grid-template: "a a a"; + gap: 1em; + align-items: center; + font-size: x-small; + color: var(--text-color-plain); +} + +fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + font-size: x-small; + color: var(--text-color-plain); +} + +/* titles of name fields*/ +fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* titles of params fields*/ +fieldset.params { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-4); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +fieldset.dropdowns { + -webkit-appearance: none; + display: flex; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: red; + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* input of name fields*/ +.names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.chat-id-color { + color: var(--chat-id-color); +} + +details { + border: 1px solid var(--border-color-2); + border-radius: 5px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; +} + +summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; +} + +details[open] { + padding: 0.5em; +} + +textarea-sec, input-sec, button-sec { + padding: 10px; + height: 40px; + align-items: center; +} + +textarea-sec::placeholder, input-sec::placeholder { + padding-left: 10px; +} + +.toggleCheckbox { + display: none; +} + +.toggleContainer { + position: relative; + display: grid; + grid-template-columns: repeat(2, 1fr); + width: fit-content; + border: 3px solid var(--border-color-2); + border-radius: 20px; + background: var(--border-color-2); + font-size: small; + cursor: pointer; + overflow: hidden; +} + +/* toggle button current state */ +.toggleContainer::before { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + content: ''; + position: absolute; + width: 50%; + height: 100%; + left: 0%; + border-radius: 20px; + transition: all 0.3s; +} + +.toggleContainer div { + padding: 6px; + text-align: center; + z-index: 1; + transition: color 0.3s; +} + +.toggleCheckbox:checked + .toggleContainer::before { + left: 50%; +} + +.toggleCheckbox:checked + .toggleContainer div:first-child { + color: var(--text-color-subtile-2); +} + +.toggleCheckbox:checked + .toggleContainer div:last-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:first-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:last-child { + color: var(--text-color-subtile-2); +} + +select { + padding: 5px; + margin-right: 5px; + border-radius: 4px; + border: 1px solid var(--secondary-color-4); + background-color: var(--primary-color-3); + color: var(--secondary-color-4); + cursor: pointer; +} + +select:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 1px var(--border-focus-shadow); +} + +.button-container { + display: flex; + justify-content: flex-end; +} + +button { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 30px #ffffff; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +button:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + font-size: x-small; + font-weight: 600; +} + +button:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +button:disabled { + color: var(--button-tertiary-text); + background-color: var(--button-tertiary-color); + border: 1px solid var(--button-tertiary-border); + font-size: x-small; + font-weight: 600; + cursor: not-allowed; +} + +.reset-button { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + width: fit-content; + height: fit-content; + font-size: x-small; + font-weight: 600; + border-radius: 50px; + overflow: hidden; +} + +.reset-button:hover { + color: var(--button-alert-text-hover); + background-color: var(--button-alert-color-hover); + border: 1px solid var(--button-alert-border-hover); + font-size: x-small; + font-weight: 600; +} + +.reset-button:active { + color: var(--button-alert-text-active); + background-color: var(--button-alert-color-active); + border: 1px solid var(--button-alert-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-grammar { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-back { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +.button-back:hover { + color: var(--button-secondary-text-hover); + background-color: var(--button-secondary-color-hover); + border: 1px solid var(--button-secondary-border-hover); + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 4px 2px; + transition: background-color 0.1s; + cursor: pointer; + border-radius: 12px; +} + +.button-back:active { + color: var(--button-secondary-text-active); + background-color: var(--button-secondary-color-active); + border: 1px solid var(--button-secondary-border-active); + font-size: x-small; + font-weight: 600; +} + +.prob-set { + padding: 0.3em; + border-bottom: 1px solid red; /* unknown */ +} + +.popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 13px rgba(0, 0, 0, 0.1); +} + +.grammar { + width: 97%; + max-width: 97%; +} + +textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + max-width: 100%; + border-radius: 8px; + border: 1px solid var(--border-color-1); + resize: none; + height: 6em; +} + +textarea:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* "props" frame */ +input[type="text"], +input[type="range"] { + padding: 5px; + border-radius: 8px; + border: 1px solid var(--border-color-1); +} + +/* "names and props" frame focused*/ +input[type="text"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +input[type="range"]:hover { + opacity: 1; +} + +input[type="range"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); + background-size: var(--slider-track-size-focus); +} + +input[type="range"]::-moz-range-thumb { + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"] { + -webkit-appearance: none; + width: 80%; + height: 1px; + border: 1px solid var(--border-color-1); + border-radius: 8px; + background: var(--border-color-2); + outline: none; + opacity: 0.7; + -webkit-transition: .2s; + transition: opacity .2s; +} + +input[type="range"]::-webkit-slider-thumb { + -webkit-appearance: none; + appearance: none; + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"]::-webkit-slider-runnable-track { + background-size: var(--slider-track-size); +} + +input[type="radio"] { + accent-color: var(--theme-nuance-color-2); +} + +.chat-input-container { + position: relative; + max-width: 97%; + min-width: 97%; +} + +.chat-input-label { + position: absolute; + top: 0; + left: 0; + color: var(--text-color-plain); + pointer-events: none; + margin-left: 5px; + margin-top: 5px; +} + +textarea#chat-input { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#chat-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.input-container { + position: relative; + box-sizing: border-box; + width: 100%; /* Setzt die Breite auf 100% */ + max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */ +} + +.input-container:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} +/* titles of name fields*/ +/* fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} */ + +/* input of name fields*/ +/* .names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} */ + +fieldset.apiKey { + width: 100%; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.apiKey { + font-family: Arial, sans-serif; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.apiKey input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey label { + display: inline-block; + width: auto; + margin-right: 5px; +} + +textarea#api_key { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#api_key:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* embedded title of the system prompt text area */ +.input-label { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + border-radius: 8px 8px 0px 0px; + padding-top: 10px; + padding-left: 13px; + padding-right: 0px; + margin-top: 1px; + margin-left: 1px; + margin-right: 20px; + text-transform: uppercase; + font-weight: 600; + font-size: small; + background: rgba(255, 255, 255, 0.5); + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); /* for safari */ + width: 97%; + /* display: block; + box-sizing: border-box; */ +} + +/* embedded title of the prompt style areas */ +.input-label-sec { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + margin-left: 13px; + margin-top: 16px; + text-transform: uppercase; + font-weight: 600; + font-size: x-small; +} + +/* system prompt input area */ +textarea.persistent-input { + padding-top: 42px; + padding-left: 11px; + width: 97%; + max-width: 97%; + height: 50px; + font-size: medium; + overscroll-behavior: contain; +} + +/* system prompt box */ +.persistent-input { + height: auto; + width: 100%; + max-width: 100%; + min-height: 50px; + padding: 3px; + transition: min-height 0.3s ease; +} + +/* chat history box */ +.persistent-input:focus { + height: auto; + min-height: 150px; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +textarea.persistent-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* prompt style input area */ +textarea.persistent-input-sec { + width: 97%; + max-width: 97%; + padding-top: 42px; + padding-left: 11px; + font-size: small; + border: 1px solid var(--border-color-1); + overscroll-behavior: contain; +} + +textarea.persistent-input-sec:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* chat history box */ +.persistent-input-sec { + height: auto; + min-height: 150px; +} + +img { + border-radius: 8px; + display: block; + margin-left: auto; + margin-right: auto; + width: 50%; +} + +/* code area background */ +pre code { + display: block; + background-color: var(--code-background-color); + color: var(--code-text-color); + padding: 0.2em 0.2em; + border-radius: 5px; +} + +/* code area text */ +code { + font-family: monospace; + font-weight: bold; + padding: 0.1em 0.3em; + border-radius: 5px; +} + +fieldset label { + margin: 0.5em 0; + display: block; +} + +fieldset label.slim { + margin: 0 0.5em; + display: inline; +} + +header { + display: flex; + justify-content: space-between; + align-items: center; + text-align: center; + padding-left: 15px; +} + +.generation-statistics:hover { + color: var(--theme-nuance-color-4); + cursor: default; +} + +footer { + font-size: 80%; + color: var(--background-color-3); + text-align: center; + cursor: default; +} + +footer a { + color: var(--background-color-4); /* Color of the link */ + text-decoration: none; /* No underlining */ + font-weight: bold; /* Bold print */ +} + +footer a:hover { + color: var(--theme-nuance-color-4); /* Color of the link when hovering */ + text-decoration: underline; /* Underlining when hovering */ +} + +.mode-chat textarea[name=prompt] { + height: 8.5em; + border: 1px solid var(--primary-color-3); +} + +.mode-completion textarea[name=prompt] { + height: 30em; + border: 1px solid var(--primary-color-3); +} + +@keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + 100% { + background-position: 100%; + } +} + +.loading { + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; +} + +.dropbtn { + color: var(--button-primary-color); + background-color: var(--background-color-1); + border: 1px solid var(--background-color-1); + transition: background-color 0.1s; + border-radius: 4px 4px 0px 0px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 2px #99999990; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 5px 20px; + display: inline-block; + cursor: pointer; + top: 0; +} + +.dropbtn svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-color); +} + +.dropbtn:hover svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-text); +} + +.dropbtn:focus { + outline: none; /* Removes the blue border that appears when the button is focused */ +} + +.dropdown { + position: relative; + display: inline-block; +} + +.dropdown-content { + /* display: none; */ + position: absolute; + right: 0; + text-align: end; + color: var(--button-secondary-color); + background-color: var(--text-color-subtile-2); + border-radius: 4px 4px 4px 4px; + min-width: 160px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + z-index: 1; + /* Verstecke den Inhalt sofort */ + opacity: 0; + visibility: hidden; + /* übergangsverzögerung für das Verschwinden */ + transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out; + transition-delay: 0.2s; +} + +#dropdown-content {transition-timing-function: ease;} + +.dropdown-content:hover { + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a { + color: var(--border-color-2); + padding: 12px 16px; + border-radius: 4px 4px 4px 4px; + text-decoration: none; + display: block; + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a:hover { + color: var(--border-color-2); + background-color: var(--text-color-subtile-1); + font-weight: 600; +} + +.dropdown:hover .dropdown-content { + /* display: block; */ + border-radius: 4px 4px 4px 4px; + /* Übergang ohne Verzögerung für das Erscheinen */ + opacity: 1; + visibility: visible; + transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s; +} + +.dropdown:hover .dropbtn { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + font-size: x-small; + font-weight: 600; + stroke: var(--button-primary-text); +} + +.dropdown:hover .dropbtn svg{ + stroke: var(--button-primary-text); +} + +/* .dropdown:active .dropbtn { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; + background-color: var(-background-color-4); +} */ + +/* .omni { + display: flex; + justify-content: space-between; + align-items: center; + padding: 0.5em; + border: 1px solid var(--border-color-3); + border-radius: 5px; + margin: 0.5em 0; +} */ diff --git a/examples/server/public/system-prompts.js b/examples/server/public/system-prompts.js new file mode 100644 index 0000000000000..f7df7d648b79f --- /dev/null +++ b/examples/server/public/system-prompts.js @@ -0,0 +1,68 @@ +export const systemPrompts = { + default: { + systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision." + }, + empty: { + systemPrompt: "" + }, + airoboros: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request." + }, + alpaca: { + systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request." + }, + atlas: { + systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)." + }, + atlas_de: { + systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)." + }, + commandrempty: { + systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n" + }, + commandrexample: { + systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available." + }, + cot: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query." + }, + deduce: { + systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer." + }, + deepseekcoder: { + systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer." + }, + jordan: { + systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information." + }, + leomistral: { + systemPrompt: "Du bist ein hilfreicher Assistent." + }, + med42: { + systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE." + }, + mistralopenorca: { + systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!" + }, + migeltot: { + systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers." + }, + orcamini: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can." + }, + samantha: { + systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha." + }, + sauerkraut: { + systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten." + }, + scarlett: { + systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI." + }, + synthia: { + systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." + }, + vicuna: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input." + }, + }; diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public/theme-beeninorder.css new file mode 100755 index 0000000000000..f6e0e2900d78c --- /dev/null +++ b/examples/server/public/theme-beeninorder.css @@ -0,0 +1,228 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration was a batman wallpaper that i have on my phone */ + +.theme-beeninorder { + +--primary-color-1: hsl(202, 11%, 19%); +--primary-color-2: hsl(202, 11%, 23%); +--primary-color-3: hsl(201, 11%, 28%); +--primary-color-4: hsl(201, 11%, 40%); + +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-4: hsl(201, 11%, 60%); + + +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(201, 11%, 19%); + --primary-color-1-hue: 201; + --primary-color-1-saturation: 11%; + --primary-color-1-lightness: 19%; + +--primary-color-2: hsl(201, 11%, 23%); + --primary-color-2-hue: 201; + --primary-color-2-saturation: 11%; + --primary-color-2-lightness: 23%; + +--primary-color-3: hsl(201, 11%, 28%); + --primary-color-3-hue: 201; + --primary-color-3-saturation: 11%; + --primary-color-3-lightness: 28%; + +--primary-color-4: hsl(201, 11%, 40%); + --primary-color-4-hue: 201; + --primary-color-4-saturation: 11%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-1-hue: 201; +--secondary-color-1-saturation: 11%; +--secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-2-hue: 201; +--secondary-color-2-saturation: 11%; +--secondary-color-2-lightness: 74%; + +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-3-hue: 201; +--secondary-color-3-saturation: 11%; +--secondary-color-3-lightness: 67%; + +--secondary-color-4: hsl(201, 11%, 60%); +--secondary-color-4-hue: 201; +--secondary-color-4-saturation: 11%; +--secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-1-hue: 44.5; + --theme-nuance-color-1-saturation: 96.7%; + --theme-nuance-color-1-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-2-hue: 44.5; + --theme-nuance-color-2-saturation: 96.7%; + --theme-nuance-color-2-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-3-hue: 44.5; + --theme-nuance-color-3-saturation: 96.7%; + --theme-nuance-color-3-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-4-hue: 44.5; + --theme-nuance-color-4-saturation: 96.7%; + --theme-nuance-color-4-lightness: 52.9%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(232, 40%, 45%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(232, 30%, 40%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(201, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public/theme-ketivah.css new file mode 100755 index 0000000000000..ee80f3c14ce88 --- /dev/null +++ b/examples/server/public/theme-ketivah.css @@ -0,0 +1,201 @@ +/* Author: Yazan Agha-Schrader */ + +.theme-ketivah { + + /* ---------- PRIMARY COLORS ----------------- */ + --primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + + --primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + + --primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + + --primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + /* ---------- SECONDARY COLORS --------------- */ + --secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + + --secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + + --secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + + --secondary-color-4: hsl(0, 0.0%, 36.1%); + --secondary-color-4-hue: 0.0; + --secondary-color-4-saturation: 0.0%; + --secondary-color-4-lightness: 36.1%; + + /* ----------- NUANCES COLORS ---------------- */ + --theme-nuance-color-1: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + + --theme-nuance-color-2: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + + --theme-nuance-color-3: hsl(165.2, 0%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + + --theme-nuance-color-4: hsl(164.9, 0%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + /* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(0.3, 80.0%, 50.0%); + --theme-orange-color: #e76f51; + --theme-yellow-color: hsl(60, 70.6%, 73.3%); + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(0.3, 70.0%, 45.0%); + + /* ------------------------------------------- */ + --background-color-1: var(--primary-color-1); + --background-color-2: var(--primary-color-2); + --background-color-3: var(--primary-color-3); + --background-color-4: var(--primary-color-4); + + --border-color-1: var(--primary-color-2); + --border-color-2: var(--primary-color-3); + --border-color-3: var(--primary-color-4); + + --border-focus-color: var(--theme-nuance-color-2); + --border-focus-shadow: var(--theme-nuance-color-1); + + --text-color-plain: var(--secondary-color-1); + --text-color-subtile-1: var(--secondary-color-2); + --text-color-subtile-2: var(--secondary-color-3); + + --code-background-color: var(--secondary-color-2); + --code-text-color: var(--primary-color-2); + + --ui-range-thumb-color: var(--primary-color-4); + --ui-range-thumb-border: var(--ui-ranger-thumb-color); + + --textarea-border-color: var(--secondary-color-4); + + --chat-id-color: var(--theme-nuance-color-4); + + /* ------------------------------------------- */ + --button-alert-text-hover: var(--primary-color-1); + --button-alert-color-hover: var(--theme-purple-color); + --button-alert-border-hover: var(--theme-purple-color); + + --button-alert-text-active: var(--primary-color-1); + --button-alert-color-active: var(--theme-red-color); + --button-alert-border-active: var(--theme-red-color); + + /* ----------- PRIMARY BUTTONS --------------- */ + /* - button should immediately catch the eye - */ + --button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color: var(--theme-nuance-color-3); + --button-primary-border: var(--theme-nuance-color-3); + + /* ---------hover---------- */ + --button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + --button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + /* ---------active--------- */ + --button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + + --button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + --button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + /* ---------- SECONDARY BUTTONS -------------- */ + /* these should NOT immediately catch the eye */ + --button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + + --button-secondary-color: var(--primary-color-3); + --button-secondary-border: var(--primary-color-3); + + /* ---------hover---------- */ + --button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-hover: var(--primary-color-4); + --button-secondary-border-hover: var(--primary-color-4); + + /* ---------active--------- */ + --button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + --button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + /* ---------- TERTIARY BUTTONS --------------- */ + /* ---------- disabled buttons --------------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + /* ---------hover---------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + } diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public/theme-mangotango.css new file mode 100755 index 0000000000000..e433802453b9d --- /dev/null +++ b/examples/server/public/theme-mangotango.css @@ -0,0 +1,216 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */ + +.theme-mangotango { + +--primary-color-1: hsl(192, 8.5%, 11.6%); +--primary-color-2: hsl(192, 8.5%, 21%); +--primary-color-3: hsl(192, 8.5%, 30%); +--primary-color-4: hsl(192, 8.5%, 40%); + +--secondary-color-1: hsl(192, 8.5%, 80%); +--secondary-color-2: hsl(192, 8.5%, 73%); +--secondary-color-3: hsl(192, 8.5%, 66%); +--secondary-color-4: hsl(192, 8.5%, 60%); + +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(192, 8.5%, 11.6%); + --primary-color-1-saturation: 8.5%; + --primary-color-1-lightness: 11.6%; + +--primary-color-2: hsl(192, 8.5%, 21%); + --primary-color-2-saturation: 8.5%; + --primary-color-2-lightness: 21%; + +--primary-color-3: hsl(192, 8.5%, 30%); + --primary-color-3-saturation: 8.5%; + --primary-color-3-lightness: 30%; + +--primary-color-4: hsl(192, 8.5%, 40%); + --primary-color-4-saturation: 8.5%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(192, 8.5%, 80%); + --secondary-color-1-saturation: 8.5%; + --secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(192, 8.5%, 73%); + --secondary-color-2-saturation: 8.5%; + --secondary-color-2-lightness: 73%; + +--secondary-color-3: hsl(192, 8.5%, 66%); + --secondary-color-3-saturation: 8.5%; + --secondary-color-3-lightness: 66%; + +--secondary-color-4: hsl(192, 8.5%, 60%); + --secondary-color-4-saturation: 8.5%; + --secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-1-saturation: 100%; + --theme-nuance-color-1-lightness: 60.2%; + +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-2-saturation: 100%; + --theme-nuance-color-2-lightness: 60.2%; + +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-3-saturation: 100%; + --theme-nuance-color-3-lightness: 60.2%; + +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-4-saturation: 100%; + --theme-nuance-color-4-lightness: 60.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(325, 60%, 50%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-blue-color: hsl(192, 95%, 40%); + --theme-purple-color: hsl(192, 80%, 35%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-blue-color); +--button-alert-border-active: var(--theme-blue-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(192, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-playground.css b/examples/server/public/theme-playground.css new file mode 100755 index 0000000000000..9d56a718248a4 --- /dev/null +++ b/examples/server/public/theme-playground.css @@ -0,0 +1,221 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */ + +.theme-playground { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + +--primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + +--primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + +--primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + +--secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + +--secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + +--secondary-color-4: hsl(0, 0%, 36.1%); + --secondary-color-4-hue: 0; + --secondary-color-4-saturation: 0%; + --secondary-color-4-lightness: 36.1%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + +--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + +--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + +--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(0.3, 80%, 50%); +--theme-orange-color: #e76f51; +--theme-yellow-color: hsl(60, 70.6%, 73.3%); +--theme-green-color: #A3BE8C; +--theme-purple-color: hsl(0.3, 70%, 45%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--primary-color-4); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public/theme-polarnight.css new file mode 100755 index 0000000000000..2bcfb33d8f183 --- /dev/null +++ b/examples/server/public/theme-polarnight.css @@ -0,0 +1,253 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-polarnight { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(220.0, 16.4%, 21.6%) ; + --primary-color-1-hue: 220.0; + --primary-color-1-saturation: 16.4%; + --primary-color-1-lightness: 21.6%; + +--primary-color-2: hsl(221.7, 16.3%, 27.6%) ; + -primary-color-2-hue: 221.7; + --primary-color-2-saturation: 16.3%; + --primary-color-2-lightness: 27.6%; + +--primary-color-3: hsl(220.0, 16.8%, 31.6%) ; + --primary-color-3-hue: 220.0; + --primary-color-3-saturation: 16.8%; + --primary-color-3-lightness: 31.6%; + +--primary-color-4: hsl(220.0, 16.5%, 35.7%); + --primary-color-4-hue: 220.0; + --primary-color-4-saturation: 16.5%; + --primary-color-4-lightness: 35.7%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(217.5, 26.7%, 94.1%); + --secondary-color-1-hue: 217.5; + --secondary-color-1-saturation: 26.7%; + --secondary-color-1-lightness: 94.1%; + +--secondary-color-2: hsl(218.2, 26.8%, 92.0%); + --secondary-color-2-hue: 218.2; + --secondary-color-2-saturation: 26.8%; + --secondary-color-2-lightness: 92.0%; + +--secondary-color-3: hsl(218.8, 27.9%, 88.0%); + --secondary-color-3-hue: 218.8; + --secondary-color-3-saturation: 27.9%; + --secondary-color-3-lightness: 88.0%; + +--secondary-color-4: hsl(218.8, 18.3%, 81.8%); + --secondary-color-4-hue: 218.8; + --secondary-color-4-saturation: 18.3%; + --secondary-color-4-lightness: 81.8%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(354.3, 42.3%, 56.5%); +--theme-orange-color: hsl(20, 85%, 50%); +--theme-yellow-color: hsl(20, 75%, 45%); +--theme-green-color: hsl( 92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------------ */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-yellow-color); +--button-alert-border-hover: var(--theme-yellow-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-orange-color); +--button-alert-border-active: var(--theme-orange-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) - 35%), + calc(var(--secondary-color-1-lightness) + 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 25%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public/theme-snowstorm.css new file mode 100755 index 0000000000000..7bb2275949273 --- /dev/null +++ b/examples/server/public/theme-snowstorm.css @@ -0,0 +1,251 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-snowstorm { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public_simplechat/datautils.mjs b/examples/server/public_simplechat/datautils.mjs new file mode 100644 index 0000000000000..75159d6b1676b --- /dev/null +++ b/examples/server/public_simplechat/datautils.mjs @@ -0,0 +1,266 @@ +//@ts-check +// Helpers to work with different data types +// by Humans for All +// + +/** + * Given the limited context size of local LLMs and , many a times when context gets filled + * between the prompt and the response, it can lead to repeating text garbage generation. + * And many a times setting penalty wrt repeatation leads to over-intelligent garbage + * repeatation with slight variations. These garbage inturn can lead to overloading of the + * available model context, leading to less valuable response for subsequent prompts/queries, + * if chat history is sent to ai model. + * + * So two simple minded garbage trimming logics are experimented below. + * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and + * * another based on char-histogram-driven garbage trimming. + * * in future characteristic of histogram over varying lengths could be used to allow for + * a more aggressive and adaptive trimming logic. + */ + + +/** + * Simple minded logic to help remove repeating garbage at end of the string. + * The repeatation needs to be perfectly matching. + * + * The logic progressively goes on probing for longer and longer substring based + * repeatation, till there is no longer repeatation. Inturn picks the one with + * the longest chain. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number} maxMatchLenThreshold + */ +export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) { + let rCnt = [0]; + let maxMatchLen = maxSubL; + let iMML = -1; + for(let subL=1; subL < maxSubL; subL++) { + rCnt.push(0); + let i; + let refS = sIn.substring(sIn.length-subL, sIn.length); + for(i=sIn.length; i > 0; i -= subL) { + let curS = sIn.substring(i-subL, i); + if (refS != curS) { + let curMatchLen = rCnt[subL]*subL; + if (maxMatchLen < curMatchLen) { + maxMatchLen = curMatchLen; + iMML = subL; + } + break; + } + rCnt[subL] += 1; + } + } + console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt); + if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) { + return {trimmed: false, data: sIn}; + } + console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen); + let iEnd = sIn.length - maxMatchLen; + return { trimmed: true, data: sIn.substring(0, iEnd) }; +} + + +/** + * Simple minded logic to help remove repeating garbage at end of the string, till it cant. + * If its not able to trim, then it will try to skip a char at end and then trim, a few times. + * This ensures that even if there are multiple runs of garbage with different patterns, the + * logic still tries to munch through them. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number | undefined} [maxMatchLenThreshold] + */ +export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) { + let sCur = sIn; + let sSaved = ""; + let iTry = 0; + while(true) { + let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold); + if (got.trimmed != true) { + if (iTry == 0) { + sSaved = got.data; + } + iTry += 1; + if (iTry >= skipMax) { + return sSaved; + } + got.data = got.data.substring(0,got.data.length-1); + } else { + iTry = 0; + } + sCur = got.data; + } +} + + +/** + * A simple minded try trim garbage at end using histogram driven characteristics. + * There can be variation in the repeatations, as long as no new char props up. + * + * This tracks the chars and their frequency in a specified length of substring at the end + * and inturn checks if moving further into the generated text from the end remains within + * the same char subset or goes beyond it and based on that either trims the string at the + * end or not. This allows to filter garbage at the end, including even if there are certain + * kind of small variations in the repeated text wrt position of seen chars. + * + * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that + * a given type of char ie numerals or alphabets or other types dont cross the specified + * maxType limit. This allows intermixed text garbage to be identified and trimmed. + * + * ALERT: This is not perfect and only provides a rough garbage identification logic. + * Also it currently only differentiates between character classes wrt english. + * + * @param {string} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) { + if (sIn.length < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + let iAlp = 0; + let iNum = 0; + let iOth = 0; + // Learn + let hist = {}; + let iUniq = 0; + for(let i=0; i= maxUniq) { + break; + } + hist[c] = 1; + } + } + console.debug("DBUG:TrimHistGarbage:", hist); + if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) { + return { trimmed: false, data: sIn }; + } + // Catch and Trim + for(let i=0; i < sIn.length; i++) { + let c = sIn[sIn.length-1-i]; + if (!(c in hist)) { + if (i < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i); + return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) }; + } + } + console.debug("DBUG:TrimHistGarbage:Trimmed fully"); + return { trimmed: true, data: "" }; +} + +/** + * Keep trimming repeatedly using hist_garbage logic, till you no longer can. + * This ensures that even if there are multiple runs of garbage with different patterns, + * the logic still tries to munch through them. + * + * @param {any} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) { + let sCur = sIn; + while (true) { + let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold); + if (!got.trimmed) { + return got.data; + } + sCur = got.data; + } +} + +/** + * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as + * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying. + * @param {string} sIn + */ +export function trim_garbage_at_end(sIn) { + let sCur = sIn; + for(let i=0; i<2; i++) { + sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72); + sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12); + } + return sCur; +} + + +/** + * NewLines array helper. + * Allow for maintaining a list of lines. + * Allow for a line to be builtup/appended part by part. + */ +export class NewLines { + + constructor() { + /** @type {string[]} */ + this.lines = []; + } + + /** + * Extracts lines from the passed string and inturn either + * append to a previous partial line or add a new line. + * @param {string} sLines + */ + add_append(sLines) { + let aLines = sLines.split("\n"); + let lCnt = 0; + for(let line of aLines) { + lCnt += 1; + // Add back newline removed if any during split + if (lCnt < aLines.length) { + line += "\n"; + } else { + if (sLines.endsWith("\n")) { + line += "\n"; + } + } + // Append if required + if (lCnt == 1) { + let lastLine = this.lines[this.lines.length-1]; + if (lastLine != undefined) { + if (!lastLine.endsWith("\n")) { + this.lines[this.lines.length-1] += line; + continue; + } + } + } + // Add new line + this.lines.push(line); + } + } + + /** + * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest] + * Optionally control whether only full lines (ie those with newline at end) will be returned + * or will a partial line without a newline at end (can only be the last line) be returned. + * @param {boolean} bFullWithNewLineOnly + */ + shift(bFullWithNewLineOnly=true) { + let line = this.lines[0]; + if (line == undefined) { + return undefined; + } + if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){ + return undefined; + } + return this.lines.shift(); + } + +} diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html index 1eb390b85a69c..f6413016fcc53 100644 --- a/examples/server/public_simplechat/index.html +++ b/examples/server/public_simplechat/index.html @@ -1,28 +1,30 @@ - SimpleChat (LlamaCPP, ...) + SimpleChat LlamaCppEtal - + +
    -
    +

    SimpleChat

    -
    - - -
    +
    @@ -30,20 +32,17 @@
    - +
    -

    Enter the system prompt above, before entering/submitting any user query.

    -

    Enter your text to the ai assistant below.

    -

    Use shift+enter for inserting enter.

    -

    Refresh the page to start over fresh.

    +

    You need to have javascript enabled.

    - +
    diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md index 5ac8258f21aca..2dc1778255256 100644 --- a/examples/server/public_simplechat/readme.md +++ b/examples/server/public_simplechat/readme.md @@ -11,14 +11,29 @@ in a simple way with minimal code from a common code base. Inturn additionally i multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their own system prompts. +This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated, +or potentially as it is being generated, in a streamed manner from the server/ai-model. + +Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you +open SimpleChat, option is provided to restore the old chat session, if a matching one exists. + The UI follows a responsive web design so that the layout can adapt to available display space in a usable enough manner, in general. -NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and -culling of old messages from the chat. +Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool +console. Parallely some of the directly useful to end-user settings can also be changed using the provided +settings ui. + +NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide +any adaptive culling of old messages nor of replacing them with summary of their content etal. However there +is a optional sliding window based chat logic, which provides a simple minded culling of old messages from +the chat history before sending to the ai model. + +NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now. +However if someone wants they can update the js file or equivalent member in gMe as needed. -NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update -the js file as needed. +NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very +limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui. ## usage @@ -29,12 +44,12 @@ http module. ### running using examples/server -bin/server -m path/model.gguf --path ../examples/server/public_simplechat [--port PORT] +./llama-server -m path/model.gguf --path examples/server/public_simplechat [--port PORT] ### running using python3's server module first run examples/server -* bin/server -m path/model.gguf +* ./llama-server -m path/model.gguf next run this web front end in examples/server/public_simplechat * cd ../examples/server/public_simplechat @@ -43,11 +58,39 @@ next run this web front end in examples/server/public_simplechat ### using the front end Open this simple web front end from your local browser + * http://127.0.0.1:PORT/index.html Once inside -* Select between chat and completion mode. By default it is set to chat mode. + +* If you want to, you can change many of the default global settings + * the base url (ie ip addr / domain name, port) + * chat (default) vs completion mode + * try trim garbage in response or not + * amount of chat history in the context sent to server/ai-model + * oneshot or streamed mode. + +* In completion mode + * one normally doesnt use a system prompt in completion mode. + * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message. + If the model requires any prefix wrt user role messages, then the end user has to + explicitly add the needed prefix, when they enter their chat message. + Similarly if the model requires any prefix to trigger assistant/ai-model response, + then the end user needs to enter the same. + This keeps the logic simple, while still giving flexibility to the end user to + manage any templating/tagging requirement wrt their messages to the model. + * the logic doesnt insert newline at the begining and end wrt the prompt message generated. + However if the chat being sent to /completions end point has more than one role's message, + then insert newline when moving from one role's message to the next role's message, so + that it can be clearly identified/distinguished. + * given that /completions endpoint normally doesnt add additional chat-templating of its + own, the above ensures that end user can create a custom single/multi message combo with + any tags/special-tokens related chat templating to test out model handshake. Or enduser + can use it just for normal completion related/based query. + * If you want to provide a system prompt, then ideally enter it first, before entering any user query. + Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting + responses with a suitable system prompt. * if chat.add_system_begin is used * you cant change the system prompt, after it is has been submitted once along with user query. * you cant set a system prompt, after you have submitted any user query @@ -55,27 +98,174 @@ Once inside * one can change the system prompt any time during chat, by changing the contents of system prompt. * inturn the updated/changed system prompt will be inserted into the chat session. * this allows for the subsequent user chatting to be driven by the new system prompt set above. + * Enter your query and either press enter or click on the submit button. If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. + * Wait for the logic to communicate with the server and get the response. * the user is not allowed to enter any fresh query during this time. * the user input box will be disabled and a working message will be shown in it. + * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent. + * just refresh the page, to reset wrt the chat history and or system prompt and start afresh. + * Using NewChat one can start independent chat sessions. * two independent chat sessions are setup by default. +* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of + interest, will display the full chat history till then wrt same, if you want full history for printing. + ## Devel note +### Reason behind this + +The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable +by developers who may not be from web frontend background (so inturn may not be familiar with template / +end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things. + +And given that the idea is also to help explore/experiment for developers, some flexibility is provided +to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects). +Skeletal logic has been implemented to explore some of the end points and ideas/implications around them. + + +### General + +Me/gMe consolidates the settings which control the behaviour into one object. +One can see the current settings, as well as change/update them using browsers devel-tool/console. +It is attached to the document object. Some of these can also be updated using the Settings UI. + + baseURL - the domain-name/ip-address and inturn the port to send the request. + + bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing + of the generated response. + + the logic assumes that the text sent from the server follows utf-8 encoding. + + in streaming mode - if there is any exception, the logic traps the same and tries to ensure + that text generated till then is not lost. + + if a very long text is being generated, which leads to no user interaction for sometime and + inturn the machine goes into power saving mode or so, the platform may stop network connection, + leading to exception. + + apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model. + + bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when + communicating with the server or only sends the latest user query/message. + + bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the + messages that get inserted into prompt field wrt /Completion endpoint. + + bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be + trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of + subsequent chat history. At the same time the actual trimmed text is shown to the user, once + when it was generated, so user can check if any useful info/data was there in the response. + + One may be able to request the ai-model to continue (wrt the last response) (if chat-history + is enabled as part of the chat-history-in-context setting), and chances are the ai-model will + continue starting from the trimmed part, thus allows long response to be recovered/continued + indirectly, in many cases. + + The histogram/freq based trimming logic is currently tuned for english language wrt its + is-it-a-alpabetic|numeral-char regex match logic. + + chatRequestOptions - maintains the list of options/fields to send along with chat request, + irrespective of whether /chat/completions or /completions endpoint. + + If you want to add additional options/fields to send to the server/ai-model, and or + modify the existing options value or remove them, for now you can update this global var + using browser's development-tools/console. + + For string and numeric fields in chatRequestOptions, including even those added by a user + at runtime by directly modifying gMe.chatRequestOptions, setting ui entries will be auto + created. + + headers - maintains the list of http headers sent when request is made to the server. By default + Content-Type is set to application/json. Additionally Authorization entry is provided, which can + be set if needed using the settings ui. + + iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. + This is disabled by default. However if enabled, then in addition to latest system message, only + the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses + from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled, + only user messages after the latest system message/prompt will be considered. + + This specified sliding window user message count also includes the latest user query. + <0 : Send entire chat history to server + 0 : Send only the system message if any to the server + >0 : Send the latest chat history from the latest system prompt, limited to specified cnt. + + +By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the +implications of loading of the ai-model's context window by chat history, wrt chat response to +some extent in a simple crude way. You may also want to control the context size enabled when +the server loads ai-model, on the server end. + + Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js may not be visible. Also remember that just refreshing/reloading page in browser or for that matter clearing site data, dont directly override site caching in all cases. Worst case you may have to change port. Or in dev tools of browser, you may be able to disable caching fully. -Concept of multiple chat sessions with different servers, as well as saving and restoring of -those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and -its instances relatively easily, however given the current goal of keeping this simple, it has -not been added, for now. + +Currently the server to communicate with is maintained globally and not as part of a specific +chat session. So if one changes the server ip/url in setting, then all chat sessions will auto +switch to this new server, when you try using those sessions. + By switching between chat.add_system_begin/anytime, one can control whether one can change the system prompt, anytime during the conversation or only at the beginning. + + +### Default setup + +By default things are setup to try and make the user experience a bit better, if possible. +However a developer when testing the server of ai-model may want to change these value. + +Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be +just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of +full chat history. This way if there is any response with garbage/repeatation, it doesnt +mess with things beyond the next question/request/query, in some ways. The trim garbage +option also tries to help avoid issues with garbage in the context to an extent. + +Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space +available wrt next query-response. However dont forget that the server when started should +also be started with a model context size of 1k or more, to be on safe side. + + The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the + internal n_predict, for now add the same here on the client side, maybe later add max_tokens + to /completions endpoint handling code on server side. + +NOTE: One may want to experiment with frequency/presence penalty fields in chatRequestOptions +wrt the set of fields sent to server along with the user query. To check how the model behaves +wrt repeatations in general in the generated text response. + +A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by +using the providing settings ui. + + +### OpenAi / Equivalent API WebService + +One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint +for a minimal chatting experimentation by setting the below. + +* the baseUrl in settings ui + * https://api.openai.com/v1 or similar + +* Wrt request body - gMe.chatRequestOptions + * model (settings ui) + * any additional fields if required in future + +* Wrt request headers - gMe.headers + * Authorization (available through settings ui) + * Bearer THE_OPENAI_API_KEY + * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so + +NOTE: Not tested, as there is no free tier api testing available. However logically this might +work. + + +## At the end + +Also a thank you to all open source and open model developers, who strive for the common good. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css index d45f50a957e4c..13bfb80b48be8 100644 --- a/examples/server/public_simplechat/simplechat.css +++ b/examples/server/public_simplechat/simplechat.css @@ -21,6 +21,17 @@ .role-user { background-color: lightgray; } +.role-trim { + background-color: lightpink; +} + +.gridx2 { + display: grid; + grid-template-columns: repeat(2, 1fr); + border-bottom-style: dotted; + border-bottom-width: thin; + border-bottom-color: lightblue; +} .flex-grow { flex-grow: 1; @@ -48,6 +59,13 @@ button { flex-direction: column; } +.ul1 { + padding-inline-start: 2vw; +} +.ul2 { + padding-inline-start: 2vw; +} + * { margin: 0.6vmin; } diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js index 3fc4dbc2026fa..25afb25649139 100644 --- a/examples/server/public_simplechat/simplechat.js +++ b/examples/server/public_simplechat/simplechat.js @@ -2,6 +2,9 @@ // A simple completions and chat/completions test related web front end logic // by Humans for All +import * as du from "./datautils.mjs"; +import * as ui from "./ui.mjs" + class Roles { static System = "system"; static User = "user"; @@ -9,26 +12,144 @@ class Roles { } class ApiEP { - static Chat = "chat"; - static Completion = "completion"; + static Type = { + Chat: "chat", + Completion: "completion", + } + static UrlSuffix = { + 'chat': `/chat/completions`, + 'completion': `/completions`, + } + + /** + * Build the url from given baseUrl and apiEp id. + * @param {string} baseUrl + * @param {string} apiEP + */ + static Url(baseUrl, apiEP) { + if (baseUrl.endsWith("/")) { + baseUrl = baseUrl.substring(0, baseUrl.length-1); + } + return `${baseUrl}${this.UrlSuffix[apiEP]}`; + } + } + let gUsageMsg = ` -

    Enter the system prompt above, before entering/submitting any user query.

    -

    Enter your text to the ai assistant below.

    -

    Use shift+enter for inserting enter.

    -

    Refresh the page to start over fresh.

    +

    Usage

    +
      +
    • System prompt above, to try control ai response characteristics.
      • +
        +
      • Completion mode - no system prompt normally.
        • +
      +
    • Use shift+enter for inserting enter/newline.
      • +
    • Enter your query to ai assistant below.
      • +
    • Default ContextWindow = [System, Last Query+Resp, Cur Query].
      • +
        +
      • ChatHistInCtxt, MaxTokens, ModelCtxt window to expand
        • +
      +
    `; + +/** @typedef {{role: string, content: string}[]} ChatMessages */ + +/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */ + class SimpleChat { - constructor() { + /** + * @param {string} chatId + */ + constructor(chatId) { + this.chatId = chatId; /** * Maintain in a form suitable for common LLM web service chat/completions' messages entry - * @type {{role: string, content: string}[]} + * @type {ChatMessages} */ this.xchat = []; this.iLastSys = -1; + this.latestResponse = ""; + } + + clear() { + this.xchat = []; + this.iLastSys = -1; + } + + ods_key() { + return `SimpleChat-${this.chatId}` + } + + save() { + /** @type {SimpleChatODS} */ + let ods = {iLastSys: this.iLastSys, xchat: this.xchat}; + localStorage.setItem(this.ods_key(), JSON.stringify(ods)); + } + + load() { + let sods = localStorage.getItem(this.ods_key()); + if (sods == null) { + return; + } + /** @type {SimpleChatODS} */ + let ods = JSON.parse(sods); + this.iLastSys = ods.iLastSys; + this.xchat = ods.xchat; + } + + /** + * Recent chat messages. + * If iRecentUserMsgCnt < 0 + * Then return the full chat history + * Else + * Return chat messages from latest going back till the last/latest system prompt. + * While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt. + * @param {number} iRecentUserMsgCnt + */ + recent_chat(iRecentUserMsgCnt) { + if (iRecentUserMsgCnt < 0) { + return this.xchat; + } + if (iRecentUserMsgCnt == 0) { + console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent"); + } + /** @type{ChatMessages} */ + let rchat = []; + let sysMsg = this.get_system_latest(); + if (sysMsg.length != 0) { + rchat.push({role: Roles.System, content: sysMsg}); + } + let iUserCnt = 0; + let iStart = this.xchat.length; + for(let i=this.xchat.length-1; i > this.iLastSys; i--) { + if (iUserCnt >= iRecentUserMsgCnt) { + break; + } + let msg = this.xchat[i]; + if (msg.role == Roles.User) { + iStart = i; + iUserCnt += 1; + } + } + for(let i = iStart; i < this.xchat.length; i++) { + let msg = this.xchat[i]; + if (msg.role == Roles.System) { + continue; + } + rchat.push({role: msg.role, content: msg.content}); + } + return rchat; + } + + /** + * Collate the latest response from the server/ai-model, as it is becoming available. + * This is mainly useful for the stream mode. + * @param {string} content + */ + append_response(content) { + this.latestResponse += content; } /** @@ -44,6 +165,7 @@ class SimpleChat { if (role == Roles.System) { this.iLastSys = this.xchat.length - 1; } + this.save(); return true; } @@ -57,11 +179,9 @@ class SimpleChat { div.replaceChildren(); } let last = undefined; - for(const x of this.xchat) { - let entry = document.createElement("p"); + for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) { + let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div); entry.className = `role-${x.role}`; - entry.innerText = `${x.role}: ${x.content}`; - div.appendChild(entry); last = entry; } if (last !== undefined) { @@ -69,17 +189,45 @@ class SimpleChat { } else { if (bClear) { div.innerHTML = gUsageMsg; + gMe.setup_load(div, this); + gMe.show_info(div); } } + return last; } /** - * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint + * Setup the fetch headers. + * It picks the headers from gMe.headers. + * It inserts Authorization only if its non-empty. + * @param {string} apiEP + */ + fetch_headers(apiEP) { + let headers = new Headers(); + for(let k in gMe.headers) { + let v = gMe.headers[k]; + if ((k == "Authorization") && (v.trim() == "")) { + continue; + } + headers.append(k, v); + } + return headers; + } + + /** + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + * The needed fields/options are picked from a global object. + * Add optional stream flag, if required. * Convert the json into string. * @param {Object} obj */ - request_jsonstr(obj) { - obj["temperature"] = 0.7; + request_jsonstr_extend(obj) { + for(let k in gMe.chatRequestOptions) { + obj[k] = gMe.chatRequestOptions[k]; + } + if (gMe.bStream) { + obj["stream"] = true; + } return JSON.stringify(obj); } @@ -88,23 +236,85 @@ class SimpleChat { */ request_messages_jsonstr() { let req = { - messages: this.xchat, + messages: this.recent_chat(gMe.iRecentUserMsgCnt), } - return this.request_jsonstr(req); + return this.request_jsonstr_extend(req); } /** * Return a string form of json object suitable for /completions + * @param {boolean} bInsertStandardRolePrefix Insert ": " as prefix wrt each role's message */ - request_prompt_jsonstr() { + request_prompt_jsonstr(bInsertStandardRolePrefix) { let prompt = ""; - for(const chat of this.xchat) { - prompt += `${chat.role}: ${chat.content}\n`; + let iCnt = 0; + for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) { + iCnt += 1; + if (iCnt > 1) { + prompt += "\n"; + } + if (bInsertStandardRolePrefix) { + prompt += `${chat.role}: `; + } + prompt += `${chat.content}`; } let req = { prompt: prompt, } - return this.request_jsonstr(req); + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for specified api endpoint. + * @param {string} apiEP + */ + request_jsonstr(apiEP) { + if (apiEP == ApiEP.Type.Chat) { + return this.request_messages_jsonstr(); + } else { + return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); + } + } + + /** + * Extract the ai-model/assistant's response from the http response got. + * Optionally trim the message wrt any garbage at the end. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + assistant = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Extract the ai-model/assistant's response from the http response got in streaming mode. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract_stream(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + if (respBody["choices"][0]["finish_reason"] !== "stop") { + assistant = respBody["choices"][0]["delta"]["content"]; + } + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; } /** @@ -163,54 +373,99 @@ class SimpleChat { return sysPrompt; } -} + /** + * Handle the multipart response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response_multipart(resp, apiEP, elDiv) { + let elP = ui.el_create_append_p("", elDiv); + if (!resp.body) { + throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body..."); + } + let tdUtf8 = new TextDecoder("utf-8"); + let rr = resp.body.getReader(); + this.latestResponse = ""; + let xLines = new du.NewLines(); + while(true) { + let { value: cur, done: done } = await rr.read(); + if (cur) { + let curBody = tdUtf8.decode(cur, {stream: true}); + console.debug("DBUG:SC:PART:Str:", curBody); + xLines.add_append(curBody); + } + while(true) { + let curLine = xLines.shift(!done); + if (curLine == undefined) { + break; + } + if (curLine.trim() == "") { + continue; + } + if (curLine.startsWith("data:")) { + curLine = curLine.substring(5); + } + let curJson = JSON.parse(curLine); + console.debug("DBUG:SC:PART:Json:", curJson); + this.append_response(this.response_extract_stream(curJson, apiEP)); + } + elP.innerText = this.latestResponse; + elP.scrollIntoView(false); + if (done) { + break; + } + } + console.debug("DBUG:SC:PART:Full:", this.latestResponse); + return this.latestResponse; + } -let gBaseURL = "http://127.0.0.1:8080"; -let gChatURL = { - 'chat': `${gBaseURL}/chat/completions`, - 'completion': `${gBaseURL}/completions`, -} -const gbCompletionFreshChatAlways = true; - - -/** - * Set the class of the children, based on whether it is the idSelected or not. - * @param {HTMLDivElement} elBase - * @param {string} idSelected - * @param {string} classSelected - * @param {string} classUnSelected - */ -function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { - for(let child of elBase.children) { - if (child.id == idSelected) { - child.className = classSelected; + /** + * Handle the oneshot response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + */ + async handle_response_oneshot(resp, apiEP) { + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + return this.response_extract(respBody, apiEP); + } + + /** + * Handle the response from the server be it in oneshot or multipart/stream mode. + * Also take care of the optional garbage trimming. + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response(resp, apiEP, elDiv) { + let theResp = { + assistant: "", + trimmed: "", + } + if (gMe.bStream) { + try { + theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv); + this.latestResponse = ""; + } catch (error) { + theResp.assistant = this.latestResponse; + this.add(Roles.Assistant, theResp.assistant); + this.latestResponse = ""; + throw error; + } } else { - child.className = classUnSelected; + theResp.assistant = await this.handle_response_oneshot(resp, apiEP); + } + if (gMe.bTrimGarbage) { + let origMsg = theResp.assistant; + theResp.assistant = du.trim_garbage_at_end(origMsg); + theResp.trimmed = origMsg.substring(theResp.assistant.length); } + this.add(Roles.Assistant, theResp.assistant); + return theResp; } -} -/** - * Create button and set it up. - * @param {string} id - * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback - * @param {string | undefined} name - * @param {string | undefined} innerText - */ -function el_create_button(id, callback, name=undefined, innerText=undefined) { - if (!name) { - name = id; - } - if (!innerText) { - innerText = id; - } - let btn = document.createElement("button"); - btn.id = id; - btn.name = name; - btn.innerText = innerText; - btn.addEventListener("click", callback); - return btn; } @@ -227,14 +482,16 @@ class MultiChatUI { this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); - this.elSelectApiEP = /** @type{HTMLSelectElement} */(document.getElementById("api-ep")); + this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading")); this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings")); this.validate_element(this.elInSystem, "system-in"); this.validate_element(this.elDivChat, "chat-div"); this.validate_element(this.elInUser, "user-in"); - this.validate_element(this.elSelectApiEP, "api-ep"); + this.validate_element(this.elDivHeading, "heading"); this.validate_element(this.elDivChat, "sessions-div"); + this.validate_element(this.elBtnSettings, "settings"); } /** @@ -275,13 +532,18 @@ class MultiChatUI { this.handle_session_switch(this.curChatId); } + this.elBtnSettings.addEventListener("click", (ev)=>{ + this.elDivChat.replaceChildren(); + gMe.show_settings(this.elDivChat); + }); + this.elBtnUser.addEventListener("click", (ev)=>{ if (this.elInUser.disabled) { return; } - this.handle_user_submit(this.curChatId, this.elSelectApiEP.value).catch((/** @type{Error} */reason)=>{ + this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{ let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; - console.debug(msg.replace("\n", ":")); + console.error(msg.replace("\n", ":")); alert(msg); this.ui_reset_userinput(); }); @@ -291,6 +553,8 @@ class MultiChatUI { // allow user to insert enter into their message using shift+enter. // while just pressing enter key will lead to submitting. if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInUser.value; + this.elInUser.value = value.substring(0,value.length-1); this.elBtnUser.click(); ev.preventDefault(); } @@ -300,6 +564,8 @@ class MultiChatUI { // allow user to insert enter into the system prompt using shift+enter. // while just pressing enter key will lead to setting the system prompt. if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInSystem.value; + this.elInSystem.value = value.substring(0,value.length-1); let chat = this.simpleChats[this.curChatId]; chat.add_system_anytime(this.elInSystem.value, this.curChatId); chat.show(this.elDivChat); @@ -315,12 +581,13 @@ class MultiChatUI { * @param {boolean} bSwitchSession */ new_chat_session(chatId, bSwitchSession=false) { - this.simpleChats[chatId] = new SimpleChat(); + this.simpleChats[chatId] = new SimpleChat(chatId); if (bSwitchSession) { this.handle_session_switch(chatId); } } + /** * Handle user query submit request, wrt specified chat session. * @param {string} chatId @@ -330,6 +597,14 @@ class MultiChatUI { let chat = this.simpleChats[chatId]; + // In completion mode, if configured, clear any previous chat history. + // So if user wants to simulate a multi-chat based completion query, + // they will have to enter the full thing, as a suitable multiline + // user input/query. + if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) { + chat.clear(); + } + chat.add_system_anytime(this.elInSystem.value, chatId); let content = this.elInUser.value; @@ -339,50 +614,29 @@ class MultiChatUI { } chat.show(this.elDivChat); - let theBody; - let theUrl = gChatURL[apiEP] - if (apiEP == ApiEP.Chat) { - theBody = chat.request_messages_jsonstr(); - } else { - theBody = chat.request_prompt_jsonstr(); - } + let theUrl = ApiEP.Url(gMe.baseURL, apiEP); + let theBody = chat.request_jsonstr(apiEP); this.elInUser.value = "working..."; this.elInUser.disabled = true; console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let theHeaders = chat.fetch_headers(apiEP); let resp = await fetch(theUrl, { method: "POST", - headers: { - "Content-Type": "application/json", - }, + headers: theHeaders, body: theBody, }); - let respBody = await resp.json(); - console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); - let assistantMsg; - if (apiEP == ApiEP.Chat) { - assistantMsg = respBody["choices"][0]["message"]["content"]; - } else { - try { - assistantMsg = respBody["choices"][0]["text"]; - } catch { - assistantMsg = respBody["content"]; - } - } - chat.add(Roles.Assistant, assistantMsg); + let theResp = await chat.handle_response(resp, apiEP, this.elDivChat); if (chatId == this.curChatId) { chat.show(this.elDivChat); + if (theResp.trimmed.length > 0) { + let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat); + p.className="role-trim"; + } } else { console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); } - // Purposefully clear at end rather than begin of this function - // so that one can switch from chat to completion mode and sequece - // in a completion mode with multiple user-assistant chat data - // from before to be sent/occur once. - if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) { - chat.xchat.length = 0; - } this.ui_reset_userinput(); } @@ -398,7 +652,7 @@ class MultiChatUI { } elDiv.replaceChildren(); // Btn for creating new chat session - let btnNew = el_create_button("New CHAT", (ev)=> { + let btnNew = ui.el_create_button("New CHAT", (ev)=> { if (this.elInUser.disabled) { console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); @@ -412,7 +666,7 @@ class MultiChatUI { } this.new_chat_session(chatIdGot, true); this.create_session_btn(elDiv, chatIdGot); - el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + ui.el_children_config_class(elDiv, chatIdGot, "session-selected", ""); }); elDiv.appendChild(btnNew); // Btns for existing chat sessions @@ -426,7 +680,7 @@ class MultiChatUI { } create_session_btn(elDiv, cid) { - let btn = el_create_button(cid, (ev)=>{ + let btn = ui.el_create_button(cid, (ev)=>{ let target = /** @type{HTMLButtonElement} */(ev.target); console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); if (this.elInUser.disabled) { @@ -435,7 +689,7 @@ class MultiChatUI { return; } this.handle_session_switch(target.id); - el_children_config_class(elDiv, target.id, "session-selected", ""); + ui.el_children_config_class(elDiv, target.id, "session-selected", ""); }); elDiv.appendChild(btn); return btn; @@ -462,17 +716,206 @@ class MultiChatUI { } -let gMuitChat; -const gChatIds = [ "Default", "Other" ]; +class Me { + + constructor() { + this.baseURL = "http://127.0.0.1:8080"; + this.defaultChatIds = [ "Default", "Other" ]; + this.multiChat = new MultiChatUI(); + this.bStream = true; + this.bCompletionFreshChatAlways = true; + this.bCompletionInsertStandardRolePrefix = false; + this.bTrimGarbage = true; + this.iRecentUserMsgCnt = 2; + this.sRecentUserMsgCnt = { + "Full": -1, + "Last0": 1, + "Last1": 2, + "Last2": 3, + "Last4": 5, + }; + this.apiEP = ApiEP.Type.Chat; + this.headers = { + "Content-Type": "application/json", + "Authorization": "", // Authorization: Bearer OPENAI_API_KEY + } + // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + this.chatRequestOptions = { + "model": "gpt-3.5-turbo", + "temperature": 0.7, + "max_tokens": 1024, + "n_predict": 1024, + //"frequency_penalty": 1.2, + //"presence_penalty": 1.2, + }; + } + + /** + * Disable console.debug by mapping it to a empty function. + */ + debug_disable() { + this.console_debug = console.debug; + console.debug = () => { + + }; + } + + /** + * Setup the load saved chat ui. + * @param {HTMLDivElement} div + * @param {SimpleChat} chat + */ + setup_load(div, chat) { + if (!(chat.ods_key() in localStorage)) { + return; + } + div.innerHTML += `

    Restore

    +

    Load previously saved chat session, if available

    `; + let btn = ui.el_create_button(chat.ods_key(), (ev)=>{ + console.log("DBUG:SimpleChat:SC:Load", chat); + chat.load(); + queueMicrotask(()=>{ + chat.show(div); + this.multiChat.elInSystem.value = chat.get_system_latest(); + }); + }); + div.appendChild(btn); + } + + /** + * Show the configurable parameters info in the passed Div element. + * @param {HTMLDivElement} elDiv + * @param {boolean} bAll + */ + show_info(elDiv, bAll=false) { + + let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv); + p.className = "role-system"; + + if (bAll) { + + ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv); + + ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv); + + ui.el_create_append_p(`bStream:${this.bStream}`, elDiv); + + ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv); + + ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv); + + ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv); + + ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv); + + ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv); + + } + + ui.el_create_append_p(`chatRequestOptions:${JSON.stringify(this.chatRequestOptions, null, " - ")}`, elDiv); + ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv); + + } + + /** + * Auto create ui input elements for fields in ChatRequestOptions + * Currently supports text and number field types. + * @param {HTMLDivElement} elDiv + */ + show_settings_chatrequestoptions(elDiv) { + let typeDict = { + "string": "text", + "number": "number", + }; + let fs = document.createElement("fieldset"); + let legend = document.createElement("legend"); + legend.innerText = "ChatRequestOptions"; + fs.appendChild(legend); + elDiv.appendChild(fs); + for(const k in this.chatRequestOptions) { + let val = this.chatRequestOptions[k]; + let type = typeof(val); + if (!((type == "string") || (type == "number"))) { + continue; + } + let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.chatRequestOptions[k], (val)=>{ + if (type == "number") { + val = Number(val); + } + this.chatRequestOptions[k] = val; + }); + fs.appendChild(inp.div); + } + } + + /** + * Show settings ui for configurable parameters, in the passed Div element. + * @param {HTMLDivElement} elDiv + */ + show_settings(elDiv) { + + let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{ + this.baseURL = val; + }); + elDiv.appendChild(inp.div); + + inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{ + this.headers["Authorization"] = val; + }); + inp.el.placeholder = "Bearer OPENAI_API_KEY"; + elDiv.appendChild(inp.div); + + let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{ + this.bStream = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{ + this.bCompletionFreshChatAlways = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{ + this.bCompletionInsertStandardRolePrefix = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{ + this.bTrimGarbage = val; + }); + elDiv.appendChild(bb.div); + + let sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{ + this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val]; + }); + elDiv.appendChild(sel.div); + + sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{ + this.apiEP = ApiEP.Type[val]; + }); + elDiv.appendChild(sel.div); + + this.show_settings_chatrequestoptions(elDiv); + + } + +} + + +/** @type {Me} */ +let gMe; function startme() { console.log("INFO:SimpleChat:StartMe:Starting..."); - gMuitChat = new MultiChatUI(); - for (let cid of gChatIds) { - gMuitChat.new_chat_session(cid); + gMe = new Me(); + gMe.debug_disable(); + document["gMe"] = gMe; + document["du"] = du; + for (let cid of gMe.defaultChatIds) { + gMe.multiChat.new_chat_session(cid); } - gMuitChat.setup_ui(gChatIds[0]); - gMuitChat.show_sessions(); + gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true); + gMe.multiChat.show_sessions(); } document.addEventListener("DOMContentLoaded", startme); diff --git a/examples/server/public_simplechat/ui.mjs b/examples/server/public_simplechat/ui.mjs new file mode 100644 index 0000000000000..b2d5b9aeab76c --- /dev/null +++ b/examples/server/public_simplechat/ui.mjs @@ -0,0 +1,211 @@ +//@ts-check +// Helpers to work with html elements +// by Humans for All +// + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +export function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + +/** + * Create a para and set it up. Optionaly append it to a passed parent. + * @param {string} text + * @param {HTMLElement | undefined} elParent + * @param {string | undefined} id + */ +export function el_create_append_p(text, elParent=undefined, id=undefined) { + let para = document.createElement("p"); + para.innerText = text; + if (id) { + para.id = id; + } + if (elParent) { + elParent.appendChild(para); + } + return para; +} + +/** + * Create a button which represents bool value using specified text wrt true and false. + * When ever user clicks the button, it will toggle the value and update the shown text. + * + * @param {string} id + * @param {{true: string, false: string}} texts + * @param {boolean} defaultValue + * @param {function(boolean):void} cb + */ +export function el_create_boolbutton(id, texts, defaultValue, cb) { + let el = document.createElement("button"); + el["xbool"] = defaultValue; + el["xtexts"] = structuredClone(texts); + el.innerText = el["xtexts"][String(defaultValue)]; + if (id) { + el.id = id; + } + el.addEventListener('click', (ev)=>{ + el["xbool"] = !el["xbool"]; + el.innerText = el["xtexts"][String(el["xbool"])]; + cb(el["xbool"]); + }) + return el; +} + +/** + * Create a div wrapped button which represents bool value using specified text wrt true and false. + * @param {string} id + * @param {string} label + * @param {{ true: string; false: string; }} texts + * @param {boolean} defaultValue + * @param {(arg0: boolean) => void} cb + * @param {string} className + */ +export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let btn = el_create_boolbutton(id, texts, defaultValue, cb); + div.appendChild(btn); + return { div: div, el: btn }; +} + + +/** + * Create a select ui element, with a set of options to select from. + * * options: an object which contains name-value pairs + * * defaultOption: the value whose name should be choosen, by default. + * * cb : the call back returns the name string of the option selected. + * + * @param {string} id + * @param {Object} options + * @param {*} defaultOption + * @param {function(string):void} cb + */ +export function el_create_select(id, options, defaultOption, cb) { + let el = document.createElement("select"); + el["xselected"] = defaultOption; + el["xoptions"] = structuredClone(options); + for(let cur of Object.keys(options)) { + let op = document.createElement("option"); + op.value = cur; + op.innerText = cur; + if (options[cur] == defaultOption) { + op.selected = true; + } + el.appendChild(op); + } + if (id) { + el.id = id; + el.name = id; + } + el.addEventListener('change', (ev)=>{ + let target = /** @type{HTMLSelectElement} */(ev.target); + console.log("DBUG:UI:Select:", id, ":", target.value); + cb(target.value); + }) + return el; +} + +/** + * Create a div wrapped select ui element, with a set of options to select from. + * + * @param {string} id + * @param {any} label + * @param {{ [x: string]: any; }} options + * @param {any} defaultOption + * @param {(arg0: string) => void} cb + * @param {string} className + */ +export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let sel = el_create_select(id, options,defaultOption, cb); + div.appendChild(sel); + return { div: div, el: sel }; +} + + +/** + * Create a input ui element. + * + * @param {string} id + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + */ +export function el_create_input(id, type, defaultValue, cb) { + let el = document.createElement("input"); + el.type = type; + el.value = defaultValue; + if (id) { + el.id = id; + } + el.addEventListener('change', (ev)=>{ + cb(el.value); + }) + return el; +} + +/** + * Create a div wrapped input. + * + * @param {string} id + * @param {string} label + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + * @param {string} className + */ +export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let el = el_create_input(id, type, defaultValue, cb); + div.appendChild(el); + return { div: div, el: el }; +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e9904263d53c7..919078f2bd920 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -17,9 +17,20 @@ #include "json.hpp" // auto generated files (update with ./deps.sh) +#include "colorthemes.css.hpp" +#include "style.css.hpp" +#include "theme-beeninorder.css.hpp" +#include "theme-ketivah.css.hpp" +#include "theme-mangotango.css.hpp" +#include "theme-playground.css.hpp" +#include "theme-polarnight.css.hpp" +#include "theme-snowstorm.css.hpp" #include "index.html.hpp" +#include "index-new.html.hpp" #include "index.js.hpp" #include "completion.js.hpp" +#include "system-prompts.js.hpp" +#include "prompt-formats.js.hpp" #include "json-schema-to-grammar.mjs.hpp" #include @@ -112,29 +123,6 @@ struct slot_params { json input_suffix; }; -struct server_params { - int32_t port = 8080; - int32_t read_timeout = 600; - int32_t write_timeout = 600; - int32_t n_threads_http = -1; - - std::string hostname = "127.0.0.1"; - std::string public_path = ""; - std::string chat_template = ""; - std::string system_prompt = ""; - - std::vector api_keys; - -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - std::string ssl_key_file = ""; - std::string ssl_cert_file = ""; -#endif - - bool slots_endpoint = true; - bool metrics_endpoint = false; - std::string slot_save_path; -}; - struct server_slot { int id; int id_task = -1; @@ -159,7 +147,7 @@ struct server_slot { int32_t n_prompt_tokens = 0; int32_t n_prompt_tokens_processed = 0; - json prompt; + json prompt; // can be either a string, array of strings or array of token ids // when a task is submitted, we first tokenize the prompt and store it here std::vector prompt_tokens; @@ -659,6 +647,9 @@ struct server_context { server_metrics metrics; + // Necessary similarity of prompt for slot selection + float slot_prompt_similarity = 0.0f; + ~server_context() { if (ctx) { llama_free(ctx); @@ -807,24 +798,88 @@ struct server_context { return prompt_tokens; } - server_slot * get_slot(int id) { - int64_t t_last = ggml_time_us(); - - server_slot * last_used = nullptr; - + server_slot * get_slot_by_id(int id) { for (server_slot & slot : slots) { - if (slot.id == id && slot.available()) { + if (slot.id == id) { return &slot; } + } + + return nullptr; + } + + server_slot * get_available_slot(const std::string & prompt) { + server_slot * ret = nullptr; + + // find the slot that has at least n% prompt similarity + if (ret == nullptr && slot_prompt_similarity != 0.0f && !prompt.empty()) { + int max_lcp_len = 0; + float similarity = 0; + + for (server_slot & slot : slots) { + // skip the slot if it is not available + if (!slot.available()) { + continue; + } + + // skip the slot if it does not contains prompt + if (!slot.prompt.is_string()) { + continue; + } + + // current slot's prompt + std::string slot_prompt = slot.prompt.get(); - // among all available slots, find the one that has been least recently used - if (slot.available() && slot.t_last_used < t_last) { - last_used = &slot; - t_last = slot.t_last_used; + // length of the current slot's prompt + int slot_prompt_len = slot_prompt.size(); + + // length of the Longest Common Prefix between the current slot's prompt and the input prompt + int lcp_len = common_part(slot_prompt, prompt); + + // fraction of the common substring length compared to the current slot's prompt length + similarity = static_cast(lcp_len) / slot_prompt_len; + + // select the current slot if the criteria match + if (lcp_len > max_lcp_len && similarity > slot_prompt_similarity) { + max_lcp_len = lcp_len; + ret = &slot; + } + } + + if (ret != nullptr) { + LOG_VERBOSE("selected slot by lcp similarity", { + {"id_slot", ret->id}, + {"max_lcp_len", max_lcp_len}, + {"similarity", similarity}, + }); + } + } + + // find the slot that has been least recently used + if (ret == nullptr) { + int64_t t_last = ggml_time_us(); + for (server_slot & slot : slots) { + // skip the slot if it is not available + if (!slot.available()) { + continue; + } + + // select the current slot if the criteria match + if (slot.t_last_used < t_last) { + t_last = slot.t_last_used; + ret = &slot; + } + } + + if (ret != nullptr) { + LOG_VERBOSE("selected slot by lru", { + {"id_slot", ret->id}, + {"t_last", t_last}, + }); } } - return last_used; + return ret; } bool launch_slot_with_task(server_slot & slot, const server_task & task) { @@ -900,16 +955,19 @@ struct server_context { slot.params.input_suffix = json_value(data, "input_suffix", default_params.input_suffix); // get prompt - { + if (!task.infill) { const auto & prompt = data.find("prompt"); if (prompt == data.end()) { - send_error(task, "Either \"prompt\" or \"messages\" must be provided", ERROR_TYPE_INVALID_REQUEST); + send_error(task, "\"prompt\" must be provided", ERROR_TYPE_INVALID_REQUEST); return false; - } else { - slot.prompt = *prompt; } - if (slot.prompt.is_array() && slot.prompt.size() == 0) { - send_error(task, "\"prompt\" cannot be an empty array", ERROR_TYPE_INVALID_REQUEST); + + if ((prompt->is_string()) || + (prompt->is_array() && prompt->size() == 1 && prompt->at(0).is_string()) || + (prompt->is_array() && !prompt->empty() && prompt->at(0).is_number_integer())) { + slot.prompt = *prompt; + } else { + send_error(task, "\"prompt\" must be a string or an array of integers", ERROR_TYPE_INVALID_REQUEST); return false; } } @@ -1250,7 +1308,7 @@ struct server_context { } json get_formated_generation(const server_slot & slot) const { - const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); + const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); std::vector samplers_sequence; @@ -1527,13 +1585,33 @@ struct server_context { switch (task.type) { case SERVER_TASK_TYPE_COMPLETION: { - server_slot * slot = get_slot(json_value(task.data, "id_slot", -1)); + const int id_slot = json_value(task.data, "id_slot", -1); + + server_slot * slot; + + if (id_slot != -1) { + slot = get_slot_by_id(id_slot); + } else { + std::string prompt; + if (task.data.contains("prompt") && task.data.at("prompt").is_string()) { + json_value(task.data, "prompt", std::string()); + } + + slot = get_available_slot(prompt); + } + if (slot == nullptr) { // if no slot is available, we defer this task for processing later LOG_VERBOSE("no slot is available", {{"id_task", task.id}}); queue_tasks.defer(task); break; } + if (!slot->available()) { + // if requested slot is unavailable, we defer this task for processing later + LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}}); + queue_tasks.defer(task); + break; + } if (task.data.contains("system_prompt")) { std::string sys_prompt = json_value(task.data, "system_prompt", std::string()); @@ -1650,11 +1728,17 @@ struct server_context { case SERVER_TASK_TYPE_SLOT_SAVE: { int id_slot = task.data.at("id_slot"); - server_slot * slot = get_slot(id_slot); + server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + if (!slot->available()) { + // if requested slot is unavailable, we defer this task for processing later + LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}}); + queue_tasks.defer(task); + break; + } const size_t token_count = slot->cache_tokens.size(); const int64_t t_start = ggml_time_us(); @@ -1685,11 +1769,17 @@ struct server_context { case SERVER_TASK_TYPE_SLOT_RESTORE: { int id_slot = task.data.at("id_slot"); - server_slot * slot = get_slot(id_slot); + server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + if (!slot->available()) { + // if requested slot is unavailable, we defer this task for processing later + LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}}); + queue_tasks.defer(task); + break; + } const int64_t t_start = ggml_time_us(); @@ -1727,11 +1817,17 @@ struct server_context { case SERVER_TASK_TYPE_SLOT_ERASE: { int id_slot = task.data.at("id_slot"); - server_slot * slot = get_slot(id_slot); + server_slot * slot = get_slot_by_id(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); break; } + if (!slot->available()) { + // if requested slot is unavailable, we defer this task for processing later + LOG_VERBOSE("requested slot is unavailable", {{"id_task", task.id}}); + queue_tasks.defer(task); + break; + } // Erase token cache const size_t n_erased = slot->cache_tokens.size(); @@ -2323,561 +2419,6 @@ struct server_context { } }; -static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) { - printf("usage: %s [options]\n", argv0); - printf("\n"); - printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); - printf(" -dt N, --defrag-thold N\n"); - printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); - printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch); - if (llama_supports_mlock()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); - } - if (llama_supports_mmap()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); - } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided my numactl\n"); - if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); - printf(" -nkvo, --no-kv-offload\n"); - printf(" disable KV offload\n"); - } - printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH); - printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); - printf(" model download url (default: unused)\n"); - printf(" -hfr REPO, --hf-repo REPO\n"); - printf(" Hugging Face model repository (default: unused)\n"); - printf(" -hff FILE, --hf-file FILE\n"); - printf(" Hugging Face model file (default: unused)\n"); - printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --rpc SERVERS comma separated list of RPC servers\n"); - printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n"); - printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n"); -#endif - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n"); - printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled"); - printf(" -spf FNAME, --system-prompt-file FNAME\n"); - printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); - printf(" -ctk TYPE, --cache-type-k TYPE\n"); - printf(" KV cache data type for K (default: f16)\n"); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: f16)\n"); - printf(" --log-format log output format: json or text (default: json)\n"); - printf(" --log-disable disables logging to a file.\n"); - printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); - printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); - printf(" --slot-save-path PATH path to save slot kv cache (default: disabled)\n"); - printf("\n"); - printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); - printf(" --chat-template JINJA_TEMPLATE\n"); - printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); - printf(" only commonly used templates are accepted:\n"); - printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n"); - printf("\n"); -} - -static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) { - gpt_params default_params; - server_params default_sparams; - - std::string arg; - bool invalid_param = false; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg == "--port") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.port = std::stoi(argv[i]); - } else if (arg == "--rpc") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rpc_servers = argv[i]; - } else if (arg == "--host") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.hostname = argv[i]; - } else if (arg == "--path") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.public_path = argv[i]; - } else if (arg == "--api-key") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.api_keys.push_back(argv[i]); - } else if (arg == "--api-key-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream key_file(argv[i]); - if (!key_file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string key; - while (std::getline(key_file, key)) { - if (key.size() > 0) { - sparams.api_keys.push_back(key); - } - } - key_file.close(); - - } -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - else if (arg == "--ssl-key-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.ssl_key_file = argv[i]; - } else if (arg == "--ssl-cert-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.ssl_cert_file = argv[i]; - } -#endif - else if (arg == "--timeout" || arg == "-to") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.read_timeout = std::stoi(argv[i]); - sparams.write_timeout = std::stoi(argv[i]); - } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model = argv[i]; - } else if (arg == "-mu" || arg == "--model-url") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_url = argv[i]; - } else if (arg == "-hfr" || arg == "--hf-repo") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.hf_repo = argv[i]; - } else if (arg == "-hff" || arg == "--hf-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.hf_file = argv[i]; - } else if (arg == "-a" || arg == "--alias") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_alias = argv[i]; - } else if (arg == "-h" || arg == "--help") { - server_print_usage(argv[0], default_params, default_sparams); - exit(0); - } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ctx = std::stoi(argv[i]); - } else if (arg == "--rope-scaling") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } - else { invalid_param = true; break; } - } else if (arg == "--rope-freq-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_base = std::stof(argv[i]); - } else if (arg == "--rope-freq-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = std::stof(argv[i]); - } else if (arg == "--yarn-ext-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_ext_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-attn-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_attn_factor = std::stof(argv[i]); - } else if (arg == "--yarn-beta-fast") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_fast = std::stof(argv[i]); - } else if (arg == "--yarn-beta-slow") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_slow = std::stof(argv[i]); - } else if (arg == "--pooling") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } - else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } - else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } - else { invalid_param = true; break; } - } else if (arg == "--defrag-thold" || arg == "-dt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.defrag_thold = std::stof(argv[i]); - } else if (arg == "--threads" || arg == "-t") { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - } else if (arg == "--grp-attn-n" || arg == "-gan") { - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_n = std::stoi(argv[i]); - } else if (arg == "--grp-attn-w" || arg == "-gaw") { - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_w = std::stoi(argv[i]); - } else if (arg == "--threads-batch" || arg == "-tb") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } else if (arg == "--threads-http") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.n_threads_http = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_batch = std::stoi(argv[i]); - } else if (arg == "-ub" || arg == "--ubatch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ubatch = std::stoi(argv[i]); - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (llama_supports_gpu_offload()) { - params.n_gpu_layers = std::stoi(argv[i]); - } else { - LOG_WARNING( - "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", - {{"n_gpu_layers", params.n_gpu_layers}}); - } - } else if (arg == "-nkvo" || arg == "--no-kv-offload") { - params.no_kv_offload = true; - } else if (arg == "--split-mode" || arg == "-sm") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_MODE_NONE; - } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } else if (arg_next == "row") { - params.split_mode = LLAMA_SPLIT_MODE_ROW; - } else { - invalid_param = true; - break; - } -#ifndef GGML_USE_CUDA - fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUDA - } else if (arg == "--tensor-split" || arg == "-ts") { - if (++i >= argc) { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) - std::string arg_next = argv[i]; - - // split string by , and / - const std::regex regex{R"([,/]+)"}; - std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; - std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= llama_max_devices()); - - for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { - if (i_device < split_arg.size()) { - params.tensor_split[i_device] = std::stof(split_arg[i_device]); - } else { - params.tensor_split[i_device] = 0.0f; - } - } -#else - LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUDA - } else if (arg == "--main-gpu" || arg == "-mg") { - if (++i >= argc) { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) - params.main_gpu = std::stoi(argv[i]); -#else - LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {}); -#endif - } else if (arg == "--lora") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(argv[i], 1.0f); - params.use_mmap = false; - } else if (arg == "--lora-scaled") { - if (++i >= argc) { - invalid_param = true; - break; - } - const char * lora_adapter = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); - params.use_mmap = false; - } else if (arg == "--lora-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_base = argv[i]; - } else if (arg == "-v" || arg == "--verbose") { -#if SERVER_VERBOSE != 1 - LOG_WARNING("server.cpp is not built with verbose logging.", {}); -#else - server_verbose = true; -#endif - } else if (arg == "--mlock") { - params.use_mlock = true; - } else if (arg == "--no-mmap") { - params.use_mmap = false; - } else if (arg == "--numa") { - if (++i >= argc) { - invalid_param = true; - break; - } else { - std::string value(argv[i]); - /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } - else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } - else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } - else { invalid_param = true; break; } - } - } else if (arg == "--embedding" || arg == "--embeddings") { - params.embedding = true; - } else if (arg == "-cb" || arg == "--cont-batching") { - params.cont_batching = true; - } else if (arg == "-fa" || arg == "--flash-attn") { - params.flash_attn = true; - } else if (arg == "-np" || arg == "--parallel") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-n" || arg == "--n-predict") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_predict = std::stoi(argv[i]); - } else if (arg == "-spf" || arg == "--system-prompt-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string system_prompt; - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(system_prompt) - ); - sparams.system_prompt = system_prompt; - } else if (arg == "-ctk" || arg == "--cache-type-k") { - params.cache_type_k = argv[++i]; - } else if (arg == "-ctv" || arg == "--cache-type-v") { - params.cache_type_v = argv[++i]; - } else if (arg == "--log-format") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (std::strcmp(argv[i], "json") == 0) { - server_log_json = true; - } else if (std::strcmp(argv[i], "text") == 0) { - server_log_json = false; - } else { - invalid_param = true; - break; - } - } else if (arg == "--log-disable") { - log_set_target(stdout); - LOG_INFO("logging to file is disabled.", {}); - } else if (arg == "--slots-endpoint-disable") { - sparams.slots_endpoint = false; - } else if (arg == "--metrics") { - sparams.metrics_endpoint = true; - } else if (arg == "--slot-save-path") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.slot_save_path = argv[i]; - // if doesn't end with DIRECTORY_SEPARATOR, add it - if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { - sparams.slot_save_path += DIRECTORY_SEPARATOR; - } - } else if (arg == "--chat-template") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (!verify_custom_template(argv[i])) { - fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); - fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); - invalid_param = true; - break; - } - sparams.chat_template = argv[i]; - } else if (arg == "--override-kv") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (!string_parse_kv_override(argv[i], params.kv_overrides)) { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } - } - - gpt_params_handle_model_default(params); - - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } -} - static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port if (req.path == "/v1/health" || req.path == "/v1/completions") { @@ -2918,16 +2459,22 @@ int main(int argc, char ** argv) { log_disable(); #endif // own arguments required by this example - gpt_params params; - server_params sparams; + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); + return 1; + } + + // TODO: not great to use extern vars + server_log_json = params.log_json; + server_verbose = params.verbosity > 0; // struct that contains llama context and inference server_context ctx_server; - server_params_parse(argc, argv, sparams, params); - - if (!sparams.system_prompt.empty()) { - ctx_server.system_prompt_set(sparams.system_prompt); + if (!params.system_prompt.empty()) { + ctx_server.system_prompt_set(params.system_prompt); } if (params.model_alias == "unknown") { @@ -2951,10 +2498,10 @@ int main(int argc, char ** argv) { std::unique_ptr svr; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") { - LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}}); + if (params.ssl_file_key != "" && params.ssl_file_cert != "") { + LOG_INFO("Running with SSL", {{"key", params.ssl_file_key}, {"cert", params.ssl_file_cert}}); svr.reset( - new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str()) + new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str()) ); } else { LOG_INFO("Running without SSL", {}); @@ -3008,26 +2555,29 @@ int main(int argc, char ** argv) { }); // set timeouts and change hostname and port - svr->set_read_timeout (sparams.read_timeout); - svr->set_write_timeout(sparams.write_timeout); + svr->set_read_timeout (params.timeout_read); + svr->set_write_timeout(params.timeout_write); - if (!svr->bind_to_port(sparams.hostname, sparams.port)) { - fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); + if (!svr->bind_to_port(params.hostname, params.port)) { + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", params.hostname.c_str(), params.port); return 1; } std::unordered_map log_data; - log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); + log_data["hostname"] = params.hostname; + log_data["port"] = std::to_string(params.port); - if (sparams.api_keys.size() == 1) { - auto key = sparams.api_keys[0]; + if (params.api_keys.size() == 1) { + auto key = params.api_keys[0]; log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); - } else if (sparams.api_keys.size() > 1) { - log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; + } else if (params.api_keys.size() > 1) { + log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded"; } + // Necessary similarity of prompt for slot selection + ctx_server.slot_prompt_similarity = params.slot_prompt_similarity; + // load the model if (!ctx_server.load_model(params)) { state.store(SERVER_STATE_ERROR); @@ -3042,10 +2592,10 @@ int main(int argc, char ** argv) { const auto model_meta = ctx_server.model_meta(); // if a custom chat template is not supplied, we will use the one that comes with the model (if any) - if (sparams.chat_template.empty()) { + if (params.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "chatml"; + params.chat_template = "chatml"; } } @@ -3057,11 +2607,11 @@ int main(int argc, char ** argv) { chat.push_back({{"role", "assistant"}, {"content", "Hi there"}}); chat.push_back({{"role", "user"}, {"content", "How are you?"}}); - const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat); + const std::string chat_example = format_chat(ctx_server.model, params.chat_template, chat); LOG_INFO("chat template", { {"chat_example", chat_example}, - {"built_in", sparams.chat_template.empty()}, + {"built_in", params.chat_template.empty()}, }); } @@ -3069,7 +2619,7 @@ int main(int argc, char ** argv) { // Middlewares // - auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) { + auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { // TODO: should we apply API key to all endpoints, including "/health" and "/models"? static const std::set protected_endpoints = { "/props", @@ -3087,7 +2637,7 @@ int main(int argc, char ** argv) { }; // If API key is not set, skip validation - if (sparams.api_keys.empty()) { + if (params.api_keys.empty()) { return true; } @@ -3102,7 +2652,7 @@ int main(int argc, char ** argv) { std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); - if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { + if (std::find(params.api_keys.begin(), params.api_keys.end(), received_api_key) != params.api_keys.end()) { return true; // API key is valid } } @@ -3157,7 +2707,7 @@ int main(int argc, char ** argv) { }; res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { + if (params.endpoint_slots && req.has_param("include_slots")) { health["slots"] = result.data.at("slots"); } @@ -3183,7 +2733,7 @@ int main(int argc, char ** argv) { }; const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { - if (!sparams.slots_endpoint) { + if (!params.endpoint_slots) { res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -3207,7 +2757,7 @@ int main(int argc, char ** argv) { }; const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { - if (!sparams.metrics_endpoint) { + if (!params.endpoint_metrics) { res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -3307,14 +2857,14 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK }; - const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { + const auto handle_slots_save = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } - std::string filepath = sparams.slot_save_path + filename; + std::string filepath = params.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_SAVE; @@ -3337,14 +2887,14 @@ int main(int argc, char ** argv) { } }; - const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { + const auto handle_slots_restore = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } - std::string filepath = sparams.slot_save_path + filename; + std::string filepath = params.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_RESTORE; @@ -3519,9 +3069,9 @@ int main(int argc, char ** argv) { res.set_content(models.dump(), "application/json; charset=utf-8"); }; - const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); + json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); const int id_task = ctx_server.queue_tasks.get_new_id(); @@ -3746,17 +3296,29 @@ int main(int argc, char ** argv) { // // register static assets routes - if (!sparams.public_path.empty()) { + if (!params.public_path.empty()) { // Set the base directory for serving static files - svr->set_base_dir(sparams.public_path); + svr->set_base_dir(params.public_path); } // using embedded static files - svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); - svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); - svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); - svr->Get("/json-schema-to-grammar.mjs", handle_static_file( - json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); + svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); + svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); + svr->Get("/json-schema-to-grammar.mjs", handle_static_file(json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + + // add new-ui files + svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8")); + svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8")); + svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8")); + svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8")); + svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8")); // register API routes svr->Get ("/health", handle_health); @@ -3775,7 +3337,7 @@ int main(int argc, char ** argv) { svr->Post("/v1/embeddings", handle_embeddings); svr->Post("/tokenize", handle_tokenize); svr->Post("/detokenize", handle_detokenize); - if (!sparams.slot_save_path.empty()) { + if (!params.slot_save_path.empty()) { // only enable slot endpoints if slot_save_path is set svr->Post("/slots/:id_slot", handle_slots_action); } @@ -3783,12 +3345,12 @@ int main(int argc, char ** argv) { // // Start the server // - if (sparams.n_threads_http < 1) { + if (params.n_threads_http < 1) { // +2 threads for monitoring endpoints - sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); + params.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } - log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + log_data["n_threads_http"] = std::to_string(params.n_threads_http); + svr->new_task_queue = [¶ms] { return new httplib::ThreadPool(params.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); diff --git a/examples/server/tests/README.md b/examples/server/tests/README.md index 83c0208f36c09..5e6cb277bc813 100644 --- a/examples/server/tests/README.md +++ b/examples/server/tests/README.md @@ -27,10 +27,8 @@ To mitigate it, you can increase values in `n_predict`, `kv_size`. ```shell cd ../../.. -mkdir build -cd build -cmake -DLLAMA_CURL=ON ../ -cmake --build . --target server +cmake -B build -DLLAMA_CURL=ON +cmake --build build --target llama-server ``` 2. Start the test: `./tests.sh` @@ -40,7 +38,7 @@ It's possible to override some scenario steps values with environment variables: | variable | description | |--------------------------|------------------------------------------------------------------------------------------------| | `PORT` | `context.server_port` to set the listening port of the server during scenario, default: `8080` | -| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/server` | +| `LLAMA_SERVER_BIN_PATH` | to change the server binary path, default: `../../../build/bin/llama-server` | | `DEBUG` | "ON" to enable steps and server verbose mode `--verbose` | | `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format | | `N_GPU_LAYERS` | number of model layers to offload to VRAM `-ngl --n-gpu-layers` | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 26d9359d7f3f8..7b5dabb019e75 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -1272,9 +1272,9 @@ def context_text(context): def start_server_background(context): if os.name == 'nt': - context.server_path = '../../../build/bin/Release/server.exe' + context.server_path = '../../../build/bin/Release/llama-server.exe' else: - context.server_path = '../../../build/bin/server' + context.server_path = '../../../build/bin/llama-server' if 'LLAMA_SERVER_BIN_PATH' in os.environ: context.server_path = os.environ['LLAMA_SERVER_BIN_PATH'] server_listen_addr = context.server_fqdn diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d8a2286e4b1df..63fde9c9faabe 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -116,13 +116,6 @@ static inline void server_log(const char * level, const char * function, int lin // chat template utils // -// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid -inline bool verify_custom_template(const std::string & tmpl) { - llama_chat_message chat[] = {{"user", "test"}}; - int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); - return res >= 0; -} - // Format given chat. If tmpl is empty, we take the template from model metadata inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { size_t alloc_size = 0; @@ -260,6 +253,13 @@ static size_t common_part(const std::vector & a, const std::vector< return i; } +static size_t common_part(const std::string & a, const std::string & b) { + size_t i; + for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} + + return i; +} + static bool ends_with(const std::string & str, const std::string & suffix) { return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix); } diff --git a/examples/simple/CMakeLists.txt b/examples/simple/CMakeLists.txt index 7da5ff6f3ac04..070cfbe7ad525 100644 --- a/examples/simple/CMakeLists.txt +++ b/examples/simple/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET simple) +set(TARGET llama-simple) add_executable(${TARGET} simple.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/simple/README.md b/examples/simple/README.md index 5d24b1046935c..49e24501cc02b 100644 --- a/examples/simple/README.md +++ b/examples/simple/README.md @@ -3,7 +3,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt. ```bash -./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" +./simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" ... diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index b0f8e0fdc4987..69a92cf7dc0c0 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -6,28 +6,27 @@ #include #include -int main(int argc, char ** argv) { - gpt_params params; +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]); - return 1 ; - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]); + LOG_TEE("\n"); +} - if (argc >= 2) { - params.model = argv[1]; - } +int main(int argc, char ** argv) { + gpt_params params; - if (argc >= 3) { - params.prompt = argv[2]; - } + params.prompt = "Hello my name is"; + params.n_predict = 32; - if (params.prompt.empty()) { - params.prompt = "Hello my name is"; + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } // total length of the sequence including the prompt - const int n_len = 32; + const int n_predict = params.n_predict; // init LLM @@ -36,9 +35,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - // model_params.n_gpu_layers = 99; // offload all layers to the GPU + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -49,12 +46,7 @@ int main(int argc, char ** argv) { // initialize the context - llama_context_params ctx_params = llama_context_default_params(); - - ctx_params.seed = 1234; - ctx_params.n_ctx = 2048; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context * ctx = llama_new_context_with_model(model, ctx_params); @@ -69,14 +61,14 @@ int main(int argc, char ** argv) { tokens_list = ::llama_tokenize(ctx, params.prompt, true); const int n_ctx = llama_n_ctx(ctx); - const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size()); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req); + LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__); - LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__); + LOG_TEE("%s: either reduce n_predict or increase n_ctx\n", __func__); return 1; } @@ -115,7 +107,7 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - while (n_cur <= n_len) { + while (n_cur <= n_predict) { // sample the next token { auto n_vocab = llama_n_vocab(model); @@ -134,7 +126,7 @@ int main(int argc, char ** argv) { const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); // is it an end of generation? - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { LOG_TEE("\n"); break; diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt index 810f3c46ac4aa..aa208e7aaeeb0 100644 --- a/examples/speculative/CMakeLists.txt +++ b/examples/speculative/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET speculative) +set(TARGET llama-speculative) add_executable(${TARGET} speculative.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 12e46fbc91a24..0939a1a6a7a38 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -27,7 +27,8 @@ struct seq_draft { int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/sycl/CMakeLists.txt b/examples/sycl/CMakeLists.txt index 69cf8932eb5c7..e4d5083e6e502 100644 --- a/examples/sycl/CMakeLists.txt +++ b/examples/sycl/CMakeLists.txt @@ -2,7 +2,7 @@ # Copyright (C) 2024 Intel Corporation # SPDX-License-Identifier: MIT -set(TARGET ls-sycl-device) +set(TARGET llama-ls-sycl-device) add_executable(${TARGET} ls-sycl-device.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/sycl/README.md b/examples/sycl/README.md index c589c2d3a489c..0e3acd35b616a 100644 --- a/examples/sycl/README.md +++ b/examples/sycl/README.md @@ -6,9 +6,9 @@ This example program provides the tools for llama.cpp for SYCL on Intel GPU. |Tool Name| Function|Status| |-|-|-| -|ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support| +|llama-ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support| -### ls-sycl-device +### llama-ls-sycl-device List all SYCL devices with ID, compute capability, max work group size, ect. @@ -23,7 +23,7 @@ source /opt/intel/oneapi/setvars.sh 3. Execute ``` -./build/bin/ls-sycl-device +./build/bin/llama-ls-sycl-device ``` Check the ID in startup log, like: diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh index 7b39a18c0681d..da0e4aaba688c 100755 --- a/examples/sycl/run-llama2.sh +++ b/examples/sycl/run-llama2.sh @@ -23,15 +23,15 @@ fi if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then echo "use $GGML_SYCL_DEVICE as main GPU" #use signle GPU only - ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none else #use multiple GPUs with same max compute units - ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 + ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 fi #use main GPU only -#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none +#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none #use multiple GPUs with same max compute units -#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 +#ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 diff --git a/examples/tokenize/CMakeLists.txt b/examples/tokenize/CMakeLists.txt index 5e6654d7e5988..b704dcae18c52 100644 --- a/examples/tokenize/CMakeLists.txt +++ b/examples/tokenize/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET tokenize) +set(TARGET llama-tokenize) add_executable(${TARGET} tokenize.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/train-text-from-scratch/CMakeLists.txt b/examples/train-text-from-scratch/CMakeLists.txt index 4459516d093d6..9a1d2a35e7731 100644 --- a/examples/train-text-from-scratch/CMakeLists.txt +++ b/examples/train-text-from-scratch/CMakeLists.txt @@ -1,4 +1,4 @@ -set(TARGET train-text-from-scratch) +set(TARGET llama-train-text-from-scratch) add_executable(${TARGET} train-text-from-scratch.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) diff --git a/examples/train-text-from-scratch/README.md b/examples/train-text-from-scratch/README.md index 1b3454069e9a3..3abae238036dc 100644 --- a/examples/train-text-from-scratch/README.md +++ b/examples/train-text-from-scratch/README.md @@ -7,7 +7,7 @@ Basic usage instructions: wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt # train -./bin/train-text-from-scratch \ +./bin/llama-train-text-from-scratch \ --vocab-model ../models/ggml-vocab-llama.gguf \ --ctx 64 --embd 256 --head 8 --layer 16 \ --checkpoint-in chk-shakespeare-256x16-LATEST.gguf \ @@ -18,7 +18,7 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s --no-checkpointing # predict -./bin/main -m ggml-shakespeare-256x16-f32.gguf +./bin/llama-cli -m ggml-shakespeare-256x16-f32.gguf ``` Output files will be saved every N iterations (config with `--save-every N`). diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index e2f85c68297b8..b779f6bd49876 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -302,7 +302,7 @@ static struct ggml_tensor * llama_build_train_graphs( const int rope_mode = 0; return ggml_rope_ext( - ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/flake.lock b/flake.lock index 451dfd32f4db8..5278fb68a3f98 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1715865404, - "narHash": "sha256-/GJvTdTpuDjNn84j82cU6bXztE0MSkdnTWClUCRub78=", + "lastModified": 1717285511, + "narHash": "sha256-iKzJcpdXih14qYVcZ9QC9XuZYnPc6T8YImb6dX166kw=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "8dc45382d5206bd292f9c2768b8058a8fd8311d9", + "rev": "2a55567fcf15b1b1c7ed712a2c6fadaec7412ea8", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1715961556, - "narHash": "sha256-+NpbZRCRisUHKQJZF3CT+xn14ZZQO+KjxIIanH3Pvn4=", + "lastModified": 1718318537, + "narHash": "sha256-4Zu0RYRcAY/VWuu6awwq4opuiD//ahpc2aFHg2CWqFY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "4a6b83b05df1a8bd7d99095ec4b4d271f2956b64", + "rev": "e9ee548d90ff586a6471b4ae80ae9cfcbceb3420", "type": "github" }, "original": { @@ -36,14 +36,14 @@ }, "nixpkgs-lib": { "locked": { - "lastModified": 1714640452, - "narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=", + "lastModified": 1717284937, + "narHash": "sha256-lIbdfCsf8LMFloheeE6N31+BMIeixqyQWbSr2vk79EQ=", "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz" }, "original": { "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz" } }, "root": { diff --git a/flake.nix b/flake.nix index 9cd3756e53e51..c69637d111784 100644 --- a/flake.nix +++ b/flake.nix @@ -63,7 +63,7 @@ # nix-repl> :lf github:ggerganov/llama.cpp # Added 13 variables. # nix-repl> outputs.apps.x86_64-linux.quantize - # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/quantize"; type = "app"; } + # { program = "/nix/store/00000000000000000000000000000000-llama.cpp/bin/llama-quantize"; type = "app"; } # ``` outputs = { self, flake-parts, ... }@inputs: @@ -159,7 +159,6 @@ windows = config.legacyPackages.llamaPackagesWindows.llama-cpp; } // lib.optionalAttrs pkgs.stdenv.isLinux { - opencl = config.packages.default.override { useOpenCL = true; }; cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; diff --git a/ggml-alloc.c b/ggml-alloc.c index 1fbd376edf410..bd367c42df44e 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -339,6 +339,7 @@ struct hash_node { }; struct tensor_alloc { + int buffer_id; size_t offset; size_t size_max; // 0 = pre-allocated, unused, or view }; @@ -349,7 +350,6 @@ struct leaf_alloc { }; struct node_alloc { - int buffer_id; struct tensor_alloc dst; struct tensor_alloc src[GGML_MAX_SRC]; }; @@ -377,7 +377,7 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t)); GGML_ASSERT(galloc->bufts != NULL); - galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs); + galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t)); GGML_ASSERT(galloc->buffers != NULL); galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *)); @@ -386,8 +386,19 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs for (int i = 0; i < n_bufs; i++) { galloc->bufts[i] = bufts[i]; galloc->buffers[i] = NULL; - size_t alignment = ggml_backend_buft_get_alignment(bufts[i]); - galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment); + + // check if the same buffer type is used multiple times and reuse the same allocator + for (int j = 0; j < i; j++) { + if (bufts[i] == bufts[j]) { + galloc->buf_tallocs[i] = galloc->buf_tallocs[j]; + break; + } + } + + if (galloc->buf_tallocs[i] == NULL) { + size_t alignment = ggml_backend_buft_get_alignment(bufts[i]); + galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment); + } } galloc->n_buffers = n_bufs; @@ -405,10 +416,30 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) { for (int i = 0; i < galloc->n_buffers; i++) { if (galloc->buffers != NULL) { - ggml_backend_buffer_free(galloc->buffers[i]); + // skip if already freed + bool freed = false; + for (int j = 0; j < i; j++) { + if (galloc->buffers[j] == galloc->buffers[i]) { + freed = true; + break; + } + } + if (!freed) { + ggml_backend_buffer_free(galloc->buffers[i]); + } } if (galloc->buf_tallocs != NULL) { - ggml_dyn_tallocr_free(galloc->buf_tallocs[i]); + // skip if already freed + bool freed = false; + for (int j = 0; j < i; j++) { + if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) { + freed = true; + break; + } + } + if (!freed) { + ggml_dyn_tallocr_free(galloc->buf_tallocs[i]); + } } } @@ -511,17 +542,18 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor } } -static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id) { +static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * node) { // graph outputs are never freed if (node->flags & GGML_TENSOR_FLAG_OUTPUT) { AT_PRINTF("not freeing output %s\n", node->name); return; } - struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; - ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); size_t offset = hn->offset; + int buffer_id = hn->buffer_id; + struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id]; + ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id]; size_t size = ggml_backend_buft_get_alloc_size(buft, node); ggml_dyn_tallocr_free_tensor(alloc, offset, size, node); hn->allocated = false; @@ -626,11 +658,11 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src_hn->allocated) { - ggml_gallocr_free_node(galloc, view_src, buffer_id); + ggml_gallocr_free_node(galloc, view_src); } } else if (p_hn->allocated) { - ggml_gallocr_free_node(galloc, parent, buffer_id); + ggml_gallocr_free_node(galloc, parent); } } AT_PRINTF("\n"); @@ -674,22 +706,25 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; struct node_alloc * node_alloc = &galloc->node_allocs[i]; - node_alloc->buffer_id = get_node_buffer_id(node_buffer_ids, i); if (node->view_src || node->data) { + node_alloc->dst.buffer_id = -1; node_alloc->dst.offset = SIZE_MAX; node_alloc->dst.size_max = 0; } else { struct hash_node * hn = ggml_gallocr_hash_get(galloc, node); - node_alloc->dst.offset = hn->offset; - node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node); + node_alloc->dst.buffer_id = hn->buffer_id; + node_alloc->dst.offset = hn->offset; + node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node); } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; if (!src || src->view_src || src->data) { + node_alloc->src[j].buffer_id = -1; node_alloc->src[j].offset = SIZE_MAX; node_alloc->src[j].size_max = 0; } else { struct hash_node * hn = ggml_gallocr_hash_get(galloc, src); + node_alloc->src[j].buffer_id = hn->buffer_id; node_alloc->src[j].offset = hn->offset; node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src); } @@ -706,9 +741,11 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf); galloc->leaf_allocs[i].buffer_id = hn->buffer_id; if (leaf->view_src || leaf->data) { + galloc->leaf_allocs[i].leaf.buffer_id = -1; galloc->leaf_allocs[i].leaf.offset = SIZE_MAX; galloc->leaf_allocs[i].leaf.size_max = 0; } else { + galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id; galloc->leaf_allocs[i].leaf.offset = hn->offset; galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf); } @@ -716,6 +753,14 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c // reallocate buffers if needed for (int i = 0; i < galloc->n_buffers; i++) { + // if the buffer type is used multiple times, we reuse the same buffer + for (int j = 0; j < i; j++) { + if (galloc->buf_tallocs[j] == galloc->buf_tallocs[i]) { + galloc->buffers[i] = galloc->buffers[j]; + break; + } + } + size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0; size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]); @@ -724,6 +769,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c #ifndef NDEBUG fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif + ggml_backend_buffer_free(galloc->buffers[i]); galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size); if (galloc->buffers[i] == NULL) { @@ -740,7 +786,8 @@ bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL); } -static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) { +static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) { + int buffer_id = tensor_alloc->buffer_id; assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max); if (tensor->view_src != NULL) { @@ -750,7 +797,7 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * // this tensor was allocated without ggml-backend return; } - ggml_backend_view_init(galloc->buffers[buffer_id], tensor); + ggml_backend_view_init(tensor); } } else { if (tensor->data == NULL) { @@ -768,8 +815,8 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * } } -static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct node_alloc * nalloc, struct tensor_alloc * talloc) { - ggml_backend_buffer_type_t buft = galloc->bufts[nalloc->buffer_id]; +static bool ggml_gallocr_node_needs_realloc(ggml_gallocr_t galloc, struct ggml_tensor * node, struct tensor_alloc * talloc) { + ggml_backend_buffer_type_t buft = talloc->buffer_id != -1 ? galloc->bufts[talloc->buffer_id] : NULL; size_t node_size = (node->data || node->view_src) ? 0 : ggml_backend_buft_get_alloc_size(buft, node); return talloc->size_max >= node_size; } @@ -793,7 +840,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph struct ggml_tensor * node = graph->nodes[i]; struct node_alloc * node_alloc = &galloc->node_allocs[i]; - if (!ggml_gallocr_node_needs_realloc(galloc, node, node_alloc, &node_alloc->dst)) { + if (!ggml_gallocr_node_needs_realloc(galloc, node, &node_alloc->dst)) { #ifndef NDEBUG fprintf(stderr, "%s: node %s is not valid\n", __func__, node->name); #endif @@ -805,7 +852,7 @@ static bool ggml_gallocr_needs_realloc(ggml_gallocr_t galloc, struct ggml_cgraph if (src == NULL) { continue; } - if (!ggml_gallocr_node_needs_realloc(galloc, src, node_alloc, &node_alloc->src[j])) { + if (!ggml_gallocr_node_needs_realloc(galloc, src, &node_alloc->src[j])) { #ifndef NDEBUG fprintf(stderr, "%s: src %d (%s) of node %s is not valid\n", __func__, j, src->name, node->name); #endif @@ -846,7 +893,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) for (int i = 0; i < graph->n_leafs; i++) { struct ggml_tensor * leaf = graph->leafs[i]; struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i]; - ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf); + ggml_gallocr_init_tensor(galloc, leaf, &leaf_alloc->leaf); } // nodes for (int i = 0; i < graph->n_nodes; i++) { @@ -857,9 +904,9 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph) if (src == NULL) { continue; } - ggml_gallocr_init_tensor(galloc, src, node_alloc->buffer_id, &node_alloc->src[j]); + ggml_gallocr_init_tensor(galloc, src, &node_alloc->src[j]); } - ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst); + ggml_gallocr_init_tensor(galloc, node, &node_alloc->dst); } return true; @@ -871,6 +918,15 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) { if (galloc->buffers[buffer_id] == NULL) { return 0; } + + for (int i = 0; i < buffer_id; i++) { + if (galloc->buffers[i] == galloc->buffers[buffer_id]) { + // this buffer is the same as a previous one due to the same buffer type being used multiple times + // only return the buffer size the first time it appears to avoid double counting + return 0; + } + } + return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]); } @@ -886,7 +942,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx, fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size); #endif for (size_t i = 0; i < *n_buffers; i++) { - ggml_backend_buffer_free(*buffers[i]); + ggml_backend_buffer_free((*buffers)[i]); } free(*buffers); return false; @@ -899,12 +955,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx, if (t->view_src == NULL) { ggml_tallocr_alloc(&tallocr, t); } else if (t->buffer == NULL) { - ggml_backend_view_init(buffer, t); + ggml_backend_view_init(t); } } else { if (t->view_src != NULL && t->buffer == NULL) { // view of a pre-allocated tensor - ggml_backend_view_init(buffer, t); + ggml_backend_view_init(t); } } } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index f121e1de420fa..36ca370867c9e 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -17,13 +17,15 @@ extern "C" { struct ggml_backend_buffer_type_i { const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft); + // allocate a buffer of this type ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); - size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment - size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); // allocation max size - size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding - bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend + // tensor alignment + size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); + // max buffer size that can be allocated + size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); + // data size needed to allocate the tensor, including padding + size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // check if tensor data is in host memory - // should be equivalent to supports_backend(buft, ggml_backend_cpu_init()) bool (*GGML_CALL is_host) (ggml_backend_buffer_type_t buft); }; @@ -92,27 +94,37 @@ extern "C" { void (*GGML_CALL synchronize)(ggml_backend_t backend); // compute graph with a plan (not used currently) + // create a new plan for a graph ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph); void (*GGML_CALL graph_plan_free) (ggml_backend_t backend, ggml_backend_graph_plan_t plan); + // update the plan with a new graph - this should be faster than creating a new plan when the graph has the same topology + void (*GGML_CALL graph_plan_update) (ggml_backend_t backend, ggml_backend_graph_plan_t plan, const struct ggml_cgraph * cgraph); + // compute the graph with the plan + enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); - // compute graph with a plan - enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan); // compute graph without a plan (async) enum ggml_status (*GGML_CALL graph_compute) (ggml_backend_t backend, struct ggml_cgraph * cgraph); - // check if the backend supports an operation + // check if the backend can compute an operation bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op); + // check if the backend can use tensors allocated in a buffer type + bool (*GGML_CALL supports_buft)(ggml_backend_t backend, ggml_backend_buffer_type_t buft); + // check if the backend wants to run an operation, even if the weights are allocated in a CPU buffer // these should be expensive operations with large batch sizes that may benefit from running on this backend // even if the weight has to be copied from the CPU temporarily bool (*GGML_CALL offload_op)(ggml_backend_t backend, const struct ggml_tensor * op); // (optional) event synchronization + // create a new event that can record events on this backend instance ggml_backend_event_t (*GGML_CALL event_new) (ggml_backend_t backend); void (*GGML_CALL event_free) (ggml_backend_event_t event); + // record an event on the backend instance that created it void (*GGML_CALL event_record) (ggml_backend_event_t event); + // wait for an event on on a different backend instance void (*GGML_CALL event_wait) (ggml_backend_t backend, ggml_backend_event_t event); + // block until an event is recorded void (*GGML_CALL event_synchronize) (ggml_backend_event_t event); }; diff --git a/ggml-backend.c b/ggml-backend.c index 9e35ce98d7ace..13c71c310c446 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -44,10 +44,6 @@ GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buf return ggml_nbytes(tensor); } -bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return buft->iface.supports_backend(buft, backend); -} - bool ggml_backend_buft_is_host(ggml_backend_buffer_type_t buft) { if (buft->iface.is_host) { return buft->iface.is_host(buft); @@ -151,7 +147,7 @@ void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) { bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) { ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer; if (dst_buf->iface.cpy_tensor) { - return src->buffer->iface.cpy_tensor(dst_buf, src, dst); + return dst_buf->iface.cpy_tensor(dst_buf, src, dst); } return false; } @@ -286,6 +282,10 @@ bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * return backend->iface.supports_op(backend, op); } +bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + return backend->iface.supports_buft(backend, buft); +} + bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op) { if (backend->iface.offload_op != NULL) { return backend->iface.offload_op(backend, op); @@ -639,12 +639,6 @@ GGML_CALL static size_t ggml_backend_cpu_buffer_type_get_alignment(ggml_backend_ GGML_UNUSED(buft); } -GGML_CALL static bool ggml_backend_cpu_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_cpu(backend); - - GGML_UNUSED(buft); -} - GGML_CALL static bool ggml_backend_cpu_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; @@ -659,7 +653,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, }, /* .context = */ NULL, @@ -715,7 +708,6 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, }, /* .context = */ NULL, @@ -836,6 +828,12 @@ GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const GGML_UNUSED(backend); } +GGML_CALL static bool ggml_backend_cpu_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + return ggml_backend_buft_is_host(buft); + + GGML_UNUSED(backend); +} + static struct ggml_backend_i cpu_backend_i = { /* .get_name = */ ggml_backend_cpu_name, /* .free = */ ggml_backend_cpu_free, @@ -846,9 +844,11 @@ static struct ggml_backend_i cpu_backend_i = { /* .synchronize = */ NULL, /* .graph_plan_create = */ ggml_backend_cpu_graph_plan_create, /* .graph_plan_free = */ ggml_backend_cpu_graph_plan_free, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ ggml_backend_cpu_graph_plan_compute, /* .graph_compute = */ ggml_backend_cpu_graph_compute, /* .supports_op = */ ggml_backend_cpu_supports_op, + /* .supports_buft = */ ggml_backend_cpu_supports_buft, /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, @@ -1055,6 +1055,9 @@ struct ggml_backend_sched { int * node_backend_ids; // [graph_size] int * leaf_backend_ids; // [graph_size] + int * prev_node_backend_ids; // [graph_size] + int * prev_leaf_backend_ids; // [graph_size] + // copy of the graph with modified inputs struct ggml_cgraph * graph; @@ -1075,6 +1078,8 @@ struct ggml_backend_sched { ggml_backend_sched_eval_callback callback_eval; void * callback_eval_user_data; + bool debug; + // align context_buffer to GGML_MEM_ALIGN #ifdef _MSC_VER __declspec(align(GGML_MEM_ALIGN)) @@ -1097,22 +1102,24 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen return -1; } -static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) { +static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor, const struct ggml_tensor * op) { ggml_backend_buffer_t buffer = tensor->buffer; if (buffer == NULL) { return -1; } - // find highest prio backend that supports the buffer type + // find highest prio backend that supports the buffer type and the op for (int i = 0; i < sched->n_backends; i++) { - if (ggml_backend_buft_supports_backend(buffer->buft, sched->backends[i])) { + if (ggml_backend_supports_buft(sched->backends[i], buffer->buft) && + ggml_backend_supports_op(sched->backends[i], op)) { return i; } } - fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n", - __func__, ggml_backend_buffer_name(buffer), tensor->name); - GGML_ASSERT(false); +#ifndef NDEBUG + fprintf(stderr, "%s: warning: no backend supports op %s with a weight with buffer type %s used in tensor %s, the weight will need to be copied\n", + __func__, ggml_op_desc(tensor), ggml_backend_buffer_name(buffer), tensor->name); +#endif return -1; } @@ -1131,7 +1138,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st // TODO: use supports_op to check if the backend supports the op // assign pre-allocated nodes to their backend - int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor); + int cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor, tensor); if (cur_backend_id != -1) { SET_CAUSE(tensor, "1.dst"); return cur_backend_id; @@ -1139,7 +1146,7 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st // view_src if (tensor->view_src != NULL) { - cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src); + cur_backend_id = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src, tensor); if (cur_backend_id != -1) { SET_CAUSE(tensor, "1.vsrc"); return cur_backend_id; @@ -1161,11 +1168,11 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st continue; } if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) { - int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src); + int src_backend_id = ggml_backend_sched_backend_from_buffer(sched, src, tensor); // check if a backend with higher prio wants to offload the op if (src_backend_id == sched->n_backends - 1) { for (int b = 0; b < src_backend_id; b++) { - if (ggml_backend_offload_op(sched->backends[b], tensor)) { + if (ggml_backend_supports_op(sched->backends[b], tensor) && ggml_backend_offload_op(sched->backends[b], tensor)) { SET_CAUSE(tensor, "1.off"); return b; } @@ -1223,10 +1230,33 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str } } -//#define DEBUG_PASS1 -//#define DEBUG_PASS2 -//#define DEBUG_PASS3 -//#define DEBUG_PASS4 +static bool ggml_backend_sched_buffer_supported(ggml_backend_sched_t sched, struct ggml_tensor * t, int backend_id) { + ggml_backend_buffer_t buf = t->view_src ? t->view_src->buffer : t->buffer; + ggml_backend_buffer_type_t buft = NULL; + + if (buf) { + // the tensor is already allocated + buft = buf->buft; + } else { + // see if the tensor already has a backend assigned, and use the buffer type of that backend + int tensor_backend_id = tensor_backend_id(t); + if (tensor_backend_id == -1 && t->view_src) { + tensor_backend_id = tensor_backend_id(t->view_src); + } + if (tensor_backend_id != -1) { + buft = sched->bufts[tensor_backend_id]; + } + } + + return buft != NULL && ggml_backend_supports_buft(sched->backends[backend_id], buft); +} + +static void ggml_backend_sched_set_if_supported(ggml_backend_sched_t sched, struct ggml_tensor * node, int cur_backend_id, int * node_backend_id) { + if (ggml_backend_supports_op(sched->backends[cur_backend_id], node)) { + *node_backend_id = cur_backend_id; + SET_CAUSE(node, "2.sup"); + } +} // assigns backends to ops and splits the graph into subgraphs that can be computed on the same backend static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) { @@ -1280,17 +1310,13 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } } -#ifdef DEBUG_PASS1 - fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); -#endif // pass 2: expand current backend assignments // assign the same backend to adjacent nodes // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend) // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops - - - // pass 2.2 expand gpu down + // ops unsupported by the backend being expanded will be left unassigned so that they can be assigned later when the locations of its inputs are known + // expand gpu down { int cur_backend_id = -1; for (int i = 0; i < graph->n_nodes; i++) { @@ -1306,13 +1332,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } else { cur_backend_id = *node_backend_id; } - } else { - *node_backend_id = cur_backend_id; - SET_CAUSE(node, "2.2"); + } else if (cur_backend_id != -1) { + ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id); } } } - // pass 2.1 expand gpu up + // expand gpu up { int cur_backend_id = -1; for (int i = graph->n_nodes - 1; i >= 0; i--) { @@ -1328,13 +1353,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } else { cur_backend_id = *node_backend_id; } - } else { - *node_backend_id = cur_backend_id; - SET_CAUSE(node, "2.1"); + } else if (cur_backend_id != -1) { + ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id); } } } - // pass 2.4 expand rest down + // expand rest down { int cur_backend_id = -1; for (int i = 0; i < graph->n_nodes; i++) { @@ -1345,13 +1369,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg int * node_backend_id = &tensor_backend_id(node); if (*node_backend_id != -1) { cur_backend_id = *node_backend_id; - } else { - *node_backend_id = cur_backend_id; - SET_CAUSE(node, "2.4"); + } else if (cur_backend_id != -1) { + ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id); } } } - // pass 2.3 expand rest up + // expand rest up { int cur_backend_id = -1; for (int i = graph->n_nodes - 1; i >= 0; i--) { @@ -1362,24 +1385,80 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg int * node_backend_id = &tensor_backend_id(node); if (*node_backend_id != -1) { cur_backend_id = *node_backend_id; - } else { - *node_backend_id = cur_backend_id; - SET_CAUSE(node, "2.3"); + } else if (cur_backend_id != -1) { + ggml_backend_sched_set_if_supported(sched, node, cur_backend_id, node_backend_id); } } } -#ifdef DEBUG_PASS2 - fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); -#endif + // pass 3: upgrade nodes to higher prio backends with compatible buffer types + // if the tensor is already in the same buffer type (*) as another higher priority backend, we should move it there + // however, we also need to verify that the sources are in compatible buffer types + // (*) the actual requirement is more relaxed, the buffer type of the backend should be supported by all the users of this tensor further down the graph + // however, this is slow to verify, so we have a more strict requirement that the buffer type is the same + // this is not uncommon since multiple backends can use host memory, with the same buffer type (eg. BLAS and CPU) + // additionally, set remaining unassigned nodes to the backend with the most supported inputs + // only nodes that could not be assigned during expansion due to the backend not supporting the op should be unassigned at this point + for (int i = 0; i < graph->n_nodes; i++) { + struct ggml_tensor * node = graph->nodes[i]; + if (ggml_is_view_op(node->op)) { + continue; + } + int * node_backend_id = &tensor_backend_id(node); + if (*node_backend_id == -1) { + // unassigned node: find the backend with the most supported inputs + int n_supported_best = -1; + for (int b = 0; b < sched->n_backends; b++) { + if (ggml_backend_supports_op(sched->backends[b], node)) { + int n_supported = 0; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + if ((tensor_backend_id(src) != -1 || tensor_backend_id(src->view_src) != -1) && ggml_backend_sched_buffer_supported(sched, src, b)) { + n_supported++; + } + } + if (n_supported > n_supported_best) { + n_supported_best = n_supported; + *node_backend_id = b; + SET_CAUSE(node, "3.best"); + } + } + } + } else { + // assigned node: upgrade to higher prio backend if possible + for (int b = 0; b < *node_backend_id; b++) { + if (sched->bufts[b] == sched->bufts[*node_backend_id] && ggml_backend_supports_op(sched->backends[b], node)) { + bool supported = true; + for (int j = 0; j < GGML_MAX_SRC; j++) { + struct ggml_tensor * src = node->src[j]; + if (src == NULL) { + continue; + } + if (!ggml_backend_sched_buffer_supported(sched, src, b)) { + supported = false; + break; + } + } + if (supported) { + *node_backend_id = b; + SET_CAUSE(node, "3.upg"); + break; + } + } + } + } + } - // pass 3: assign backends to remaining src from dst and view_src + // pass 4: assign backends to remaining src from dst and view_src for (int i = 0; i < graph->n_nodes; i++) { struct ggml_tensor * node = graph->nodes[i]; int * cur_backend_id = &tensor_backend_id(node); if (node->view_src != NULL && *cur_backend_id == -1) { *cur_backend_id = tensor_backend_id(node->view_src); - SET_CAUSE(node, "3.vsrc"); + SET_CAUSE(node, "4.vsrc"); } for (int j = 0; j < GGML_MAX_SRC; j++) { struct ggml_tensor * src = node->src[j]; @@ -1391,17 +1470,14 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg if (src->view_src != NULL) { // views are always on the same backend as the source *src_backend_id = tensor_backend_id(src->view_src); - SET_CAUSE(src, "3.vsrc"); + SET_CAUSE(src, "4.vsrc"); } else { *src_backend_id = *cur_backend_id; - SET_CAUSE(src, "3.cur"); + SET_CAUSE(src, "4.cur"); } } } } -#ifdef DEBUG_PASS3 - fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); -#endif // pass 4: split graph, find tensors that need to be copied { @@ -1448,10 +1524,12 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } // check if the split has too many inputs + // FIXME: count the number of inputs instead of only checking when full if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) { const size_t id = hash_id(src); int src_backend_id = sched->tensor_backend_id[id]; - if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL) { + bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id); + if (src_backend_id != cur_backend_id && sched->tensor_copies[hash_id(src)][cur_backend_id][0] == NULL && !supported) { //printf("starting new split because of too many inputs: node %s, input %s\n", node->name, src->name); need_new_split = true; break; @@ -1486,7 +1564,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg const int src_backend_id = tensor_backend_id(src); assert(src_backend_id != -1); // all inputs should be assigned by now - if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) { + if (src->flags & GGML_TENSOR_FLAG_INPUT && sched->n_copies > 1) { size_t id = hash_id(src); if (sched->tensor_copies[id][src_backend_id][0] == NULL) { ggml_backend_t backend = sched->backends[src_backend_id]; @@ -1511,7 +1589,8 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } } - if (src_backend_id != node_backend_id) { + bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id); + if (src_backend_id != cur_backend_id && !supported) { // create a copy of the input in the split's backend const size_t id = hash_id(src); if (sched->tensor_copies[id][cur_backend_id][0] == NULL) { @@ -1537,9 +1616,21 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg split->i_end = graph->n_nodes; sched->n_splits = i_split + 1; } -#ifdef DEBUG_PASS4 - fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph); -#endif + + if (sched->debug) { + ggml_backend_sched_print_assignments(sched, graph); + } + + // swap node_backend_ids and leaf_backend_ids and prevs + { + int * tmp = sched->node_backend_ids; + sched->node_backend_ids = sched->prev_node_backend_ids; + sched->prev_node_backend_ids = tmp; + + tmp = sched->leaf_backend_ids; + sched->leaf_backend_ids = sched->prev_leaf_backend_ids; + sched->prev_leaf_backend_ids = tmp; + } // create copies of the graph for each split // TODO: avoid this copy @@ -1613,8 +1704,26 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg } static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) { + bool backend_ids_changed = false; + for (int i = 0; i < sched->graph->n_nodes; i++) { + if (sched->node_backend_ids[i] != sched->prev_node_backend_ids[i] && + sched->bufts[sched->node_backend_ids[i]] != sched->bufts[sched->prev_node_backend_ids[i]]) { + backend_ids_changed = true; + break; + } + } + if (!backend_ids_changed) { + for (int i = 0; i < sched->graph->n_leafs; i++) { + if (sched->leaf_backend_ids[i] != sched->prev_leaf_backend_ids[i] && + sched->bufts[sched->leaf_backend_ids[i]] != sched->bufts[sched->prev_leaf_backend_ids[i]]) { + backend_ids_changed = true; + break; + } + } + } + // allocate graph - if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { + if (backend_ids_changed || !ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) { // the re-allocation may cause the split inputs to be moved to a different address ggml_backend_sched_synchronize(sched); #ifndef NDEBUG @@ -1727,6 +1836,8 @@ ggml_backend_sched_t ggml_backend_sched_new( struct ggml_backend_sched * sched = calloc(1, sizeof(struct ggml_backend_sched)); + sched->debug = getenv("GGML_SCHED_DEBUG") != NULL; + // initialize hash table sched->hash_set = ggml_hash_set_new(graph_size); sched->tensor_backend_id = calloc(sched->hash_set.size, sizeof(sched->tensor_backend_id[0])); @@ -1735,6 +1846,8 @@ ggml_backend_sched_t ggml_backend_sched_new( const size_t nodes_size = graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2; sched->node_backend_ids = calloc(nodes_size, sizeof(sched->node_backend_ids[0])); sched->leaf_backend_ids = calloc(nodes_size, sizeof(sched->leaf_backend_ids[0])); + sched->prev_node_backend_ids = calloc(nodes_size, sizeof(sched->prev_node_backend_ids[0])); + sched->prev_leaf_backend_ids = calloc(nodes_size, sizeof(sched->prev_leaf_backend_ids[0])); sched->n_backends = n_backends; @@ -1747,7 +1860,7 @@ ggml_backend_sched_t ggml_backend_sched_new( for (int b = 0; b < n_backends; b++) { sched->backends[b] = backends[b]; sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]); - GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b])); + GGML_ASSERT(ggml_backend_supports_buft(backends[b], sched->bufts[b])); if (sched->n_copies > 1) { for (int c = 0; c < sched->n_copies; c++) { sched->events[b][c] = ggml_backend_event_new(backends[b]); @@ -1779,6 +1892,8 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) { free(sched->tensor_copies); free(sched->node_backend_ids); free(sched->leaf_backend_ids); + free(sched->prev_node_backend_ids); + free(sched->prev_leaf_backend_ids); free(sched); } @@ -1864,6 +1979,15 @@ int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) { return sched->n_copies; } +int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched) { + return sched->n_backends; +} + +ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i) { + GGML_ASSERT(i >= 0 && i < sched->n_backends); + return sched->backends[i]; +} + size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) { int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); @@ -1875,6 +1999,7 @@ void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct gg int backend_index = ggml_backend_sched_backend_id(sched, backend); GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends); tensor_backend_id(node) = backend_index; + SET_CAUSE(node, "usr"); } ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) { @@ -1887,15 +2012,15 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, // utils -void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +void ggml_backend_view_init(struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); - tensor->buffer = buffer; + tensor->buffer = tensor->view_src->buffer; tensor->data = (char *)tensor->view_src->data + tensor->view_offs; - ggml_backend_buffer_init_tensor(buffer, tensor); + ggml_backend_buffer_init_tensor(tensor->buffer, tensor); } void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) { @@ -1954,7 +2079,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te struct ggml_tensor * dst = node_copies[id]; if (dst->view_src != NULL) { graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src); - ggml_backend_view_init(dst->view_src->buffer, dst); + ggml_backend_view_init(dst); } else { ggml_backend_tensor_copy(src, dst); diff --git a/ggml-backend.h b/ggml-backend.h index 744b6a77457d7..4a38eeb5c23bd 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -23,7 +23,6 @@ extern "C" { GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft); GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); - GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); // buffer @@ -74,6 +73,7 @@ extern "C" { GGML_API enum ggml_status ggml_backend_graph_compute (ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API enum ggml_status ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph); GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op); + GGML_API bool ggml_backend_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft); GGML_API bool ggml_backend_offload_op(ggml_backend_t backend, const struct ggml_tensor * op); // tensor copy between different backends @@ -90,7 +90,7 @@ extern "C" { GGML_API void ggml_backend_event_free (ggml_backend_event_t event); GGML_API void ggml_backend_event_record (ggml_backend_event_t event); GGML_API void ggml_backend_event_synchronize(ggml_backend_event_t event); - GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event + GGML_API void ggml_backend_event_wait (ggml_backend_t backend, ggml_backend_event_t event); // // CPU backend @@ -119,7 +119,7 @@ extern "C" { GGML_API size_t ggml_backend_reg_get_count(void); GGML_API size_t ggml_backend_reg_find_by_name(const char * name); - GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params] + GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is backend_name:params (params is optional) GGML_API const char * ggml_backend_reg_get_name(size_t i); GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i); @@ -182,6 +182,9 @@ extern "C" { // Initialize backend buffers from a measure graph GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); + GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched); + GGML_API ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i); + // Get the number of splits of the last graph GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched); GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched); @@ -225,7 +228,7 @@ extern "C" { // Tensor initialization GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); - GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor); #ifdef __cplusplus diff --git a/ggml-blas.cpp b/ggml-blas.cpp new file mode 100644 index 0000000000000..d709a357bbf29 --- /dev/null +++ b/ggml-blas.cpp @@ -0,0 +1,363 @@ +#include "ggml-blas.h" +#include "ggml-backend-impl.h" + +#include +#include + +#if defined(GGML_USE_ACCELERATE) +# include +#elif defined(GGML_BLAS_USE_MKL) +# include +#else +# include +# ifdef BLIS_ENABLE_CBLAS +# include +# endif +#endif + +struct ggml_backend_blas_context { + int n_threads = GGML_DEFAULT_N_THREADS; + std::unique_ptr work_data; + size_t work_size = 0; +#ifndef GGML_USE_OPENMP + std::vector> tasks; +#endif +}; + +// helper function to determine if it is better to use BLAS or not +// for large matrices, BLAS is faster +static bool ggml_backend_blas_use_blas(const struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + const int64_t ne10 = src1->ne[0]; + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + + // TODO: find the optimal values for these + if (ggml_is_contiguous(src0) && + ggml_is_contiguous(src1) && + src1->type == GGML_TYPE_F32 && + (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { + + /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ + return true; + } + + return false; +} + +static void ggml_backend_blas_mul_mat(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const enum ggml_type type = src0->type; + + GGML_ASSERT(ne0 == ne01); + GGML_ASSERT(ne1 == ne11); + GGML_ASSERT(ne2 == ne12); + GGML_ASSERT(ne3 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == ggml_type_size(type)); + GGML_ASSERT(nb10 == ggml_type_size(src1->type)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; + + const int64_t ne_plane = ne01*ne00; + const size_t desired_wsize = type == GGML_TYPE_F32 ? 0 : ne03*ne02*ne_plane*sizeof(float); + + if (ctx->work_size < desired_wsize) { + ctx->work_data.reset(new char[desired_wsize]); + ctx->work_size = desired_wsize; + } + void * wdata = ctx->work_data.get(); + + // convert src0 to float + if (type != GGML_TYPE_F32) { + ggml_type_traits_t type_traits = ggml_internal_get_type_traits(type); + ggml_to_float_t const to_float = type_traits.to_float; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + const void * x = (char *) src0->data + i02*nb02 + i03*nb03; + float * const wplane = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane; + + const int min_cols_per_thread = 4096; + const int min_rows_per_thread = std::max((int)(min_cols_per_thread/ne00), 1); + const int n_threads = std::max(std::min(ctx->n_threads, (int)(ne01/min_rows_per_thread)), 1); + +#ifdef GGML_USE_OPENMP + #pragma omp parallel for num_threads(n_threads) + for (int64_t i01 = 0; i01 < ne01; i01++) { + to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); + } +#else + for (int i = 1; i < n_threads; i++) { + const int64_t start = i*ne01/n_threads; + const int64_t end = (i + 1)*ne01/n_threads; + if (start < end) { + ctx->tasks.push_back(std::async(std::launch::async, [=]() { + for (int64_t i01 = start; i01 < end; i01++) { + to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); + } + })); + } + } + { + // reuse the current thread for the first task + const int64_t start = 0; + const int64_t end = ne01/n_threads; + for (int64_t i01 = start; i01 < end; i01++) { + to_float((const char *) x + i01*nb01, wplane + i01*ne00, ne00); + } + } +#endif + } + } + +#ifndef GGML_USE_OPENMP + // wait for all tasks to finish + for (auto & task : ctx->tasks) { + task.get(); + } + ctx->tasks.clear(); +#endif + } + +#if defined(OPENBLAS_VERSION) + openblas_set_num_threads(ctx->n_threads); +#endif + +#if defined(BLIS_ENABLE_CBLAS) + bli_thread_set_num_threads(ctx->n_threads); +#endif + + for (int64_t i13 = 0; i13 < ne13; i13++) { + for (int64_t i12 = 0; i12 < ne12; i12++) { + const int64_t i03 = i13/r3; + const int64_t i02 = i12/r2; + + const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); + const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13); + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + + if (type != GGML_TYPE_F32) { + x = (float *) wdata + i02*ne_plane + i03*ne02*ne_plane; + } + + cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, + ne1, ne01, ne10, + 1.0f, y, ne10, + x, ne00, + 0.0f, d, ne01); + } + } +} + +static void ggml_backend_blas_out_prod(ggml_backend_blas_context * ctx, struct ggml_tensor * dst) { + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(ne0 == ne00); + GGML_ASSERT(ne1 == ne10); + GGML_ASSERT(ne2 == ne02); + GGML_ASSERT(ne02 == ne12); + GGML_ASSERT(ne3 == ne13); + GGML_ASSERT(ne03 == ne13); + + // we don't support permuted src0 or src1 + GGML_ASSERT(nb00 == sizeof(float)); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + // GGML_ASSERT(nb0 <= nb1); + // GGML_ASSERT(nb1 <= nb2); + // GGML_ASSERT(nb2 <= nb3); + + // Arguments to ggml_compute_forward_out_prod (expressed as major,minor) + // src0: (k,n) + // src1: (k,m) + // dst: (m,n) + // + // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f) + // Also expressed as (major,minor) + // a: (m,k): so src1 transposed + // b: (k,n): so src0 + // c: (m,n) + // + // However, if ggml_is_transposed(src1) is true, then + // src1->data already contains a transposed version, so sgemm mustn't + // transpose it further. + + int n = src0->ne[0]; + int k = src0->ne[1]; + int m = src1->ne[0]; + + CBLAS_TRANSPOSE transposeA; + int lda; + + if (!ggml_is_transposed(src1)) { + transposeA = CblasTrans; + lda = m; + } else { + transposeA = CblasNoTrans; + lda = k; + } + + float * a = (float *) ((char *) src1->data); + float * b = (float *) ((char *) src0->data); + float * c = (float *) ((char *) dst->data); + + cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n); + + GGML_UNUSED(ctx); +} + +// backend interface + +GGML_CALL static const char * ggml_backend_blas_name(ggml_backend_t backend) { + return "BLAS"; + + GGML_UNUSED(backend); +} + +GGML_CALL static void ggml_backend_blas_free(ggml_backend_t backend) { + ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; + delete ctx; + delete backend; +} + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_blas_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_cpu_buffer_type(); + + GGML_UNUSED(backend); +} + +GGML_CALL static enum ggml_status ggml_backend_blas_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend->context; + + for (int i = 0; i < cgraph->n_nodes; i++) { + struct ggml_tensor * node = cgraph->nodes[i]; + + switch (node->op) { + case GGML_OP_MUL_MAT: + ggml_backend_blas_mul_mat(ctx, node); + break; + + case GGML_OP_OUT_PROD: + ggml_backend_blas_out_prod(ctx, node); + break; + + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + break; + + default: + fprintf(stderr, "%s: unsupported op %s\n", __func__, ggml_op_desc(node)); + GGML_ASSERT(false); + } + } + + return GGML_STATUS_SUCCESS; + + GGML_UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_blas_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { + const struct ggml_tensor * src0 = op->src[0]; + const struct ggml_tensor * src1 = op->src[1]; + + return (op->op == GGML_OP_MUL_MAT && ggml_backend_blas_use_blas(op)) || + (op->op == GGML_OP_OUT_PROD && op->src[0]->type == GGML_TYPE_F32 && + op->src[1]->type == GGML_TYPE_F32 && + ggml_is_matrix(src0) && + ggml_is_matrix(src1) && + ggml_is_contiguous(src0) && + (ggml_is_contiguous(src1) || ggml_is_transposed(src1))); + + GGML_UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_blas_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + return ggml_backend_buft_is_host(buft); + + GGML_UNUSED(backend); +} + +static struct ggml_backend_i blas_backend_i = { + /* .get_name = */ ggml_backend_blas_name, + /* .free = */ ggml_backend_blas_free, + /* .get_default_buffer_type = */ ggml_backend_blas_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_blas_graph_compute, + /* .supports_op = */ ggml_backend_blas_supports_op, + /* .supports_buft = */ ggml_backend_blas_supports_buft, + /* .offload_op = */ NULL, + /* .event_new = */ NULL, + /* .event_free = */ NULL, + /* .event_record = */ NULL, + /* .event_wait = */ NULL, + /* .event_synchronize = */ NULL, +}; + +static ggml_guid_t ggml_backend_blas_guid(void) { + static ggml_guid guid = { 0x12, 0xa8, 0xae, 0xf4, 0xc0, 0x1e, 0x61, 0x97, 0x8f, 0xeb, 0x33, 0x04, 0xa1, 0x33, 0x51, 0x2d }; + return &guid; +} + +ggml_backend_t ggml_backend_blas_init(void) { + ggml_backend_blas_context * ctx = new ggml_backend_blas_context; + + ggml_backend_t backend = new ggml_backend { + /* .guid = */ ggml_backend_blas_guid(), + /* .interface = */ blas_backend_i, + /* .context = */ ctx, + }; + +#if !defined(NDEBUG) && defined(OPENBLAS_VERSION) && defined(GGML_USE_OPENMP) + if (openblas_get_parallel() != OPENBLAS_OPENMP) { + fprintf(stderr, "%s: warning: ggml is using OpenMP, but OpenBLAS was compiled without OpenMP support\n", __func__); + } +#endif + +#if !defined(NDEBUG) && defined(BLIS_ENABLE_CBLAS) && defined(GGML_USE_OPENMP) && !defined(BLIS_ENABLE_OPENMP) + fprintf(stderr, "%s: warning: ggml is using OpenMP, but BLIS was compiled without OpenMP support\n", __func__); +#endif + + return backend; +} + +GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend) { + return backend != NULL && ggml_guid_matches(backend->guid, ggml_backend_blas_guid()); +} + +void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads) { + GGML_ASSERT(ggml_backend_is_blas(backend_blas)); + + ggml_backend_blas_context * ctx = (ggml_backend_blas_context *)backend_blas->context; + ctx->n_threads = n_threads; +} diff --git a/ggml-blas.h b/ggml-blas.h new file mode 100644 index 0000000000000..f2e37de06f609 --- /dev/null +++ b/ggml-blas.h @@ -0,0 +1,23 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + + +#ifdef __cplusplus +extern "C" { +#endif + +// backend API +GGML_API GGML_CALL ggml_backend_t ggml_backend_blas_init(void); + +GGML_API GGML_CALL bool ggml_backend_is_blas(ggml_backend_t backend); + +// number of threads used for conversion to float +// for openblas and blis, this will also set the number of threads used for blas operations +GGML_API GGML_CALL void ggml_backend_blas_set_n_threads(ggml_backend_t backend_blas, int n_threads); + + +#ifdef __cplusplus +} +#endif diff --git a/ggml-common.h b/ggml-common.h index 77e6bfba4b11b..e8efceb760d40 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -123,12 +123,18 @@ typedef sycl::half2 ggml_half2; #define QI1_S (QK_K / (4*QR1_S)) #define QR1_S 8 +#define QI1_M (QK_K / (4*QR1_M)) +#define QR1_M 8 + #define QI4_NL (QK4_NL / (4*QR4_NL)) #define QR4_NL 2 #define QI4_XS (QK_K / (4*QR4_XS)) #define QR4_XS 8 +#define QI3_S (QK_K / (4*QR3_S)) +#define QR3_S 8 + #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP #define QK4_0 32 diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b82167cbf7227..b8298ab205e60 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -119,6 +119,20 @@ int ggml_cuda_get_device() { return id; } +static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { + ggml_cuda_set_device(device); +#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) + auto res = hipMallocManaged(ptr, size); + if (res == hipSuccess) { + // if error we "need" to know why... + CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device)); + } + return res; +#else + return cudaMalloc(ptr, size); +#endif +} + static ggml_cuda_device_info ggml_cuda_init() { #ifdef __HIP_PLATFORM_AMD__ // Workaround for a rocBLAS bug when using multiple graphics cards: @@ -174,13 +188,15 @@ static ggml_cuda_device_info ggml_cuda_init() { info.default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; + info.devices[id].nsm = prop.multiProcessorCount; + info.devices[id].smpb = prop.sharedMemPerBlock; #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + info.devices[id].smpbo = prop.sharedMemPerBlock; info.devices[id].cc = 100*prop.major + 10*prop.minor + CC_OFFSET_AMD; #else + info.devices[id].smpbo = prop.sharedMemPerBlockOptin; info.devices[id].cc = 100*prop.major + 10*prop.minor; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - info.devices[id].smpb = prop.sharedMemPerBlock; - info.devices[id].nsm = prop.multiProcessorCount; } for (int id = 0; id < info.device_count; ++id) { @@ -271,7 +287,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); ggml_cuda_set_device(device); - CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); + CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device)); *actual_size = look_ahead_size; pool_size += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC @@ -529,6 +545,10 @@ GGML_CALL static const char * ggml_backend_cuda_buffer_type_name(ggml_backend_bu return ctx->name.c_str(); } +static bool ggml_backend_buft_is_cuda(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_cuda_buffer_type_name; +} + GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; @@ -537,7 +557,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 void * dev_ptr; - cudaError_t err = cudaMalloc(&dev_ptr, size); + cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); if (err != cudaSuccess) { // clear the error cudaGetLastError(); @@ -571,24 +591,12 @@ GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backen GGML_UNUSED(buft); } -GGML_CALL static bool ggml_backend_cuda_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - if (!ggml_backend_is_cuda(backend)) { - return false; - } - - ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; - ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; - - return buft_ctx->device == cuda_ctx->device; -} - static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { /* .get_name = */ ggml_backend_cuda_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, /* .is_host = */ NULL, }; @@ -619,88 +627,22 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device) { // cuda split buffer -static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { - int64_t min_compute_capability = INT_MAX; - int64_t max_compute_capability = INT_MIN; +static int64_t get_row_rounding(const std::array & tensor_split) { + int64_t row_rounding = 0; for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { - if (tensor_split[id] < (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { - if (min_compute_capability > ggml_cuda_info().devices[id].cc) { - min_compute_capability = ggml_cuda_info().devices[id].cc; - } - if (max_compute_capability < ggml_cuda_info().devices[id].cc) { - max_compute_capability = ggml_cuda_info().devices[id].cc; - } + if (tensor_split[id] >= (id + 1 < ggml_backend_cuda_get_device_count() ? tensor_split[id + 1] : 1.0f)) { + continue; } - } -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return max_compute_capability >= CC_RDNA2 ? 128 : 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - return max_compute_capability >= CC_RDNA2 ? 128 : 32; - case GGML_TYPE_Q3_K: - return min_compute_capability < CC_RDNA2 ? 128 : 64; - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - return max_compute_capability >= CC_RDNA2 ? 128 : 64; - default: - GGML_ASSERT(false); - } -#else - switch(type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - return max_compute_capability >= CC_VOLTA ? 128 : 64; - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 64; - case GGML_TYPE_F16: - case GGML_TYPE_F32: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - return max_compute_capability >= CC_VOLTA ? 128 : 64; - case GGML_TYPE_Q6_K: - return 64; - default: - GGML_ASSERT(false); + const int cc = ggml_cuda_info().devices[id].cc; + row_rounding = std::max(row_rounding, (int64_t)get_mmq_y_host(cc, get_mmq_x_max_host(cc))); } -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + return row_rounding; } static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tensor * tensor, const std::array & tensor_split, int id) { const int64_t nrows = ggml_nrows(tensor); - const int64_t rounding = get_row_rounding(tensor->type, tensor_split); + const int64_t rounding = get_row_rounding(tensor_split); *row_low = id == 0 ? 0 : nrows*tensor_split[id]; *row_low -= *row_low % rounding; @@ -798,7 +740,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first ggml_cuda_set_device(id); char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); + CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id)); // set padding to 0 to avoid possible NaN values if (size > original_size) { @@ -915,6 +857,10 @@ GGML_CALL static const char * ggml_backend_cuda_split_buffer_type_name(ggml_back GGML_UNUSED(buft); } +static bool ggml_backend_buft_is_cuda_split(ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_cuda_split_buffer_type_name; +} + GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_split_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { // since we don't know the exact split after rounding, we cannot allocate the device buffers at this point // instead, we allocate them for each tensor separately in init_tensor @@ -958,12 +904,6 @@ GGML_CALL static size_t ggml_backend_cuda_split_buffer_type_get_alloc_size(ggml_ return total_size; } -GGML_CALL static bool ggml_backend_cuda_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_cuda(backend); - - GGML_UNUSED(buft); -} - GGML_CALL static bool ggml_backend_cuda_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; @@ -976,7 +916,6 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, }; @@ -1076,7 +1015,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, /* .context = */ nullptr, @@ -1399,10 +1337,30 @@ static void ggml_cuda_set_peer_access(const int n_tokens, int main_device) { GGML_UNUSED(main_device); } +static cudaError_t ggml_cuda_Memcpy2DPeerAsync( + void * dst, int dstDevice, size_t dpitch, void * src, int srcDevice, size_t spitch, size_t width, size_t height, cudaStream_t stream) { + +#if !defined(GGML_USE_HIPBLAS) + // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices + cudaMemcpy3DPeerParms p = {}; + p.dstDevice = dstDevice; + p.dstPtr = make_cudaPitchedPtr(dst, dpitch, dpitch, height); + p.srcDevice = srcDevice; + p.srcPtr = make_cudaPitchedPtr(src, spitch, spitch, height); + p.extent = make_cudaExtent(width, height, 1); + return cudaMemcpy3DPeerAsync(&p, stream); +#else + // HIP does not support cudaMemcpy3DPeerAsync or vmm pools + GGML_UNUSED(dstDevice); + GGML_UNUSED(srcDevice); + return cudaMemcpy2DAsync(dst, dpitch, src, spitch, width, height, cudaMemcpyDeviceToDevice, stream); +#endif // !defined(GGML_USE_HIPBLAS) +} + static void ggml_cuda_op_mul_mat( ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_cuda_op_mul_mat_t op, - const bool convert_src1_to_q8_1) { + quantize_cuda_t quantize_src1) { const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; @@ -1459,7 +1417,9 @@ static void ggml_cuda_op_mul_mat( } struct dev_data { - ggml_cuda_pool_alloc src0_dd_alloc; + int cc; + + ggml_cuda_pool_alloc src0_dd_alloc; ggml_cuda_pool_alloc src1_ddf_alloc; ggml_cuda_pool_alloc src1_ddq_alloc; ggml_cuda_pool_alloc dst_dd_alloc; @@ -1478,6 +1438,8 @@ static void ggml_cuda_op_mul_mat( int used_devices = 0; for (int id = 0; id < ggml_backend_cuda_get_device_count(); ++id) { + dev[id].cc = ggml_cuda_info().devices[id].cc; + // by default, use all rows dev[id].row_low = 0; dev[id].row_high = ne01; @@ -1485,7 +1447,7 @@ static void ggml_cuda_op_mul_mat( // for multi GPU, get the row boundaries from tensor split // and round to mul_mat_q tile sizes if (split) { - const int64_t rounding = get_row_rounding(src0->type, tensor_split); + const int64_t rounding = get_row_rounding(tensor_split); if (id != 0) { dev[id].row_low = ne01*tensor_split[id]; @@ -1528,11 +1490,15 @@ static void ggml_cuda_op_mul_mat( dev[id].src1_ddf = dev[id].src1_ddf_alloc.alloc(ctx.pool(id), ggml_nelements(src1)); } - if (convert_src1_to_q8_1) { - dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); + if (quantize_src1) { + size_t src_1_ddq_size = nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs; + if (quantize_src1 == quantize_mmq_q8_1_cuda) { + src_1_ddq_size += get_mmq_x_max_host(dev[id].cc)*sizeof(block_q8_1_mmq); + } + dev[id].src1_ddq = dev[id].src1_ddq_alloc.alloc(ctx.pool(id), src_1_ddq_size); if (src1_on_device && src1_is_contiguous) { - quantize_row_q8_1_cuda(dev[id].src1_ddf, dev[id].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); + quantize_src1(dev[id].src1_ddf, dev[id].src1_ddq, ne10, ne11, ne12*ne13, src1_padded_col_size, src0->type, stream); CUDA_CHECK(cudaGetLastError()); } } @@ -1578,7 +1544,12 @@ static void ggml_cuda_op_mul_mat( const int64_t i03 = i0 / ne12; const int64_t i02 = i0 % ne12; - const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; + size_t src1_ddq_i_offset = i0*ne11 * src1_padded_col_size*q8_1_ts/q8_1_bs; + if (quantize_src1 == quantize_mmq_q8_1_cuda) { + src1_ddq_i_offset += src1_col_0 * sizeof(block_q8_1_mmq); + } else { + src1_ddq_i_offset += src1_col_0 * src1_padded_col_size*q8_1_ts/q8_1_bs; + } // for split tensors the data begins at i0 == i0_offset_low char * src0_dd_i = dev[id].src0_dd + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; @@ -1595,10 +1566,17 @@ static void ggml_cuda_op_mul_mat( // copy src0, src1 to device if necessary if (src1_is_contiguous) { if (id != ctx.device) { - if (convert_src1_to_q8_1) { + if (quantize_src1) { char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset; - CUDA_CHECK(cudaMemcpyPeerAsync(src1_ddq_i, id, src1_ddq_i_source, ctx.device, - src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); + if (quantize_src1 == quantize_mmq_q8_1_cuda) { + const size_t pitch = ne11*sizeof(block_q8_1_mmq); + const size_t width = src1_ncols*sizeof(block_q8_1_mmq); + const size_t height = src1_padded_col_size/(4*QK8_1); + CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync(src1_ddq_i, id, pitch, src1_ddq_i_source, ctx.device, pitch, width, height, stream)); + } else { + CUDA_CHECK(cudaMemcpyPeerAsync( + src1_ddq_i, id, src1_ddq_i_source, ctx.device, src1_ncols*src1_padded_col_size*q8_1_ts/q8_1_bs, stream)); + } } else { float * src1_ddf_i_source = (float *) src1->data; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; @@ -1613,8 +1591,8 @@ static void ggml_cuda_op_mul_mat( GGML_ASSERT(false); } - if (convert_src1_to_q8_1 && !src1_is_contiguous) { - quantize_row_q8_1_cuda(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); + if (quantize_src1 && !src1_is_contiguous) { + quantize_src1(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, 1, src1_padded_col_size, src0->type, stream); CUDA_CHECK(cudaGetLastError()); } @@ -1639,22 +1617,8 @@ static void ggml_cuda_op_mul_mat( float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); dhf_dst_i += src1_col_0*ne0 + dev[id].row_low; -#if !defined(GGML_USE_HIPBLAS) - // cudaMemcpy2DAsync may fail with copies between vmm pools of different devices - cudaMemcpy3DPeerParms p = {}; - p.dstDevice = ctx.device; - p.dstPtr = make_cudaPitchedPtr(dhf_dst_i, ne0*sizeof(float), row_diff, src1_ncols); - p.srcDevice = id; - p.srcPtr = make_cudaPitchedPtr(dst_dd_i, row_diff*sizeof(float), row_diff, src1_ncols); - p.extent = make_cudaExtent(row_diff*sizeof(float), src1_ncols, 1); - CUDA_CHECK(cudaMemcpy3DPeerAsync(&p, stream)); -#else - // HIP does not support cudaMemcpy3DPeerAsync or vmm pools - CUDA_CHECK(cudaMemcpy2DAsync(dhf_dst_i, ne0*sizeof(float), - dst_dd_i, row_diff*sizeof(float), - row_diff*sizeof(float), src1_ncols, - cudaMemcpyDeviceToDevice, stream)); -#endif + CUDA_CHECK(ggml_cuda_Memcpy2DPeerAsync( + dhf_dst_i, ctx.device, ne0*sizeof(float), dst_dd_i, id, row_diff*sizeof(float), row_diff*sizeof(float), src1_ncols, stream)); } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -1856,7 +1820,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co } } #else - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 // use cublasGemmStridedBatchedEx CUBLAS_CHECK( @@ -1993,13 +1957,13 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor // KQ + KQV multi-batch ggml_cuda_mul_mat_batched_cublas(ctx, src0, src1, dst); } else if (use_dequantize_mul_mat_vec) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, nullptr); } else if (use_mul_mat_vec_q) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, quantize_row_q8_1_cuda); } else if (use_mul_mat_q) { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, true); + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_q, quantize_mmq_q8_1_cuda); } else { - ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); + ggml_cuda_op_mul_mat(ctx, src0, src1, dst, ggml_cuda_op_mul_mat_cublas, nullptr); } } @@ -2303,6 +2267,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_SQR: ggml_cuda_op_sqr(ctx, dst); break; + case GGML_OP_SQRT: + ggml_cuda_op_sqrt(ctx, dst); + break; case GGML_OP_CLAMP: ggml_cuda_op_clamp(ctx, dst); break; @@ -2510,9 +2477,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool use_cuda_graph = true; bool cuda_graph_update_required = false; - // pointer to CUDA cpy kernel, which is required to identify + // vector of pointers to CUDA cpy kernels, which are required to identify // kernel parameters which need updated in the graph for each token - void * ggml_cuda_cpy_fn_ptr = nullptr; + std::vector ggml_cuda_cpy_fn_ptrs; if (cuda_ctx->cuda_graph->graph == nullptr) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { @@ -2588,9 +2555,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (node->op == GGML_OP_CPY) { // store the copy op parameter which changes with each token. cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); - if (ggml_cuda_cpy_fn_ptr == nullptr) { - // store a pointer to the copy op CUDA kernel to identify it later - ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + // store a pointer to each copy op CUDA kernel to identify it later + void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) { + ggml_cuda_cpy_fn_ptrs.push_back(ptr); } } @@ -2687,10 +2655,8 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (cuda_graph_update_required) { // Extract nodes from graph - if (cuda_ctx->cuda_graph->num_nodes == 0) { - // First call with null argument gets number of nodes in graph - CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes)); - } + // First call with null argument gets number of nodes in graph + CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes)); // Subsequent call with non-null argument gets nodes cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes); cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes); @@ -2720,7 +2686,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured int k = 0; for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { - if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) { + if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) { char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); @@ -2767,7 +2733,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: - return true; + return ggml_is_contiguous(op->src[0]); default: return false; } @@ -2867,11 +2833,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: + case GGML_OP_SQRT: case GGML_OP_CLAMP: case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + return true; case GGML_OP_ROPE: + return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: @@ -2888,10 +2857,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128; #else - if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) { + if (op->src[0]->ne[0] == 128) { + return true; + } + if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { return true; } - return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA && + op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) default: return false; @@ -2900,6 +2873,20 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons GGML_UNUSED(backend); } +GGML_CALL static bool ggml_backend_cuda_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + if (ggml_backend_buft_is_cuda_split(buft)) { + return true; + } + + if (ggml_backend_buft_is_cuda(buft)) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; + ggml_backend_cuda_buffer_type_context * buft_ctx = (ggml_backend_cuda_buffer_type_context *)buft->context; + return buft_ctx->device == cuda_ctx->device; + } + + return false; +} + GGML_CALL static bool ggml_backend_cuda_offload_op(ggml_backend_t backend, const ggml_tensor * op) { const int min_batch_size = 32; @@ -2972,9 +2959,11 @@ static ggml_backend_i ggml_backend_cuda_interface = { /* .synchronize = */ ggml_backend_cuda_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_cuda_graph_compute, /* .supports_op = */ ggml_backend_cuda_supports_op, + /* .supports_buft = */ ggml_backend_cuda_supports_buft, /* .offload_op = */ ggml_backend_cuda_offload_op, /* .event_new = */ ggml_backend_cuda_event_new, /* .event_free = */ ggml_backend_cuda_event_free, diff --git a/ggml-cuda/argsort.cu b/ggml-cuda/argsort.cu index 1641440617779..15757ca18e4d7 100644 --- a/ggml-cuda/argsort.cu +++ b/ggml-cuda/argsort.cu @@ -73,6 +73,7 @@ static void argsort_f32_i32_cuda(const float * x, int * dst, const int ncols, co const dim3 block_nums(1, nrows, 1); const size_t shared_mem = ncols_pad * sizeof(int); + // FIXME: this limit could be raised by ~2-4x on Ampere or newer GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb); if (order == GGML_SORT_ORDER_ASC) { diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 8f6fd71cfea35..de7c2e4349ede 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -79,13 +79,8 @@ #define cudaHostRegisterReadOnly hipHostRegisterReadOnly #define cudaHostUnregister hipHostUnregister #define cudaLaunchHostFunc hipLaunchHostFunc -#ifdef GGML_HIP_UMA -#define cudaMalloc hipMallocManaged -#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size) -#else #define cudaMalloc hipMalloc #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) -#endif #define cudaMemcpy hipMemcpy #define cudaMemcpyAsync hipMemcpyAsync #define cudaMemcpyPeerAsync hipMemcpyPeerAsync @@ -144,6 +139,7 @@ #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 +#define CC_TURING 750 #define CC_AMPERE 800 #define CC_OFFSET_AMD 1000000 #define CC_RDNA1 (CC_OFFSET_AMD + 1010) @@ -165,7 +161,7 @@ #endif #define MMVQ_MAX_BATCH_SIZE 8 // max batch size to use MMVQ kernels -#define MMQ_MAX_BATCH_SIZE 32 // max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 64 // max batch size to use MMQ kernels when tensor cores are available #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -331,9 +327,21 @@ static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int #endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 #endif // defined(GGML_USE_HIPBLAS) -#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#if (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL +#define FP16_AVAILABLE +#endif // (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL + +#if defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 +#define FAST_FP16_AVAILABLE +#endif // defined(FP16_AVAILABLE) && __CUDA_ARCH__ != 610 + +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#define FP16_MMA_AVAILABLE +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA -#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING +#define INT8_MMA_AVAILABLE +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_TURING static bool fast_fp16_available(const int cc) { return cc >= CC_PASCAL && cc != 610; @@ -343,6 +351,10 @@ static bool fp16_mma_available(const int cc) { return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; } +static bool int8_mma_available(const int cc) { + return cc < CC_OFFSET_AMD && cc >= CC_TURING; +} + [[noreturn]] static __device__ void no_device_code( const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { @@ -384,7 +396,7 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { } static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #pragma unroll @@ -417,7 +429,7 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { } static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX return __float2half(fmaxf(__half2float(a), __half2float(b))); @@ -489,6 +501,161 @@ static __device__ __forceinline__ float get_alibi_slope( return powf(base, exph); } +template +struct ggml_cuda_type_traits; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = 1; + static constexpr int qr = 1; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK4_0; + static constexpr int qr = QR4_0; + static constexpr int qi = QI4_0; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK4_1; + static constexpr int qr = QR4_1; + static constexpr int qi = QI4_1; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK5_0; + static constexpr int qr = QR5_0; + static constexpr int qi = QI5_0; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK5_1; + static constexpr int qr = QR5_1; + static constexpr int qi = QI5_1; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK8_0; + static constexpr int qr = QR8_0; + static constexpr int qi = QI8_0; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR2_K; + static constexpr int qi = QI2_K; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR3_K; + static constexpr int qi = QI3_K; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR4_K; + static constexpr int qi = QI4_K; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR5_K; + static constexpr int qi = QI5_K; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR6_K; + static constexpr int qi = QI6_K; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR2_XXS; + static constexpr int qi = QI2_XXS; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR2_XS; + static constexpr int qi = QI2_XS; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR2_S; + static constexpr int qi = QI2_S; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR3_XXS; + static constexpr int qi = QI3_XXS; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR1_S; + static constexpr int qi = QI1_S; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR1_M; + static constexpr int qi = QI1_M; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK4_NL; + static constexpr int qr = QR4_NL; + static constexpr int qi = QI4_NL; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR4_XS; + static constexpr int qi = QI4_XS; +}; + +template<> +struct ggml_cuda_type_traits { + static constexpr int qk = QK_K; + static constexpr int qr = QR3_S; + static constexpr int qi = QI3_S; +}; + +static int get_mmq_x_max_host(const int cc) { +#ifdef CUDA_USE_TENSOR_CORES + return cc >= CC_VOLTA && cc < CC_OFFSET_AMD ? MMQ_MAX_BATCH_SIZE : 64; +#else + return cc >= CC_VOLTA && cc < CC_OFFSET_AMD ? 128 : 64; +#endif // CUDA_USE_TENSOR_CORES +} + +// Round rows to this value for --split-mode row: +static int get_mmq_y_host(const int cc, const int mmq_x) { + return cc >= CC_VOLTA && mmq_x >= 32 ? 128 : 64; +} + ////////////////////// struct ggml_cuda_device_info { @@ -498,6 +665,7 @@ struct ggml_cuda_device_info { int cc; // compute capability int nsm; // number of streaming multiprocessors size_t smpb; // max. shared memory per block + size_t smpbo; // max. shared memory per block (with opt-in) bool vmm; // virtual memory support size_t vmm_granularity; // granularity of virtual memory size_t total_vram; diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu index 2941d2f1770a8..dac10ec36b0bd 100644 --- a/ggml-cuda/concat.cu +++ b/ggml-cuda/concat.cu @@ -1,15 +1,69 @@ #include "concat.cuh" -static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { +// contiguous kernels +static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; } - // operation + int offset_dst = nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; + + if (nidx < ne00) { // src0 + int offset_src = + nidx + + blockIdx.y * ne00 + + blockIdx.z * ne00 * gridDim.y; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + (nidx - ne00) + + blockIdx.y * (ne0 - ne00) + + blockIdx.z * (ne0 - ne00) * gridDim.y; + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + + if (blockIdx.y < ne01) { // src0 + int offset_src = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + nidx + + (blockIdx.y - ne01) * ne0 + + blockIdx.z * ne0 * (gridDim.y - ne01); + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + if (blockIdx.z < ne02) { // src0 int offset_src = nidx + @@ -25,25 +79,118 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, } } -static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { +static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; dim3 gridDim(num_blocks, ne1, ne2); - concat_f32<<>>(x, y, dst, ne0, ne02); + if (dim == 0) { + concat_f32_dim0<<>>(x, y, dst, ne0, ne00); + return; + } + if (dim == 1) { + concat_f32_dim1<<>>(x, y, dst, ne0, ne01); + return; + } + concat_f32_dim2<<>>(x, y, dst, ne0, ne02); } +// non-contiguous kernel (slow) +static __global__ void concat_f32_non_cont( + const char * src0, + const char * src1, + char * dst, + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne03, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, + int64_t /*ne10*/, + int64_t /*ne11*/, + int64_t /*ne12*/, + int64_t /*ne13*/, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, + int64_t ne0, + int64_t /*ne1*/, + int64_t /*ne2*/, + int64_t /*ne3*/, + uint64_t nb0, + uint64_t nb1, + uint64_t nb2, + uint64_t nb3, + int32_t dim) { + const int64_t i3 = blockIdx.z; + const int64_t i2 = blockIdx.y; + const int64_t i1 = blockIdx.x; + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + + const float * x; + + for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); + } else { + x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); + } + + float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; + } +} + + void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const float * src0_d = (const float *)src0->data; - const float * src1_d = (const float *)src1->data; - float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + const int32_t dim = ((int32_t *) dst->op_params)[0]; + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + const float * src0_d = (const float *)src0->data; + const float * src1_d = (const float *)src1->data; - for (int i3 = 0; i3 < dst->ne[3]; i3++) { - concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream); + float * dst_d = (float *)dst->data; + + if (dim != 3) { + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_cuda( + src0_d + i3 * (src0->nb[3] / 4), + src1_d + i3 * (src1->nb[3] / 4), + dst_d + i3 * ( dst->nb[3] / 4), + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], dim, stream); + } + } else { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream)); + } + } else { + dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]); + concat_f32_non_cont<<>>( + (const char *)src0->data, + (const char *)src1->data, + ( char *)dst->data, + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], + dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim); } } diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu index 47d4d5d9e91da..174489e0665d3 100644 --- a/ggml-cuda/dmmv.cu +++ b/ggml-cuda/dmmv.cu @@ -422,10 +422,22 @@ static __device__ void convert_f16(const void * vx, const int64_t ib, const int v.y = x[ib + iqs + 1]; } -template +static constexpr __device__ dequantize_kernel_t get_dequantize_kernel(ggml_type type) { + return type == GGML_TYPE_Q4_0 ? dequantize_q4_0 : + type == GGML_TYPE_Q4_1 ? dequantize_q4_1 : + type == GGML_TYPE_Q5_0 ? dequantize_q5_0 : + type == GGML_TYPE_Q5_1 ? dequantize_q5_1 : + type == GGML_TYPE_Q8_0 ? dequantize_q8_0 : + type == GGML_TYPE_F16 ? convert_f16 : + nullptr; +} + +template static __global__ void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows) { - // qk = quantized weights per x block - // qr = number of quantized weights per data value in x block + constexpr int qk = ggml_cuda_type_traits::qk; // quantized weights per x block + constexpr int qr = ggml_cuda_type_traits::qr; // number of quantized weights per data value in x block + constexpr dequantize_kernel_t dequantize_kernel = get_dequantize_kernel(type); + const int64_t row = (int64_t)blockIdx.x*blockDim.y + threadIdx.y; if (row >= nrows) { @@ -493,7 +505,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -502,7 +514,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -511,7 +523,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -520,7 +532,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -529,7 +541,7 @@ static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -580,7 +592,7 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - dequantize_mul_mat_vec<1, 1, convert_f16> + dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index 1dd519bdee7f1..37b3b99323b20 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -1,4 +1,8 @@ +#pragma once + #include "common.cuh" +#include "convert.cuh" +#include "vecdotq.cuh" #include @@ -34,11 +38,523 @@ typedef void (* fattn_kernel_t)( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, const int ne3); +typedef half (*vec_dot_KQ_f16_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); +typedef float (*vec_dot_KQ_f32_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; + GGML_UNUSED(Q_v); + + half sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_0; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_1; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid4d8 = __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid4d8 + m4s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_0; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */; + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_1; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid5d8 = __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid5d8 + m5s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_0; + const int iqs = k_KQ % QI8_0; + + const int v = get_int_from_int8(K_q8_0[ib].qs, iqs); + + T Q_d; + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]); + } else { + const float2 * Q_ds = (const float2 *) Q_ds_v; + Q_d = Q_ds[k_KQ_0/WARP_SIZE].x; + } + + sum += vec_dot_q8_0_q8_1_impl(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d); + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) { + + const half2 * K_h2 = (const half2 *) K_c; + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_h2 = (const half2 *) Q_v; + + half2 sum2 = make_half2(0.0f, 0.0f); + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[k_KQ]; + sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE]; + } + + return __low2half(sum2) + __high2half(sum2); + } +#endif // FP16_AVAILABLE + + const float2 * Q_f2 = (const float2 *) Q_v; + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[k_KQ]; + sum += __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x; + sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y; + } + + return sum; +} + +template +static __device__ __forceinline__ void quantize_q8_1_to_shared( + const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) { + + float vals[sizeof(int)] = {0.0f}; +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + vals[l] = scale * x[4*threadIdx.x + l]; + } + + float amax = fabsf(vals[0]); + float sum = vals[0]; +#pragma unroll + for (int l = 1; l < sizeof(int); ++l) { + amax = fmaxf(amax, fabsf(vals[l])); + sum += vals[l]; + } +#pragma unroll + for (int mask = QI8_1/2; mask > 0; mask >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32)); + sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, 32); + } + + const float d = amax / 127; + int q32 = 0; + int8_t * q8 = (int8_t *) &q32; + + if (d != 0.0f) { +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + q8[l] = roundf(vals[l] / d); + } + } + + yq32[threadIdx.x] = q32; + if (threadIdx.x % QI8_1 == 0) { + if (std::is_same::value) { + ((half2 *) yds)[threadIdx.x/QI8_1] = make_half2(d, sum); + } else { + ((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum); + } + } +} + +typedef half (*dequantize_1_f16_t)(const void *, const int64_t); +typedef float (*dequantize_1_f32_t)(const void *, const int64_t); + +template +static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) { + const block_q4_0 * x = (const block_q4_0 *) vx; + + const int64_t ib = i / QK4_0; + const int iqs = i % (QK4_0/2); + const int shift = (i % QK4_0) / (QK4_0/2); + + const T d = x[ib].d; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F) - 8; + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) { + const block_q4_1 * x = (const block_q4_1 *) vx; + + const int64_t ib = i / QK4_1; + const int iqs = i % (QK4_1/2); + const int shift = (i % QK4_1) / (QK4_1/2); + + const half2 dm = x[ib].dm; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) { + const block_q5_0 * x = (const block_q5_0 *) vx; + + const int64_t ib = i / QK5_0; + const int idq = i % QK5_0; + const int iqs = i % (QK5_0/2); + const int shift = (i % QK5_0) / (QK5_0/2); + + const T d = x[ib].d; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh) - 16; + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) { + const block_q5_1 * x = (const block_q5_1 *) vx; + + const int64_t ib = i / QK5_1; + const int idq = i % QK5_1; + const int iqs = i % (QK5_1/2); + const int shift = (i % QK5_1) / (QK5_1/2); + + const half2 dm = x[ib].dm; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh); + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) { + const block_q8_0 * x = (const block_q8_0 *) vx; + + const int64_t ib = i / QK8_0; + const int iqs = i % QK8_0; + + const T d = x[ib].d; + const int q = x[ib].qs[iqs]; + +#ifdef FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) { + const half * x = (const half *) vx; + + return x[i]; +} + +template +constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +template +constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + template // D == head size #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) @@ -83,8 +599,32 @@ static __global__ void flash_attn_combine_results( dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; } +static void on_no_fattn_vec_case(const int D) { + if (D == 64) { + fprintf(stderr, "Unsupported KV type combination for head_size 64.\n"); + fprintf(stderr, "By default only f16 KV cache is supported.\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n"); + GGML_ASSERT(false); + } else if (D == 128) { + fprintf(stderr, "Unsupported KV type combination for head_size 128.\n"); + fprintf(stderr, "Supported combinations:\n"); + fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n"); + fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n"); + fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n"); + GGML_ASSERT(false); + } else { + fprintf(stderr, "Unsupported KV type combination for head_size 256.\n"); + fprintf(stderr, "Only f16 is supported.\n"); + GGML_ASSERT(false); + } +} + template -void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { +void launch_fattn( + ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, + const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V +) { const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; @@ -94,8 +634,6 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern ggml_tensor * KQV = dst; GGML_ASSERT(Q->type == GGML_TYPE_F32); - GGML_ASSERT(K->type == GGML_TYPE_F16); - GGML_ASSERT(V->type == GGML_TYPE_F16); GGML_ASSERT(KQV->type == GGML_TYPE_F32); GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); @@ -107,9 +645,49 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern ggml_cuda_pool & pool = ctx.pool(); cudaStream_t main_stream = ctx.stream(); + ggml_cuda_pool_alloc K_f16(pool); + ggml_cuda_pool_alloc V_f16(pool); ggml_cuda_pool_alloc dst_tmp(pool); ggml_cuda_pool_alloc dst_tmp_meta(pool); + char * K_data = (char *) K->data; + size_t nb11 = K->nb[1]; + size_t nb12 = K->nb[2]; + size_t nb13 = K->nb[3]; + + char * V_data = (char *) V->data; + size_t nb21 = V->nb[1]; + size_t nb22 = V->nb[2]; + size_t nb23 = V->nb[3]; + + if (need_f16_K && K->type != GGML_TYPE_F16) { + K_f16.alloc(ggml_nelements(K)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type); + to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream); + K_data = (char *) K_f16.ptr; + + const size_t bs = ggml_blck_size(K->type); + const size_t ts = ggml_type_size(K->type); + + nb11 = nb11*bs*sizeof(half)/ts; + nb12 = nb12*bs*sizeof(half)/ts; + nb13 = nb13*bs*sizeof(half)/ts; + } + + if (need_f16_V && V->type != GGML_TYPE_F16) { + V_f16.alloc(ggml_nelements(V)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); + to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); + V_data = (char *) V_f16.ptr; + + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); + + nb21 = nb21*bs*sizeof(half)/ts; + nb22 = nb22*bs*sizeof(half)/ts; + nb23 = nb23*bs*sizeof(half)/ts; + } + if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); @@ -133,8 +711,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern fattn_kernel<<>>( (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, + K_data, + V_data, mask ? ((const char *) mask->data) : nullptr, (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, scale, max_bias, m0, m1, n_head_log2, @@ -142,7 +720,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern K->ne[0], K->ne[1], K->ne[2], K->ne[3], mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], + nb11, nb12, nb13, + nb21, nb22, nb23, KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] ); CUDA_CHECK(cudaGetLastError()); diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index cdb5eaff79535..c6c35134d4db5 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -36,11 +36,14 @@ static __global__ void flash_attn_tile_ext_f16( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, const int ne3) { -#if FP16_AVAILABLE +#ifdef FP16_AVAILABLE //In this kernel Q, K, V are matrices while i, j, k are matrix indices. const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. @@ -275,13 +278,13 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 5a3de2918c7a3..15e22f495ffaa 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, @@ -272,13 +275,13 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu deleted file mode 100644 index 808e8f36246a7..0000000000000 --- a/ggml-cuda/fattn-vec-f16.cu +++ /dev/null @@ -1,330 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f16.cuh" - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ half KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -HALF_MAX_HALF; - } - half2 * KQ2 = (half2 *) KQ; - - half kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -HALF_MAX_HALF; - } - half kqsum[ncols] = {0.0f}; - - __shared__ half kqmax_shared[ncols][WARP_SIZE]; - __shared__ half kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - half2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); - Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); - } - } - - half2 VKQ[ncols] = {{0.0f, 0.0f}}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, - // see https://github.com/ggerganov/llama.cpp/pull/7061 . - // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). - half kqmax_new = kqmax[0]; - half kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - half2 sum2[ncols] = {{0.0f, 0.0f}}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] = warp_reduce_sum(sum2[j]); - half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); - - if (ncols == 1) { - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - } else { - kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); - } - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const half val = hexp(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= __half2half2(KQ_max_scale); - } - - __syncthreads(); - -#pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; - } - - half2 V_k; - reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ncols > 2 && ic0 + j_VKQ >= ne01) { - break; - } - - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - ggml_tensor * KQV = dst; - ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 256: { - constexpr int D = 256; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: - GGML_ASSERT(false); - break; - } -} - -template -void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - - if (Q->ne[1] == 1) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f16_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh index c7023610ab2d4..02a4ad072bbd6 100644 --- a/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -1,5 +1,397 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#ifdef FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V); + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); + + const half * maskh = (const half *) mask + ne11*ic0; + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ half KQ[ncols*D]; + half2 * KQ2 = (half2 *) KQ; + + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; + + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)]; + half2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + } + + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + + half2 VKQ[ncols] = {{0.0f, 0.0f}}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); + } + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid); + reinterpret_cast(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, tid); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu deleted file mode 100644 index b4652301b87e0..0000000000000 --- a/ggml-cuda/fattn-vec-f32.cu +++ /dev/null @@ -1,279 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f32.cuh" - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f32( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ float KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -FLT_MAX/2.0f; - } - - float kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -FLT_MAX/2.0f; - } - float kqsum[ncols] = {0.0f}; - - __shared__ float kqmax_shared[ncols][WARP_SIZE]; - __shared__ float kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - float2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - Q_h2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); - Q_h2[j][i0/WARP_SIZE].x *= scale; - Q_h2[j][i0/WARP_SIZE].y *= scale; - } - } - - float VKQ[ncols] = {0.0f}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - float kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - float sum[ncols] = {0.0f}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x; - sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum[j] = warp_reduce_sum(sum[j]); - sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; - - kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]); - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum[j]; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - float kqmax_new_j = kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - float kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const float val = expf(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= KQ_max_scale; - } - - __syncthreads(); - -#pragma unroll - for (int k = 0; k < D; ++k) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { - break; - } - - const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]); -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_ki*KQ[j*D + k]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ncols > 2 && ic0 + j_VKQ >= ne01) { - break; - } - - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - float dst_val = VKQ[j_VKQ]; - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -} - -template -void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f32_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh index 614d54ae39253..11a5e355fd40b 100644 --- a/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -1,3 +1,374 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V); + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -FLT_MAX/2.0f; + } + + float kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -FLT_MAX/2.0f; + } + float kqsum[ncols] = {0.0f}; + + __shared__ float kqmax_shared[ncols][WARP_SIZE]; + __shared__ float kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers: + float2 Q_f2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)]; + float2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_f2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_f2[j][i0/WARP_SIZE].x *= scale; + Q_f2[j][i0/WARP_SIZE].y *= scale; + } + } + } + + float VKQ[ncols] = {0.0f}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const float val = expf(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= KQ_max_scale; + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < D; ++k) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { + break; + } + + const float V_ki = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, tid); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_ki*KQ[j*D + k]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + float dst_val = VKQ[j_VKQ]; + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f32_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-wmma-f16.cuh b/ggml-cuda/fattn-wmma-f16.cuh new file mode 100644 index 0000000000000..ae23222426097 --- /dev/null +++ b/ggml-cuda/fattn-wmma-f16.cuh @@ -0,0 +1,490 @@ +#include "common.cuh" +#include "fattn-common.cuh" + +#ifdef FP16_MMA_AVAILABLE +#include +#endif // FP16_MMA_AVAILABLE + +// D == head size, VKQ_stride == num VKQ rows calculated in parallel: +template +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#ifdef FP16_MMA_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); + static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); + constexpr int frag_m = ncols == 8 ? 32 : 16; + constexpr int frag_n = ncols == 8 ? 8 : 16; + static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); + typedef nvcuda::wmma::fragment frag_a_K; + typedef nvcuda::wmma::fragment frag_a_V; + typedef nvcuda::wmma::fragment frag_b; + typedef nvcuda::wmma::fragment frag_c_KQ; + typedef nvcuda::wmma::fragment frag_c_VKQ; + + constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. + constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. + static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); + + // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: + constexpr int D_padded = D + 8; + constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; + constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0); + const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0; + const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2); + + const int stride_Q = nb01 / sizeof(float); + const int stride_KV = nb11 / sizeof(half); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); + + frag_b Q_b[D/16][ncols/frag_n]; + + // A single buffer for temporarily holding tiles of KQ and VKQ parts: + constexpr int mem_KQ = ncols*kqs_padded*kqar; + constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; + __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; + float * KQ_f = (float *) KQ; + half2 * KQ2 = (half2 *) KQ; + + float KQ_rowsum_f[ncols/nwarps] = {0.0f}; + float KQ_max_f[ncols/nwarps]; + float KQ_max_scale_f[ncols/nwarps] = {0.0f}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_f[j] = -FLT_MAX/2.0f; + } + + half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + half2 KQ_max_h2[ncols/nwarps]; + half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); + } + + __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. + half2 * VKQ2 = (half2 *) VKQ; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); + } + } + + // Convert Q to half and apply scale, temporarily store in KQ: +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f; + } + } + + __syncthreads(); + + // Load Q into tensor core fragments/registers since it will be used frequently: +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 16) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded); + } + } + + __syncthreads(); + + // Iterate over ne11 == previous tokens: + for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) { + // Calculate tile of KQ: +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { + frag_c_KQ KQ_c[ncols/frag_n]; +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f); + } +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { + frag_a_K K_a; + nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); + } + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + + // Calculate softmax for each KQ column using the current max. value. + // The divisor is stored in KQ_rowsum and will be applied at the end. +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (std::is_same::value) { + float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k]; + } + + float KQ_max_new = KQ_max_f[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; + KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); + } + KQ_max_new = warp_reduce_max(KQ_max_new); + + const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; + KQ_max_scale_f[j0/nwarps] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_max_scale_f[j0/nwarps] = 0.0f; + } + KQ_max_f[j0/nwarps] = KQ_max_new; + + float KQ_rowsum_add = 0.0f; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps]; + KQ_f_tmp[k0/WARP_SIZE] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_f_tmp[k0/WARP_SIZE] = 0.0f; + } + KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE]; + KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; + } else { + half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k]; + } + + half2 KQ_max_new = KQ_max_h2[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); + KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); + } + KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); + const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; + KQ_max_scale_h2[j0/nwarps] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; + KQ_max_h2[j0/nwarps] = KQ_max_new; + + half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps]; + KQ2_tmp[k0/WARP_SIZE] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask; + KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE]; + KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; + } + } + + __syncthreads(); + + frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + nvcuda::wmma::load_matrix_sync( + KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], + KQ + j0*(kqar*kqs_padded) + k, + kqar*kqs_padded); + } + } + + frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f); + } + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + + frag_a_V v_a; + nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); + } + } + } + + __syncthreads(); + + const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync( + KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), + VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], + D_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + half2 VKQ_scale; + if (std::is_same::value) { + VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); + } else { + VKQ_scale = KQ_max_scale_h2[j0/nwarps]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + + half2 VKQ_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int l = 0; l < VKQ_ratio; ++l) { + VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; + } + VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j_VKQ = j0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + + float KQ_rowsum_j; + if (std::is_same::value) { + KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; + } else { + KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); + } + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + float dst_val = VKQ[j_VKQ*D_padded + i]; + if (parallel_blocks == 1) { + dst_val /= KQ_rowsum_j; + } + dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val; + } + + if (parallel_blocks == 1 || threadIdx.x != 0) { + continue; + } + + float2 dst_meta_val; + if (std::is_same::value) { + dst_meta_val.x = KQ_max_f[j0/nwarps]; + } else { + dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); + } + dst_meta_val.y = KQ_rowsum_j; + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val; + } +#else + NO_DEVICE_CODE; +#endif // FP16_MMA_AVAILABLE +} + +constexpr int get_max_power_of_2(int x) { + return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; +} + +static_assert(get_max_power_of_2(1) == 1, "Test failed."); +static_assert(get_max_power_of_2(2) == 2, "Test failed."); +static_assert(get_max_power_of_2(4) == 4, "Test failed."); +static_assert(get_max_power_of_2(6) == 2, "Test failed."); + +// Number of VKQ rows calculated in parallel: +constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { + return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; +} + +static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); +static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); + +template +void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + constexpr int nwarps = 4; + + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; + const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + + if (4*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); + return; + } + if (2*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); + return; + } + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); +} + +#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \ + template void ggml_cuda_flash_attn_ext_wmma_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, float); +// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 8, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index af7c95232ddf3..38d30b2102631 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -4,519 +4,38 @@ #include "fattn-tile-f32.cuh" #include "fattn-vec-f16.cuh" #include "fattn-vec-f32.cuh" +#include "fattn-wmma-f16.cuh" #include "fattn.cuh" #include -#if FP16_MMA_AVAILABLE -#include -#endif - -// D == head size, VKQ_stride == num VKQ rows calculated in parallel: -template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_MMA_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); - static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); - constexpr int frag_m = ncols == 8 ? 32 : 16; - constexpr int frag_n = ncols == 8 ? 8 : 16; - static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); - typedef nvcuda::wmma::fragment frag_a_K; - typedef nvcuda::wmma::fragment frag_a_V; - typedef nvcuda::wmma::fragment frag_b; - typedef nvcuda::wmma::fragment frag_c_KQ; - typedef nvcuda::wmma::fragment frag_c_VKQ; - - constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. - constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. - static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); - - // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: - constexpr int D_padded = D + 8; - constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; - constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0); - const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0; - const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2); - - const int stride_Q = nb01 / sizeof(float); - const int stride_KV = nb11 / sizeof(half); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - const half2 slope2 = make_half2(slopef, slopef); - - frag_b Q_b[D/16][ncols/frag_n]; - - // A single buffer for temporarily holding tiles of KQ and VKQ parts: - constexpr int mem_KQ = ncols*kqs_padded*kqar; - constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; - __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; - float * KQ_f = (float *) KQ; - half2 * KQ2 = (half2 *) KQ; - - float KQ_rowsum_f[ncols/nwarps] = {0.0f}; - float KQ_max_f[ncols/nwarps]; - float KQ_max_scale_f[ncols/nwarps] = {0.0f}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_f[j] = -FLT_MAX/2.0f; - } - - half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - half2 KQ_max_h2[ncols/nwarps]; - half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); - } - - __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. - half2 * VKQ2 = (half2 *) VKQ; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D/2 && i >= D/2) { - break; - } - VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); - } - } - - // Convert Q to half and apply scale, temporarily store in KQ: -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D && i >= D) { - break; - } - KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f; - } - } - - __syncthreads(); - - // Load Q into tensor core fragments/registers since it will be used frequently: -#pragma unroll - for (int i0 = 0; i0 < D; i0 += 16) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded); - } - } - - __syncthreads(); - - // Iterate over ne11 == previous tokens: - for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) { - // Calculate tile of KQ: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { - frag_c_KQ KQ_c[ncols/frag_n]; -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f); - } -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { - frag_a_K K_a; - nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); - } - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major); - } - } - - __syncthreads(); - - // Calculate softmax for each KQ column using the current max. value. - // The divisor is stored in KQ_rowsum and will be applied at the end. -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k]; - } - - float KQ_max_new = KQ_max_f[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; - KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); - } - KQ_max_new = warp_reduce_max(KQ_max_new); - - const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; - KQ_max_scale_f[j0/nwarps] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_max_scale_f[j0/nwarps] = 0.0f; - } - KQ_max_f[j0/nwarps] = KQ_max_new; - - float KQ_rowsum_add = 0.0f; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps]; - KQ_f_tmp[k0/WARP_SIZE] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_f_tmp[k0/WARP_SIZE] = 0.0f; - } - KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE]; - KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; - } else { - half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k]; - } - - half2 KQ_max_new = KQ_max_h2[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); - KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); - } - KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); - const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; - KQ_max_scale_h2[j0/nwarps] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; - KQ_max_h2[j0/nwarps] = KQ_max_new; - - half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps]; - KQ2_tmp[k0/WARP_SIZE] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask; - KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE]; - KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; - } - } - - __syncthreads(); - - frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - nvcuda::wmma::load_matrix_sync( - KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], - KQ + j0*(kqar*kqs_padded) + k, - kqar*kqs_padded); - } - } - - frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; -#pragma unroll - for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f); - } - -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - - frag_a_V v_a; - nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); - } - } - } - - __syncthreads(); - - const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::store_matrix_sync( - KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), - VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], - D_padded, nvcuda::wmma::mem_col_major); - } - } - - __syncthreads(); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - half2 VKQ_scale; - if (std::is_same::value) { - VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); - } else { - VKQ_scale = KQ_max_scale_h2[j0/nwarps]; - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D/2 && i >= D/2) { - break; - } - - half2 VKQ_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int l = 0; l < VKQ_ratio; ++l) { - VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; - } - VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j_VKQ = j0 + threadIdx.y; - if (ic0 + j_VKQ >= ne01) { - return; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - - float KQ_rowsum_j; - if (std::is_same::value) { - KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; - } else { - KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); - } - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D && i >= D) { - break; - } - float dst_val = VKQ[j_VKQ*D_padded + i]; - if (parallel_blocks == 1) { - dst_val /= KQ_rowsum_j; - } - dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val; - } - - if (parallel_blocks == 1 || threadIdx.x != 0) { - continue; - } - - float2 dst_meta_val; - if (std::is_same::value) { - dst_meta_val.x = KQ_max_f[j0/nwarps]; - } else { - dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); - } - dst_meta_val.y = KQ_rowsum_j; - dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val; - } -#else - NO_DEVICE_CODE; -#endif // FP16_MMA_AVAILABLE -} - -constexpr int get_max_power_of_2(int x) { - return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; -} - -static_assert(get_max_power_of_2(1) == 1, "Test failed."); -static_assert(get_max_power_of_2(2) == 2, "Test failed."); -static_assert(get_max_power_of_2(4) == 4, "Test failed."); -static_assert(get_max_power_of_2(6) == 2, "Test failed."); - -// Number of VKQ rows calculated in parallel: -constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { - return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; -} - -static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); -static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); - -template -void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; - const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; - - if (4*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 4; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - if (2*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 2; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - constexpr int parallel_blocks = 1; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); -} - -void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { +static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - ggml_cuda_set_device(ctx.device); - const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int32_t precision = KQV->op_params[2]; - // On AMD the tile kernels perform poorly, use the vec kernel instead: - if (cc >= CC_OFFSET_AMD) { - if (precision == GGML_PREC_DEFAULT) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); - } else { - ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); - } - return; - } - - if (!fast_fp16_available(cc)) { - if (Q->ne[1] <= 8) { - ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); - } else { - ggml_cuda_flash_attn_ext_tile_f32(ctx, dst); - } - return; - } - - if (!fp16_mma_available(cc)) { - if (Q->ne[1] <= 8) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); - } else { - ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); - } - return; - } - if (precision != GGML_PREC_DEFAULT) { - if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { - ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); - return; - } - if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; - constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); break; default: GGML_ASSERT(false); @@ -524,25 +43,24 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } } else { constexpr int cols_per_block = 32; - constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); break; // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); + // ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); // break; default: GGML_ASSERT(false); @@ -552,26 +70,20 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst return; } - if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - return; - } - if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) { constexpr int cols_per_block = 8; - constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -582,25 +94,24 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst if (Q->ne[1] <= 32) { constexpr int cols_per_block = 16; - constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -610,29 +121,225 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } constexpr int cols_per_block = 32; - constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); break; default: GGML_ASSERT(false); break; } - return; +} +#define FATTN_VEC_F16_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f16_case(ctx, dst); \ + return; \ + } \ + +static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 ) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#else + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); +} + +#define FATTN_VEC_F32_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f32_case(ctx, dst); \ + return; \ + } \ + +static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#else + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); +} + +void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; + + ggml_cuda_set_device(ctx.device); + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; + const int32_t precision = KQV->op_params[2]; + + // On AMD the tile kernels perform poorly, use the vec kernel instead: + if (cc >= CC_OFFSET_AMD) { + if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } + return; + } + + if (!fast_fp16_available(cc)) { + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f32(ctx, dst); + } + return; + } + + if (!fp16_mma_available(cc)) { + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); + } + return; + } + + if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + return; + } else if(Q->ne[0] <= 128) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + return; + } + } + + ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst); } diff --git a/ggml-cuda/mma.cuh b/ggml-cuda/mma.cuh new file mode 100644 index 0000000000000..63e07fbc21291 --- /dev/null +++ b/ggml-cuda/mma.cuh @@ -0,0 +1,161 @@ +#include "common.cuh" + +struct mma_int_A_I16K4 { + static constexpr int I = 16; + static constexpr int K = 4; + static constexpr int ne = 2; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_i(const int l) { + const int ret = (l%2) * (I/2) + threadIdx.x / K; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < I); + return ret; + } + + static __device__ __forceinline__ int get_k(const int /* l */) { + const int ret = threadIdx.x % K; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_A_I16K8 { + static constexpr int I = 16; + static constexpr int K = 8; + static constexpr int ne = 4; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_i(const int l) { + const int ret = (l%2) * (I/2) + threadIdx.x / (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < I); + return ret; + } + + static __device__ __forceinline__ int get_k(const int l) { + const int ret = (l/2) * (K/2) + threadIdx.x % (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_B_J8K4 { + static constexpr int J = 8; + static constexpr int K = 4; + static constexpr int ne = 1; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_j(const int /* l */) { + const int ret = threadIdx.x / K; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < J); + return ret; + } + + static __device__ __forceinline__ int get_k(const int /* l */) { + const int ret = threadIdx.x % K; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_B_J8K8 { + static constexpr int J = 8; + static constexpr int K = 8; + static constexpr int ne = 2; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_j(const int /* l */) { + const int ret = threadIdx.x / (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < J); + return ret; + } + + static __device__ __forceinline__ int get_k(const int l) { + const int ret = l * (K/2) + threadIdx.x % (K/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < K); + return ret; + } +}; + +struct mma_int_C_I16J8 { + static constexpr int I = 16; + static constexpr int J = 8; + static constexpr int ne = 4; + + int x[ne] = {0}; + + static __device__ __forceinline__ int get_i(const int l) { + const int ret = (l/2) * (I/2) + threadIdx.x / (J/2); + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < I); + return ret; + } + + static __device__ __forceinline__ int get_j(const int l) { + const int ret = 2 * (threadIdx.x % (J/2)) + l%2; + GGML_CUDA_ASSUME(ret >= 0); + GGML_CUDA_ASSUME(ret < J); + return ret; + } + + __device__ __forceinline__ void mma_K4(const mma_int_A_I16K4 & mma_A, const mma_int_B_J8K4 & mma_B) { +#ifdef INT8_MMA_AVAILABLE +#if __CUDA_ARCH__ >= CC_AMPERE + asm("mma.sync.aligned.m16n8k16.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5}, {%6}, {%0, %1, %2, %3};" + : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_B.x[0])); +#else + // On Turing m16n8k16 mma is not available, use 2x m8n8k16 mma instead: + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[0]), "+r"(x[1]) + : "r"(mma_A.x[0]), "r"(mma_B.x[0])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[1]), "r"(mma_B.x[0])); +#endif // __CUDA_ARCH__ >= CC_AMPERE +#else + GGML_UNUSED(mma_A); + GGML_UNUSED(mma_B); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE + } + + __device__ __forceinline__ void mma_K8(const mma_int_A_I16K8 & mma_A, const mma_int_B_J8K8 & mma_B) { +#ifdef INT8_MMA_AVAILABLE +#if __CUDA_ARCH__ >= CC_AMPERE + asm("mma.sync.aligned.m16n8k32.row.col.s32.s8.s8.s32 {%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%0, %1, %2, %3};" + : "+r"(x[0]), "+r"(x[1]), "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[0]), "r"(mma_A.x[1]), "r"(mma_A.x[2]), "r"(mma_A.x[3]), "r"(mma_B.x[0]), "r"(mma_B.x[1])); +#else + // On Turing m16n8k32 mma is not available, use 4x m8n8k16 mma instead: + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[0]), "+r"(x[1]) + : "r"(mma_A.x[0]), "r"(mma_B.x[0])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[1]), "r"(mma_B.x[0])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[0]), "+r"(x[1]) + : "r"(mma_A.x[2]), "r"(mma_B.x[1])); + asm("mma.sync.aligned.m8n8k16.row.col.s32.s8.s8.s32 {%0, %1}, {%2}, {%3}, {%0, %1};" + : "+r"(x[2]), "+r"(x[3]) + : "r"(mma_A.x[3]), "r"(mma_B.x[1])); +#endif // __CUDA_ARCH__ >= CC_AMPERE +#else + GGML_UNUSED(mma_A); + GGML_UNUSED(mma_B); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE + } +}; diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index c0a66d9b61802..1d6b9e6982b6e 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -1,1450 +1,4 @@ #include "mmq.cuh" -#include "vecdotq.cuh" - -typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc); -typedef void (*load_tiles_cuda_t)( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row); -typedef float (*vec_dot_q_mul_mat_cuda_t)( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); -typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); -typedef void (mul_mat_q_t)( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst); - -struct mmq_arch_config_t { - int x; - int y; - int nwarps; -}; - -struct mmq_config_t { - mmq_arch_config_t rdna2; - mmq_arch_config_t rdna1; - mmq_arch_config_t ampere; - mmq_arch_config_t pascal; -}; - -constexpr mmq_config_t MMQ_CONFIG_Q4_0 = { -// x y nwarps - { 64, 128, 8}, - { 64, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q4_1 = { -// x y nwarps - { 64, 128, 8}, - { 64, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q5_0 = { -// x y nwarps - { 64, 128, 8}, - { 64, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - {128, 64, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q5_1 = { -// x y nwarps - { 64, 128, 8}, - { 64, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - {128, 64, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q8_0 = { -// x y nwarps - { 64, 128, 8}, - { 64, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - {128, 64, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q2_K = { -// x y nwarps - { 64, 128, 8}, - {128, 32, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q3_K = { -// x y nwarps - {128, 64, 8}, - { 32, 128, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - {128, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q4_K = { -// x y nwarps - { 64, 128, 8}, - { 32, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q5_K = { -// x y nwarps - { 64, 128, 8}, - { 32, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 128, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; -constexpr mmq_config_t MMQ_CONFIG_Q6_K = { -// x y nwarps - { 64, 128, 8}, - { 32, 64, 8}, -#ifdef CUDA_USE_TENSOR_CORES - { 4, 32, 4}, -#else - { 64, 64, 4}, -#endif // CUDA_USE_TENSOR_CORES - { 64, 64, 8}, -}; - -// ------------------------------------------------------------ - -template static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); - GGML_UNUSED(x_sc); - - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI4_0) + mmq_y/QI4_0]; - - *x_ql = tile_x_qs; - *x_dm = (half2 *) tile_x_d; -} - -template static __device__ __forceinline__ void load_tiles_q4_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI4_0; - const int kqsx = k % QI4_0; - - const block_q4_0 * bx0 = (const block_q4_0 *) vx; - - float * x_dmf = (float *) x_dm; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); - // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d; - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI4_0; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) { - int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d; - } -} - -static __device__ __forceinline__ float vec_dot_q4_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const float * x_dmf = (const float *) x_dm; - - int u[2*VDR_Q4_0_Q8_1_MMQ]; - -#pragma unroll - for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE]; - } - - return vec_dot_q4_0_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0], - y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} - -template static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_1) + mmq_y/QI4_1]; - - *x_ql = tile_x_qs; - *x_dm = tile_x_dm; -} - -template static __device__ __forceinline__ void load_tiles_q4_1( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI4_1; - const int kqsx = k % QI4_1; - - const block_q4_1 * bx0 = (const block_q4_1 *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI4_1; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) { - int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm; - } -} - -static __device__ __forceinline__ float vec_dot_q4_1_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - - int u[2*VDR_Q4_1_Q8_1_MMQ]; - -#pragma unroll - for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE]; - } - - return vec_dot_q4_1_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1], - y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} - -template static __device__ __forceinline__ void allocate_tiles_q5_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI5_0) + mmq_y/QI5_0]; - - *x_ql = tile_x_ql; - *x_dm = (half2 *) tile_x_d; -} - -template static __device__ __forceinline__ void load_tiles_q5_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI5_0; - const int kqsx = k % QI5_0; - - const block_q5_0 * bx0 = (const block_q5_0 *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx; - - const int ql = get_int_from_uint8(bxi->qs, kqsx); - const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0)); - - int qs0 = (ql >> 0) & 0x0F0F0F0F; - qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 - qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 - qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 - qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 - qs0 = __vsubss4(qs0, 0x10101010); // subtract 16 - - x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; - - int qs1 = (ql >> 4) & 0x0F0F0F0F; - qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 - qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 - qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 - qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 - qs1 = __vsubss4(qs1, 0x10101010); // subtract 16 - - x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI5_0; - const int kbxd = k % blocks_per_tile_x_row; - float * x_dmf = (float *) x_dm; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) { - int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d; - } -} - -static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0; - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; - - int u[2*VDR_Q5_0_Q8_1_MMQ]; - -#pragma unroll - for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; - } - - return vec_dot_q8_0_q8_1_impl - (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} - - -template static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_1) + mmq_y/QI5_1]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; -} - -template static __device__ __forceinline__ void load_tiles_q5_1( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI5_1; - const int kqsx = k % QI5_1; - - const block_q5_1 * bx0 = (const block_q5_1 *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx; - - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); - const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1)); - - int qs0 = (ql >> 0) & 0x0F0F0F0F; - qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 - qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 - qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 - qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 - - x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; - - int qs1 = (ql >> 4) & 0x0F0F0F0F; - qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 - qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 - qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 - qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 - - x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI5_1; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) { - int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm; - } -} - -static __device__ __forceinline__ float vec_dot_q5_1_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); - const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1; - - int u[2*VDR_Q5_1_Q8_1_MMQ]; - -#pragma unroll - for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) { - u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; - u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE]; - } - - return vec_dot_q8_1_q8_1_impl - (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); -} - -template static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - __shared__ int tile_x_qs[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ float tile_x_d[mmq_y * (WARP_SIZE/QI8_0) + mmq_y/QI8_0]; - - *x_ql = tile_x_qs; - *x_dm = (half2 *) tile_x_d; -} - -template static __device__ __forceinline__ void load_tiles_q8_0( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI8_0; - const int kqsx = k % QI8_0; - float * x_dmf = (float *) x_dm; - - const block_q8_0 * bx0 = (const block_q8_0 *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI8_0; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) { - int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row; - - if (need_check) { - i = min(i, i_max); - } - - const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d; - } -} - -static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); GGML_UNUSED(x_sc); - - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; - - return vec_dot_q8_0_q8_1_impl - (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0], - y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]); -} - -template static __device__ __forceinline__ void allocate_tiles_q2_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); - - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI2_K) + mmq_y/QI2_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; -} - -template static __device__ __forceinline__ void load_tiles_q2_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI2_K; - const int kqsx = k % QI2_K; - - const block_q2_K * bx0 = (const block_q2_K *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI2_K; - const int kbxd = k % blocks_per_tile_x_row; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) { - int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { - int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); - - if (need_check) { - i = min(i, i_max); - } - - const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4); - - x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4)); - } -} - -static __device__ __forceinline__ float vec_dot_q2_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); - - const int kbx = k / QI2_K; - const int ky = (k % QI2_K) * QR2_K; - const float * y_df = (const float *) y_ds; - - int v[QR2_K*VDR_Q2_K_Q8_1_MMQ]; - - const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2); - const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2)); - -#pragma unroll - for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) { - v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303; - } - - const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4; - - const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE; - return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]); -} - -template static __device__ __forceinline__ void allocate_tiles_q3_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI3_K) + mmq_y/QI3_K]; - __shared__ int tile_x_qh[mmq_y * (WARP_SIZE/2) + mmq_y/2]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/4) + mmq_y/4]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_qh = tile_x_qh; - *x_sc = tile_x_sc; -} - -template static __device__ __forceinline__ void load_tiles_q3_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI3_K; - const int kqsx = k % QI3_K; - - const block_q3_K * bx0 = (const block_q3_K *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI3_K; - const int kbxd = k % blocks_per_tile_x_row; - float * x_dmf = (float *) x_dm; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) { - int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) { - int i = i0 + i_offset * 2 + k / (WARP_SIZE/2); - - if (need_check) { - i = min(i, i_max); - } - - const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2); - - // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted - x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2)); - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { - int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); - - if (need_check) { - i = min(i, i_max); - } - - const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4); - - const int ksc = k % (QI3_K/4); - - const int ksc_low = ksc % (QI3_K/8); - const int shift_low = 4 * (ksc / (QI3_K/8)); - const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; - - const int ksc_high = QI3_K/8; - const int shift_high = 2 * ksc; - const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; - - const int sc = __vsubss4(sc_low | sc_high, 0x20202020); - - x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc; - } -} - -static __device__ __forceinline__ float vec_dot_q3_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - - const int kbx = k / QI3_K; - const int ky = (k % QI3_K) * QR3_K; - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; - - const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; - - int v[QR3_K*VDR_Q3_K_Q8_1_MMQ]; - -#pragma unroll - for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) { - const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2); - const int shift = 2 * ((ky % 32) / 8); - const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303; - - const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8); - const int vlh = (vh << 2) & 0x04040404; - - v[l] = __vsubss4(vll, vlh); - } - - const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE; - return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]); -} - -template static __device__ __forceinline__ void allocate_tiles_q4_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); - - __shared__ int tile_x_ql[mmq_y * (WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI4_K) + mmq_y/QI4_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; -} - -template static __device__ __forceinline__ void load_tiles_q4_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI4_K; // == 0 if QK_K == 256 - const int kqsx = k % QI4_K; // == k if QK_K == 256 - - const block_q4_K * bx0 = (const block_q4_K *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx; - - x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) { - int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8); - - const int * scales = (const int *) bxi->scales; - - const int ksc = k % (WARP_SIZE/8); - - // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 - int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits - scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits - - x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; - } -} - -static __device__ __forceinline__ float vec_dot_q4_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); - - const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8); - - const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE; - return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8, - x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]); -} - -template static __device__ __forceinline__ void allocate_tiles_q5_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); - - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI5_K) + mmq_y/QI5_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; -} - -template static __device__ __forceinline__ void load_tiles_q5_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI5_K; // == 0 if QK_K == 256 - const int kqsx = k % QI5_K; // == k if QK_K == 256 - - const block_q5_K * bx0 = (const block_q5_K *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx; - const int ky = QR5_K*kqsx; - - const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); - const int ql0 = (ql >> 0) & 0x0F0F0F0F; - const int ql1 = (ql >> 4) & 0x0F0F0F0F; - - const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); - const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; - const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; - - const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0; - const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4); - - x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0; - x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1; - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) { - int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8); - - const int * scales = (const int *) bxi->scales; - - const int ksc = k % (WARP_SIZE/8); - - // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 - int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits - scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits - - x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; - } -} - -static __device__ __forceinline__ float vec_dot_q5_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); - - const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8); - - const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k; - const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE; - return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8, - x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]); -} - -template static __device__ __forceinline__ void allocate_tiles_q6_K(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { - GGML_UNUSED(x_qh); - - __shared__ int tile_x_ql[mmq_y * (2*WARP_SIZE) + mmq_y]; - __shared__ half2 tile_x_dm[mmq_y * (WARP_SIZE/QI6_K) + mmq_y/QI6_K]; - __shared__ int tile_x_sc[mmq_y * (WARP_SIZE/8) + mmq_y/8]; - - *x_ql = tile_x_ql; - *x_dm = tile_x_dm; - *x_sc = tile_x_sc; -} - -template static __device__ __forceinline__ void load_tiles_q6_K( - const void * __restrict__ vx, int * __restrict__ x_ql, half2 * __restrict__ x_dm, int * __restrict__ x_qh, - int * __restrict__ x_sc, const int & i_offset, const int & i_max, const int & k, const int & blocks_per_row) { - GGML_UNUSED(x_qh); - - GGML_CUDA_ASSUME(i_offset >= 0); - GGML_CUDA_ASSUME(i_offset < nwarps); - GGML_CUDA_ASSUME(k >= 0); - GGML_CUDA_ASSUME(k < WARP_SIZE); - - const int kbx = k / QI6_K; // == 0 if QK_K == 256 - const int kqsx = k % QI6_K; // == k if QK_K == 256 - - const block_q6_K * bx0 = (const block_q6_K *) vx; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { - int i = i0 + i_offset; - - if (need_check) { - i = min(i, i_max); - } - - const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx; - const int ky = QR6_K*kqsx; - - const int ql = get_int_from_uint8(bxi->ql, kqsx); - const int ql0 = (ql >> 0) & 0x0F0F0F0F; - const int ql1 = (ql >> 4) & 0x0F0F0F0F; - - const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); - const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; - const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; - - const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0; - const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2); - - x_ql[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020); - x_ql[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020); - } - - const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256 - const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 - float * x_dmf = (float *) x_dm; - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) { - int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd; - - x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d; - } - -#pragma unroll - for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { - int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; - - if (need_check) { - i = min(i, i_max); - } - - const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4; - - x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8)); - } -} - -static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( - const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, - const int * __restrict__ y_qs, const half2 * __restrict__ y_ds, const int & i, const int & j, const int & k) { - GGML_UNUSED(x_qh); - - const float * x_dmf = (const float *) x_dm; - const float * y_df = (const float *) y_ds; - - const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]); - - const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k; - const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE; - return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); -} - -template -static __device__ __forceinline__ void mul_mat_q( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - - const block_q_t * x = (const block_q_t *) vx; - const block_q8_1 * y = (const block_q8_1 *) vy; - - const int blocks_per_row_x = ncols_x / qk; - const int blocks_per_col_y = nrows_y / QK8_1; - const int blocks_per_warp = WARP_SIZE / qi; - - const int & ncols_dst = ncols_y; - - const int row_dst_0 = blockIdx.x*mmq_y; - const int & row_x_0 = row_dst_0; - - const int col_dst_0 = blockIdx.y*mmq_x; - const int & col_y_0 = col_dst_0; - - int * tile_x_ql = nullptr; - half2 * tile_x_dm = nullptr; - int * tile_x_qh = nullptr; - int * tile_x_sc = nullptr; - - allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc); - - __shared__ int tile_y_qs[mmq_x * WARP_SIZE]; - __shared__ half2 tile_y_ds[mmq_x * WARP_SIZE/QI8_1]; - - float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}}; - - for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) { - - load_tiles(x + row_x_0*blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, - threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x); - -#pragma unroll - for (int ir = 0; ir < qr; ++ir) { - const int kqs = ir*WARP_SIZE + threadIdx.x; - const int kbxd = kqs / QI8_1; - -#pragma unroll - for (int i = 0; i < mmq_x; i += nwarps) { - const int col_y_eff = min(col_y_0 + threadIdx.y + i, ncols_y-1); // to prevent out-of-bounds memory accesses - - const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd]; - - const int index_y = (threadIdx.y + i) * WARP_SIZE + kqs % WARP_SIZE; - tile_y_qs[index_y] = get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1); - } - -#pragma unroll - for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) { - const int ids = (ids0 + threadIdx.y * QI8_1 + threadIdx.x / (WARP_SIZE/QI8_1)) % mmq_x; - const int kby = threadIdx.x % (WARP_SIZE/QI8_1); - const int col_y_eff = min(col_y_0 + ids, ncols_y-1); - - // if the sum is not needed it's faster to transform the scale to f32 ahead of time - const half2 * dsi_src = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + ir*(WARP_SIZE/QI8_1) + kby].ds; - half2 * dsi_dst = &tile_y_ds[ids * (WARP_SIZE/QI8_1) + kby]; - if (need_sum) { - *dsi_dst = *dsi_src; - } else { - float * dfi_dst = (float *) dsi_dst; - *dfi_dst = __low2float(*dsi_src); - } - } - - __syncthreads(); - -// #pragma unroll // unrolling this loop causes too much register pressure - for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) { -#pragma unroll - for (int j = 0; j < mmq_x; j += nwarps) { -#pragma unroll - for (int i = 0; i < mmq_y; i += WARP_SIZE) { - sum[i/WARP_SIZE][j/nwarps] += vec_dot( - tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs, tile_y_ds, - threadIdx.x + i, threadIdx.y + j, k); - } - } - } - - __syncthreads(); - } - } - -#pragma unroll - for (int j = 0; j < mmq_x; j += nwarps) { - const int col_dst = col_dst_0 + j + threadIdx.y; - - if (col_dst >= ncols_dst) { - return; - } - -#pragma unroll - for (int i = 0; i < mmq_y; i += WARP_SIZE) { - const int row_dst = row_dst_0 + threadIdx.x + i; - - if (row_dst >= nrows_dst) { - continue; - } - - dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps]; - } - } -} - -static constexpr __device__ mmq_arch_config_t get_arch_config_device(mmq_config_t mmq_config) { - -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - -#if defined(RDNA3) || defined(RDNA2) - return mmq_config.rdna2; -#else - return mmq_config.rdna1; -#endif // defined(RDNA3) || defined(RDNA2) - -#else - -#if __CUDA_ARCH__ >= CC_VOLTA - return mmq_config.ampere; -#else - return mmq_config.pascal; -#endif // __CUDA_ARCH__ >= CC_VOLTA - -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_0.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q4_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_0); - - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.pascal.nwarps, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q4_1( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_1); - - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_0.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q5_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_0); - - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_1.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q5_1( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_1); - - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q8_0.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - mul_mat_q8_0( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q8_0); - - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q2_K.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q2_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q2_K); - - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.pascal.nwarps, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q3_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q3_K); - - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q4_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_K); - - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_K.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -mul_mat_q5_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_K); - - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -template static __global__ void -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q6_K.rdna2.nwarps, 2) -#endif // defined(RDNA3) || defined(RDNA2) -#elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) -#endif // __CUDA_ARCH__ < CC_VOLTA - mul_mat_q6_K( - const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) { - -#if __CUDA_ARCH__ >= MIN_CC_DP4A - constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q6_K); - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); -#else - GGML_UNUSED(get_arch_config_device); - GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat); - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A -} - -#define MMQ_SWITCH_CASE(type_suffix) \ - case GGML_TYPE_Q##type_suffix: if (row_diff % arch_config.y == 0) { \ - const bool need_check = false; \ - mul_mat_q##type_suffix<<>> \ - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ - } else { \ - const bool need_check = true; \ - mul_mat_q##type_suffix<<>> \ - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ - } break; \ void ggml_cuda_op_mul_mat_q( ggml_backend_cuda_context & ctx, @@ -1454,12 +8,16 @@ void ggml_cuda_op_mul_mat_q( const int64_t ne00 = src0->ne[0]; + const int64_t nb01 = src0->nb[1]; + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; GGML_ASSERT(ne10 % QK8_1 == 0); const int64_t ne0 = dst->ne[0]; const int64_t row_diff = row_high - row_low; + const int64_t stride00 = nb01 / ggml_type_size(src0->type); int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; @@ -1468,73 +26,39 @@ void ggml_cuda_op_mul_mat_q( // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = id == ctx.device ? ne0 : row_diff; - mmq_config_t mmq_config; + const mmq_args args = {src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stride00, src1_padded_row_size, src1_ncols, ne11, nrows_dst}; switch (src0->type) { case GGML_TYPE_Q4_0: - mmq_config = MMQ_CONFIG_Q4_0; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q4_1: - mmq_config = MMQ_CONFIG_Q4_1; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q5_0: - mmq_config = MMQ_CONFIG_Q5_0; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q5_1: - mmq_config = MMQ_CONFIG_Q5_1; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q8_0: - mmq_config = MMQ_CONFIG_Q8_0; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q2_K: - mmq_config = MMQ_CONFIG_Q2_K; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q3_K: - mmq_config = MMQ_CONFIG_Q3_K; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q4_K: - mmq_config = MMQ_CONFIG_Q4_K; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q5_K: - mmq_config = MMQ_CONFIG_Q5_K; + mul_mat_q_case(args, stream); break; case GGML_TYPE_Q6_K: - mmq_config = MMQ_CONFIG_Q6_K; - break; - default: - GGML_ASSERT(false); + mul_mat_q_case(args, stream); break; - } - - mmq_arch_config_t arch_config; - if (compute_capability >= CC_RDNA2) { - arch_config = mmq_config.rdna2; - } else if (compute_capability >= CC_OFFSET_AMD) { - arch_config = mmq_config.rdna1; - } else if (compute_capability >= CC_VOLTA) { - arch_config = mmq_config.ampere; - } else if (compute_capability >= MIN_CC_DP4A) { - arch_config = mmq_config.pascal; - } else { - GGML_ASSERT(false); - } - - const int block_num_x = (row_diff + arch_config.y - 1) / arch_config.y; - const int block_num_y = (src1_ncols + arch_config.x - 1) / arch_config.x; - const dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, arch_config.nwarps, 1); - - switch (src0->type) { - MMQ_SWITCH_CASE(4_0) - MMQ_SWITCH_CASE(4_1) - MMQ_SWITCH_CASE(5_0) - MMQ_SWITCH_CASE(5_1) - MMQ_SWITCH_CASE(8_0) - MMQ_SWITCH_CASE(2_K) - MMQ_SWITCH_CASE(3_K) - MMQ_SWITCH_CASE(4_K) - MMQ_SWITCH_CASE(5_K) - MMQ_SWITCH_CASE(6_K) default: GGML_ASSERT(false); break; diff --git a/ggml-cuda/mmq.cuh b/ggml-cuda/mmq.cuh index 807817c4a715f..6d57974fb4e7c 100644 --- a/ggml-cuda/mmq.cuh +++ b/ggml-cuda/mmq.cuh @@ -1,4 +1,2133 @@ +#pragma once + #include "common.cuh" +#include "vecdotq.cuh" +#include "mma.cuh" + +#include +#include + +#define MMQ_TILE_Y_K (WARP_SIZE + WARP_SIZE/QI8_1) + +typedef void (*load_tiles_mmq_t)( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride); +typedef void (*vec_dot_mmq_t)( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0); +typedef void (*mmq_write_back_t)(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1); + +struct block_q8_1_mmq { + half2 ds[4]; + int8_t qs[4*QK8_1]; +}; +static_assert(sizeof(block_q8_1_mmq) == 4*QK8_1 + 4*sizeof(half2), "Unexpected block_q8_1_mmq size"); +static_assert(sizeof(block_q8_1_mmq) == 4*sizeof(block_q8_1), "Unexpected block_q8_1_mmq size"); + +struct tile_x_sizes { + int qs; + int dm; + int sc; +}; + +// get_mmq_x_max_host is in common.cuh so that it can be used to determine the correct way to round for --split-mode row + +static constexpr __device__ int get_mmq_x_max_device() { +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + return 64; +#else +#if __CUDA_ARCH__ >= CC_VOLTA +#ifdef CUDA_USE_TENSOR_CORES + return MMQ_MAX_BATCH_SIZE; +#else + return 128; +#endif // CUDA_USE_TENSOR_CORES +#else + return 64; +#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +} + +// get_mmq_y_host is in common.cuh so that it can be used to determine the correct way to round for --split-mode row + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +static constexpr __device__ int get_mmq_y_device(int mmq_x) { + return mmq_x >= 32 ? 128 : 64; +} +#else +#if __CUDA_ARCH__ >= CC_VOLTA +static constexpr __device__ int get_mmq_y_device(int mmq_x) { + return mmq_x >= 32 ? 128 : 64; +} +#else +static constexpr __device__ int get_mmq_y_device(int /*mmq_x*/) { + return 64; +} +#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + +#define TILE_X_SIZES_Q4_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_0 + mmq_y/QI4_0, 0} +#define TILE_X_SIZES_Q4_1 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_1 + mmq_y/QI4_1, 0} +#define TILE_X_SIZES_Q5_0 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_0 + mmq_y/QI5_0, 0} +#define TILE_X_SIZES_Q5_1 tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_1 + mmq_y/QI5_1, 0} +#define TILE_X_SIZES_Q8_0 tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI8_0 + mmq_y/QI8_0, 0} +#define TILE_X_SIZES_Q2_K tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE + mmq_y, 0} +#define TILE_X_SIZES_Q3_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI3_K + mmq_y/QI3_K, mmq_y*WARP_SIZE/4 + mmq_y/4} +#define TILE_X_SIZES_Q4_K tile_x_sizes{mmq_y*WARP_SIZE + mmq_y, mmq_y*WARP_SIZE/QI4_K + mmq_y/QI4_K, mmq_y*WARP_SIZE/8 + mmq_y/8} +#define TILE_X_SIZES_Q5_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI5_K + mmq_y/QI5_K, mmq_y*WARP_SIZE/8 + mmq_y/8} +#define TILE_X_SIZES_Q6_K tile_x_sizes{mmq_y*WARP_SIZE*2 + mmq_y, mmq_y*WARP_SIZE/QI6_K + mmq_y/QI6_K, mmq_y*WARP_SIZE/8 + mmq_y/8} + +#define GET_TILE_X_SIZES_BODY \ + return type == GGML_TYPE_Q4_0 ? TILE_X_SIZES_Q4_0 : \ + type == GGML_TYPE_Q4_1 ? TILE_X_SIZES_Q4_1 : \ + type == GGML_TYPE_Q5_0 ? TILE_X_SIZES_Q5_0 : \ + type == GGML_TYPE_Q5_1 ? TILE_X_SIZES_Q5_1 : \ + type == GGML_TYPE_Q8_0 ? TILE_X_SIZES_Q8_0 : \ + type == GGML_TYPE_Q2_K ? TILE_X_SIZES_Q2_K : \ + type == GGML_TYPE_Q3_K ? TILE_X_SIZES_Q3_K : \ + type == GGML_TYPE_Q4_K ? TILE_X_SIZES_Q4_K : \ + type == GGML_TYPE_Q5_K ? TILE_X_SIZES_Q5_K : \ + type == GGML_TYPE_Q6_K ? TILE_X_SIZES_Q6_K : \ + tile_x_sizes{0, 0, 0} + +static tile_x_sizes get_tile_x_sizes_host(const ggml_type type, const int mmq_y) { + GET_TILE_X_SIZES_BODY; +} + +template +static constexpr __device__ tile_x_sizes get_tile_x_sizes_device(ggml_type type) { + GET_TILE_X_SIZES_BODY; +} + +// ------------------------------------------------------------ + +template static __device__ __forceinline__ void load_tiles_q4_0( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + GGML_UNUSED(x_sc); + + const int kbx = threadIdx.x / QI4_0; + const int kqsx = threadIdx.x % QI4_0; + + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbx; + + x_qs[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_0; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) { + int i = i0 + threadIdx.y * QI4_0 + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_0 * bxi = (const block_q4_0 *) x + kbx0 + i*stride + kbxd; + + x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d; + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_0_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + GGML_UNUSED(x_sc); + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2)); + + int u[2*VDR_Q4_0_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_0) % WARP_SIZE]; + } + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_0_q8_1_impl + (&x_qs[i*(WARP_SIZE + 1) + k0], u, x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0], + y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_0_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l) % QI4_0; + const int shift = 4*(mma_A::get_k(l) / QI4_0); + + A.x[l] = __vsubss4((x_qs[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F, 0x08080808); + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += dA[l/2]*__low2float(dsB[l%2])*C.x[l]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q4_1( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + GGML_UNUSED(x_sc); + + const int kbx = threadIdx.x / QI4_1; + const int kqsx = threadIdx.x % QI4_1; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbx; + + x_qs[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_1; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) { + int i = i0 + threadIdx.y * QI4_1 + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_1 * bxi = (const block_q4_1 *) x + kbx0 + i*stride + kbxd; + + x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm; + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_1_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + GGML_UNUSED(x_sc); + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2)); + + int u[2*VDR_Q4_1_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI4_1) % WARP_SIZE]; + } + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_1_q8_1_impl + (&x_qs[i*(WARP_SIZE + 1) + k0], u, x_dm[i*(WARP_SIZE/QI4_1) + i/QI4_1 + k0/QI4_1], + y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_1_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + half2 dmA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l) % QI4_0; + const int shift = 4*(mma_A::get_k(l) / QI4_0); + + A.x[l] = (x_qs[i*(WARP_SIZE + 1) + k] >> shift) & 0x0F0F0F0F; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI4_0) + i/QI4_0 + k0/QI4_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const half2 dmA_dsB = dmA[l/2]*dsB[l%2]; + sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB); + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q5_0( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + GGML_UNUSED(x_sc); + + const int kbx = threadIdx.x / QI5_0; + const int kqsx = threadIdx.x % QI5_0; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbx; + + const int ql = get_int_from_uint8(bxi->qs, kqsx); + const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_0)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + qs0 = __vsubss4(qs0, 0x10101010); // subtract 16 + + x_qs[i * (2*WARP_SIZE + 1) + 2*threadIdx.x+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + qs1 = __vsubss4(qs1, 0x10101010); // subtract 16 + + x_qs[i * (2*WARP_SIZE + 1) + 2*threadIdx.x+1] = qs1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_0; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) { + int i = i0 + threadIdx.y * QI5_0 + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_0 * bxi = (const block_q5_0 *) x + kbx0 + i*stride + kbxd; + + x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d; + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_0_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + GGML_UNUSED(x_sc); + + const float * x_dmf = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2)); + const int index_bx = i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0; + + int u[2*VDR_Q5_0_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_0) % WARP_SIZE]; + } + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl + (&x_qs[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dmf[index_bx], y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_0_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = 2*(k0 + mma_A::get_k(l) % QI5_0) + mma_A::get_k(l) / QI5_0; + + A.x[l] = x_qs[i*(2*WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI5_0) + i/QI5_0 + k0/QI5_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += dA[l/2]*dB[l%2]*C.x[l]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q5_1( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + GGML_UNUSED(x_sc); + + const int kbx = threadIdx.x / QI5_1; + const int kqsx = threadIdx.x % QI5_1; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbx; + + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (threadIdx.x % QI5_1)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + + x_qs[i * (2*WARP_SIZE + 1) + 2*threadIdx.x+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + + x_qs[i * (2*WARP_SIZE + 1) + 2*threadIdx.x+1] = qs1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_1; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) { + int i = i0 + threadIdx.y * QI5_1 + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_1 * bxi = (const block_q5_1 *) x + kbx0 + i*stride + kbxd; + + x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm; + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_1_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + GGML_UNUSED(x_sc); + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int kyqs = k0 % (QI8_1/2) + QI8_1 * (k0 / (QI8_1/2)); + const int index_bx = i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1; + + int u[2*VDR_Q5_1_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j*MMQ_TILE_Y_K + (kyqs + l + QI5_1) % WARP_SIZE]; + } + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_1_q8_1_impl + (&x_qs[i*(2*WARP_SIZE + 1) + 2*k0], u, x_dm[index_bx], y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_1_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + mma_A A; + half2 dmA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = 2*(k0 + mma_A::get_k(l) % QI5_1) + mma_A::get_k(l) / QI5_1; + + A.x[l] = x_qs[i*(2*WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI5_1) + i/QI5_1 + k0/QI5_1]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + (2*k0/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const half2 dmA_dsB = dmA[l/2]*dsB[l%2]; + sum[(j0/B.J)*C.ne + l] += __low2float(dmA_dsB)*C.x[l] + __high2float(dmA_dsB); + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q8_0( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + GGML_UNUSED(x_sc); + + const int kbx = threadIdx.x / QI8_0; + const int kqsx = threadIdx.x % QI8_0; + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbx; + + x_qs[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_int8(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI8_0; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) { + int i = i0 + threadIdx.y * QI8_0 + threadIdx.x / blocks_per_tile_x_row; + + if (need_check) { + i = min(i, i_max); + } + + const block_q8_0 * bxi = (const block_q8_0 *) x + kbx0 + i*stride + kbxd; + + x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d; + } +} + +template +static __device__ __forceinline__ void vec_dot_q8_0_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + GGML_UNUSED(x_sc); + + const float * x_dmf = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q8_0_q8_1_impl + (&x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + k0], x_dmf[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0], + y_df[j*MMQ_TILE_Y_K + k0/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q8_0_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + GGML_UNUSED(x_sc); + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + mma_A A; + float dA[mma_C::ne/2]; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l); + + A.x[l] = x_qs[i*(WARP_SIZE + 1) + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI8_0) + i/QI8_0 + k0/QI8_0]; + } + + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C; + mma_B B; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = k0 + mma_B::get_k(l); + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + k0/QI8_1]; + } + + C.mma_K8(A, B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/B.J)*C.ne + l] += C.x[l]*dA[l/2]*dB[l%2]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q2_K( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + + const int kbx = threadIdx.x / QI2_K; + const int kqsx = threadIdx.x % QI2_K; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q2_K * bxi = (const block_q2_K *) x + kbx0 + i*stride + kbx; + + const int x_ql_0 = get_int_from_uint8(bxi->qs, kqsx); + +#pragma unroll + for (int l = 0; l < QR2_K; ++l) { + const int k = kbx*QI2_K + (kqsx/8)*8 + l*2 + (kqsx % 8)/4; + + int x_qs_k = ((x_ql_0 >> (2*l)) & 0x03030303) << (2*(kqsx % 4)); + x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 1, WARP_SIZE); + x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 2, WARP_SIZE); + + if (kqsx % QR2_K != 0) { + continue; + } + + x_qs[i*(WARP_SIZE + 1) + k] = x_qs_k; + } + + const int sc_m = bxi->scales[kqsx]; +#ifdef FAST_FP16_AVAILABLE + const half2 x_dm_ik = __hmul2(bxi->dm, make_half2(sc_m & 0x0F, sc_m >> 4)); +#else + const float2 bxi_dmf = __half22float2(bxi->dm); + const half2 x_dm_ik = make_half2(bxi_dmf.x*(sc_m & 0x0F), bxi_dmf.y*(sc_m >> 4)); +#endif // FAST_FP16_AVAILABLE + + x_dm[i*(WARP_SIZE + 1) + threadIdx.x] = x_dm_ik; + } +} + +template +static __device__ __forceinline__ void vec_dot_q2_K_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q2_K_q8_1_impl_mmq( + &x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR2_K*k0) % WARP_SIZE], + &x_dm[i*(WARP_SIZE + 1) + k0], y_df[j*MMQ_TILE_Y_K + ((QR2_K*k0) % WARP_SIZE)/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q2_K_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + + typedef mma_int_A_I16K4 mma_A; + typedef mma_int_B_J8K4 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + + mma_A A[2]; + float dA[mma_C::ne/2][2]; + float mA[mma_C::ne/2][2]; + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int shift = 2*mma_A::get_k(l); + + A[0].x[l] = (x_qs[i*(WARP_SIZE + 1) + k0 + 0] >> shift) & 0x03030303; + A[1].x[l] = (x_qs[i*(WARP_SIZE + 1) + k0 + 1] >> shift) & 0x03030303; + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + +#pragma unroll + for (int kk = 0; kk < 2; ++kk) { + const float2 dm = __half22float2(x_dm[i*(WARP_SIZE + 1) + k0 + kk]); + + dA[l][kk] = dm.x; + mA[l][kk] = dm.y; + } + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C Cd[2]; + mma_C Cm[2]; + mma_B B[2]; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (4*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0]; + B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + ((4*k0)/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + Cd[0].mma_K4(A[0], B[0]); + Cd[1].mma_K4(A[1], B[1]); + + mma_A A1; + A1.x[0] = 0x01010101; + A1.x[1] = 0x01010101; + Cm[0].mma_K4(A1, B[0]); + Cm[1].mma_K4(A1, B[1]); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/mma_B::J)*mma_C::ne + l] += (Cd[0].x[l]*dA[l/2][0] + Cd[1].x[l]*dA[l/2][1] - Cm[0].x[l]*mA[l/2][0] - Cm[1].x[l]*mA[l/2][1])*dB[l%2]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q3_K( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + + const int kbx = threadIdx.x / QI3_K; + const int kqsx = threadIdx.x % QI3_K; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbx; + + const int x_ql_0 = get_int_from_uint8(bxi->qs, kqsx); + const int x_qh_0 = get_int_from_uint8(bxi->hmask, kqsx % (QI3_K/2)) >> (4 * (kqsx / (QI3_K/2))); + +#pragma unroll + for (int l = 0; l < QR3_K; ++l) { + const int k = kbx*(QR3_K*QI3_K) + (kqsx/8)*32 + l*8 + kqsx % 8; + + const int x_ql_k = (x_ql_0 >> (2*l)) & 0x03030303; + const int x_qh_k = ((x_qh_0 >> l) << 2) & 0x04040404; + + int x_qs_k = (x_ql_k | x_qh_k) << (4*(k%2)); + x_qs_k |= __shfl_xor_sync(0xFFFFFFFF, x_qs_k, 1, WARP_SIZE); + + if (kqsx % 2 != 0) { + continue; + } + + x_qs[i*(2*WARP_SIZE + 1) + k/2] = x_qs_k; + } + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI3_K; + const int kbxd = threadIdx.x % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) { + int i = (i0 + threadIdx.y * QI3_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + kbxd; + + x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + threadIdx.y * 4 + threadIdx.x / (WARP_SIZE/4); + + if (need_check) { + i = min(i, i_max); + } + + const block_q3_K * bxi = (const block_q3_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/4)) / (QI3_K/4); + + const int ksc = threadIdx.x % (QI3_K/4); + + const int ksc_low = ksc % (QI3_K/8); + const int shift_low = 4 * (ksc / (QI3_K/8)); + const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; + + const int ksc_high = QI3_K/8; + const int shift_high = 2 * ksc; + const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; + + const int sc = __vsubss4(sc_low | sc_high, 0x20202020); + + x_sc[i * (WARP_SIZE/4) + i / 4 + threadIdx.x % (WARP_SIZE/4)] = sc; + } +} + +template +static __device__ __forceinline__ void vec_dot_q3_K_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int kbx = k0 / QI3_K; + const int ky = (k0 % QI3_K) * QR3_K; + + const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q3_K_q8_1_impl_mmq( + &x_qs[i*(2*WARP_SIZE + 1) + 2*k0], &y_qs[j*MMQ_TILE_Y_K + (k0*QR3_K) % WARP_SIZE], scales, + x_df[i*(WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[j*MMQ_TILE_Y_K + ((k0*QR3_K) % WARP_SIZE)/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q3_K_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + + typedef mma_int_A_I16K4 mma_A; + typedef mma_int_B_J8K4 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + + mma_A A[2]; + int scA[mma_C::ne/2][2]; + float dA[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = QR3_K*k0 + mma_A::get_k(l); + + A[0].x[l] = (x_qs[i*(2*WARP_SIZE + 1) + k/2 + 0] >> (4*(k%2))) & 0x0F0F0F0F; + A[1].x[l] = (x_qs[i*(2*WARP_SIZE + 1) + k/2 + mma_A::K/2] >> (4*(k%2))) & 0x0F0F0F0F; + A[0].x[l] = __vsubss4(A[0].x[l], 0x04040404); + A[1].x[l] = __vsubss4(A[1].x[l], 0x04040404); + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + const int kbx = k0 / QI3_K; + const int ky = (k0 % QI3_K) * QR3_K; + const int8_t * sc = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; + + scA[l][0] = sc[0]; + scA[l][1] = sc[1]; + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI3_K) + i/QI3_K + k0/QI3_K]; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + mma_C C[2]; + mma_B B[2]; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (4*k0 + mma_B::get_k(l)) % WARP_SIZE; + + B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0]; + B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + ((4*k0)/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C[0].mma_K4(A[0], B[0]); + C[1].mma_K4(A[1], B[1]); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/mma_B::J)*mma_C::ne + l] += (C[0].x[l]*scA[l/2][0] + C[1].x[l]*scA[l/2][1])*dA[l/2]*dB[l%2]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q4_K( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + + const int kbx = 0; // threadIdx.x / QI4_K + const int kqsx = threadIdx.x; // threadIdx.x % QI4_K + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbx; + + x_qs[i * (WARP_SIZE + 1) + threadIdx.x] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256 + const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256 + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) { + int i = (i0 + threadIdx.y * QI4_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + kbxd; + + x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q4_K * bxi = (const block_q4_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI4_K/8); + + const int * scales = (const int *) bxi->scales; + + const int ksc = threadIdx.x % (WARP_SIZE/8); + + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_K_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2*((k0 % 16) / 8); + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q4_K_q8_1_impl_mmq( + &x_qs[i*(WARP_SIZE + 1) + k0], &y_qs[j*MMQ_TILE_Y_K + (QR4_K*k0) % WARP_SIZE], sc, sc+8, + x_dm[i*(WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[j*MMQ_TILE_Y_K + ((QR4_K*k0) % WARP_SIZE)/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q4_K_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + + mma_A A[2]; + int scA[mma_C::ne/2][2]; + int mA[mma_C::ne/2][2]; + half2 dmA[mma_C::ne/2]; +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q4_K_Q8_1_MMQ; kvdr += 4) { +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = k0 + mma_A::get_k(l); + + A[kvdr/4].x[l] = (x_qs[i*(WARP_SIZE + 1) + k] >> kvdr) & 0x0F0F0F0F; + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8); + const uint8_t * m = sc + 8; + + scA[l][kvdr/4] = sc[kvdr/4]; + mA[l][kvdr/4] = m[kvdr/4]; + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K]; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + float tmpd[mma_C::ne] = {0.0f}; + float tmpm[mma_C::ne] = {0.0f}; + +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A[kvdr/4], B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) * __low2float(dsB[l%2]); + tmpm[l] += mA[l/2][kvdr/4] * __high2float(dsB[l%2]); + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q5_K( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + + const int kbx = 0; // threadIdx.x / QI5_K + const int kqsx = threadIdx.x; // threadIdx.x % QI5_K + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbx; + const int ky = QR5_K*kqsx; + + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; + + const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); + const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; + const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; + + const int kq0 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + 0; + const int kq1 = ky - ky % (QI5_K/2) + threadIdx.x % (QI5_K/4) + (QI5_K/4); + + x_qs[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0; + x_qs[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256 + const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256 + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) { + int i = (i0 + threadIdx.y * QI5_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + kbxd; + + x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q5_K * bxi = (const block_q5_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / (QI5_K/8); + + const int * scales = (const int *) bxi->scales; + + const int ksc = threadIdx.x % (WARP_SIZE/8); + + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_K_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8); + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q5_K_q8_1_impl_mmq( + &x_qs[i*(QR5_K*WARP_SIZE + 1) + QR5_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR5_K*k0) % WARP_SIZE], sc, sc+8, + x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[j*MMQ_TILE_Y_K + ((QR5_K*k0) % WARP_SIZE)/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q5_K_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + + typedef mma_int_A_I16K8 mma_A; + typedef mma_int_B_J8K8 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const int * y_qs = (const int *) y + 4; + const half2 * y_ds = (const half2 *) y; + + const int i0 = threadIdx.y*mma_A::I; + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); + + mma_A A[2]; + int scA[mma_C::ne/2][2]; + int mA[mma_C::ne/2][2]; + half2 dmA[mma_C::ne/2]; +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) { +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = QR5_K*k0 + QR5_K*kvdr + mma_A::get_k(l); + + A[kvdr/4].x[l] = x_qs[i*(QR5_K*WARP_SIZE + 1) + k]; + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/16]) + 2 * ((k0 % 16) / 8); + const uint8_t * m = sc + 8; + + scA[l][kvdr/4] = sc[kvdr/4]; + mA[l][kvdr/4] = m[kvdr/4]; + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dmA[l] = x_dm[i*(WARP_SIZE/QI5_K) + i/QI5_K + k0/QI5_K]; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + float tmpd[mma_C::ne] = {0.0f}; + float tmpm[mma_C::ne] = {0.0f}; + +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q5_K_Q8_1_MMQ; kvdr += 4) { + mma_C C; + mma_B B; + half2 dsB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE; + + B.x[l] = y_qs[j*MMQ_TILE_Y_K + k]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dsB[l] = y_ds[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C.mma_K8(A[kvdr/4], B); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + tmpd[l] += (C.x[l]*scA[l/2][kvdr/4]) * __low2float(dsB[l%2]); + tmpm[l] += mA[l/2][kvdr/4] * __high2float(dsB[l%2]); + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/mma_B::J)*mma_C::ne + l] += __low2float(dmA[l/2])*tmpd[l] - __high2float(dmA[l/2])*tmpm[l]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template static __device__ __forceinline__ void load_tiles_q6_K( + const char * __restrict__ x, int * __restrict__ x_qs, half2 * __restrict__ x_dm, + int * __restrict__ x_sc, const int & kbx0, const int & i_max, const int & stride) { + + const int kbx = 0; // threadIdx.x / QI6_K + const int kqsx = threadIdx.x; // threadIdx.x % QI6_K + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + threadIdx.y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbx; + const int ky = QR6_K*kqsx; + + const int ql = get_int_from_uint8(bxi->ql, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; + + const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); + const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; + const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; + + const int kq0 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + 0; + const int kq1 = ky - ky % QI6_K + threadIdx.x % (QI6_K/2) + (QI6_K/2); + + x_qs[i * (2*WARP_SIZE + 1) + kq0] = __vsubss4(ql0 | qh0, 0x20202020); + x_qs[i * (2*WARP_SIZE + 1) + kq1] = __vsubss4(ql1 | qh1, 0x20202020); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256 + const int kbxd = threadIdx.x % blocks_per_tile_x_row; // == 0 if QK_K == 256 + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) { + int i = (i0 + threadIdx.y * QI6_K + threadIdx.x / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + kbxd; + + x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + threadIdx.y * 8 + threadIdx.x / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = min(i, i_max); + } + + const block_q6_K * bxi = (const block_q6_K *) x + kbx0 + i*stride + (threadIdx.x % (WARP_SIZE/8)) / 4; + + x_sc[i * (WARP_SIZE/8) + i / 8 + threadIdx.x % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, threadIdx.x % (QI6_K/8)); + } +} + +template +static __device__ __forceinline__ void vec_dot_q6_K_q8_1_dp4a( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { + + const float * x_dmf = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]); + + sum[j0/nwarps*mmq_y/WARP_SIZE + i0/WARP_SIZE] += vec_dot_q6_K_q8_1_impl_mmq( + &x_qs[i*(QR6_K*WARP_SIZE + 1) + QR6_K*k0], &y_qs[j*MMQ_TILE_Y_K + (QR6_K*k0) % WARP_SIZE], sc, + x_dmf[i*(WARP_SIZE/QI6_K) + i/QI6_K], &y_df[j*MMQ_TILE_Y_K + ((QR6_K*k0) % WARP_SIZE)/QI8_1]); + } + } +} + +template +static __device__ __forceinline__ void vec_dot_q6_K_q8_1_mma( + const int * __restrict__ x_qs, const half2 * __restrict__ x_dm, const int * __restrict__ x_sc, + const int * __restrict__ y, float * __restrict__ sum, const int & k0) { +#ifdef INT8_MMA_AVAILABLE + + typedef mma_int_A_I16K4 mma_A; + typedef mma_int_B_J8K4 mma_B; + typedef mma_int_C_I16J8 mma_C; + + const float * x_df = (const float *) x_dm; + const int * y_qs = (const int *) y + 4; + const float * y_df = (const float *) y; + + const int i0 = threadIdx.y*mma_A::I; +#ifdef INT8_MMA_AVAILABLE + static_assert(nwarps*mma_A::I == mmq_y, "nwarps*mma_A::I != mmq_y"); +#endif // INT8_MMA_AVAILABLE + + mma_A A[4]; + int scA[mma_C::ne/2][4]; + float dA[mma_C::ne/2]; +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) { +#pragma unroll + for (int l = 0; l < mma_A::ne; ++l) { + const int i = i0 + mma_A::get_i(l); + const int k = QR6_K*k0 + QR6_K*kvdr + mma_A::get_k(l); + + A[kvdr/2 + 0].x[l] = x_qs[i*(QR6_K*WARP_SIZE + 1) + k + 0]; + A[kvdr/2 + 1].x[l] = x_qs[i*(QR6_K*WARP_SIZE + 1) + k + mma_A::K]; + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k0/8]); + + scA[l][kvdr/2 + 0] = sc[kvdr/2 + 0]; + scA[l][kvdr/2 + 1] = sc[kvdr/2 + 1]; + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int i = i0 + mma_C::get_i(2*l); + + dA[l] = x_df[i*(WARP_SIZE/QI6_K) + i/QI6_K + k0/QI6_K]; + } + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_int_B_J8K8::J) { + float tmp[mma_C::ne] = {0.0f}; + +#pragma unroll + for (int kvdr = 0; kvdr < VDR_Q6_K_Q8_1_MMQ; kvdr += 4) { + mma_C C[2]; + mma_B B[2]; + float dB[mma_C::ne/2]; + +#pragma unroll + for (int l = 0; l < mma_B::ne; ++l) { + const int j = j0 + mma_B::get_j(l); + const int k = (2*k0 + 2*kvdr + mma_B::get_k(l)) % WARP_SIZE; + + B[0].x[l] = y_qs[j*MMQ_TILE_Y_K + k + 0]; + B[1].x[l] = y_qs[j*MMQ_TILE_Y_K + k + mma_B::K]; + } +#pragma unroll + for (int l = 0; l < mma_C::ne/2; ++l) { + const int j = j0 + mma_C::get_j(l); + + dB[l] = y_df[j*MMQ_TILE_Y_K + ((2*k0 + 2*kvdr)/QI8_1) % (WARP_SIZE/QI8_1)]; + } + + C[0].mma_K4(A[kvdr/2 + 0], B[0]); + C[1].mma_K4(A[kvdr/2 + 1], B[1]); + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + tmp[l] += (C[0].x[l]*scA[l/2][kvdr/2 + 0] + C[1].x[l]*scA[l/2][kvdr/2 + 1])*dB[l%2]; + } + } + +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + sum[(j0/mma_B::J)*mma_C::ne + l] += tmp[l]*dA[l/2]; + } + } +#else + GGML_UNUSED(x_qs); GGML_UNUSED(x_dm); GGML_UNUSED(x_sc); GGML_UNUSED(y); GGML_UNUSED(sum); GGML_UNUSED(k0); + NO_DEVICE_CODE; +#endif // INT8_MMA_AVAILABLE +} + +template +static __device__ __forceinline__ void mmq_write_back_dp4a(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) { +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += nwarps) { + const int j = blockIdx.y*mmq_x + j0 + threadIdx.y; + + if (j >= ne1) { + return; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += WARP_SIZE) { + const int i = blockIdx.x*mmq_y + i0 + threadIdx.x; + + if (need_check && i >= ne0) { + continue; + } + + dst[j*ne0 + i] = sum[(j0/nwarps) * (mmq_y/WARP_SIZE) + i0/WARP_SIZE]; + } + } +} + +template +static __device__ __forceinline__ void mmq_write_back_mma(const float * __restrict__ sum, float * __restrict__ dst, const int & ne0, const int & ne1) { + typedef mma_int_C_I16J8 mma_C; + + const int i0 = threadIdx.y*mma_C::I; +#ifdef INT8_MMA_AVAILABLE + static_assert(nwarps*mma_C::I == mmq_y, "nwarps*mma_C::I != mmq_y"); +#endif // INT8_MMA_AVAILABLE + +#pragma unroll + for (int j0 = 0; j0 < mmq_x; j0 += mma_C::J) { +#pragma unroll + for (int l = 0; l < mma_C::ne; ++l) { + const int j = blockIdx.y*mmq_x + j0 + mma_C::get_j(l); + + if (j >= ne1) { + continue; + } + + const int i = blockIdx.x*mmq_y + i0 + mma_C::get_i(l); + + if (need_check && i >= ne0) { + continue; + } + + dst[j*ne0 + i] = sum[(j0/mma_C::J)*mma_C::ne + l]; + } + } +} + +// ------------------------------------------------------------------------------------------------------------------------------------- + +template +struct mmq_type_traits; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q4_0_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_0; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q4_0_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q4_0_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q4_1_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_1; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q4_1_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q4_1_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q5_0_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_0; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q5_0_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q5_0_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q5_1_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_1; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q5_1_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q5_1_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q8_0_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q8_0; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q8_0_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q8_0_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q2_K_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q2_K; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q2_K_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q2_K_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q3_K_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q3_K; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q3_K_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q3_K_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q4_K_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q4_K; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q4_K_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q4_K_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q5_K_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q5_K; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q5_K_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q5_K_q8_1_dp4a; +}; + +template +struct mmq_type_traits { + static constexpr int vdr = VDR_Q6_K_Q8_1_MMQ; + static constexpr load_tiles_mmq_t load_tiles = load_tiles_q6_K; + static constexpr vec_dot_mmq_t vec_dot_mma = vec_dot_q6_K_q8_1_mma; + static constexpr vec_dot_mmq_t vec_dot_dp4a = vec_dot_q6_K_q8_1_dp4a; +}; + +static bool mmq_need_sum(const ggml_type type_x) { + switch (type_x) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return true; + case GGML_TYPE_Q5_0: + return false; + case GGML_TYPE_Q5_1: + return true; + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + return false; + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + return true; + case GGML_TYPE_Q6_K: + return false; + default: + GGML_ASSERT(false); + break; + } + return false; +} + +template +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#if defined(RDNA3) || defined(RDNA2) + __launch_bounds__(WARP_SIZE*nwarps, 2) +#endif // defined(RDNA3) || defined(RDNA2) +#else +#if __CUDA_ARCH__ >= CC_VOLTA + __launch_bounds__(WARP_SIZE*nwarps, 1) +#else + __launch_bounds__(WARP_SIZE*nwarps, 2) +#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +static __global__ void mul_mat_q( + const char * __restrict__ x, const char * __restrict__ yc, float * __restrict__ dst, + const int ne00, const int ne01, const int stride01, const int ne10, const int ne11, const int stride11, const int ne0) { + + // Skip unused template specializations for faster compilation: + if (mmq_x > get_mmq_x_max_device()) { + NO_DEVICE_CODE; + return; + } + + constexpr int qk = ggml_cuda_type_traits::qk; + constexpr int qr = ggml_cuda_type_traits::qr; + constexpr int qi = ggml_cuda_type_traits::qi; + constexpr int mmq_y = get_mmq_y_device(mmq_x); + constexpr int vdr = mmq_type_traits::vdr; + constexpr load_tiles_mmq_t load_tiles = mmq_type_traits::load_tiles; + +#ifdef INT8_MMA_AVAILABLE + constexpr vec_dot_mmq_t vec_dot = mmq_type_traits::vec_dot_mma; + constexpr mmq_write_back_t write_back = mmq_write_back_mma; +#else + constexpr vec_dot_mmq_t vec_dot = mmq_type_traits::vec_dot_dp4a; + constexpr mmq_write_back_t write_back = mmq_write_back_dp4a; +#endif // INT8_MMA_AVAILABLE + + constexpr tile_x_sizes txs = get_tile_x_sizes_device(type); + + extern __shared__ char data_mul_mat_q[]; + int * tile_x_qs = (int *) data_mul_mat_q; + half2 * tile_x_dm = (half2 *) (tile_x_qs + txs.qs); + int * tile_x_sc = (int *) (tile_x_dm + txs.dm); + int * tile_y = (int *) (tile_x_sc + txs.sc); // [mmq_x * (WARP_SIZE + WARP_SIZE/QI8_1)] + + const int blocks_per_row_x = ne00 / qk; + const int blocks_per_warp = WARP_SIZE / qi; + + const int & ne1 = ne11; + + const int tile_x_max_i = ne01 - blockIdx.x*mmq_y - 1; + + const int * y = (const int *) yc + blockIdx.y*(mmq_x*sizeof(block_q8_1_mmq)/sizeof(int)); + + float sum[mmq_x*mmq_y / (nwarps*WARP_SIZE)] = {0.0f}; + + for (int kb0 = 0; kb0 < blocks_per_row_x; kb0 += blocks_per_warp) { + + load_tiles(x, tile_x_qs, tile_x_dm, tile_x_sc, stride01*blockIdx.x*mmq_y + kb0, tile_x_max_i, stride01); + +#pragma unroll + for (int kr = 0; kr < qr; ++kr) { + const int * by0 = y + stride11*(kb0*(qk*sizeof(block_q8_1_mmq) / (4*QK8_1*sizeof(int))) + kr*sizeof(block_q8_1_mmq)/sizeof(int)); +#pragma unroll + for (int l0 = 0; l0 < mmq_x*MMQ_TILE_Y_K; l0 += nwarps*WARP_SIZE) { + int l = l0 + threadIdx.y*WARP_SIZE + threadIdx.x; + + tile_y[l] = by0[l]; + } + + __syncthreads(); + +// #pragma unroll // unrolling this loop causes too much register pressure + for (int k0 = kr*WARP_SIZE/qr; k0 < (kr+1)*WARP_SIZE/qr; k0 += vdr) { + vec_dot(tile_x_qs, tile_x_dm, tile_x_sc, tile_y, sum, k0); + } + + __syncthreads(); + } + } + + write_back(sum, dst, ne0, ne1); +} + +struct mmq_args { + const char * x; const char * y; float * dst; + int64_t ne00; int64_t ne01; int64_t stride01; + int64_t ne10; int64_t ne11; int64_t stride11; + int64_t ne0; +}; + +constexpr int mmq_get_nwarps(int mmq_x) { + return mmq_x >= 32 ? 8 : 4; +} + +static int mmq_get_shmem(const ggml_type type, const int mmq_x, const int mmq_y) { + const tile_x_sizes txs = get_tile_x_sizes_host(type, mmq_y); + const int nwarps = mmq_get_nwarps(mmq_x); + + const int shmem_x = txs.qs*sizeof(int) + txs.dm*sizeof(half2) + txs.sc*sizeof(int); + const int shmem_y = mmq_x*WARP_SIZE*sizeof(int) + mmq_x*(WARP_SIZE/QI8_1)*sizeof(half2); + return shmem_x + GGML_PAD(shmem_y, nwarps*WARP_SIZE*sizeof(int)); +} + +template +static void launch_mul_mat_q(const mmq_args & args, cudaStream_t stream) { + const int id = ggml_cuda_get_device(); + const int cc = ggml_cuda_info().devices[id].cc; + const int mmq_y = get_mmq_y_host(cc, mmq_x); + + const int block_num_x = (args.ne01 + mmq_y - 1) / mmq_y; + const int block_num_y = (args.ne11 + mmq_x - 1) / mmq_x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, nwarps, 1); + + const int shmem = mmq_get_shmem(type, mmq_x, mmq_y); + +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + static bool shmem_limit_raised[GGML_CUDA_MAX_DEVICES] = {false}; + if (!shmem_limit_raised[id]) { + CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); + CUDA_CHECK(cudaFuncSetAttribute(mul_mat_q, cudaFuncAttributeMaxDynamicSharedMemorySize, shmem)); + shmem_limit_raised[id] = true; + } +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + + if (args.ne01 % mmq_y == 0) { + const bool need_check = false; + mul_mat_q<<>> + (args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + } else { + const bool need_check = true; + mul_mat_q<<>> + (args.x, args.y, args.dst, args.ne00, args.ne01, args.stride01, args.ne10, args.ne11, args.stride11, args.ne0); + } +} + +template +void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) { + const int id = ggml_cuda_get_device(); + const int nsm = ggml_cuda_info().devices[id].nsm; + const int cc = ggml_cuda_info().devices[id].cc; + const int smpbo = ggml_cuda_info().devices[id].smpbo; + + const int mmq_x_max = get_mmq_x_max_host(cc); + const int mmq_y = get_mmq_y_host(cc, mmq_x_max); + const int block_num_y = (args.ne01 + mmq_y - 1) / mmq_y; + + int mmq_x_best = 0; + int nwaves_best = INT_MAX; + + for (int mmq_x = 8; mmq_x <= mmq_x_max && nwaves_best > 1; mmq_x += 8) { + const int block_num_x = (args.ne11 + mmq_x - 1) / mmq_x; + const int nwaves = (block_num_x*block_num_y + nsm - 1) / nsm; + + if (nwaves < nwaves_best && mmq_get_shmem(type, mmq_x, mmq_y) <= smpbo) { + mmq_x_best = mmq_x; + nwaves_best = nwaves; + } + } + + switch (mmq_x_best) { + case 8: + launch_mul_mat_q(args, stream); + break; + case 16: + launch_mul_mat_q(args, stream); + break; + case 24: + launch_mul_mat_q(args, stream); + break; + case 32: + launch_mul_mat_q(args, stream); + break; + case 40: + launch_mul_mat_q(args, stream); + break; + case 48: + launch_mul_mat_q(args, stream); + break; + case 56: + launch_mul_mat_q(args, stream); + break; + case 64: + launch_mul_mat_q(args, stream); + break; + case 72: + launch_mul_mat_q(args, stream); + break; + case 80: + launch_mul_mat_q(args, stream); + break; + case 88: + launch_mul_mat_q(args, stream); + break; + case 96: + launch_mul_mat_q(args, stream); + break; + case 104: + launch_mul_mat_q(args, stream); + break; + case 112: + launch_mul_mat_q(args, stream); + break; + case 120: + launch_mul_mat_q(args, stream); + break; + case 128: + launch_mul_mat_q(args, stream); + break; + default: + fprintf(stderr, "mmq_x_best=%d\n", mmq_x_best); + GGML_ASSERT(false); + break; + } +} + +#define DECL_MMQ_CASE(type) \ + template void mul_mat_q_case(const mmq_args & args, cudaStream_t stream) \ + +extern DECL_MMQ_CASE(GGML_TYPE_Q4_0); +extern DECL_MMQ_CASE(GGML_TYPE_Q4_1); +extern DECL_MMQ_CASE(GGML_TYPE_Q5_0); +extern DECL_MMQ_CASE(GGML_TYPE_Q5_1); +extern DECL_MMQ_CASE(GGML_TYPE_Q8_0); +extern DECL_MMQ_CASE(GGML_TYPE_Q2_K); +extern DECL_MMQ_CASE(GGML_TYPE_Q3_K); +extern DECL_MMQ_CASE(GGML_TYPE_Q4_K); +extern DECL_MMQ_CASE(GGML_TYPE_Q5_K); +extern DECL_MMQ_CASE(GGML_TYPE_Q6_K); + +// ------------------------------------------------------------------------------------------------------------------------- void ggml_cuda_op_mul_mat_q( ggml_backend_cuda_context & ctx, diff --git a/ggml-cuda/mmvq.cu b/ggml-cuda/mmvq.cu index 65cc1bcaad697..e8d157169544f 100644 --- a/ggml-cuda/mmvq.cu +++ b/ggml-cuda/mmvq.cu @@ -1,9 +1,47 @@ #include "mmvq.cuh" #include "vecdotq.cuh" -typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); +typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs); + +static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) { + return type == GGML_TYPE_Q4_0 ? vec_dot_q4_0_q8_1 : + type == GGML_TYPE_Q4_1 ? vec_dot_q4_1_q8_1 : + type == GGML_TYPE_Q5_0 ? vec_dot_q5_0_q8_1 : + type == GGML_TYPE_Q5_1 ? vec_dot_q5_1_q8_1 : + type == GGML_TYPE_Q8_0 ? vec_dot_q8_0_q8_1 : + type == GGML_TYPE_Q2_K ? vec_dot_q2_K_q8_1 : + type == GGML_TYPE_Q3_K ? vec_dot_q3_K_q8_1 : + type == GGML_TYPE_Q4_K ? vec_dot_q4_K_q8_1 : + type == GGML_TYPE_Q5_K ? vec_dot_q5_K_q8_1 : + type == GGML_TYPE_Q6_K ? vec_dot_q6_K_q8_1 : + type == GGML_TYPE_IQ2_XXS ? vec_dot_iq2_xxs_q8_1 : + type == GGML_TYPE_IQ2_XS ? vec_dot_iq2_xs_q8_1 : + type == GGML_TYPE_IQ2_S ? vec_dot_iq2_s_q8_1 : + type == GGML_TYPE_IQ3_XXS ? vec_dot_iq3_xxs_q8_1 : + type == GGML_TYPE_IQ1_S ? vec_dot_iq1_s_q8_1 : + type == GGML_TYPE_IQ1_M ? vec_dot_iq1_m_q8_1 : + type == GGML_TYPE_IQ4_NL ? vec_dot_iq4_nl_q8_1 : + type == GGML_TYPE_IQ4_XS ? vec_dot_iq4_xs_q8_1 : + type == GGML_TYPE_IQ3_S ? vec_dot_iq3_s_q8_1 : + nullptr; +} + +static constexpr __device__ int get_vdr_mmvq(ggml_type type) { + return type == GGML_TYPE_Q4_0 ? VDR_Q4_0_Q8_1_MMVQ : + type == GGML_TYPE_Q4_1 ? VDR_Q4_1_Q8_1_MMVQ : + type == GGML_TYPE_Q5_0 ? VDR_Q5_0_Q8_1_MMVQ : + type == GGML_TYPE_Q5_1 ? VDR_Q5_1_Q8_1_MMVQ : + type == GGML_TYPE_Q8_0 ? VDR_Q8_0_Q8_1_MMVQ : + type == GGML_TYPE_Q2_K ? VDR_Q2_K_Q8_1_MMVQ : + type == GGML_TYPE_Q3_K ? VDR_Q3_K_Q8_1_MMVQ : + type == GGML_TYPE_Q4_K ? VDR_Q4_K_Q8_1_MMVQ : + type == GGML_TYPE_Q5_K ? VDR_Q5_K_Q8_1_MMVQ : + type == GGML_TYPE_Q6_K ? VDR_Q6_K_Q8_1_MMVQ : + type == GGML_TYPE_IQ4_NL ? VDR_Q4_K_Q8_1_MMVQ : + 1; +} -template +template #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) // tell the compiler to use as many registers as it wants, see nwarps definition below __launch_bounds__((ncols_y <= 4 ? 4 : 2)*WARP_SIZE, 1) @@ -12,6 +50,12 @@ static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols_x, const int nrows_x, const int nrows_y, const int nrows_dst) { + constexpr int qk = ggml_cuda_type_traits::qk; + constexpr int qi = ggml_cuda_type_traits::qi; + constexpr int vdr = get_vdr_mmvq(type); + + constexpr vec_dot_q_cuda_t vec_dot_q_cuda = get_vec_dot_q_cuda(type); + #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) && (defined(RDNA2) || defined(RDNA3)) constexpr int nwarps = 1; constexpr int rows_per_cuda_block = 1; @@ -29,7 +73,6 @@ static __global__ void mul_mat_vec_q( // partial sum for each thread float tmp[ncols_y][rows_per_cuda_block] = {0.0f}; - const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; for (int kbx = tid / (qi/vdr); kbx < blocks_per_row_x; kbx += blocks_per_iter) { @@ -42,8 +85,7 @@ static __global__ void mul_mat_vec_q( for (int j = 0; j < ncols_y; ++j) { #pragma unroll for (int i = 0; i < rows_per_cuda_block; ++i) { - tmp[j][i] += vec_dot_q_cuda( - &x[kbx + (row0 + i)*blocks_per_row_x], &y[j*blocks_per_col_y + kby], kqs); + tmp[j][i] += vec_dot_q_cuda(vx, &y[j*blocks_per_col_y + kby], (row0 + i)*blocks_per_row_x + kbx, kqs); } } } @@ -75,18 +117,18 @@ static __global__ void mul_mat_vec_q( tmp[j][i] = warp_reduce_sum(tmp[j][i]); } - if (threadIdx.x < rows_per_cuda_block) { + if (threadIdx.x < rows_per_cuda_block && (rows_per_cuda_block == 1 || row0 + threadIdx.x < nrows_dst)) { dst[j*nrows_dst + row0 + threadIdx.x] = tmp[j][threadIdx.x]; } } } -template +template static void mul_mat_vec_q_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - GGML_ASSERT(ncols_x % qk == 0); + GGML_ASSERT(ncols_x % ggml_blck_size(type) == 0); GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); int id = ggml_cuda_get_device(); @@ -124,36 +166,28 @@ static void mul_mat_vec_q_cuda( switch (ncols_y) { case 1: - mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 2: - mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 3: - mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 4: - mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 5: - mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 6: - mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 7: - mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; case 8: - mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); + mul_mat_vec_q<<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, nrows_dst); break; default: GGML_ASSERT(false); @@ -165,152 +199,133 @@ static void mul_mat_vec_q4_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q4_1_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q5_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q5_1_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q8_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q2_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q3_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q4_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q5_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_q6_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq2_xxs_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq2_xs_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq2_s_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq3_xxs_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq1_s_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq1_m_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq4_nl_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq4_xs_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } static void mul_mat_vec_iq3_s_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { - mul_mat_vec_q_cuda - (vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); + mul_mat_vec_q_cuda(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst, stream); } void ggml_cuda_op_mul_mat_vec_q( diff --git a/ggml-cuda/norm.cu b/ggml-cuda/norm.cu index 86f7745344994..30866d51274fb 100644 --- a/ggml-cuda/norm.cu +++ b/ggml-cuda/norm.cu @@ -170,6 +170,8 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -188,6 +190,8 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -202,6 +206,8 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); diff --git a/ggml-cuda/quantize.cu b/ggml-cuda/quantize.cu index 7578c4b6c7cab..b4678682238d3 100644 --- a/ggml-cuda/quantize.cu +++ b/ggml-cuda/quantize.cu @@ -1,22 +1,23 @@ #include "quantize.cuh" +#include -static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx_padded) { - const int64_t ix = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; +static __global__ void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int64_t kx, const int64_t kx0_padded) { + const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; - if (ix >= kx_padded) { + if (ix0 >= kx0_padded) { return; } - const int64_t iy = (int64_t)blockDim.y*blockIdx.y + threadIdx.y; + const int64_t ix1 = blockIdx.y; - const int64_t i_padded = (int64_t)iy*kx_padded + ix; + const int64_t i_padded = ix1*kx0_padded + ix0; block_q8_1 * y = (block_q8_1 *) vy; const int64_t ib = i_padded / QK8_1; // block index const int64_t iqs = i_padded % QK8_1; // quant index - const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; + const float xi = ix0 < kx ? x[ix1*kx + ix0] : 0.0f; float amax = fabsf(xi); float sum = xi; @@ -36,10 +37,76 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest reinterpret_cast(y[ib].ds.y) = sum; } -void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream) { - const int64_t block_num_x = (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; - const dim3 num_blocks(block_num_x, ky, 1); +template +static __global__ void quantize_mmq_q8_1( + const float * __restrict__ x, void * __restrict__ vy, const int64_t kx0, const int64_t kx1, const int64_t kx0_padded) { + + const int64_t ix0 = (int64_t)blockDim.x*blockIdx.x + threadIdx.x; + + if (ix0 >= kx0_padded) { + return; + } + + const int64_t ix1 = kx1*blockIdx.z + blockIdx.y; + + block_q8_1_mmq * y = (block_q8_1_mmq *) vy; + + const int64_t ib0 = blockIdx.z*(gridDim.y*gridDim.x*blockDim.x/(4*QK8_1)); // first block of channel + const int64_t ib = ib0 + (ix0 / (4*QK8_1))*kx1 + blockIdx.y; // block index in channel + const int64_t iqs = ix0 % (4*QK8_1); // quant index in block + + const float xi = ix0 < kx0 ? x[ix1*kx0 + ix0] : 0.0f; + float amax = fabsf(xi); + + amax = warp_reduce_max(amax); + + float sum; + if (need_sum) { + sum = warp_reduce_sum(xi); + } + + const float d = amax / 127; + const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); + + y[ib].qs[iqs] = q; + + if (iqs % QK8_1 != 0) { + return; + } + + if (need_sum) { + y[ib].ds[iqs/QK8_1] = make_half2(d, sum); + } else { + ((float *) y[ib].ds)[iqs/QK8_1] = d; + } +} + +void quantize_row_q8_1_cuda( + const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, + const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) { + + GGML_ASSERT(kx0_padded % QK8_1 == 0); + + const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; + const dim3 num_blocks(block_num_x, kx1*channels, 1); const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1); - quantize_q8_1<<>>(x, vy, kx, kx_padded); + quantize_q8_1<<>>(x, vy, kx0, kx0_padded); + + GGML_UNUSED(type_x); } +void quantize_mmq_q8_1_cuda( + const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, + const int64_t kx0_padded, const ggml_type type_x, cudaStream_t stream) { + + GGML_ASSERT(kx0_padded % (4*QK8_1) == 0); + + const int64_t block_num_x = (kx0_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; + const dim3 num_blocks(block_num_x, kx1, channels); + const dim3 block_size(CUDA_QUANTIZE_BLOCK_SIZE, 1, 1); + if (mmq_need_sum(type_x)) { + quantize_mmq_q8_1<<>>(x, vy, kx0, kx1, kx0_padded); + } else { + quantize_mmq_q8_1<<>>(x, vy, kx0, kx1, kx0_padded); + } +} diff --git a/ggml-cuda/quantize.cuh b/ggml-cuda/quantize.cuh index b37a4752f2d24..486c9360a46fd 100644 --- a/ggml-cuda/quantize.cuh +++ b/ggml-cuda/quantize.cuh @@ -1,5 +1,20 @@ +#pragma once + #include "common.cuh" +#include "mmq.cuh" + +#include #define CUDA_QUANTIZE_BLOCK_SIZE 256 -void quantize_row_q8_1_cuda(const float * x, void * vy, const int64_t kx, const int64_t ky, const int64_t kx_padded, cudaStream_t stream); +typedef void (*quantize_cuda_t)( + const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded, + const ggml_type type_x, cudaStream_t stream); + +void quantize_row_q8_1_cuda( + const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded, + const ggml_type type_x, cudaStream_t stream); + +void quantize_mmq_q8_1_cuda( + const float * x, void * vy, const int64_t kx0, const int64_t kx1, const int64_t channels, const int64_t kx0_padded, + const ggml_type type_x, cudaStream_t stream); diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 50f2cf415ef60..596fb7c135058 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -1,7 +1,7 @@ #include "rope.cuh" struct rope_corr_dims { - float v[4]; + float v[2]; }; static __device__ float rope_yarn_ramp(const float low, const float high, const int i0) { @@ -13,8 +13,7 @@ static __device__ float rope_yarn_ramp(const float low, const float high, const // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static __device__ void rope_yarn( float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta -) { + float * cos_theta, float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; @@ -29,27 +28,38 @@ static __device__ void rope_yarn( *sin_theta = sinf(theta) * mscale; } -// rope == RoPE == rotary positional embedding -template -static __global__ void rope( - const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - float ext_factor, float attn_factor, rope_corr_dims corr_dims -) { - const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); +template +static __global__ void rope_norm( + const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) { + const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y); - if (col >= ncols) { + if (i0 >= ne0) { return; } const int row = blockDim.x*blockIdx.x + threadIdx.x; - const int i = row*ncols + col; + + if (i0 >= n_dims) { + const int i = row*ne0 + i0; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; + } + + const int i = row*ne0 + i0; const int i2 = row/p_delta_rows; - const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*powf(freq_base, -float(col)/ncols); + const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f); + + const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f; + + float cos_theta; + float sin_theta; - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + 1]; @@ -58,23 +68,20 @@ static __global__ void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +template static __global__ void rope_neox( - const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, const float * freq_factors -) { - const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); + const T * x, T * dst, int ne0, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors) { + const int i0 = 2*(blockDim.y*blockIdx.y + threadIdx.y); - if (col >= ncols) { + if (i0 >= ne0) { return; } const int row = blockDim.x*blockIdx.x + threadIdx.x; - const int ib = col / n_dims; - const int ic = col % n_dims; - if (ib > 0) { - const int i = row*ncols + ib*n_dims + ic; + if (i0 >= n_dims) { + const int i = row*ne0 + i0; dst[i + 0] = x[i + 0]; dst[i + 1] = x[i + 1]; @@ -82,18 +89,17 @@ static __global__ void rope_neox( return; } - const int i = row*ncols + ib*n_dims + ic/2; + const int i = row*ne0 + i0/2; const int i2 = row/p_delta_rows; - float cur_rot = inv_ndims * ic - ib; + const float theta_base = pos[i2]*powf(theta_scale, i0/2.0f); - const int p = has_pos ? pos[i2] : 0; - const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f)/freq_factor; + float cos_theta; + float sin_theta; - float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + n_dims/2]; @@ -102,145 +108,81 @@ static __global__ void rope_neox( dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; } -static __global__ void rope_glm_f32( - const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - int n_ctx -) { - const int col = blockDim.x*blockIdx.x + threadIdx.x; - const int half_n_dims = ncols/4; - - if (col >= half_n_dims) { - return; - } - - const int row = blockDim.y*blockIdx.y + threadIdx.y; - const int i = row*ncols + col; - const int i2 = row/p_delta_rows; - - const float col_theta_scale = powf(freq_base, -2.0f*col/ncols); - // FIXME: this is likely wrong - const int p = pos != nullptr ? pos[i2] : 0; - - const float theta = min(p, n_ctx - 2)*freq_scale*col_theta_scale; - const float sin_theta = sinf(theta); - const float cos_theta = cosf(theta); - - const float x0 = x[i + 0]; - const float x1 = x[i + half_n_dims]; - - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta; - - const float block_theta = ((float)max(p - n_ctx - 2, 0))*col_theta_scale; - const float sin_block_theta = sinf(block_theta); - const float cos_block_theta = cosf(block_theta); - - const float x2 = x[i + half_n_dims * 2]; - const float x3 = x[i + half_n_dims * 3]; - - dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta; - dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; -} - - template -static void rope_cuda( - const T * x, T * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream -) { - GGML_ASSERT(ncols % 2 == 0); +static void rope_norm_cuda( + const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { + GGML_ASSERT(ne0 % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); - const dim3 block_nums(nrows, num_blocks_x, 1); - if (pos == nullptr) { - rope<<>>( - x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims - ); + const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); + const dim3 block_nums(nr, n_blocks_x, 1); + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + + if (freq_factors == nullptr) { + rope_norm<<>>( + x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); } else { - rope<<>>( - x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims - ); + rope_norm<<>>( + x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); } } template static void rope_neox_cuda( - const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream -) { - GGML_ASSERT(ncols % 2 == 0); + const T * x, T * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { + GGML_ASSERT(ne0 % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); - const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); - const dim3 block_nums(nrows, num_blocks_x, 1); + const int n_blocks_x = (ne0 + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); + const dim3 block_nums(nr, n_blocks_x, 1); const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; - if (pos == nullptr) { - if (freq_factors == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors - ); - } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors ); - } } else { - if (freq_factors == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + rope_neox<<>>( + x, dst, ne0, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors ); - } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors - ); - } } } -static void rope_glm_f32_cuda( - const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, int n_ctx, cudaStream_t stream -) { - GGML_ASSERT(ncols % 4 == 0); - const dim3 block_dims(CUDA_ROPE_BLOCK_SIZE/4, 1, 1); - const int num_blocks_x = (ncols + CUDA_ROPE_BLOCK_SIZE - 1) / CUDA_ROPE_BLOCK_SIZE; - const dim3 block_nums(num_blocks_x, nrows, 1); - rope_glm_f32<<>>(x, dst, ncols, pos, freq_scale, p_delta_rows, freq_base, n_ctx); -} - -static void rope_cuda_f16( - const half * x, half * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) { +static void rope_norm_cuda_f16( + const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_cuda(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_norm_cuda(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } -static void rope_cuda_f32( - const float * x, float * dst, int ncols, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) { +static void rope_norm_cuda_f32( + const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_cuda(x, dst, ncols, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_norm_cuda(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } static void rope_neox_cuda_f16( - const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + const half * x, half * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); + rope_neox_cuda(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } static void rope_neox_cuda_f32( - const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, + const float * x, float * dst, int ne0, int n_dims, int nr, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); + rope_neox_cuda(x, dst, ne0, n_dims, nr, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -254,22 +196,29 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); GGML_ASSERT(src0->type == dst->type); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - const int64_t nrows = ggml_nrows(src0); + const int64_t nr = ggml_nrows(src0); - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; // RoPE alteration for extended context - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); @@ -277,38 +226,28 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - const float * freq_factors = nullptr; - const int32_t * pos = nullptr; - const bool is_neox = mode & 2; - const bool is_glm = mode & 4; - pos = (const int32_t *) src1_d; + const int32_t * pos = (const int32_t *) src1_d; - if (is_neox) { - if (src2 != nullptr) { - freq_factors = (const float *) src2->data; - } - } else { - GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + const float * freq_factors = nullptr; + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; } rope_corr_dims corr_dims; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v); // compute - if (is_glm) { - GGML_ASSERT(false); - rope_glm_f32_cuda(src0_d, dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, stream); - } else if (is_neox) { + if (is_neox) { if (src0->type == GGML_TYPE_F32) { rope_neox_cuda_f32( - (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_cuda_f16( - (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream ); } else { @@ -316,14 +255,14 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { } } else { if (src0->type == GGML_TYPE_F32) { - rope_cuda_f32( - (const float *)src0_d, (float *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + rope_norm_cuda_f32( + (const float *)src0_d, (float *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, stream ); } else if (src0->type == GGML_TYPE_F16) { - rope_cuda_f16( - (const half *)src0_d, (half *)dst_d, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + rope_norm_cuda_f16( + (const half *)src0_d, (half *)dst_d, ne00, n_dims, nr, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, freq_factors, stream ); } else { GGML_ASSERT(false); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index ce64f2f2ce28b..c24abae1f138c 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -130,6 +130,7 @@ static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, cons const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + // FIXME: this limit could be raised by ~2-4x on Ampere or newer if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) { switch (ncols_x) { case 32: diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu new file mode 100644 index 0000000000000..6696a238476d8 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu new file mode 100644 index 0000000000000..dd070db2853f5 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu new file mode 100644 index 0000000000000..54dcde6f52324 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu new file mode 100644 index 0000000000000..4ec22f791912d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu new file mode 100644 index 0000000000000..3c15bf7f0ef16 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu new file mode 100644 index 0000000000000..7e61b5fdcdbca --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu new file mode 100644 index 0000000000000..fdb15b580cff8 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 0000000000000..0f7c417d2c0c8 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 0000000000000..851f33c43f040 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 0000000000000..763809cbeb44c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 0000000000000..f2a276e50e5fa --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 0000000000000..cb227f6f5ce1f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu new file mode 100644 index 0000000000000..97ac0520c71d1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 0000000000000..c772b42634fe6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 0000000000000..5cb7430819e4e --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 0000000000000..98a709d171446 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 0000000000000..4f2f947ae81e6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 0000000000000..11f96b6f65cee --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu new file mode 100644 index 0000000000000..b39bdc0611c0d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 0000000000000..bbd6a2c7f491c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 0000000000000..9d84ff2b19175 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 0000000000000..bc8a5bff684ff --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 0000000000000..a679100c83807 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 0000000000000..8f21bccf7f8da --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu new file mode 100644 index 0000000000000..858b00fd74191 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 0000000000000..0fc8011fac5fc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 0000000000000..261fdf623e098 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 0000000000000..0fb8247383063 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 0000000000000..a9d9d089bd314 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 0000000000000..7d7b27920aa3e --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu new file mode 100644 index 0000000000000..a092ee2d50957 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 0000000000000..db55927a19457 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 0000000000000..c3c21cefae047 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 0000000000000..35dd9f520802c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 0000000000000..050c22ac7c6c7 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 0000000000000..de4866c5e65ce --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu new file mode 100644 index 0000000000000..57a10bc4be4a3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu new file mode 100644 index 0000000000000..e0f08b46a7e35 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu new file mode 100644 index 0000000000000..1c8e8a467a8aa --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu new file mode 100644 index 0000000000000..cefed83fb9562 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu new file mode 100644 index 0000000000000..aede6e3588195 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu new file mode 100644 index 0000000000000..1a1a92c788fbd --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu new file mode 100644 index 0000000000000..ad667473d110b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu new file mode 100644 index 0000000000000..c499f455da971 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu new file mode 100644 index 0000000000000..8286ebf373627 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu new file mode 100644 index 0000000000000..4587868825d21 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu new file mode 100644 index 0000000000000..d89103ce0c68f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu new file mode 100644 index 0000000000000..bb75fd42ff17d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu new file mode 100644 index 0000000000000..b1629817e79e3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu new file mode 100644 index 0000000000000..d8657604dab80 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 0000000000000..2e5bd2f1a3acc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 0000000000000..be5f302d9f1d4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 0000000000000..8dd91cd72eb60 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 0000000000000..4cb791502a157 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 0000000000000..09dea426736e9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu new file mode 100644 index 0000000000000..0fbb607694f25 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 0000000000000..2aeab83b20d21 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 0000000000000..599415b494741 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 0000000000000..e4f8e3083bb6b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 0000000000000..34d166527e93a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 0000000000000..4bebef45a37cb --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu new file mode 100644 index 0000000000000..326468da2fb24 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 0000000000000..511b58f4ecc72 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 0000000000000..d9906d142e159 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 0000000000000..f61c183abbaf7 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 0000000000000..c10450fd29e76 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 0000000000000..2d5cb195c41dc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu new file mode 100644 index 0000000000000..b384f34d7d921 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 0000000000000..446e293b16edc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 0000000000000..6f430298899c7 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 0000000000000..1cd8ba88fd650 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 0000000000000..1ee2eab65a1c9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 0000000000000..2bc77816a5d4e --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu new file mode 100644 index 0000000000000..d55ced08bc940 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 0000000000000..8361e99c4e4a4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 0000000000000..7507a67c4c5e9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 0000000000000..61f050b235ff2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 0000000000000..d4a49d9c9912a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 0000000000000..d146278976211 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu new file mode 100644 index 0000000000000..e73f917a1f186 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu new file mode 100644 index 0000000000000..d40825dfc21f0 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu new file mode 100644 index 0000000000000..b5c6869f4ec42 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu new file mode 100644 index 0000000000000..4e21b0ccaef16 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu new file mode 100644 index 0000000000000..2eac321b370df --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu new file mode 100644 index 0000000000000..f7d2c3b4e0a12 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu new file mode 100644 index 0000000000000..a013f400bd33b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu new file mode 100644 index 0000000000000..2d94e65c28c29 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, float); +DECL_FATTN_WMMA_F16_CASE(80, 16, float); +DECL_FATTN_WMMA_F16_CASE(96, 16, float); +DECL_FATTN_WMMA_F16_CASE(112, 16, float); +DECL_FATTN_WMMA_F16_CASE(128, 16, float); +DECL_FATTN_WMMA_F16_CASE(256, 16, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu new file mode 100644 index 0000000000000..c3d9df3c44313 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu @@ -0,0 +1,9 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, float); +DECL_FATTN_WMMA_F16_CASE(80, 32, float); +DECL_FATTN_WMMA_F16_CASE(96, 32, float); +DECL_FATTN_WMMA_F16_CASE(112, 32, float); +DECL_FATTN_WMMA_F16_CASE(128, 32, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu new file mode 100644 index 0000000000000..bb680e401f7da --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, half); +DECL_FATTN_WMMA_F16_CASE(80, 16, half); +DECL_FATTN_WMMA_F16_CASE(96, 16, half); +DECL_FATTN_WMMA_F16_CASE(112, 16, half); +DECL_FATTN_WMMA_F16_CASE(128, 16, half); +DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu new file mode 100644 index 0000000000000..073f71b1f3e26 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, half); +DECL_FATTN_WMMA_F16_CASE(80, 32, half); +DECL_FATTN_WMMA_F16_CASE(96, 32, half); +DECL_FATTN_WMMA_F16_CASE(112, 32, half); +DECL_FATTN_WMMA_F16_CASE(128, 32, half); +DECL_FATTN_WMMA_F16_CASE(256, 32, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu new file mode 100644 index 0000000000000..d30710c5fa21b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu @@ -0,0 +1,8 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 8, half); +DECL_FATTN_WMMA_F16_CASE(96, 8, half); +DECL_FATTN_WMMA_F16_CASE(128, 8, half); +DECL_FATTN_WMMA_F16_CASE(256, 8, half); diff --git a/ggml-cuda/template-instances/generate_cu_files.py b/ggml-cuda/template-instances/generate_cu_files.py new file mode 100755 index 0000000000000..ea58d09680231 --- /dev/null +++ b/ggml-cuda/template-instances/generate_cu_files.py @@ -0,0 +1,75 @@ +#!/usr/bin/env python3 + +from glob import glob +import os + +TYPES_KV = ["GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_F16"] + +SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f{vkq_size}.cuh" + +DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v}); +""" + +SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +""" + +SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n" + +TYPES_MMQ = [ + "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", + "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K" +] + +SOURCE_MMQ = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE({type}); +""" + + +def get_short_name(long_quant_name): + return long_quant_name.replace("GGML_TYPE_", "").lower() + + +def get_head_sizes(type_k, type_v): + if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16": + return [64, 128, 256] + if type_k == "GGML_TYPE_F16": + return [64, 128] + return [128] + + +for filename in glob("*.cu"): + os.remove(filename) + +for vkq_size in [16, 32]: + for type_k in TYPES_KV: + for type_v in TYPES_KV: + for head_size in get_head_sizes(type_k, type_v): + with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f: + f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v)) + +for kq_acc_t in ["half", "float"]: + for cols_per_block in [8, 16, 32]: + if kq_acc_t == "float" and cols_per_block == 8: + continue + + with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f: + f.write(SOURCE_FATTN_WMMA_START) + + for head_size in [64, 80, 96, 112, 128, 256]: + if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32 + continue + if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance + continue + f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size)) + +for type in TYPES_MMQ: + with open(f"mmq-instance-{get_short_name(type)}.cu", "w") as f: + f.write(SOURCE_MMQ.format(type=type)) diff --git a/ggml-cuda/template-instances/mmq-instance-q2_k.cu b/ggml-cuda/template-instances/mmq-instance-q2_k.cu new file mode 100644 index 0000000000000..6415369dc1d95 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q2_k.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q2_K); diff --git a/ggml-cuda/template-instances/mmq-instance-q3_k.cu b/ggml-cuda/template-instances/mmq-instance-q3_k.cu new file mode 100644 index 0000000000000..ffb6213af83ea --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q3_k.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q3_K); diff --git a/ggml-cuda/template-instances/mmq-instance-q4_0.cu b/ggml-cuda/template-instances/mmq-instance-q4_0.cu new file mode 100644 index 0000000000000..0c0b0c8a8ed22 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/mmq-instance-q4_1.cu b/ggml-cuda/template-instances/mmq-instance-q4_1.cu new file mode 100644 index 0000000000000..ee67f6942a8fc --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/mmq-instance-q4_k.cu b/ggml-cuda/template-instances/mmq-instance-q4_k.cu new file mode 100644 index 0000000000000..9eeb3cd7f3cc1 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q4_k.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q4_K); diff --git a/ggml-cuda/template-instances/mmq-instance-q5_0.cu b/ggml-cuda/template-instances/mmq-instance-q5_0.cu new file mode 100644 index 0000000000000..cc57fb9753c9e --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/mmq-instance-q5_1.cu b/ggml-cuda/template-instances/mmq-instance-q5_1.cu new file mode 100644 index 0000000000000..721ac790c44f4 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/mmq-instance-q5_k.cu b/ggml-cuda/template-instances/mmq-instance-q5_k.cu new file mode 100644 index 0000000000000..a2e90ffd5d0aa --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q5_k.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q5_K); diff --git a/ggml-cuda/template-instances/mmq-instance-q6_k.cu b/ggml-cuda/template-instances/mmq-instance-q6_k.cu new file mode 100644 index 0000000000000..470938fef8a05 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q6_k.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q6_K); diff --git a/ggml-cuda/template-instances/mmq-instance-q8_0.cu b/ggml-cuda/template-instances/mmq-instance-q8_0.cu new file mode 100644 index 0000000000000..974477bbb73a8 --- /dev/null +++ b/ggml-cuda/template-instances/mmq-instance-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../mmq.cuh" + +DECL_MMQ_CASE(GGML_TYPE_Q8_0); diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu index ac03d5c6fce54..f9e208011e2a8 100644 --- a/ggml-cuda/unary.cu +++ b/ggml-cuda/unary.cu @@ -92,6 +92,15 @@ static __global__ void sqr_f32(const float * x, float * dst, const int k) { dst[i] = x[i] * x[i]; } +static __global__ void sqrt_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = sqrtf(x[i]); +} + static void gelu_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_GELU_BLOCK_SIZE - 1) / CUDA_GELU_BLOCK_SIZE; gelu_f32<<>>(x, dst, k); @@ -142,12 +151,19 @@ static void sqr_f32_cuda(const float * x, float * dst, const int k, cudaStream_t sqr_f32<<>>(x, dst, k); } +static void sqrt_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SQRT_BLOCK_SIZE - 1) / CUDA_SQRT_BLOCK_SIZE; + sqrt_f32<<>>(x, dst, k); +} + void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -160,6 +176,8 @@ void ggml_cuda_op_silu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -172,6 +190,8 @@ void ggml_cuda_op_gelu_quick(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -184,6 +204,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -196,6 +218,8 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -208,6 +232,8 @@ void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -220,6 +246,8 @@ void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -232,6 +260,8 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -244,6 +274,8 @@ void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -259,8 +291,24 @@ void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); sqr_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); } + +void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *)src0->data; + float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous(src0)); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqrt_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); +} diff --git a/ggml-cuda/unary.cuh b/ggml-cuda/unary.cuh index a1d07c04fcd43..4cfb0479e7169 100644 --- a/ggml-cuda/unary.cuh +++ b/ggml-cuda/unary.cuh @@ -8,6 +8,7 @@ #define CUDA_HARDSIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 +#define CUDA_SQRT_BLOCK_SIZE 256 void ggml_cuda_op_gelu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); @@ -28,3 +29,5 @@ void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_leaky_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_sqr(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void ggml_cuda_op_sqrt(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh index 5ebdddcc745de..3b12d656616be 100644 --- a/ggml-cuda/vecdotq.cuh +++ b/ggml-cuda/vecdotq.cuh @@ -180,8 +180,8 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp #define VDR_Q8_0_Q8_1_MMVQ 2 #define VDR_Q8_0_Q8_1_MMQ 8 -template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl( - const int * v, const int * u, const float & d8_0, const float & d8_1) { +template static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl( + const int * v, const int * u, const T & d8_0, const T & d8_1) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; @@ -192,7 +192,7 @@ template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp sumi = __dp4a(v[i], u[i], sumi); } - return d8_0*d8_1 * sumi; + return d8_0*d8_1 * ((T) sumi); #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -265,36 +265,31 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( // contiguous u/y values static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( - const int * __restrict__ v, const int * __restrict__ u, const uint8_t * __restrict__ scales, - const half2 & dm2, const float & d8) { + const int * __restrict__ v, const int * __restrict__ u, const half2 * dm2, const float & d8) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - int sumi_d = 0; - int sumi_m = 0; + float sumf_d = 0.0f; + float sumf_m = 0.0f; #pragma unroll for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) { - int sumi_d_sc = 0; - - const int sc = scales[i0 / (QI8_1/2)]; - - // fill int with 4x m - int m = sc >> 4; - m |= m << 8; - m |= m << 16; + const float2 dm2f = __half22float2(dm2[i0/(QI8_1/2)]); + int sumi_d = 0; + int sumi_m = 0; + const int vi0 = v[i0/(QI8_1/2)]; #pragma unroll for (int i = i0; i < i0 + QI8_1/2; ++i) { - sumi_d_sc = __dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product - sumi_m = __dp4a(m, u[i], sumi_m); // multiply sum of q8_1 values with m + const int vi = (vi0 >> (2*(i % (QI8_1/2)))) & 0x03030303; + sumi_d = __dp4a(vi, u[i], sumi_d); // SIMD dot product + sumi_m = __dp4a(0x01010101, u[i], sumi_m); } - sumi_d += sumi_d_sc * (sc & 0xF); + sumf_d += dm2f.x * sumi_d; + sumf_m += dm2f.y * sumi_m; } - const float2 dm2f = __half22float2(dm2); - - return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m); + return d8*(sumf_d - sumf_m); #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -352,8 +347,10 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) { int sumi_sc = 0; +#pragma unroll for (int i = i0; i < i0 + QI8_1/2; ++i) { - sumi_sc = __dp4a(v[i], u[i], sumi_sc); // SIMD dot product + const int vi = __vsubss4((v[i/2] >> (4*(i%2))) & 0x0F0F0F0F, 0x04040404); + sumi_sc = __dp4a(vi, u[i], sumi_sc); // SIMD dot product } sumi += sumi_sc * scales[i0 / (QI8_1/2)]; @@ -566,9 +563,9 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( } static __device__ __forceinline__ float vec_dot_q4_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq + kbx; int v[VDR_Q4_0_Q8_1_MMVQ]; int u[2*VDR_Q4_0_Q8_1_MMVQ]; @@ -585,9 +582,9 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1( static __device__ __forceinline__ float vec_dot_q4_1_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; + const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq + kbx; int v[VDR_Q4_1_Q8_1_MMVQ]; int u[2*VDR_Q4_1_Q8_1_MMVQ]; @@ -603,9 +600,9 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1( } static __device__ __forceinline__ float vec_dot_q5_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; + const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq + kbx; int vl[VDR_Q5_0_Q8_1_MMVQ]; int vh[VDR_Q5_0_Q8_1_MMVQ]; @@ -623,9 +620,9 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1( } static __device__ __forceinline__ float vec_dot_q5_1_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; + const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq + kbx; int vl[VDR_Q5_1_Q8_1_MMVQ]; int vh[VDR_Q5_1_Q8_1_MMVQ]; @@ -643,9 +640,9 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1( } static __device__ __forceinline__ float vec_dot_q8_0_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; + const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq + kbx; int v[VDR_Q8_0_Q8_1_MMVQ]; int u[VDR_Q8_0_Q8_1_MMVQ]; @@ -656,13 +653,13 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1( u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); } - return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); + return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); } static __device__ __forceinline__ float vec_dot_q2_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q2_K * bq2_K = (const block_q2_K *) vbq; + const block_q2_K * bq2_K = (const block_q2_K *) vbq + kbx; const int bq8_offset = QR2_K * (iqs / QI8_1); const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); @@ -683,9 +680,9 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1( } static __device__ __forceinline__ float vec_dot_q3_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q3_K * bq3_K = (const block_q3_K *) vbq; + const block_q3_K * bq3_K = (const block_q3_K *) vbq + kbx; const int bq8_offset = QR3_K * (iqs / (QI3_K/2)); const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); @@ -710,9 +707,9 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( } static __device__ __forceinline__ float vec_dot_q4_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q4_K * bq4_K = (const block_q4_K *) vbq; + const block_q4_K * bq4_K = (const block_q4_K *) vbq + kbx; int v[2]; int u[2*QR4_K]; @@ -756,9 +753,9 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( } static __device__ __forceinline__ float vec_dot_q5_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q5_K * bq5_K = (const block_q5_K *) vbq; + const block_q5_K * bq5_K = (const block_q5_K *) vbq + kbx; int vl[2]; int vh[2]; @@ -802,9 +799,9 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( } static __device__ __forceinline__ float vec_dot_q6_K_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_q6_K * bq6_K = (const block_q6_K *) vbq; + const block_q6_K * bq6_K = (const block_q6_K *) vbq + kbx; const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4); const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8); @@ -828,8 +825,8 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( } static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq + kbx; #if QR2_XXS == 8 const int ib32 = iqs; @@ -872,9 +869,9 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( } static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; + const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq + kbx; const int ib32 = iqs; const uint16_t * q2 = bq2->qs + 4*ib32; @@ -911,9 +908,9 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( // TODO static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq2_s * bq2 = (const block_iq2_s *) vbq; + const block_iq2_s * bq2 = (const block_iq2_s *) vbq + kbx; const int ib32 = iqs; const int8_t * q8 = bq8_1[ib32].qs; @@ -951,9 +948,9 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( } static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq + kbx; const int ib32 = iqs; const uint8_t * q3 = bq2->qs + 8*ib32; @@ -981,9 +978,9 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( // TODO: don't use lookup table for signs static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq3_s * bq2 = (const block_iq3_s *) vbq; + const block_iq3_s * bq2 = (const block_iq3_s *) vbq + kbx; const int ib32 = iqs; const uint8_t * qs = bq2->qs + 8*ib32; @@ -1008,8 +1005,8 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( } static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const block_iq1_s * bq1 = (const block_iq1_s *) vbq; + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + const block_iq1_s * bq1 = (const block_iq1_s *) vbq + kbx; const int ib32 = iqs; int sumi = 0; @@ -1039,8 +1036,8 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( } static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { - const block_iq1_m * bq1 = (const block_iq1_m *) vbq; + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { + const block_iq1_m * bq1 = (const block_iq1_m *) vbq + kbx; const int ib32 = iqs; int sumi[2] = {0, 0}; @@ -1094,9 +1091,9 @@ static __device__ __forceinline__ void get_int_from_table_16(const uint32_t & q4 #endif static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { - const block_iq4_nl * bq = (const block_iq4_nl *) vbq; + const block_iq4_nl * bq = (const block_iq4_nl *) vbq + kbx; #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics const uint16_t * q4 = (const uint16_t *)bq->qs + 2*iqs; @@ -1128,10 +1125,10 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( } static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( - const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; + const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq + kbx; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; // iqs is 0...7 @@ -1149,6 +1146,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( } return d * (sumi1 + sumi2); #else - return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); + return vec_dot_iq4_xs_q8_1(vbq, bq8_1, kbx, iqs); #endif } diff --git a/ggml-impl.h b/ggml-impl.h index 362d40f4d1d8b..1d23361906c34 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -17,7 +17,7 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) -#if defined(_WIN32) +#if defined(_MSC_VER) #define m512bh(p) p #define m512i(p) p @@ -144,6 +144,10 @@ extern "C" { #endif #endif +#if defined(__ARM_FEATURE_SVE) +#include +#endif + // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 6c6058b2a95b1..ed5f2e3494ba4 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -22,6 +22,7 @@ #include "shaderop_mul_mat_q4_1.h" #include "shaderop_mul_mat_q6_k.h" #include "shaderop_mul_mat_mat_f32.h" +#include "shaderop_getrows_f32.h" #include "shaderop_getrows_f16.h" #include "shaderop_getrows_q4_0.h" #include "shaderop_getrows_q4_1.h" @@ -1146,6 +1147,14 @@ static void ggml_vk_get_rows( seq.record(s_algo); } +template +static void ggml_vk_get_rows_f32(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f32_comp_spv, + kp::shader_data::op_getrows_f32_comp_spv_len); + + ggml_vk_get_rows(spirv, "f32", sizeof(float), 0, std::forward(args)...); +} + template static void ggml_vk_get_rows_f16(Args&&... args) { const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv, @@ -1183,7 +1192,7 @@ static void ggml_vk_rope( const std::shared_ptr& inB, const std::shared_ptr& out, uint32_t inAOff, uint32_t inBOff, uint32_t outOff, - ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_orig_ctx, + ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, int32_t ne01, int32_t ne02, int32_t ne03, uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, @@ -1212,14 +1221,14 @@ static void ggml_vk_rope( struct PushConstants { uint32_t inAOff, inBOff, outOff; - int32_t n_dims, mode, n_orig_ctx; + int32_t n_dims, mode, n_ctx_orig; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; uint32_t nb00, nb01, nb02, nb03; int32_t ne0; uint32_t nb0, nb1, nb2, nb3; } pushConsts { safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size), - n_dims, mode, n_orig_ctx, + n_dims, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, nb00, nb01, nb02, nb03, ne0, @@ -1331,7 +1340,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { case GGML_UNARY_OP_RELU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: - return true; + return ggml_is_contiguous(op->src[0]); default: ; } @@ -1371,6 +1380,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { return op->ne[3] == 1; case GGML_OP_GET_ROWS: switch (op->src[0]->type) { + case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -1597,7 +1607,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -1662,7 +1671,9 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_GET_ROWS: { - if (src0t == GGML_TYPE_F16) { + if (src0t == GGML_TYPE_F32) { + ggml_vk_get_rows_f32(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else if (src0t == GGML_TYPE_F16) { ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); } else if (src0t == GGML_TYPE_Q4_0) { ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); @@ -1681,13 +1692,16 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml #pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); +#pragma message("TODO: update rope NORM mode to match NEOX mode") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") + GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); @@ -1697,7 +1711,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); ggml_vk_rope( - seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_orig_ctx, + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 ); @@ -1888,18 +1902,12 @@ static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_ return ctx->max_alloc; } -static bool ggml_backend_kompute_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - GGML_UNUSED(buft); - return ggml_backend_is_kompute(backend); -} - static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = { /* .get_name = */ ggml_backend_kompute_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_kompute_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_kompute_buffer_type_get_alignment, /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .supports_backend = */ ggml_backend_kompute_buffer_type_supports_backend, /* .is_host = */ NULL, }; @@ -1959,6 +1967,11 @@ static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struc return ggml_vk_supports_op(op); } +static bool ggml_backend_kompute_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + GGML_UNUSED(backend); + return buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name; +} + static struct ggml_backend_i kompute_backend_i = { /* .get_name = */ ggml_backend_kompute_name, /* .free = */ ggml_backend_kompute_free, @@ -1969,9 +1982,11 @@ static struct ggml_backend_i kompute_backend_i = { /* .synchronize = */ NULL, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_kompute_graph_compute, /* .supports_op = */ ggml_backend_kompute_supports_op, + /* .supports_buft = */ ggml_backend_kompute_supports_buft, /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, diff --git a/ggml-metal.h b/ggml-metal.h index a5c542189c295..e7543ae795d28 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -1,7 +1,7 @@ // An interface allowing to compute ggml_cgraph with Metal // // This is a fully functional interface that extends ggml with GPU support for Apple devices. -// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.) +// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.) // // How it works? // diff --git a/ggml-metal.m b/ggml-metal.m index c9e570dbf5a3a..f894274cacc93 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -35,6 +35,10 @@ GGML_METAL_KERNEL_TYPE_MUL_ROW, GGML_METAL_KERNEL_TYPE_DIV, GGML_METAL_KERNEL_TYPE_DIV_ROW, + GGML_METAL_KERNEL_TYPE_REPEAT_F32, + GGML_METAL_KERNEL_TYPE_REPEAT_F16, + GGML_METAL_KERNEL_TYPE_REPEAT_I32, + GGML_METAL_KERNEL_TYPE_REPEAT_I16, GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, GGML_METAL_KERNEL_TYPE_CLAMP, @@ -168,8 +172,10 @@ GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, - GGML_METAL_KERNEL_TYPE_ROPE_F32, - GGML_METAL_KERNEL_TYPE_ROPE_F16, + GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, + GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, + GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, + GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, @@ -184,9 +190,9 @@ GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, + //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, + //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, @@ -485,6 +491,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32, repeat_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); @@ -618,8 +628,10 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32, rope_norm_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16, rope_norm_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32, rope_neox_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16, rope_neox_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); @@ -634,9 +646,9 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); @@ -732,7 +744,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: - return true; + return ggml_is_contiguous(op->src[0]); default: return false; } @@ -746,6 +758,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ACC: case GGML_OP_MUL: case GGML_OP_DIV: + case GGML_OP_REPEAT: case GGML_OP_SCALE: case GGML_OP_CLAMP: case GGML_OP_SQR: @@ -770,6 +783,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: + if (op->src[1]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[2]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[0]->ne[0] == 256) { + return false; + } return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: @@ -976,10 +998,10 @@ static enum ggml_status ggml_metal_graph_compute( switch (dst->op) { case GGML_OP_CONCAT: { - const int64_t nb = ne00; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + const int32_t dim = ((int32_t *) dst->op_params)[0]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1008,7 +1030,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; const int nth = MIN(1024, ne0); @@ -1018,11 +1040,14 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_MUL: case GGML_OP_DIV: { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + const size_t offs = 0; bool bcast_row = false; - int64_t nb = ne00; + int64_t nb = ne00; // used by the "row" kernels id pipeline = nil; @@ -1091,6 +1116,42 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } } break; + case GGML_OP_REPEAT: + { + id pipeline; + + switch (src0t) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; + case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; + default: GGML_ASSERT(false); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ACC: { GGML_ASSERT(src0t == GGML_TYPE_F32); @@ -1468,7 +1529,6 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -1802,9 +1862,10 @@ static enum ggml_status ggml_metal_graph_compute( // ne21 = n_rows const int dst_rows = ne20*ne21; const int dst_rows_min = n_as; + const int dst_rows_max = (ctx->device.maxThreadgroupMemoryLength - 32 - 8192)/4; // max size of the rowids array in the kernel shared buffer - GGML_ASSERT(dst_rows <= 2048); + GGML_ASSERT(dst_rows <= dst_rows_max); // for now the matrix-matrix multiplication kernel only works on A14+/M1+ SoCs // AMD GPU and older A-chips will reuse matrix-vector multiplication kernel @@ -2136,6 +2197,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_RMS_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous_1(src0)); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -2163,6 +2225,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_GROUP_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous(src0)); //float eps; //memcpy(&eps, dst->op_params, sizeof(float)); @@ -2196,6 +2259,8 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_NORM: { + GGML_ASSERT(ggml_is_contiguous_1(src0)); + float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -2225,7 +2290,7 @@ static enum ggml_status ggml_metal_graph_compute( const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; float freq_base; float freq_scale; @@ -2242,22 +2307,23 @@ static enum ggml_status ggml_metal_graph_compute( memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); const bool is_neox = mode & 2; - const bool is_glm = mode & 4; - GGML_ASSERT(!is_glm && "GLM RoPE not implemented in Metal"); + id pipeline = nil; if (!is_neox) { - GGML_ASSERT(id_src2 == nil && "TODO: freq_factors not implemented for !is_neox"); + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NORM_F16].pipeline; break; + default: GGML_ASSERT(false); + }; + } else { + switch (src0->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_NEOX_F16].pipeline; break; + default: GGML_ASSERT(false); + }; } - id pipeline = nil; - - switch (src0->type) { - case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F32].pipeline; break; - case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ROPE_F16].pipeline; break; - default: GGML_ASSERT(false); - }; - [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -2285,14 +2351,13 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; - [encoder setBytes:&mode length:sizeof( int) atIndex:22]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:23]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:24]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:25]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:28]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:29]; + [encoder setBytes:&n_ctx_orig length:sizeof( int) atIndex:22]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2573,7 +2638,7 @@ static enum ggml_status ggml_metal_graph_compute( case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; default: { GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); @@ -2586,7 +2651,7 @@ static enum ggml_status ggml_metal_graph_compute( switch (ne00) { case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; default: { GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); @@ -2980,12 +3045,6 @@ GGML_CALL static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend UNUSED(buft); } -GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); - - UNUSED(buft); -} - GGML_CALL static bool ggml_backend_metal_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return true; @@ -3000,7 +3059,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes - /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend, /* .is_host = */ ggml_backend_metal_buffer_type_is_host, }, /* .context = */ NULL, @@ -3115,6 +3173,12 @@ GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, con return ggml_metal_supports_op(metal_ctx, op); } +GGML_CALL static bool ggml_backend_metal_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + return buft->iface.get_name == ggml_backend_metal_buffer_type_get_name; + + UNUSED(backend); +} + static struct ggml_backend_i ggml_backend_metal_i = { /* .get_name = */ ggml_backend_metal_name, /* .free = */ ggml_backend_metal_free, @@ -3125,9 +3189,11 @@ GGML_CALL static bool ggml_backend_metal_supports_op(ggml_backend_t backend, con /* .synchronize = */ NULL, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_metal_graph_compute, /* .supports_op = */ ggml_backend_metal_supports_op, + /* .supports_buft = */ ggml_backend_metal_supports_buft, /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, diff --git a/ggml-metal.metal b/ggml-metal.metal index 8ff70d7a79ca7..e2796fd601281 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -168,6 +168,53 @@ kernel void kernel_div( } } +template +kernel void kernel_repeat( + device const char * src0, + device char * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 % ne03; + const int64_t i02 = i2 % ne02; + const int64_t i01 = i1 % ne01; + + device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; + device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + const int i00 = i0 % ne00; + *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); + } +} + +typedef decltype(kernel_repeat) kernel_repeat_t; + +template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat; + // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( @@ -1607,8 +1654,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - thread float * cos_theta, thread float * sin_theta -) { + thread float * cos_theta, thread float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; @@ -1625,19 +1671,20 @@ static void rope_yarn( // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get // `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) { - return n_dims * log(n_orig_ctx / (n_rot * 2 * M_PI_F)) / (2 * log(base)); +static float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * log(n_ctx_orig / (n_rot * 2 * M_PI_F)) / (2 * log(base)); } static void rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims - dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base))); - dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base))); + dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))); + dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base))); } -typedef void (rope_t)( +template +kernel void kernel_rope_norm( device const void * src0, device const int32_t * src1, device const float * src2, @@ -1660,8 +1707,7 @@ typedef void (rope_t)( constant uint64_t & nb3, constant int & n_past, constant int & n_dims, - constant int & mode, - constant int & n_orig_ctx, + constant int & n_ctx_orig, constant float & freq_base, constant float & freq_scale, constant float & ext_factor, @@ -1670,10 +1716,52 @@ typedef void (rope_t)( constant float & beta_slow, uint tiitg[[thread_index_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]], - uint3 tgpig[[threadgroup_position_in_grid]]); + uint3 tgpig[[threadgroup_position_in_grid]]) { + const int64_t i3 = tgpig[2]; + const int64_t i2 = tgpig[1]; + const int64_t i1 = tgpig[0]; + + float corr_dims[2]; + rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); + + device const int32_t * pos = src1; + + const float theta_base = (float) pos[i2]; + const float inv_ndims = -1.f/n_dims; + + float cos_theta; + float sin_theta; + + for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { + if (i0 < n_dims) { + const int64_t ic = i0/2; + + const float theta = theta_base * pow(freq_base, inv_ndims*i0); + + const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; + + rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); + + device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = src[0]; + const float x1 = src[1]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; + } else { + device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_data[0] = src[0]; + dst_data[1] = src[1]; + } + } +} template -kernel void kernel_rope( +kernel void kernel_rope_neox( device const void * src0, device const int32_t * src1, device const float * src2, @@ -1696,8 +1784,7 @@ kernel void kernel_rope( constant uint64_t & nb3, constant int & n_past, constant int & n_dims, - constant int & mode, - constant int & n_orig_ctx, + constant int & n_ctx_orig, constant float & freq_base, constant float & freq_scale, constant float & ext_factor, @@ -1711,73 +1798,53 @@ kernel void kernel_rope( const int64_t i2 = tgpig[1]; const int64_t i1 = tgpig[0]; - const bool is_neox = mode & 2; - float corr_dims[2]; - rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); device const int32_t * pos = src1; - const int64_t p = pos[i2]; - - const float theta_0 = (float)p; + const float theta_base = (float) pos[i2]; const float inv_ndims = -1.f/n_dims; - if (!is_neox) { - for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { - - const float theta = theta_0 * pow(freq_base, inv_ndims*i0); - float cos_theta, sin_theta; - rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); - - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const T x0 = src[0]; - const T x1 = src[1]; + float cos_theta; + float sin_theta; - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[1] = x0*sin_theta + x1*cos_theta; - } - } else { - for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) { - if (ic < n_dims) { - const int64_t ib = 0; - - // simplified from `(ib * n_dims + ic) * inv_ndims` - const float cur_rot = inv_ndims*ic - ib; - const float freq_factor = src2 != src0 ? src2[ic/2] : 1.0f; - - const float theta = theta_0 * pow(freq_base, cur_rot) / freq_factor; + for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { + if (i0 < n_dims) { + const int64_t ic = i0/2; - float cos_theta, sin_theta; - rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + const float theta = theta_base * pow(freq_base, inv_ndims*i0); - const int64_t i0 = ib*n_dims + ic/2; + const float freq_factor = src2 != src0 ? src2[ic] : 1.0f; - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + rope_yarn(theta/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); - const float x0 = src[0]; - const float x1 = src[n_dims/2]; + device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } else { - const int64_t i0 = ic; + const float x0 = src[0]; + const float x1 = src[n_dims/2]; - device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } else { + device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } -template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; -template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; +typedef decltype(kernel_rope_norm) kernel_rope_norm_t; +typedef decltype(kernel_rope_neox) kernel_rope_neox_t; + +template [[host_name("kernel_rope_norm_f32")]] kernel kernel_rope_norm_t kernel_rope_norm; +template [[host_name("kernel_rope_norm_f16")]] kernel kernel_rope_norm_t kernel_rope_norm; + +template [[host_name("kernel_rope_neox_f32")]] kernel kernel_rope_neox_t kernel_rope_neox; +template [[host_name("kernel_rope_neox_f16")]] kernel kernel_rope_neox_t kernel_rope_neox; typedef void (im2col_t)( device const float * x, @@ -2418,7 +2485,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; -template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; +//template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; template // head size, queries per threadgroup, cache items per threadgroup kernel void kernel_flash_attn_ext_vec_f16( @@ -2696,7 +2763,7 @@ kernel void kernel_flash_attn_ext_vec_f16( } template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; -template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; +//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; kernel void kernel_cpy_f16_f16( device const half * src0, @@ -3319,31 +3386,30 @@ kernel void kernel_concat( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, + constant int32_t & dim, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0; + device const float * x; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i02 < ne02) { - ((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0]; - src0_ptr += ntg.x*nb00; + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); } else { - ((device float *)dst_ptr)[0] = ((device float *)src1_ptr)[0]; - src1_ptr += ntg.x*nb10; + x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); } - dst_ptr += ntg.x*nb0; + + device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; } } diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp deleted file mode 100644 index e28566a7bdbd7..0000000000000 --- a/ggml-opencl.cpp +++ /dev/null @@ -1,2305 +0,0 @@ -#include "ggml.h" -#include "ggml-opencl.h" -#include "ggml-backend-impl.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#define CL_TARGET_OPENCL_VERSION 120 -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#define CL_DMMV_LOCAL_SIZE 32 - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 1 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -#define MULTILINE_QUOTE(...) #__VA_ARGS__ -static std::string program_source = MULTILINE_QUOTE( - -typedef char int8_t; -typedef uchar uint8_t; -typedef short int16_t; -typedef ushort uint16_t; -typedef int int32_t; -typedef uint uint32_t; - -struct __attribute__ ((packed)) block_q4_0 -{ - half d; - uint8_t qs[QK4_0 / 2]; -}; - -struct __attribute__ ((packed)) block_q4_1 -{ - half d; - half m; - uint8_t qs[QK4_1 / 2]; -}; - -struct __attribute__ ((packed)) block_q5_0 -{ - half d; - uint32_t qh; - uint8_t qs[QK5_0 / 2]; -}; - -struct __attribute__ ((packed)) block_q5_1 -{ - half d; - half m; - uint32_t qh; - uint8_t qs[QK5_1 / 2]; -}; - -struct __attribute__ ((packed)) block_q8_0 -{ - half d; - int8_t qs[QK8_0]; -}; - -struct __attribute__((packed)) block_q2_K -{ - uint8_t scales[16]; - uint8_t qs[64]; - half d; - half dmin; -}; - -struct __attribute__((packed)) block_q3_K -{ - uint8_t hmask[32]; - uint8_t qs[64]; - uint8_t scales[12]; - half d; -}; - -struct __attribute__((packed)) block_q4_K -{ - half d; - half dmin; - uint8_t scales[12]; - uint8_t qs[128]; -}; - -struct __attribute__((packed)) block_q5_K -{ - half d; - half dmin; - uint8_t scales[12]; - uint8_t qh[32]; - uint8_t qs[128]; -}; - -struct __attribute__((packed)) block_q6_K -{ - uint8_t ql[128]; - uint8_t qh[64]; - int8_t scales[16]; - half d; -}; - -__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) { - const uint i = get_global_id(0); - - y[i] = vload_half(0, &x[i]); -} - -void dequantize_q4_0(__global const struct block_q4_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - const uint8_t vui = x[ib].qs[iqs]; - - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; - - *v0 = (vi0 - 8)*d; - *v1 = (vi1 - 8)*d; -} -void dequantize_q4_1(__global const struct block_q4_1* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - const float m = vload_half(0, &x[ib].m); - - const uint8_t vui = x[ib].qs[iqs]; - - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; - - *v0 = vi0*d + m; - *v1 = vi1*d + m; -} -void dequantize_q5_0(__global const struct block_q5_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - uint32_t qh = x[ib].qh; - - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16; - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1) - 16; - - *v0 = x0*d; - *v1 = x1*d; -} -void dequantize_q5_1(__global const struct block_q5_1* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - const float m = vload_half(0, &x[ib].m); - - uint32_t qh = x[ib].qh; - - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0); - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1); - - *v0 = x0*d + m; - *v1 = x1*d + m; -} -void dequantize_q8_0(__global const struct block_q8_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - const int8_t vi0 = x[ib].qs[iqs + 0]; - const int8_t vi1 = x[ib].qs[iqs + 1]; - - *v0 = vi0*d; - *v1 = vi1*d; -} -void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float* v1){ - *v0 = vload_half(0, &x[ib + 0]); - *v1 = vload_half(0, &x[ib + 1]); -} -); - -static std::string k_quants_source = MULTILINE_QUOTE( -inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m) -{ - if (j < 4) - { - *d = q[j] & 63; - *m = q[j + 4] & 63; - } - else - { - *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4); - *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4); - } -} - -__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int n = tid / 32; - const int l = tid - 32 * n; - const int is = 8 * n + l / 16; - - const uint8_t q = x[i].qs[32 * n + l]; - __global float *y = yy + get_group_id(0) * QK_K + 128 * n; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4); - y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4); - y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4); - y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4); -} - -__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy) -{ - int r = get_local_id(0) / 4; - int i = get_group_id(0) + get_global_offset(0); - int tid = r / 2; - int is0 = r % 2; - int l0 = 16 * is0 + 4 * (get_local_id(0) % 4); - int n = tid / 4; - int j = tid - 4 * n; - - uint8_t m = 1 << (4 * n + j); - int is = 8 * n + 2 * j + is0; - int shift = 2 * j; - - int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4) - : is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4) - : is < 12 ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4) - : (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4); - float d_all = vload_half(0, &x[i].d); - float dl = d_all * (us - 32); - - __global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j; - const __global uint8_t *q = x[i].qs + 32 * n; - const __global uint8_t *hm = x[i].hmask; - - for (int l = l0; l < l0 + 4; ++l) - y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -} - -__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int il = tid / 8; - const int ir = tid % 8; - const int is = 2 * il; - const int n = 4; - - __global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint8_t *q = x[i].qs + 32 * il + n * ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, &sc, &m); - float d1 = dall * sc; - float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, &sc, &m); - float d2 = dall * sc; - float m2 = dmin * m; - for (int l = 0; l < n; ++l) - { - y[l + 0] = d1 * (q[l] & 0xF) - m1; - y[l + 32] = d2 * (q[l] >> 4) - m2; - } -} - -__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int il = tid / 16; - const int ir = tid % 16; - const int is = 2 * il; - - __global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir; - __global const uint8_t *qh = x[i].qh + 2 * ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, &sc, &m); - const float d1 = dall * sc; - const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, &sc, &m); - const float d2 = dall * sc; - const float m2 = dmin * m; - - uint8_t hm = 1 << (2 * il); - y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1; - y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1; - hm <<= 1; - y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2; - y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2; -} - -__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; - - __global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il; - - const float d = vload_half(0, &x[i].d); - - __global const uint8_t *ql = x[i].ql + 64 * ip + il; - const uint8_t qh = x[i].qh[32 * ip + il]; - __global const int8_t *sc = x[i].scales + is; - - y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); - y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); - y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -} - -__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q2_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 16/K_QUANTS_PER_ITERATION; - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int s_offset = 8*im; - const int y_offset = 128*im + l0; - - tmp[16 * ix + tid] = 0; - - uint32_t aux[4]; - const uint8_t * d = (const uint8_t *)aux; - const uint8_t * m = (const uint8_t *)(aux + 2); - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * q = x[i].qs + q_offset; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset); - aux[0] = a[0] & 0x0f0f0f0f; - aux[1] = a[1] & 0x0f0f0f0f; - aux[2] = (a[0] >> 4) & 0x0f0f0f0f; - aux[3] = (a[1] >> 4) & 0x0f0f0f0f; - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) - + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) - + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) - + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) - + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) - + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) - + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) - +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); - sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] - + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - - } - tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q3_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop - const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0....15 or 0...7 - - const uint8_t m = 1 << (4*im); - - const int l0 = n*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int y_offset = 128*im + l0; - - uint16_t utmp[4]; - const int8_t * s = (const int8_t *)utmp; - - const uint16_t s_shift = 4*im; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * q = x[i].qs + q_offset; - __global const uint8_t * h = x[i].hmask + l0; - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); - utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); - utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); - utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); - - const float d = vload_half(0, &x[i].d); - - float sum = 0; - for (int l = 0; l < n; ++l) { - sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) - + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) - + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) - + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); - sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) - + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) - + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) - + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); - } - tmp[16 * ix + tid] += d * sum; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - //to rename it later, just to test now - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int row = get_group_id(0); - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...15 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; - - const int step = 8/K_QUANTS_PER_ITERATION; - - const int il = tid/step; // 0...3 - const int ir = tid - step*il;// 0...3 - const int n = 2*K_QUANTS_PER_ITERATION; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - __global const struct block_q4_K * x = xx + ib0; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const uint8_t * q1 = x[i].qs + q_offset; - __global const uint8_t * q2 = q1 + 64; - __global const float * y1 = yy + i*QK_K + y_offset; - __global const float * y2 = y1 + 128; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 s = (float4)(0.f); - float smin = 0; - for (int l = 0; l < n; ++l) { - s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4); - s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4); - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int row = get_group_id(0); - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - const int tid = get_local_id(0)/2; // 0...15 - const int ix = get_local_id(0)%2; - - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 2; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - __global const struct block_q5_K * x = xx + ib0; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2) { - - __global const uint8_t * ql1 = x[i].qs + q_offset; - __global const uint8_t * ql2 = ql1 + 64; - __global const uint8_t * qh = x[i].qh + l0; - __global const float * y1 = yy + i*QK_K + y_offset; - __global const float * y2 = y1 + 128; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 sum = (float4)(0.f); - float smin = 0; - for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) - + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) - + y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) - + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) - + y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; - } - tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) { - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q6_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - -\n#if K_QUANTS_PER_ITERATION == 1\n - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 - const int is = 0; - -\n#else\n - - const int l0 = 4 * in; // 0, 4, 8, ..., 28 - const int is = in / 4; - -\n#endif\n - - const int ql_offset = 64*im + l0; - const int qh_offset = 32*im + l0; - const int s_offset = 8*im + is; - const int y_offset = 128*im + l0; - - tmp[16 * ix + tid] = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * ql = x[i].ql + ql_offset; - __global const uint8_t * qh = x[i].qh + qh_offset; - __global const int8_t * s = x[i].scales + s_offset; - - const float d = vload_half(0, &x[i].d); - -\n#if K_QUANTS_PER_ITERATION == 1\n - float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) - + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) - + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) - + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) - + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) - + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) - + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) - +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); - tmp[16 * ix + tid] += sum; -\n#else\n - float sum = 0; - for (int l = 0; l < 4; ++l) { - sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) - + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) - + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) - + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); - } - tmp[16 * ix + tid] += sum; -\n#endif\n - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} -); - - -std::string dequant_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2; - - if (i >= get_global_size(0)) { - return; - } - - const uint qk = QUANT_K; - const uint qr = QUANT_R; - - const int ib = i/qk + get_global_offset(0); // block index - const int iqs = (i%qk)/qr; // quant index - const int iybs = i - i%qk; // y block start index - const int y_offset = qr == 1 ? 1 : qk/2; - - // dequantize - float v0, v1; - DEQUANT_FUNC(x, ib, iqs, &v0, &v1); - y[iybs + iqs + 0] = v0; - y[iybs + iqs + y_offset] = v1; -} -); - -std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) { - const int local_size = get_local_size(0); - const int row = get_group_id(0); - const int tid = get_local_id(0); - - const uint qk = QUANT_K; - const uint qr = QUANT_R; - - const int col_step = local_size * 2; - const int y_offset = qr == 1 ? 1 : qk/2; - - x += get_global_offset(0); - - tmp[tid] = 0; - - for (int col = tid*2; col < ncols; col += col_step) { - const int ib = (row*ncols + col)/qk; // block index - const int iqs = (col%qk)/qr; // quant index - const int iybs = col - col%qk; // y block start index - - // dequantize - float v0, v1; - DEQUANT_FUNC(x, ib, iqs, &v0, &v1); - - // matrix multiplication - tmp[tid] += v0 * y[iybs + iqs + 0]; - tmp[tid] += v1 * y[iybs + iqs + y_offset]; - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=local_size/2; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -); - - -std::string mul_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0); - - if (i >= get_global_size(0)) { - return; - } - - dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky]; -} -); - -std::string add_template = MULTILINE_QUOTE( -__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0); - - if (i >= get_global_size(0)) { - return; - } - - dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky]; -} -); - -#define CL_CHECK(err) \ - do { \ - cl_int err_ = (err); \ - if (err_ != CL_SUCCESS) { \ - fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \ - #err, err_, __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -#define CLBLAST_CHECK(err) \ - do { \ - CLBlastStatusCode err_ = (err); \ - if (err_ != CLBlastSuccess) { \ - fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \ - #err, err_, __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -std::array dequant_str_keys = { - "KERNEL_NAME", "X_TYPE", "QUANT_K", "QUANT_R", "DEQUANT_FUNC" -}; - -std::array dequant_str_values = { - "dequantize_row_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0", - "dequantize_row_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1", - "dequantize_row_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0", - "dequantize_row_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1", - "dequantize_row_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0", - "convert_row_f16", "half", "1", "1", "convert_f16" -}; - -std::array dequant_mul_mat_vec_str_values = { - "dequantize_mul_mat_vec_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0", - "dequantize_mul_mat_vec_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1", - "dequantize_mul_mat_vec_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0", - "dequantize_mul_mat_vec_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1", - "dequantize_mul_mat_vec_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0", - "convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16" -}; - -std::array mul_str_keys = { - "KERNEL_NAME", "TYPE" -}; -std::array mul_str_values = { - "mul_f32", "float" -}; - -static std::string& replace(std::string& s, const std::string& from, const std::string& to) { - size_t pos = 0; - while ((pos = s.find(from, pos)) != std::string::npos) { - s.replace(pos, from.length(), to); - pos += to.length(); - } - return s; -} - -static std::string generate_kernels() { - std::stringstream src; - src << program_source << '\n'; - src << k_quants_source << '\n'; - for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) { - std::string dequant_kernel = dequant_template; - std::string dmmv_kernel = dequant_mul_mat_vec_template; - for (size_t j = 0; j < dequant_str_keys.size(); j++) { - replace(dequant_kernel, dequant_str_keys[j], dequant_str_values[i + j]); - replace(dmmv_kernel, dequant_str_keys[j], dequant_mul_mat_vec_str_values[i + j]); - } - src << dequant_kernel << '\n'; - src << dmmv_kernel << '\n'; - } - for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) { - std::string mul_kernel = mul_template; - for (size_t j = 0; j < mul_str_keys.size(); j++) { - replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]); - } - src << mul_kernel << '\n'; - } - src << add_template << '\n'; - - return src.str(); -} - -static cl_platform_id platform; -static cl_device_id device; -static cl_context context; -static cl_command_queue queue; -static cl_program program; -static cl_kernel convert_row_f16_cl; -static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl; -static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl; -static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl; -static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl; -static cl_kernel mul_f32_cl; -static cl_kernel add_f32_cl; -static bool fp16_support; - -static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) { - cl_program p; - char *program_log; - size_t program_size; - size_t log_size; - int err; - - program_size = strlen(program_buffer); - - p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err); - if(err < 0) { - fprintf(stderr, "OpenCL error creating program"); - exit(1); - } - - std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math " - "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 " - "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION); - - err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL); - if(err < 0) { - - clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size); - program_log = (char*) malloc(log_size + 1); - program_log[log_size] = '\0'; - clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL); - fprintf(stderr, "ggml_opencl: kernel compile error:\n\n%s\n", program_log); - free(program_log); - exit(1); - } - - return p; -} - -void ggml_cl_init(void) { - static bool initialized = false; - if (initialized) { - return; - } - initialized = true; - - cl_int err; - - struct cl_device; - struct cl_platform { - cl_platform_id id; - unsigned number; - char name[128]; - char vendor[128]; - struct cl_device * devices; - unsigned n_devices; - struct cl_device * default_device; - }; - - struct cl_device { - struct cl_platform * platform; - cl_device_id id; - unsigned number; - cl_device_type type; - char name[128]; - }; - - enum { NPLAT = 16, NDEV = 16 }; - - struct cl_platform platforms[NPLAT]; - unsigned n_platforms = 0; - struct cl_device devices[NDEV]; - unsigned n_devices = 0; - struct cl_device * default_device = NULL; - - platform = NULL; - device = NULL; - - cl_platform_id platform_ids[NPLAT]; - CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms)); - - for (unsigned i = 0; i < n_platforms; i++) { - struct cl_platform * p = &platforms[i]; - p->number = i; - p->id = platform_ids[i]; - CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL)); - CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL)); - - cl_device_id device_ids[NDEV]; - cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices); - if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) { - p->n_devices = 0; - } else { - CL_CHECK(clGetDeviceIDsError); - } - p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL; - p->default_device = NULL; - - for (unsigned j = 0; j < p->n_devices; j++) { - struct cl_device * d = &devices[n_devices]; - d->number = n_devices++; - d->id = device_ids[j]; - d->platform = p; - CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL)); - CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL)); - - if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) { - p->default_device = d; - } - } - - if (default_device == NULL && p->default_device != NULL) { - default_device = p->default_device; - } - } - - if (n_devices == 0) { - fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n"); - exit(1); - } - - char * user_platform_string = getenv("GGML_OPENCL_PLATFORM"); - char * user_device_string = getenv("GGML_OPENCL_DEVICE"); - int user_platform_number = -1; - int user_device_number = -1; - - unsigned n; - if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) { - user_platform_number = (int)n; - } - if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) { - user_device_number = (int)n; - } - if (user_platform_number != -1 && user_device_number != -1) { - cl_platform* platform = &platforms[user_platform_number]; - if ((unsigned)user_device_number >= platform->n_devices) { - fprintf(stderr, "ggml_opencl: invalid device number %d\n", user_device_number); - exit(1); - } - default_device = &platform->devices[user_device_number]; - } else { - - struct cl_device * selected_devices = devices; - unsigned n_selected_devices = n_devices; - - if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) { - for (unsigned i = 0; i < n_platforms; i++) { - struct cl_platform * p = &platforms[i]; - if (strstr(p->name, user_platform_string) != NULL || - strstr(p->vendor, user_platform_string) != NULL) { - user_platform_number = (int)i; - break; - } - } - if (user_platform_number == -1) { - fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string); - exit(1); - } - } - if (user_platform_number != -1) { - struct cl_platform * p = &platforms[user_platform_number]; - selected_devices = p->devices; - n_selected_devices = p->n_devices; - default_device = p->default_device; - if (n_selected_devices == 0) { - fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name); - exit(1); - } - } - - if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) { - for (unsigned i = 0; i < n_selected_devices; i++) { - struct cl_device * d = &selected_devices[i]; - if (strstr(d->name, user_device_string) != NULL) { - user_device_number = d->number; - break; - } - } - if (user_device_number == -1) { - fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string); - exit(1); - } - } - if (user_device_number != -1) { - selected_devices = &devices[user_device_number]; - n_selected_devices = 1; - default_device = &selected_devices[0]; - } - - GGML_ASSERT(n_selected_devices > 0); - - if (default_device == NULL) { - default_device = &selected_devices[0]; - } - } - - fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name); - fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name); - if (default_device->type != CL_DEVICE_TYPE_GPU) { - fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name); - } - - platform = default_device->platform->id; - device = default_device->id; - - size_t ext_str_size; - clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size); - char *ext_buffer = (char *)alloca(ext_str_size + 1); - clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL); - ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated - // Disabled due to faulty outputs - // Check if ext_buffer contains cl_khr_fp16 - fp16_support = false; // strstr(ext_buffer, "cl_khr_fp16") != NULL; - // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false"); - - cl_context_properties properties[] = { - (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0 - }; - - CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err)); - - CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err), - (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err : - (queue = clCreateCommandQueue(context, device, 0, &err), err) - ))); - - const std::string kernel_src = generate_kernels(); - - program = build_program_from_source(context, device, kernel_src.c_str()); - - // FP16 to FP32 kernel - CL_CHECK((convert_row_f16_cl = clCreateKernel(program, "convert_row_f16", &err), err)); - - // Dequantize kernels - CL_CHECK((dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err), err)); - CL_CHECK((dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err), err)); - CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err)); - CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err)); - CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err)); - CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err)); - CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err)); - CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err)); - CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err)); - CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err)); - CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err)); - - // dequant mul mat kernel - CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q4_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_1", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_0", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err)); - CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err)); - - // mul kernel - CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err)); - - CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err)); -} - -static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - return &dequantize_row_q4_0_cl; - case GGML_TYPE_Q4_1: - return &dequantize_row_q4_1_cl; - case GGML_TYPE_Q5_0: - return &dequantize_row_q5_0_cl; - case GGML_TYPE_Q5_1: - return &dequantize_row_q5_1_cl; - case GGML_TYPE_Q8_0: - return &dequantize_row_q8_0_cl; - case GGML_TYPE_Q2_K: - return &dequantize_block_q2_k_cl; - case GGML_TYPE_Q3_K: - return &dequantize_block_q3_k_cl; - case GGML_TYPE_Q4_K: - return &dequantize_block_q4_k_cl; - case GGML_TYPE_Q5_K: - return &dequantize_block_q5_k_cl; - case GGML_TYPE_Q6_K: - return &dequantize_block_q6_k_cl; - case GGML_TYPE_F16: - return &convert_row_f16_cl; - default: - return nullptr; - } -} - -static size_t ggml_cl_global_denom(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - return 4; - case GGML_TYPE_Q4_K: - return 8; - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - return 4; - case GGML_TYPE_F16: - default: - return 1; - } -} - -static size_t ggml_cl_local_size(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - return 0; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - return 64; - case GGML_TYPE_Q4_K: - return 32; - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - return 64; - case GGML_TYPE_F16: - default: - return 0; - } -} - -static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - return &dequantize_mul_mat_vec_q4_0_cl; - case GGML_TYPE_Q4_1: - return &dequantize_mul_mat_vec_q4_1_cl; - case GGML_TYPE_Q5_0: - return &dequantize_mul_mat_vec_q5_0_cl; - case GGML_TYPE_Q5_1: - return &dequantize_mul_mat_vec_q5_1_cl; - case GGML_TYPE_Q8_0: - return &dequantize_mul_mat_vec_q8_0_cl; - case GGML_TYPE_F16: - return &convert_mul_mat_vec_f16_cl; - case GGML_TYPE_Q2_K: - return &dequantize_mul_mat_vec_q2_K_cl; - case GGML_TYPE_Q3_K: - return &dequantize_mul_mat_vec_q3_K_cl; - case GGML_TYPE_Q4_K: - return &dequantize_mul_mat_vec_q4_K_cl; - case GGML_TYPE_Q5_K: - return &dequantize_mul_mat_vec_q5_K_cl; - case GGML_TYPE_Q6_K: - return &dequantize_mul_mat_vec_q6_K_cl; - default: - return nullptr; - } -} - -// buffer pool for cl -#define MAX_CL_BUFFERS 256 - -struct scoped_spin_lock { - std::atomic_flag& lock; - scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { - while (lock.test_and_set(std::memory_order_acquire)) { - ; // spin - } - } - ~scoped_spin_lock() { - lock.clear(std::memory_order_release); - } - scoped_spin_lock(const scoped_spin_lock&) = delete; - scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; -}; - -struct cl_buffer { - cl_mem mem; - size_t size = 0; -}; - -static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS]; -static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT; - -static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) { - scoped_spin_lock lock(g_cl_pool_lock); - cl_int err; - - int best_i = -1; - size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs - int worst_i = -1; - size_t worst_size = 0; //largest unused buffer seen so far - for (int i = 0; i < MAX_CL_BUFFERS; ++i) { - cl_buffer &b = g_cl_buffer_pool[i]; - if (b.size > 0 && b.size >= size && b.size < best_size) - { - best_i = i; - best_size = b.size; - } - if (b.size > 0 && b.size > worst_size) - { - worst_i = i; - worst_size = b.size; - } - } - if(best_i!=-1) //found the smallest buffer that fits our needs - { - cl_buffer& b = g_cl_buffer_pool[best_i]; - cl_mem mem = b.mem; - *actual_size = b.size; - b.size = 0; - return mem; - } - if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory - { - cl_buffer& b = g_cl_buffer_pool[worst_i]; - cl_mem mem = b.mem; - b.size = 0; - clReleaseMemObject(mem); - } - cl_mem mem; - CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err)); - *actual_size = size; - return mem; -} - -static void ggml_cl_pool_free(cl_mem mem, size_t size) { - scoped_spin_lock lock(g_cl_pool_lock); - - for (int i = 0; i < MAX_CL_BUFFERS; ++i) { - cl_buffer& b = g_cl_buffer_pool[i]; - if (b.size == 0) { - b.mem = mem; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n"); - clReleaseMemObject(mem); -} - -void ggml_cl_free_data(const struct ggml_tensor* tensor) { - if (tensor->backend != GGML_BACKEND_TYPE_GPU) { - return; - } - - cl_mem mem = (cl_mem)tensor->extra; - clReleaseMemObject(mem); -} - -static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) { - cl_int err; - const uint64_t ne0 = src->ne[0]; - const uint64_t ne1 = src->ne[1]; - const uint64_t nb0 = src->nb[0]; - const uint64_t nb1 = src->nb[1]; - const uint64_t nb2 = src->nb[2]; - const uint64_t nb3 = src->nb[3]; - const enum ggml_type type = src->type; - const size_t ts = ggml_type_size(type); - const size_t bs = ggml_blck_size(type); - const uint64_t row_size = ts*ne0/bs; - - const char * x = (const char *) src->data + i2*nb2 + i3*nb3; - if (nb0 == ts && nb1 == row_size) { - return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev); - } - if (nb0 == ts) { - const size_t buffer_origin[3] = { offset, 0, 0 }; - const size_t host_origin[3] = { 0, 0, 0 }; - const size_t region[3] = { row_size, ne1, 1 }; - return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev); - } - std::vector events; - if (ev && ne1>1) events.reserve(ne1-1); - for (uint64_t i1 = 0; i1 < ne1; i1++) { - // pretend the row is a matrix with cols=1 - const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 }; - const size_t host_origin[3] = { 0, 0, 0 }; - const size_t region[3] = { ts, ne0/bs, 1 }; - // if an event is requested, make the last write wait for all previous writes to complete - if (ev && i1) { - events.push_back(*ev); - } - cl_uint nevents = i1 == ne1-1 ? events.size() : 0U; - err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev); - if (err != CL_SUCCESS) { - for (auto event : events) { - clReleaseEvent(event); - } - return err; - } - } - for (auto event : events) { - CL_CHECK(clReleaseEvent(event)); - } - return CL_SUCCESS; -} - -static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - size_t x_size; - size_t d_size; - - cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 - cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. - cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst - - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - cl_event ev; - - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); - - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int i1 = i13*ne12*ne11 + i12*ne11; - - cl_int x_offset = 0; - cl_int y_offset = i1*ne10; - cl_int d_offset = 0; - - size_t global = ne00 * ne01; - cl_int ky = ne10 * ne11; - - CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - - CL_CHECK(clReleaseEvent(ev)); - CL_CHECK(clFinish(queue)); - - // copy dst to host - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL)); - } - } - ggml_cl_pool_free(d_X, x_size); - ggml_cl_pool_free(d_D, d_size); -} - -void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - ggml_cl_mul_f32(src0, src1, dst); -} - -static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - size_t x_size; - size_t d_size; - - cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 - cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. - cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst - - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - cl_event ev; - - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); - - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int i1 = i13*ne12*ne11 + i12*ne11; - - cl_int x_offset = 0; - cl_int y_offset = i1*ne10; - cl_int d_offset = 0; - - size_t global = ne00 * ne01; - cl_int ky = ne10 * ne11; - - CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - - CL_CHECK(clReleaseEvent(ev)); - CL_CHECK(clFinish(queue)); - - // copy dst to host - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL)); - } - } - ggml_cl_pool_free(d_X, x_size); - ggml_cl_pool_free(d_D, d_size); -} - -void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - ggml_cl_add_f32(src0, src1, dst); -} - -static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const float alpha = 1.0f; - const float beta = 0.0f; - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - - size_t x_size; - size_t y_size; - size_t d_size; - cl_mem d_X; - if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT - d_X = (cl_mem) src0->extra; - } else { - d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); - } - cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); - - size_t x_offset = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_TYPE_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else { - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - } - - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { - // copy src1 to device - if (src1->backend == GGML_BACKEND_TYPE_CPU) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - } - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // copy dst to host - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); - } - } - } - } - } - - if (src0->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_X, x_size); - } - if (src1->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_Y, y_size); - } - if (dst->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_D, d_size); - } -} - -static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) { - GGML_ASSERT(fp16_support); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f); - const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f); - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - - GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne); - GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne); - ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata; - - size_t x_size; - size_t y_size; - size_t d_size; - cl_mem d_X; - if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT - d_X = (cl_mem) src0->extra; - } else { - d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); - } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size); - - bool src1_cont_rows = nb10 == sizeof(float); - bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float); - - size_t x_offset = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_TYPE_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else { - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - } - - // FIXME: convert on device - - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { - // convert src1 to fp16 - // TODO: use multiple threads - char * src1i = (char *) src1->data + i13*nb13 + i12*nb12; - if (src1_cont_rows) { - if (src1_cont_cols) { - ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11); - } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10); - } - } - } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - for (int64_t i10 = 0; i10 < ne10; i10++) { - // very slow due to no inlining - tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10)); - } - } - } - - // copy src1 to device - CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL)); - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // copy dst to host, then convert to float - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_fp16_to_fp32_row(tmp, d, d_ne); - } else { - // FIXME: convert dst to fp32 on device - } - } - } - } - } - - if (src0->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_X, x_size); - } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); -} - -static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - const ggml_type type = src0->type; - const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const float alpha = 1.0f; - const float beta = 0.0f; - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice - const size_t q_sz = ggml_type_size(type) * x_bps; - - size_t x_size; - size_t y_size; - size_t d_size; - size_t q_size; - cl_mem d_X; - if (!mul_mat_vec) { - d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); - } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); - cl_mem d_Q; - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - d_Q = ggml_cl_pool_malloc(q_sz, &q_size); - } - - cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type); - cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type); - GGML_ASSERT(to_fp32_cl != nullptr); - - const size_t global_denom = ggml_cl_global_denom(type); - const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type); - - size_t ev_idx = 0; - std::vector events; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy and dequantize src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - // copy src0 to device if necessary - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - events.emplace_back(); - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++)); - } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { - d_Q = (cl_mem) src0->extra; - } else { - GGML_ASSERT(false); - } - - if (!mul_mat_vec) { - // convert src0 to fp32 on device - const size_t global = x_ne / global_denom; - const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; - CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); - } - - int64_t i12 = i02 * r2; - int64_t e12 = i12 + r2; - events.reserve(e12 - i12); - for (; i12 < e12; i12++) { - if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel - // copy src1 to device - events.emplace_back(); - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++)); - - // compute - const size_t global = ne01 * local; - const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; - const cl_int ncols = ne00; - events.emplace_back(); - CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL)); - CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++)); - } else { // CLBlast matrix matrix multiplication - // copy src1 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - - // wait for conversion - CL_CHECK(clFinish(queue)); - - // compute - events.emplace_back(); - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, 0, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, events.data() + ev_idx++); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - } - - // copy dst to host - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL)); - for (auto *event : events) { - clReleaseEvent(event); - } - - ev_idx = 0; - events.clear(); - } - } - } - } - - if (!mul_mat_vec) { - ggml_cl_pool_free(d_X, x_size); - } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - ggml_cl_pool_free(d_Q, q_size); - } -} - - -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) { - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - src1->type == GGML_TYPE_F32 && - dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) { - return true; - } - - return false; -} - -static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) { - // If device doesn't support FP16 - if (!fp16_support) { - return false; - } - - size_t src0_sz = ggml_nbytes(src0); - size_t src1_sz = ggml_nbytes(src1); - - // mul_mat_q: src0 is converted to fp32 on device - size_t mul_mat_q_transfer = src0_sz + src1_sz; - - // mul_mat_f16: src1 is converted to fp16 on cpu - size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1); - - // choose the smaller one to transfer to the device - // TODO: this is not always the best choice due to the overhead of converting to fp16 - return mul_mat_f16_transfer < mul_mat_q_transfer; -} - -void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) { - GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst)); - - if (src0->type == GGML_TYPE_F32) { - ggml_cl_mul_mat_f32(src0, src1, dst); - } - else if (src0->type == GGML_TYPE_F16) { - if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) { - ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize); - } - else { - ggml_cl_mul_mat_q_f32(src0, src1, dst); - } - } - else if (ggml_is_quantized(src0->type)) { - ggml_cl_mul_mat_q_f32(src0, src1, dst); - } - else { - GGML_ASSERT(false); - } -} - -size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) { - return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]); - } - return 0; -} - -void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) { - const int64_t ne0 = tensor->ne[0]; - const int64_t ne1 = tensor->ne[1]; - const int64_t ne2 = tensor->ne[2]; - const int64_t ne3 = tensor->ne[3]; - - const ggml_type type = tensor->type; - const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type)); - const size_t q_sz = s_sz * (size_t) (ne2 * ne3); - - size_t q_size; - cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size); - - tensor->data = data; - // copy tensor to device - size_t offset = 0; - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL)); - offset += s_sz; - } - } - - CL_CHECK(clFinish(queue)); - - tensor->extra = dst; - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); -} - -// ggml-backend - -// buffer - -struct ggml_backend_opencl_buffer_context { - ~ggml_backend_opencl_buffer_context() { - if (buffer) { - clReleaseMemObject(buffer); - } - for (auto * sub_buffer : sub_buffers) { - clReleaseMemObject(sub_buffer); - } - } - - cl_mem buffer; - std::vector sub_buffers; -}; - -static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000; - -static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) { - return "OpenCL"; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - delete ctx; -} - -static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) { - return cl_ptr_base; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - if (tensor->view_src != NULL && tensor->view_offs == 0) { - tensor->extra = tensor->view_src->extra; - } else { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)}; - cl_int err; - cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); - CL_CHECK(err); - ctx->sub_buffers.push_back(sub_buffer); - tensor->extra = sub_buffer; - } - tensor->backend = GGML_BACKEND_TYPE_GPU; -} - -static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - cl_mem tensor_buffer = (cl_mem) tensor->extra; - CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - cl_mem tensor_buffer = (cl_mem) tensor->extra; - CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); -} - -static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - for (auto * sub_buffer : ctx->sub_buffers) { - clReleaseMemObject(sub_buffer); - } - ctx->sub_buffers.clear(); -} - -static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = { - /* .get_name = */ ggml_backend_opencl_buffer_get_name, - /* .free_buffer = */ ggml_backend_opencl_buffer_free_buffer, - /* .get_base = */ ggml_backend_opencl_buffer_get_base, - /* .init_tensor = */ ggml_backend_opencl_buffer_init_tensor, - /* .set_tensor = */ ggml_backend_opencl_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_opencl_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_opencl_buffer_clear, - /* .reset = */ ggml_backend_opencl_buffer_reset, -}; - -// buffer type - -static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) { - return "OpenCL"; - - GGML_UNUSED(buffer_type); -} - -static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { - ggml_cl_init(); - - cl_int err; - cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err); - if (err != CL_SUCCESS) { - fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0); - return nullptr; - } - - ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}}; - - return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size); -} - -static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { - // FIXME: not thread safe, device may not be initialized yet - static cl_uint alignment = -1; - if (alignment == (cl_uint)-1) { - ggml_cl_init(); - clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL); - alignment /= 8; // bits to bytes - } - return alignment; - - GGML_UNUSED(buffer_type); -} - -static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) { - static size_t max_size = -1; - if (max_size == (size_t)-1) { - ggml_cl_init(); - clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL); - } - return max_size; -} - -static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) { - //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend - return ggml_backend_is_cpu(backend); - - GGML_UNUSED(buffer_type); -} - -static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { - /* .get_name = */ ggml_backend_opencl_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_opencl_buffer_type_get_max_size, - /* .get_alloc_size = */ NULL, - /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, - /* .is_host = */ NULL, -}; - - -ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() { - static ggml_backend_buffer_type buffer_type = { - /* .iface = */ ggml_backend_opencl_buffer_type_interface, - /* .context = */ nullptr, - }; - - return &buffer_type; -} - -#if 0 -// host buffer type - -static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return "CL_Host"; - - GGML_UNUSED(buft); -} - -static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) { - return "CL_Host"; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_cl_host_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_cl_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_opencl_host_buffer_name; - buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_opencl_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .context = */ nullptr, - }; - - return &ggml_backend_opencl_buffer_type_host; -} - -// backend - -static const char * ggml_backend_opencl_name(ggml_backend_t backend) { - return "OpenCL"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_opencl_free(ggml_backend_t backend) { - GGML_UNUSED(backend); -} - -static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_opencl_buffer_type(); - - GGML_UNUSED(backend); -} - -static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { - for (int i = 0; i < graph->n_nodes; ++i) { - ggml_tensor * node = graph->nodes[i]; - - if (ggml_is_empty(node)) { - continue; - } - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0); - break; - case GGML_OP_MUL: - ggml_cl_mul(node->src[0], node->src[1], node); - break; - default: - GGML_ASSERT(false); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_MUL_MAT: - return ggml_cl_can_mul_mat(op->src[0], op->src[1], op); - case GGML_OP_MUL: - // return ggml_can_repeat_rows(op->src[1], op->src[0]); - return true; - default: - return false; - } - - GGML_UNUSED(backend); -} - -static ggml_backend_i opencl_backend_i = { - /* .get_name = */ ggml_backend_opencl_name, - /* .free = */ ggml_backend_opencl_free, - /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_opencl_graph_compute, - /* .supports_op = */ ggml_backend_opencl_supports_op, -}; - -ggml_backend_t ggml_backend_opencl_init() { - ggml_backend_t backend = new ggml_backend { - /* .interface = */ opencl_backend_i, - /* .context = */ nullptr - }; - - return backend; -} - -bool ggml_backend_is_opencl(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_opencl_name; -} -#endif diff --git a/ggml-opencl.h b/ggml-opencl.h deleted file mode 100644 index 257a6be6af5ec..0000000000000 --- a/ggml-opencl.h +++ /dev/null @@ -1,36 +0,0 @@ -#pragma once - -#include "ggml.h" -#include "ggml-backend.h" - -#ifdef __cplusplus -extern "C" { -#endif - -GGML_API void ggml_cl_init(void); - -GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst); -GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); - -// GGML_API void * ggml_cl_host_malloc(size_t size); -// GGML_API void ggml_cl_host_free(void * ptr); - -GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); - -GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); - -// backend API - -// GGML_API ggml_backend_t ggml_backend_opencl_init(void); - -// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend); - -GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void); -// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void); - -#ifdef __cplusplus -} -#endif diff --git a/ggml-quants.c b/ggml-quants.c index bb01ce93cb969..0b346c11e6b2a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -4,8 +4,6 @@ #include "ggml-quants.h" #include "ggml-impl.h" -#define GGML_COMMON_IMPL_C -#include "ggml-common.h" #include #include @@ -1078,6 +1076,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) } vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + } #elif defined(__loongarch_asx) for (int i = 0; i < nb; i++) { @@ -1437,6 +1436,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) accv = vec_add(accv, vec_sld(accv, accv, 4)); accv = vec_add(accv, vec_sld(accv, accv, 8)); y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + } #elif defined(__loongarch_asx) for (int i = 0; i < nb; i++) { @@ -3813,7 +3813,44 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + const svbool_t ptrueh = svptrue_pat_b8(SV_VL16); + const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh); + + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q4_0 * restrict x0 = &x[i + 0]; + const block_q4_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -4076,12 +4113,13 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); const vector unsigned char v4 = vec_splats((unsigned char)0x4); const vector signed char v8 = vec_splats((signed char)0x8); vector float vsumf0 = vec_splats(0.0f); -#pragma GCC unroll 4 +#pragma GCC unroll 8 for (int i = 0; i < nb; i++) { __builtin_prefetch(x[i].qs, 0, 1); __builtin_prefetch(y[i].qs, 0, 1); @@ -4103,9 +4141,10 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - qv0 = vec_add(qv0, qv1); + vector signed int vsumi0 = v0; - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi0 = vec_sum4s(qv1, vsumi0); vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); } @@ -4479,6 +4518,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); const vector unsigned char v4 = vec_splats((unsigned char)0x4); vector float vsumf0 = vec_splats(0.0f); @@ -4500,15 +4540,13 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r vector signed char q8y0 = vec_xl( 0, y[i].qs); vector signed char q8y1 = vec_xl(16, y[i].qs); - vector signed char q4x0 = vec_and(qxs, lowMask); - vector signed char q4x1 = vec_sr(qxs, v4); - - vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); + vector unsigned char q4x0 = (vector unsigned char)vec_and(qxs, lowMask); + vector unsigned char q4x1 = (vector unsigned char)vec_sr(qxs, v4); - qv0 = vec_add(qv0, qv1); + vector signed int vsumi0 = v0; - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + vsumi0 = vec_msum(q8y0, q4x0, vsumi0); + vsumi0 = vec_msum(q8y1, q4x1, vsumi0); vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); } @@ -5210,6 +5248,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); const vector unsigned char v4 = vec_splats((unsigned char)0x4); vector float vsumf0 = vec_splats(0.0f); @@ -5235,18 +5274,16 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r vector signed char qxs = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char q5x0 = vec_or(vec_and(qxs, lowMask), qh0); - vector signed char q5x1 = vec_or(vec_sr(qxs, v4), qh1); + vector unsigned char q5x0 = (vector unsigned char)vec_or(vec_and(qxs, lowMask), qh0); + vector unsigned char q5x1 = (vector unsigned char)vec_or(vec_sr(qxs, v4), qh1); vector signed char q8y0 = vec_xl( 0, y[i].qs); vector signed char q8y1 = vec_xl( 16, y[i].qs); - vector signed short qv0 = vec_add(vec_mule(q5x0, q8y0), vec_mulo(q5x0, q8y0)); - vector signed short qv1 = vec_add(vec_mule(q5x1, q8y1), vec_mulo(q5x1, q8y1)); + vector signed int vsumi0 = v0; - qv0 = vec_add(qv0, qv1); - - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); + vsumi0 = vec_msum(q8y0, q5x0, vsumi0); + vsumi0 = vec_msum(q8y1, q5x1, vsumi0); vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); } @@ -5384,7 +5421,32 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q8_0 * restrict x0 = &x[i + 0]; + const block_q8_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); + const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -5461,9 +5523,10 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r *s = sumf; #elif defined(__POWER9_VECTOR__) + const vector signed int v0 = vec_splats((int32_t)0); vector float vsumf0 = vec_splats(0.0f); -#pragma GCC unroll 4 +#pragma GCC unroll 8 for (int i = 0; i < nb; i++) { __builtin_prefetch(x[i].qs, 0, 1); __builtin_prefetch(y[i].qs, 0, 1); @@ -5482,13 +5545,13 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r vector signed short qv2 = vec_mule(q8x1, q8y1); vector signed short qv3 = vec_mulo(q8x1, q8y1); - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackh(qv1)); - vector signed int vsumi1 = vec_add(vec_unpackl(qv0), vec_unpackl(qv1)); - vector signed int vsumi2 = vec_add(vec_unpackh(qv2), vec_unpackh(qv3)); - vector signed int vsumi3 = vec_add(vec_unpackl(qv2), vec_unpackl(qv3)); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; - vsumi0 = vec_add(vsumi0, vsumi2); - vsumi1 = vec_add(vsumi1, vsumi3); + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); + vsumi0 = vec_sum4s(qv2, vsumi0); + vsumi1 = vec_sum4s(qv3, vsumi1); vsumi0 = vec_add(vsumi0, vsumi1); @@ -5876,6 +5939,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0x3); const vector signed char lowScaleMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); const vector unsigned char v2 = vec_splats((unsigned char)0x2); const vector unsigned char v6 = vec_splats((unsigned char)0x6); const vector unsigned char v4 = vec_splats((unsigned char)0x4); @@ -5913,15 +5977,17 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; for (int j = 0; j < QK_K/128; ++j) { __builtin_prefetch(q2, 0, 1); @@ -5931,14 +5997,14 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char qxs1 = (vector signed char)vec_xl(16, q2); q2 += 32; - vector signed char q2x00 = vec_and(qxs0, lowMask); - vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); - vector signed char q2x10 = vec_and(qxs1, lowMask); - vector signed char q2x11 = vec_and(vec_sr(qxs1, v2), lowMask); - vector signed char q2x12 = vec_and(vec_sr(qxs1, v4), lowMask); - vector signed char q2x13 = vec_and(vec_sr(qxs1, v6), lowMask); + vector unsigned char q2x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q2x01 = (vector unsigned char)vec_and(vec_sr(qxs0, v2), lowMask); + vector unsigned char q2x02 = (vector unsigned char)vec_and(vec_sr(qxs0, v4), lowMask); + vector unsigned char q2x03 = (vector unsigned char)vec_and(vec_sr(qxs0, v6), lowMask); + vector unsigned char q2x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q2x11 = (vector unsigned char)vec_and(vec_sr(qxs1, v2), lowMask); + vector unsigned char q2x12 = (vector unsigned char)vec_and(vec_sr(qxs1, v4), lowMask); + vector unsigned char q2x13 = (vector unsigned char)vec_and(vec_sr(qxs1, v6), lowMask); vector signed char q8y00 = vec_xl( 0, q8); vector signed char q8y10 = vec_xl( 16, q8); @@ -5950,45 +6016,36 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char q8y13 = vec_xl(112, q8); q8 += 128; - vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); - vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); - vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); - vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); - vector signed short qv4 = vec_add(vec_mule(q2x10, q8y10), vec_mulo(q2x10, q8y10)); - vector signed short qv5 = vec_add(vec_mule(q2x11, q8y11), vec_mulo(q2x11, q8y11)); - vector signed short qv6 = vec_add(vec_mule(q2x12, q8y12), vec_mulo(q2x12, q8y12)); - vector signed short qv7 = vec_add(vec_mule(q2x13, q8y13), vec_mulo(q2x13, q8y13)); - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - vector signed short vs4 = vec_splat(vscales_h, 4); - vector signed short vs5 = vec_splat(vscales_h, 5); - vector signed short vs6 = vec_splat(vscales_h, 6); - vector signed short vs7 = vec_splat(vscales_h, 7); + vector signed int qv0 = vec_msum(q8y00, q2x00, v0); + vector signed int qv1 = vec_msum(q8y01, q2x01, v0); + vector signed int qv2 = vec_msum(q8y02, q2x02, v0); + vector signed int qv3 = vec_msum(q8y03, q2x03, v0); + vector signed int qv4 = vec_msum(q8y10, q2x10, v0); + vector signed int qv5 = vec_msum(q8y11, q2x11, v0); + vector signed int qv6 = vec_msum(q8y12, q2x12, v0); + vector signed int qv7 = vec_msum(q8y13, q2x13, v0); + + vector signed short vscales_07 = vec_unpackh(vscales); + vector signed int vscales_03 = vec_unpackh(vscales_07); + vector signed int vscales_47 = vec_unpackl(vscales_07); + vector signed int vs0 = vec_splat(vscales_03, 0); + vector signed int vs1 = vec_splat(vscales_03, 1); + vector signed int vs2 = vec_splat(vscales_03, 2); + vector signed int vs3 = vec_splat(vscales_03, 3); + vector signed int vs4 = vec_splat(vscales_47, 0); + vector signed int vs5 = vec_splat(vscales_47, 1); + vector signed int vs6 = vec_splat(vscales_47, 2); + vector signed int vs7 = vec_splat(vscales_47, 3); vscales = vec_sld(vscales, vscales, 8); - qv0 = vec_mul(qv0, vs0); - qv1 = vec_mul(qv1, vs2); - qv2 = vec_mul(qv2, vs4); - qv3 = vec_mul(qv3, vs6); - - qv0 = vec_madd(qv4, vs1, qv0); - qv1 = vec_madd(qv5, vs3, qv1); - qv2 = vec_madd(qv6, vs5, qv2); - qv3 = vec_madd(qv7, vs7, qv3); - - vsumi0 = vec_add(vec_unpackh(qv0), vsumi0); - vsumi1 = vec_add(vec_unpackh(qv1), vsumi1); - vsumi2 = vec_add(vec_unpackh(qv2), vsumi2); - vsumi3 = vec_add(vec_unpackh(qv3), vsumi3); - - vsumi4 = vec_add(vec_unpackl(qv0), vsumi4); - vsumi5 = vec_add(vec_unpackl(qv1), vsumi5); - vsumi6 = vec_add(vec_unpackl(qv2), vsumi6); - vsumi7 = vec_add(vec_unpackl(qv3), vsumi7); + vsumi0 = vec_add(vec_mul(qv0, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv1, vs2), vsumi1); + vsumi2 = vec_add(vec_mul(qv2, vs4), vsumi2); + vsumi3 = vec_add(vec_mul(qv3, vs6), vsumi3); + vsumi4 = vec_add(vec_mul(qv4, vs1), vsumi4); + vsumi5 = vec_add(vec_mul(qv5, vs3), vsumi5); + vsumi6 = vec_add(vec_mul(qv6, vs5), vsumi6); + vsumi7 = vec_add(vec_mul(qv7, vs7), vsumi7); } vsumi0 = vec_add(vsumi0, vsumi4); @@ -6026,6 +6083,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); @@ -6578,6 +6636,9 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0x3); + const vector signed char lowMask1 = vec_splats((int8_t)0xf); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); const vector signed char v1 = vec_splats((signed char)0x1); const vector unsigned char v2 = vec_splats((unsigned char)0x2); const vector unsigned char v3 = vec_splats((unsigned char)0x3); @@ -6595,30 +6656,33 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - uint32_t aux[3]; - uint32_t utmp[4]; + UNUSED(kmask1); + UNUSED(kmask2); - memcpy(aux, x[i].scales, 12); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(u0, lowMask1); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = (vector signed char)vec_mergeh((vector signed int)u2, (vector signed int)vec_sr(u2, v2)); + vector signed char u30 = vec_sl(vec_and(u3, lowMask), v4); + vector signed char u31 = vec_and(u3, lowMask2); - vector signed char vscales = (vector signed char)vec_xl( 0, utmp); + u1 = vec_or(u1, u30); + u2 = vec_or(vec_sr(u0, v4), u31); + + vector signed char vscales = (vector signed char)vec_mergeh((vector signed long long)u1, (vector signed long long)u2); vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].hmask); vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].hmask); vscales = vec_sub(vscales, off); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); - + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -6692,23 +6756,14 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed short qv12 = vec_add(vec_mule(q3x12, q8y12), vec_mulo(q3x12, q8y12)); vector signed short qv13 = vec_add(vec_mule(q3x13, q8y13), vec_mulo(q3x13, q8y13)); - vector signed int vsum0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsum1 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsum2 = vec_add(vec_mule(qv02, vs4), vec_mulo(qv02, vs4)); - vector signed int vsum3 = vec_add(vec_mule(qv03, vs6), vec_mulo(qv03, vs6)); - vector signed int vsum4 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsum5 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - vector signed int vsum6 = vec_add(vec_mule(qv12, vs5), vec_mulo(qv12, vs5)); - vector signed int vsum7 = vec_add(vec_mule(qv13, vs7), vec_mulo(qv13, vs7)); - - vsumi0 = vec_add(vsum0, vsumi0); - vsumi1 = vec_add(vsum1, vsumi1); - vsumi2 = vec_add(vsum2, vsumi2); - vsumi3 = vec_add(vsum3, vsumi3); - vsumi4 = vec_add(vsum4, vsumi4); - vsumi5 = vec_add(vsum5, vsumi5); - vsumi6 = vec_add(vsum6, vsumi6); - vsumi7 = vec_add(vsum7, vsumi7); + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs2, vsumi1); + vsumi2 = vec_msum(qv02, vs4, vsumi2); + vsumi3 = vec_msum(qv03, vs6, vsumi3); + vsumi4 = vec_msum(qv10, vs1, vsumi4); + vsumi5 = vec_msum(qv11, vs3, vsumi5); + vsumi6 = vec_msum(qv12, vs5, vsumi6); + vsumi7 = vec_msum(qv13, vs7, vsumi7); } vsumi0 = vec_add(vsumi0, vsumi4); @@ -6745,6 +6800,8 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; // Set up scales memcpy(aux, x[i].scales, 12); __m128i scales128 = lsx_set_w( @@ -6766,29 +6823,32 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int bit = 0; int is = 0; + __m256i xvbit; - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; for (int j = 0; j < QK_K/128; ++j) { // load low 2 bits const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit); // prepare low and high bits const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); - const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); - const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); - const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); - const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; // load Q8 quants @@ -7202,6 +7262,10 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); + const vector unsigned char v2 = vec_splats((uint8_t)2); const vector unsigned char v4 = vec_splats((unsigned char)0x4); vector float vsumf0 = vec_splats(0.0f); @@ -7220,15 +7284,24 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - memcpy(utmp, x[i].scales, 12); + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); - vector signed char utmps = (vector signed char)vec_xl( 0, utmp); vector signed short vscales = vec_unpackh(utmps); vector signed short q4xmins = vec_unpackl(utmps); vector signed short q4xmins0 = vec_mergeh(q4xmins, q4xmins); @@ -7244,14 +7317,10 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r vsumf2 = vec_nmsub(vec_ctf(prod2, 0), vdmin, vsumf2); vsumf3 = vec_nmsub(vec_ctf(prod3, 0), vdmin, vsumf3); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -7266,14 +7335,14 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char qxs3 = (vector signed char)vec_xl(48, q4); q4 += 64; - vector signed char q4x00 = vec_and(qxs0, lowMask); - vector signed char q4x01 = vec_sr(qxs0, v4); - vector signed char q4x10 = vec_and(qxs1, lowMask); - vector signed char q4x11 = vec_sr(qxs1, v4); - vector signed char q4x20 = vec_and(qxs2, lowMask); - vector signed char q4x21 = vec_sr(qxs2, v4); - vector signed char q4x30 = vec_and(qxs3, lowMask); - vector signed char q4x31 = vec_sr(qxs3, v4); + vector unsigned char q4x00 = (vector unsigned char)vec_and(qxs0, lowMask); + vector unsigned char q4x01 = (vector unsigned char)vec_sr(qxs0, v4); + vector unsigned char q4x10 = (vector unsigned char)vec_and(qxs1, lowMask); + vector unsigned char q4x11 = (vector unsigned char)vec_sr(qxs1, v4); + vector unsigned char q4x20 = (vector unsigned char)vec_and(qxs2, lowMask); + vector unsigned char q4x21 = (vector unsigned char)vec_sr(qxs2, v4); + vector unsigned char q4x30 = (vector unsigned char)vec_and(qxs3, lowMask); + vector unsigned char q4x31 = (vector unsigned char)vec_sr(qxs3, v4); vector signed char q8y00 = vec_xl( 0, q8); vector signed char q8y10 = vec_xl( 16, q8); @@ -7285,41 +7354,33 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char q8y31 = vec_xl(112, q8); q8 += 128; - vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); - vector signed short qv20 = vec_add(vec_mule(q4x20, q8y20), vec_mulo(q4x20, q8y20)); - vector signed short qv21 = vec_add(vec_mule(q4x21, q8y21), vec_mulo(q4x21, q8y21)); - vector signed short qv30 = vec_add(vec_mule(q4x30, q8y30), vec_mulo(q4x30, q8y30)); - vector signed short qv31 = vec_add(vec_mule(q4x31, q8y31), vec_mulo(q4x31, q8y31)); - - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); - vector signed short vs2 = vec_splat(vscales, 2); - vector signed short vs3 = vec_splat(vscales, 3); + vector signed int qv00 = vec_msum(q8y00, q4x00, v0); + vector signed int qv01 = vec_msum(q8y01, q4x01, v0); + vector signed int qv10 = vec_msum(q8y10, q4x10, v0); + vector signed int qv11 = vec_msum(q8y11, q4x11, v0); + vector signed int qv20 = vec_msum(q8y20, q4x20, v0); + vector signed int qv21 = vec_msum(q8y21, q4x21, v0); + vector signed int qv30 = vec_msum(q8y30, q4x30, v0); + vector signed int qv31 = vec_msum(q8y31, q4x31, v0); + + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); + vector signed int vs2 = vec_splat(vscales_h, 2); + vector signed int vs3 = vec_splat(vscales_h, 3); vscales = vec_sld(vscales, vscales, 8); - qv00 = vec_add(qv00, qv10); - qv10 = vec_add(qv01, qv11); - qv20 = vec_add(qv20, qv30); - qv30 = vec_add(qv21, qv31); + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv01, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv20, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv21, vs3), vsumi3); - vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); - vsumi2 = vec_add(vec_mule(qv10, vs1), vsumi2); - vsumi3 = vec_add(vec_mulo(qv10, vs1), vsumi3); - vsumi4 = vec_add(vec_mule(qv20, vs2), vsumi4); - vsumi5 = vec_add(vec_mulo(qv20, vs2), vsumi5); - vsumi6 = vec_add(vec_mule(qv30, vs3), vsumi6); - vsumi7 = vec_add(vec_mulo(qv30, vs3), vsumi7); + vsumi0 = vec_add(vec_mul(qv10, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv11, vs1), vsumi1); + vsumi2 = vec_add(vec_mul(qv30, vs2), vsumi2); + vsumi3 = vec_add(vec_mul(qv31, vs3), vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -7337,6 +7398,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); @@ -7349,6 +7413,11 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -7388,16 +7457,17 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + ft_union fi; fi.i = __lsx_vpickve2gr_w(acc_m, 0); *s = hsum_float_8(acc) + fi.f ; - #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -7812,6 +7882,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed char lowMask1 = vec_splats((int8_t)0x3f); + const vector signed char lowMask2 = vec_splats((int8_t)0x30); + const vector int v0 = vec_splats((int32_t)0); const vector unsigned char v1 = vec_splats((unsigned char)0x1); const vector unsigned char v2 = vec_splats((unsigned char)0x2); const vector unsigned char v3 = vec_splats((unsigned char)0x3); @@ -7830,18 +7903,27 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); vector float vdmin = vec_mul(vxmin, vyd); - memcpy(utmp, x[i].scales, 12); + UNUSED(kmask1); + UNUSED(kmask2); + UNUSED(kmask3); + UNUSED(utmp); - utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); - const uint32_t uaux = utmp[1] & kmask1; - utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); - utmp[2] = uaux; - utmp[0] &= kmask1; + vector signed char u0 = (vector signed char)vec_xl_len(x[i].scales, 8); + vector signed char u1 = vec_and(vec_sr(u0, v2), lowMask2); + vector signed char u2 = (vector signed char)vec_xl_len(x[i].scales + 8, 4); + vector signed char u3 = vec_sr(u2, v4); + + vector signed char u30 = u1; + vector signed char u31 = (vector signed char)vec_mergeh((vector signed int)vec_and(u2, lowMask), (vector signed int)u3); + + u1 = vec_and(u0, lowMask1); + u2 = vec_or(u30, u31); + + vector signed char utmps = (vector signed char)vec_mergeh((vector signed int)u1, (vector signed int)u2); vector signed short q8ysums0 = vec_xl( 0, y[i].bsums); vector signed short q8ysums1 = vec_xl(16, y[i].bsums); - vector signed char utmps = (vector signed char)vec_xl( 0, utmp); vector signed short vscales = vec_unpackh(utmps); vector signed short q5xmins = vec_unpackl(utmps); @@ -7861,10 +7943,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); vector signed char qxhs1 = (vector signed char)vec_xl(16, x[i].qh); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -7889,10 +7971,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r qxhs0 = vec_sr(qxhs0, v2); qxhs1 = vec_sr(qxhs1, v2); - vector signed char q5x00 = vec_or(q5h00, qxs00); - vector signed char q5x01 = vec_or(q5h01, qxs01); - vector signed char q5x10 = vec_or(q5h10, qxs10); - vector signed char q5x11 = vec_or(q5h11, qxs11); + vector unsigned char q5x00 = (vector unsigned char)vec_or(q5h00, qxs00); + vector unsigned char q5x01 = (vector unsigned char)vec_or(q5h01, qxs01); + vector unsigned char q5x10 = (vector unsigned char)vec_or(q5h10, qxs10); + vector unsigned char q5x11 = (vector unsigned char)vec_or(q5h11, qxs11); vector signed char q8y00 = vec_xl( 0, q8); vector signed char q8y10 = vec_xl(16, q8); @@ -7900,22 +7982,20 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed char q8y11 = vec_xl(48, q8); q8 += 64; - vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); + vector signed int qv00 = vec_msum(q8y00, q5x00, v0); + vector signed int qv01 = vec_msum(q8y01, q5x01, v0); + vector signed int qv10 = vec_msum(q8y10, q5x10, v0); + vector signed int qv11 = vec_msum(q8y11, q5x11, v0); - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); + vector signed int vscales_h = vec_unpackh(vscales); + vector signed int vs0 = vec_splat(vscales_h, 0); + vector signed int vs1 = vec_splat(vscales_h, 1); vscales = vec_sld(vscales, vscales, 12); - qv00 = vec_add(qv00, qv10); - qv01 = vec_add(qv01, qv11); - - vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); - vsumi2 = vec_add(vec_mule(qv01, vs1), vsumi2); - vsumi3 = vec_add(vec_mulo(qv01, vs1), vsumi3); + vsumi0 = vec_add(vec_mul(qv00, vs0), vsumi0); + vsumi1 = vec_add(vec_mul(qv10, vs0), vsumi1); + vsumi2 = vec_add(vec_mul(qv01, vs1), vsumi2); + vsumi3 = vec_add(vec_mul(qv11, vs1), vsumi3); } vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); @@ -7935,6 +8015,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); const __m128i mzero = __lsx_vldi(0); @@ -7953,6 +8036,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -7971,6 +8059,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256i sumi = __lasx_xvldi(0); int bit = 0; + __m256i xvbit; for (int j = 0; j < QK_K/64; ++j) { @@ -7979,13 +8068,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); hmask = __lasx_xvslli_h(hmask, 1); + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); hmask = __lasx_xvslli_h(hmask, 1); @@ -7999,10 +8090,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r p16_1 = lasx_madd_h(scale_1, p16_1); sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + } __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } *s = hsum_float_8(acc) + summs; @@ -8463,6 +8556,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); const vector unsigned char v2 = vec_splats((unsigned char)0x2); const vector unsigned char v3 = vec_splats((unsigned char)0x3); const vector unsigned char v4 = vec_splats((unsigned char)0x4); @@ -8479,14 +8573,14 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; + vector signed int vsumi4 = v0; + vector signed int vsumi5 = v0; + vector signed int vsumi6 = v0; + vector signed int vsumi7 = v0; const uint8_t * restrict q6 = x[i].ql; const uint8_t * restrict qh = x[i].qh; @@ -8566,23 +8660,14 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed short vs6 = vec_splat(vscales, 6); vector signed short vs7 = vec_splat(vscales, 7); - vsumi0 = vec_add(vec_mule(qv00, vs0), vsumi0); - vsumi1 = vec_add(vec_mulo(qv00, vs0), vsumi1); - vsumi2 = vec_add(vec_mule(qv01, vs4), vsumi2); - vsumi3 = vec_add(vec_mulo(qv01, vs4), vsumi3); - vsumi4 = vec_add(vec_mule(qv10, vs1), vsumi4); - vsumi5 = vec_add(vec_mulo(qv10, vs1), vsumi5); - vsumi6 = vec_add(vec_mule(qv11, vs5), vsumi6); - vsumi7 = vec_add(vec_mulo(qv11, vs5), vsumi7); - - vsumi0 = vec_add(vec_mule(qv20, vs2), vsumi0); - vsumi1 = vec_add(vec_mulo(qv20, vs2), vsumi1); - vsumi2 = vec_add(vec_mule(qv21, vs6), vsumi2); - vsumi3 = vec_add(vec_mulo(qv21, vs6), vsumi3); - vsumi4 = vec_add(vec_mule(qv30, vs3), vsumi4); - vsumi5 = vec_add(vec_mulo(qv30, vs3), vsumi5); - vsumi6 = vec_add(vec_mule(qv31, vs7), vsumi6); - vsumi7 = vec_add(vec_mulo(qv31, vs7), vsumi7); + vsumi0 = vec_msum(qv00, vs0, vsumi0); + vsumi1 = vec_msum(qv01, vs4, vsumi1); + vsumi2 = vec_msum(qv10, vs1, vsumi2); + vsumi3 = vec_msum(qv11, vs5, vsumi3); + vsumi4 = vec_msum(qv20, vs2, vsumi4); + vsumi5 = vec_msum(qv21, vs6, vsumi5); + vsumi6 = vec_msum(qv30, vs3, vsumi6); + vsumi7 = vec_msum(qv31, vs7, vsumi7); } vsumi0 = vec_add(vsumi0, vsumi4); @@ -8863,6 +8948,7 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void *s = 0.125f * hsum_float_8(accumf); #elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); vector float vsumf2 = vec_splats(0.0f); @@ -8875,14 +8961,10 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint16_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -8929,21 +9011,12 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void vector signed short vscales01 = vec_splats((int16_t)(2*ls0+1)); vector signed short vscales23 = vec_splats((int16_t)(2*ls1+1)); - vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -9335,6 +9408,7 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * *s = 0.125f * hsum_float_8(accumf); #elif defined(__POWER9_VECTOR__) + const vector int v0 = vec_splats((int32_t)0); vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); vector float vsumf2 = vec_splats(0.0f); @@ -9347,14 +9421,10 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint16_t * restrict q2 = x[i].qs; const uint8_t * restrict sc = x[i].scales; @@ -9402,21 +9472,12 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -9639,6 +9700,8 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); vector float vsumf2 = vec_splats(0.0f); @@ -9653,14 +9716,10 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint8_t * restrict q2 = x[i].qs; const uint8_t * restrict qh = x[i].qh; @@ -9720,21 +9779,12 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * vector signed short vscales2 = vec_splats((int16_t)(2*ls2+1)); vector signed short vscales3 = vec_splats((int16_t)(2*ls3+1)); - vsumi0 = vec_add(vec_mule(qv0, vscales0), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales1), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales2), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales3), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales0), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales1), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales2), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales3), vsumi7); + vsumi0 = vec_msum(qv0, vscales0, vsumi0); + vsumi1 = vec_msum(qv1, vscales1, vsumi1); + vsumi2 = vec_msum(qv2, vscales2, vsumi2); + vsumi3 = vec_msum(qv3, vscales3, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -9972,6 +10022,8 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void #elif defined(__POWER9_VECTOR__) const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); vector float vsumf2 = vec_splats(0.0f); @@ -9982,14 +10034,10 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void vector float vyd = vec_splats(y[i].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; const uint8_t * restrict q3 = x[i].qs; const uint32_t * restrict signs = (const uint32_t *)(x[i].qs + QK_K/4); @@ -10034,21 +10082,12 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -10338,6 +10377,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * static const uint8_t k_mask2[16] = {0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80,}; + const vector int v0 = vec_splats((int32_t)0); + vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); vector float vsumf2 = vec_splats(0.0f); @@ -10358,14 +10399,10 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const uint8_t * restrict sc = x[i].scales; const int8_t * restrict q8 = y[i].qs; - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; for (int j = 0; j < QK_K/32; j += 2) { __builtin_prefetch(q3, 0, 1); @@ -10419,21 +10456,12 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * vector signed short vscales01 = (vector signed short)vec_splats((uint16_t)(2*ls0+1)); vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); - vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -10714,10 +10742,6 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void vector signed int vsumi1 = vec_splats((int32_t)0); vector signed int vsumi2 = vec_splats((int32_t)0); vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); vector signed int vsumi8 = vec_splats((int32_t)0); const uint8_t * restrict q1 = x[i].qs; @@ -10759,14 +10783,10 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void vector signed short vscales23 = (vector signed short)vec_splats((uint16_t)(2*ls1+1)); vector signed short vscales = vec_sld(vscales23, vscales01, 8); - vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); vector signed short q8ysums = vec_xl_len(qs, 8); qs += 4; @@ -10781,11 +10801,6 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void vsumi8 = vec_add(vec_mule(q8ysum, vscales), vsumi8); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -11179,6 +11194,7 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector signed int v0 = vec_splats((int32_t)0); const vector unsigned char v4 = vec_splats((unsigned char)0x4); vector float vsumf0 = vec_splats(0.0f); @@ -11209,8 +11225,11 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * vector signed short qv0 = vec_add(vec_mule(q4x0, q8y0), vec_mulo(q4x0, q8y0)); vector signed short qv1 = vec_add(vec_mule(q4x1, q8y1), vec_mulo(q4x1, q8y1)); - vector signed int vsumi0 = vec_add(vec_unpackh(qv0), vec_unpackl(qv0)); - vector signed int vsumi1 = vec_add(vec_unpackh(qv1), vec_unpackl(qv1)); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + + vsumi0 = vec_sum4s(qv0, vsumi0); + vsumi1 = vec_sum4s(qv1, vsumi1); vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); @@ -11365,6 +11384,7 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); + const vector int v0 = vec_splats((int32_t)0); const vector unsigned char v4 = vec_splats((unsigned char)0x4); vector float vsumf0 = vec_splats(0.0f); @@ -11380,14 +11400,10 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * vector float vyd = vec_splats(y[ibl].d); vector float vd = vec_mul(vxd, vyd); - vector signed int vsumi0 = vec_splats((int32_t)0); - vector signed int vsumi1 = vec_splats((int32_t)0); - vector signed int vsumi2 = vec_splats((int32_t)0); - vector signed int vsumi3 = vec_splats((int32_t)0); - vector signed int vsumi4 = vec_splats((int32_t)0); - vector signed int vsumi5 = vec_splats((int32_t)0); - vector signed int vsumi6 = vec_splats((int32_t)0); - vector signed int vsumi7 = vec_splats((int32_t)0); + vector signed int vsumi0 = v0; + vector signed int vsumi1 = v0; + vector signed int vsumi2 = v0; + vector signed int vsumi3 = v0; uint16_t h = x[ibl].scales_h; @@ -11432,21 +11448,12 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * vector signed short vscales01 = vec_splats((int16_t)ls0); vector signed short vscales23 = vec_splats((int16_t)ls1); - vsumi0 = vec_add(vec_mule(qv0, vscales01), vsumi0); - vsumi1 = vec_add(vec_mule(qv1, vscales01), vsumi1); - vsumi2 = vec_add(vec_mule(qv2, vscales23), vsumi2); - vsumi3 = vec_add(vec_mule(qv3, vscales23), vsumi3); - vsumi4 = vec_add(vec_mulo(qv0, vscales01), vsumi4); - vsumi5 = vec_add(vec_mulo(qv1, vscales01), vsumi5); - vsumi6 = vec_add(vec_mulo(qv2, vscales23), vsumi6); - vsumi7 = vec_add(vec_mulo(qv3, vscales23), vsumi7); + vsumi0 = vec_msum(qv0, vscales01, vsumi0); + vsumi1 = vec_msum(qv1, vscales01, vsumi1); + vsumi2 = vec_msum(qv2, vscales23, vsumi2); + vsumi3 = vec_msum(qv3, vscales23, vsumi3); } - vsumi0 = vec_add(vsumi0, vsumi4); - vsumi1 = vec_add(vsumi1, vsumi5); - vsumi2 = vec_add(vsumi2, vsumi6); - vsumi3 = vec_add(vsumi3, vsumi7); - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); @@ -13049,7 +13056,7 @@ static int iq1_find_best_neighbour(const uint16_t * restrict neighbours, const u const float * restrict xval, const float * restrict weight, float * scale, int8_t * restrict L, int ngrid) { int num_neighbors = neighbours[0]; GGML_ASSERT(num_neighbors > 0); - float best_score = 0; + float best_score = -FLT_MAX; int grid_index = -1; for (int j = 1; j <= num_neighbors; ++j) { const int8_t * pg = (const int8_t *)(grid + neighbours[j]); @@ -13247,7 +13254,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy sumw[j+1] = sumw[j] + weight[i]; } } - float best_score = 0, scale = max; + float best_score = -FLT_MIN, scale = max; int besti1 = -1, besti2 = -1, best_shift = 0; for (int i1 = 0; i1 <= block_size; ++i1) { for (int i2 = i1; i2 <= block_size; ++i2) { @@ -13423,7 +13430,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy idx[2*j] = j; } qsort(pairs, block_size, 2*sizeof(float), iq1_sort_helper); - float best_score = 0, scale = max; + float best_score = -FLT_MIN, scale = max; int besti1 = -1, besti2 = -1, best_k = -1; // 0: +, + // 1: +, - diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index cc1d3ace1ddac..b01ad267446fb 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #ifdef _WIN32 @@ -47,6 +48,7 @@ struct socket_t { sockfd_t fd; socket_t(sockfd_t fd) : fd(fd) {} ~socket_t() { + GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd); #ifdef _WIN32 closesocket(this->fd); #else @@ -71,9 +73,13 @@ struct rpc_tensor { uint64_t view_offs; uint64_t data; char name[GGML_MAX_NAME]; + + char padding[4]; }; #pragma pack(pop) +static_assert(sizeof(rpc_tensor) % 8 == 0, "rpc_tensor size must be multiple of 8"); + // RPC commands enum rpc_cmd { ALLOC_BUFFER = 0, @@ -97,7 +103,7 @@ static ggml_guid_t ggml_backend_rpc_guid() { } struct ggml_backend_rpc_buffer_type_context { - std::shared_ptr sock; + std::string endpoint; std::string name; size_t alignment; size_t max_size; @@ -106,8 +112,6 @@ struct ggml_backend_rpc_buffer_type_context { struct ggml_backend_rpc_context { std::string endpoint; std::string name; - std::shared_ptr sock; - ggml_backend_buffer_type_t buft; }; struct ggml_backend_rpc_buffer_context { @@ -231,14 +235,13 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) { return true; } -static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { - std::string str(endpoint); - size_t pos = str.find(':'); +static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { + size_t pos = endpoint.find(':'); if (pos == std::string::npos) { return false; } - host = str.substr(0, pos); - port = std::stoi(str.substr(pos + 1)); + host = endpoint.substr(0, pos); + port = std::stoi(endpoint.substr(pos + 1)); return true; } @@ -273,6 +276,44 @@ static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cm // RPC client-side implementation +static std::shared_ptr get_socket(const std::string & endpoint) { + static std::mutex mutex; + std::lock_guard lock(mutex); + static std::unordered_map> sockets; + static bool initialized = false; + + auto it = sockets.find(endpoint); + if (it != sockets.end()) { + if (auto sock = it->second.lock()) { + return sock; + } + } + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return nullptr; + } +#ifdef _WIN32 + if (!initialized) { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + return nullptr; + } + initialized = true; + } +#else + UNUSED(initialized); +#endif + auto sock = socket_connect(host.c_str(), port); + if (sock == nullptr) { + return nullptr; + } + GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd); + sockets[endpoint] = sock; + return sock; +} + GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; return ctx->name.c_str(); @@ -442,7 +483,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer std::vector input(input_size, 0); memcpy(input.data(), &size, sizeof(size)); std::vector output; - bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); + auto sock = get_socket(buft_ctx->endpoint); + bool status = send_rpc_cmd(sock, ALLOC_BUFFER, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | @@ -453,7 +495,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer if (remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, remote_size); return buffer; } else { @@ -502,26 +544,15 @@ GGML_CALL static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend return ggml_nbytes(tensor); } -GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - if (!ggml_backend_is_rpc(backend)) { - return false; - } - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; - ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - return buft_ctx->sock == rpc_ctx->sock; -} - static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { /* .get_name = */ ggml_backend_rpc_buffer_type_name, /* .alloc_buffer = */ ggml_backend_rpc_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_rpc_buffer_type_get_alignment, /* .get_max_size = */ ggml_backend_rpc_get_max_size, /* .get_alloc_size = */ ggml_backend_rpc_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_rpc_buffer_type_supports_backend, /* .is_host = */ NULL, }; - GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; @@ -530,16 +561,13 @@ GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context; - delete buft_ctx; - delete rpc_ctx->buft; delete rpc_ctx; delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - return ctx->buft; + return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); } GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { @@ -575,9 +603,8 @@ static void serialize_graph(const ggml_cgraph * cgraph, std::vector & o int output_size = sizeof(uint32_t) + n_nodes * sizeof(uint64_t) + sizeof(uint32_t) + n_tensors * sizeof(rpc_tensor); output.resize(output_size, 0); memcpy(output.data(), &n_nodes, sizeof(n_nodes)); - uint64_t * out_nodes = (uint64_t *)(output.data() + sizeof(n_nodes)); for (uint32_t i = 0; i < n_nodes; i++) { - out_nodes[i] = reinterpret_cast(cgraph->nodes[i]); + memcpy(output.data() + sizeof(n_nodes) + i * sizeof(uint64_t), &cgraph->nodes[i], sizeof(uint64_t)); } uint32_t * out_ntensors = (uint32_t *)(output.data() + sizeof(n_nodes) + n_nodes * sizeof(uint64_t)); *out_ntensors = n_tensors; @@ -590,7 +617,8 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t std::vector input; serialize_graph(cgraph, input); std::vector output; - bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); + auto sock = get_socket(rpc_ctx->endpoint); + bool status = send_rpc_cmd(sock, GRAPH_COMPUTE, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 1); return (enum ggml_status)output[0]; @@ -599,8 +627,17 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t GGML_CALL static bool ggml_backend_rpc_supports_op(ggml_backend_t backend, const ggml_tensor * op) { UNUSED(backend); UNUSED(op); - GGML_ASSERT(false && "not implemented"); - return false; + //TODO: call the remote backend and cache the results + return true; +} + +GGML_CALL static bool ggml_backend_rpc_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_rpc_buffer_type_name) { + return false; + } + ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; + ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; + return buft_ctx->endpoint == rpc_ctx->endpoint; } static ggml_backend_i ggml_backend_rpc_interface = { @@ -613,9 +650,11 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .synchronize = */ ggml_backend_rpc_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_rpc_graph_compute, /* .supports_op = */ ggml_backend_rpc_supports_op, + /* .supports_buft = */ ggml_backend_rpc_supports_buft, /* .offload_op = */ NULL, /* .event_new = */ NULL, /* .event_free = */ NULL, @@ -624,65 +663,48 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .event_synchronize = */ NULL, }; -static std::unordered_map instances; - GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; -} - -GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { - std::string endpoint_str(endpoint); - if (instances.find(endpoint_str) != instances.end()) { - return instances[endpoint_str]; - } -#ifdef _WIN32 - { - WSADATA wsaData; - int res = WSAStartup(MAKEWORD(2, 2), &wsaData); - if (res != 0) { - return nullptr; - } - } -#endif - fprintf(stderr, "Connecting to %s\n", endpoint); - std::string host; - int port; - if (!parse_endpoint(endpoint, host, port)) { - return nullptr; - } - auto sock = socket_connect(host.c_str(), port); + static std::mutex mutex; + std::lock_guard lock(mutex); + // NOTE: buffer types are allocated and never freed; this is by design + static std::unordered_map buft_map; + auto it = buft_map.find(endpoint); + if (it != buft_map.end()) { + return it->second; + } + auto sock = get_socket(endpoint); if (sock == nullptr) { return nullptr; } size_t alignment = get_alignment(sock); size_t max_size = get_max_size(sock); ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { - /* .sock = */ sock, - /* .name = */ "RPC" + std::to_string(sock->fd), + /* .endpoint = */ endpoint, + /* .name = */ "RPC[" + std::string(endpoint) + "]", /* .alignment = */ alignment, - /* .max_size = */ max_size + /* .max_size = */ max_size }; ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { /* .iface = */ ggml_backend_rpc_buffer_type_interface, /* .context = */ buft_ctx }; + buft_map[endpoint] = buft; + return buft; +} +GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC" + std::to_string(sock->fd), - /* .sock = */ sock, - /* .buft = */ buft + /* .endpoint = */ endpoint, + /* .name = */ "RPC[" + std::string(endpoint) + "]", }; - instances[endpoint] = new ggml_backend { + ggml_backend_t backend = new ggml_backend { /* .guid = */ ggml_backend_rpc_guid(), /* .interface = */ ggml_backend_rpc_interface, /* .context = */ ctx }; - - return instances[endpoint]; + return backend; } GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { @@ -706,14 +728,13 @@ static void get_device_memory(const std::shared_ptr & sock, size_t * f } GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - if (backend == nullptr) { + auto sock = get_socket(endpoint); + if (sock == nullptr) { *free = 0; *total = 0; return; } - ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - get_device_memory(ctx->sock, free, total); + get_device_memory(sock, free, total); } // RPC server-side implementation @@ -1018,7 +1039,9 @@ bool rpc_server::graph_compute(const std::vector & input, std::vector tensor_map; for (uint32_t i = 0; i < n_nodes; i++) { - graph->nodes[i] = create_node(nodes[i], ctx, tensor_ptrs, tensor_map); + int64_t id; + memcpy(&id, &nodes[i], sizeof(id)); + graph->nodes[i] = create_node(id, ctx, tensor_ptrs, tensor_map); } ggml_status status = ggml_backend_graph_compute(backend, graph); // output serialization format: | status (1 byte) | diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 496ec61c3c28a..6bd42b9609882 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -36,6 +36,8 @@ #include "ggml.h" #include "ggml-backend-impl.h" +#include "ggml-sycl/backend.hpp" + /* Following definition copied from DPCT head files, which are used by ggml-sycl.cpp */ @@ -74,2978 +76,15 @@ Following definition copied from DPCT head files, which are used by ggml-sycl.cp #else #define __dpct_align__(n) __attribute__((aligned(n))) #define __dpct_inline__ __inline__ __attribute__((always_inline)) -#endif - -#if defined(_MSC_VER) -#define __dpct_noinline__ __declspec(noinline) -#else -#define __dpct_noinline__ __attribute__((noinline)) -#endif - - -std::string get_device_type_name(const sycl::device &Device) { - auto DeviceType = Device.get_info(); - switch (DeviceType) { - case sycl::info::device_type::cpu: - return "cpu"; - case sycl::info::device_type::gpu: - return "gpu"; - case sycl::info::device_type::host: - return "host"; - case sycl::info::device_type::accelerator: - return "acc"; - default: - return "unknown"; - } -} - -std::string get_device_backend_and_type(const sycl::device &device) { - std::stringstream device_type; - sycl::backend backend = device.get_backend(); - device_type << backend << ":" << get_device_type_name(device); - return device_type.str(); -} - -namespace dpct -{ - typedef sycl::queue *queue_ptr; - typedef sycl::event *event_ptr; - typedef char *device_ptr; - typedef uint8_t byte_t; - typedef sycl::buffer buffer_t; - - /// SYCL default exception handler - inline auto exception_handler = [](sycl::exception_list exceptions) - { - for (std::exception_ptr const &e : exceptions) - { - try - { - std::rethrow_exception(e); - } - catch (sycl::exception const &e) - { - std::cerr << "Caught asynchronous SYCL exception:" << std::endl - << e.what() << std::endl - << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - } - } - }; - - enum error_code - { - success = 0, - default_error = 999 - }; - - enum memcpy_direction - { - host_to_host, - host_to_device, - device_to_host, - device_to_device, - automatic - }; - - enum memory_region - { - global = 0, // device global memory - constant, // device constant memory - local, // device local memory - shared, // memory which can be accessed by host and device - }; - - enum class library_data_t : unsigned char - { - real_float = 0, - complex_float, - real_double, - complex_double, - real_half, - complex_half, - real_bfloat16, - complex_bfloat16, - real_int4, - complex_int4, - real_uint4, - complex_uint4, - real_int8, - complex_int8, - real_uint8, - complex_uint8, - real_int16, - complex_int16, - real_uint16, - complex_uint16, - real_int32, - complex_int32, - real_uint32, - complex_uint32, - real_int64, - complex_int64, - real_uint64, - complex_uint64, - real_int8_4, - real_int8_32, - real_uint8_4, - library_data_t_size - }; - - template - struct DataType - { - using T2 = T; - }; - template - struct DataType> - { - using T2 = std::complex; - }; - - static void destroy_event(event_ptr event) - { - delete event; - } - - static inline unsigned int get_tid() - { -#if defined(__linux__) - return syscall(SYS_gettid); -#elif defined(_WIN64) - return GetCurrentThreadId(); -#else -#error "Only support Windows and Linux." -#endif - } - - namespace detail - { - static void get_version(const sycl::device &dev, int &major, int &minor) - { - // Version string has the following format: - // a. OpenCL - // b. - // c. e.g gfx1030 - std::string ver; - ver = dev.get_info(); - std::string::size_type i = 0; - while (i < ver.size()) { - if (isdigit(ver[i])) - break; - i++; - } - major = std::stoi(&(ver[i])); - while (i < ver.size()) { - if (ver[i] == '.') - break; - i++; - } - if (i < ver.size()) { - // a. and b. - i++; - minor = std::stoi(&(ver[i])); - } else { - // c. - minor = 0; - } - } - - template - class generic_error_type - { - public: - generic_error_type() = default; - generic_error_type(T value) : value{value} {} - operator T() const { return value; } - - private: - T value; - }; - - } // namespace detail - - /// Pitched 2D/3D memory data. - class pitched_data - { - public: - pitched_data() : pitched_data(nullptr, 0, 0, 0) {} - pitched_data(void *data, size_t pitch, size_t x, size_t y) - : _data(data), _pitch(pitch), _x(x), _y(y) {} - - void *get_data_ptr() { return _data; } - void set_data_ptr(void *data) { _data = data; } - - size_t get_pitch() { return _pitch; } - void set_pitch(size_t pitch) { _pitch = pitch; } - - size_t get_x() { return _x; } - void set_x(size_t x) { _x = x; }; - - size_t get_y() { return _y; } - void set_y(size_t y) { _y = y; } - - private: - void *_data; - size_t _pitch, _x, _y; - }; - - class device_info - { - public: - // get interface - const char *get_name() const { return _name; } - char *get_name() { return _name; } - template , - std::enable_if_t> || - std::is_same_v, - int> = 0> - auto get_max_work_item_sizes() const - { - if constexpr (std::is_same_v>) - return sycl::range<3>(_max_work_item_sizes_i[0], - _max_work_item_sizes_i[1], - _max_work_item_sizes_i[2]); - else - { - return _max_work_item_sizes_i; - } - } - template , - std::enable_if_t> || - std::is_same_v, - int> = 0> - auto get_max_work_item_sizes() - { - if constexpr (std::is_same_v>) - return sycl::range<3>(_max_work_item_sizes_i[0], - _max_work_item_sizes_i[1], - _max_work_item_sizes_i[2]); - else - { - return _max_work_item_sizes_i; - } - } - bool get_host_unified_memory() const { return _host_unified_memory; } - int get_major_version() const { return _major; } - int get_minor_version() const { return _minor; } - int get_integrated() const { return _integrated; } - int get_max_clock_frequency() const { return _frequency; } - int get_max_compute_units() const { return _max_compute_units; } - int get_max_work_group_size() const { return _max_work_group_size; } - int get_max_sub_group_size() const { return _max_sub_group_size; } - int get_max_work_items_per_compute_unit() const - { - return _max_work_items_per_compute_unit; - } - int get_max_register_size_per_work_group() const - { - return _max_register_size_per_work_group; - } - template || - std::is_same_v, - int> = 0> - auto get_max_nd_range_size() const - { - if constexpr (std::is_same_v) - return _max_nd_range_size; - else - return _max_nd_range_size_i; - } - template || - std::is_same_v, - int> = 0> - auto get_max_nd_range_size() - { - if constexpr (std::is_same_v) - return _max_nd_range_size; - else - return _max_nd_range_size_i; - } - size_t get_global_mem_size() const { return _global_mem_size; } - size_t get_local_mem_size() const { return _local_mem_size; } - size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; } - /// Returns the maximum clock rate of device's global memory in kHz. If - /// compiler does not support this API then returns default value 3200000 kHz. - unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } - /// Returns the maximum bus width between device and memory in bits. If - /// compiler does not support this API then returns default value 64 bits. - unsigned int get_memory_bus_width() const { return _memory_bus_width; } - uint32_t get_device_id() const { return _device_id; } - std::array get_uuid() const { return _uuid; } - /// Returns global memory cache size in bytes. - unsigned int get_global_mem_cache_size() const - { - return _global_mem_cache_size; - } - - // set interface - void set_name(const char *name) - { - size_t length = strlen(name); - if (length < 256) - { - std::memcpy(_name, name, length + 1); - } - else - { - std::memcpy(_name, name, 255); - _name[255] = '\0'; - } - } - void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes) - { - for (int i = 0; i < 3; ++i) - _max_work_item_sizes_i[i] = max_work_item_sizes[i]; - } - [[deprecated]] void - set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes) - { - for (int i = 0; i < 3; ++i) - { - _max_work_item_sizes_i[i] = max_work_item_sizes[i]; - } - } - void set_host_unified_memory(bool host_unified_memory) - { - _host_unified_memory = host_unified_memory; - } - void set_major_version(int major) { _major = major; } - void set_minor_version(int minor) { _minor = minor; } - void set_integrated(int integrated) { _integrated = integrated; } - void set_max_clock_frequency(int frequency) { _frequency = frequency; } - void set_max_compute_units(int max_compute_units) - { - _max_compute_units = max_compute_units; - } - void set_global_mem_size(size_t global_mem_size) - { - _global_mem_size = global_mem_size; - } - void set_local_mem_size(size_t local_mem_size) - { - _local_mem_size = local_mem_size; - } - void set_max_mem_alloc_size(size_t max_mem_alloc_size) - { - _max_mem_alloc_size = max_mem_alloc_size; - } - void set_max_work_group_size(int max_work_group_size) - { - _max_work_group_size = max_work_group_size; - } - void set_max_sub_group_size(int max_sub_group_size) - { - _max_sub_group_size = max_sub_group_size; - } - void - set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit) - { - _max_work_items_per_compute_unit = max_work_items_per_compute_unit; - } - void set_max_nd_range_size(int max_nd_range_size[]) - { - for (int i = 0; i < 3; i++) - { - _max_nd_range_size[i] = max_nd_range_size[i]; - _max_nd_range_size_i[i] = max_nd_range_size[i]; - } - } - void set_memory_clock_rate(unsigned int memory_clock_rate) - { - _memory_clock_rate = memory_clock_rate; - } - void set_memory_bus_width(unsigned int memory_bus_width) - { - _memory_bus_width = memory_bus_width; - } - void - set_max_register_size_per_work_group(int max_register_size_per_work_group) - { - _max_register_size_per_work_group = max_register_size_per_work_group; - } - void set_device_id(uint32_t device_id) - { - _device_id = device_id; - } - void set_uuid(std::array uuid) - { - _uuid = std::move(uuid); - } - void set_global_mem_cache_size(unsigned int global_mem_cache_size) - { - _global_mem_cache_size = global_mem_cache_size; - } - - private: - char _name[256]; - int _max_work_item_sizes_i[3]; - bool _host_unified_memory = false; - int _major; - int _minor; - int _integrated = 0; - int _frequency; - // Set estimated value 3200000 kHz as default value. - unsigned int _memory_clock_rate = 3200000; - // Set estimated value 64 bits as default value. - unsigned int _memory_bus_width = 64; - unsigned int _global_mem_cache_size; - int _max_compute_units; - int _max_work_group_size; - int _max_sub_group_size; - int _max_work_items_per_compute_unit; - int _max_register_size_per_work_group; - size_t _global_mem_size; - size_t _local_mem_size; - size_t _max_mem_alloc_size; - size_t _max_nd_range_size[3]; - int _max_nd_range_size_i[3]; - uint32_t _device_id; - std::array _uuid; - }; - - static int get_major_version(const sycl::device &dev) - { - int major, minor; - detail::get_version(dev, major, minor); - return major; - } - - static int get_minor_version(const sycl::device &dev) - { - int major, minor; - detail::get_version(dev, major, minor); - return minor; - } - - static void get_device_info(device_info &out, const sycl::device &dev) - { - device_info prop; - prop.set_name(dev.get_info().c_str()); - - int major, minor; - detail::get_version(dev, major, minor); - prop.set_major_version(major); - prop.set_minor_version(minor); - - prop.set_max_work_item_sizes( -#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902) - // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes - // is an enum class element - dev.get_info()); -#else - // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by - // an int - dev.get_info>()); -#endif - prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations)); - - prop.set_max_clock_frequency( - dev.get_info() * 1000); - - prop.set_max_compute_units( - dev.get_info()); - prop.set_max_work_group_size( - dev.get_info()); - prop.set_global_mem_size(dev.get_info()); - prop.set_local_mem_size(dev.get_info()); - prop.set_max_mem_alloc_size(dev.get_info()); - -#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) - if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) - { - unsigned int tmp = - dev.get_info(); - if (tmp != 0) - prop.set_memory_clock_rate(1000 * tmp); - } - if (dev.has(sycl::aspect::ext_intel_memory_bus_width)) - { - prop.set_memory_bus_width( - dev.get_info()); - } - if (dev.has(sycl::aspect::ext_intel_device_id)) - { - prop.set_device_id( - dev.get_info()); - } - if (dev.has(sycl::aspect::ext_intel_device_info_uuid)) - { - prop.set_uuid(dev.get_info()); - } -#elif defined(_MSC_VER) && !defined(__clang__) -#pragma message("get_device_info: querying memory_clock_rate and \ - memory_bus_width are not supported by the compiler used. \ - Use 3200000 kHz as memory_clock_rate default value. \ - Use 64 bits as memory_bus_width default value.") -#else -#warning "get_device_info: querying memory_clock_rate and \ - memory_bus_width are not supported by the compiler used. \ - Use 3200000 kHz as memory_clock_rate default value. \ - Use 64 bits as memory_bus_width default value." -#endif - - size_t max_sub_group_size = 1; - std::vector sub_group_sizes = - dev.get_info(); - - for (const auto &sub_group_size : sub_group_sizes) - { - if (max_sub_group_size < sub_group_size) - max_sub_group_size = sub_group_size; - } - - prop.set_max_sub_group_size(max_sub_group_size); - - prop.set_max_work_items_per_compute_unit( - dev.get_info()); - int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF}; - prop.set_max_nd_range_size(max_nd_range_size); - - // Estimates max register size per work group, feel free to update the value - // according to device properties. - prop.set_max_register_size_per_work_group(65536); - - prop.set_global_mem_cache_size( - dev.get_info()); - out = prop; - } - - /// dpct device extension - class device_ext : public sycl::device - { - typedef std::mutex mutex_type; - - public: - device_ext() : sycl::device(), _ctx(*this) {} - ~device_ext() - { - std::lock_guard lock(m_mutex); - clear_queues(); - } - device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this) - { - std::lock_guard lock(m_mutex); - init_queues(); - } - - int is_native_atomic_supported() { return 0; } - int get_major_version() const - { - return dpct::get_major_version(*this); - } - - int get_minor_version() const - { - return dpct::get_minor_version(*this); - } - - int get_max_compute_units() const - { - return get_device_info().get_max_compute_units(); - } - - /// Return the maximum clock frequency of this device in KHz. - int get_max_clock_frequency() const - { - return get_device_info().get_max_clock_frequency(); - } - - int get_integrated() const { return get_device_info().get_integrated(); } - - int get_max_sub_group_size() const - { - return get_device_info().get_max_sub_group_size(); - } - - int get_max_register_size_per_work_group() const - { - return get_device_info().get_max_register_size_per_work_group(); - } - - int get_max_work_group_size() const - { - return get_device_info().get_max_work_group_size(); - } - - int get_mem_base_addr_align() const - { - return get_info(); - } - - size_t get_global_mem_size() const - { - return get_device_info().get_global_mem_size(); - } - - size_t get_max_mem_alloc_size() const - { - return get_device_info().get_max_mem_alloc_size(); - } - - /// Get the number of bytes of free and total memory on the SYCL device. - /// \param [out] free_memory The number of bytes of free memory on the SYCL device. - /// \param [out] total_memory The number of bytes of total memory on the SYCL device. - void get_memory_info(size_t &free_memory, size_t &total_memory) - { - total_memory = get_device_info().get_global_mem_size(); - const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not " - "supported (export/set ZES_ENABLE_SYSMAN=1 to support), " - "use total memory as free memory"; -#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) - if (!has(sycl::aspect::ext_intel_free_memory)) - { - std::cerr << warning_info << std::endl; - free_memory = total_memory; - } - else - { - free_memory = get_info(); - } -#else - std::cerr << warning_info << std::endl; - free_memory = total_memory; -#if defined(_MSC_VER) && !defined(__clang__) -#pragma message("Querying the number of bytes of free memory is not supported") -#else -#warning "Querying the number of bytes of free memory is not supported" -#endif -#endif - } - - void get_device_info(device_info &out) const - { - dpct::get_device_info(out, *this); - } - - device_info get_device_info() const - { - device_info prop; - dpct::get_device_info(prop, *this); - return prop; - } - - void reset() - { - std::lock_guard lock(m_mutex); - clear_queues(); - init_queues(); - } - - sycl::queue &in_order_queue() { return *_q_in_order; } - - sycl::queue &out_of_order_queue() { return *_q_out_of_order; } - - sycl::queue &default_queue() - { - return in_order_queue(); - } - - void queues_wait_and_throw() - { - std::unique_lock lock(m_mutex); - std::vector> current_queues( - _queues); - lock.unlock(); - for (const auto &q : current_queues) - { - q->wait_and_throw(); - } - // Guard the destruct of current_queues to make sure the ref count is safe. - lock.lock(); - } - - sycl::queue *create_queue(bool enable_exception_handler = false) - { - return create_in_order_queue(enable_exception_handler); - } - - sycl::queue *create_queue(sycl::context context, sycl::device device, - bool enable_exception_handler = false) { - return create_in_order_queue(context, device, enable_exception_handler); - } - - sycl::queue *create_in_order_queue(bool enable_exception_handler = false) { - std::lock_guard lock(m_mutex); - return create_queue_impl(enable_exception_handler, - sycl::property::queue::in_order()); - } - - sycl::queue *create_in_order_queue(sycl::context context, sycl::device device, - bool enable_exception_handler = false) { - std::lock_guard lock(m_mutex); - return create_queue_impl(context, device, enable_exception_handler, - sycl::property::queue::in_order()); - } - - sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) { - std::lock_guard lock(m_mutex); - return create_queue_impl(enable_exception_handler); - } - - void destroy_queue(sycl::queue *&queue) - { - std::lock_guard lock(m_mutex); - _queues.erase(std::remove_if(_queues.begin(), _queues.end(), - [=](const std::shared_ptr &q) -> bool - { - return q.get() == queue; - }), - _queues.end()); - queue = nullptr; - } - void set_saved_queue(sycl::queue *q) - { - std::lock_guard lock(m_mutex); - _saved_queue = q; - } - sycl::queue *get_saved_queue() const - { - std::lock_guard lock(m_mutex); - return _saved_queue; - } - sycl::context get_context() const { return _ctx; } - - private: - void clear_queues() - { - _queues.clear(); - _q_in_order = _q_out_of_order = _saved_queue = nullptr; - } - - void init_queues() - { - _q_in_order = create_queue_impl(true, sycl::property::queue::in_order()); - _q_out_of_order = create_queue_impl(true); - _saved_queue = &default_queue(); - } - - /// Caller should acquire resource \p m_mutex before calling this function. - template - sycl::queue *create_queue_impl(bool enable_exception_handler, - Properties... properties) - { - sycl::async_handler eh = {}; - if (enable_exception_handler) - { - eh = exception_handler; - } - _queues.push_back(std::make_shared( - _ctx, *this, eh, - sycl::property_list( -#ifdef DPCT_PROFILING_ENABLED - sycl::property::queue::enable_profiling(), -#endif - properties...))); - - return _queues.back().get(); - } - - template - sycl::queue *create_queue_impl(sycl::context context, sycl::device device, - bool enable_exception_handler, - Properties... properties) { - sycl::async_handler eh = {}; - if (enable_exception_handler) { - eh = exception_handler; - } - _queues.push_back(std::make_shared( - context, device, eh, - sycl::property_list( - #ifdef DPCT_PROFILING_ENABLED - sycl::property::queue::enable_profiling(), - #endif - properties...))); - - return _queues.back().get(); - } - - void get_version(int &major, int &minor) const - { - detail::get_version(*this, major, minor); - } - sycl::queue *_q_in_order, *_q_out_of_order; - sycl::queue *_saved_queue; - sycl::context _ctx; - std::vector> _queues; - mutable mutex_type m_mutex; - }; - - /// device manager - class dev_mgr - { - public: - device_ext ¤t_device() - { - unsigned int dev_id = current_device_id(); - check_id(dev_id); - return *_devs[dev_id]; - } - device_ext &cpu_device() const - { - std::lock_guard lock(m_mutex); - if (_cpu_device == -1) - { - throw std::runtime_error("no valid cpu device"); - } - else - { - return *_devs[_cpu_device]; - } - } - device_ext &get_device(unsigned int id) const - { - std::lock_guard lock(m_mutex); - check_id(id); - return *_devs[id]; - } - unsigned int current_device_id() const - { - std::lock_guard lock(m_mutex); - auto it = _thread2dev_map.find(get_tid()); - if (it != _thread2dev_map.end()) - return it->second; - return DEFAULT_DEVICE_ID; - } - - /// Select device with a device ID. - /// \param [in] id The id of the device which can - /// be obtained through get_device_id(const sycl::device). - void select_device(unsigned int id) - { - std::lock_guard lock(m_mutex); - check_id(id); - _thread2dev_map[get_tid()] = id; - } - unsigned int device_count() { return _devs.size(); } - - unsigned int get_device_id(const sycl::device &dev) - { - unsigned int id = 0; - for (auto dev_item : _devs) - { - if (*dev_item == dev) - { - break; - } - id++; - } - return id; - } - - template - std::enable_if_t< - std::is_invocable_r_v> - select_device(const DeviceSelector &selector = sycl::gpu_selector_v) - { - sycl::device selected_device = sycl::device(selector); - unsigned int selected_device_id = get_device_id(selected_device); - select_device(selected_device_id); - } - - /// Returns the instance of device manager singleton. - static dev_mgr &instance() - { - static dev_mgr d_m; - return d_m; - } - dev_mgr(const dev_mgr &) = delete; - dev_mgr &operator=(const dev_mgr &) = delete; - dev_mgr(dev_mgr &&) = delete; - dev_mgr &operator=(dev_mgr &&) = delete; - - private: - mutable std::recursive_mutex m_mutex; - static bool compare_dev(sycl::device &device1, sycl::device &device2) - { - dpct::device_info prop1; - dpct::get_device_info(prop1, device1); - dpct::device_info prop2; - dpct::get_device_info(prop2, device2); - return prop1.get_max_compute_units() > prop2.get_max_compute_units(); - } - static int convert_backend_index(std::string & backend) { - if (backend == "ext_oneapi_level_zero:gpu") return 0; - if (backend == "opencl:gpu") return 1; - if (backend == "ext_oneapi_cuda:gpu") return 2; - if (backend == "ext_oneapi_hip:gpu") return 3; - if (backend == "opencl:cpu") return 4; - if (backend == "opencl:acc") return 5; - printf("convert_backend_index: can't handle backend=%s\n", backend.c_str()); - GGML_ASSERT(false); - } - static bool compare_backend(std::string &backend1, std::string &backend2) { - return convert_backend_index(backend1) < convert_backend_index(backend2); - } - dev_mgr() - { - sycl::device default_device = - sycl::device(sycl::default_selector_v); - _devs.push_back(std::make_shared(default_device)); - - std::vector sycl_all_devs; - // Collect other devices except for the default device. - if (default_device.is_cpu()) - _cpu_device = 0; - - auto Platforms = sycl::platform::get_platforms(); - // Keep track of the number of devices per backend - std::map DeviceNums; - std::map> backend_devices; - - while (!Platforms.empty()) { - auto Platform = Platforms.back(); - Platforms.pop_back(); - auto devices = Platform.get_devices(); - std::string backend_type = get_device_backend_and_type(devices[0]); - for (const auto &device : devices) { - backend_devices[backend_type].push_back(device); - } - } - - std::vector keys; - for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) { - keys.push_back(it->first); - } - std::sort(keys.begin(), keys.end(), compare_backend); - - for (auto &key : keys) { - std::vector devs = backend_devices[key]; - std::sort(devs.begin(), devs.end(), compare_dev); - for (const auto &dev : devs) { - sycl_all_devs.push_back(dev); - } - } - - for (auto &dev : sycl_all_devs) - { - if (dev == default_device) - { - continue; - } - _devs.push_back(std::make_shared(dev)); - if (_cpu_device == -1 && dev.is_cpu()) - { - _cpu_device = _devs.size() - 1; - } - } - } - void check_id(unsigned int id) const - { - if (id >= _devs.size()) - { - throw std::runtime_error("invalid device id"); - } - } - std::vector> _devs; - /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current - /// thread id in _thread2dev_map, which means default device should be used - /// for the current thread. - const unsigned int DEFAULT_DEVICE_ID = 0; - /// thread-id to device-id map. - std::map _thread2dev_map; - int _cpu_device = -1; - }; - - static inline sycl::queue &get_default_queue() - { - return dev_mgr::instance().current_device().default_queue(); - } - - namespace detail - { - enum class pointer_access_attribute - { - host_only = 0, - device_only, - host_device, - end - }; - - static pointer_access_attribute get_pointer_attribute(sycl::queue &q, - const void *ptr) - { - switch (sycl::get_pointer_type(ptr, q.get_context())) - { - case sycl::usm::alloc::unknown: - return pointer_access_attribute::host_only; - case sycl::usm::alloc::device: - return pointer_access_attribute::device_only; - case sycl::usm::alloc::shared: - case sycl::usm::alloc::host: - return pointer_access_attribute::host_device; - } - } - - template - inline constexpr std::uint64_t get_type_combination_id(ArgT Val) - { - static_assert((unsigned char)library_data_t::library_data_t_size <= - std::numeric_limits::max() && - "library_data_t size exceeds limit."); - static_assert(std::is_same_v, "Unsupported ArgT"); - return (std::uint64_t)Val; - } - - template - inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal, - RestT... RestVal) - { - static_assert((std::uint8_t)library_data_t::library_data_t_size <= - std::numeric_limits::max() && - "library_data_t size exceeds limit."); - static_assert(sizeof...(RestT) <= 8 && "Too many parameters"); - static_assert(std::is_same_v, "Unsupported FirstT"); - return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal); - } - - class mem_mgr - { - mem_mgr() - { - // Reserved address space, no real memory allocation happens here. -#if defined(__linux__) - mapped_address_space = - (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE, - MAP_PRIVATE | MAP_ANONYMOUS, -1, 0); -#elif defined(_WIN64) - mapped_address_space = (byte_t *)VirtualAlloc( - NULL, // NULL specified as the base address parameter - mapped_region_size, // Size of allocation - MEM_RESERVE, // Allocate reserved pages - PAGE_NOACCESS); // Protection = no access -#else -#error "Only support Windows and Linux." -#endif - next_free = mapped_address_space; - }; - - public: - using buffer_id_t = int; - - struct allocation - { - buffer_t buffer; - byte_t *alloc_ptr; - size_t size; - }; - - ~mem_mgr() - { -#if defined(__linux__) - munmap(mapped_address_space, mapped_region_size); -#elif defined(_WIN64) - VirtualFree(mapped_address_space, 0, MEM_RELEASE); -#else -#error "Only support Windows and Linux." -#endif - }; - - mem_mgr(const mem_mgr &) = delete; - mem_mgr &operator=(const mem_mgr &) = delete; - mem_mgr(mem_mgr &&) = delete; - mem_mgr &operator=(mem_mgr &&) = delete; - - /// Allocate - void *mem_alloc(size_t size) - { - if (!size) - return nullptr; - std::lock_guard lock(m_mutex); - if (next_free + size > mapped_address_space + mapped_region_size) - { - throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool"); - } - // Allocation - sycl::range<1> r(size); - buffer_t buf(r); - allocation A{buf, next_free, size}; - // Map allocation to device pointer - void *result = next_free; - m_map.emplace(next_free + size, A); - // Update pointer to the next free space. - next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1); - - return result; - } - - /// Deallocate - void mem_free(const void *ptr) - { - if (!ptr) - return; - std::lock_guard lock(m_mutex); - auto it = get_map_iterator(ptr); - m_map.erase(it); - } - - /// map: device pointer -> allocation(buffer, alloc_ptr, size) - allocation translate_ptr(const void *ptr) - { - std::lock_guard lock(m_mutex); - auto it = get_map_iterator(ptr); - return it->second; - } - - /// Check if the pointer represents device pointer or not. - bool is_device_ptr(const void *ptr) const - { - std::lock_guard lock(m_mutex); - return (mapped_address_space <= ptr) && - (ptr < mapped_address_space + mapped_region_size); - } - - /// Returns the instance of memory manager singleton. - static mem_mgr &instance() - { - static mem_mgr m; - return m; - } - - private: - std::map m_map; - mutable std::mutex m_mutex; - byte_t *mapped_address_space; - byte_t *next_free; - const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024; - const size_t alignment = 256; - /// This padding may be defined to some positive value to debug - /// out of bound accesses. - const size_t extra_padding = 0; - - std::map::iterator get_map_iterator(const void *ptr) - { - auto it = m_map.upper_bound((byte_t *)ptr); - if (it == m_map.end()) - { - // Not a virtual pointer. - throw std::runtime_error("can not get buffer from non-virtual pointer"); - } - const allocation &alloc = it->second; - if (ptr < alloc.alloc_ptr) - { - // Out of bound. - // This may happen if there's a gap between allocations due to alignment - // or extra padding and pointer points to this gap. - throw std::runtime_error("invalid virtual pointer"); - } - return it; - } - }; - - template - class accessor; - template - class memory_traits - { - public: - static constexpr sycl::access::target target = - sycl::access::target::device; - static constexpr sycl::access_mode mode = - (Memory == constant) ? sycl::access_mode::read - : sycl::access_mode::read_write; - static constexpr size_t type_size = sizeof(T); - using element_t = - typename std::conditional::type; - using value_t = typename std::remove_cv::type; - template - using accessor_t = typename std::conditional< - Memory == local, sycl::local_accessor, - sycl::accessor>::type; - using pointer_t = T *; - }; - - static inline void *dpct_malloc(size_t size, sycl::queue &q) - { - return sycl::malloc_device(size, q.get_device(), q.get_context()); - } - -#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F)) - static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z, - sycl::queue &q) - { - pitch = PITCH_DEFAULT_ALIGN(x); - return dpct_malloc(pitch * y * z, q); - } - - /** - * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q. - * @tparam valueT The type of the element to be set. - * @param [in] q The queue in which the operation is done. - * @param [in] dev_ptr Pointer to the virtual device memory address. - * @param [in] value The value to be set. - * @param [in] size Number of elements to be set to the value. - * @return An event representing the memset operation. - */ - template - static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr, - valueT value, size_t size) - { - return q.fill(dev_ptr, value, size); - } - - /** - * @brief Sets \p value to the 3D memory region pointed by \p data in \p q. - * @tparam valueT The type of the element to be set. - * @param [in] q The queue in which the operation is done. - * @param [in] data Pointer to the pitched device memory region. - * @param [in] value The value to be set. - * @param [in] size 3D memory region by number of elements. - * @return An event list representing the memset operations. - */ - template - static inline std::vector - dpct_memset(sycl::queue &q, pitched_data data, valueT value, - sycl::range<3> size) - { - std::vector event_list; - size_t slice = data.get_pitch() * data.get_y(); - unsigned char *data_surface = (unsigned char *)data.get_data_ptr(); - for (size_t z = 0; z < size.get(2); ++z) - { - unsigned char *data_ptr = data_surface; - for (size_t y = 0; y < size.get(1); ++y) - { - event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0))); - data_ptr += data.get_pitch(); - } - data_surface += slice; - } - return event_list; - } - - /** - * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q. - * @tparam valueT The type of the element to be set. - * @param [in] q The queue in which the operation is done. - * @param [in] ptr Pointer to the virtual device memory. - * @param [in] pitch The pitch size by number of elements, including padding. - * @param [in] val The value to be set. - * @param [in] x The width of memory region by number of elements. - * @param [in] y The height of memory region by number of elements. - * @return An event list representing the memset operations. - */ - template - static inline std::vector - dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x, - size_t y) - { - return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val, - sycl::range<3>(x, y, 1)); - } - - static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr, - const void *from_ptr, - memcpy_direction dir) - { - switch (dir) - { - case memcpy_direction::host_to_host: - case memcpy_direction::host_to_device: - case memcpy_direction::device_to_host: - case memcpy_direction::device_to_device: - return dir; - case memcpy_direction::automatic: - { - // table[to_attribute][from_attribute] - static const memcpy_direction - direction_table[static_cast(pointer_access_attribute::end)] - [static_cast(pointer_access_attribute::end)] = - {{memcpy_direction::host_to_host, - memcpy_direction::device_to_host, - memcpy_direction::host_to_host}, - {memcpy_direction::host_to_device, - memcpy_direction::device_to_device, - memcpy_direction::device_to_device}, - {memcpy_direction::host_to_host, - memcpy_direction::device_to_device, - memcpy_direction::device_to_device}}; - return direction_table[static_cast(get_pointer_attribute( - q, to_ptr))][static_cast(get_pointer_attribute(q, from_ptr))]; - } - default: - throw std::runtime_error("dpct_memcpy: invalid direction value"); - } - } - - static sycl::event - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, - memcpy_direction direction, - const std::vector &dep_events = {}) - { - if (!size) - return sycl::event{}; - return q.memcpy(to_ptr, from_ptr, size, dep_events); - GGML_UNUSED(direction); - } - - // Get actual copy range and make sure it will not exceed range. - static inline size_t get_copy_range(sycl::range<3> size, size_t slice, - size_t pitch) - { - return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); - } - - static inline size_t get_offset(sycl::id<3> id, size_t slice, - size_t pitch) - { - return slice * id.get(2) + pitch * id.get(1) + id.get(0); - } - - /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr - /// and \p from_range to another specified by \p to_ptr and \p to_range. - static inline std::vector - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, - sycl::range<3> to_range, sycl::range<3> from_range, - sycl::id<3> to_id, sycl::id<3> from_id, - sycl::range<3> size, memcpy_direction direction, - const std::vector &dep_events = {}) - { - // RAII for host pointer - class host_buffer - { - void *_buf; - size_t _size; - sycl::queue &_q; - const std::vector &_deps; // free operation depends - - public: - host_buffer(size_t size, sycl::queue &q, - const std::vector &deps) - : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} - void *get_ptr() const { return _buf; } - size_t get_size() const { return _size; } - ~host_buffer() - { - if (_buf) - { - _q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(_deps); - cgh.host_task([buf = _buf] { std::free(buf); }); }); - } - } - }; - std::vector event_list; - - size_t to_slice = to_range.get(1) * to_range.get(0), - from_slice = from_range.get(1) * from_range.get(0); - unsigned char *to_surface = - (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); - const unsigned char *from_surface = - (const unsigned char *)from_ptr + - get_offset(from_id, from_slice, from_range.get(0)); - - if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) - { - return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), - direction, dep_events)}; - } - direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); - size_t size_slice = size.get(1) * size.get(0); - switch (direction) - { - case host_to_host: - for (size_t z = 0; z < size.get(2); ++z) - { - unsigned char *to_ptr = to_surface; - const unsigned char *from_ptr = from_surface; - if (to_range.get(0) == from_range.get(0) && - to_range.get(0) == size.get(0)) - { - event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, - direction, dep_events)); - } - else - { - for (size_t y = 0; y < size.get(1); ++y) - { - event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), - direction, dep_events)); - to_ptr += to_range.get(0); - from_ptr += from_range.get(0); - } - } - to_surface += to_slice; - from_surface += from_slice; - } - break; - case host_to_device: - { - host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, - event_list); - std::vector host_events; - if (to_slice == size_slice) - { - // Copy host data to a temp host buffer with the shape of target. - host_events = - dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, - host_to_host, dep_events); - } - else - { - // Copy host data to a temp host buffer with the shape of target. - host_events = dpct_memcpy( - q, buf.get_ptr(), from_surface, to_range, from_range, - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, - // If has padding data, not sure whether it is useless. So fill temp - // buffer with it. - std::vector{ - dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), - device_to_host, dep_events)}); - } - // Copy from temp host buffer to device with only one submit. - event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), - buf.get_size(), host_to_device, - host_events)); - break; - } - case device_to_host: - { - host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, - event_list); - // Copy from host temp buffer to host target with reshaping. - event_list = dpct_memcpy( - q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), - sycl::id<3>(0, 0, 0), size, host_to_host, - // Copy from device to temp host buffer with only one submit. - std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, - buf.get_size(), - device_to_host, dep_events)}); - break; - } - case device_to_device: - event_list.push_back(q.submit([&](sycl::handler &cgh){ - cgh.depends_on(dep_events); - cgh.parallel_for( - size, - [=](sycl::id<3> id) { - to_surface[get_offset(id, to_slice, to_range.get(0))] = - from_surface[get_offset(id, from_slice, from_range.get(0))]; - }); })); - break; - default: - throw std::runtime_error("dpct_memcpy: invalid direction value"); - } - return event_list; - } - - /// memcpy 2D/3D matrix specified by pitched_data. - static inline std::vector - dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, - pitched_data from, sycl::id<3> from_id, sycl::range<3> size, - memcpy_direction direction = automatic) - { - return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), - sycl::range<3>(to.get_pitch(), to.get_y(), 1), - sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, - size, direction); - } - - /// memcpy 2D matrix with pitch. - static inline std::vector - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, - size_t to_pitch, size_t from_pitch, size_t x, size_t y, - memcpy_direction direction = automatic) - { - return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), - sycl::range<3>(from_pitch, y, 1), - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), - sycl::range<3>(x, y, 1), direction); - } - - namespace deprecated - { - - template - class usm_allocator - { - private: - using Alloc = sycl::usm_allocator; - Alloc _impl; - - public: - using value_type = typename std::allocator_traits::value_type; - using pointer = typename std::allocator_traits::pointer; - using const_pointer = typename std::allocator_traits::const_pointer; - using void_pointer = typename std::allocator_traits::void_pointer; - using const_void_pointer = - typename std::allocator_traits::const_void_pointer; - using reference = typename std::allocator_traits::value_type &; - using const_reference = - const typename std::allocator_traits::value_type &; - using difference_type = - typename std::allocator_traits::difference_type; - using size_type = typename std::allocator_traits::size_type; - using propagate_on_container_copy_assignment = typename std::allocator_traits< - Alloc>::propagate_on_container_copy_assignment; - using propagate_on_container_move_assignment = typename std::allocator_traits< - Alloc>::propagate_on_container_move_assignment; - using propagate_on_container_swap = - typename std::allocator_traits::propagate_on_container_swap; - using is_always_equal = - typename std::allocator_traits::is_always_equal; - - template - struct rebind - { - typedef usm_allocator other; - }; - - usm_allocator() : _impl(dpct::get_default_queue()) {} - ~usm_allocator() {} - usm_allocator(const usm_allocator &other) : _impl(other._impl) {} - usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {} - pointer address(reference r) { return &r; } - const_pointer address(const_reference r) { return &r; } - pointer allocate(size_type cnt, const_void_pointer hint = nullptr) - { - return std::allocator_traits::allocate(_impl, cnt, hint); - } - void deallocate(pointer p, size_type cnt) - { - std::allocator_traits::deallocate(_impl, p, cnt); - } - size_type max_size() const - { - return std::allocator_traits::max_size(_impl); - } - bool operator==(const usm_allocator &other) const { return _impl == other._impl; } - bool operator!=(const usm_allocator &other) const { return _impl != other._impl; } - }; - - } // namespace deprecated - - inline void dpct_free(void *ptr, - const sycl::queue &q) - { - if (ptr) - { - sycl::free(ptr, q.get_context()); - } - } - - template - inline auto get_memory(const void *x) - { - T *new_x = reinterpret_cast(const_cast(x)); - return new_x; - } - - template - inline typename DataType::T2 get_value(const T *s, sycl::queue &q) - { - using Ty = typename DataType::T2; - Ty s_h; - if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) - detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host) - .wait(); - else - s_h = *reinterpret_cast(s); - return s_h; - } - - } // namespace detail - - template - inline auto get_value(const T *s, sycl::queue &q) - { - return detail::get_value(s, q); - } - - namespace detail - { - template - inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, int k, - const void *alpha, const void *a, int lda, const void *b, - int ldb, const void *beta, void *c, int ldc) - { - Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); - Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); - auto data_a = get_memory(a); - auto data_b = get_memory(b); - auto data_c = get_memory(c); - oneapi::mkl::blas::column_major::gemm( - q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, - data_b, ldb, beta_value, data_c, ldc); - } - - template - class vectorized_binary - { - public: - inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) - { - VecT v4; - for (size_t i = 0; i < v4.size(); ++i) - { - v4[i] = binary_op(a[i], b[i]); - } - return v4; - } - }; - - template - class vectorized_binary< - VecT, BinaryOperation, - std::void_t>> - { - public: - inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) - { - return binary_op(a, b).template as(); - } - }; - - template - inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, int k, - const void *alpha, const void **a, int lda, - const void **b, int ldb, const void *beta, void **c, - int ldc, int batch_size) - { - struct matrix_info_t - { - oneapi::mkl::transpose transpose_info[2]; - Ts value_info[2]; - std::int64_t size_info[3]; - std::int64_t ld_info[3]; - std::int64_t groupsize_info; - }; - - Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); - Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); - - matrix_info_t *matrix_info = - (matrix_info_t *)std::malloc(sizeof(matrix_info_t)); - matrix_info->transpose_info[0] = a_trans; - matrix_info->transpose_info[1] = b_trans; - matrix_info->value_info[0] = alpha_value; - matrix_info->value_info[1] = beta_value; - matrix_info->size_info[0] = m; - matrix_info->size_info[1] = n; - matrix_info->size_info[2] = k; - matrix_info->ld_info[0] = lda; - matrix_info->ld_info[1] = ldb; - matrix_info->ld_info[2] = ldc; - matrix_info->groupsize_info = batch_size; - - sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( - q, matrix_info->transpose_info, matrix_info->transpose_info + 1, - matrix_info->size_info, matrix_info->size_info + 1, - matrix_info->size_info + 2, matrix_info->value_info, - reinterpret_cast(a), matrix_info->ld_info, - reinterpret_cast(b), matrix_info->ld_info + 1, - matrix_info->value_info + 1, reinterpret_cast(c), - matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info)); - - q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(e); - cgh.host_task([=] { std::free(matrix_info); }); }); - } - - template - inline void - gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, - int k, const void *alpha, const void *a, int lda, - long long int stride_a, const void *b, int ldb, - long long int stride_b, const void *beta, void *c, - int ldc, long long int stride_c, int batch_size) - { - Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); - Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); - auto data_a = get_memory(a); - auto data_b = get_memory(b); - auto data_c = get_memory(c); - oneapi::mkl::blas::column_major::gemm_batch( - q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, - stride_a, data_b, ldb, stride_b, beta_value, - data_c, ldc, stride_c, batch_size); - } - - } // namespace detail - - template - inline unsigned vectorized_binary(unsigned a, unsigned b, - const BinaryOperation binary_op) - { - sycl::vec v0{a}, v1{b}; - auto v2 = v0.as(); - auto v3 = v1.as(); - auto v4 = - detail::vectorized_binary()(v2, v3, binary_op); - v0 = v4.template as>(); - return v0; - } - - static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size, - memcpy_direction direction = automatic, - sycl::queue &q = dpct::get_default_queue()) - { - detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction); - } - - static inline unsigned int select_device(unsigned int id) - { - dev_mgr::instance().select_device(id); - return id; - } - - template - T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, - unsigned int logical_sub_group_size = 32) - { - unsigned int id = g.get_local_linear_id(); - unsigned int start_index = - id / logical_sub_group_size * logical_sub_group_size; - unsigned int target_offset = (id % logical_sub_group_size) ^ mask; - return sycl::select_from_group(g, x, - target_offset < logical_sub_group_size - ? start_index + target_offset - : id); - } - - template - sycl::vec extract_and_sign_or_zero_extend4(T val) - { - return sycl::vec(val) - .template as, int8_t, uint8_t>, 4>>() - .template convert(); - } - - template - using dot_product_acc_t = - std::conditional_t && std::is_unsigned_v, - uint32_t, int32_t>; - - template - inline auto dp4a(T1 a, T2 b, T3 c) - { - dot_product_acc_t res = c; - auto va = extract_and_sign_or_zero_extend4(a); - auto vb = extract_and_sign_or_zero_extend4(b); - res += va[0] * vb[0]; - res += va[1] * vb[1]; - res += va[2] * vb[2]; - res += va[3] * vb[3]; - return res; - } - - struct sub_sat - { - template - auto operator()(const T x, const T y) const - { - return sycl::sub_sat(x, y); - } - }; - - template - inline T vectorized_min(T a, T b) - { - sycl::vec v0{a}, v1{b}; - auto v2 = v0.template as(); - auto v3 = v1.template as(); - auto v4 = sycl::min(v2, v3); - v0 = v4.template as>(); - return v0; - } - - inline float pow(const float a, const int b) { return sycl::pown(a, b); } - inline double pow(const double a, const int b) { return sycl::pown(a, b); } - inline float pow(const float a, const float b) { return sycl::pow(a, b); } - inline double pow(const double a, const double b) { return sycl::pow(a, b); } - template - inline typename std::enable_if_t, T> - pow(const T a, const U b) - { - return sycl::pow(a, static_cast(b)); - } - template - inline typename std::enable_if_t, double> - pow(const T a, const U b) - { - return sycl::pow(static_cast(a), static_cast(b)); - } - - inline double min(const double a, const float b) - { - return sycl::fmin(a, static_cast(b)); - } - inline double min(const float a, const double b) - { - return sycl::fmin(static_cast(a), b); - } - inline float min(const float a, const float b) { return sycl::fmin(a, b); } - inline double min(const double a, const double b) { return sycl::fmin(a, b); } - inline std::uint32_t min(const std::uint32_t a, const std::int32_t b) - { - return sycl::min(a, static_cast(b)); - } - inline std::uint32_t min(const std::int32_t a, const std::uint32_t b) - { - return sycl::min(static_cast(a), b); - } - inline std::int32_t min(const std::int32_t a, const std::int32_t b) - { - return sycl::min(a, b); - } - inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b) - { - return sycl::min(a, b); - } - inline std::uint64_t min(const std::uint64_t a, const std::int64_t b) - { - return sycl::min(a, static_cast(b)); - } - inline std::uint64_t min(const std::int64_t a, const std::uint64_t b) - { - return sycl::min(static_cast(a), b); - } - inline std::int64_t min(const std::int64_t a, const std::int64_t b) - { - return sycl::min(a, b); - } - inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b) - { - return sycl::min(a, b); - } - inline std::uint64_t min(const std::uint64_t a, const std::int32_t b) - { - return sycl::min(a, static_cast(b)); - } - inline std::uint64_t min(const std::int32_t a, const std::uint64_t b) - { - return sycl::min(static_cast(a), b); - } - inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b) - { - return sycl::min(a, static_cast(b)); - } - inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b) - { - return sycl::min(static_cast(a), b); - } - // max function overloads. - // For floating-point types, `float` or `double` arguments are acceptable. - // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or - // `std::int64_t` type arguments are acceptable. - inline double max(const double a, const float b) - { - return sycl::fmax(a, static_cast(b)); - } - inline double max(const float a, const double b) - { - return sycl::fmax(static_cast(a), b); - } - inline float max(const float a, const float b) { return sycl::fmax(a, b); } - inline double max(const double a, const double b) { return sycl::fmax(a, b); } - inline std::uint32_t max(const std::uint32_t a, const std::int32_t b) - { - return sycl::max(a, static_cast(b)); - } - inline std::uint32_t max(const std::int32_t a, const std::uint32_t b) - { - return sycl::max(static_cast(a), b); - } - inline std::int32_t max(const std::int32_t a, const std::int32_t b) - { - return sycl::max(a, b); - } - inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b) - { - return sycl::max(a, b); - } - inline std::uint64_t max(const std::uint64_t a, const std::int64_t b) - { - return sycl::max(a, static_cast(b)); - } - inline std::uint64_t max(const std::int64_t a, const std::uint64_t b) - { - return sycl::max(static_cast(a), b); - } - inline std::int64_t max(const std::int64_t a, const std::int64_t b) - { - return sycl::max(a, b); - } - inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b) - { - return sycl::max(a, b); - } - inline std::uint64_t max(const std::uint64_t a, const std::int32_t b) - { - return sycl::max(a, static_cast(b)); - } - inline std::uint64_t max(const std::int32_t a, const std::uint64_t b) - { - return sycl::max(static_cast(a), b); - } - inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b) - { - return sycl::max(a, static_cast(b)); - } - inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b) - { - return sycl::max(static_cast(a), b); - } - - inline void - has_capability_or_fail(const sycl::device &dev, - const std::initializer_list &props) - { - for (const auto &it : props) - { - if (dev.has(it)) - continue; - switch (it) - { - case sycl::aspect::fp64: - throw std::runtime_error("'double' is not supported in '" + - dev.get_info() + - "' device"); - break; - case sycl::aspect::fp16: - throw std::runtime_error("'half' is not supported in '" + - dev.get_info() + - "' device"); - break; - default: -#define __SYCL_ASPECT(ASPECT, ID) \ - case sycl::aspect::ASPECT: \ - return #ASPECT; -#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID) -#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE) - auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string - { - switch (AspectNum) - { -#include -#include - default: - return "unknown aspect"; - } - }; -#undef __SYCL_ASPECT_DEPRECATED_ALIAS -#undef __SYCL_ASPECT_DEPRECATED -#undef __SYCL_ASPECT - throw std::runtime_error( - "'" + getAspectNameStr(it) + "' is not supported in '" + - dev.get_info() + "' device"); - } - break; - } - } - - static inline unsigned int get_current_device_id() - { - return dev_mgr::instance().current_device_id(); - } - - static inline device_ext &get_current_device() - { - return dev_mgr::instance().current_device(); - } - - static inline sycl::queue &get_in_order_queue() - { - return dev_mgr::instance().current_device().in_order_queue(); - } - - static sycl::event - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, - memcpy_direction direction, - const std::vector &dep_events = {}) - { - if (!size) - return sycl::event{}; - return q.memcpy(to_ptr, from_ptr, size, dep_events); - GGML_UNUSED(direction); - } - - // Get actual copy range and make sure it will not exceed range. - static inline size_t get_copy_range(sycl::range<3> size, size_t slice, - size_t pitch) - { - return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); - } - - static inline size_t get_offset(sycl::id<3> id, size_t slice, - size_t pitch) - { - return slice * id.get(2) + pitch * id.get(1) + id.get(0); - } - - /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr - /// and \p from_range to another specified by \p to_ptr and \p to_range. - static inline std::vector - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, - sycl::range<3> to_range, sycl::range<3> from_range, - sycl::id<3> to_id, sycl::id<3> from_id, - sycl::range<3> size, memcpy_direction direction, - const std::vector &dep_events = {}) - { - // RAII for host pointer - class host_buffer - { - void *_buf; - size_t _size; - sycl::queue &_q; - const std::vector &_deps; // free operation depends - - public: - host_buffer(size_t size, sycl::queue &q, - const std::vector &deps) - : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} - void *get_ptr() const { return _buf; } - size_t get_size() const { return _size; } - ~host_buffer() - { - if (_buf) - { - _q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(_deps); - cgh.host_task([buf = _buf] { std::free(buf); }); }); - } - } - }; - std::vector event_list; - - size_t to_slice = to_range.get(1) * to_range.get(0), - from_slice = from_range.get(1) * from_range.get(0); - unsigned char *to_surface = - (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); - const unsigned char *from_surface = - (const unsigned char *)from_ptr + - get_offset(from_id, from_slice, from_range.get(0)); - - if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) - { - return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), - direction, dep_events)}; - } - direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction); - size_t size_slice = size.get(1) * size.get(0); - switch (direction) - { - case host_to_host: - for (size_t z = 0; z < size.get(2); ++z) - { - unsigned char *to_ptr = to_surface; - const unsigned char *from_ptr = from_surface; - if (to_range.get(0) == from_range.get(0) && - to_range.get(0) == size.get(0)) - { - event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, - direction, dep_events)); - } - else - { - for (size_t y = 0; y < size.get(1); ++y) - { - event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), - direction, dep_events)); - to_ptr += to_range.get(0); - from_ptr += from_range.get(0); - } - } - to_surface += to_slice; - from_surface += from_slice; - } - break; - case host_to_device: - { - host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, - event_list); - std::vector host_events; - if (to_slice == size_slice) - { - // Copy host data to a temp host buffer with the shape of target. - host_events = - dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, - host_to_host, dep_events); - } - else - { - // Copy host data to a temp host buffer with the shape of target. - host_events = dpct_memcpy( - q, buf.get_ptr(), from_surface, to_range, from_range, - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, - // If has padding data, not sure whether it is useless. So fill temp - // buffer with it. - std::vector{ - dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), - device_to_host, dep_events)}); - } - // Copy from temp host buffer to device with only one submit. - event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), - buf.get_size(), host_to_device, - host_events)); - break; - } - case device_to_host: - { - host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, - event_list); - // Copy from host temp buffer to host target with reshaping. - event_list = dpct_memcpy( - q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), - sycl::id<3>(0, 0, 0), size, host_to_host, - // Copy from device to temp host buffer with only one submit. - std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, - buf.get_size(), - device_to_host, dep_events)}); - break; - } - case device_to_device: - event_list.push_back(q.submit([&](sycl::handler &cgh) - { - cgh.depends_on(dep_events); - cgh.parallel_for( - size, - [=](sycl::id<3> id) { - to_surface[get_offset(id, to_slice, to_range.get(0))] = - from_surface[get_offset(id, from_slice, from_range.get(0))]; - }); })); - break; - default: - throw std::runtime_error("dpct_memcpy: invalid direction value"); - } - return event_list; - } - - /// memcpy 2D/3D matrix specified by pitched_data. - static inline std::vector - dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, - pitched_data from, sycl::id<3> from_id, sycl::range<3> size, - memcpy_direction direction = automatic) - { - return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), - sycl::range<3>(to.get_pitch(), to.get_y(), 1), - sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, - size, direction); - } - - /// memcpy 2D matrix with pitch. - static inline std::vector - dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, - size_t to_pitch, size_t from_pitch, size_t x, size_t y, - memcpy_direction direction = automatic) - { - return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), - sycl::range<3>(from_pitch, y, 1), - sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), - sycl::range<3>(x, y, 1), direction); - } - - inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, int k, - const void *alpha, const void *a, library_data_t a_type, - int lda, const void *b, library_data_t b_type, int ldb, - const void *beta, void *c, library_data_t c_type, int ldc, - library_data_t scaling_type) - { - if (scaling_type == library_data_t::real_float && - c_type == library_data_t::complex_float) - { - scaling_type = library_data_t::complex_float; - } - else if (scaling_type == library_data_t::real_double && - c_type == library_data_t::complex_double) - { - scaling_type = library_data_t::complex_double; - } - - std::uint64_t key = - detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); - switch (key) - { - case detail::get_type_combination_id( - library_data_t::real_float, library_data_t::real_float, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_double, library_data_t::real_double, - library_data_t::real_double, library_data_t::real_double): - { - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_float, library_data_t::complex_float, - library_data_t::complex_float, library_data_t::complex_float): - { - detail::gemm_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_double, library_data_t::complex_double, - library_data_t::complex_double, library_data_t::complex_double): - { - detail::gemm_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_half): - { - detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, - lda, b, ldb, beta, c, ldc); - break; - } -#ifdef __INTEL_MKL__ - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, b, - ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_float): - { - float alpha_value = - dpct::get_value(reinterpret_cast(alpha), q); - float beta_value = - dpct::get_value(reinterpret_cast(beta), q); - sycl::half alpha_half(alpha_value); - sycl::half beta_half(beta_value); - detail::gemm_impl(q, a_trans, b_trans, m, n, k, &alpha_half, - a, lda, b, ldb, &beta_half, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_bfloat16, library_data_t::real_float): - { - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_int32, library_data_t::real_int32): - { - float alpha_float = - dpct::get_value(reinterpret_cast(alpha), q); - float beta_float = - dpct::get_value(reinterpret_cast(beta), q); - detail::gemm_impl( - q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc); - break; - } -#endif // __INTEL_MKL__ - default: - throw std::runtime_error("the combination of data type is unsupported"); - } - } // gemm() - - /// Computes a batch of matrix-matrix product with general matrices. - /// \param [in] q The queue where the routine should be executed. - /// \param [in] a_trans Specifies the operation applied to A. - /// \param [in] b_trans Specifies the operation applied to B. - /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. - /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. - /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). - /// \param [in] alpha Scaling factor for the matrix-matrix product. - /// \param [in] a Input matrix A. - /// \param [in] a_type Data type of the matrix A. - /// \param [in] lda Leading dimension of A. - /// \param [in] b Input matrix B. - /// \param [in] b_type Data type of the matrix B. - /// \param [in] ldb Leading dimension of B. - /// \param [in] beta Scaling factor for matrix C. - /// \param [in, out] c Input/Output matrix C. - /// \param [in] c_type Data type of the matrix C. - /// \param [in] ldc Leading dimension of C. - /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. - /// \param [in] scaling_type Data type of the scaling factors. - inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, int k, - const void *alpha, const void *a[], - library_data_t a_type, int lda, const void *b[], - library_data_t b_type, int ldb, const void *beta, - void *c[], library_data_t c_type, int ldc, - int batch_size, library_data_t scaling_type) - { - if (scaling_type == library_data_t::real_float && - c_type == library_data_t::complex_float) - { - scaling_type = library_data_t::complex_float; - } - else if (scaling_type == library_data_t::real_double && - c_type == library_data_t::complex_double) - { - scaling_type = library_data_t::complex_double; - } - - std::uint64_t key = - detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); - switch (key) - { - case detail::get_type_combination_id( - library_data_t::real_float, library_data_t::real_float, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_double, library_data_t::real_double, - library_data_t::real_double, library_data_t::real_double): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_float, library_data_t::complex_float, - library_data_t::complex_float, library_data_t::complex_float): - { - detail::gemm_batch_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_double, library_data_t::complex_double, - library_data_t::complex_double, library_data_t::complex_double): - { - detail::gemm_batch_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_half): - { - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, - a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } -#ifdef __INTEL_MKL__ - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_bfloat16, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, - b, ldb, beta, c, ldc, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_int32, library_data_t::real_int32): - { - float alpha_float = - dpct::get_value(reinterpret_cast(alpha), q); - float beta_float = - dpct::get_value(reinterpret_cast(beta), q); - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, &alpha_float, - a, lda, b, ldb, &beta_float, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, - batch_size); - break; - } -#endif - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_float): - { - float alpha_value = - dpct::get_value(reinterpret_cast(alpha), q); - float beta_value = - dpct::get_value(reinterpret_cast(beta), q); - sycl::half alpha_half(alpha_value); - sycl::half beta_half(beta_value); - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc, - batch_size); - break; - } - default: - throw std::runtime_error("the combination of data type is unsupported"); - } - } - - /// Computes a batch of matrix-matrix product with general matrices. - /// \param [in] q The queue where the routine should be executed. - /// \param [in] a_trans Specifies the operation applied to A. - /// \param [in] b_trans Specifies the operation applied to B. - /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. - /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. - /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). - /// \param [in] alpha Scaling factor for the matrix-matrix product. - /// \param [in] a Input matrix A. - /// \param [in] a_type Data type of the matrix A. - /// \param [in] lda Leading dimension of A. - /// \param [in] stride_a Stride between the different A matrices. - /// \param [in] b Input matrix B. - /// \param [in] b_type Data type of the matrix B. - /// \param [in] ldb Leading dimension of B. - /// \param [in] stride_b Stride between the different B matrices. - /// \param [in] beta Scaling factor for matrix C. - /// \param [in, out] c Input/Output matrix C. - /// \param [in] c_type Data type of the matrix C. - /// \param [in] ldc Leading dimension of C. - /// \param [in] stride_c Stride between the different C matrices. - /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. - /// \param [in] scaling_type Data type of the scaling factors. - inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, - oneapi::mkl::transpose b_trans, int m, int n, int k, - const void *alpha, const void *a, library_data_t a_type, - int lda, long long int stride_a, const void *b, - library_data_t b_type, int ldb, long long int stride_b, - const void *beta, void *c, library_data_t c_type, - int ldc, long long int stride_c, int batch_size, - library_data_t scaling_type) - { - if (scaling_type == library_data_t::real_float && - c_type == library_data_t::complex_float) - { - scaling_type = library_data_t::complex_float; - } - else if (scaling_type == library_data_t::real_double && - c_type == library_data_t::complex_double) - { - scaling_type = library_data_t::complex_double; - } - - std::uint64_t key = - detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); - switch (key) - { - case detail::get_type_combination_id( - library_data_t::real_float, library_data_t::real_float, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_double, library_data_t::real_double, - library_data_t::real_double, library_data_t::real_double): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_float, library_data_t::complex_float, - library_data_t::complex_float, library_data_t::complex_float): - { - detail::gemm_batch_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::complex_double, library_data_t::complex_double, - library_data_t::complex_double, library_data_t::complex_double): - { - detail::gemm_batch_impl, std::complex, - std::complex, std::complex>( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_half): - { - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, - a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } -#ifdef __INTEL_MKL__ - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_bfloat16, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_bfloat16, library_data_t::real_bfloat16, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, - stride_a, b, ldb, stride_b, beta, c, ldc, - stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_int32, library_data_t::real_int32): - { - detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, - a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_int8, library_data_t::real_int8, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_float, library_data_t::real_float): - { - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, - beta, c, ldc, stride_c, batch_size); - break; - } -#endif - case detail::get_type_combination_id( - library_data_t::real_half, library_data_t::real_half, - library_data_t::real_half, library_data_t::real_float): - { - float alpha_value = - dpct::get_value(reinterpret_cast(alpha), q); - float beta_value = - dpct::get_value(reinterpret_cast(beta), q); - sycl::half alpha_half(alpha_value); - sycl::half beta_half(beta_value); - detail::gemm_batch_impl( - q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b, - &beta_half, c, ldc, stride_c, batch_size); - break; - } - default: - throw std::runtime_error("the combination of data type is unsupported"); - } - } - - static inline void - async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr, - size_t from_pitch, size_t x, size_t y, - memcpy_direction direction = automatic, - sycl::queue &q = get_default_queue()) - { - detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y, - direction); - } - - using err0 = detail::generic_error_type; - using err1 = detail::generic_error_type; - - static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) { - detail::dpct_free(ptr, q); - } - - /// dpct accessor used as device function parameter. - template class accessor; - template class accessor { - public: - using memory_t = detail::memory_traits; - using element_t = typename memory_t::element_t; - using pointer_t = typename memory_t::pointer_t; - using accessor_t = typename memory_t::template accessor_t<3>; - accessor(pointer_t data, const sycl::range<3> &in_range) - : _data(data), _range(in_range) {} - template - accessor(typename std::enable_if::type &acc) - : accessor(acc, acc.get_range()) {} - accessor(const accessor_t &acc, const sycl::range<3> &in_range) - : accessor(acc.get_pointer(), in_range) {} - accessor operator[](size_t index) const { - sycl::range<2> sub(_range.get(1), _range.get(2)); - return accessor(_data + index * sub.size(), sub); - } - - pointer_t get_ptr() const { return _data; } - - private: - pointer_t _data; - sycl::range<3> _range; - }; - template class accessor { - public: - using memory_t = detail::memory_traits; - using element_t = typename memory_t::element_t; - using pointer_t = typename memory_t::pointer_t; - using accessor_t = typename memory_t::template accessor_t<2>; - accessor(pointer_t data, const sycl::range<2> &in_range) - : _data(data), _range(in_range) {} - template - accessor(typename std::enable_if::type &acc) - : accessor(acc, acc.get_range()) {} - accessor(const accessor_t &acc, const sycl::range<2> &in_range) - : accessor(acc.get_pointer(), in_range) {} - - pointer_t operator[](size_t index) const { - return _data + _range.get(1) * index; - } - - pointer_t get_ptr() const { return _data; } - - private: - pointer_t _data; - sycl::range<2> _range; - }; - - namespace detail { - /// Device variable with address space of shared, global or constant. - template class device_memory { - public: - using accessor_t = - typename detail::memory_traits::template accessor_t; - using value_t = typename detail::memory_traits::value_t; - using dpct_accessor_t = dpct::accessor; - - device_memory() : device_memory(sycl::range(1)) {} - - /// Constructor of 1-D array with initializer list - device_memory(const sycl::range &in_range, - std::initializer_list &&init_list) - : device_memory(in_range) { - assert(init_list.size() <= in_range.size()); - _host_ptr = (value_t *)std::malloc(_size); - std::memset(_host_ptr, 0, _size); - std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T)); - } - - /// Constructor of 2-D array with initializer list - template - device_memory( - const typename std::enable_if>::type &in_range, - std::initializer_list> &&init_list) - : device_memory(in_range) { - assert(init_list.size() <= in_range[0]); - _host_ptr = (value_t *)std::malloc(_size); - std::memset(_host_ptr, 0, _size); - auto tmp_data = _host_ptr; - for (auto sub_list : init_list) { - assert(sub_list.size() <= in_range[1]); - std::memcpy(tmp_data, sub_list.begin(), - sub_list.size() * sizeof(T)); - tmp_data += in_range[1]; - } - } - - /// Constructor with range - device_memory(const sycl::range &range_in) - : _size(range_in.size() * sizeof(T)), _range(range_in), - _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) { - static_assert( - (Memory == global) || (Memory == constant) || (Memory == shared), - "device memory region should be global, constant or shared"); - // Make sure that singleton class mem_mgr and dev_mgr will destruct - // later than this. - detail::mem_mgr::instance(); - dev_mgr::instance(); - } - - /// Constructor with range - template - device_memory(Args... Arguments) - : device_memory(sycl::range(Arguments...)) {} - - ~device_memory() { - if (_device_ptr && !_reference) - dpct::dpct_free(_device_ptr); - if (_host_ptr) - std::free(_host_ptr); - } - - /// Allocate memory with default queue, and init memory if has initial - /// value. - void init() { init(dpct::get_default_queue()); } - /// Allocate memory with specified queue, and init memory if has initial - /// value. - void init(sycl::queue &q) { - if (_device_ptr) - return; - if (!_size) - return; - allocate_device(q); - if (_host_ptr) - detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size, - host_to_device); - } - - /// The variable is assigned to a device pointer. - void assign(value_t *src, size_t size) { - this->~device_memory(); - new (this) device_memory(src, size); - } - - /// Get memory pointer of the memory object, which is virtual pointer when - /// usm is not used, and device pointer when usm is used. - value_t *get_ptr() { return get_ptr(get_default_queue()); } - /// Get memory pointer of the memory object, which is virtual pointer when - /// usm is not used, and device pointer when usm is used. - value_t *get_ptr(sycl::queue &q) { - init(q); - return _device_ptr; - } - - /// Get the device memory object size in bytes. - size_t get_size() { return _size; } - - template - typename std::enable_if::type &operator[](size_t index) { - init(); - return _device_ptr[index]; - } - - /// Get dpct::accessor with dimension info for the device memory object - /// when usm is used and dimension is greater than 1. - template - typename std::enable_if::type - get_access(sycl::handler &cgh) { - return dpct_accessor_t((T *)_device_ptr, _range); - } - - private: - device_memory(value_t *memory_ptr, size_t size) - : _size(size), _range(size / sizeof(T)), _reference(true), - _device_ptr(memory_ptr) {} - - void allocate_device(sycl::queue &q) { - #ifndef DPCT_USM_LEVEL_NONE - if (Memory == shared) { - _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(), - q.get_context()); - return; - } - #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY - if (Memory == constant) { - _device_ptr = (value_t *)sycl::malloc_device( - _size, q.get_device(), q.get_context(), - sycl::ext::oneapi::property::usm::device_read_only()); - return; - } - #endif - #endif - _device_ptr = (value_t *)detail::dpct_malloc(_size, q); - } - - size_t _size; - sycl::range _range; - bool _reference; - value_t *_host_ptr; - value_t *_device_ptr; - }; - template - class device_memory : public device_memory { - public: - using base = device_memory; - using value_t = typename base::value_t; - using accessor_t = - typename detail::memory_traits::template accessor_t<0>; - - /// Constructor with initial value. - device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {} - - /// Default constructor - device_memory() : base(1) {} - }; - } // namespace detail - - template - using global_memory = detail::device_memory; - template - using constant_memory = detail::device_memory; - template - using shared_memory = detail::device_memory; - - -} // COPY from DPCT head files - -#define GGML_COMMON_DECL_SYCL -#define GGML_COMMON_IMPL_SYCL -#include "ggml-common.h" - -static int g_ggml_sycl_debug=0; -#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) fprintf(stderr, __VA_ARGS__);}while(0) - -#define CHECK_TRY_ERROR(expr) \ - [&]() { \ - try { \ - expr; \ - return dpct::success; \ - } catch (std::exception const &e) { \ - std::cerr << e.what()<< "\nException caught at file:" << __FILE__ \ - << ", line:" << __LINE__ <<", func:"<<__func__<< std::endl; \ - return dpct::default_error; \ - } \ - }() - -// #define DEBUG_SYCL_MALLOC - -static int g_work_group_size = 0; -// typedef sycl::half ggml_fp16_t; - -#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP -#define VER_4VEC 610 //todo for hardward optimize. -#define VER_GEN9 700 //todo for hardward optimize. -#define VER_GEN12 1000000 //todo for hardward optimize. -#define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize. - -#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares - - -//define for XMX in Intel GPU -//TODO: currently, it's not used for XMX really. -#define SYCL_USE_XMX - -// max batch size to use MMQ kernels when tensor cores are available -#define XMX_MAX_BATCH_SIZE 32 - - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -// dmmv = dequantize_mul_mat_vec -#ifndef GGML_SYCL_DMMV_X -#define GGML_SYCL_DMMV_X 32 -#endif -#ifndef GGML_SYCL_MMV_Y -#define GGML_SYCL_MMV_Y 1 -#endif - -enum ggml_sycl_backend_gpu_mode { - SYCL_UNSET_GPU_MODE = -1, - SYCL_SINGLE_GPU_MODE = 0, - SYCL_MUL_GPU_MODE -}; - -static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); - -static void crash(){ - int *ptr = NULL; - *ptr = 0; -} - -static void ggml_sycl_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) { - fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg); - fprintf(stderr, " in function %s at %s:%d\n", func, file, line); - GGML_ASSERT(!"SYCL error"); -} - -#define SYCL_CHECK(err) do { \ - auto err_ = (err); if (err_ != 0) ggml_sycl_error( \ - #err, __func__, __FILE__, __LINE__, \ - "Meet error in this line code!"); \ -} while (0) - -#if DPCT_COMPAT_RT_VERSION >= 11100 -#define GGML_SYCL_ASSUME(x) __builtin_assume(x) -#else -#define GGML_SYCL_ASSUME(x) -#endif // DPCT_COMPAT_RT_VERSION >= 11100 +#endif -#ifdef GGML_SYCL_F16 -typedef sycl::half dfloat; // dequantize float -typedef sycl::half2 dfloat2; +#if defined(_MSC_VER) +#define __dpct_noinline__ __declspec(noinline) #else -typedef float dfloat; // dequantize float -typedef sycl::float2 dfloat2; -#endif //GGML_SYCL_F16 - -#define MMVQ_MAX_BATCH_SIZE 8 - -static const int8_t kvalues_iq4nl[16]={-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; +#define __dpct_noinline__ __attribute__((noinline)) +#endif bool ggml_sycl_loaded(void); -void * ggml_sycl_host_malloc(size_t size); -void ggml_sycl_host_free(void * ptr); -bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); void ggml_sycl_free_data(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers(struct ggml_tensor * tensor); void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor); @@ -3059,9 +98,7 @@ void ggml_sycl_free_scratch(void); void ggml_sycl_get_device_description(int device, char * description, size_t description_size); bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); -int get_main_device(); -void print_ggml_tensor(const char*name, struct ggml_tensor *src); -void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); void dev2dev_memcpy(sycl::queue &q_dst, sycl::queue &q_src, void *ptr_dst, const void *ptr_src, size_t size) { @@ -3104,25 +141,26 @@ static __dpct_inline__ int get_int_from_uint8_aligned(const uint8_t *x8, template using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y, - int k, dpct::queue_ptr stream); + int k, queue_ptr stream); typedef to_t_sycl_t to_fp32_sycl_t; typedef to_t_sycl_t to_fp16_sycl_t; typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); typedef void (*cpy_kernel_t)(const char * cx, char * cdst); -typedef void (*ggml_sycl_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +typedef void (*ggml_sycl_func_t)(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); typedef void (*ggml_sycl_op_mul_mat_t)( + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, - const dpct::queue_ptr &stream); -typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0, + const queue_ptr &stream); +typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream); + const queue_ptr &main_stream); typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm, @@ -3140,387 +178,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms, const int &i, const int &j, const int &k); -#define WARP_SIZE 32 -#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses - -#define SYCL_GELU_BLOCK_SIZE 256 -#define SYCL_SILU_BLOCK_SIZE 256 -#define SYCL_TANH_BLOCK_SIZE 256 -#define SYCL_RELU_BLOCK_SIZE 256 -#define SYCL_HARDSIGMOID_BLOCK_SIZE 256 -#define SYCL_HARDSWISH_BLOCK_SIZE 256 -#define SYCL_SQR_BLOCK_SIZE 256 -#define SYCL_CPY_BLOCK_SIZE 32 -#define SYCL_SCALE_BLOCK_SIZE 256 -#define SYCL_CLAMP_BLOCK_SIZE 256 -#define SYCL_ROPE_BLOCK_SIZE 256 -#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 -#define SYCL_QUANTIZE_BLOCK_SIZE 256 -#define SYCL_DEQUANTIZE_BLOCK_SIZE 256 -#define SYCL_GET_ROWS_BLOCK_SIZE 256 -#define SYCL_UPSCALE_BLOCK_SIZE 256 -#define SYCL_CONCAT_BLOCK_SIZE 256 -#define SYCL_PAD_BLOCK_SIZE 256 -#define SYCL_ACC_BLOCK_SIZE 256 -#define SYCL_IM2COL_BLOCK_SIZE 256 -#define SYCL_POOL2D_BLOCK_SIZE 256 - -// dmmv = dequantize_mul_mat_vec -#ifndef GGML_SYCL_DMMV_X -#define GGML_SYCL_DMMV_X 32 -#endif -#ifndef GGML_SYCL_MMV_Y -#define GGML_SYCL_MMV_Y 1 -#endif - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 2 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE -#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128 -#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE - -#define MUL_MAT_SRC1_COL_STRIDE 128 - -#define MAX_STREAMS 8 -static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = {{0}}; - -struct ggml_tensor_extra_gpu { - void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors - dpct::event_ptr - events[GGML_SYCL_MAX_DEVICES] - [MAX_STREAMS]; // events for synchronizing multiple GPUs -}; - -class sycl_gpu_mgr { - public: - std::vector gpus; - std::vector devices; - sycl::queue *first_queue; - sycl::context co_ctx; - int max_compute_units = 0; - int work_group_size = 0; - std::string gpus_list = ""; - - /* - Use all GPUs with same top max compute units - */ - sycl_gpu_mgr() { - detect_sycl_gpu_list_with_max_cu(); - get_allow_gpus(); - create_context_with_gpus(); - } - - /* - Only use the assigned GPU - */ - sycl_gpu_mgr(int main_gpu_id) { - sycl::device device = dpct::dev_mgr::instance().get_device(main_gpu_id); - dpct::device_info prop; - dpct::get_device_info(prop, device); - gpus.push_back(main_gpu_id); - devices.push_back(device); - work_group_size = prop.get_max_work_group_size(); - max_compute_units = prop.get_max_compute_units(); - - get_allow_gpus(); - create_context_with_gpus(); - } - - void create_context_with_gpus() { - sycl::context ctx = sycl::context(devices); - assert(gpus.size() > 0); - first_queue = dpct::get_current_device().create_queue(ctx, devices[0]); - co_ctx = first_queue->get_context(); - } - - sycl::context &get_co_ctx() { return co_ctx; } - - void get_allow_gpus() { - gpus_list = ""; - for (size_t i = 0; i < gpus.size(); ++i) { - gpus_list += std::to_string(gpus[i]); - gpus_list += ","; - } - if (gpus_list.length() > 1) { - gpus_list.pop_back(); - } - } - - bool is_allowed_gpu(int device_id) { - return std::find(gpus.begin(), gpus.end(), device_id) != gpus.end(); - } - - void detect_sycl_gpu_list_with_max_cu() try { - int device_count = dpct::dev_mgr::instance().device_count(); - - for (int id = 0; id < device_count; id++) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - if (!device.is_gpu()) - continue; - dpct::device_info prop; - dpct::get_device_info(prop, device); - if (max_compute_units < prop.get_max_compute_units()) - max_compute_units = prop.get_max_compute_units(); - } - - for (int id = 0; id < device_count; id++) { - sycl::device device = dpct::dev_mgr::instance().get_device(id); - if (!device.is_gpu()) - continue; - dpct::device_info prop; - dpct::get_device_info(prop, device); - if (max_compute_units == prop.get_max_compute_units() && - is_ext_oneapi_device(device)) { - gpus.push_back(id); - devices.push_back(device); - work_group_size = prop.get_max_work_group_size(); - } - } - return; - } catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); - } - - int get_gpu_count() { return (int)gpus.size(); } - - int get_index(int id) { - for (int i = 0; i < (int)gpus.size(); i++) { - if (gpus[i] == id) - return i; - } - printf("miss to get device index by id=%d\n", id); - GGML_ASSERT(false); - } - - int get_next_index(int id) { - int cur_index = get_index(id); - for (int i = cur_index + 1; i < (int)gpus.size(); i++) { - if (gpus[i] == id) - return i; - } - GGML_ASSERT(false); - } - - bool is_ext_oneapi_device(const sycl::device &dev) { - sycl::backend dev_backend = dev.get_backend(); - if (dev_backend == sycl::backend::ext_oneapi_level_zero || - dev_backend == sycl::backend::ext_oneapi_cuda || - dev_backend == sycl::backend::ext_oneapi_hip) - return true; - return false; - } -}; - -static sycl_gpu_mgr *g_sycl_gpu_mgr = NULL; -static int g_device_count = -1; -static int g_all_sycl_device_count = -1; -static int g_main_device = -1; -static int g_main_device_id = -1; -static bool g_ggml_backend_sycl_buffer_type_initialized = false; - -static std::array g_default_tensor_split = {}; - -static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; - -static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = SYCL_UNSET_GPU_MODE; - -struct sycl_device_capabilities { - int cc; // compute capability - bool vmm; // virtual memory support - size_t vmm_granularity; // granularity of virtual memory - int device_id; -}; - -static sycl_device_capabilities g_device_caps[GGML_SYCL_MAX_DEVICES] = { {0, false, 0, -1} }; - -struct sycl_device_id2index { - int index; -}; - -static void * g_scratch_buffer = nullptr; -static size_t g_scratch_size = 0; // disabled by default -static size_t g_scratch_offset = 0; - -static dpct::queue_ptr g_sycl_handles[GGML_SYCL_MAX_DEVICES] = {nullptr}; - -int get_main_device(){ - return g_main_device; -} - -[[noreturn]] -static void bad_arch(const sycl::stream &stream_ct1) { - stream_ct1 << "ERROR: ggml-sycl was compiled without support for the " - "current GPU architecture.\n"; - // __trap(); - std::exit(1); - - (void) bad_arch; // suppress unused function warning -} - -/* -device_index: device index from 0 to n (continue numbers). - It is used for device select/set in SYCL backend internal data structure. -*/ -void check_allow_gpu_index(const int device_index) { - if (device_index >= g_device_count) { - char error_buf[256]; - snprintf(error_buf, sizeof(error_buf), - "%s error: device_index:%d is out of range: [0-%d]", __func__, - device_index, g_device_count - 1); - fprintf(stderr, "%s\n", error_buf); - assert(false); - } -} - -/* -device_id: device ID is shown by ggml_backend_sycl_print_sycl_devices(). - It is only used to set current working device. -*/ -void check_allow_gpu_id(const int device_id) { - if (!g_sycl_gpu_mgr->is_allowed_gpu(device_id)) { - char error_buf[256]; - snprintf(error_buf, sizeof(error_buf), - "error: cannot set device=%d, which is not allowed. Please " - "set GPU ID in: [%s]", - device_id, g_sycl_gpu_mgr->gpus_list.c_str()); - fprintf(stderr, "%s\n", error_buf); - throw std::invalid_argument(error_buf); - } -} - -int get_current_device_id() { - return dpct::dev_mgr::instance().current_device_id(); -} - -inline dpct::err0 ggml_sycl_set_device(const int device) try { - - int device_id = g_sycl_gpu_mgr->gpus[device]; - check_allow_gpu_id(device_id); - - int current_device_id; - SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); - - // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d, - // current_device_id=%d\n", device, current_device); - if (device_id == current_device_id) { - return 0; - } - - return CHECK_TRY_ERROR(dpct::select_device(device_id)); -} catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - crash(); - std::exit(1); -} - -void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){ - if(!g_ggml_sycl_debug) return; - if(!src){ - printf("GGML Tensor:%s skip to save for NULL pointer\n", name); - return; - } - char filename[1024]; - sprintf(filename, "%s.txt", name); - printf("GGML Tensor:%s save to %s\n", name, filename); - - size_t total_size = total_elements*sizeof(float); - float *local_buf = NULL; - if(src_on_device) { - local_buf = (float *) ggml_sycl_host_malloc(total_size); - ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - main_stream->memcpy(local_buf, src, total_size).wait(); - } - else { - local_buf = (float *)src; - } - - std::ofstream logfile; - logfile.open(filename); - for(size_t i=0; imemcpy(local_buf, src, total_size).wait(); - } - else { - local_buf = (sycl::half *)src; - } - - std::ofstream logfile; - logfile.open(filename); - for(size_t i=0; ibackend == GGML_BACKEND_TYPE_GPU || src->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - float *src_data =NULL; - if(src_on_device) { - ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; - src_data = (float*)src_extra->data_device[g_main_device]; - } - else { - src_data = (float *)src->data; - } - - log_ggml_var_device(name, src_data, total_elements, src_on_device); -} - -static int log_file_name_idx=0; -void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt) { - stop_cnt = 4; - if(log_file_name_idx>=stop_cnt) return; - char filename[1280]; - sprintf(filename, "%s_%07d", name, log_file_name_idx); - log_file_name_idx++; - print_ggml_tensor(filename, src); -} - static __dpct_inline__ float warp_reduce_sum(float x, const sycl::nd_item<3> &item_ct1) { #pragma unroll @@ -8830,12 +5487,11 @@ static void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +template static void rope_neox( const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims -, - const sycl::nd_item<3> &item_ct1) { + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, + const float * freq_factors, const sycl::nd_item<3> &item_ct1) { const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + item_ct1.get_local_id(1)); @@ -8863,8 +5519,10 @@ static void rope_neox( float cur_rot = inv_ndims * ic - ib; const int p = has_pos ? pos[i2] : 0; + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + const float theta_base = - p * freq_scale * dpct::pow(theta_scale, col / 2.0f); + p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -8876,49 +5534,6 @@ static void rope_neox( dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; } -static void rope_glm_f32( - const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, - int n_ctx -, const sycl::nd_item<3> &item_ct1) { - const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - const int half_n_dims = ncols/4; - - if (col >= half_n_dims) { - return; - } - - const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i = row*ncols + col; - const int i2 = row/p_delta_rows; - - const float col_theta_scale = dpct::pow(freq_base, -2.0f * col / ncols); - // FIXME: this is likely wrong - const int p = pos != nullptr ? pos[i2] : 0; - - const float theta = sycl::min(p, n_ctx - 2) * freq_scale * col_theta_scale; - const float sin_theta = sycl::sin((float)theta); - const float cos_theta = sycl::cos((float)theta); - - const float x0 = x[i + 0]; - const float x1 = x[i + half_n_dims]; - - dst[i + 0] = x0*cos_theta - x1*sin_theta; - dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta; - - const float block_theta = - ((float)sycl::max(p - n_ctx - 2, 0)) * col_theta_scale; - const float sin_block_theta = sycl::sin((float)block_theta); - const float cos_block_theta = sycl::cos((float)block_theta); - - const float x2 = x[i + half_n_dims * 2]; - const float x3 = x[i + half_n_dims * 3]; - - dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta; - dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; -} - static void k_sum_rows_f32(const float * x, float * dst, const int ncols, const sycl::nd_item<3> &item_ct1) { const int row = item_ct1.get_group(1); @@ -9099,6 +5714,7 @@ static void soft_max_f32(const float * x, const float * mask, float * dst, const // find the sum of exps in the block tmp = warp_reduce_sum(tmp, item_ct1); if (block_size > WARP_SIZE) { + item_ct1.barrier(sycl::access::fence_space::local_space); if (warp_id == 0) { buf[lane_id] = 0.f; } @@ -9246,10 +5862,10 @@ static void pool2d_nchw_kernel( } template -static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, +static void get_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const void *src0_dd, const int32_t *src1_dd, float *dst_dd, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_TENSOR_BINARY_OP_LOCALS @@ -9281,10 +5897,10 @@ static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, } template -static void get_rows_sycl_float(const ggml_tensor *src0, +static void get_rows_sycl_float(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const src0_t *src0_dd, const int32_t *src1_dd, - float *dst_dd, dpct::queue_ptr stream) { + float *dst_dd, queue_ptr stream) { GGML_TENSOR_BINARY_OP_LOCALS @@ -9321,10 +5937,11 @@ static void get_rows_sycl_float(const ggml_tensor *src0, template struct bin_bcast_sycl { template - void operator()(const struct ggml_tensor *src0, + void operator()(ggml_backend_sycl_context & ctx, + const struct ggml_tensor *src0, const struct ggml_tensor *src1, struct ggml_tensor *dst, const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_TENSOR_BINARY_OP_LOCALS @@ -9461,7 +6078,7 @@ struct bin_bcast_sycl { static void acc_f32_sycl(const float *x, const float *y, float *dst, const int n_elements, const int ne10, const int ne11, const int ne12, const int nb1, const int nb2, - const int offset, dpct::queue_ptr stream) { + const int offset, queue_ptr stream) { int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9474,7 +6091,7 @@ static void acc_f32_sycl(const float *x, const float *y, float *dst, } static void gelu_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9486,7 +6103,7 @@ static void gelu_f32_sycl(const float *x, float *dst, const int k, } static void silu_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9498,7 +6115,7 @@ static void silu_f32_sycl(const float *x, float *dst, const int k, } static void gelu_quick_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9510,7 +6127,7 @@ static void gelu_quick_f32_sycl(const float *x, float *dst, const int k, } static void tanh_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9522,7 +6139,7 @@ static void tanh_f32_sycl(const float *x, float *dst, const int k, } static void relu_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9534,7 +6151,7 @@ static void relu_f32_sycl(const float *x, float *dst, const int k, } static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_HARDSIGMOID_BLOCK_SIZE - 1) / SYCL_HARDSIGMOID_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9546,7 +6163,7 @@ static void hardsigmoid_f32_sycl(const float *x, float *dst, const int k, } static void hardswish_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_HARDSWISH_BLOCK_SIZE - 1) / SYCL_HARDSWISH_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9559,7 +6176,7 @@ static void hardswish_f32_sycl(const float *x, float *dst, const int k, static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, const float negative_slope, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9571,7 +6188,7 @@ static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, } static void sqr_f32_sycl(const float *x, float *dst, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -9584,7 +6201,7 @@ static void sqr_f32_sycl(const float *x, float *dst, const int k, static void norm_f32_sycl(const float *x, float *dst, const int ncols, const int nrows, const float eps, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); if (ncols < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -9602,7 +6219,8 @@ static void norm_f32_sycl(const float *x, float *dst, const int ncols, }); }); } else { - const int work_group_size = g_work_group_size; + // FIXME: 1024 from cuda + const int work_group_size = GROUP_SIZE; const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:17: The work-group size passed to the SYCL kernel may exceed @@ -9627,7 +6245,7 @@ static void norm_f32_sycl(const float *x, float *dst, const int ncols, static void group_norm_f32_sycl(const float *x, float *dst, const int num_groups, const int group_size, - const int ne_elements, dpct::queue_ptr stream) { + const int ne_elements, queue_ptr stream) { static const float eps = 1e-6f; if (group_size < 1024) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -9648,7 +6266,7 @@ static void group_norm_f32_sycl(const float *x, float *dst, }); }); } else { - const int work_group_size = g_work_group_size; + const int work_group_size = GROUP_SIZE; const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:18: The work-group size passed to the SYCL kernel may exceed @@ -9677,7 +6295,7 @@ static void group_norm_f32_sycl(const float *x, float *dst, static void concat_f32_sycl(const float *x, const float *y, float *dst, const int ne0, int ne1, int ne2, int ne02, - dpct::queue_ptr stream) { + queue_ptr stream) { int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE; sycl::range<3> gridDim(ne2, ne1, num_blocks); stream->parallel_for( @@ -9692,7 +6310,7 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int ne13, const float sf0, const float sf1, - const float sf2, const float sf3, dpct::queue_ptr stream) { + const float sf2, const float sf3, queue_ptr stream) { int dst_size = ne10 * ne11 * ne12 * ne13; int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); @@ -9705,7 +6323,7 @@ static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const i static void pad_f32_sycl(const float *x, float *dst, const int ne00, const int ne01, const int ne02, const int ne0, - const int ne1, const int ne2, dpct::queue_ptr stream) { + const int ne1, const int ne2, queue_ptr stream) { int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; sycl::range<3> gridDim(ne2, ne1, num_blocks); stream->parallel_for( @@ -9718,7 +6336,7 @@ static void pad_f32_sycl(const float *x, float *dst, const int ne00, static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols, const int nrows, const float eps, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); if (ncols < 1024) { @@ -9737,7 +6355,7 @@ static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols, }); }); } else { - const int work_group_size = g_work_group_size; + const int work_group_size = GROUP_SIZE; const sycl::range<3> block_dims(1, 1, work_group_size); /* DPCT1049:19: The work-group size passed to the SYCL kernel may exceed @@ -9762,7 +6380,7 @@ static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols, static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, const int ky, const int kx_padded, - dpct::queue_ptr stream) { + queue_ptr stream) { const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE; const sycl::range<3> num_blocks(1, ky, block_num_x); const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE); @@ -9781,7 +6399,7 @@ static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, template static void dequantize_block_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + 2*SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / (2*SYCL_DEQUANTIZE_BLOCK_SIZE); { dpct::has_capability_or_fail(stream->get_device(), @@ -9799,7 +6417,7 @@ static void dequantize_block_sycl(const void *__restrict__ vx, template static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9816,7 +6434,7 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9833,7 +6451,7 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; { @@ -9851,7 +6469,7 @@ static void dequantize_row_q4_0_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb32 = k / 32; const int nb = (k + 255) / 256; { @@ -9870,7 +6488,7 @@ static void dequantize_row_q4_1_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9887,7 +6505,7 @@ static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9904,7 +6522,7 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9921,7 +6539,7 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9942,7 +6560,7 @@ static void dequantize_row_iq1_s_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9963,7 +6581,7 @@ static void dequantize_row_iq1_m_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -9984,7 +6602,7 @@ static void dequantize_row_iq2_xxs_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10005,7 +6623,7 @@ static void dequantize_row_iq2_xs_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10025,7 +6643,7 @@ static void dequantize_row_iq2_s_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10046,7 +6664,7 @@ static void dequantize_row_iq3_xxs_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = k / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10066,7 +6684,7 @@ static void dequantize_row_iq3_s_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = (k + QK_K - 1) / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10087,7 +6705,7 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, template static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int nb = (k + QK_K - 1) / QK_K; { dpct::has_capability_or_fail(stream->get_device(), @@ -10110,7 +6728,7 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int k, template static void convert_unary_sycl(const void *__restrict__ vx, dst_t *__restrict__ y, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; { dpct::has_capability_or_fail(stream->get_device(), @@ -10231,7 +6849,7 @@ static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead @@ -10253,7 +6871,7 @@ static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y, static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10274,7 +6892,7 @@ static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y, static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10295,7 +6913,7 @@ static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y, static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10316,7 +6934,7 @@ static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y, static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10337,7 +6955,7 @@ static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y, static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 const int block_num_y = (nrows + ny - 1) / ny; @@ -10353,7 +6971,7 @@ static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y, static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; @@ -10369,7 +6987,7 @@ static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; @@ -10385,7 +7003,7 @@ static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const sycl::range<3> block_dims(1, 1, 32); stream->parallel_for( @@ -10398,7 +7016,7 @@ static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int ny = 2 / K_QUANTS_PER_ITERATION; const int block_num_y = (nrows + ny - 1) / ny; @@ -10414,7 +7032,7 @@ static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10436,7 +7054,7 @@ static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK4_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10460,7 +7078,7 @@ static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK4_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10484,7 +7102,7 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK5_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10508,7 +7126,7 @@ static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK5_1 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10532,7 +7150,7 @@ static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK8_0 == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10556,7 +7174,7 @@ static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10580,7 +7198,7 @@ static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10604,7 +7222,7 @@ static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10628,7 +7246,7 @@ static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10652,7 +7270,7 @@ static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10677,7 +7295,7 @@ static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10698,7 +7316,7 @@ static void mul_mat_vec_iq2_xxs_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10723,7 +7341,7 @@ static void mul_mat_vec_iq2_xs_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10748,7 +7366,7 @@ static void mul_mat_vec_iq2_s_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10773,7 +7391,7 @@ static void mul_mat_vec_iq3_xxs_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10797,7 +7415,7 @@ static void mul_mat_vec_iq3_s_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10822,7 +7440,7 @@ static void mul_mat_vec_iq1_s_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10843,7 +7461,7 @@ static void mul_mat_vec_iq1_m_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK4_NL == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10865,7 +7483,7 @@ static void mul_mat_vec_iq4_nl_q8_1_sycl(const void *vx, const void *vy, static void mul_mat_vec_iq4_xs_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ncols % QK_K == 0); const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; const sycl::range<3> block_nums(1, 1, block_num_y); @@ -10888,12 +7506,12 @@ static void ggml_mul_mat_q4_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11003,12 +7621,12 @@ static void ggml_mul_mat_q4_1_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11118,12 +7736,12 @@ static void ggml_mul_mat_q5_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11233,12 +7851,12 @@ static void ggml_mul_mat_q5_1_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11348,12 +7966,12 @@ static void ggml_mul_mat_q8_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11463,12 +8081,12 @@ static void ggml_mul_mat_q2_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11584,12 +8202,12 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11711,12 +8329,12 @@ static void ggml_mul_mat_q4_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11832,12 +8450,12 @@ static void ggml_mul_mat_q5_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -11953,12 +8571,12 @@ static void ggml_mul_mat_q6_K_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, - dpct::queue_ptr stream) try { + queue_ptr stream) try { int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); - const int compute_capability = g_device_caps[id].cc; + const int compute_capability = ggml_sycl_info().devices[id].cc; int mmq_x, mmq_y, nwarps; if (compute_capability >= VER_GEN13) { @@ -12075,7 +8693,7 @@ static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y, const int nrows_x, const int nchannels_x, const int nchannels_y, - dpct::queue_ptr stream) { + queue_ptr stream) { const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -12095,7 +8713,7 @@ static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y, static void ggml_mul_mat_vec_nc_f16_f32_sycl( const void *vx, const float *y, float *dst, const int ncols_x, const int nrows_x, const int row_stride_x, const int nchannels_x, - const int nchannels_y, const int channel_stride_x, dpct::queue_ptr stream) { + const int nchannels_y, const int channel_stride_x, queue_ptr stream) { const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -12119,7 +8737,7 @@ ggml_cpy_f16_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int nb01, const int nb02, const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, - const int nb13, dpct::queue_ptr stream) { + const int nb13, queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12146,7 +8764,7 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12173,7 +8791,7 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12200,7 +8818,7 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ne % QK8_0 == 0); const int num_blocks = ne / QK8_0; @@ -12222,7 +8840,7 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; @@ -12244,7 +8862,7 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; @@ -12266,7 +8884,7 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12293,7 +8911,7 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12320,7 +8938,7 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; { @@ -12340,7 +8958,7 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, } static void scale_f32_sycl(const float *x, float *dst, const float scale, - const int k, dpct::queue_ptr stream) { + const int k, queue_ptr stream) { const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -12353,7 +8971,7 @@ static void scale_f32_sycl(const float *x, float *dst, const float scale, static void clamp_f32_sycl(const float *x, float *dst, const float min, const float max, const int k, - dpct::queue_ptr stream) { + queue_ptr stream) { const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE; stream->parallel_for( sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * @@ -12368,7 +8986,7 @@ template static void rope_sycl(const T *x, T *dst, int ncols, int nrows, const int32_t *pos, float freq_scale, int p_delta_rows, float freq_base, float ext_factor, float attn_factor, - rope_corr_dims corr_dims, dpct::queue_ptr stream) { + rope_corr_dims corr_dims, queue_ptr stream) { GGML_ASSERT(ncols % 2 == 0); const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); @@ -12413,7 +9031,7 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, const int32_t *pos, float freq_scale, int p_delta_rows, float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, - dpct::queue_ptr stream) { + const float * freq_factors, queue_ptr stream) { GGML_ASSERT(ncols % 2 == 0); const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); @@ -12423,59 +9041,53 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { - /* - DPCT1049:42: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, - item_ct1); - }); + if (freq_factors == nullptr) { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, + item_ct1); + }); + } else { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, + item_ct1); + }); + } } else { - /* - DPCT1049:43: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, item_ct1); - }); + if (freq_factors == nullptr) { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); + }); + } else { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); + }); + } } } -static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows, - const int32_t *pos, float freq_scale, - int p_delta_rows, float freq_base, int n_ctx, - dpct::queue_ptr stream) { - GGML_ASSERT(ncols % 4 == 0); - const sycl::range<3> block_dims(1, 1, SYCL_ROPE_BLOCK_SIZE / 4); - const int num_blocks_x = (ncols + SYCL_ROPE_BLOCK_SIZE - 1) / SYCL_ROPE_BLOCK_SIZE; - const sycl::range<3> block_nums(1, nrows, num_blocks_x); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_glm_f32(x, dst, ncols, pos, freq_scale, - p_delta_rows, freq_base, n_ctx, - item_ct1); - }); -} - static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, dpct::queue_ptr stream) { + const int nrows, queue_ptr stream) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); const sycl::range<3> block_nums(1, nrows, 1); stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), @@ -12495,7 +9107,7 @@ static int next_power_of_2(int x) { static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const int nrows, ggml_sort_order order, - dpct::queue_ptr stream) { + queue_ptr stream) { // bitonic sort requires ncols to be power of 2 const int ncols_pad = next_power_of_2(ncols); @@ -12503,8 +9115,6 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, const sycl::range<3> block_nums(1, nrows, 1); const size_t shared_mem = ncols_pad * sizeof(int); - // GGML_ASSERT(shared_mem <= ggml_cuda_info().devices[ggml_cuda_get_device()].smpb); - if (order == GGML_SORT_ORDER_ASC) { stream->submit([&](sycl::handler &cgh) { sycl::local_accessor dpct_local_acc_ct1( @@ -12541,7 +9151,7 @@ static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, static void diag_mask_inf_f32_sycl(const float *x, float *dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, - dpct::queue_ptr stream) { + queue_ptr stream) { const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1); const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE; const sycl::range<3> block_nums(1, block_num_x, nrows_x); @@ -12557,7 +9167,7 @@ template static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, - const size_t n_local_scratch, dpct::queue_ptr stream) { + const size_t n_local_scratch, queue_ptr stream) { stream->submit([&](sycl::handler &cgh) { sycl::local_accessor local_buf_acc(n_local_scratch, cgh); @@ -12575,9 +9185,9 @@ static void soft_max_f32_submitter(const float * x, const float * mask, float * static void soft_max_f32_sycl(const float * x, const float * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, - dpct::queue_ptr stream) { + queue_ptr stream) { int nth = WARP_SIZE; - int max_block_size = g_work_group_size; + int max_block_size = GROUP_SIZE; while (nth < ncols_x && nth < max_block_size) nth *= 2; if (nth>max_block_size) nth = max_block_size; @@ -12644,257 +9254,40 @@ static void soft_max_f32_sycl(const float * x, const float * mask, soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); - break; - } - } else { - soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, - max_bias, m0, m1, n_head_log2, block_nums, - block_dims, WARP_SIZE, stream); - } -} - -template -static void im2col_sycl(const float *x, T *dst, int IW, int IH, - int OW, int OH, int KW, int KH, int IC, - int offset_delta, int s0, int s1, int p0, - int p1, int d0, int d1, - dpct::queue_ptr stream) { - const int parallel_elements = OW * KW * KH; - const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE; - sycl::range<3> block_nums(IC, OH, num_blocks); - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * - sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), - sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), - [=](sycl::nd_item<3> item_ct1) { - im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH, - parallel_elements, (IC * KH * KW), s0, s1, p0, - p1, d0, d1, item_ct1); - }); - } -} - -// buffer pool for sycl -#define MAX_SYCL_BUFFERS 256 - -struct scoped_spin_lock { - std::atomic_flag& lock; - scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { - while (lock.test_and_set(std::memory_order_acquire)) { - ; // spin - } - } - ~scoped_spin_lock() { - lock.clear(std::memory_order_release); - } - scoped_spin_lock(const scoped_spin_lock&) = delete; - scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; -}; - -static std::atomic_flag g_sycl_pool_lock = ATOMIC_FLAG_INIT; - -// #define DEBUG_SYCL_MALLOC -struct sycl_buffer { - void * ptr = nullptr; - size_t size = 0; -}; - -static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS]; -static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0}; - -static void *ggml_sycl_pool_malloc_leg(int device_index, size_t size, size_t *actual_size) try { - scoped_spin_lock lock(g_sycl_pool_lock); - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg device_index %d size=%lu\n", device_index, size); -#ifdef DEBUG_SYCL_MALLOC - int nnz = 0; - size_t max_size = 0; -#endif - size_t best_diff = 1ull << 36; - int ibest = -1; - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; - if (b.ptr != nullptr) { -#ifdef DEBUG_SYCL_MALLOC - ++nnz; - if (b.size > max_size) max_size = b.size; -#endif - if (b.size >= size) { - size_t diff = b.size - size; - if (diff < best_diff) { - best_diff = diff; - ibest = i; - if (!best_diff) { - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p and rm in pool\n", ptr); - return ptr; - } - } - } - } - } - if (ibest >= 0) { - sycl_buffer& b = g_sycl_buffer_pool[device_index][ibest]; - void * ptr = b.ptr; - *actual_size = b.size; - b.ptr = nullptr; - b.size = 0; - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p and rm in pool\n", ptr); - return ptr; - } - void * ptr; - size_t look_ahead_size = (size_t) (1.05 * size); - look_ahead_size = 256 * ((look_ahead_size + 255)/256); - - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( - look_ahead_size, *stream))); - *actual_size = look_ahead_size; - g_sycl_pool_size[device_index] += look_ahead_size; - -#ifdef DEBUG_SYCL_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); -#endif - // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); - return ptr; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_pool_free_leg(int device_index, void *ptr, size_t size) try { - scoped_spin_lock lock(g_sycl_pool_lock); - const dpct::queue_ptr stream = g_syclStreams[device_index][0]; - for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { - sycl_buffer& b = g_sycl_buffer_pool[device_index][i]; - if (b.ptr == nullptr) { - b.ptr = ptr; - b.size = size; - return; + break; } - } - fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n"); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream))); - g_sycl_pool_size[device_index] -= size; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -// pool with virtual memory -/* -DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported. -*/ -// static std::vector -// g_sycl_pool_handles[GGML_SYCL_MAX_DEVICES]; -static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0}; -static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0}; - -static void *ggml_sycl_pool_malloc_vmm(int device_index, size_t size, size_t *actual_size) try { - GGML_UNUSED(device_index); - GGML_UNUSED(size); - GGML_UNUSED(actual_size); - return NULL; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_pool_free_vmm(int device_index, void *ptr, size_t size) try { - scoped_spin_lock lock(g_sycl_pool_lock); -#ifdef DEBUG_SYCL_MALLOC - printf("sycl pool[%d]: freed %llu bytes at %llx\n", device_index, (unsigned long long) size, ptr); -#endif - - g_sycl_pool_used[device_index] -= size; - - // all deallocations must be in reverse order of the allocations - GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[device_index] + g_sycl_pool_used[device_index])); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void *ggml_sycl_pool_malloc(int device_index, size_t size, size_t *actual_size) try { - if (g_device_caps[device_index].vmm) { - return ggml_sycl_pool_malloc_vmm(device_index, size, actual_size); - } else { - return ggml_sycl_pool_malloc_leg(device_index, size, actual_size); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static void ggml_sycl_pool_free(int device_index, void *ptr, size_t size) try { - if (g_device_caps[device_index].vmm) { - ggml_sycl_pool_free_vmm(device_index, ptr, size); } else { - ggml_sycl_pool_free_leg(device_index, ptr, size); + soft_max_f32_submitter(x, mask, dst, ncols_x, nrows_y, scale, + max_bias, m0, m1, n_head_log2, block_nums, + block_dims, WARP_SIZE, stream); } } -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - - -template -struct sycl_pool_alloc { - int device_index = -1; - int device_id = -1; - T * ptr = nullptr; - size_t actual_size = 0; - - // size is in number of elements - T * alloc(size_t size) { - GGML_ASSERT(ptr == nullptr); - device_id = get_current_device_id(); - device_index = g_sycl_gpu_mgr->get_index(device_id); - ptr = (T *) ggml_sycl_pool_malloc(device_index, size * sizeof(T), &this->actual_size); - // GGML_SYCL_DEBUG("sycl_pool_alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); - return ptr; - } - - sycl_pool_alloc(size_t size) { - alloc(size); - } - ~sycl_pool_alloc() { - if (ptr != nullptr) { - ggml_sycl_pool_free(device_index, ptr, actual_size); - } - } +template +static void im2col_sycl(const float *x, T *dst, int IW, int IH, + int OW, int OH, int KW, int KH, int IC, + int offset_delta, int s0, int s1, int p0, + int p1, int d0, int d1, + queue_ptr stream) { + const int parallel_elements = OW * KW * KH; + const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE; + sycl::range<3> block_nums(IC, OH, num_blocks); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); - T * get() { - return ptr; + stream->parallel_for( + sycl::nd_range<3>(block_nums * + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH, + parallel_elements, (IC * KH * KW), s0, s1, p0, + p1, d0, d1, item_ct1); + }); } +} - sycl_pool_alloc() = default; - sycl_pool_alloc(const sycl_pool_alloc &) = delete; - sycl_pool_alloc(sycl_pool_alloc &&) = delete; - sycl_pool_alloc& operator=(const sycl_pool_alloc &) = delete; - sycl_pool_alloc& operator=(sycl_pool_alloc &&) = delete; -}; static bool g_sycl_loaded = false; @@ -12946,21 +9339,6 @@ void ggml_backend_sycl_print_sycl_devices() { } } -void print_gpu_device_list() { - GGML_ASSERT(g_sycl_gpu_mgr); - - char* hint=NULL; - if (g_ggml_sycl_backend_gpu_mode == SYCL_SINGLE_GPU_MODE) { - hint = "use %d SYCL GPUs: [%s] with Max compute units:%d\n"; - } else { - hint = "detect %d SYCL GPUs: [%s] with top Max compute units:%d\n"; - } - fprintf(stderr, hint, - g_sycl_gpu_mgr->get_gpu_count(), - g_sycl_gpu_mgr->gpus_list.c_str(), - g_sycl_gpu_mgr->max_compute_units); -} - int get_sycl_env(const char *env_name, int default_val) { char *user_device_string = getenv(env_name); int user_number = default_val; @@ -12982,11 +9360,11 @@ int get_work_group_size(int user_device_id) { return prop.get_max_work_group_size(); } -static void ggml_init_sycl() try { +static void ggml_check_sycl() try { static bool initialized = false; if (!initialized) { - fprintf(stderr, "[SYCL] call ggml_init_sycl\n"); + fprintf(stderr, "[SYCL] call ggml_check_sycl\n"); g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); fprintf(stderr, "%s: GGML_SYCL_DEBUG: %d\n", __func__, g_ggml_sycl_debug); @@ -13023,106 +9401,189 @@ catch (sycl::exception const &exc) { std::exit(1); } -void ggml_init_by_gpus(int device_count) try { - g_device_count = device_count; - g_work_group_size = g_sycl_gpu_mgr->work_group_size; - - int64_t total_vram = 0; - - print_gpu_device_list(); +static ggml_sycl_device_info ggml_sycl_init() { + ggml_sycl_device_info info = {}; - for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { - g_device_caps[id].vmm = 0; - g_device_caps[id].device_id = -1; - g_device_caps[id].cc = 0; - g_tensor_split[id] = 0; - g_default_tensor_split[id] = 0; + info.device_count = dpct::dev_mgr::instance().device_count(); + if (info.device_count == 0) { + fprintf(stderr, "%s: failed to initialize " GGML_SYCL_NAME ": %s\n", __func__); + return info; } - for (int i = 0; i < g_device_count; ++i) { - int device_id = g_sycl_gpu_mgr->gpus[i]; - g_device_caps[i].vmm = 0; + GGML_ASSERT(info.device_count <= GGML_SYCL_MAX_DEVICES); + + int64_t total_vram = 0; +#if defined(GGML_SYCL_FORCE_MMQ) + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_FORCE_MMQ: no\n", __func__); +#endif +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif + fprintf(stderr, "%s: found %d " GGML_SYCL_NAME " devices:\n", __func__, info.device_count); + for (int i = 0; i < info.device_count; ++i) { + info.devices[i].vmm = 0; dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); + prop, dpct::dev_mgr::instance().get_device(i)))); - g_default_tensor_split[i] = total_vram; + info.default_tensor_split[i] = total_vram; total_vram += prop.get_global_mem_size(); - g_device_caps[i].cc = + info.devices[i].cc = 100 * prop.get_major_version() + 10 * prop.get_minor_version(); } - for (int i = 0; i < g_device_count; ++i) { - g_default_tensor_split[i] /= total_vram; + for (int id = 0; id < info.device_count; ++id) { + info.default_tensor_split[id] /= total_vram; } + return info; +} - for (int i = 0; i < g_device_count; ++i) { - SYCL_CHECK(ggml_sycl_set_device(i)); +const ggml_sycl_device_info & ggml_sycl_info() { + static ggml_sycl_device_info info = ggml_sycl_init(); + return info; +} - // create sycl streams - for (int is = 0; is < MAX_STREAMS; ++is) { - SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[i][is] = - dpct::get_current_device().create_queue( - g_sycl_gpu_mgr->get_co_ctx(), dpct::get_current_device()))); - } +/* +device_index: device index from 0 to n (continue numbers). + It is used for device select/set in SYCL backend internal data structure. +*/ +inline void check_allow_gpu_index(const int device_index) { + if (device_index >= ggml_sycl_info().device_count) { + char error_buf[256]; + snprintf( + error_buf, + sizeof(error_buf), + "%s error: device_index:%d is out of range: [0-%d]", + __func__, + device_index, + ggml_sycl_info().device_count - 1); + fprintf(stderr, "%s\n", error_buf); + assert(false); + } +} - const dpct::queue_ptr stream = g_syclStreams[i][0]; - // create sycl handle - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[i] = stream)); +// buffer pool for sycl (legacy) +struct ggml_sycl_pool_leg : public ggml_sycl_pool { + static const int MAX_SYCL_BUFFERS = 256; + + int device; + queue_ptr qptr; + struct ggml_sycl_buffer { + void * ptr = nullptr; + size_t size = 0; + }; + + ggml_sycl_buffer buffer_pool[MAX_SYCL_BUFFERS] = {}; + size_t pool_size = 0; + + explicit ggml_sycl_pool_leg(queue_ptr qptr_, int device_) : + qptr(qptr_), + device(device_) { } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} -void *ggml_sycl_host_malloc(size_t size) try { - if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { - return nullptr; + ~ggml_sycl_pool_leg() { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer & b = buffer_pool[i]; + if (b.ptr != nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(b.ptr, *qptr))); + pool_size -= b.size; + } + } + GGML_ASSERT(pool_size == 0); } - void * ptr = nullptr; - //allow to use dpct::get_in_order_queue() for host malloc - dpct::err0 err = CHECK_TRY_ERROR( - ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); + void * alloc(size_t size, size_t * actual_size) override { +#ifdef DEBUG_sycl_MALLOC + int nnz = 0; + size_t max_size = 0; +#endif + size_t best_diff = 1ull << 36; + int ibest = -1; + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr != nullptr) { +#ifdef DEBUG_sycl_MALLOC + ++nnz; + if (b.size > max_size) max_size = b.size; +#endif + if (b.size >= size) { + size_t diff = b.size - size; + if (diff < best_diff) { + best_diff = diff; + ibest = i; + if (!best_diff) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + } + } + } + } + if (ibest >= 0) { + ggml_sycl_buffer& b = buffer_pool[ibest]; + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + return ptr; + } + void * ptr; + size_t look_ahead_size = (size_t) (1.05 * size); + + SYCL_CHECK( + CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( + look_ahead_size, *qptr))); + *actual_size = look_ahead_size; + pool_size += look_ahead_size; + + #ifdef DEBUG_SYCL_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); + #endif + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg look_ahead_size=%lu, return %p\n", look_ahead_size, ptr); + return ptr; + } - if (err != 0) { - // clear the error - fprintf( - stderr, - "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", - size / 1024.0 / 1024.0, - "syclGetErrorString is not supported"); - return nullptr; + void free(void * ptr, size_t size) override { + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + ggml_sycl_buffer& b = buffer_pool[i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_sycl_BUFFERS\n"); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *qptr))); + pool_size -= size; } +}; - return ptr; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); +std::unique_ptr ggml_backend_sycl_context::new_pool_for_device(queue_ptr qptr, int device) { + // TBD: NO VMM support + // if (ggml_sycl_info().devices[device].vmm) { + // return std::unique_ptr(new ggml_sycl_pool_vmm(device)); + // } + return std::unique_ptr(new ggml_sycl_pool_leg(qptr, device)); } -void ggml_sycl_host_free(void *ptr) try { - //allow to use dpct::get_in_order_queue() for host malloc - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue()))); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} +// TBD pool with virtual memory management +// struct ggml_sycl_pool_vmm : public ggml_sycl_pool static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, const struct ggml_tensor *src, int64_t i3, int64_t i2, int64_t i1_low, int64_t i1_high, - dpct::queue_ptr stream) try { + queue_ptr stream) try { dpct::memcpy_direction kind; char * src_ptr; @@ -13188,10 +9649,10 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_op_get_rows(const ggml_tensor *src0, +static void ggml_sycl_op_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_d, const float *src1_d, - float *dst_d, const dpct::queue_ptr &stream) { + float *dst_d, const queue_ptr &stream) { GGML_ASSERT(src1->type == GGML_TYPE_I32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -13204,26 +9665,26 @@ static void ggml_sycl_op_get_rows(const ggml_tensor *src0, switch (src0->type) { case GGML_TYPE_F16: - get_rows_sycl_float(src0, src1, dst, (const sycl::half *)src0_d, + get_rows_sycl_float(ctx, src0, src1, dst, (const sycl::half *)src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_F32: - get_rows_sycl_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl_float(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_Q4_0: - get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_Q4_1: - get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_Q5_0: - get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_Q5_1: - get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; case GGML_TYPE_Q8_0: - get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + get_rows_sycl(ctx, src0, src1, dst, src0_d, src1_i32, dst_d, stream); break; default: // TODO: k-quants @@ -13234,25 +9695,25 @@ static void ggml_sycl_op_get_rows(const ggml_tensor *src0, } template -inline void ggml_sycl_op_bin_bcast(const ggml_tensor *src0, +inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, (sycl::half *)dst_dd, main_stream); } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { - op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, + op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, main_stream); } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { - op()(src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, + op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, main_stream); } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { - op()(src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, + op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, main_stream); } else { fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, @@ -13261,30 +9722,30 @@ inline void ggml_sycl_op_bin_bcast(const ggml_tensor *src0, } } -static void ggml_sycl_op_repeat(const ggml_tensor *src0, +static void ggml_sycl_op_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_d, const float *src1_d, float *dst_d, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { - ggml_sycl_op_bin_bcast>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream); + ggml_sycl_op_bin_bcast>(ctx, dst, src0, dst, nullptr, src0_d, dst_d, main_stream); (void) src1; (void) src1_d; } -inline void ggml_sycl_op_add(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_add(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { - ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_sycl_op_acc(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_acc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13301,26 +9762,26 @@ inline void ggml_sycl_op_acc(const ggml_tensor *src0, const ggml_tensor *src1, (void) dst; } -inline void ggml_sycl_op_mul(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_mul(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { - ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_sycl_op_div(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_div(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { - ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + ggml_sycl_op_bin_bcast>(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); } -inline void ggml_sycl_op_gelu(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13332,10 +9793,10 @@ inline void ggml_sycl_op_gelu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_silu(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_silu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13347,11 +9808,11 @@ inline void ggml_sycl_op_silu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_gelu_quick(const ggml_tensor *src0, +inline void ggml_sycl_op_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13363,10 +9824,10 @@ inline void ggml_sycl_op_gelu_quick(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13377,10 +9838,10 @@ inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13392,11 +9853,11 @@ inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0, +static void ggml_sycl_op_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13408,10 +9869,10 @@ static void ggml_sycl_op_hardsigmoid(const ggml_tensor *src0, (void) src1_dd; } -static void ggml_sycl_op_hardswish(const ggml_tensor *src0, +static void ggml_sycl_op_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, - float *dst_dd, const dpct::queue_ptr &main_stream) { + float *dst_dd, const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13423,11 +9884,11 @@ static void ggml_sycl_op_hardswish(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, +inline void ggml_sycl_op_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13442,10 +9903,10 @@ inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13457,10 +9918,10 @@ inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13478,11 +9939,11 @@ inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_group_norm(const ggml_tensor *src0, +inline void ggml_sycl_op_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13496,11 +9957,15 @@ inline void ggml_sycl_op_group_norm(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_concat(const ggml_tensor *src0, +inline void ggml_sycl_op_concat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { +#pragma message("TODO: generalize concat kernel for dim != 2") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7563") + int dim = dst->op_params[0]; + GGML_ASSERT(dim == 2); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13514,11 +9979,11 @@ inline void ggml_sycl_op_concat(const ggml_tensor *src0, (void) dst; } -inline void ggml_sycl_op_upscale(const ggml_tensor *src0, +inline void ggml_sycl_op_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -13537,10 +10002,10 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_pad(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_pad(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); @@ -13555,11 +10020,11 @@ inline void ggml_sycl_op_pad(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_rms_norm(const ggml_tensor *src0, +inline void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -13578,11 +10043,11 @@ inline void ggml_sycl_op_rms_norm(const ggml_tensor *src0, } inline void ggml_sycl_op_mul_mat_q( - const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, - const dpct::queue_ptr &stream) try { + const queue_ptr &stream) try { const int64_t ne00 = src0->ne[0]; @@ -13599,7 +10064,7 @@ inline void ggml_sycl_op_mul_mat_q( // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && device_id == g_main_device ? ne0 : row_diff; + const int64_t nrows_dst = device_id == ctx.device ? ne0 : row_diff; switch (src0->type) { case GGML_TYPE_Q4_0: @@ -13650,13 +10115,13 @@ catch (sycl::exception const &exc) { static int64_t get_row_rounding(ggml_type type, const std::array & tensor_split) { int64_t min_compute_capability = INT_MAX; int64_t max_compute_capability = INT_MIN; - for (int i = 0; i < g_device_count; ++i) { - if (tensor_split[i] < (i + 1 < g_device_count ? tensor_split[i + 1] : 1.0f)) { - if (min_compute_capability > g_device_caps[i].cc) { - min_compute_capability = g_device_caps[i].cc; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + if (tensor_split[i] < (i + 1 < ggml_sycl_info().device_count ? tensor_split[i + 1] : 1.0f)) { + if (min_compute_capability > ggml_sycl_info().devices[i].cc) { + min_compute_capability = ggml_sycl_info().devices[i].cc; } - if (max_compute_capability < g_device_caps[i].cc) { - max_compute_capability = g_device_caps[i].cc; + if (max_compute_capability < ggml_sycl_info().devices[i].cc) { + max_compute_capability = ggml_sycl_info().devices[i].cc; } } } @@ -13696,11 +10161,12 @@ static int64_t get_row_rounding(ggml_type type, const std::arrayne[0]; GGML_ASSERT(ne10 % QK8_1 == 0); @@ -13714,7 +10180,7 @@ inline void ggml_sycl_op_mul_mat_vec_q( // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into - const int64_t nrows_dst = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne00 : row_diff; + const int64_t nrows_dst = id == ctx.device ? ne00 : row_diff; switch (src0->type) { case GGML_TYPE_Q4_0: @@ -13788,11 +10254,12 @@ inline void ggml_sycl_op_mul_mat_vec_q( inline void ggml_sycl_op_dequantize_mul_mat_vec( + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, - const dpct::queue_ptr &stream) { + const queue_ptr &stream) { const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; @@ -13801,7 +10268,7 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics #ifdef GGML_SYCL_F16 - sycl_pool_alloc src1_dfloat_a; + ggml_sycl_pool_alloc src1_dfloat_a(ctx.pool()); sycl::half *src1_dfloat = nullptr; // dfloat == half bool src1_convert_f16 = @@ -13867,11 +10334,12 @@ inline void ggml_sycl_op_dequantize_mul_mat_vec( } inline void ggml_sycl_op_mul_mat_sycl( + ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, float *dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, - const dpct::queue_ptr &stream) try { + const queue_ptr &stream) try { GGML_ASSERT(src0_dd_i != nullptr); GGML_ASSERT(src1_ddf_i != nullptr); @@ -13890,7 +10358,7 @@ inline void ggml_sycl_op_mul_mat_sycl( // the main device has a larger memory buffer to hold the results from all GPUs // ldc == nrows of the matrix that cuBLAS writes into - int ldc = dst->backend == GGML_BACKEND_TYPE_GPU && id == g_main_device ? ne0 : row_diff; + int ldc = id == ctx.device ? ne0 : row_diff; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check @@ -13902,7 +10370,7 @@ inline void ggml_sycl_op_mul_mat_sycl( dst->op_params[0] == GGML_PREC_DEFAULT) { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); - sycl_pool_alloc src0_as_f16; + ggml_sycl_pool_alloc src0_as_f16(ctx.pool()); if (src0->type != GGML_TYPE_F16) { const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type); GGML_ASSERT(to_fp16_sycl != nullptr); @@ -13914,7 +10382,7 @@ inline void ggml_sycl_op_mul_mat_sycl( ? (const sycl::half *)src0_dd_i : src0_as_f16.get(); - sycl_pool_alloc src1_as_f16; + ggml_sycl_pool_alloc src1_as_f16(ctx.pool()); if (src1->type != GGML_TYPE_F16) { const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); GGML_ASSERT(to_fp16_sycl != nullptr); @@ -13925,26 +10393,24 @@ inline void ggml_sycl_op_mul_mat_sycl( const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16 ? (const sycl::half *)src1->data + src1_padded_row_size : src1_as_f16.get(); - sycl_pool_alloc dst_f16(row_diff * src1_ncols); + ggml_sycl_pool_alloc dst_f16(ctx.pool(), row_diff * src1_ncols); const sycl::half alpha_f16 = 1.0f; const sycl::half beta_f16 = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( - *g_sycl_handles[id], oneapi::mkl::transpose::trans, + *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00, src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16, dst_f16.get(), dpct::library_data_t::real_half, ldc, dpct::library_data_t::real_half))); - g_sycl_handles[id]->wait(); const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); } else { // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); - sycl_pool_alloc src0_ddq_as_f32; - sycl_pool_alloc src1_ddq_as_f32; + ggml_sycl_pool_alloc src0_ddq_as_f32(ctx.pool()); + ggml_sycl_pool_alloc src1_ddq_as_f32(ctx.pool()); if (src0->type != GGML_TYPE_F32) { const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); GGML_ASSERT(to_fp32_sycl != nullptr); @@ -13963,14 +10429,12 @@ inline void ggml_sycl_op_mul_mat_sycl( const float alpha = 1.0f; const float beta = 0.0f; - SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( - *g_sycl_handles[id], oneapi::mkl::transpose::trans, + *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, - dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, - src1_ddf1_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), + dpct::get_value(&alpha, *stream), src0_ddf_i, ne00, + src1_ddf1_i, ne10, dpct::get_value(&beta, *stream), dst_dd_i, ldc))); - g_sycl_handles[id]->wait(); } (void) dst; (void) src1_ddq_i; @@ -13982,13 +10446,11 @@ catch (sycl::exception const &exc) { std::exit(1); } -inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_rope(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + const queue_ptr &main_stream) { + const ggml_tensor * src2 = dst->src[2]; GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); @@ -14002,8 +10464,8 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; // RoPE alteration for extended context float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; @@ -14014,6 +10476,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; const int32_t * pos = nullptr; if ((mode & 1) == 0) { GGML_ASSERT(src1->type == GGML_TYPE_I32); @@ -14022,26 +10485,35 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, } const bool is_neox = mode & 2; - const bool is_glm = mode & 4; + +#pragma message("TODO: update rope NORM mode to match NEOX mode") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7634") + + if (is_neox) { + pos = (const int32_t *) src1_dd; + + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } rope_corr_dims corr_dims; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v); // compute - if (is_glm) { - GGML_ASSERT(false); - rope_glm_f32_sycl(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); - } else if (is_neox) { + if (is_neox) { if (src0->type == GGML_TYPE_F32) { rope_neox_sycl( (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream + attn_factor, corr_dims, freq_factors, main_stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, attn_factor, corr_dims, - main_stream); + freq_factors, main_stream); } else { GGML_ASSERT(false); } @@ -14065,10 +10537,10 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -static void ggml_sycl_op_pool2d(const ggml_tensor *src0, +static void ggml_sycl_op_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, - float *dst_dd, const dpct::queue_ptr &main_stream) { + float *dst_dd, const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14107,11 +10579,11 @@ static void ggml_sycl_op_pool2d(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_im2col(const ggml_tensor *src0, +inline void ggml_sycl_op_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -14148,11 +10620,11 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, (void) src0_dd; } -inline void ggml_sycl_op_sum_rows(const ggml_tensor *src0, +inline void ggml_sycl_op_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14167,11 +10639,11 @@ inline void ggml_sycl_op_sum_rows(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_argsort(const ggml_tensor *src0, +inline void ggml_sycl_op_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_I32); @@ -14188,11 +10660,11 @@ inline void ggml_sycl_op_argsort(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_diag_mask_inf(const ggml_tensor *src0, +inline void ggml_sycl_op_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14210,11 +10682,11 @@ inline void ggml_sycl_op_diag_mask_inf(const ggml_tensor *src0, (void) src1_dd; } -inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, +inline void ggml_sycl_op_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14237,10 +10709,10 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, nrows_x, nrows_y, scale, max_bias, main_stream); } -inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_scale(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14260,10 +10732,10 @@ inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_clamp(const ggml_tensor *src0, const ggml_tensor *src1, +inline void ggml_sycl_op_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { + const queue_ptr &main_stream) { GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -14285,7 +10757,7 @@ inline void ggml_sycl_op_clamp(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -static void ggml_sycl_op_flatten(const ggml_tensor *src0, +static void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const ggml_sycl_op_flatten_t op) try { const int64_t nrows0 = ggml_nrows(src0); @@ -14300,66 +10772,22 @@ static void ggml_sycl_op_flatten(const ggml_tensor *src0, ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU; - // dd = data device - float * src0_ddf = nullptr; - float * src1_ddf = nullptr; - float * dst_ddf = nullptr; - - sycl_pool_alloc src0_f; - sycl_pool_alloc src1_f; - sycl_pool_alloc dst_f; + float * src0_ddf = (float *) src0->data; + float * src1_ddf = use_src1 ? (float *) src1->data : nullptr; + float * dst_ddf = (float *) dst->data; - ggml_sycl_set_device(g_main_device); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - // GGML_SYCL_DEBUG("g_main_device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", - // g_main_device, main_stream, src0_on_device, src1_on_device, dst_on_device); - - if (src0_on_device) { - src0_ddf = (float *) src0_extra->data_device[g_main_device]; - } else { - src0_ddf = src0_f.alloc(ggml_nelements(src0)); - // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0); - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream)); - } + ggml_sycl_pool_alloc src0_f(ctx.pool()); + ggml_sycl_pool_alloc src1_f(ctx.pool()); + ggml_sycl_pool_alloc dst_f(ctx.pool()); - if (use_src1) { - if (src1_on_device) { - src1_ddf = (float *) src1_extra->data_device[g_main_device]; - } else { - src1_ddf = src1_f.alloc(ggml_nelements(src1)); - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); - } - } - if (dst_on_device) { - dst_ddf = (float *) dst_extra->data_device[g_main_device]; - } else { - dst_ddf = dst_f.alloc(ggml_nelements(dst)); - } + ggml_sycl_set_device(ctx.device); + queue_ptr main_stream = ctx.stream(); + // GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device); - // GGML_SYCL_DEBUG("op src0=%p, src1=%p, dst=%p, src0_ddf=%p, src1_ddf=%p, dst_ddf=%p, main_stream=%p\n", - // src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); // do the computation - op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); - /* - DPCT1010:89: SYCL uses exceptions to report errors and does not use the - error codes. The call was replaced with 0. You need to rewrite this code. - */ - SYCL_CHECK(0); - - // copy dst to host if necessary - if (!dst_on_device) { - SYCL_CHECK(CHECK_TRY_ERROR( - main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)).wait())); - } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_current_device().queues_wait_and_throw())); - } + op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); // print_ggml_tensor("tensor", dst); } catch (sycl::exception const &exc) { @@ -14369,7 +10797,7 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_set_peer_access(const int n_tokens) { +static void ggml_sycl_set_peer_access(const int n_tokens, int main_device) { static bool peer_access_enabled = false; const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE; @@ -14379,19 +10807,18 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { } #ifdef NDEBUG - for (int i = 0; i < g_device_count; ++i) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { SYCL_CHECK(ggml_sycl_set_device(i)); - // SYCL_CHECK(syclDeviceSynchronize()); } - for (int i = 0; i < g_device_count; ++i) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { SYCL_CHECK(ggml_sycl_set_device(i)); - for (int id_other = 0; id_other < g_device_count; ++id_other) { + for (int id_other = 0; id_other < ggml_sycl_info().device_count; ++id_other) { if (i == id_other) { continue; } - if (i != g_main_device && id_other != g_main_device) { + if (i != main_device && id_other != main_device) { continue; } @@ -14415,7 +10842,7 @@ struct ggml_backend_sycl_split_buffer_type_context { std::array tensor_split; }; -static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, +static void ggml_sycl_op_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, ggml_sycl_op_mul_mat_t op, const bool convert_src1_to_q8_1) try { @@ -14450,7 +10877,6 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - const bool src0_on_device = src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; const bool src0_is_contiguous = ggml_is_contiguous(src0); const bool src1_is_contiguous = ggml_is_contiguous(src1); @@ -14470,10 +10896,10 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } struct dev_data { - sycl_pool_alloc src0_dd_alloc; - sycl_pool_alloc src1_ddf_alloc; - sycl_pool_alloc src1_ddq_alloc; - sycl_pool_alloc dst_dd_alloc; + ggml_sycl_pool_alloc src0_dd_alloc; + ggml_sycl_pool_alloc src1_ddf_alloc; + ggml_sycl_pool_alloc src1_ddq_alloc; + ggml_sycl_pool_alloc dst_dd_alloc; char *src0_dd = nullptr; float *src1_ddf = nullptr; // float @@ -14487,9 +10913,9 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, dev_data dev[GGML_SYCL_MAX_DEVICES]; int used_devices = 0; - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + queue_ptr main_stream = ctx.stream(); - for (int i = 0; i < g_device_count; ++i) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { // by default, use all rows dev[i].row_low = 0; dev[i].row_high = ne01; @@ -14506,7 +10932,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } } - if (i != g_device_count - 1) { + if (i != ggml_sycl_info().device_count - 1) { dev[i].row_high = ne01*tensor_split[i + 1]; if (dev[i].row_high < ne01) { dev[i].row_high -= dev[i].row_high % rounding; @@ -14515,33 +10941,33 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } } - for (int i = 0; i < g_device_count; ++i) { - if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) { continue; } used_devices++; - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool src1_on_device = i == ctx.device; + const bool dst_on_device = i == ctx.device; ggml_sycl_set_device(i); - dpct::queue_ptr stream = g_syclStreams[i][0]; + queue_ptr stream = ctx.stream(i, 0); - if (src0_on_device && src0_is_contiguous) { - dev[i].src0_dd = (char *) src0_extra->data_device[i]; + if (src0_is_contiguous) { + dev[i].src0_dd = (char *) src0->data; } else { - dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ggml_nbytes(src0)); + dev[i].src0_dd = dev[i].src0_dd_alloc.alloc(ctx.pool(i), ggml_nbytes(src0)); } if (src1_on_device && src1_is_contiguous) { - dev[i].src1_ddf = (float *) src1_extra->data_device[i]; + dev[i].src1_ddf = (float *) src1->data; } else { - dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ggml_nelements(src1)); + dev[i].src1_ddf = dev[i].src1_ddf_alloc.alloc(ctx.pool(i), ggml_nelements(src1)); } if (convert_src1_to_q8_1) { - dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); + dev[i].src1_ddq = dev[i].src1_ddq_alloc.alloc(ctx.pool(i), nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs); if (src1_on_device && src1_is_contiguous) { quantize_row_q8_1_sycl(dev[i].src1_ddf, dev[i].src1_ddq, ne10, nrows1, src1_padded_col_size, stream); @@ -14555,53 +10981,53 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } if (dst_on_device) { - dev[i].dst_dd = (float *) dst_extra->data_device[i]; + dev[i].dst_dd = (float *) dst->data; } else { const size_t size_dst_ddf = split ? (dev[i].row_high - dev[i].row_low)*ne1 : ggml_nelements(dst); - dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(size_dst_ddf); + dev[i].dst_dd = dev[i].dst_dd_alloc.alloc(ctx.pool(i), size_dst_ddf); } } // if multiple devices are used they need to wait for the main device // here an event is recorded that signals that the main device has finished calculating the input data if (split && used_devices > 1) { - ggml_sycl_set_device(g_main_device); + ggml_sycl_set_device(ctx.device); /* DPCT1024:91: The original code returned the error code that was further consumed by the program logic. This original code was replaced with 0. You may need to rewrite the program logic consuming the error code. */ SYCL_CHECK(CHECK_TRY_ERROR( - *src0_extra->events[g_main_device][0] = - g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier())); + *src0_extra->events[ctx.device][0] = + ctx.stream()->ext_oneapi_submit_barrier())); } const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { - const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; + const int64_t is = split ? (src1_col_0/src1_col_stride) % GGML_SYCL_MAX_STREAMS : 0; const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; - for (int i = 0; i < g_device_count; ++i) { - if ((!split && i != g_main_device) || dev[i].row_low == dev[i].row_high) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + if ((!split && i != ctx.device) || dev[i].row_low == dev[i].row_high) { continue; } - const bool src1_on_device = src1->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; - const bool dst_on_device = dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device; + const bool src1_on_device = i == ctx.device; + const bool dst_on_device = i == ctx.device; const int64_t row_diff = dev[i].row_high - dev[i].row_low; ggml_sycl_set_device(i); - dpct::queue_ptr stream = g_syclStreams[i][is]; + queue_ptr stream = ctx.stream(i, is); // wait for main GPU data if necessary - if (split && (i != g_main_device || is != 0)) { + if (split && (i != ctx.device || is != 0)) { /* DPCT1009:163: SYCL uses exceptions to report errors and does not use the error codes. The original code was commented out and a warning string was inserted. You need to rewrite this code. */ SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( - {*src0_extra->events[g_main_device][0]}))); + {*src0_extra->events[ctx.device][0]}))); } for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { @@ -14618,22 +11044,22 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // the main device memory buffer can be on VRAM scratch, with space for all partial results // in that case an offset on dst_ddf_i is needed - if (dst->backend == GGML_BACKEND_TYPE_GPU && i == g_main_device) { + if (i == ctx.device) { dst_dd_i += dev[i].row_low; // offset is 0 if no tensor split } // copy src0, src1 to device if necessary - if (src1->backend == GGML_BACKEND_TYPE_GPU && src1_is_contiguous) { - if (i != g_main_device) { + if (src1_is_contiguous) { + if (i != ctx.device) { if (convert_src1_to_q8_1) { - char * src1_ddq_i_source = dev[g_main_device].src1_ddq + src1_ddq_i_offset; + char * src1_ddq_i_source = dev[ctx.device].src1_ddq + src1_ddq_i_offset; SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( src1_ddq_i, src1_ddq_i_source, src1_ncols * src1_padded_col_size * q8_1_ts / q8_1_bs).wait())); } else { - float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device]; + float * src1_ddf_i_source = (float *) src1_extra->data_device[ctx.device]; src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; SYCL_CHECK(CHECK_TRY_ERROR(dev2dev_memcpy(*stream, *main_stream, @@ -14641,14 +11067,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, src1_ncols * ne10 * sizeof(float)))); } } - } else if (src1->backend == GGML_BACKEND_TYPE_CPU || (src1_on_device && !src1_is_contiguous)) { + } else if (src1_on_device && !src1_is_contiguous) { SYCL_CHECK(ggml_sycl_cpy_tensor_2d( src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); } else { GGML_ASSERT(false); } - if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_TYPE_CPU || !src1_is_contiguous)) { + if (convert_src1_to_q8_1 && !src1_is_contiguous) { quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); /* DPCT1010:92: SYCL uses exceptions to report errors and does @@ -14658,14 +11084,14 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, SYCL_CHECK(0); } - if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { + if (src1_col_0 == 0 && !src0_is_contiguous && i02 % i02_divisor == 0) { SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, dev[i].row_low, dev[i].row_high, stream)); } if (src1->type == GGML_TYPE_F16) { src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; } // do the computation - SYCL_CHECK(CHECK_TRY_ERROR(op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, + SYCL_CHECK(CHECK_TRY_ERROR(op(ctx, src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, dev[i].row_low, dev[i].row_high, src1_ncols, src1_padded_col_size, stream))); /* DPCT1010:93: SYCL uses exceptions to report errors and does not @@ -14676,17 +11102,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, // copy dst to host or other device if necessary if (!dst_on_device) { - void * dst_off_device; - dpct::memcpy_direction kind; - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - dst_off_device = dst->data; - kind = dpct::device_to_host; - } else if (dst->backend == GGML_BACKEND_TYPE_GPU) { - dst_off_device = dst_extra->data_device[g_main_device]; - kind = dpct::device_to_device; - } else { - GGML_ASSERT(false); - } + void * dst_off_device = dst->data; if (split) { // src0 = weight matrix is saved as a transposed matrix for better memory layout. // dst is NOT transposed. @@ -14697,27 +11113,10 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); dhf_dst_i += src1_col_0*ne0 + dev[i].row_low; - //todo, dirty solution. Need be updated when device2device memcpy() is supported. - if (kind == dpct::device_to_device) { - size_t dst_size = ggml_nbytes_pad(dst); - float *host_buf = (float *)malloc(dst_size); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - host_buf, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, dpct::device_to_host, *stream))); - dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[i]).queues_wait_and_throw(); - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), host_buf, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, dpct::host_to_device, *main_stream))); - dpct::dev_mgr::instance().get_device(g_sycl_gpu_mgr->gpus[g_main_device]).queues_wait_and_throw(); - free(host_buf); - } else { - SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( - dhf_dst_i, ne0 * sizeof(float), dst_dd_i, - row_diff * sizeof(float), row_diff * sizeof(float), - src1_ncols, kind, *stream))); - } + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, dpct::device_to_device, *stream))); } else { float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); @@ -14729,7 +11128,7 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } // add event for the main device to wait on until other device is done - if (split && (i != g_main_device || is != 0)) { + if (split && (i != ctx.device || is != 0)) { /* DPCT1024:94: The original code returned the error code that was further consumed by the program logic. This original @@ -14745,28 +11144,22 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, } // main device waits for all other devices to be finished - if (split && g_device_count > 1) { + if (split && ggml_sycl_info().device_count > 1) { int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; - is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; + is_max = is_max <= GGML_SYCL_MAX_STREAMS ? is_max : GGML_SYCL_MAX_STREAMS; - ggml_sycl_set_device(g_main_device); - for (int i = 0; i < g_device_count; ++i) { + ggml_sycl_set_device(ctx.device); + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { if (dev[i].row_low == dev[i].row_high) { continue; } for (int64_t is = 0; is < is_max; ++is) { SYCL_CHECK(CHECK_TRY_ERROR( - g_syclStreams[g_main_device][0]->ext_oneapi_submit_barrier( + ctx.stream()->ext_oneapi_submit_barrier( {*src0_extra->events[i][is]}))); } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::get_current_device().queues_wait_and_throw())); - } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -14775,149 +11168,134 @@ catch (sycl::exception const &exc) { } -static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_repeat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_repeat); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_get_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_get_rows); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_add(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_add); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_acc(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_acc); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_mul(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_mul); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_div(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_div); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_gelu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_silu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_silu); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_gelu_quick); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_tanh); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_relu); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_hardsigmoid(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardsigmoid); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardsigmoid); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_hardswish(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_hardswish); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_hardswish); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_leaky_relu(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_leaky_relu); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_sqr(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sqr); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_group_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_group_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_concat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_concat); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_upscale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_upscale); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_pad(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pad); GGML_SYCL_DEBUG("call %s done\n", __func__); } -static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_rms_norm(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_SYCL_DEBUG("call %s\n", __func__); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rms_norm); GGML_SYCL_DEBUG("call %s done\n", __func__); } -bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (!g_sycl_loaded) return false; - - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - src1->type == GGML_TYPE_F32 && - dst->type == GGML_TYPE_F32 && - (ne0 >= 32 && ne1 >= 32 && ne10 >= 32); -} - -static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, +static void ggml_sycl_mul_mat_vec_p021(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); @@ -14933,17 +11311,12 @@ static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; - - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + queue_ptr main_stream = ctx.stream(); - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + void * src0_ddq = src0->data; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); } @@ -14953,7 +11326,7 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, +static void ggml_sycl_mul_mat_vec_nc(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); @@ -14972,17 +11345,12 @@ static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, const int64_t ne12 = src1->ne[2]; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + queue_ptr main_stream = ctx.stream(); - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; - - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; - - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + void * src0_ddq = src0->data; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; const int64_t row_stride_x = nb01 / sizeof(sycl::half); const int64_t channel_stride_x = nb02 / sizeof(sycl::half); @@ -15020,7 +11388,8 @@ static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; } -static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, +static void ggml_sycl_mul_mat_batched_sycl(ggml_backend_sycl_context & ctx, + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { GGML_ASSERT(!ggml_is_transposed(src0)); @@ -15032,27 +11401,20 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t ne_dst = ggml_nelements(dst); - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + queue_ptr main_stream = ctx.stream();; bool no_mixed_dtypes = main_stream->get_backend() == sycl::backend::ext_oneapi_cuda || main_stream->get_backend() == sycl::backend::ext_oneapi_hip; - SYCL_CHECK( - CHECK_TRY_ERROR(g_sycl_handles[g_main_device] = main_stream)); - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - void * src0_ddq = src0_extra->data_device[g_main_device]; + void * src0_ddq = src0->data; sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; - - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; - - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + float * src1_ddf = (float *) src1->data; + float * dst_ddf = (float *) dst->data; // convert src1 to fp16 - sycl_pool_alloc src1_f16_alloc; + ggml_sycl_pool_alloc src1_f16_alloc(ctx.pool()); if (src1->type != GGML_TYPE_F16) { const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); const int64_t ne_src1 = ggml_nelements(src1); @@ -15063,7 +11425,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, sycl::half *src1_f16 = src1->type == GGML_TYPE_F16 ? (sycl::half *)src1_ddf : src1_f16_alloc.get(); - sycl_pool_alloc dst_f16; + ggml_sycl_pool_alloc dst_f16(ctx.pool()); char * dst_t; dpct::library_data_t cu_compute_type = dpct::library_data_t::real_float; @@ -15108,10 +11470,10 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, + *main_stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const char *)src0_as_f16, dpct::library_data_t::real_half, nb01 / nb00, nb02 / nb00, @@ -15122,8 +11484,8 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, } else { const int ne23 = ne12*ne13; - sycl_pool_alloc ptrs_src(2*ne23); - sycl_pool_alloc< void *> ptrs_dst(1*ne23); + ggml_sycl_pool_alloc ptrs_src(ctx.pool(), 2*ne23); + ggml_sycl_pool_alloc< void *> ptrs_dst(ctx.pool(), 1*ne23); sycl::range<3> block_dims(1, ne12, ne13); /* @@ -15152,7 +11514,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, }); } SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( - *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, + *main_stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, (const void **)(ptrs_src.get() + 0 * ne23), dpct::library_data_t::real_half, nb01 / nb00, @@ -15173,283 +11535,172 @@ catch (sycl::exception const &exc) { std::exit(1); } - -static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const bool all_on_device = - (src0->backend == GGML_BACKEND_TYPE_GPU || src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT) && - (src1->backend == GGML_BACKEND_TYPE_GPU) && - ( dst->backend == GGML_BACKEND_TYPE_GPU); - - const bool split = src0->backend == GGML_BACKEND_TYPE_GPU_SPLIT; - - int64_t min_compute_capability = INT_MAX; - for (int i = 0; i < g_device_count; ++i) { - if (min_compute_capability > g_device_caps[i].cc && g_tensor_split[i] < (i + 1 < g_device_count ? g_tensor_split[i + 1] : 1.0f)) { - min_compute_capability = g_device_caps[i].cc; - } - } - -#ifdef SYCL_USE_XMX - const bool use_xmx = true; -#else - const bool use_xmx = false; -#endif - - // debug helpers - //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); - //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); - //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); - //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); - //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); - //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - - if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - // KQ single-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n"); - ggml_sycl_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - // KQV single-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); - ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { - // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); - ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); - } else if (src0->type == GGML_TYPE_F32) { - // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); - } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - // GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n"); - if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) { -#ifdef GGML_SYCL_FORCE_DMMV - const bool use_mul_mat_vec_q = false; -#else - bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); - use_mul_mat_vec_q = use_mul_mat_vec_q || - (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) || - (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) || - (src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) || - (src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M); - - -#endif // GGML_SYCL_FORCE_DMMV - - if (use_mul_mat_vec_q) { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); - } else { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); - } - } else { - bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); - - if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) { - use_mul_mat_q = false; - } - - if (use_mul_mat_q) { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); - } else { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); - } - } - } else { - GGML_ASSERT(false); - } +inline bool ggml_sycl_supports_mmq(enum ggml_type type) { + // TODO: accuracy issues in MMQ + return false; } -#if 0 -template -static __global__ void k_compute_batched_ptrs_id( - const void ** ptrs_src, void ** ptrs_dst, - int ne12, int ne13, - int ne23, - int nb02, int nb03, - int nb12, int nb13, - int nb2, int nb3, - int r2, int r3, - ggml_type src0_type, half * src0_as_f16, int64_t src0_ne, - const half * src1_f16, half * dst_f16, - const int32_t * ids, const int id, - Srcs... src0s) { - - int i = ids[id]; - - half * src0_f16; - const void * srcs_ar[] = { (const half *) src0s... }; - if (src0_type == GGML_TYPE_F16) { - src0_f16 = (half *) srcs_ar[i]; - } else { - src0_f16 = src0_as_f16; - if (item_ct1.get_local_id(2) == 0 && threadIdx.y == 0) { - const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type); - to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget); - } - } - - int i13 = blockIdx.x * blockDim.x + item_ct1.get_local_id(2); - int i12 = blockIdx.y * blockDim.y + threadIdx.y; - - if (i13 >= ne13 || i12 >= ne12) { - return; +bool ggml_sycl_supports_dmmv(enum ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_F16: + return true; + default: + return false; } - - int i03 = i13 / r3; - int i02 = i12 / r2; - - ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03; - ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2; - ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; } -static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { - const struct ggml_tensor * ids = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src00 = dst->src[2]; +static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const bool split = ggml_backend_buffer_is_sycl_split(src0->buffer); - const int id = dst->op_params[0]; + int64_t min_compute_capability = INT_MAX; - GGML_ASSERT(!ggml_is_transposed(src00)); - GGML_ASSERT(!ggml_is_transposed(src1)); + if (split) { + ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *) src0->buffer->buft->context; + auto & tensor_split = buft_ctx->tensor_split; + for (int id = 0; id < ggml_sycl_info().device_count; ++id) { + // skip devices that are not going to do any work: + if (tensor_split[id] >= (id + 1 < ggml_sycl_info().device_count ? tensor_split[id + 1] : 1.0f)) { + continue; + } - GGML_ASSERT(src00->backend != GGML_BACKEND_TYPE_GPU_SPLIT); - GGML_ASSERT(src1->type == GGML_TYPE_F32); + if (min_compute_capability > ggml_sycl_info().devices[id].cc) { + min_compute_capability = ggml_sycl_info().devices[id].cc; + } + } + } else { + min_compute_capability = ggml_sycl_info().devices[ctx.device].cc; + } - GGML_TENSOR_LOCALS(int64_t, ne0, src00, ne); + // check data types and tensor shapes for custom matrix multiplication kernels: + bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1; - //const int64_t nb01 = src00->nb[1]; - GGML_TENSOR_LOCALS(int64_t, nb0, src00, nb); + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; - GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; - GGML_TENSOR_LOCALS(int64_t, nb1, src1, nb); - //const int64_t nb11 = src1->nb[1]; + // mmvq and mmq need the __dp4a instruction which is available for gen12+ + // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e + use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); +#ifdef SYCL_USE_XMX + use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); +#endif // SYCL_USE_XMX - const int64_t ne1 = ggml_nelements(src1); - const int64_t ne = ggml_nelements(dst); + if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + // KQ single-batch + ggml_sycl_mul_mat_vec_p021(ctx, src0, src1, dst); + } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + // KQV single-batch + ggml_sycl_mul_mat_vec_nc(ctx, src0, src1, dst); + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { + // KQ + KQV multi-batch + ggml_sycl_mul_mat_batched_sycl(ctx, src0, src1, dst); + } else if (use_dequantize_mul_mat_vec) { + ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); + } else if (use_mul_mat_vec_q) { + ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { + ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_q, true); + } else { + ggml_sycl_op_mul_mat(ctx, src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); + } +} - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - syclStream_t main_stream = g_syclStreams[g_main_device][0]; - SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device], main_stream)); +struct mmid_row_mapping { + int32_t i1; + int32_t i2; +}; - //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - //void * src0_ddq = src0_extra->data_device[g_main_device]; - //half * src0_as_f16 = (half *) src0_ddq; +__dpct_inline__ static void k_copy_src1_to_contiguous( + const char *__restrict__ src1_original, char *__restrict__ src1_contiguous, + int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping, + const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, + int64_t ne11, int64_t ne10, size_t nb11, size_t nb12, + const sycl::nd_item<3> &item_ct1, int &src1_row) { + int32_t iid1 = item_ct1.get_group(2); + int32_t id = item_ct1.get_group(1); - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; - float * src1_ddf = (float *) src1_extra->data_device[g_main_device]; + const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); - ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - float * dst_ddf = (float *) dst_extra->data_device[g_main_device]; + if (row_id_i != i02) { + return; + } - // convert src1 to fp16 - const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); - GGML_ASSERT(to_fp16_sycl != nullptr); + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; - size_t src1_as = 0; - half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne1 * sizeof(half), &src1_as); - to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream); + if (item_ct1.get_local_id(2) == 0) { + src1_row = + dpct::atomic_fetch_add( + cur_src1_row, 1); + row_mapping[src1_row] = {id, iid1}; + } + /* + DPCT1065:194: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better + performance if there is no access to global memory. + */ + item_ct1.barrier(); - size_t dst_as = 0; - half * dst_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, ne * sizeof(half), &dst_as); + const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); + float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); - GGML_ASSERT(ne12 % ne02 == 0); - GGML_ASSERT(ne13 % ne03 == 0); +#pragma unroll + for (int i = item_ct1.get_local_id(2); i < ne10; + i += item_ct1.get_local_range(2)) { + src1_row_contiguous[i] = src1_row_original[i]; + } +} - // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; +__dpct_inline__ static void k_copy_dst_from_contiguous( + char *__restrict__ dst_original, const char *__restrict__ dst_contiguous, + const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1, + size_t nb2, const sycl::nd_item<3> &item_ct1) { + int32_t i = item_ct1.get_group(2); - const half alpha_f16 = 1.0f; - const half beta_f16 = 0.0f; - - // use syclGemmBatchedEx - const int ne23 = ne12*ne13; - - const void ** ptrs_src = nullptr; - void ** ptrs_dst = nullptr; - - size_t ptrs_src_s = 0; - size_t ptrs_dst_s = 0; - - ptrs_src = (const void **) ggml_sycl_pool_malloc(g_main_device, 2*ne23*sizeof(void *), &ptrs_src_s); - ptrs_dst = ( void **) ggml_sycl_pool_malloc(g_main_device, 1*ne23*sizeof(void *), &ptrs_dst_s); - - int64_t src0_ne = ggml_nelements(src00); - half * src0_as_f16 = nullptr; - size_t src0_as = 0; - if (src00->type != GGML_TYPE_F16) { - src0_as_f16 = (half *) ggml_sycl_pool_malloc(g_main_device, src0_ne * sizeof(half), &src0_as); - } - - static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); - dim3 block_dims(ne13, ne12); - k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>( - ptrs_src, ptrs_dst, - ne12, ne13, - ne23, - ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half), - nb12, nb13, - dst->nb[2], dst->nb[3], - r2, r3, - src00->type, src0_as_f16, src0_ne, - src1_as_f16, dst_f16, - (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device], id, - dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device] : nullptr, - dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device] : nullptr, - dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device] : nullptr, - dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device] : nullptr - ); - SYCL_CHECK(syclGetLastError()); + const int32_t i1 = row_mapping[i].i1; + const int32_t i2 = row_mapping[i].i2; - SYCL_CHECK( - syclGemmBatchedEx(g_sycl_handles[g_main_device], CUBLAS_OP_T, CUBLAS_OP_N, - ne01, ne11, ne10, - &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00, - (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10, - &beta_f16, ( void **) (ptrs_dst + 0*ne23), SYCL_R_16F, ne01, - ne23, - CUBLAS_COMPUTE_16F, - CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); + float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); - if (src0_as != 0) { - ggml_sycl_pool_free(g_main_device, src0_as_f16, src0_as); - } - if (ptrs_src_s != 0) { - ggml_sycl_pool_free(g_main_device, ptrs_src, ptrs_src_s); - } - if (ptrs_dst_s != 0) { - ggml_sycl_pool_free(g_main_device, ptrs_dst, ptrs_dst_s); +#pragma unroll + for (int j = item_ct1.get_local_id(2); j < ne0; + j += item_ct1.get_local_range(2)) { + dst_row_original[j] = dst_row_contiguous[j]; } - - const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); - to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream); - - ggml_sycl_pool_free(g_main_device, src1_as_f16, src1_as); - ggml_sycl_pool_free(g_main_device, dst_f16, dst_as); } -#endif -static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, +static void ggml_sycl_mul_mat_id(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && - "mul_mat_id does not support split buffers"); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers"); + const ggml_tensor *ids = dst->src[2]; - const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; + GGML_TENSOR_BINARY_OP_LOCALS - const size_t nb11 = src1->nb[1]; - const size_t nb1 = dst->nb[1]; + const queue_ptr stream = ctx.stream(); - const int32_t id = ((int32_t *)dst->op_params)[0]; - const int32_t n_as = src0->ne[2]; + const int64_t n_as = ne02; + const int64_t n_ids = ids->ne[0]; std::vector ids_host(ggml_nbytes(ids)); - const char *ids_dev = (const char *)ids->data; + const char * ids_dev = (const char *) ids->data; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); @@ -15479,100 +11730,150 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, char *src0_original = src1->backend == GGML_BACKEND_TYPE_CPU ? (char *)src0->data - : (char *)src0_extra->data_device[g_main_device]; + : (char *)src0_extra->data_device[ctx.device]; char *src1_original = src1->backend == GGML_BACKEND_TYPE_CPU ? (char *)src1->data - : (char *)src1_extra->data_device[g_main_device]; + : (char *)src1_extra->data_device[ctx.device]; char *dst_original = dst->backend == GGML_BACKEND_TYPE_CPU ? (char *)dst->data - : (char *)dst_extra->data_device[g_main_device]; + : (char *)dst_extra->data_device[ctx.device]; src0_row.ne[2] = 1; src0_row.ne[3] = 1; - src0_row.nb[3] = src0->nb[2]; - - if (src1->ne[1] == 1) { - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = - *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] + - id * ids->nb[0]); - - GGML_ASSERT(row_id >= 0 && row_id < n_as); - - src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; - src1_row_extra.data_device[g_main_device] = - src1_original + i01 * src1->nb[1]; - dst_row_extra.data_device[g_main_device] = - dst_original + i01 * dst->nb[1]; - - ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); + src0_row.nb[3] = nb02; + + src1_row.ne[1] = 1; + src1_row.ne[2] = 1; + src1_row.ne[3] = 1; + src1_row.nb[2] = nb11; + src1_row.nb[3] = nb11; + + dst_row.ne[1] = 1; + dst_row.ne[2] = 1; + dst_row.ne[3] = 1; + dst_row.nb[2] = nb1; + dst_row.nb[3] = nb1; + if (ne12 == 1) { + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + GGML_ASSERT(i02 >= 0 && i02 < n_as); + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + const int64_t i1 = id; + const int64_t i2 = i12; + + src0_row_extra.data_device[ctx.device] = + src0_original + i02*nb02; + src1_row_extra.data_device[ctx.device] = + src1_original + + i11*nb11 + i12*nb12; + dst_row_extra.data_device[ctx.device] = + dst_original + i1*nb1 + i2*nb2; + + ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row); + } } } else { - sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); - sycl_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); + ggml_sycl_pool_alloc src1_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(src1)); + ggml_sycl_pool_alloc dst_contiguous(ctx.pool(), sizeof(float)*ggml_nelements(dst)); - src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); - dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); + src1_row_extra.data_device[ctx.device] = src1_contiguous.get(); + dst_row_extra.data_device[ctx.device] = dst_contiguous.get(); - for (int32_t row_id = 0; row_id < n_as; ++row_id) { + for (int64_t i02 = 0; i02 < n_as; i02++) { int64_t num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - if (row_id_i != row_id) { - continue; - } + GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); - GGML_ASSERT(row_id >= 0 && row_id < n_as); + if (row_id_i != i02) { + continue; + } - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, - src1_original + i01 * nb11, nb11))); - num_src1_rows++; + num_src1_rows++; + } } if (num_src1_rows == 0) { continue; } - src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; + ggml_sycl_pool_alloc dev_cur_src1_row(ctx.pool(), 1); + ggml_sycl_pool_alloc dev_row_mapping(ctx.pool(), num_src1_rows); + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memset(dev_cur_src1_row.get(), 0, sizeof(int)))); + + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u)); + sycl::range<3> grid_dims(1, n_ids, ids->ne[1]); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor src1_row_acc(cgh); + + char *__restrict src1_contiguous_get = + src1_contiguous.get(); + int *__restrict dev_cur_src1_row_get = + dev_cur_src1_row.get(); + mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); + size_t ids_nb_ct6 = ids->nb[1]; + size_t ids_nb_ct7 = ids->nb[0]; + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_src1_to_contiguous( + src1_original, src1_contiguous_get, + dev_cur_src1_row_get, + dev_row_mapping_get, ids_dev, i02, + ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12, + item_ct1, src1_row_acc); + }); + }); + } + + src0_row_extra.data_device[ctx.device] = src0_original + i02*nb02; + + GGML_ASSERT(nb11 == sizeof(float)*ne10); + GGML_ASSERT(nb1 == sizeof(float)*ne0); src1_row.ne[1] = num_src1_rows; - dst_row.ne[1] = num_src1_rows; src1_row.nb[1] = nb11; src1_row.nb[2] = num_src1_rows*nb11; src1_row.nb[3] = num_src1_rows*nb11; + dst_row.ne[1] = num_src1_rows; dst_row.nb[1] = nb1; dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1; - ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); - - num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); - - if (row_id_i != row_id) { - continue; - } - - GGML_ASSERT(row_id >= 0 && row_id < n_as); + ggml_sycl_mul_mat(ctx, &src0_row, &src1_row, &dst_row); - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( - dst_original + i01 * nb1, - dst_contiguous.get() + num_src1_rows * nb1, nb1))); - num_src1_rows++; + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u)); + sycl::range<3> grid_dims(1, 1, num_src1_rows); + stream->submit([&](sycl::handler &cgh) { + const char *__restrict dst_contiguous_get = + dst_contiguous.get(); + const mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_dst_from_contiguous(dst_original, + dst_contiguous_get, + dev_row_mapping_get, + ne0, nb1, nb2, item_ct1); + }); + }); } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15580,35 +11881,29 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_scale); +static void ggml_sycl_scale(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_scale); } -static void ggml_sycl_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_clamp); +static void ggml_sycl_clamp(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_clamp); } -static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, +static void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { const int64_t ne = ggml_nelements(src0); GGML_ASSERT(ne == ggml_nelements(src1)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); GGML_TENSOR_BINARY_OP_LOCALS; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - dpct::queue_ptr main_stream = g_syclStreams[g_main_device][0]; - - const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; - const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + SYCL_CHECK(ggml_sycl_set_device(ctx.device)); + queue_ptr main_stream = ctx.stream(); - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; + char * src0_ddc = (char *) src0->data; + char * src1_ddc = (char *) src1->data; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); @@ -15642,44 +11937,44 @@ catch (sycl::exception const &exc) { std::exit(1); } -static void ggml_sycl_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_dup(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // TODO: why do we pass dst as src1 here? - ggml_sycl_cpy(src0, dst, nullptr); + ggml_sycl_cpy(ctx, src0, dst, nullptr); (void) src1; } -static void ggml_sycl_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_diag_mask_inf); +static void ggml_sycl_diag_mask_inf(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_diag_mask_inf); } -static void ggml_sycl_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_soft_max); +static void ggml_sycl_soft_max(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_soft_max); } -static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_rope(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_rope); } -static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); +static void ggml_sycl_pool2d(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_pool2d); } -static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col); +static void ggml_sycl_im2col(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_im2col); } -static void ggml_sycl_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_sum_rows(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sum_rows); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_sum_rows); } -static void ggml_sycl_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_argsort(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_contiguous(src0)); - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_argsort); + ggml_sycl_op_flatten(ctx, src0, src1, dst, ggml_sycl_op_argsort); } -static void ggml_sycl_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_sycl_nop(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { (void) src0; (void) src1; (void) dst; @@ -15691,211 +11986,18 @@ static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_spl return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); } -void ggml_sycl_free_data(struct ggml_tensor *tensor) try { - if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_TYPE_GPU && tensor->backend != GGML_BACKEND_TYPE_GPU_SPLIT) ) { - return; - } - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - - for (int i = 0; i < g_device_count; ++i) { - const dpct::queue_ptr stream = g_syclStreams[i][0]; - if (extra->data_device[i] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(i)); - SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[i], *stream))); - } - - for (int64_t is = 0; is < MAX_STREAMS; ++is) { - if (extra->events[i][is] != nullptr) { - SYCL_CHECK(ggml_sycl_set_device(i)); - SYCL_CHECK(CHECK_TRY_ERROR( - dpct::destroy_event(extra->events[i][is]))); - } - } - } - - delete extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr; -static size_t g_temp_tensor_extra_index = 0; - -static ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() { - if (g_temp_tensor_extras == nullptr) { - g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES]; - } - - size_t alloc_index = g_temp_tensor_extra_index; - g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES; - ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; - memset(extra, 0, sizeof(*extra)); - - return extra; -} - -static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, - bool scratch, bool force_inplace, - bool no_alloc) try { - if (scratch && g_scratch_size == 0) { - return; - } - - tensor->backend = GGML_BACKEND_TYPE_GPU; - - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_CPU) { - const ggml_op src0_op = tensor->src[0]->op; - if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) { - ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc); - } - } - if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_TYPE_CPU) { - ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc); - } - - if (scratch && no_alloc) { - return; - } - - ggml_tensor_extra_gpu * extra; - - const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) || - tensor->op == GGML_OP_VIEW || - force_inplace; - const size_t size = ggml_nbytes(tensor); - - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - - if (inplace && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) { - ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; - char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; - size_t offset = 0; - if (tensor->op == GGML_OP_VIEW) { - memcpy(&offset, tensor->op_params, sizeof(size_t)); - } - extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src0_ddc + offset; - } else if (tensor->op == GGML_OP_CPY) { - ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; - void * src1_ddv = src1_extra->data_device[g_main_device]; - extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = src1_ddv; - } else if (scratch) { - GGML_ASSERT(size <= g_scratch_size); - if (g_scratch_offset + size > g_scratch_size) { - g_scratch_offset = 0; - } - - char * data = (char *) g_scratch_buffer; - if (data == nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR( - data = (char *)sycl::malloc_device( - g_scratch_size, *stream))); - g_scratch_buffer = data; - } - extra = ggml_sycl_alloc_temp_tensor_extra(); - extra->data_device[g_main_device] = data + g_scratch_offset; - - g_scratch_offset += size; - - GGML_ASSERT(g_scratch_offset <= g_scratch_size); - } else { // allocate new buffers outside of scratch - void * data; - SYCL_CHECK(CHECK_TRY_ERROR(data = (void *)sycl::malloc_device( - size, *stream))); - SYCL_CHECK(CHECK_TRY_ERROR( - (*stream).memset(data, 0, size).wait())); - extra = new ggml_tensor_extra_gpu; - memset(extra, 0, sizeof(*extra)); - extra->data_device[g_main_device] = data; - } - - tensor->extra = extra; -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(ggml_is_contiguous(tensor)); - - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - SYCL_CHECK(ggml_sycl_set_device(g_main_device)); - const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - SYCL_CHECK(CHECK_TRY_ERROR((*stream) - .memcpy(extra->data_device[g_main_device], - tensor->data, ggml_nbytes(tensor)) - .wait())); -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_assign_buffers(struct ggml_tensor * tensor) { - ggml_sycl_assign_buffers_impl(tensor, true, false, false); -} - -void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor) { - ggml_sycl_assign_buffers_impl(tensor, true, false, true); -} - -void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor) { - ggml_sycl_assign_buffers_impl(tensor, false, false, false); -} - -void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) { - ggml_sycl_assign_buffers_impl(tensor, false, true, false); -} - void ggml_sycl_set_main_device(const int main_device) try { - if (g_main_device == main_device) return; + if (dpct::get_current_device_id() == main_device) return; check_allow_gpu_index(main_device); - g_main_device = main_device; - g_main_device_id = g_sycl_gpu_mgr->gpus[main_device]; + dpct::select_device(main_device); if (g_ggml_sycl_debug) { dpct::device_info prop; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(g_main_device_id)))); + prop, dpct::dev_mgr::instance().get_device(main_device)))); fprintf(stderr, "Using device %d (%s) as main device\n", - g_main_device_id, prop.get_name()); - } -} -catch (sycl::exception const &exc) { - std::cerr << exc.what() << "Exception caught at file:" << __FILE__ - << ", line:" << __LINE__ << std::endl; - std::exit(1); -} - -void ggml_sycl_set_scratch_size(const size_t scratch_size) { - // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously - // it still won't always work as expected, but it's better than nothing - if (scratch_size > g_scratch_size) { - ggml_sycl_free_scratch(); - } - g_scratch_size = std::max(g_scratch_size, scratch_size); -} - -void ggml_sycl_free_scratch() try { - if (g_scratch_buffer == nullptr) { - return; + main_device, prop.get_name()); } - ggml_sycl_set_device(g_main_device); - const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - - SYCL_CHECK(CHECK_TRY_ERROR( - sycl::free(g_scratch_buffer, *stream))); - g_scratch_buffer = nullptr; } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -15903,26 +12005,10 @@ catch (sycl::exception const &exc) { std::exit(1); } -bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct ggml_tensor * tensor) { if (!g_sycl_loaded) return false; ggml_sycl_func_t func; - const bool any_on_device = tensor->backend == GGML_BACKEND_TYPE_GPU - || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU || tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT)) - || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_TYPE_GPU); - - if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { - return false; - } - - if (tensor->op == GGML_OP_MUL_MAT) { - if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { -#ifndef NDEBUG - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); -#endif - return false; - } - } switch (tensor->op) { case GGML_OP_REPEAT: @@ -15995,13 +12081,13 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ func = ggml_sycl_rms_norm; break; case GGML_OP_MUL_MAT: - if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { return false; } func = ggml_sycl_mul_mat; break; case GGML_OP_MUL_MAT_ID: - if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) { + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { return false; } func = ggml_sycl_mul_mat_id; @@ -16053,17 +12139,11 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ return false; } - if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_TYPE_GPU_SPLIT) { - ggml_sycl_set_peer_access(tensor->src[1]->ne[1]); + if (tensor->src[0] != nullptr && ggml_backend_buffer_is_sycl_split(tensor->src[0]->buffer)) { + ggml_sycl_set_peer_access(tensor->src[1]->ne[1], ctx.device); } - if (params->ith != 0) { - return true; - } - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return true; - } - func(tensor->src[0], tensor->src[1], tensor); + func(ctx, tensor->src[0], tensor->src[1], tensor); return true; } @@ -16071,13 +12151,9 @@ GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_gpu_list\n"); for(int i=0;igpus.size();i++){ + for (int i=0;i< ggml_sycl_info().device_count;i++){ if (i>=max_len) break; - id_list[i] = g_sycl_gpu_mgr->gpus[i]; + id_list[i] = i; } return; } @@ -16105,9 +12181,8 @@ GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *descr size_t description_size) try { GGML_SYCL_DEBUG("[SYCL] call ggml_sycl_get_device_description\n"); dpct::device_info prop; - int device_id = g_sycl_gpu_mgr->gpus[device]; SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( - prop, dpct::dev_mgr::instance().get_device(device_id)))); + prop, dpct::dev_mgr::instance().get_device(device)))); snprintf(description, description_size, "%s", prop.get_name()); } catch (sycl::exception const &exc) { @@ -16131,9 +12206,8 @@ GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, device information which may not be supported by all compilers or runtimes. You may need to adjust the code. */ - int device_id = g_sycl_gpu_mgr->gpus[device]; SYCL_CHECK(CHECK_TRY_ERROR( - dpct::dev_mgr::instance().get_device(device_id).get_memory_info(*free, *total))); + dpct::dev_mgr::instance().get_device(device).get_memory_info(*free, *total))); } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -16152,32 +12226,21 @@ catch (sycl::exception const &exc) { struct ggml_backend_sycl_buffer_context { int device; void * dev_ptr = nullptr; - ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; - size_t temp_tensor_extra_index = 0; + queue_ptr stream; std::string name; - ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : - device(device), dev_ptr(dev_ptr) { + ggml_backend_sycl_buffer_context(int device, void * dev_ptr, queue_ptr stream) : + device(device), dev_ptr(dev_ptr), stream(stream) { check_allow_gpu_index(device); - int id = g_sycl_gpu_mgr->gpus[device]; - name = (GGML_SYCL_NAME + std::to_string(id)); + name = (GGML_SYCL_NAME + std::to_string(device)); } - ~ ggml_backend_sycl_buffer_context() { - delete[] temp_tensor_extras; - } - ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() { - if (temp_tensor_extras == nullptr) { - temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES]; + ~ggml_backend_sycl_buffer_context() { + if (dev_ptr != nullptr) { + ggml_sycl_set_device(device); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(dev_ptr, *stream))); } - - size_t alloc_index = temp_tensor_extra_index; - temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES; - ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; - memset(extra, 0, sizeof(*extra)); - - return extra; } }; @@ -16194,10 +12257,7 @@ static void ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; - SYCL_CHECK( - CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream))); delete ctx; } catch (sycl::exception const &exc) { @@ -16223,11 +12283,6 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, return; } - ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra(); - - extra->data_device[ctx->device] = tensor->data; - tensor->backend = GGML_BACKEND_TYPE_GPU; - tensor->extra = extra; if (ggml_is_quantized(tensor->type)) { // initialize padding to 0 to avoid possible NaN values @@ -16235,7 +12290,7 @@ ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset( + SYCL_CHECK(CHECK_TRY_ERROR(ctx->stream->memset( (char *)tensor->data + original_size, 0, padded_size - original_size).wait())); } @@ -16251,19 +12306,17 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor *tensor, const void *data, size_t offset, size_t size) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; + auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); SYCL_CHECK( CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); char* host_buf = (char*)malloc(size); memcpy(host_buf, data, size); SYCL_CHECK( - CHECK_TRY_ERROR((*stream) - .memcpy((char *)tensor->data + offset, host_buf, size) + CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) .wait())); free(host_buf); } @@ -16277,19 +12330,14 @@ static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor *tensor, void *data, size_t offset, size_t size) try { - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; - - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); + auto stream = dpct::dev_mgr::instance().get_device(ctx->device).default_queue(); SYCL_CHECK(CHECK_TRY_ERROR( - (*stream) - .memcpy(data, (const char *)tensor->data + offset, size) + stream.memcpy(data, (const char *)tensor->data + offset, size) .wait())); } catch (sycl::exception const &exc) { @@ -16304,7 +12352,7 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, ggml_tensor *dst) try { if (ggml_backend_buffer_is_sycl(src->buffer)) { ggml_backend_sycl_buffer_context * src_ctx = (ggml_backend_sycl_buffer_context *)src->buffer->context; - ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + ggml_backend_sycl_buffer_context * dst_ctx = (ggml_backend_sycl_buffer_context *)dst->buffer->context; ggml_sycl_set_device(src_ctx->device); /* @@ -16328,8 +12376,8 @@ ggml_backend_sycl_buffer_cpy_tensor(ggml_backend_buffer_t buffer, was inserted. You need to rewrite this code. */ - dpct::queue_ptr stream_dst = g_syclStreams[dst_ctx->device][0]; - dpct::queue_ptr stream_src = g_syclStreams[src_ctx->device][0]; + queue_ptr stream_dst = dst_ctx->stream; + queue_ptr stream_src = src_ctx->stream; size_t size = ggml_nbytes(src); //todo. it's dirty solutino to walkaroud known issue:device2device cross GPUs. @@ -16364,7 +12412,7 @@ static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); - const dpct::queue_ptr stream = g_syclStreams[ctx->device][0]; + queue_ptr stream = ctx->stream; SYCL_CHECK( CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); @@ -16394,11 +12442,9 @@ static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { struct ggml_backend_sycl_buffer_type_context { int device; std::string name; -}; -struct ggml_backend_sycl_context { - int device; - std::string name; + // each buffer type has its own stream + queue_ptr stream = nullptr; }; GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -16411,13 +12457,13 @@ ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) try { ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; ggml_sycl_set_device(buft_ctx->device); - const dpct::queue_ptr stream = g_syclStreams[buft_ctx->device][0]; + const queue_ptr stream = buft_ctx->stream; size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 void * dev_ptr; SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( size, *stream))); - ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr); + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(buft_ctx->device, dev_ptr, buft_ctx->stream); return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); } catch (sycl::exception const &exc) { @@ -16452,45 +12498,67 @@ GGML_CALL static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backen UNUSED(buft); } -GGML_CALL static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - if (!ggml_backend_is_sycl(backend)) { - return false; - } - ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; - ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - return buft_ctx->device == sycl_ctx->device; -} - static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .get_name = */ ggml_backend_sycl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, /* .is_host = */ nullptr, }; -ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device_index) { +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { + static std::mutex mutex; + std::lock_guard lock(mutex); + + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_buffer_type\n"); + + if (device>=ggml_sycl_info().device_count or device<0) { + printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", + device, ggml_sycl_info().device_count-1); + GGML_ASSERT(device=g_device_count or device_index<0) { + int device = ctx->device; + if (device>=ggml_sycl_info().device_count or device<0) { printf("ggml_backend_sycl_buffer_type error: device_index:%d is out of range [0, %d], miss to call ggml_backend_sycl_set_single_device()\n", - device_index, g_device_count-1); - GGML_ASSERT(device_indexgpus[i])}, + /* .context = */ new ggml_backend_sycl_buffer_type_context{i, GGML_SYCL_NAME + std::to_string(i), ctx->stream(i, 0)}, }; } - g_ggml_backend_sycl_buffer_type_initialized = true; + ggml_backend_sycl_buffer_type_initialized = true; } - return &ggml_backend_sycl_buffer_types[device_index]; + return &ggml_backend_sycl_buffer_types[device]; } // sycl split buffer type @@ -16500,7 +12568,7 @@ static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tens *row_low = id == 0 ? 0 : nrows*tensor_split[id]; *row_low -= *row_low % rounding; - if (id == g_device_count - 1) { + if (id == ggml_sycl_info().device_count - 1) { *row_high = nrows; } else { *row_high = nrows*tensor_split[id + 1]; @@ -16511,9 +12579,8 @@ static void get_row_split(int64_t * row_low, int64_t * row_high, const ggml_tens struct ggml_backend_sycl_split_buffer_context { ~ggml_backend_sycl_split_buffer_context() try { for (ggml_tensor_extra_gpu * extra : tensor_extras) { - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; - for (int64_t is = 0; is < MAX_STREAMS; ++is) { + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { if (extra->events[i][is] != nullptr) { /* DPCT1009:206: SYCL uses exceptions to report errors and @@ -16534,7 +12601,7 @@ struct ggml_backend_sycl_split_buffer_context { */ ggml_sycl_set_device(i); SYCL_CHECK(CHECK_TRY_ERROR(sycl::free( - extra->data_device[i], *g_syclStreams[i][0]))); + extra->data_device[i], *(streams[i])))); } } delete extra; @@ -16547,6 +12614,7 @@ struct ggml_backend_sycl_split_buffer_context { } std::vector tensor_extras; + std::vector streams; }; GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backend_buffer_t buffer) { @@ -16555,10 +12623,9 @@ GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backe UNUSED(buffer); } -// unused at the moment -//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { -// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -//} +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +} GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; @@ -16585,9 +12652,9 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu{}; ctx->tensor_extras.push_back(extra); + ctx->streams.push_back(&(dpct::get_current_device().default_queue())); - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { int64_t row_low, row_high; get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); @@ -16607,6 +12674,7 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, // FIXME: do not crash if cudaMalloc fails // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; char * buf; /* DPCT1009:208: SYCL uses exceptions to report errors and does not use the @@ -16614,7 +12682,7 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, was inserted. You need to rewrite this code. */ SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( - size, *g_syclStreams[i][0]))); + size, *stream))); // set padding to 0 to avoid possible NaN values if (size > original_size) { @@ -16624,14 +12692,14 @@ ggml_backend_sycl_split_buffer_init_tensor(ggml_backend_buffer_t buffer, string was inserted. You need to rewrite this code. */ SYCL_CHECK(CHECK_TRY_ERROR( - (*g_syclStreams[i][0]) + (*stream) .memset(buf + original_size, 0, size - original_size) .wait())); } extra->data_device[i] = buf; - for (int64_t is = 0; is < MAX_STREAMS; ++is) { + for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) { /* DPCT1009:210: SYCL uses exceptions to report errors and does not use the error codes. The original code was commented out and a warning @@ -16658,14 +12726,14 @@ ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; const int64_t ne0 = tensor->ne[0]; const size_t nb1 = tensor->nb[1]; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { int64_t row_low, row_high; get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); @@ -16690,8 +12758,9 @@ ggml_backend_sycl_split_buffer_set_tensor(ggml_backend_buffer_t buffer, was inserted. You need to rewrite this code. */ ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; SYCL_CHECK(CHECK_TRY_ERROR( - (*g_syclStreams[i][0]) + (*stream) .memcpy(extra->data_device[i], buf_host, original_size) .wait())); } @@ -16710,14 +12779,14 @@ ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, GGML_ASSERT(offset == 0); GGML_ASSERT(size == ggml_nbytes(tensor)); + ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; ggml_backend_sycl_split_buffer_type_context * buft_ctx = (ggml_backend_sycl_split_buffer_type_context *)buffer->buft->context; const int64_t ne0 = tensor->ne[0]; const size_t nb1 = tensor->nb[1]; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *)tensor->extra; - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { int64_t row_low, row_high; get_row_split(&row_low, &row_high, tensor, buft_ctx->tensor_split, i); @@ -16742,8 +12811,9 @@ ggml_backend_sycl_split_buffer_get_tensor(ggml_backend_buffer_t buffer, was inserted. You need to rewrite this code. */ ggml_sycl_set_device(i); + const queue_ptr stream = ctx->streams[i]; SYCL_CHECK(CHECK_TRY_ERROR( - (*g_syclStreams[i][0]) + (*stream) .memcpy(buf_host, extra->data_device[i], original_size) .wait())); } @@ -16799,8 +12869,7 @@ GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_ const int64_t ne0 = tensor->ne[0]; - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { int64_t row_low, row_high; get_row_split(&row_low, &row_high, tensor, ctx->tensor_split, i); @@ -16820,12 +12889,6 @@ GGML_CALL static size_t ggml_backend_sycl_split_buffer_type_get_alloc_size(ggml_ return total_size; } -GGML_CALL static bool ggml_backend_sycl_split_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_sycl(backend); - - UNUSED(buft); -} - GGML_CALL static bool ggml_backend_sycl_split_buffer_type_is_host(ggml_backend_buffer_type_t buft) { return false; @@ -16838,13 +12901,15 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_split_buffer_type_interface /* .get_alignment = */ ggml_backend_sycl_split_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_sycl_split_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_sycl_split_buffer_type_supports_backend, /* .is_host = */ ggml_backend_sycl_split_buffer_type_is_host, }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split) { + static std::mutex mutex; + std::lock_guard lock(mutex); + GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_split_buffer_type\n"); - ggml_init_sycl(); + ggml_check_sycl(); // FIXME: this is not thread safe static std::map, struct ggml_backend_buffer_type> buft_map; @@ -16852,16 +12917,14 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const f bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + GGML_SYCL_MAX_DEVICES, [](float x) { return x == 0.0f; }); if (all_zero) { - tensor_split_arr = g_default_tensor_split; + tensor_split_arr = ggml_sycl_info().default_tensor_split; } else { float split_sum = 0.0f; - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { tensor_split_arr[i] = split_sum; split_sum += tensor_split[i]; } - for (int i = 0; i < g_device_count; ++i) { - // int id = g_sycl_gpu_mgr->gpus[i]; + for (int i = 0; i < ggml_sycl_info().device_count; ++i) { tensor_split_arr[i] /= split_sum; } } @@ -16924,7 +12987,6 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, /* .context = */ nullptr, @@ -16960,9 +13022,11 @@ GGML_CALL static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, const void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + + GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); + const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); + SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( (char *)tensor->data + offset, data, size).wait())); } catch (sycl::exception const &exc) { @@ -16976,9 +13040,11 @@ GGML_CALL static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, void *data, size_t offset, size_t size) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer; + + GGML_ASSERT(buf->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); + const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); + SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( data, (const char *)tensor->data + offset, size).wait())); } catch (sycl::exception const &exc) { @@ -16997,7 +13063,8 @@ GGML_CALL static bool ggml_backend_sycl_cpy_tensor_async(ggml_backend_t backend, error codes. The original code was commented out and a warning string was inserted. You need to rewrite this code. */ - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); + SYCL_CHECK(CHECK_TRY_ERROR((stream)->memcpy( dst->data, src->data, ggml_nbytes(dst)).wait())); return true; } @@ -17012,7 +13079,8 @@ catch (sycl::exception const &exc) { static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; - SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); + const queue_ptr stream = sycl_ctx->stream(sycl_ctx->device, 0); + SYCL_CHECK(CHECK_TRY_ERROR((stream)->wait())); UNUSED(backend); } @@ -17026,28 +13094,21 @@ GGML_CALL static ggml_status ggml_backend_sycl_graph_compute(ggml_backend_t back ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; ggml_sycl_set_main_device(sycl_ctx->device); - ggml_compute_params params = {}; - params.type = GGML_TASK_TYPE_COMPUTE; - params.ith = 0; + for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { continue; } #ifndef NDEBUG - assert(node->backend == GGML_BACKEND_TYPE_GPU || node->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); - assert(node->extra != nullptr); - for (int j = 0; j < GGML_MAX_SRC; j++) { if (node->src[j] != nullptr) { - assert(node->src[j]->backend == GGML_BACKEND_TYPE_GPU || node->src[j]->backend == GGML_BACKEND_TYPE_GPU_SPLIT); assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); - assert(node->src[j]->extra != nullptr); } } #endif - bool ok = ggml_sycl_compute_forward(¶ms, node); + bool ok = ggml_sycl_compute_forward(*sycl_ctx, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } @@ -17068,7 +13129,7 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: - return true; + return ggml_is_contiguous(op->src[0]); default: return false; } @@ -17189,6 +13250,14 @@ GGML_CALL static bool ggml_backend_sycl_offload_op(ggml_backend_t backend, const GGML_UNUSED(backend); } +GGML_CALL static bool ggml_backend_sycl_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_sycl_buffer_type_name) { + return false; + } + ggml_backend_sycl_buffer_type_context * buft_ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + ggml_backend_sycl_context * sycl_ctx = (ggml_backend_sycl_context *)backend->context; + return buft_ctx->device == sycl_ctx->device; +} static ggml_backend_i ggml_backend_sycl_interface = { /* .get_name = */ ggml_backend_sycl_name, @@ -17200,9 +13269,11 @@ static ggml_backend_i ggml_backend_sycl_interface = { /* .synchronize = */ ggml_backend_sycl_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_sycl_graph_compute, /* .supports_op = */ ggml_backend_sycl_supports_op, + /* .supports_buft = */ ggml_backend_sycl_supports_buft, /* .offload_op = */ ggml_backend_sycl_offload_op, /* .event_new = */ NULL, /* .event_free = */ NULL, @@ -17218,16 +13289,14 @@ static ggml_guid_t ggml_backend_sycl_guid() { GGML_CALL ggml_backend_t ggml_backend_sycl_init(int device) { GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_init\n"); - ggml_init_sycl(); + ggml_check_sycl(); check_allow_gpu_index(device); - // not strictly necessary, but it may reduce the overhead of the first graph_compute - ggml_sycl_set_main_device(device); - int id = g_sycl_gpu_mgr->gpus[device]; - ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context { - /* .device = */ device, - /* .name = */ GGML_SYCL_NAME + std::to_string(id), + ggml_backend_sycl_context * ctx = new ggml_backend_sycl_context(device); + if (ctx == nullptr) { + fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + return nullptr; }; ggml_backend_t sycl_backend = new ggml_backend { @@ -17245,8 +13314,7 @@ bool ggml_backend_is_sycl(ggml_backend_t backend) { GGML_CALL int ggml_backend_sycl_get_device_count() { GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_count\n"); - if (!g_sycl_gpu_mgr) g_sycl_gpu_mgr = new sycl_gpu_mgr(); - return g_sycl_gpu_mgr->get_gpu_count(); + return ggml_sycl_info().device_count; } GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { @@ -17256,60 +13324,14 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, UNUSED(params); } -GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id) { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_index\n"); - return g_sycl_gpu_mgr->get_index(device_id); -} - -GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index) { - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_get_device_id\n"); - return g_sycl_gpu_mgr->gpus[device_index]; -} - -GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id) { - ggml_init_sycl(); - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_single_device_mode\n"); - fprintf(stderr, "ggml_backend_sycl_set_single_device: use single device: [%d]\n", main_gpu_id); - GGML_ASSERT(main_gpu_idget_gpu_count()); - g_ggml_backend_sycl_buffer_type_initialized = false; -} - -GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode() { - ggml_init_sycl(); - GGML_SYCL_DEBUG("[SYCL] call ggml_backend_sycl_set_mul_device_mode\n"); - - if (g_ggml_sycl_backend_gpu_mode == SYCL_MUL_GPU_MODE) { - return; - } - - fprintf(stderr, "ggml_backend_sycl_set_mul_device_mode: true\n"); - - if (g_sycl_gpu_mgr) { - delete g_sycl_gpu_mgr; - } - g_sycl_gpu_mgr = new sycl_gpu_mgr(); - g_ggml_sycl_backend_gpu_mode = SYCL_MUL_GPU_MODE; - ggml_init_by_gpus(g_sycl_gpu_mgr->get_gpu_count()); - g_ggml_backend_sycl_buffer_type_initialized = false; -} - extern "C" int ggml_backend_sycl_reg_devices(); int ggml_backend_sycl_reg_devices() { - ggml_backend_sycl_set_mul_device_mode(); - assert(g_device_count>0); - for (int i = 0; i < g_device_count; i++) { - int id = g_sycl_gpu_mgr->gpus[i]; + assert(ggml_sycl_info().device_count>0); + for (int i = 0; i < ggml_sycl_info().device_count; i++) { char name[128]; - snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, id); + snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i); ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i); } - return g_device_count; + return ggml_sycl_info().device_count; } diff --git a/ggml-sycl.h b/ggml-sycl.h index a9f776fc1dd59..451938fc4151d 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -8,14 +8,12 @@ #include "ggml.h" #include "ggml-backend.h" +#include "ggml-sycl/presets.hpp" #ifdef __cplusplus extern "C" { #endif -#define GGML_SYCL_MAX_DEVICES 48 -#define GGML_SYCL_NAME "SYCL" - // backend API GGML_API ggml_backend_t ggml_backend_sycl_init(int device); @@ -33,13 +31,6 @@ GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len); GGML_API GGML_CALL void ggml_sycl_get_device_description(int device, char *description, size_t description_size); GGML_API GGML_CALL int ggml_backend_sycl_get_device_count(); GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total); -GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id); - -// TODO: these are temporary -// ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670 -GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index); -GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id); -GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode(); // SYCL doesn't support registering host memory, keep here for reference // GGML_API GGML_CALL bool ggml_backend_sycl_register_host_buffer(void * buffer, size_t size); diff --git a/ggml-sycl/backend.hpp b/ggml-sycl/backend.hpp new file mode 100644 index 0000000000000..88bae59678bdd --- /dev/null +++ b/ggml-sycl/backend.hpp @@ -0,0 +1,18 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_BACKEND_HPP +#define GGML_SYCL_BACKEND_HPP + +#include "common.hpp" + +#endif // GGML_SYCL_BACKEND_HPP diff --git a/ggml-sycl/common.cpp b/ggml-sycl/common.cpp new file mode 100644 index 0000000000000..e878f4f50f09e --- /dev/null +++ b/ggml-sycl/common.cpp @@ -0,0 +1,53 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#include "common.hpp" + +int get_current_device_id() { + return dpct::dev_mgr::instance().current_device_id(); +} + +void* ggml_sycl_host_malloc(size_t size) try { + if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { + return nullptr; + } + + void* ptr = nullptr; + // allow to use dpct::get_in_order_queue() for host malloc + dpct::err0 err = CHECK_TRY_ERROR( + ptr = (void*)sycl::malloc_host(size, dpct::get_in_order_queue())); + + if (err != 0) { + // clear the error + fprintf( + stderr, + "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", + size / 1024.0 / 1024.0, + "syclGetErrorString is not supported"); + return nullptr; + } + + return ptr; +} catch (sycl::exception const& exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_host_free(void* ptr) try { + // allow to use dpct::get_in_order_queue() for host malloc + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue()))); +} catch (sycl::exception const& exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} diff --git a/ggml-sycl/common.hpp b/ggml-sycl/common.hpp new file mode 100644 index 0000000000000..414c37eed0d5d --- /dev/null +++ b/ggml-sycl/common.hpp @@ -0,0 +1,298 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_COMMON_HPP +#define GGML_SYCL_COMMON_HPP + +#include +#include + +#include "dpct/helper.hpp" +#include "presets.hpp" + +#define GGML_COMMON_DECL_SYCL +#define GGML_COMMON_IMPL_SYCL +#include "ggml-common.h" + +void* ggml_sycl_host_malloc(size_t size); +void ggml_sycl_host_free(void* ptr); + +static int g_ggml_sycl_debug = 0; +#define GGML_SYCL_DEBUG(...) \ + do { \ + if (g_ggml_sycl_debug) \ + fprintf(stderr, __VA_ARGS__); \ + } while (0) + +#define CHECK_TRY_ERROR(expr) \ + [&]() { \ + try { \ + expr; \ + return dpct::success; \ + } catch (std::exception const& e) { \ + std::cerr << e.what() << "\nException caught at file:" << __FILE__ \ + << ", line:" << __LINE__ << ", func:" << __func__ \ + << std::endl; \ + return dpct::default_error; \ + } \ + }() + +// #define DEBUG_SYCL_MALLOC + +static int g_work_group_size = 0; +// typedef sycl::half ggml_fp16_t; + +#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP +#define VER_4VEC 610 // todo for hardward optimize. +#define VER_GEN9 700 // todo for hardward optimize. +#define VER_GEN12 1000000 // todo for hardward optimize. +#define VER_GEN13 (VER_GEN12 + 1030) // todo for hardward optimize. + +#define GGML_SYCL_MAX_NODES 8192 // TODO: adapt to hardwares + +// define for XMX in Intel GPU +// TODO: currently, it's not used for XMX really. +#if !defined(GGML_SYCL_FORCE_MMQ) + #define SYCL_USE_XMX +#endif + +// max batch size to use MMQ kernels when tensor cores are available +#define MMQ_MAX_BATCH_SIZE 32 + +#if defined(_MSC_VER) +#pragma warning(disable : 4244 4267) // possible loss of data +#endif + +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_SYCL_DMMV_X +#define GGML_SYCL_DMMV_X 32 +#endif +#ifndef GGML_SYCL_MMV_Y +#define GGML_SYCL_MMV_Y 1 +#endif + +typedef sycl::queue *queue_ptr; + +enum ggml_sycl_backend_gpu_mode { + SYCL_UNSET_GPU_MODE = -1, + SYCL_SINGLE_GPU_MODE = 0, + SYCL_MUL_GPU_MODE +}; + +static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); + +static void crash() { + int* ptr = NULL; + *ptr = 0; +} + +[[noreturn]] static void ggml_sycl_error( + const char* stmt, + const char* func, + const char* file, + const int line, + const char* msg) { + fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg); + fprintf(stderr, " in function %s at %s:%d\n", func, file, line); + GGML_ASSERT(!"SYCL error"); +} + +#define SYCL_CHECK(err) \ + do { \ + auto err_ = (err); \ + if (err_ != 0) \ + ggml_sycl_error( \ + #err, \ + __func__, \ + __FILE__, \ + __LINE__, \ + "Meet error in this line code!"); \ + } while (0) + +#if DPCT_COMPAT_RT_VERSION >= 11100 +#define GGML_SYCL_ASSUME(x) __builtin_assume(x) +#else +#define GGML_SYCL_ASSUME(x) +#endif // DPCT_COMPAT_RT_VERSION >= 11100 + +#ifdef GGML_SYCL_F16 +typedef sycl::half dfloat; // dequantize float +typedef sycl::half2 dfloat2; +#else +typedef float dfloat; // dequantize float +typedef sycl::float2 dfloat2; +#endif // GGML_SYCL_F16 + +#define MMVQ_MAX_BATCH_SIZE 8 + +static const int8_t kvalues_iq4nl[16]={-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; + +static int g_all_sycl_device_count = -1; +static bool g_ggml_backend_sycl_buffer_type_initialized = false; + +static ggml_sycl_backend_gpu_mode g_ggml_sycl_backend_gpu_mode = + SYCL_UNSET_GPU_MODE; + +static void* g_scratch_buffer = nullptr; +static size_t g_scratch_size = 0; // disabled by default +static size_t g_scratch_offset = 0; + +[[noreturn]] static inline void bad_arch(const sycl::stream& stream_ct1) { + stream_ct1 << "ERROR: ggml-sycl was compiled without support for the " + "current GPU architecture.\n"; + // __trap(); + std::exit(1); + + (void)bad_arch; // suppress unused function warning +} + +int get_current_device_id(); + +inline dpct::err0 ggml_sycl_set_device(const int device) try { + + int current_device_id; + SYCL_CHECK(CHECK_TRY_ERROR(current_device_id = get_current_device_id())); + + // GGML_SYCL_DEBUG("ggml_sycl_set_device device_id=%d, + // current_device_id=%d\n", device, current_device); + if (device == current_device_id) { + return 0; + } + + return CHECK_TRY_ERROR(dpct::select_device(device)); +} catch (sycl::exception const& exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + crash(); + std::exit(1); +} + +////////////////////// + +struct ggml_sycl_device_info { + int device_count; + + struct sycl_device_info { + int cc; // compute capability + // int nsm; // number of streaming multiprocessors + // size_t smpb; // max. shared memory per block + bool vmm; // virtual memory support + size_t total_vram; + }; + + sycl_device_info devices[GGML_SYCL_MAX_DEVICES] = {}; + + std::array default_tensor_split = {}; +}; + +const ggml_sycl_device_info & ggml_sycl_info(); + +struct ggml_sycl_pool { + virtual ~ggml_sycl_pool() = default; + + virtual void * alloc(size_t size, size_t * actual_size) = 0; + virtual void free(void * ptr, size_t size) = 0; +}; + +template +struct ggml_sycl_pool_alloc { + ggml_sycl_pool * pool = nullptr; + T * ptr = nullptr; + size_t actual_size = 0; + + explicit ggml_sycl_pool_alloc(ggml_sycl_pool & pool) : pool(&pool) { + } + + ggml_sycl_pool_alloc(ggml_sycl_pool & pool, size_t size) : pool(&pool) { + alloc(size); + } + + ~ggml_sycl_pool_alloc() { + if (ptr != nullptr) { + pool->free(ptr, actual_size); + } + } + + // size is in number of elements + T * alloc(size_t size) { + GGML_ASSERT(pool != nullptr); + GGML_ASSERT(ptr == nullptr); + ptr = (T *) pool->alloc(size * sizeof(T), &this->actual_size); + return ptr; + } + + T * alloc(ggml_sycl_pool & pool, size_t size) { + this->pool = &pool; + return alloc(size); + } + + T * get() { + return ptr; + } + + ggml_sycl_pool_alloc() = default; + ggml_sycl_pool_alloc(const ggml_sycl_pool_alloc &) = delete; + ggml_sycl_pool_alloc(ggml_sycl_pool_alloc &&) = delete; + ggml_sycl_pool_alloc& operator=(const ggml_sycl_pool_alloc &) = delete; + ggml_sycl_pool_alloc& operator=(ggml_sycl_pool_alloc &&) = delete; +}; + +// backend interface + +struct ggml_tensor_extra_gpu { + void* data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split + // tensors + dpct::event_ptr events[GGML_SYCL_MAX_DEVICES] + [GGML_SYCL_MAX_STREAMS]; // events for synchronizing multiple GPUs +}; + +struct ggml_backend_sycl_context { + int device; + std::string name; + + queue_ptr qptrs[GGML_SYCL_MAX_DEVICES][GGML_SYCL_MAX_STREAMS] = { { nullptr } }; + + explicit ggml_backend_sycl_context(int device) : + device(device), + name(GGML_SYCL_NAME + std::to_string(device)) { + } + + queue_ptr stream(int device, int stream) { + if (qptrs[device][stream] == nullptr) { + qptrs[device][stream] = &(dpct::get_current_device().default_queue()); + } + return qptrs[device][stream]; + } + + queue_ptr stream() { + return stream(device, 0); + } + + // pool + std::unique_ptr pools[GGML_SYCL_MAX_DEVICES]; + + static std::unique_ptr new_pool_for_device(queue_ptr qptr, int device); + + ggml_sycl_pool & pool(int device) { + if (pools[device] == nullptr) { + pools[device] = new_pool_for_device(stream(device,0), device); + } + return *pools[device]; + } + + ggml_sycl_pool & pool() { + return pool(device); + } +}; + + +#endif // GGML_SYCL_COMMON_HPP diff --git a/ggml-sycl/dpct/helper.hpp b/ggml-sycl/dpct/helper.hpp new file mode 100644 index 0000000000000..017fd6ee13268 --- /dev/null +++ b/ggml-sycl/dpct/helper.hpp @@ -0,0 +1,2980 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_DPCT_HELPER_HPP +#define GGML_SYCL_DPCT_HELPER_HPP + +#include +#include +#include +#include + +#include "ggml.h" + +#if defined(__linux__) +#include +#elif defined(_WIN64) +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#else +#error "Only support Windows and Linux." +#endif + +#if defined(__linux__) +#include +#include +#endif +#if defined(_WIN64) +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#endif + +#define DPCT_COMPATIBILITY_TEMP (900) + +#if defined(_MSC_VER) +#define __dpct_align__(n) __declspec(align(n)) +#define __dpct_inline__ __forceinline +#else +#define __dpct_align__(n) __attribute__((aligned(n))) +#define __dpct_inline__ __inline__ __attribute__((always_inline)) +#endif + +#if defined(_MSC_VER) +#define __dpct_noinline__ __declspec(noinline) +#else +#define __dpct_noinline__ __attribute__((noinline)) +#endif + +inline std::string get_device_type_name(const sycl::device &Device) { + auto DeviceType = Device.get_info(); + switch (DeviceType) { + case sycl::info::device_type::cpu: + return "cpu"; + case sycl::info::device_type::gpu: + return "gpu"; + case sycl::info::device_type::host: + return "host"; + case sycl::info::device_type::accelerator: + return "acc"; + default: + return "unknown"; + } +} + +inline std::string get_device_backend_and_type(const sycl::device &device) { + std::stringstream device_type; + sycl::backend backend = device.get_backend(); + device_type << backend << ":" << get_device_type_name(device); + return device_type.str(); +} + +namespace dpct +{ + typedef sycl::queue *queue_ptr; + typedef sycl::event *event_ptr; + typedef char *device_ptr; + typedef uint8_t byte_t; + typedef sycl::buffer buffer_t; + + /// SYCL default exception handler + inline auto exception_handler = [](sycl::exception_list exceptions) + { + for (std::exception_ptr const &e : exceptions) + { + try + { + std::rethrow_exception(e); + } + catch (sycl::exception const &e) + { + std::cerr << "Caught asynchronous SYCL exception:" << std::endl + << e.what() << std::endl + << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + } + } + }; + + enum error_code + { + success = 0, + default_error = 999 + }; + + enum memcpy_direction + { + host_to_host, + host_to_device, + device_to_host, + device_to_device, + automatic + }; + + enum memory_region + { + global = 0, // device global memory + constant, // device constant memory + local, // device local memory + shared, // memory which can be accessed by host and device + }; + + enum class library_data_t : unsigned char + { + real_float = 0, + complex_float, + real_double, + complex_double, + real_half, + complex_half, + real_bfloat16, + complex_bfloat16, + real_int4, + complex_int4, + real_uint4, + complex_uint4, + real_int8, + complex_int8, + real_uint8, + complex_uint8, + real_int16, + complex_int16, + real_uint16, + complex_uint16, + real_int32, + complex_int32, + real_uint32, + complex_uint32, + real_int64, + complex_int64, + real_uint64, + complex_uint64, + real_int8_4, + real_int8_32, + real_uint8_4, + library_data_t_size + }; + + template + struct DataType + { + using T2 = T; + }; + template + struct DataType> + { + using T2 = std::complex; + }; + + static void destroy_event(event_ptr event) + { + delete event; + } + + static inline unsigned int get_tid() + { +#if defined(__linux__) + return syscall(SYS_gettid); +#elif defined(_WIN64) + return GetCurrentThreadId(); +#else +#error "Only support Windows and Linux." +#endif + } + + namespace detail + { + static void get_version(const sycl::device &dev, int &major, int &minor) + { + // Version string has the following format: + // a. OpenCL + // b. + // c. e.g gfx1030 + std::string ver; + ver = dev.get_info(); + std::string::size_type i = 0; + while (i < ver.size()) { + if (isdigit(ver[i])) + break; + i++; + } + major = std::stoi(&(ver[i])); + while (i < ver.size()) { + if (ver[i] == '.') + break; + i++; + } + if (i < ver.size()) { + // a. and b. + i++; + minor = std::stoi(&(ver[i])); + } else { + // c. + minor = 0; + } + } + + template + class generic_error_type + { + public: + generic_error_type() = default; + generic_error_type(T value) : value{value} {} + operator T() const { return value; } + + private: + T value; + }; + + } // namespace detail + + /// Pitched 2D/3D memory data. + class pitched_data + { + public: + pitched_data() : pitched_data(nullptr, 0, 0, 0) {} + pitched_data(void *data, size_t pitch, size_t x, size_t y) + : _data(data), _pitch(pitch), _x(x), _y(y) {} + + void *get_data_ptr() { return _data; } + void set_data_ptr(void *data) { _data = data; } + + size_t get_pitch() { return _pitch; } + void set_pitch(size_t pitch) { _pitch = pitch; } + + size_t get_x() { return _x; } + void set_x(size_t x) { _x = x; }; + + size_t get_y() { return _y; } + void set_y(size_t y) { _y = y; } + + private: + void *_data; + size_t _pitch, _x, _y; + }; + + class device_info + { + public: + // get interface + const char *get_name() const { return _name; } + char *get_name() { return _name; } + template , + std::enable_if_t> || + std::is_same_v, + int> = 0> + auto get_max_work_item_sizes() const + { + if constexpr (std::is_same_v>) + return sycl::range<3>(_max_work_item_sizes_i[0], + _max_work_item_sizes_i[1], + _max_work_item_sizes_i[2]); + else + { + return _max_work_item_sizes_i; + } + } + template , + std::enable_if_t> || + std::is_same_v, + int> = 0> + auto get_max_work_item_sizes() + { + if constexpr (std::is_same_v>) + return sycl::range<3>(_max_work_item_sizes_i[0], + _max_work_item_sizes_i[1], + _max_work_item_sizes_i[2]); + else + { + return _max_work_item_sizes_i; + } + } + bool get_host_unified_memory() const { return _host_unified_memory; } + int get_major_version() const { return _major; } + int get_minor_version() const { return _minor; } + int get_integrated() const { return _integrated; } + int get_max_clock_frequency() const { return _frequency; } + int get_max_compute_units() const { return _max_compute_units; } + int get_max_work_group_size() const { return _max_work_group_size; } + int get_max_sub_group_size() const { return _max_sub_group_size; } + int get_max_work_items_per_compute_unit() const + { + return _max_work_items_per_compute_unit; + } + int get_max_register_size_per_work_group() const + { + return _max_register_size_per_work_group; + } + template || + std::is_same_v, + int> = 0> + auto get_max_nd_range_size() const + { + if constexpr (std::is_same_v) + return _max_nd_range_size; + else + return _max_nd_range_size_i; + } + template || + std::is_same_v, + int> = 0> + auto get_max_nd_range_size() + { + if constexpr (std::is_same_v) + return _max_nd_range_size; + else + return _max_nd_range_size_i; + } + size_t get_global_mem_size() const { return _global_mem_size; } + size_t get_local_mem_size() const { return _local_mem_size; } + size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; } + /// Returns the maximum clock rate of device's global memory in kHz. If + /// compiler does not support this API then returns default value 3200000 kHz. + unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } + /// Returns the maximum bus width between device and memory in bits. If + /// compiler does not support this API then returns default value 64 bits. + unsigned int get_memory_bus_width() const { return _memory_bus_width; } + uint32_t get_device_id() const { return _device_id; } + std::array get_uuid() const { return _uuid; } + /// Returns global memory cache size in bytes. + unsigned int get_global_mem_cache_size() const + { + return _global_mem_cache_size; + } + + // set interface + void set_name(const char *name) + { + size_t length = strlen(name); + if (length < 256) + { + std::memcpy(_name, name, length + 1); + } + else + { + std::memcpy(_name, name, 255); + _name[255] = '\0'; + } + } + void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes) + { + for (int i = 0; i < 3; ++i) + _max_work_item_sizes_i[i] = max_work_item_sizes[i]; + } + [[deprecated]] void + set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes) + { + for (int i = 0; i < 3; ++i) + { + _max_work_item_sizes_i[i] = max_work_item_sizes[i]; + } + } + void set_host_unified_memory(bool host_unified_memory) + { + _host_unified_memory = host_unified_memory; + } + void set_major_version(int major) { _major = major; } + void set_minor_version(int minor) { _minor = minor; } + void set_integrated(int integrated) { _integrated = integrated; } + void set_max_clock_frequency(int frequency) { _frequency = frequency; } + void set_max_compute_units(int max_compute_units) + { + _max_compute_units = max_compute_units; + } + void set_global_mem_size(size_t global_mem_size) + { + _global_mem_size = global_mem_size; + } + void set_local_mem_size(size_t local_mem_size) + { + _local_mem_size = local_mem_size; + } + void set_max_mem_alloc_size(size_t max_mem_alloc_size) + { + _max_mem_alloc_size = max_mem_alloc_size; + } + void set_max_work_group_size(int max_work_group_size) + { + _max_work_group_size = max_work_group_size; + } + void set_max_sub_group_size(int max_sub_group_size) + { + _max_sub_group_size = max_sub_group_size; + } + void + set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit) + { + _max_work_items_per_compute_unit = max_work_items_per_compute_unit; + } + void set_max_nd_range_size(int max_nd_range_size[]) + { + for (int i = 0; i < 3; i++) + { + _max_nd_range_size[i] = max_nd_range_size[i]; + _max_nd_range_size_i[i] = max_nd_range_size[i]; + } + } + void set_memory_clock_rate(unsigned int memory_clock_rate) + { + _memory_clock_rate = memory_clock_rate; + } + void set_memory_bus_width(unsigned int memory_bus_width) + { + _memory_bus_width = memory_bus_width; + } + void + set_max_register_size_per_work_group(int max_register_size_per_work_group) + { + _max_register_size_per_work_group = max_register_size_per_work_group; + } + void set_device_id(uint32_t device_id) + { + _device_id = device_id; + } + void set_uuid(std::array uuid) + { + _uuid = std::move(uuid); + } + void set_global_mem_cache_size(unsigned int global_mem_cache_size) + { + _global_mem_cache_size = global_mem_cache_size; + } + + private: + char _name[256]; + int _max_work_item_sizes_i[3]; + bool _host_unified_memory = false; + int _major; + int _minor; + int _integrated = 0; + int _frequency; + // Set estimated value 3200000 kHz as default value. + unsigned int _memory_clock_rate = 3200000; + // Set estimated value 64 bits as default value. + unsigned int _memory_bus_width = 64; + unsigned int _global_mem_cache_size; + int _max_compute_units; + int _max_work_group_size; + int _max_sub_group_size; + int _max_work_items_per_compute_unit; + int _max_register_size_per_work_group; + size_t _global_mem_size; + size_t _local_mem_size; + size_t _max_mem_alloc_size; + size_t _max_nd_range_size[3]; + int _max_nd_range_size_i[3]; + uint32_t _device_id; + std::array _uuid; + }; + + static int get_major_version(const sycl::device &dev) + { + int major, minor; + detail::get_version(dev, major, minor); + return major; + } + + static int get_minor_version(const sycl::device &dev) + { + int major, minor; + detail::get_version(dev, major, minor); + return minor; + } + + static void get_device_info(device_info &out, const sycl::device &dev) + { + device_info prop; + prop.set_name(dev.get_info().c_str()); + + int major, minor; + detail::get_version(dev, major, minor); + prop.set_major_version(major); + prop.set_minor_version(minor); + + prop.set_max_work_item_sizes( +#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902) + // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes + // is an enum class element + dev.get_info()); +#else + // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by + // an int + dev.get_info>()); +#endif + prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations)); + + prop.set_max_clock_frequency( + dev.get_info() * 1000); + + prop.set_max_compute_units( + dev.get_info()); + prop.set_max_work_group_size( + dev.get_info()); + prop.set_global_mem_size(dev.get_info()); + prop.set_local_mem_size(dev.get_info()); + prop.set_max_mem_alloc_size(dev.get_info()); + +#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) + if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) + { + unsigned int tmp = + dev.get_info(); + if (tmp != 0) + prop.set_memory_clock_rate(1000 * tmp); + } + if (dev.has(sycl::aspect::ext_intel_memory_bus_width)) + { + prop.set_memory_bus_width( + dev.get_info()); + } + if (dev.has(sycl::aspect::ext_intel_device_id)) + { + prop.set_device_id( + dev.get_info()); + } + if (dev.has(sycl::aspect::ext_intel_device_info_uuid)) + { + prop.set_uuid(dev.get_info()); + } +#elif defined(_MSC_VER) && !defined(__clang__) +#pragma message("get_device_info: querying memory_clock_rate and \ + memory_bus_width are not supported by the compiler used. \ + Use 3200000 kHz as memory_clock_rate default value. \ + Use 64 bits as memory_bus_width default value.") +#else +#warning "get_device_info: querying memory_clock_rate and \ + memory_bus_width are not supported by the compiler used. \ + Use 3200000 kHz as memory_clock_rate default value. \ + Use 64 bits as memory_bus_width default value." +#endif + + size_t max_sub_group_size = 1; + std::vector sub_group_sizes = + dev.get_info(); + + for (const auto &sub_group_size : sub_group_sizes) + { + if (max_sub_group_size < sub_group_size) + max_sub_group_size = sub_group_size; + } + + prop.set_max_sub_group_size(max_sub_group_size); + + prop.set_max_work_items_per_compute_unit( + dev.get_info()); + int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF}; + prop.set_max_nd_range_size(max_nd_range_size); + + // Estimates max register size per work group, feel free to update the value + // according to device properties. + prop.set_max_register_size_per_work_group(65536); + + prop.set_global_mem_cache_size( + dev.get_info()); + out = prop; + } + + /// dpct device extension + class device_ext : public sycl::device + { + typedef std::mutex mutex_type; + + public: + device_ext() : sycl::device(), _ctx(*this) {} + ~device_ext() + { + std::lock_guard lock(m_mutex); + clear_queues(); + } + device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this) + { + std::lock_guard lock(m_mutex); + init_queues(); + } + + int is_native_atomic_supported() { return 0; } + int get_major_version() const + { + return dpct::get_major_version(*this); + } + + int get_minor_version() const + { + return dpct::get_minor_version(*this); + } + + int get_max_compute_units() const + { + return get_device_info().get_max_compute_units(); + } + + /// Return the maximum clock frequency of this device in KHz. + int get_max_clock_frequency() const + { + return get_device_info().get_max_clock_frequency(); + } + + int get_integrated() const { return get_device_info().get_integrated(); } + + int get_max_sub_group_size() const + { + return get_device_info().get_max_sub_group_size(); + } + + int get_max_register_size_per_work_group() const + { + return get_device_info().get_max_register_size_per_work_group(); + } + + int get_max_work_group_size() const + { + return get_device_info().get_max_work_group_size(); + } + + int get_mem_base_addr_align() const + { + return get_info(); + } + + size_t get_global_mem_size() const + { + return get_device_info().get_global_mem_size(); + } + + size_t get_max_mem_alloc_size() const + { + return get_device_info().get_max_mem_alloc_size(); + } + + /// Get the number of bytes of free and total memory on the SYCL device. + /// \param [out] free_memory The number of bytes of free memory on the SYCL device. + /// \param [out] total_memory The number of bytes of total memory on the SYCL device. + void get_memory_info(size_t &free_memory, size_t &total_memory) + { + total_memory = get_device_info().get_global_mem_size(); + const char *warning_info = "get_memory_info: [warning] ext_intel_free_memory is not " + "supported (export/set ZES_ENABLE_SYSMAN=1 to support), " + "use total memory as free memory"; +#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) + if (!has(sycl::aspect::ext_intel_free_memory)) + { + std::cerr << warning_info << std::endl; + free_memory = total_memory; + } + else + { + free_memory = get_info(); + } +#else + std::cerr << warning_info << std::endl; + free_memory = total_memory; +#if defined(_MSC_VER) && !defined(__clang__) +#pragma message("Querying the number of bytes of free memory is not supported") +#else +#warning "Querying the number of bytes of free memory is not supported" +#endif +#endif + } + + void get_device_info(device_info &out) const + { + dpct::get_device_info(out, *this); + } + + device_info get_device_info() const + { + device_info prop; + dpct::get_device_info(prop, *this); + return prop; + } + + void reset() + { + std::lock_guard lock(m_mutex); + clear_queues(); + init_queues(); + } + + sycl::queue &in_order_queue() { return *_q_in_order; } + + sycl::queue &out_of_order_queue() { return *_q_out_of_order; } + + sycl::queue &default_queue() + { + return in_order_queue(); + } + + void queues_wait_and_throw() + { + std::unique_lock lock(m_mutex); + std::vector> current_queues( + _queues); + lock.unlock(); + for (const auto &q : current_queues) + { + q->wait_and_throw(); + } + // Guard the destruct of current_queues to make sure the ref count is safe. + lock.lock(); + } + + sycl::queue *create_queue(bool enable_exception_handler = false) + { + return create_in_order_queue(enable_exception_handler); + } + + sycl::queue *create_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + return create_in_order_queue(context, device, enable_exception_handler); + } + + sycl::queue *create_in_order_queue(bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_in_order_queue(sycl::context context, sycl::device device, + bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(context, device, enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) { + std::lock_guard lock(m_mutex); + return create_queue_impl(enable_exception_handler); + } + + void destroy_queue(sycl::queue *&queue) + { + std::lock_guard lock(m_mutex); + _queues.erase(std::remove_if(_queues.begin(), _queues.end(), + [=](const std::shared_ptr &q) -> bool + { + return q.get() == queue; + }), + _queues.end()); + queue = nullptr; + } + void set_saved_queue(sycl::queue *q) + { + std::lock_guard lock(m_mutex); + _saved_queue = q; + } + sycl::queue *get_saved_queue() const + { + std::lock_guard lock(m_mutex); + return _saved_queue; + } + sycl::context get_context() const { return _ctx; } + + private: + void clear_queues() + { + _queues.clear(); + _q_in_order = _q_out_of_order = _saved_queue = nullptr; + } + + void init_queues() + { + _q_in_order = create_queue_impl(true, sycl::property::queue::in_order()); + _q_out_of_order = create_queue_impl(true); + _saved_queue = &default_queue(); + } + + /// Caller should acquire resource \p m_mutex before calling this function. + template + sycl::queue *create_queue_impl(bool enable_exception_handler, + Properties... properties) + { + sycl::async_handler eh = {}; + if (enable_exception_handler) + { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + _ctx, *this, eh, + sycl::property_list( +#ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), +#endif + properties...))); + + return _queues.back().get(); + } + + template + sycl::queue *create_queue_impl(sycl::context context, sycl::device device, + bool enable_exception_handler, + Properties... properties) { + sycl::async_handler eh = {}; + if (enable_exception_handler) { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + context, device, eh, + sycl::property_list( + #ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), + #endif + properties...))); + + return _queues.back().get(); + } + + void get_version(int &major, int &minor) const + { + detail::get_version(*this, major, minor); + } + sycl::queue *_q_in_order, *_q_out_of_order; + sycl::queue *_saved_queue; + sycl::context _ctx; + std::vector> _queues; + mutable mutex_type m_mutex; + }; + + /// device manager + class dev_mgr + { + public: + device_ext ¤t_device() + { + unsigned int dev_id = current_device_id(); + check_id(dev_id); + return *_devs[dev_id]; + } + device_ext &cpu_device() const + { + std::lock_guard lock(m_mutex); + if (_cpu_device == -1) + { + throw std::runtime_error("no valid cpu device"); + } + else + { + return *_devs[_cpu_device]; + } + } + device_ext &get_device(unsigned int id) const + { + std::lock_guard lock(m_mutex); + check_id(id); + return *_devs[id]; + } + unsigned int current_device_id() const + { + std::lock_guard lock(m_mutex); + auto it = _thread2dev_map.find(get_tid()); + if (it != _thread2dev_map.end()) + return it->second; + return DEFAULT_DEVICE_ID; + } + + /// Select device with a device ID. + /// \param [in] id The id of the device which can + /// be obtained through get_device_id(const sycl::device). + void select_device(unsigned int id) + { + std::lock_guard lock(m_mutex); + check_id(id); + _thread2dev_map[get_tid()] = id; + } + unsigned int device_count() { return _devs.size(); } + + unsigned int get_device_id(const sycl::device &dev) + { + unsigned int id = 0; + for (auto dev_item : _devs) + { + if (*dev_item == dev) + { + break; + } + id++; + } + return id; + } + + template + std::enable_if_t< + std::is_invocable_r_v> + select_device(const DeviceSelector &selector = sycl::gpu_selector_v) + { + sycl::device selected_device = sycl::device(selector); + unsigned int selected_device_id = get_device_id(selected_device); + select_device(selected_device_id); + } + + /// Returns the instance of device manager singleton. + static dev_mgr &instance() + { + static dev_mgr d_m; + return d_m; + } + dev_mgr(const dev_mgr &) = delete; + dev_mgr &operator=(const dev_mgr &) = delete; + dev_mgr(dev_mgr &&) = delete; + dev_mgr &operator=(dev_mgr &&) = delete; + + private: + mutable std::recursive_mutex m_mutex; + static bool compare_dev(sycl::device &device1, sycl::device &device2) + { + sycl::backend backend1 = device1.get_backend(); + sycl::backend backend2 = device2.get_backend(); + // levelzero backends always come first + if(backend1 == sycl::backend::ext_oneapi_level_zero && backend2 != sycl::backend::ext_oneapi_level_zero) return true; + if(backend1 != sycl::backend::ext_oneapi_level_zero && backend2 == sycl::backend::ext_oneapi_level_zero) return false; + dpct::device_info prop1; + dpct::get_device_info(prop1, device1); + dpct::device_info prop2; + dpct::get_device_info(prop2, device2); + return prop1.get_max_compute_units() > prop2.get_max_compute_units(); + } + static int convert_backend_index(std::string & backend) { + if (backend == "ext_oneapi_level_zero:gpu") return 0; + if (backend == "opencl:gpu") return 1; + if (backend == "ext_oneapi_cuda:gpu") return 2; + if (backend == "ext_oneapi_hip:gpu") return 3; + if (backend == "opencl:cpu") return 4; + if (backend == "opencl:acc") return 5; + printf("convert_backend_index: can't handle backend=%s\n", backend.c_str()); + GGML_ASSERT(false); + } + static bool compare_backend(std::string &backend1, std::string &backend2) { + return convert_backend_index(backend1) < convert_backend_index(backend2); + } + dev_mgr() + { + sycl::device default_device = + sycl::device(sycl::default_selector_v); + _devs.push_back(std::make_shared(default_device)); + + std::vector sycl_all_devs; + // Collect other devices except for the default device. + if (default_device.is_cpu()) + _cpu_device = 0; + + auto Platforms = sycl::platform::get_platforms(); + // Keep track of the number of devices per backend + std::map DeviceNums; + std::map> backend_devices; + + while (!Platforms.empty()) { + auto Platform = Platforms.back(); + Platforms.pop_back(); + auto devices = Platform.get_devices(); + std::string backend_type = get_device_backend_and_type(devices[0]); + for (const auto &device : devices) { + backend_devices[backend_type].push_back(device); + } + } + + std::vector keys; + for(auto it = backend_devices.begin(); it != backend_devices.end(); ++it) { + keys.push_back(it->first); + } + std::sort(keys.begin(), keys.end(), compare_backend); + + for (auto &key : keys) { + std::vector devs = backend_devices[key]; + std::sort(devs.begin(), devs.end(), compare_dev); + for (const auto &dev : devs) { + sycl_all_devs.push_back(dev); + } + } + + for (auto &dev : sycl_all_devs) + { + if (dev == default_device) + { + continue; + } + _devs.push_back(std::make_shared(dev)); + if (_cpu_device == -1 && dev.is_cpu()) + { + _cpu_device = _devs.size() - 1; + } + } + } + void check_id(unsigned int id) const + { + if (id >= _devs.size()) + { + throw std::runtime_error("invalid device id"); + } + } + std::vector> _devs; + /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current + /// thread id in _thread2dev_map, which means default device should be used + /// for the current thread. + const unsigned int DEFAULT_DEVICE_ID = 0; + /// thread-id to device-id map. + std::map _thread2dev_map; + int _cpu_device = -1; + }; + + static inline sycl::queue &get_default_queue() + { + return dev_mgr::instance().current_device().default_queue(); + } + + namespace detail + { + enum class pointer_access_attribute + { + host_only = 0, + device_only, + host_device, + end + }; + + static pointer_access_attribute get_pointer_attribute(sycl::queue &q, + const void *ptr) + { + switch (sycl::get_pointer_type(ptr, q.get_context())) + { + case sycl::usm::alloc::unknown: + return pointer_access_attribute::host_only; + case sycl::usm::alloc::device: + return pointer_access_attribute::device_only; + case sycl::usm::alloc::shared: + case sycl::usm::alloc::host: + return pointer_access_attribute::host_device; + } + } + + template + inline constexpr std::uint64_t get_type_combination_id(ArgT Val) + { + static_assert((unsigned char)library_data_t::library_data_t_size <= + std::numeric_limits::max() && + "library_data_t size exceeds limit."); + static_assert(std::is_same_v, "Unsupported ArgT"); + return (std::uint64_t)Val; + } + + template + inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal, + RestT... RestVal) + { + static_assert((std::uint8_t)library_data_t::library_data_t_size <= + std::numeric_limits::max() && + "library_data_t size exceeds limit."); + static_assert(sizeof...(RestT) <= 8 && "Too many parameters"); + static_assert(std::is_same_v, "Unsupported FirstT"); + return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal); + } + + class mem_mgr + { + mem_mgr() + { + // Reserved address space, no real memory allocation happens here. +#if defined(__linux__) + mapped_address_space = + (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE, + MAP_PRIVATE | MAP_ANONYMOUS, -1, 0); +#elif defined(_WIN64) + mapped_address_space = (byte_t *)VirtualAlloc( + NULL, // NULL specified as the base address parameter + mapped_region_size, // Size of allocation + MEM_RESERVE, // Allocate reserved pages + PAGE_NOACCESS); // Protection = no access +#else +#error "Only support Windows and Linux." +#endif + next_free = mapped_address_space; + }; + + public: + using buffer_id_t = int; + + struct allocation + { + buffer_t buffer; + byte_t *alloc_ptr; + size_t size; + }; + + ~mem_mgr() + { +#if defined(__linux__) + munmap(mapped_address_space, mapped_region_size); +#elif defined(_WIN64) + VirtualFree(mapped_address_space, 0, MEM_RELEASE); +#else +#error "Only support Windows and Linux." +#endif + }; + + mem_mgr(const mem_mgr &) = delete; + mem_mgr &operator=(const mem_mgr &) = delete; + mem_mgr(mem_mgr &&) = delete; + mem_mgr &operator=(mem_mgr &&) = delete; + + /// Allocate + void *mem_alloc(size_t size) + { + if (!size) + return nullptr; + std::lock_guard lock(m_mutex); + if (next_free + size > mapped_address_space + mapped_region_size) + { + throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool"); + } + // Allocation + sycl::range<1> r(size); + buffer_t buf(r); + allocation A{buf, next_free, size}; + // Map allocation to device pointer + void *result = next_free; + m_map.emplace(next_free + size, A); + // Update pointer to the next free space. + next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1); + + return result; + } + + /// Deallocate + void mem_free(const void *ptr) + { + if (!ptr) + return; + std::lock_guard lock(m_mutex); + auto it = get_map_iterator(ptr); + m_map.erase(it); + } + + /// map: device pointer -> allocation(buffer, alloc_ptr, size) + allocation translate_ptr(const void *ptr) + { + std::lock_guard lock(m_mutex); + auto it = get_map_iterator(ptr); + return it->second; + } + + /// Check if the pointer represents device pointer or not. + bool is_device_ptr(const void *ptr) const + { + std::lock_guard lock(m_mutex); + return (mapped_address_space <= ptr) && + (ptr < mapped_address_space + mapped_region_size); + } + + /// Returns the instance of memory manager singleton. + static mem_mgr &instance() + { + static mem_mgr m; + return m; + } + + private: + std::map m_map; + mutable std::mutex m_mutex; + byte_t *mapped_address_space; + byte_t *next_free; + const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024; + const size_t alignment = 256; + /// This padding may be defined to some positive value to debug + /// out of bound accesses. + const size_t extra_padding = 0; + + std::map::iterator get_map_iterator(const void *ptr) + { + auto it = m_map.upper_bound((byte_t *)ptr); + if (it == m_map.end()) + { + // Not a virtual pointer. + throw std::runtime_error("can not get buffer from non-virtual pointer"); + } + const allocation &alloc = it->second; + if (ptr < alloc.alloc_ptr) + { + // Out of bound. + // This may happen if there's a gap between allocations due to alignment + // or extra padding and pointer points to this gap. + throw std::runtime_error("invalid virtual pointer"); + } + return it; + } + }; + + template + class accessor; + template + class memory_traits + { + public: + static constexpr sycl::access::target target = + sycl::access::target::device; + static constexpr sycl::access_mode mode = + (Memory == constant) ? sycl::access_mode::read + : sycl::access_mode::read_write; + static constexpr size_t type_size = sizeof(T); + using element_t = + typename std::conditional::type; + using value_t = typename std::remove_cv::type; + template + using accessor_t = typename std::conditional< + Memory == local, sycl::local_accessor, + sycl::accessor>::type; + using pointer_t = T *; + }; + + static inline void *dpct_malloc(size_t size, sycl::queue &q) + { + return sycl::malloc_device(size, q.get_device(), q.get_context()); + } + +#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F)) + static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z, + sycl::queue &q) + { + pitch = PITCH_DEFAULT_ALIGN(x); + return dpct_malloc(pitch * y * z, q); + } + + /** + * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] dev_ptr Pointer to the virtual device memory address. + * @param [in] value The value to be set. + * @param [in] size Number of elements to be set to the value. + * @return An event representing the memset operation. + */ + template + static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr, + valueT value, size_t size) + { + return q.fill(dev_ptr, value, size); + } + + /** + * @brief Sets \p value to the 3D memory region pointed by \p data in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] data Pointer to the pitched device memory region. + * @param [in] value The value to be set. + * @param [in] size 3D memory region by number of elements. + * @return An event list representing the memset operations. + */ + template + static inline std::vector + dpct_memset(sycl::queue &q, pitched_data data, valueT value, + sycl::range<3> size) + { + std::vector event_list; + size_t slice = data.get_pitch() * data.get_y(); + unsigned char *data_surface = (unsigned char *)data.get_data_ptr(); + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *data_ptr = data_surface; + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0))); + data_ptr += data.get_pitch(); + } + data_surface += slice; + } + return event_list; + } + + /** + * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] ptr Pointer to the virtual device memory. + * @param [in] pitch The pitch size by number of elements, including padding. + * @param [in] val The value to be set. + * @param [in] x The width of memory region by number of elements. + * @param [in] y The height of memory region by number of elements. + * @return An event list representing the memset operations. + */ + template + static inline std::vector + dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x, + size_t y) + { + return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val, + sycl::range<3>(x, y, 1)); + } + + static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr, + const void *from_ptr, + memcpy_direction dir) + { + switch (dir) + { + case memcpy_direction::host_to_host: + case memcpy_direction::host_to_device: + case memcpy_direction::device_to_host: + case memcpy_direction::device_to_device: + return dir; + case memcpy_direction::automatic: + { + // table[to_attribute][from_attribute] + static const memcpy_direction + direction_table[static_cast(pointer_access_attribute::end)] + [static_cast(pointer_access_attribute::end)] = + {{memcpy_direction::host_to_host, + memcpy_direction::device_to_host, + memcpy_direction::host_to_host}, + {memcpy_direction::host_to_device, + memcpy_direction::device_to_device, + memcpy_direction::device_to_device}, + {memcpy_direction::host_to_host, + memcpy_direction::device_to_device, + memcpy_direction::device_to_device}}; + return direction_table[static_cast(get_pointer_attribute( + q, to_ptr))][static_cast(get_pointer_attribute(q, from_ptr))]; + } + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + } + + static sycl::event + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction, + const std::vector &dep_events = {}) + { + if (!size) + return sycl::event{}; + return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); + } + + // Get actual copy range and make sure it will not exceed range. + static inline size_t get_copy_range(sycl::range<3> size, size_t slice, + size_t pitch) + { + return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); + } + + static inline size_t get_offset(sycl::id<3> id, size_t slice, + size_t pitch) + { + return slice * id.get(2) + pitch * id.get(1) + id.get(0); + } + + /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr + /// and \p from_range to another specified by \p to_ptr and \p to_range. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + sycl::range<3> to_range, sycl::range<3> from_range, + sycl::id<3> to_id, sycl::id<3> from_id, + sycl::range<3> size, memcpy_direction direction, + const std::vector &dep_events = {}) + { + // RAII for host pointer + class host_buffer + { + void *_buf; + size_t _size; + sycl::queue &_q; + const std::vector &_deps; // free operation depends + + public: + host_buffer(size_t size, sycl::queue &q, + const std::vector &deps) + : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} + void *get_ptr() const { return _buf; } + size_t get_size() const { return _size; } + ~host_buffer() + { + if (_buf) + { + _q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(_deps); + cgh.host_task([buf = _buf] { std::free(buf); }); }); + } + } + }; + std::vector event_list; + + size_t to_slice = to_range.get(1) * to_range.get(0), + from_slice = from_range.get(1) * from_range.get(0); + unsigned char *to_surface = + (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); + const unsigned char *from_surface = + (const unsigned char *)from_ptr + + get_offset(from_id, from_slice, from_range.get(0)); + + if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) + { + return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), + direction, dep_events)}; + } + direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + size_t size_slice = size.get(1) * size.get(0); + switch (direction) + { + case host_to_host: + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *to_ptr = to_surface; + const unsigned char *from_ptr = from_surface; + if (to_range.get(0) == from_range.get(0) && + to_range.get(0) == size.get(0)) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, + direction, dep_events)); + } + else + { + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), + direction, dep_events)); + to_ptr += to_range.get(0); + from_ptr += from_range.get(0); + } + } + to_surface += to_slice; + from_surface += from_slice; + } + break; + case host_to_device: + { + host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, + event_list); + std::vector host_events; + if (to_slice == size_slice) + { + // Copy host data to a temp host buffer with the shape of target. + host_events = + dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, + host_to_host, dep_events); + } + else + { + // Copy host data to a temp host buffer with the shape of target. + host_events = dpct_memcpy( + q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, + // If has padding data, not sure whether it is useless. So fill temp + // buffer with it. + std::vector{ + dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), + device_to_host, dep_events)}); + } + // Copy from temp host buffer to device with only one submit. + event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), + buf.get_size(), host_to_device, + host_events)); + break; + } + case device_to_host: + { + host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, + event_list); + // Copy from host temp buffer to host target with reshaping. + event_list = dpct_memcpy( + q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), + sycl::id<3>(0, 0, 0), size, host_to_host, + // Copy from device to temp host buffer with only one submit. + std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, + buf.get_size(), + device_to_host, dep_events)}); + break; + } + case device_to_device: + event_list.push_back(q.submit([&](sycl::handler &cgh){ + cgh.depends_on(dep_events); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_surface[get_offset(id, to_slice, to_range.get(0))] = + from_surface[get_offset(id, from_slice, from_range.get(0))]; + }); })); + break; + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + return event_list; + } + + /// memcpy 2D/3D matrix specified by pitched_data. + static inline std::vector + dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, + pitched_data from, sycl::id<3> from_id, sycl::range<3> size, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), + sycl::range<3>(to.get_pitch(), to.get_y(), 1), + sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, + size, direction); + } + + /// memcpy 2D matrix with pitch. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + size_t to_pitch, size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), + sycl::range<3>(from_pitch, y, 1), + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), + sycl::range<3>(x, y, 1), direction); + } + + namespace deprecated + { + + template + class usm_allocator + { + private: + using Alloc = sycl::usm_allocator; + Alloc _impl; + + public: + using value_type = typename std::allocator_traits::value_type; + using pointer = typename std::allocator_traits::pointer; + using const_pointer = typename std::allocator_traits::const_pointer; + using void_pointer = typename std::allocator_traits::void_pointer; + using const_void_pointer = + typename std::allocator_traits::const_void_pointer; + using reference = typename std::allocator_traits::value_type &; + using const_reference = + const typename std::allocator_traits::value_type &; + using difference_type = + typename std::allocator_traits::difference_type; + using size_type = typename std::allocator_traits::size_type; + using propagate_on_container_copy_assignment = typename std::allocator_traits< + Alloc>::propagate_on_container_copy_assignment; + using propagate_on_container_move_assignment = typename std::allocator_traits< + Alloc>::propagate_on_container_move_assignment; + using propagate_on_container_swap = + typename std::allocator_traits::propagate_on_container_swap; + using is_always_equal = + typename std::allocator_traits::is_always_equal; + + template + struct rebind + { + typedef usm_allocator other; + }; + + usm_allocator() : _impl(dpct::get_default_queue()) {} + ~usm_allocator() {} + usm_allocator(const usm_allocator &other) : _impl(other._impl) {} + usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {} + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + pointer allocate(size_type cnt, const_void_pointer hint = nullptr) + { + return std::allocator_traits::allocate(_impl, cnt, hint); + } + void deallocate(pointer p, size_type cnt) + { + std::allocator_traits::deallocate(_impl, p, cnt); + } + size_type max_size() const + { + return std::allocator_traits::max_size(_impl); + } + bool operator==(const usm_allocator &other) const { return _impl == other._impl; } + bool operator!=(const usm_allocator &other) const { return _impl != other._impl; } + }; + + } // namespace deprecated + + inline void dpct_free(void *ptr, + const sycl::queue &q) + { + if (ptr) + { + sycl::free(ptr, q.get_context()); + } + } + + template + inline auto get_memory(const void *x) + { + T *new_x = reinterpret_cast(const_cast(x)); + return new_x; + } + + template + inline typename DataType::T2 get_value(const T *s, sycl::queue &q) + { + using Ty = typename DataType::T2; + Ty s_h; + if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) + detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host) + .wait(); + else + s_h = *reinterpret_cast(s); + return s_h; + } + + } // namespace detail + + template + inline auto get_value(const T *s, sycl::queue &q) + { + return detail::get_value(s, q); + } + + namespace detail + { + template + inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, int lda, const void *b, + int ldb, const void *beta, void *c, int ldc) + { + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + auto data_a = get_memory(a); + auto data_b = get_memory(b); + auto data_c = get_memory(c); + oneapi::mkl::blas::column_major::gemm( + q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, + data_b, ldb, beta_value, data_c, ldc); + } + + template + class vectorized_binary + { + public: + inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) + { + VecT v4; + for (size_t i = 0; i < v4.size(); ++i) + { + v4[i] = binary_op(a[i], b[i]); + } + return v4; + } + }; + + template + class vectorized_binary< + VecT, BinaryOperation, + std::void_t>> + { + public: + inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) + { + return binary_op(a, b).template as(); + } + }; + + template + inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void **a, int lda, + const void **b, int ldb, const void *beta, void **c, + int ldc, int batch_size) + { + struct matrix_info_t + { + oneapi::mkl::transpose transpose_info[2]; + Ts value_info[2]; + std::int64_t size_info[3]; + std::int64_t ld_info[3]; + std::int64_t groupsize_info; + }; + + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + + matrix_info_t *matrix_info = + (matrix_info_t *)std::malloc(sizeof(matrix_info_t)); + matrix_info->transpose_info[0] = a_trans; + matrix_info->transpose_info[1] = b_trans; + matrix_info->value_info[0] = alpha_value; + matrix_info->value_info[1] = beta_value; + matrix_info->size_info[0] = m; + matrix_info->size_info[1] = n; + matrix_info->size_info[2] = k; + matrix_info->ld_info[0] = lda; + matrix_info->ld_info[1] = ldb; + matrix_info->ld_info[2] = ldc; + matrix_info->groupsize_info = batch_size; + + sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( + q, matrix_info->transpose_info, matrix_info->transpose_info + 1, + matrix_info->size_info, matrix_info->size_info + 1, + matrix_info->size_info + 2, matrix_info->value_info, + reinterpret_cast(a), matrix_info->ld_info, + reinterpret_cast(b), matrix_info->ld_info + 1, + matrix_info->value_info + 1, reinterpret_cast(c), + matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info)); + + q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(e); + cgh.host_task([=] { std::free(matrix_info); }); }); + } + + template + inline void + gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, + int k, const void *alpha, const void *a, int lda, + long long int stride_a, const void *b, int ldb, + long long int stride_b, const void *beta, void *c, + int ldc, long long int stride_c, int batch_size) + { + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + auto data_a = get_memory(a); + auto data_b = get_memory(b); + auto data_c = get_memory(c); + oneapi::mkl::blas::column_major::gemm_batch( + q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, + stride_a, data_b, ldb, stride_b, beta_value, + data_c, ldc, stride_c, batch_size); + } + + } // namespace detail + + template + inline unsigned vectorized_binary(unsigned a, unsigned b, + const BinaryOperation binary_op) + { + sycl::vec v0{a}, v1{b}; + auto v2 = v0.as(); + auto v3 = v1.as(); + auto v4 = + detail::vectorized_binary()(v2, v3, binary_op); + v0 = v4.template as>(); + return v0; + } + + static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction = automatic, + sycl::queue &q = dpct::get_default_queue()) + { + detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction); + } + + static inline unsigned int select_device(unsigned int id) + { + dev_mgr::instance().select_device(id); + return id; + } + + template + T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, + unsigned int logical_sub_group_size = 32) + { + unsigned int id = g.get_local_linear_id(); + unsigned int start_index = + id / logical_sub_group_size * logical_sub_group_size; + unsigned int target_offset = (id % logical_sub_group_size) ^ mask; + return sycl::select_from_group(g, x, + target_offset < logical_sub_group_size + ? start_index + target_offset + : id); + } + + template + sycl::vec extract_and_sign_or_zero_extend4(T val) + { + return sycl::vec(val) + .template as, int8_t, uint8_t>, 4>>() + .template convert(); + } + + template + using dot_product_acc_t = + std::conditional_t && std::is_unsigned_v, + uint32_t, int32_t>; + + template + inline auto dp4a(T1 a, T2 b, T3 c) + { + dot_product_acc_t res = c; + auto va = extract_and_sign_or_zero_extend4(a); + auto vb = extract_and_sign_or_zero_extend4(b); + res += va[0] * vb[0]; + res += va[1] * vb[1]; + res += va[2] * vb[2]; + res += va[3] * vb[3]; + return res; + } + + struct sub_sat + { + template + auto operator()(const T x, const T y) const + { + return sycl::sub_sat(x, y); + } + }; + + template + inline T vectorized_min(T a, T b) + { + sycl::vec v0{a}, v1{b}; + auto v2 = v0.template as(); + auto v3 = v1.template as(); + auto v4 = sycl::min(v2, v3); + v0 = v4.template as>(); + return v0; + } + + inline float pow(const float a, const int b) { return sycl::pown(a, b); } + inline double pow(const double a, const int b) { return sycl::pown(a, b); } + inline float pow(const float a, const float b) { return sycl::pow(a, b); } + inline double pow(const double a, const double b) { return sycl::pow(a, b); } + template + inline typename std::enable_if_t, T> + pow(const T a, const U b) + { + return sycl::pow(a, static_cast(b)); + } + template + inline typename std::enable_if_t, double> + pow(const T a, const U b) + { + return sycl::pow(static_cast(a), static_cast(b)); + } + + inline double min(const double a, const float b) + { + return sycl::fmin(a, static_cast(b)); + } + inline double min(const float a, const double b) + { + return sycl::fmin(static_cast(a), b); + } + inline float min(const float a, const float b) { return sycl::fmin(a, b); } + inline double min(const double a, const double b) { return sycl::fmin(a, b); } + inline std::uint32_t min(const std::uint32_t a, const std::int32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint32_t min(const std::int32_t a, const std::uint32_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::int32_t min(const std::int32_t a, const std::int32_t b) + { + return sycl::min(a, b); + } + inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::int64_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::int64_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::int64_t min(const std::int64_t a, const std::int64_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::int32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::int32_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + // max function overloads. + // For floating-point types, `float` or `double` arguments are acceptable. + // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or + // `std::int64_t` type arguments are acceptable. + inline double max(const double a, const float b) + { + return sycl::fmax(a, static_cast(b)); + } + inline double max(const float a, const double b) + { + return sycl::fmax(static_cast(a), b); + } + inline float max(const float a, const float b) { return sycl::fmax(a, b); } + inline double max(const double a, const double b) { return sycl::fmax(a, b); } + inline std::uint32_t max(const std::uint32_t a, const std::int32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint32_t max(const std::int32_t a, const std::uint32_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::int32_t max(const std::int32_t a, const std::int32_t b) + { + return sycl::max(a, b); + } + inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::int64_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::int64_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::int64_t max(const std::int64_t a, const std::int64_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::int32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::int32_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + + inline void + has_capability_or_fail(const sycl::device &dev, + const std::initializer_list &props) + { + for (const auto &it : props) + { + if (dev.has(it)) + continue; + switch (it) + { + case sycl::aspect::fp64: + throw std::runtime_error("'double' is not supported in '" + + dev.get_info() + + "' device"); + break; + case sycl::aspect::fp16: + throw std::runtime_error("'half' is not supported in '" + + dev.get_info() + + "' device"); + break; + default: +#define __SYCL_ASPECT(ASPECT, ID) \ + case sycl::aspect::ASPECT: \ + return #ASPECT; +#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID) +#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE) + auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string + { + switch (AspectNum) + { +#include +#include + default: + return "unknown aspect"; + } + }; +#undef __SYCL_ASPECT_DEPRECATED_ALIAS +#undef __SYCL_ASPECT_DEPRECATED +#undef __SYCL_ASPECT + throw std::runtime_error( + "'" + getAspectNameStr(it) + "' is not supported in '" + + dev.get_info() + "' device"); + } + break; + } + } + + static inline unsigned int get_current_device_id() + { + return dev_mgr::instance().current_device_id(); + } + + static inline device_ext &get_current_device() + { + return dev_mgr::instance().current_device(); + } + + static inline sycl::queue &get_in_order_queue() + { + return dev_mgr::instance().current_device().in_order_queue(); + } + + static sycl::event + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction, + const std::vector &dep_events = {}) + { + if (!size) + return sycl::event{}; + return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); + } + + // Get actual copy range and make sure it will not exceed range. + static inline size_t get_copy_range(sycl::range<3> size, size_t slice, + size_t pitch) + { + return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); + } + + static inline size_t get_offset(sycl::id<3> id, size_t slice, + size_t pitch) + { + return slice * id.get(2) + pitch * id.get(1) + id.get(0); + } + + /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr + /// and \p from_range to another specified by \p to_ptr and \p to_range. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + sycl::range<3> to_range, sycl::range<3> from_range, + sycl::id<3> to_id, sycl::id<3> from_id, + sycl::range<3> size, memcpy_direction direction, + const std::vector &dep_events = {}) + { + // RAII for host pointer + class host_buffer + { + void *_buf; + size_t _size; + sycl::queue &_q; + const std::vector &_deps; // free operation depends + + public: + host_buffer(size_t size, sycl::queue &q, + const std::vector &deps) + : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} + void *get_ptr() const { return _buf; } + size_t get_size() const { return _size; } + ~host_buffer() + { + if (_buf) + { + _q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(_deps); + cgh.host_task([buf = _buf] { std::free(buf); }); }); + } + } + }; + std::vector event_list; + + size_t to_slice = to_range.get(1) * to_range.get(0), + from_slice = from_range.get(1) * from_range.get(0); + unsigned char *to_surface = + (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); + const unsigned char *from_surface = + (const unsigned char *)from_ptr + + get_offset(from_id, from_slice, from_range.get(0)); + + if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) + { + return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), + direction, dep_events)}; + } + direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + size_t size_slice = size.get(1) * size.get(0); + switch (direction) + { + case host_to_host: + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *to_ptr = to_surface; + const unsigned char *from_ptr = from_surface; + if (to_range.get(0) == from_range.get(0) && + to_range.get(0) == size.get(0)) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, + direction, dep_events)); + } + else + { + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), + direction, dep_events)); + to_ptr += to_range.get(0); + from_ptr += from_range.get(0); + } + } + to_surface += to_slice; + from_surface += from_slice; + } + break; + case host_to_device: + { + host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, + event_list); + std::vector host_events; + if (to_slice == size_slice) + { + // Copy host data to a temp host buffer with the shape of target. + host_events = + dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, + host_to_host, dep_events); + } + else + { + // Copy host data to a temp host buffer with the shape of target. + host_events = dpct_memcpy( + q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, + // If has padding data, not sure whether it is useless. So fill temp + // buffer with it. + std::vector{ + dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), + device_to_host, dep_events)}); + } + // Copy from temp host buffer to device with only one submit. + event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), + buf.get_size(), host_to_device, + host_events)); + break; + } + case device_to_host: + { + host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, + event_list); + // Copy from host temp buffer to host target with reshaping. + event_list = dpct_memcpy( + q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), + sycl::id<3>(0, 0, 0), size, host_to_host, + // Copy from device to temp host buffer with only one submit. + std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, + buf.get_size(), + device_to_host, dep_events)}); + break; + } + case device_to_device: + event_list.push_back(q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_surface[get_offset(id, to_slice, to_range.get(0))] = + from_surface[get_offset(id, from_slice, from_range.get(0))]; + }); })); + break; + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + return event_list; + } + + /// memcpy 2D/3D matrix specified by pitched_data. + static inline std::vector + dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, + pitched_data from, sycl::id<3> from_id, sycl::range<3> size, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), + sycl::range<3>(to.get_pitch(), to.get_y(), 1), + sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, + size, direction); + } + + /// memcpy 2D matrix with pitch. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + size_t to_pitch, size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), + sycl::range<3>(from_pitch, y, 1), + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), + sycl::range<3>(x, y, 1), direction); + } + + inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, library_data_t a_type, + int lda, const void *b, library_data_t b_type, int ldb, + const void *beta, void *c, library_data_t c_type, int ldc, + library_data_t scaling_type) + { + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, + lda, b, ldb, beta, c, ldc); + break; + } +#ifdef __INTEL_MKL__ + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, b, + ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_impl(q, a_trans, b_trans, m, n, k, &alpha_half, + a, lda, b, ldb, &beta_half, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + float alpha_float = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_float = + dpct::get_value(reinterpret_cast(beta), q); + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc); + break; + } +#endif // __INTEL_MKL__ + default: + throw std::runtime_error("the combination of data type is unsupported"); + } + } // gemm() + + /// Computes a batch of matrix-matrix product with general matrices. + /// \param [in] q The queue where the routine should be executed. + /// \param [in] a_trans Specifies the operation applied to A. + /// \param [in] b_trans Specifies the operation applied to B. + /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. + /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. + /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). + /// \param [in] alpha Scaling factor for the matrix-matrix product. + /// \param [in] a Input matrix A. + /// \param [in] a_type Data type of the matrix A. + /// \param [in] lda Leading dimension of A. + /// \param [in] b Input matrix B. + /// \param [in] b_type Data type of the matrix B. + /// \param [in] ldb Leading dimension of B. + /// \param [in] beta Scaling factor for matrix C. + /// \param [in, out] c Input/Output matrix C. + /// \param [in] c_type Data type of the matrix C. + /// \param [in] ldc Leading dimension of C. + /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. + /// \param [in] scaling_type Data type of the scaling factors. + inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a[], + library_data_t a_type, int lda, const void *b[], + library_data_t b_type, int ldb, const void *beta, + void *c[], library_data_t c_type, int ldc, + int batch_size, library_data_t scaling_type) + { + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } +#ifdef __INTEL_MKL__ + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, + b, ldb, beta, c, ldc, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + float alpha_float = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_float = + dpct::get_value(reinterpret_cast(beta), q); + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, &alpha_float, + a, lda, b, ldb, &beta_float, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } +#endif + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc, + batch_size); + break; + } + default: + throw std::runtime_error("the combination of data type is unsupported"); + } + } + + /// Computes a batch of matrix-matrix product with general matrices. + /// \param [in] q The queue where the routine should be executed. + /// \param [in] a_trans Specifies the operation applied to A. + /// \param [in] b_trans Specifies the operation applied to B. + /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. + /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. + /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). + /// \param [in] alpha Scaling factor for the matrix-matrix product. + /// \param [in] a Input matrix A. + /// \param [in] a_type Data type of the matrix A. + /// \param [in] lda Leading dimension of A. + /// \param [in] stride_a Stride between the different A matrices. + /// \param [in] b Input matrix B. + /// \param [in] b_type Data type of the matrix B. + /// \param [in] ldb Leading dimension of B. + /// \param [in] stride_b Stride between the different B matrices. + /// \param [in] beta Scaling factor for matrix C. + /// \param [in, out] c Input/Output matrix C. + /// \param [in] c_type Data type of the matrix C. + /// \param [in] ldc Leading dimension of C. + /// \param [in] stride_c Stride between the different C matrices. + /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. + /// \param [in] scaling_type Data type of the scaling factors. + inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, library_data_t a_type, + int lda, long long int stride_a, const void *b, + library_data_t b_type, int ldb, long long int stride_b, + const void *beta, void *c, library_data_t c_type, + int ldc, long long int stride_c, int batch_size, + library_data_t scaling_type) + { + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } +#ifdef __INTEL_MKL__ + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, + stride_a, b, ldb, stride_b, beta, c, ldc, + stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } +#endif + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b, + &beta_half, c, ldc, stride_c, batch_size); + break; + } + default: + throw std::runtime_error("the combination of data type is unsupported"); + } + } + + static inline void + async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr, + size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic, + sycl::queue &q = get_default_queue()) + { + detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y, + direction); + } + + using err0 = detail::generic_error_type; + using err1 = detail::generic_error_type; + + static inline void dpct_free(void *ptr, sycl::queue &q = get_default_queue()) { + detail::dpct_free(ptr, q); + } + + /// dpct accessor used as device function parameter. + template class accessor; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<3>; + accessor(pointer_t data, const sycl::range<3> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<3> &in_range) + : accessor(acc.get_pointer(), in_range) {} + accessor operator[](size_t index) const { + sycl::range<2> sub(_range.get(1), _range.get(2)); + return accessor(_data + index * sub.size(), sub); + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<3> _range; + }; + template class accessor { + public: + using memory_t = detail::memory_traits; + using element_t = typename memory_t::element_t; + using pointer_t = typename memory_t::pointer_t; + using accessor_t = typename memory_t::template accessor_t<2>; + accessor(pointer_t data, const sycl::range<2> &in_range) + : _data(data), _range(in_range) {} + template + accessor(typename std::enable_if::type &acc) + : accessor(acc, acc.get_range()) {} + accessor(const accessor_t &acc, const sycl::range<2> &in_range) + : accessor(acc.get_pointer(), in_range) {} + + pointer_t operator[](size_t index) const { + return _data + _range.get(1) * index; + } + + pointer_t get_ptr() const { return _data; } + + private: + pointer_t _data; + sycl::range<2> _range; + }; + + namespace detail { + /// Device variable with address space of shared, global or constant. + template class device_memory { + public: + using accessor_t = + typename detail::memory_traits::template accessor_t; + using value_t = typename detail::memory_traits::value_t; + using dpct_accessor_t = dpct::accessor; + + device_memory() : device_memory(sycl::range(1)) {} + + /// Constructor of 1-D array with initializer list + device_memory(const sycl::range &in_range, + std::initializer_list &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range.size()); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + std::memcpy(_host_ptr, init_list.begin(), init_list.size() * sizeof(T)); + } + + /// Constructor of 2-D array with initializer list + template + device_memory( + const typename std::enable_if>::type &in_range, + std::initializer_list> &&init_list) + : device_memory(in_range) { + assert(init_list.size() <= in_range[0]); + _host_ptr = (value_t *)std::malloc(_size); + std::memset(_host_ptr, 0, _size); + auto tmp_data = _host_ptr; + for (auto sub_list : init_list) { + assert(sub_list.size() <= in_range[1]); + std::memcpy(tmp_data, sub_list.begin(), + sub_list.size() * sizeof(T)); + tmp_data += in_range[1]; + } + } + + /// Constructor with range + device_memory(const sycl::range &range_in) + : _size(range_in.size() * sizeof(T)), _range(range_in), + _reference(false), _host_ptr(nullptr), _device_ptr(nullptr) { + static_assert( + (Memory == global) || (Memory == constant) || (Memory == shared), + "device memory region should be global, constant or shared"); + // Make sure that singleton class mem_mgr and dev_mgr will destruct + // later than this. + detail::mem_mgr::instance(); + dev_mgr::instance(); + } + + /// Constructor with range + template + device_memory(Args... Arguments) + : device_memory(sycl::range(Arguments...)) {} + + ~device_memory() { + if (_device_ptr && !_reference) + dpct::dpct_free(_device_ptr); + if (_host_ptr) + std::free(_host_ptr); + } + + /// Allocate memory with default queue, and init memory if has initial + /// value. + void init() { init(dpct::get_default_queue()); } + /// Allocate memory with specified queue, and init memory if has initial + /// value. + void init(sycl::queue &q) { + if (_device_ptr) + return; + if (!_size) + return; + allocate_device(q); + if (_host_ptr) + detail::dpct_memcpy(q, _device_ptr, _host_ptr, _size, + host_to_device); + } + + /// The variable is assigned to a device pointer. + void assign(value_t *src, size_t size) { + this->~device_memory(); + new (this) device_memory(src, size); + } + + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr() { return get_ptr(get_default_queue()); } + /// Get memory pointer of the memory object, which is virtual pointer when + /// usm is not used, and device pointer when usm is used. + value_t *get_ptr(sycl::queue &q) { + init(q); + return _device_ptr; + } + + /// Get the device memory object size in bytes. + size_t get_size() { return _size; } + + template + typename std::enable_if::type &operator[](size_t index) { + init(); + return _device_ptr[index]; + } + + /// Get dpct::accessor with dimension info for the device memory object + /// when usm is used and dimension is greater than 1. + template + typename std::enable_if::type + get_access([[maybe_unused]] sycl::handler &cgh) { + return dpct_accessor_t((T *)_device_ptr, _range); + } + + private: + device_memory(value_t *memory_ptr, size_t size) + : _size(size), _range(size / sizeof(T)), _reference(true), + _device_ptr(memory_ptr) {} + + void allocate_device(sycl::queue &q) { + #ifndef DPCT_USM_LEVEL_NONE + if (Memory == shared) { + _device_ptr = (value_t *)sycl::malloc_shared(_size, q.get_device(), + q.get_context()); + return; + } + #ifdef SYCL_EXT_ONEAPI_USM_DEVICE_READ_ONLY + if (Memory == constant) { + _device_ptr = (value_t *)sycl::malloc_device( + _size, q.get_device(), q.get_context(), + sycl::ext::oneapi::property::usm::device_read_only()); + return; + } + #endif + #endif + _device_ptr = (value_t *)detail::dpct_malloc(_size, q); + } + + size_t _size; + sycl::range _range; + bool _reference; + value_t *_host_ptr; + value_t *_device_ptr; + }; + template + class device_memory : public device_memory { + public: + using base = device_memory; + using value_t = typename base::value_t; + using accessor_t = + typename detail::memory_traits::template accessor_t<0>; + + /// Constructor with initial value. + device_memory(const value_t &val) : base(sycl::range<1>(1), {val}) {} + + /// Default constructor + device_memory() : base(1) {} + }; + } // namespace detail + + template + using global_memory = detail::device_memory; + template + using constant_memory = detail::device_memory; + template + using shared_memory = detail::device_memory; + + + template + inline T atomic_fetch_add(T *addr, T operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T atomic_fetch_add(T *addr, T operand, + sycl::memory_order memoryOrder) { + switch (memoryOrder) { + case sycl::memory_order::relaxed: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::acq_rel: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::seq_cst: + return atomic_fetch_add(addr, operand); + default: + assert(false && "Invalid memory_order for atomics. Valid memory_order for " + "atomics are: sycl::memory_order::relaxed, " + "sycl::memory_order::acq_rel, sycl::memory_order::seq_cst!"); + } + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand, + sycl::memory_order memoryOrder) { + atomic_fetch_add(addr, operand, memoryOrder); + } + +} // COPY from DPCT head files + +#endif // GGML_SYCL_DPCT_HELPER_HPP diff --git a/ggml-sycl/presets.hpp b/ggml-sycl/presets.hpp new file mode 100644 index 0000000000000..dcf0261102e91 --- /dev/null +++ b/ggml-sycl/presets.hpp @@ -0,0 +1,69 @@ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// + +#ifndef GGML_SYCL_PRESETS_HPP +#define GGML_SYCL_PRESETS_HPP + +#define GGML_SYCL_MAX_STREAMS 8 +#define GGML_SYCL_MAX_BUFFERS 256 +#define GGML_SYCL_MAX_DEVICES 48 +#define GGML_SYCL_NAME "SYCL" + +// FIXME: 1024 from cuda +#define GROUP_SIZE 1024 +#define WARP_SIZE 32 +#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses + +#define SYCL_GELU_BLOCK_SIZE 256 +#define SYCL_SILU_BLOCK_SIZE 256 +#define SYCL_TANH_BLOCK_SIZE 256 +#define SYCL_RELU_BLOCK_SIZE 256 +#define SYCL_HARDSIGMOID_BLOCK_SIZE 256 +#define SYCL_HARDSWISH_BLOCK_SIZE 256 +#define SYCL_SQR_BLOCK_SIZE 256 +#define SYCL_CPY_BLOCK_SIZE 32 +#define SYCL_SCALE_BLOCK_SIZE 256 +#define SYCL_CLAMP_BLOCK_SIZE 256 +#define SYCL_ROPE_BLOCK_SIZE 256 +#define SYCL_ALIBI_BLOCK_SIZE 32 +#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 +#define SYCL_QUANTIZE_BLOCK_SIZE 256 +#define SYCL_DEQUANTIZE_BLOCK_SIZE 256 +#define SYCL_GET_ROWS_BLOCK_SIZE 256 +#define SYCL_UPSCALE_BLOCK_SIZE 256 +#define SYCL_CONCAT_BLOCK_SIZE 256 +#define SYCL_PAD_BLOCK_SIZE 256 +#define SYCL_ACC_BLOCK_SIZE 256 +#define SYCL_IM2COL_BLOCK_SIZE 256 +#define SYCL_POOL2D_BLOCK_SIZE 256 + +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_SYCL_DMMV_X +#define GGML_SYCL_DMMV_X 32 +#endif +#ifndef GGML_SYCL_MMV_Y +#define GGML_SYCL_MMV_Y 1 +#endif + +#ifndef K_QUANTS_PER_ITERATION +#define K_QUANTS_PER_ITERATION 2 +#else +static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); +#endif + +#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE +#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128 +#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE + +#define MUL_MAT_SRC1_COL_STRIDE 128 + +#endif // GGML_SYCL_PRESETS_HPP diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e8cb5f52cdb30..e97a8c6b7ae6c 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -2103,7 +2103,7 @@ unsigned char dequant_f32_data[] = { }; const uint64_t dequant_f32_len = 3200; -unsigned char dequant_q2_K_data[] = { +unsigned char dequant_q2_k_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x10,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, @@ -2442,9 +2442,9 @@ unsigned char dequant_q2_K_data[] = { 0x01,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q2_K_len = 4032; +const uint64_t dequant_q2_k_len = 4032; -unsigned char dequant_q3_K_data[] = { +unsigned char dequant_q3_k_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, @@ -2847,7 +2847,7 @@ unsigned char dequant_q3_K_data[] = { 0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q3_K_len = 4804; +const uint64_t dequant_q3_k_len = 4804; unsigned char dequant_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -3728,7 +3728,7 @@ unsigned char dequant_q4_1_data[] = { }; const uint64_t dequant_q4_1_len = 5248; -unsigned char dequant_q4_K_data[] = { +unsigned char dequant_q4_k_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xae,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, @@ -4224,7 +4224,7 @@ unsigned char dequant_q4_K_data[] = { 0x34,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q4_K_len = 5916; +const uint64_t dequant_q4_k_len = 5916; unsigned char dequant_q5_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -5323,7 +5323,7 @@ unsigned char dequant_q5_1_data[] = { }; const uint64_t dequant_q5_1_len = 6412; -unsigned char dequant_q5_K_data[] = { +unsigned char dequant_q5_k_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x9e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, @@ -5824,9 +5824,9 @@ unsigned char dequant_q5_K_data[] = { 0xf8,0x00,0x02,0x00,0x88,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q5_K_len = 5980; +const uint64_t dequant_q5_k_len = 5980; -unsigned char dequant_q6_K_data[] = { +unsigned char dequant_q6_k_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x0d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, @@ -6185,7 +6185,7 @@ unsigned char dequant_q6_K_data[] = { 0x03,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q6_K_len = 4272; +const uint64_t dequant_q6_k_len = 4272; unsigned char dequant_q8_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -6720,6 +6720,367 @@ unsigned char diag_mask_inf_f32_data[] = { }; const uint64_t diag_mask_inf_f32_len = 1504; +unsigned char div_f32_data[] = 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+0x5e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x34,0x01,0x00,0x00, +0x47,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00, +0x5d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x62,0x01,0x00,0x00, +0xf8,0x00,0x02,0x00,0x62,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, +}; +const uint64_t div_f32_len = 4276; + unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -11260,12 +11621,12 @@ unsigned char matmul_f16_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -11299,12 +11660,12 @@ unsigned char matmul_f16_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -11419,7 +11780,7 @@ unsigned char matmul_f16_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xfc,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x30,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x30,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x38,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x7e,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,0x7e,0x02,0x00,0x00, @@ -12130,12 +12491,12 @@ unsigned char matmul_f16_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -12170,13 +12531,13 @@ unsigned char matmul_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -12294,7 +12655,7 @@ unsigned char matmul_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x68,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xae,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xaf,0x02,0x00,0x00, @@ -13061,12 +13422,12 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -13102,12 +13463,12 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -13218,7 +13579,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x30,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x38,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x7e,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x7f,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -13924,12 +14285,12 @@ unsigned char matmul_f16_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -13963,12 +14324,12 @@ unsigned char matmul_f16_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -14084,7 +14445,7 @@ unsigned char matmul_f16_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x80,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x81,0x02,0x00,0x00, @@ -14796,12 +15157,12 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -14836,13 +15197,13 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -14964,7 +15325,7 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x73,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb9,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xba,0x02,0x00,0x00, @@ -15740,12 +16101,12 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -15781,12 +16142,12 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -15899,7 +16260,7 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfc,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x36,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x7c,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x7d,0x02,0x00,0x00, @@ -16598,12 +16959,12 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -16637,13 +16998,13 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -16756,7 +17117,7 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xfc,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x36,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x7c,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x7d,0x02,0x00,0x00,0x7c,0x02,0x00,0x00, @@ -17458,12 +17819,12 @@ unsigned char matmul_f16_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -17497,13 +17858,13 @@ unsigned char matmul_f16_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -17615,7 +17976,7 @@ unsigned char matmul_f16_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfc,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x36,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x7c,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x7d,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -18217,235 +18578,237 @@ unsigned char matmul_f16_fp32_data[] = { }; const uint64_t matmul_f16_fp32_len = 10276; -unsigned char matmul_f32_data[] = { +unsigned char matmul_f32_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00, -0x52,0x01,0x00,0x00,0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 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0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, @@ -18478,7 +18841,7 @@ unsigned char matmul_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x80,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x81,0x02,0x00,0x00, @@ -18728,3120 +19091,47838 @@ unsigned char matmul_f32_data[] = { 0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0xc0,0x02,0x00,0x00, 0x59,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x5c,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x79,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, +0x41,0x00,0x06,0x00,0x5d,0x01,0x00,0x00,0x5e,0x01,0x00,0x00, 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matmul_id_f32_f16_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x31,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0x4f,0x01,0x00,0x00, +0x5a,0x01,0x00,0x00,0x86,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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+0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t matmul_id_f32_f32_fp32_len = 10680; + +unsigned char matmul_id_q2_k_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x75,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x29,0x01,0x00,0x00, +0x63,0x01,0x00,0x00,0x94,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_id_q2_k_f32_fp32_len = 11572; + +unsigned char matmul_id_q3_k_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xff,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00, +0x90,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0xb5,0x01,0x00,0x00, +0x14,0x02,0x00,0x00,0x1f,0x02,0x00,0x00,0x4c,0x03,0x00,0x00, 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matmul_id_q3_k_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x28,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x10,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, +0x91,0x00,0x00,0x00,0x44,0x01,0x00,0x00,0xb5,0x01,0x00,0x00, +0x30,0x02,0x00,0x00,0x38,0x02,0x00,0x00,0x7a,0x03,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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+0x6f,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t matmul_id_q3_k_f32_fp32_len = 13476; + +unsigned char matmul_id_q4_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x42,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x11,0x01,0x00,0x00, +0x32,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6e,0x01,0x00,0x00, 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10124; +const uint64_t matmul_id_q4_k_f32_aligned_len = 13336; -unsigned char matmul_f32_f16_data[] = { +unsigned char matmul_id_q4_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, -0x06,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x52,0x01,0x00,0x00, -0x3a,0x02,0x00,0x00,0x83,0x02,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+81200,12 @@ unsigned char matmul_q2_k_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -25420,20 +81232,20 @@ unsigned char matmul_q2_k_f32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -25514,7 +81326,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00, @@ -25545,7 +81357,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xb5,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb9,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xd4,0x01,0x00,0x00,0x84,0x00,0x00,0x00, @@ -25567,7 +81379,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x44,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x78,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x78,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xc6,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00, @@ -25676,7 +81488,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -25706,517 +81518,516 @@ unsigned char matmul_q2_k_f32_data[] = { 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-0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x02,0x00,0x00, -0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00,0xf4,0x02,0x00,0x00, -0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x02,0x00,0x00, -0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, -0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00, -0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x02,0x00,0x00, -0xf3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x88,0x02,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +0xb0,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xe8,0x02,0x00,0x00,0xf6,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa6,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa8,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x02,0x00,0x00, +0xf4,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xec,0x02,0x00,0x00,0xf3,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x88,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + }; -const uint64_t matmul_q2_k_f32_len = 11240; +const uint64_t matmul_q2_k_f32_len = 11220; unsigned char matmul_q2_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -26332,12 +82143,12 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -26373,12 +82184,12 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -26516,7 +82327,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x99,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xa1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe7,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe8,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -27316,13 +83127,13 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -27357,12 +83168,12 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -27493,7 +83304,7 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x78,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbf,0x02,0x00,0x00, @@ -28243,13 +84054,13 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -28275,20 +84086,20 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -28370,7 +84181,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x65,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x64,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x66,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x65,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x67,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x66,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -28399,7 +84210,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xb1,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb5,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xd0,0x01,0x00,0x00,0x84,0x00,0x00,0x00, @@ -28420,7 +84231,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3f,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x71,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x79,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xbf,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xc0,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -28528,7 +84339,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, @@ -28586,483 +84397,482 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0xff,0x02,0x00,0x00,0xeb,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xf6,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 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-0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +0xa9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa2,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xe1,0x02,0x00,0x00,0xef,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x9f,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa1,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x8a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x8a,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00, +0xed,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x87,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x82,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xe5,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x81,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + }; -const uint64_t matmul_q2_k_f32_fp32_len = 11120; +const uint64_t matmul_q2_k_f32_fp32_len = 11100; unsigned char matmul_q3_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -29179,12 +84989,12 @@ unsigned char matmul_q3_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -29211,19 +85021,19 @@ unsigned char matmul_q3_k_f32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -29307,7 +85117,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0xe7,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0xe6,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe8,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xe7,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe9,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xe8,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x07,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -29337,7 +85147,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0x31,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x35,0x02,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x39,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x54,0x02,0x00,0x00, @@ -29359,7 +85169,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x00,0x03,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x46,0x03,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x47,0x03,0x00,0x00, @@ -29466,7 +85276,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, @@ -29524,645 +85334,643 @@ unsigned char matmul_q3_k_f32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0x82,0x03,0x00,0x00,0xeb,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xf6,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 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-0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x6f,0x03,0x00,0x00, -0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x03,0x00,0x00, -0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, +0x28,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x11,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x11,0x03,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x6a,0x03,0x00,0x00,0x70,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0e,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x10,0x03,0x00,0x00,0xf9,0x00,0x02,0x00, +0x09,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00, +0x6f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x06,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x03,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q3_k_f32_len = 13144; +const uint64_t matmul_q3_k_f32_len = 13124; unsigned char matmul_q3_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -30282,12 +86090,12 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -30322,13 +86130,13 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -30467,7 +86275,7 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe5,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x19,0x03,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x19,0x03,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x67,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x68,0x03,0x00,0x00,0x67,0x03,0x00,0x00, @@ -30773,7 +86581,7 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x87,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x30,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x30,0x01,0x00,0x00,0x90,0x01,0x00,0x00, -0x8f,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0x71,0x00,0x04,0x00, +0x8f,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00, 0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x01,0x00,0x00, 0x91,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00, @@ -31425,12 +87233,12 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -31466,12 +87274,12 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -31604,7 +87412,7 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xf0,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xf8,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x3e,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x3f,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -31910,7 +87718,7 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x8d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x30,0x01,0x00,0x00, 0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x30,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x92,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x91,0x01,0x00,0x00, 0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x93,0x01,0x00,0x00, @@ -32511,12 +88319,12 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -32543,20 +88351,20 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -32639,7 +88447,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xe4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe6,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xe5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xe5,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xe6,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x05,0x02,0x00,0x00,0x80,0x00,0x00,0x00, @@ -32668,7 +88476,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x2d,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x35,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00, @@ -32689,7 +88497,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xf1,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xf1,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x3f,0x03,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x40,0x03,0x00,0x00,0x3f,0x03,0x00,0x00, @@ -32796,7 +88604,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -32853,639 +88661,637 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x74,0x00,0x00,0x00,0x77,0x03,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 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-0x38,0x00,0x01,0x00, +0x21,0x03,0x00,0x00,0xf9,0x00,0x02,0x00,0x0a,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x63,0x03,0x00,0x00,0x69,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x07,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x09,0x03,0x00,0x00,0xf9,0x00,0x02,0x00, +0x02,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x65,0x03,0x00,0x00, +0x68,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xff,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q3_k_f32_fp32_len = 13024; +const uint64_t matmul_q3_k_f32_fp32_len = 13004; unsigned char matmul_q4_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -33595,12 +89401,12 @@ unsigned char matmul_q4_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -33627,20 +89433,20 @@ unsigned char matmul_q4_0_f32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -33712,7 +89518,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x36,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x35,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x37,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x36,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x38,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x37,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x56,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -33742,7 +89548,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x80,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x88,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, @@ -33764,7 +89570,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4f,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x95,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x96,0x02,0x00,0x00, @@ -33873,7 +89679,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -33930,7 +89736,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x74,0x00,0x00,0x00,0xcf,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00, 0xd3,0x02,0x00,0x00,0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00, @@ -34483,12 +90289,12 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -34524,12 +90330,12 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -34660,7 +90466,7 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb8,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -35413,13 +91219,13 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -35454,12 +91260,12 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -35582,7 +91388,7 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x40,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x40,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00, @@ -36285,13 +92091,13 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -36317,20 +92123,20 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -36402,7 +92208,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x33,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x34,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x34,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -36431,7 +92237,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x7c,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x84,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, @@ -36452,7 +92258,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x40,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x40,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00, @@ -36561,7 +92367,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -36618,7 +92424,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0xc8,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xcc,0x02,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, @@ -37163,12 +92969,12 @@ unsigned char matmul_q4_1_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -37195,19 +93001,19 @@ unsigned char matmul_q4_1_f32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -37278,7 +93084,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00, @@ -37309,7 +93115,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x89,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0x84,0x00,0x00,0x00, @@ -37331,7 +93137,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x18,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x4c,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x4c,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x9a,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x9b,0x02,0x00,0x00,0x9a,0x02,0x00,0x00, @@ -37438,7 +93244,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -37495,7 +93301,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x74,0x00,0x00,0x00,0xd4,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00, 0xd8,0x02,0x00,0x00,0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00, @@ -38057,12 +93863,12 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -38097,13 +93903,13 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -38232,7 +94038,7 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x77,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbd,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, @@ -38993,12 +94799,12 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -39034,12 +94840,12 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -39161,7 +94967,7 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x45,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x4d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x93,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -39871,12 +95677,12 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -39903,20 +95709,20 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -39987,7 +95793,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x39,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x38,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x3a,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x39,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x3b,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x3a,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x59,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -40016,7 +95822,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x84,0x00,0x00,0x00, @@ -40037,7 +95843,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x45,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x4d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x93,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -40144,7 +95950,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -40201,7 +96007,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0xcd,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xd1,0x02,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, @@ -40755,12 +96561,12 @@ unsigned char matmul_q4_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -40787,19 +96593,19 @@ unsigned char matmul_q4_k_f32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -40881,7 +96687,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xa1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xa2,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xa2,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xa3,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -40912,7 +96718,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf0,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -40934,7 +96740,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb3,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xbb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x01,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x02,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -41041,7 +96847,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -41098,561 +96904,559 @@ unsigned char matmul_q4_k_f32_data[] = { 0x3b,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x3f,0x03,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0x2f,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xe1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe3,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0xcc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xcc,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x25,0x03,0x00,0x00,0x2d,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xc9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xcb,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xc4,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xc4,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x27,0x03,0x00,0x00,0x2c,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q4_k_f32_len = 12072; +const uint64_t matmul_q4_k_f32_len = 12052; unsigned char matmul_q4_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -41769,12 +97573,12 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -41809,13 +97613,13 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -41954,7 +97758,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd5,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xdd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x23,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x24,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -42197,7 +98001,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x52,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00, 0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, +0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, 0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0xa9,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00, 0x56,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -42214,7 +98018,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x66,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x65,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x67,0x01,0x00,0x00,0x66,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, 0x68,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x67,0x01,0x00,0x00, @@ -42824,12 +98628,12 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -42865,12 +98669,12 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -43001,7 +98805,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x79,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xab,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xab,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xf9,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xfa,0x02,0x00,0x00,0xf9,0x02,0x00,0x00, @@ -43244,7 +99048,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0xd0,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x52,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x54,0x01,0x00,0x00, -0x53,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x53,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -43261,7 +99065,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,0x64,0x01,0x00,0x00, 0x63,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x82,0x00,0x00,0x00, +0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x92,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x66,0x01,0x00,0x00, 0x65,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x67,0x01,0x00,0x00,0x60,0x01,0x00,0x00, @@ -43820,12 +99624,12 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -43852,20 +99656,20 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 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0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -43946,7 +99750,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x01,0x00,0x00, @@ -43976,7 +99780,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xed,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf0,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0b,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -43996,7 +99800,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xb4,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xfa,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xfb,0x02,0x00,0x00, @@ -44103,7 +99907,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, @@ -44161,554 +99965,552 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0xf9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xf9,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xe5,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xe5,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x1a,0x03,0x00,0x00,0x2a,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xe4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0xdd,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdd,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x03,0x00,0x00, +0x28,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xda,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xdc,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0xc5,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xc5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x1e,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xc2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xc4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbd,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xbd,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x20,0x03,0x00,0x00,0x25,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xba,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q4_k_f32_fp32_len = 11952; +const uint64_t matmul_q4_k_f32_fp32_len = 11932; unsigned char matmul_q5_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -44821,12 +100623,12 @@ unsigned char matmul_q5_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -44853,20 +100655,20 @@ unsigned char matmul_q5_0_f32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -44945,7 +100747,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x5f,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x5e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x60,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x5f,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x61,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x60,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7f,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -44975,7 +100777,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0xa9,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xae,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb1,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xcc,0x01,0x00,0x00, @@ -44997,7 +100799,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x78,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbf,0x02,0x00,0x00, @@ -45106,7 +100908,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -45163,7 +100965,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x74,0x00,0x00,0x00,0xfa,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00, 0xfe,0x02,0x00,0x00,0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00, @@ -45752,12 +101554,12 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -45793,12 +101595,12 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -45936,7 +101738,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5f,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x93,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x9b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe1,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe2,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -46725,13 +102527,13 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -46766,12 +102568,12 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -46901,7 +102703,7 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x69,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x69,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xb7,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00, @@ -47640,13 +103442,13 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -47672,20 +103474,20 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -47764,7 +103566,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x5d,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x5d,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x5e,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -47793,7 +103595,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xa5,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xaa,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00, @@ -47814,7 +103616,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x69,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x69,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xb7,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00, @@ -47923,7 +103725,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -47980,7 +103782,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xf3,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf7,0x02,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, @@ -48559,13 +104361,13 @@ unsigned char matmul_q5_1_f32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -48591,20 +104393,20 @@ unsigned char matmul_q5_1_f32_data[] = { 0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -48678,7 +104480,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x59,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x5a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x5a,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x5b,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -48709,7 +104511,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xa9,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xac,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -48731,7 +104533,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x73,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -48838,7 +104640,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -48895,7 +104697,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0xf5,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0xf9,0x02,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, @@ -49480,13 +105282,13 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -49521,12 +105323,12 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -49659,7 +105461,7 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5a,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x8e,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x8e,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00, @@ -50444,12 +106246,12 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -50484,13 +106286,13 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -50614,7 +106416,7 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb2,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xb3,0x02,0x00,0x00, @@ -51349,12 +107151,12 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -51381,19 +107183,19 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -51467,7 +107269,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00, @@ -51497,7 +107299,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xa5,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xa8,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc3,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -51517,7 +107319,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb2,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xb3,0x02,0x00,0x00, @@ -51624,7 +107426,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, @@ -51682,7 +107484,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0xf2,0x02,0x00,0x00,0xeb,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -52261,12 +108063,12 @@ unsigned char matmul_q5_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -52293,19 +108095,19 @@ unsigned char matmul_q5_k_f32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -52390,7 +108192,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x06,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xc6,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xc7,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xc7,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xc8,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -52421,7 +108223,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x15,0x02,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x16,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x19,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x34,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -52443,7 +108245,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd8,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xe0,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x26,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x27,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -52550,7 +108352,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, 0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00, 0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, @@ -52607,598 +108409,597 @@ unsigned char matmul_q5_k_f32_data[] = { 0x60,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00, -0xea,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xea,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x64,0x03,0x00,0x00, 0xeb,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xee,0x00,0x00,0x00,0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0xe7,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, 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+0x4c,0x03,0x00,0x00,0x51,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xe8,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + }; -const uint64_t matmul_q5_k_f32_len = 12596; +const uint64_t matmul_q5_k_f32_len = 12576; unsigned char matmul_q5_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -53318,12 +109119,12 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -53358,13 +109159,13 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -53506,7 +109307,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xfa,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x02,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x48,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x49,0x03,0x00,0x00,0x48,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -53755,7 +109556,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x5d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, +0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0xa9,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x62,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -53772,7 +109573,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x71,0x01,0x00,0x00, -0x70,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x70,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x72,0x01,0x00,0x00,0x71,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x73,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,0x72,0x01,0x00,0x00, @@ -54416,12 +110217,12 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -54457,12 +110258,12 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -54596,7 +110397,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x9e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xd0,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xd0,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x1e,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00, @@ -54845,7 +110646,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0xd0,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, -0x5e,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x5e,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -54862,7 +110663,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x6f,0x01,0x00,0x00, 0x6e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00, -0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x82,0x00,0x00,0x00, +0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x92,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x71,0x01,0x00,0x00, 0x70,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x72,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, @@ -55456,12 +111257,12 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -55488,20 +111289,20 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -55585,7 +111386,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0xc4,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00, @@ -55615,7 +111416,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x11,0x02,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x12,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x15,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x30,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, @@ -55635,7 +111436,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xd9,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x1f,0x03,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x20,0x03,0x00,0x00, @@ -55742,7 +111543,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 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+0xe7,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xe9,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0xe2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xe2,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x45,0x03,0x00,0x00,0x4a,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xdf,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xe1,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + }; -const uint64_t matmul_q5_k_f32_fp32_len = 12476; +const uint64_t matmul_q5_k_f32_fp32_len = 12456; unsigned char matmul_q6_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -56501,12 +112301,12 @@ unsigned char matmul_q6_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -56533,19 +112333,19 @@ unsigned char matmul_q6_k_f32_data[] = { 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -56629,7 +112429,7 @@ unsigned char matmul_q6_k_f32_data[] = { 0x8b,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x8a,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x8c,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x8b,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x8d,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x8c,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xab,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -56659,7 +112459,7 @@ unsigned char matmul_q6_k_f32_data[] = { 0xd5,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xd9,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xda,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xdd,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf8,0x01,0x00,0x00, @@ -56681,7 +112481,7 @@ unsigned char matmul_q6_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xa4,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xea,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xeb,0x02,0x00,0x00, @@ -56788,7 +112588,7 @@ unsigned char matmul_q6_k_f32_data[] = { 0x94,0x00,0x00,0x00,0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9e,0x00,0x00,0x00,0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, @@ -56846,530 +112646,528 @@ unsigned char matmul_q6_k_f32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xec,0x00,0x00,0x00,0x26,0x03,0x00,0x00,0xeb,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00, 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-0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x0e,0x03,0x00,0x00,0x14,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, -0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00, -0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x13,0x03,0x00,0x00, -0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00, -0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, +0xcc,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb5,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x0e,0x03,0x00,0x00,0x14,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb2,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0xad,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00, +0x13,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xaa,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q6_k_f32_len = 11764; +const uint64_t matmul_q6_k_f32_len = 11744; unsigned char matmul_q6_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -57489,12 +113287,12 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -57529,13 +113327,13 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -57674,7 +113472,7 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x89,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xbd,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xbd,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00, @@ -58517,12 +114315,12 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -58558,12 +114356,12 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -58696,7 +114494,7 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x62,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x94,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe2,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe3,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -59488,12 +115286,12 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -59520,20 +115318,20 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -59616,7 +115414,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x88,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x89,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x89,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x8a,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -59645,7 +115443,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0xd1,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xd5,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd6,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd9,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x01,0x00,0x00, @@ -59666,7 +115464,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x63,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x95,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x95,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xe3,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00, @@ -59773,7 +115571,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x9d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x17,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0xa1,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa3,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x84,0x00,0x05,0x00, @@ -59830,524 +115628,522 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x74,0x00,0x00,0x00,0x1b,0x03,0x00,0x00,0x84,0x00,0x05,0x00, 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+0xc5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xae,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x07,0x03,0x00,0x00,0x0d,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xab,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0xa6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x09,0x03,0x00,0x00, +0x0c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xa3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q6_k_f32_fp32_len = 11644; +const uint64_t matmul_q6_k_f32_fp32_len = 11624; unsigned char matmul_q8_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -60457,12 +116253,12 @@ unsigned char matmul_q8_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -60489,20 +116285,20 @@ unsigned char matmul_q8_0_f32_data[] = { 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -60574,7 +116370,7 @@ unsigned char matmul_q8_0_f32_data[] = { 0x37,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x36,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x38,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x01,0x00,0x00, -0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x39,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x38,0x01,0x00,0x00, 0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x57,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, @@ -60604,7 +116400,7 @@ unsigned char matmul_q8_0_f32_data[] = { 0x81,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, @@ -60626,7 +116422,7 @@ unsigned char matmul_q8_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 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+0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x61,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xba,0x02,0x00,0x00,0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_q8_0_f32_len = 10608; +const uint64_t matmul_q8_0_f32_len = 10588; unsigned char matmul_q8_0_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -61349,12 +117143,12 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -61390,12 +117184,12 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -61526,7 +117320,7 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x73,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -62283,13 +118077,13 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -62324,12 +118118,12 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -62452,7 +118246,7 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x41,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x41,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0x8f,0x02,0x00,0x00, @@ -63159,13 +118953,13 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -63191,20 +118985,20 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -63276,7 +119070,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x34,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x35,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x35,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0x36,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x80,0x00,0x00,0x00, @@ -63305,7 +119099,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x7d,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, @@ -63326,7 +119120,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x41,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x41,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0x8f,0x02,0x00,0x00, @@ -63433,7 +119227,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x9a,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x00,0x00,0x00,0x9b,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 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mul_mat_vec_id_q3_k_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x5c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x25,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x84,0x00,0x00,0x00, +0xac,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x37,0x03,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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mul_mat_vec_id_q6_k_f32_len = 11612; + +unsigned char mul_mat_vec_id_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0e,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x46,0x00,0x00,0x00, +0x4e,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0x71,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xba,0x00,0x00,0x00, +0xf8,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x1b,0x00,0x00,0x00, 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= 3808; + +unsigned char mul_mat_vec_nc_f16_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xb3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x6f,0x00,0x00,0x00,0x7a,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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mul_mat_vec_q4_0_f32_f32_data[] = { +unsigned char mul_mat_vec_q2_k_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x29,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, -0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x47,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x53,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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4012; +const uint64_t mul_mat_vec_q2_k_f32_f32_len = 8356; -unsigned char mul_mat_vec_q4_K_f16_f32_data[] = { +unsigned char mul_mat_vec_q3_k_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x85,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -69819,870 +129844,839 @@ unsigned char mul_mat_vec_q4_K_f16_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00, -0x23,0x02,0x00,0x00,0xc6,0x03,0x00,0x00,0x10,0x00,0x06,0x00, 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mul_mat_vec_q5_0_f16_f32_data[] = { +unsigned char mul_mat_vec_q4_0_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x80,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x63,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0x72,0x00,0x00,0x00, -0x77,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0xb5,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x2d,0x01,0x00,0x00, +0x00,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x50,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x83,0x00,0x00,0x00, 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+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t mul_mat_vec_q5_1_f32_f32_len = 4352; -unsigned char mul_mat_vec_q5_K_f32_f32_data[] = { +unsigned char mul_mat_vec_q5_k_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xf8,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -74123,1068 +136054,1111 @@ unsigned char mul_mat_vec_q5_K_f32_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x59,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, 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mul_mat_vec_q5_k_f32_f32_len = 12776; -unsigned char mul_mat_vec_q6_K_f32_f32_data[] = { +unsigned char mul_mat_vec_q6_k_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xde,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x26,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -76193,1631 +138235,2632 @@ unsigned char mul_mat_vec_q6_K_f32_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00, -0xc7,0x00,0x00,0x00,0xd8,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 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+0x38,0x00,0x01,0x00, }; -const uint64_t rope_f16_len = 3156; +const uint64_t rope_neox_f16_len = 3952; -unsigned char rope_f32_data[] = { +unsigned char rope_neox_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x17,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x4a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x8b,0x00,0x00,0x00, -0xac,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x2b,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x89,0x00,0x00,0x00, +0x8f,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xca,0x00,0x00,0x00, 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+0x05,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x3b,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x40,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00, +0x41,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x2c,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x52,0x00,0x00,0x00, 0xcd,0xcc,0xcc,0x3d,0x17,0x00,0x04,0x00,0x65,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x20,0x00,0x04,0x00, @@ -78709,894 +142159,560 @@ unsigned char rope_f32_data[] = { 0x69,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x07,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x72,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x2d,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x88,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x89,0x00,0x00,0x00,0x88,0x00,0x00,0x00, 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+0x4d,0x00,0x00,0x00,0xc3,0x00,0x05,0x00,0x28,0x00,0x00,0x00, +0x64,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x4c,0x00,0x00,0x00,0xaa,0x00,0x05,0x00,0x2d,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xf7,0x00,0x03,0x00,0x68,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfa,0x00,0x04,0x00,0x66,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x68,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x00,0x00,0x00, +0x41,0x00,0x05,0x00,0x1b,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x3a,0x00,0x00,0x00,0x70,0x00,0x00,0x00, +0x6c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x68,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x68,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t sum_rows_f32_len = 2112; + diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 79ce1479f16ca..f389934ead3ed 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1,5 +1,5 @@ #include "ggml-vulkan.h" - +#include #ifdef GGML_VULKAN_RUN_TESTS #include #endif @@ -8,13 +8,15 @@ #include #include +#include #include -#include #include #include #include #include #include +#include +#include #include "ggml.h" #include "ggml-backend-impl.h" @@ -56,6 +58,12 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA } \ } while (0) +#ifdef GGML_VULKAN_DEBUG +#define VK_LOG_DEBUG(msg) std::cerr << msg << std::endl +#else +#define VK_LOG_DEBUG(msg) ((void) 0) +#endif // GGML_VULKAN_DEBUG + struct ggml_backend_vk_context; struct vk_queue { @@ -137,6 +145,7 @@ struct vk_device { vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_div_f32; vk_pipeline pipeline_add_f32; vk_pipeline pipeline_scale_f32; vk_pipeline pipeline_sqr_f32; @@ -149,16 +158,15 @@ struct vk_device { vk_pipeline pipeline_relu_f32; vk_pipeline pipeline_diag_mask_inf_f32; vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; - vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; vk_pipeline pipeline_argsort_f32; + vk_pipeline pipeline_sum_rows_f32; std::vector pipelines; ~vk_device() { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "destroy device " << name << std::endl; -#endif + VK_LOG_DEBUG("destroy device " << name); device.destroyCommandPool(compute_queue.pool); if (!single_queue) { device.destroyCommandPool(transfer_queue.pool); @@ -193,9 +201,7 @@ struct vk_buffer_struct { if (size == 0) { return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("~vk_buffer_struct(" << buffer << ", " << size << ")"); device->device.freeMemory(device_memory); device->device.destroyBuffer(buffer); @@ -226,17 +232,27 @@ typedef std::vector vk_sequence; struct vk_mat_mat_push_constants { uint32_t M; uint32_t N; uint32_t K; - uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t k_split; - uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; + uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; - uint32_t expert_stride_b; uint32_t expert_stride_d; - uint32_t idx; uint32_t nbi1; uint32_t n_as; + uint32_t k_split; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; }; - struct vk_mat_vec_push_constants { uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; +}; + +struct vk_mat_mat_id_push_constants { + uint32_t M; uint32_t N; uint32_t K; + uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11; +}; +struct vk_mat_vec_id_push_constants { + uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t nei0; uint32_t ne11; }; struct vk_op_push_constants { @@ -271,26 +287,15 @@ struct vk_op_diag_mask_push_constants { struct vk_op_rope_push_constants { uint32_t ncols; + uint32_t n_dims; float freq_scale; uint32_t p_delta_rows; float freq_base; float ext_factor; float attn_factor; - float corr_dims[4]; -}; - -struct vk_op_rope_neox_push_constants { - uint32_t ncols; - uint32_t ndims; - float freq_scale; - uint32_t p_delta_rows; - float freq_base; - float ext_factor; - float attn_factor; - float corr_dims[4]; + float corr_dims[2]; float theta_scale; - float inv_ndims; - uint32_t has_freq_facs; + uint32_t has_ff; }; struct vk_op_soft_max_push_constants { @@ -333,15 +338,12 @@ struct vk_context { }; struct ggml_tensor_extra_gpu { - bool ready; - size_t ctx_idx; vk_buffer_ref buffer_gpu; uint64_t offset; void reset() { - ready = false; ctx_idx = 0; buffer_gpu.reset(); offset = 0; @@ -356,6 +358,49 @@ struct ggml_vk_garbage_collector { std::vector contexts; }; +#if defined(GGML_VULKAN_MEMORY_DEBUG) || defined(GGML_VULKAN_DEBUG) +#include + +#define VK_LOG_MEMORY(msg) std::cerr << "ggml_vulkan memory: " << msg << std::endl + +static std::string format_size(size_t size) { + const size_t kib = 1024; + const size_t mib = kib * 1024; + const size_t gib = mib * 1024; + + std::ostringstream oss; + oss << std::fixed << std::setprecision(2); + + if (size >= gib) { + oss << static_cast(size) / gib << " GiB"; + } else if (size >= mib) { + oss << static_cast(size) / mib << " MiB"; + } else if (size >= kib) { + oss << static_cast(size) / kib << " KiB"; + } else { + oss << size << " B"; + } + + return oss.str(); +} + +static std::mutex log_mutex; + +class vk_memory_logger { +public: + vk_memory_logger(): total_device(0), total_host(0) {} + void log_allocation(vk_buffer_ref buf_ref, size_t size); + void log_deallocation(vk_buffer_ref buf_ref); + +private: + std::map allocations; // Track allocations + size_t total_device; + size_t total_host; +}; +#else +#define VK_LOG_MEMORY(msg) ((void) 0) +#endif // GGML_VULKAN_MEMORY_DEBUG + struct ggml_backend_vk_context { std::string name; @@ -380,8 +425,45 @@ struct ggml_backend_vk_context { bool initialized; size_t idx; + +#ifdef GGML_VULKAN_MEMORY_DEBUG + vk_memory_logger memory_logger; +#endif }; +#ifdef GGML_VULKAN_MEMORY_DEBUG +void vk_memory_logger::log_allocation(vk_buffer_ref buf_ref, size_t size) { + std::lock_guard guard(log_mutex); + vk_buffer buf = buf_ref.lock(); + const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); + const std::string type = device ? "device" : "host"; + allocations[buf->buffer] = size; + total_device += device ? size : 0; + total_host += device ? 0 : size; + VK_LOG_MEMORY("VULKAN" << buf->ctx->idx << ": +" << format_size(size) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); +} + +void vk_memory_logger::log_deallocation(vk_buffer_ref buf_ref) { + if (buf_ref.expired() || buf_ref.lock()->size == 0) { + return; + } + + std::lock_guard guard(log_mutex); + vk_buffer buf = buf_ref.lock(); + const bool device = bool(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eDeviceLocal); + std::string type = device ? "device" : "host"; + auto it = allocations.find(buf->buffer); + total_device -= device ? it->second : 0; + total_host -= device ? 0 : it->second; + if (it != allocations.end()) { + VK_LOG_MEMORY("VULKAN" << buf->ctx->idx << ": -" << format_size(it->second) << " " << type << " at " << buf->buffer << ". Total device: " << format_size(total_device) << ", total host: " << format_size(total_host)); + allocations.erase(it); + } else { + VK_LOG_MEMORY("ERROR VULKAN" << buf->ctx->idx << ": Attempted to deallocate unknown " << type << " memory at " << buf->buffer); + } +} +#endif // GGML_VULKAN_MEMORY_DEBUG + struct vk_instance_t { vk::Instance instance; @@ -394,15 +476,11 @@ struct vk_instance_t { }; static std::shared_ptr ggml_vk_get_device(size_t idx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_get_device(" << idx << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_get_device(" << idx << ")"); static std::weak_ptr devices[GGML_VK_MAX_DEVICES]; if (devices[idx].expired()) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "Initializing new vk_device" << std::endl; -#endif + VK_LOG_DEBUG("Initializing new vk_device"); std::shared_ptr device = std::make_shared(); device->initialized = false; devices[idx] = device; @@ -429,9 +507,7 @@ static vk_instance_t vk_instance; GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")"); GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT @@ -532,9 +608,7 @@ static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& } static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_destroy_pipeline(" << pipeline->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_pipeline_destroy_pipeline(" << pipeline->name << ")"); for (auto& pool : pipeline->descriptor_pools) { device.destroyDescriptorPool(pool); } @@ -552,9 +626,7 @@ static void ggml_vk_destroy_pipeline(vk::Device& device, vk_pipeline& pipeline) } static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_pipeline_allocate_descriptor_sets(" << pipeline->name << ", " << n << ")"); if (pipeline->descriptor_sets.size() >= pipeline->descriptor_set_idx + n) { // Enough descriptors are available return; @@ -584,16 +656,12 @@ static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx } static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_pipeline_cleanup(" << pipeline->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_pipeline_cleanup(" << pipeline->name << ")"); pipeline->descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_cmd_buffer()"); if (q.cmd_buffers.size() > q.cmd_buffer_idx) { // Reuse command buffer return q.cmd_buffers[q.cmd_buffer_idx++]; @@ -613,9 +681,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx } static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_submission()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_submission()"); vk_submission s; s.buffer = ggml_vk_create_cmd_buffer(ctx, q); s.wait_semaphores = std::move(wait_semaphores); @@ -624,9 +690,7 @@ static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk } static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")"); if (ctx->seqs.empty()) { return; } @@ -700,9 +764,7 @@ static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { } static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_find_queue_family_index()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_find_queue_family_index()"); const uint32_t qfsize = queue_family_props.size(); // Try with avoid preferences first @@ -748,9 +810,7 @@ static uint32_t ggml_vk_find_queue_family_index(std::vectorgc.contexts.emplace_back(); vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1]; memset((void *) result, 0, sizeof(vk_context)); @@ -776,9 +834,7 @@ static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_que } static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); @@ -788,9 +844,7 @@ static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * } static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_timeline_semaphore()"); if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; @@ -809,9 +863,7 @@ static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { } static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_queue_cleanup()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_queue_cleanup()"); // Requires command buffers to be done ctx->device->device.resetCommandPool(q.pool); @@ -831,9 +883,7 @@ static uint32_t find_properties(const vk::PhysicalDeviceMemoryProperties* mem_pr } static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags, vk::MemoryPropertyFlags fallback_flags = vk::MemoryPropertyFlags(0)) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_buffer(device " << ctx->idx << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_buffer(device " << ctx->idx << ", " << size << ", " << to_string(req_flags) << ", " << to_string(fallback_flags) << ")"); vk_buffer buf = std::make_shared(); if (size == 0) { @@ -893,8 +943,8 @@ static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t siz buf->device = ctx->device; -#ifdef GGML_VULKAN_DEBUG - std::cerr << "Created buffer " << buf->buffer << std::endl; +#ifdef GGML_VULKAN_MEMORY_DEBUG + ctx->memory_logger.log_allocation(buf, size); #endif return buf; @@ -929,6 +979,14 @@ static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, siz } static void ggml_vk_destroy_buffer(vk_buffer& buf) { + if (buf == nullptr) { + return; + } + +#ifdef GGML_VULKAN_MEMORY_DEBUG + buf->ctx->memory_logger.log_deallocation(buf); +#endif + buf.reset(); } @@ -937,9 +995,7 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { } static void ggml_vk_sync_buffers(vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_sync_buffers()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_sync_buffers()"); const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; ctx->s->buffer.pipelineBarrier( @@ -953,9 +1009,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { } static void ggml_vk_wait_events(vk_context * ctx, std::vector&& events) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_wait_events()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_wait_events()"); if (events.empty()) { return; } @@ -990,9 +1044,7 @@ static bool ggml_vk_build_shader(ggml_type type) { } static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_load_shaders(" << ctx->name << ")"); const std::shared_ptr device = ctx->device; @@ -1028,7 +1080,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared(); - /*ctx->device->pipeline_matmul_id_f32 = std::make_shared(); + ctx->device->pipeline_matmul_id_f32 = std::make_shared(); ctx->device->pipeline_matmul_id_f16_f32 = std::make_shared(); ctx->device->pipeline_matmul_id_f16 = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared(); @@ -1040,15 +1092,15 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared(); - ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared();*/ + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared(); if (device->fp16) { - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_len, matmul_f32_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_len, matmul_f32_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); @@ -1078,12 +1130,12 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); @@ -1141,103 +1193,103 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/ + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_len, matmul_id_f32_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_len, matmul_id_f32_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); } else { - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_f32_fp32_len, matmul_f32_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_f32_aligned_fp32_len, matmul_f32_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); @@ -1330,134 +1382,137 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/ + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_f32_fp32_len, matmul_id_f32_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_f32_aligned_fp32_len, matmul_id_f32_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); } // mul mat vec + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32_f32", mul_mat_vec_q2_K_f32_f32_len, mul_mat_vec_q2_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32_f32", mul_mat_vec_q3_K_f32_f32_len, mul_mat_vec_q3_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32_f32", mul_mat_vec_q4_K_f32_f32_len, mul_mat_vec_q4_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32_f32", mul_mat_vec_q5_K_f32_f32_len, mul_mat_vec_q5_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32_f32", mul_mat_vec_q6_K_f32_f32_len, mul_mat_vec_q6_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f32_f32", mul_mat_vec_q2_k_f32_f32_len, mul_mat_vec_q2_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f32_f32", mul_mat_vec_q3_k_f32_f32_len, mul_mat_vec_q3_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f32_f32", mul_mat_vec_q4_k_f32_f32_len, mul_mat_vec_q4_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f32_f32", mul_mat_vec_q5_k_f32_f32_len, mul_mat_vec_q5_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f32_f32", mul_mat_vec_q6_k_f32_f32_len, mul_mat_vec_q6_k_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f16_f32", mul_mat_vec_q2_K_f16_f32_len, mul_mat_vec_q2_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f16_f32", mul_mat_vec_q3_K_f16_f32_len, mul_mat_vec_q3_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f16_f32", mul_mat_vec_q4_K_f16_f32_len, mul_mat_vec_q4_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f16_f32", mul_mat_vec_q5_K_f16_f32_len, mul_mat_vec_q5_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f16_f32", mul_mat_vec_q6_K_f16_f32_len, mul_mat_vec_q6_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - - /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_K_f32", mul_mat_vec_id_q2_K_f32_len, mul_mat_vec_id_q2_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_K_f32", mul_mat_vec_id_q3_K_f32_len, mul_mat_vec_id_q3_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_K_f32", mul_mat_vec_id_q4_K_f32_len, mul_mat_vec_id_q4_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_K_f32", mul_mat_vec_id_q5_K_f32_len, mul_mat_vec_id_q5_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_K_f32", mul_mat_vec_id_q6_K_f32_len, mul_mat_vec_id_q6_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);*/ + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_k_f16_f32", mul_mat_vec_q2_k_f16_f32_len, mul_mat_vec_q2_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_k_f16_f32", mul_mat_vec_q3_k_f16_f32_len, mul_mat_vec_q3_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_k_f16_f32", mul_mat_vec_q4_k_f16_f32_len, mul_mat_vec_q4_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_k_f16_f32", mul_mat_vec_q5_k_f16_f32_len, mul_mat_vec_q5_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_k_f16_f32", mul_mat_vec_q6_k_f16_f32_len, mul_mat_vec_q6_k_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_k_f32", mul_mat_vec_id_q2_k_f32_len, mul_mat_vec_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_k_f32", mul_mat_vec_id_q3_k_f32_len, mul_mat_vec_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_f32", mul_mat_vec_id_q4_k_f32_len, mul_mat_vec_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_f32", mul_mat_vec_id_q5_k_f32_len, mul_mat_vec_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_f32", mul_mat_vec_id_q6_k_f32_len, mul_mat_vec_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); // dequant shaders ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -1466,11 +1521,11 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_k", dequant_q2_k_len, dequant_q2_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_k", dequant_q3_k_len, dequant_q3_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_k", dequant_q4_k_len, dequant_q4_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_k", dequant_q5_k_len, dequant_q5_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_k", dequant_q6_k_len, dequant_q6_k_data, "main", 2, 5 * sizeof(uint32_t), {256 * 64, 1, 1}, {}, 1); // get_rows ggml_vk_create_pipeline(ctx, ctx->device->pipeline_get_rows[GGML_TYPE_F32 ], "get_rows_f32", get_rows_f32_len, get_rows_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); @@ -1505,6 +1560,8 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); @@ -1520,21 +1577,21 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_norm_f16, "rope_norm_f16", rope_norm_f16_len, rope_norm_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); } static void ggml_vk_print_gpu_info(size_t idx) { GGML_ASSERT(idx < vk_instance.device_indices.size()); size_t dev_num = vk_instance.device_indices[idx]; -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_print_gpu_info(" << dev_num << ")"); GGML_ASSERT(vk_instance.initialized); std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); @@ -1550,8 +1607,10 @@ static void ggml_vk_print_gpu_info(size_t idx) { vk::PhysicalDeviceProperties2 props2; vk::PhysicalDeviceMaintenance3Properties props3; vk::PhysicalDeviceSubgroupProperties subgroup_props; + vk::PhysicalDeviceDriverProperties driver_props; props2.pNext = &props3; props3.pNext = &subgroup_props; + subgroup_props.pNext = &driver_props; physical_device.getProperties2(&props2); const size_t subgroup_size = subgroup_props.subgroupSize; @@ -1595,7 +1654,7 @@ static void ggml_vk_print_gpu_info(size_t idx) { fp16 = fp16 && vk12_features.shaderFloat16; std::string device_name = props2.properties.deviceName.data(); - std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + std::cerr << GGML_VK_NAME << idx << ": " << device_name << " (" << driver_props.driverName << ") | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; @@ -1609,9 +1668,7 @@ void ggml_vk_instance_init() { if (vk_instance_initialized) { return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_instance_init()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_instance_init()"); vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; @@ -1691,7 +1748,71 @@ void ggml_vk_instance_init() { vk::PhysicalDeviceProperties props = devices[i].getProperties(); if (props.deviceType == vk::PhysicalDeviceType::eDiscreteGpu) { - vk_instance.device_indices.push_back(i); + // Check if there are two physical devices corresponding to the same GPU + auto old_device = std::find_if( + vk_instance.device_indices.begin(), + vk_instance.device_indices.end(), + [&devices, &props](const size_t k){ return devices[k].getProperties().deviceID == props.deviceID; } + ); + if (old_device == vk_instance.device_indices.end()) { + vk_instance.device_indices.push_back(i); + } else { + // There can be two physical devices corresponding to the same GPU if there are 2 different drivers + // This can cause error when splitting layers aross the devices, need to keep only 1 + VK_LOG_DEBUG("Device " << i << " and device " << *old_device << " have the same device id"); + + vk::PhysicalDeviceProperties2 old_prop; + vk::PhysicalDeviceDriverProperties old_driver; + old_prop.pNext = &old_driver; + devices[*old_device].getProperties2(&old_prop); + + vk::PhysicalDeviceProperties2 new_prop; + vk::PhysicalDeviceDriverProperties new_driver; + new_prop.pNext = &new_driver; + devices[i].getProperties2(&new_prop); + + std::map driver_priorities {}; + int old_priority = std::numeric_limits::max(); + int new_priority = std::numeric_limits::max(); + + // Check https://registry.khronos.org/vulkan/specs/1.3-extensions/man/html/VkDriverId.html for the list of driver id + // Smaller number -> higher priority + switch (old_prop.properties.vendorID) { + case VK_VENDOR_ID_AMD: + driver_priorities[vk::DriverId::eMesaRadv] = 1; + driver_priorities[vk::DriverId::eAmdOpenSource] = 2; + driver_priorities[vk::DriverId::eAmdProprietary] = 3; + break; + case VK_VENDOR_ID_INTEL: + driver_priorities[vk::DriverId::eIntelOpenSourceMESA] = 1; + driver_priorities[vk::DriverId::eIntelProprietaryWindows] = 2; + break; + case VK_VENDOR_ID_NVIDIA: + driver_priorities[vk::DriverId::eNvidiaProprietary] = 1; +#if defined(VK_API_VERSION_1_3) && VK_HEADER_VERSION >= 235 + driver_priorities[vk::DriverId::eMesaNvk] = 2; +#endif + break; + } + + if (driver_priorities.count(old_driver.driverID)) { + old_priority = driver_priorities[old_driver.driverID]; + } + if (driver_priorities.count(new_driver.driverID)) { + new_priority = driver_priorities[new_driver.driverID]; + } + + if (new_priority < old_priority) { + auto r = std::remove(vk_instance.device_indices.begin(), vk_instance.device_indices.end(), *old_device); + vk_instance.device_indices.erase(r, vk_instance.device_indices.end()); + vk_instance.device_indices.push_back(i); + + VK_LOG_DEBUG("Prioritize device " << i << " driver " << new_driver.driverName << " over device " << *old_device << " driver " << old_driver.driverName); + } + else { + VK_LOG_DEBUG("Prioritize device " << *old_device << " driver " << old_driver.driverName << " over device " << i << " driver " << new_driver.driverName << std::endl); + } + } } } @@ -1713,9 +1834,7 @@ void ggml_vk_instance_init() { static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { GGML_ASSERT(idx < vk_instance.device_indices.size()); size_t dev_num = vk_instance.device_indices[idx]; -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_init(" << ctx->name << ", " << dev_num << ")"); ggml_vk_instance_init(); std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); @@ -1888,9 +2007,7 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { } static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_get_to_fp16()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_get_to_fp16()"); switch (type) { case GGML_TYPE_F32: case GGML_TYPE_Q4_0: @@ -1912,9 +2029,7 @@ static vk_pipeline ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type } static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_get_mul_mat_mat_pipeline()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_pipeline()"); if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f32; } @@ -1949,10 +2064,33 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; } +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { + VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); + GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); + + switch (a_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + break; + default: + return nullptr; + } + + return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type]; +} + static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_get_mul_mat_mat_id_pipeline()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_get_mul_mat_mat_id_pipeline()"); if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_id_f32; } @@ -1984,13 +2122,12 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type]; } -static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; -#endif - GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { + VK_LOG_DEBUG("ggml_vk_get_dequantize_mul_mat_vec()"); + GGML_ASSERT(b_type == GGML_TYPE_F32); switch (a_type) { + case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -2007,13 +2144,13 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type]; + return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[a_type]; } static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_pool_malloc(" << size << ")"); + VK_LOG_MEMORY("ggml_vk_pool_malloc"); + int best_i = -1; size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs int worst_i = -1; @@ -2041,13 +2178,11 @@ static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) ggml_vk_destroy_buffer(b); } - return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); + return ggml_vk_create_buffer_device(ctx, size); } static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_pool_free(" << buffer->size << ")"); for (int i = 0; i < MAX_VK_BUFFERS; ++i) { vk_buffer& b = ctx->buffer_pool[i]; if (b == nullptr) { @@ -2068,6 +2203,8 @@ static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_ } } + VK_LOG_MEMORY("ggml_vk_create_buffer_temp(" << size << ")"); + // Otherwise create new buffer vk_buffer buf = ggml_vk_pool_malloc(ctx, size); ctx->gc.temp_buffers.push_back(buf); @@ -2076,9 +2213,7 @@ static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_ } static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; -#endif + VK_LOG_MEMORY("ggml_vk_host_malloc(" << size << ")"); vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); @@ -2100,9 +2235,7 @@ static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) { if (ptr == nullptr) { return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; -#endif + VK_LOG_MEMORY("ggml_vk_host_free(" << ptr << ")"); vk_buffer buf; size_t index; for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { @@ -2154,9 +2287,11 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context const uint32_t wg0 = CEIL_DIV(elements[0], pipeline->wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_dispatch_pipeline(" << pipeline->name << ", {"; + for (auto& buffer : buffers) { + std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.size << "), "; + } + std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))"); std::vector descriptor_buffer_infos; std::vector write_descriptor_sets; GGML_ASSERT(pipeline->descriptor_set_idx < pipeline->descriptor_sets.size()); @@ -2189,9 +2324,7 @@ static void ggml_vk_end_submission(vk_submission& s, std::vector w } static void ggml_vk_ctx_end(vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")"); if (ctx->s == nullptr) { return; } @@ -2201,9 +2334,7 @@ static void ggml_vk_ctx_end(vk_context * ctx) { } static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_ctx_begin(" << ctx << ")"); if (subctx->s != nullptr) { ggml_vk_ctx_end(subctx); } @@ -2213,9 +2344,7 @@ static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx } static size_t ggml_vk_align_size(size_t width, size_t align) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_align_size(" << width << ", " << align << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_align_size(" << width << ", " << align << ")"); return CEIL_DIV(width, align) * align; } @@ -2229,6 +2358,7 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { + VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")"); ggml_vk_destroy_buffer(ctx->sync_staging); ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, @@ -2237,9 +2367,7 @@ static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, si } static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_write_nc_async(" << tensor << ")"); GGML_ASSERT(!ggml_is_contiguous(tensor)); // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { @@ -2344,9 +2472,7 @@ static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_cont } static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")"); // Make sure ctx owns the buffer GGML_ASSERT(dst->ctx == ctx); @@ -2381,9 +2507,7 @@ static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_cont subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "STAGING" << std::endl; -#endif + VK_LOG_DEBUG("STAGING"); // Staging buffer required vk_buffer staging = ctx->staging; @@ -2418,16 +2542,12 @@ static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_cont } static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_write_async(" << size << ")"); return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging); } static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_write_2d(" << width << ", " << height << ")"); // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); @@ -2452,16 +2572,12 @@ static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& ds } static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_write(" << size << ")"); ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1); } static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")"); GGML_ASSERT(width > 0); GGML_ASSERT(height > 0); GGML_ASSERT(src != nullptr); @@ -2495,9 +2611,7 @@ static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_conte return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "STAGING" << std::endl; -#endif + VK_LOG_DEBUG("STAGING"); // Fall back to staging buffer vk_buffer staging = ctx->staging; @@ -2524,9 +2638,7 @@ static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context } static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_read(" << offset << ", " << size << ")"); if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); @@ -2548,9 +2660,7 @@ static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, s } static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_copy_async(" << size << ")"); // Make sure both buffers are on same ctx GGML_ASSERT(src->ctx == dst->ctx); @@ -2561,9 +2671,7 @@ static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t d static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { if (src->ctx == dst->ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")"); // Copy within the device ggml_backend_vk_context * ctx = src->ctx; @@ -2575,9 +2683,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr VK_CHECK(ctx->device->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); ctx->device->device.resetFences({ ctx->fence }); } else { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")"); // Copy device to device ggml_backend_vk_context * src_ctx = src->ctx; ggml_backend_vk_context * dst_ctx = dst->ctx; @@ -2595,9 +2701,7 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr } static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")"); // Make sure ctx owns the buffer GGML_ASSERT(dst->ctx == ctx); @@ -2612,9 +2716,7 @@ static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, } static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")"); const uint64_t ne0 = src->ne[0]; const uint64_t ne1 = src->ne[1]; const uint64_t nb0 = src->nb[0]; @@ -2642,9 +2744,7 @@ static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * su } static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_d2h_tensor_2d()"); const uint64_t ne0 = dst->ne[0]; const uint64_t ne1 = dst->ne[1]; const uint64_t ne2 = dst->ne[2]; @@ -2668,9 +2768,7 @@ static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * su } static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"); // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { // return 4; // } @@ -2702,9 +2800,7 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline_intel(ggml_backend_vk_context * } static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline(" << m << ", " << n << ", " << aligned << ")"); switch (ctx->device->vendor_id) { case VK_VENDOR_ID_AMD: return ggml_vk_guess_matmul_pipeline_amd(ctx, mmp, m, n, aligned); @@ -2726,9 +2822,7 @@ static vk_pipeline ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, } static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")"); return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align; } @@ -2736,22 +2830,19 @@ static void ggml_vk_matmul( ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, - uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, - uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; -#endif + uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3) { + VK_LOG_DEBUG("ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ")"); ggml_vk_sync_buffers(subctx); if (split_k == 1) { - const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; + const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k, ne02, ne12, broadcast2, broadcast3 }; ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch }); return; } GGML_ASSERT(batch_stride_d == m * n); - const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; + const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3 }; // Make sure enough workgroups get assigned for split k to work ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); ggml_vk_sync_buffers(subctx); @@ -2761,29 +2852,18 @@ static void ggml_vk_matmul( static void ggml_vk_matmul_id( ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, - vk_subbuffer&& ids, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& a, vk_subbuffer&& split_k_buffer, + vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, - uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, - uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; -#endif - ggml_vk_sync_buffers(subctx); - if (split_k == 1) { - const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, d, a }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch }); - return; - } - - GGML_ASSERT(batch_stride_d == m * n); - - const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; - // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, split_k_buffer, a }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); + uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11) { + VK_LOG_DEBUG("ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), " << + "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " << + "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " << + "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")"); ggml_vk_sync_buffers(subctx); - const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, + nei0, nei1, nbi1, ne11 }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids }, sizeof(vk_mat_mat_id_push_constants), &pc, { m, nei1, n_as }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -2809,10 +2889,8 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_ } static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; - std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")"); const int tensor_type_size = ggml_type_size(tensor->type); const uint32_t ne = ggml_nelements(tensor); @@ -2829,11 +2907,9 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context } static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + VK_LOG_DEBUG("ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -2908,7 +2984,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); vk_buffer d_X; @@ -2917,12 +2993,12 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su uint64_t y_buf_offset = 0; if (!src0_uma) { d_Qx = extra_src0->buffer_gpu.lock(); - qx_buf_offset = extra_src0->offset; + qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); } if (!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { @@ -2997,17 +3073,16 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, - ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, - 0, 0, 0, 0, 1 + ne01, ne11, ne10, + ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21, + split_k, ne12*ne13, ne02, ne12, r2, r3 ); // NOLINT } static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + VK_LOG_DEBUG("ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT @@ -3072,7 +3147,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); vk_buffer d_X; uint64_t x_buf_offset = 0; @@ -3080,12 +3155,12 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context uint64_t y_buf_offset = 0; if(!src0_uma) { d_Qx = extra_src0->buffer_gpu.lock(); - qx_buf_offset = extra_src0->offset; + qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); } if(!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { @@ -3150,19 +3225,17 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context // compute const vk_mat_vec_push_constants pc = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, - (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), + (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, }; ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1}); } static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + VK_LOG_DEBUG("ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT @@ -3204,14 +3277,14 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); - const uint64_t qx_buf_offset = extra_src0->offset; + const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); if (!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qx != nullptr); } @@ -3231,11 +3304,9 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c } static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + VK_LOG_DEBUG("ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); @@ -3281,14 +3352,14 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); - const uint64_t qx_buf_offset = extra_src0->offset; + const uint64_t qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); if (!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qx != nullptr); } @@ -3308,29 +3379,25 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; -#endif - if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + VK_LOG_DEBUG("ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")"); + if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) { ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); - } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) { ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); - } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); } else { ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst); } } -/*static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", backend=" << ids->backend << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif - GGML_ASSERT(src0->type == GGML_TYPE_I32); +static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ids->type == GGML_TYPE_I32); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3342,16 +3409,18 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const uint64_t ne12 = src1->ne[2]; const uint64_t ne13 = src1->ne[3]; - const uint32_t nb11 = src1->nb[1]; + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + GGML_ASSERT(nei0 * nei1 <= 2048); + + const uint32_t nbi1 = ids->nb[1]; + const uint32_t nbi2 = ids->nb[2]; const uint64_t ne20 = dst->ne[0]; const uint64_t ne21 = dst->ne[1]; + const uint64_t ne22 = dst->ne[2]; + const uint64_t ne23 = dst->ne[3]; - const uint64_t r2 = ne12 / ne02; - const uint64_t r3 = ne13 / ne03; - - const uint32_t nbi1 = src0->nb[1]; - const uint32_t idx = ((uint32_t *) dst->op_params)[0]; const uint64_t n_as = ne02; GGML_ASSERT(n_as <= 8); @@ -3365,15 +3434,20 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, size_t qx_buf_offset = 0; vk_buffer d_Qy; size_t qy_buf_offset = 0; + vk_buffer d_ids; + size_t ids_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool ids_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); + ggml_vk_host_get(ctx, ids->data, d_ids, ids_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; + ids_uma = d_ids != nullptr; } const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); @@ -3393,41 +3467,44 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, // Not implemented GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; + const uint64_t x_ne = ne01 * ne00; + const uint64_t y_ne = ne11 * ne10; + const uint64_t d_ne = ne21 * ne20; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); - const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8; + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, nei1)); + const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 8; - const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - - vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, nei1, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t ids_sz = nbi2; const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); - GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); vk_buffer d_X; uint64_t x_buf_offset = 0; vk_buffer d_Y; uint64_t y_buf_offset = 0; if (!src0_uma) { d_Qx = extra_src0->buffer_gpu.lock(); - qx_buf_offset = extra_src0->offset; + qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); } if (!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qy != nullptr); } + if (!ids_uma) { + d_ids = extra_ids->buffer_gpu.lock(); + ids_buf_offset = extra_ids->offset + ids->view_offs; + GGML_ASSERT(d_ids != nullptr); + } if (qx_needs_dequant) { d_X = ctx->prealloc_x; GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); @@ -3469,9 +3546,6 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, if (qy_needs_dequant) { ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1); } - if (split_k > 1) { - ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1); - } if (x_non_contig) { ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); @@ -3496,23 +3570,24 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, } // compute - ggml_vk_matmul( + ggml_vk_matmul_id( ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, - { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, - ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, - nb11 / ggml_type_size(src1->type), ne20, idx, nbi1, n_as + { d_D, d_buf_offset, d_sz * ne22 * ne23 }, { d_ids, ids_buf_offset, ids_sz }, + ne01, ne21, ne10, ne10, ne10, ne01, + stride_batch_x, stride_batch_y, ne20*ne21, + n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11 ); // NOLINT } -static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; - std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; -#endif +static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)"); GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ids->type == GGML_TYPE_I32); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3524,36 +3599,41 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte const uint64_t ne12 = src1->ne[2]; const uint64_t ne13 = src1->ne[3]; - GGML_ASSERT(ne11 == 1); + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + + const uint64_t nbi2 = ids->nb[2]; + + GGML_ASSERT(nei1 == 1); const uint64_t ne20 = dst->ne[0]; const uint64_t ne21 = dst->ne[1]; const uint64_t ne22 = dst->ne[2]; const uint64_t ne23 = dst->ne[3]; - const uint64_t nb22 = dst->nb[2]; - const uint64_t nb23 = dst->nb[3]; - - const uint64_t r2 = ne12 / ne02; - const uint64_t r3 = ne13 / ne03; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra; vk_buffer d_Qx; size_t qx_buf_offset = 0; vk_buffer d_Qy; size_t qy_buf_offset = 0; + vk_buffer d_ids; + size_t ids_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool ids_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); + ggml_vk_host_get(ctx, ids->data, d_ids, ids_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; + ids_uma = d_ids != nullptr; } const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); @@ -3569,16 +3649,17 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte const uint64_t x_ne = ne01 * ne00; const uint64_t y_ne = ne11 * ne10; - const uint64_t d_ne = ne11 * ne01; + const uint64_t d_ne = ne21 * ne20; const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t ids_sz = nbi2; const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); - const uint64_t d_buf_offset = extra->offset; + const uint64_t d_buf_offset = extra->offset + dst->view_offs; GGML_ASSERT(d_D != nullptr); vk_buffer d_X; uint64_t x_buf_offset = 0; @@ -3586,14 +3667,19 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte uint64_t y_buf_offset = 0; if(!src0_uma) { d_Qx = extra_src0->buffer_gpu.lock(); - qx_buf_offset = extra_src0->offset; + qx_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_Qx != nullptr); } if(!src1_uma) { d_Qy = extra_src1->buffer_gpu.lock(); - qy_buf_offset = extra_src1->offset; + qy_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Qy != nullptr); } + if(!ids_uma) { + d_ids = extra_ids->buffer_gpu.lock(); + ids_buf_offset = extra_ids->offset + ids->view_offs; + GGML_ASSERT(d_ids != nullptr); + } if (qx_needs_dequant) { d_X = ctx->prealloc_x; } else { @@ -3619,7 +3705,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(ctx, src0->type, src1->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); @@ -3642,27 +3728,32 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } - uint32_t stride_batch_x = ne00*ne01; uint32_t stride_batch_y = ne10*ne11; - if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { - stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); - } - if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); } // compute - const vk_mat_vec_push_constants pc = { + const vk_mat_vec_id_push_constants pc = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, - (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, - stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), - // 0, 0, 0, 0, 1 + (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21), + (uint32_t)nei0, (uint32_t)ne11, }; ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1}); -}*/ + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, + { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23}, { d_ids, ids_buf_offset, ids_sz } }, + sizeof(vk_mat_vec_id_push_constants), &pc, { (uint32_t)ne01, (uint32_t)nei0, 1 }); +} + +static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { + VK_LOG_DEBUG("ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")"); + if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst); + } else { + ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst); + } +} static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat @@ -3699,9 +3790,9 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; const vk_buffer src_buf = extra_src0->buffer_gpu.lock(); - const uint64_t src_offset = extra_src0->offset; + const uint64_t src_offset = extra_src0->offset + src0->view_offs; vk_buffer dst_buf = extra->buffer_gpu.lock(); - const uint64_t dst_offset = extra->offset; + const uint64_t dst_offset = extra->offset + dst->view_offs; std::vector copies; @@ -3754,6 +3845,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_mul_f32; } return nullptr; + case GGML_OP_DIV: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_div_f32; + } + return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_scale_f32; @@ -3823,11 +3919,6 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const { const int mode = ((const int32_t *) dst->op_params)[2]; const bool is_neox = mode & 2; - const bool is_glm = mode & 4; - - if (is_glm) { - return nullptr; - } if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { @@ -3838,10 +3929,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return ctx->device->pipeline_rope_f32; + return ctx->device->pipeline_rope_norm_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return ctx->device->pipeline_rope_f16; + return ctx->device->pipeline_rope_norm_f16; } } return nullptr; @@ -3851,6 +3942,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_argsort_f32; } return nullptr; + case GGML_OP_SUM_ROWS: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_sum_rows_f32; + } + return nullptr; default: return nullptr; } @@ -3873,6 +3969,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_GET_ROWS: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: @@ -3884,18 +3981,16 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { template static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + VK_LOG_DEBUG("ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; } if (src2 != nullptr) { std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; } - std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; -#endif + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")"); GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT - GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT + GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0)); // NOLINT GGML_ASSERT(dst->extra != nullptr); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3918,6 +4013,12 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne23 = use_src2 ? src2->ne[3] : 0; const uint64_t ne2 = ne20 * ne21; + const uint64_t ned0 = dst->ne[0]; + const uint64_t ned1 = dst->ne[1]; + const uint64_t ned2 = dst->ne[2]; + const uint64_t ned3 = dst->ne[3]; + const uint64_t ned = ned0 * ned1; + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; @@ -3967,10 +4068,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } } - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; - uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; - uint64_t d_sz = ggml_type_size(dst->type) * ne0; + uint64_t x_sz = ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0; + uint64_t y_sz = use_src1 ? ggml_type_size(src1->type) * ne1 : 0; + uint64_t z_sz = use_src2 ? ggml_type_size(src2->type) * ne2 : 0; + uint64_t d_sz = ggml_type_size(dst->type) * ned; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3980,21 +4081,21 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = ((extra->offset + dst->view_offs) / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if(!src0_uma) { d_X = extra_src0->buffer_gpu.lock(); - x_buf_offset = extra_src0->offset; + x_buf_offset = extra_src0->offset + src0->view_offs; GGML_ASSERT(d_X != nullptr); } if (use_src1 && !src1_uma) { d_Y = extra_src1->buffer_gpu.lock(); - y_buf_offset = extra_src1->offset; + y_buf_offset = extra_src1->offset + src1->view_offs; GGML_ASSERT(d_Y != nullptr); } if (use_src2 && !src2_uma) { d_Z = extra_src2->buffer_gpu.lock(); - z_buf_offset = extra_src2->offset; + z_buf_offset = extra_src2->offset + src2->view_offs; GGML_ASSERT(d_Z != nullptr); } @@ -4028,6 +4129,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_SOFT_MAX: + case GGML_OP_SUM_ROWS: elements = { (uint32_t)ggml_nrows(src0), 1, 1 }; break; case GGML_OP_DIAG_MASK_INF: @@ -4056,7 +4158,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c z_sz *= ne22 * ne23; } if (d_sz != VK_WHOLE_SIZE) { - d_sz *= ne02 * ne03; + d_sz *= ned2 * ned3; } } @@ -4072,24 +4174,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (op == GGML_OP_ROPE) { - const int mode = ((int32_t *) dst->op_params)[2]; - const bool is_neox = mode & 2; - - if (is_neox) { - // Empty src2 is possible in rope, but the shader needs a buffer - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; - } else { - subbuf_z = { d_X, 0, d_X->size }; - } - - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + // Empty src2 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; } else { - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + subbuf_z = { d_X, 0, d_X->size }; } + + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src2) { ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); @@ -4193,6 +4287,21 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, cons }); } +static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_DIV, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); +} + static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; const uint32_t src0_type_size = ggml_type_size(src0->type); @@ -4238,7 +4347,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + const uint32_t d_offset = ((extra->offset + dst->view_offs) % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), @@ -4296,9 +4405,9 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; - const int mode = ((int32_t *) dst->op_params)[2]; + // const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; const float freq_base = ((float *) dst->op_params)[5]; const float freq_scale = ((float *) dst->op_params)[6]; const float ext_factor = ((float *) dst->op_params)[7]; @@ -4306,28 +4415,16 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con const float beta_fast = ((float *) dst->op_params)[9]; const float beta_slow = ((float *) dst->op_params)[10]; - const bool is_neox = mode & 2; - const bool is_glm = mode & 4; + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); - GGML_ASSERT(!is_glm); + const float theta_scale = powf(freq_base, -2.0f/n_dims); - float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); - - if (is_neox) { - const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { - (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], - freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims, - src2 != nullptr, - }); - } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { - (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], - freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} - }); - } + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { + (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1]}, theta_scale, + src2 != nullptr, + }); } static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { @@ -4342,10 +4439,6 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, GGML_ASSERT(ncols_pad <= 1024); - std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl; - - std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { ncols, ncols_pad, @@ -4353,6 +4446,10 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, }); } +static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }); +} + #ifdef GGML_VULKAN_RUN_TESTS static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { @@ -4389,9 +4486,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 template static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")"); const size_t x_ne = m * k * batch; const size_t y_ne = k * n * batch; const size_t d_ne = m * n * batch; @@ -4548,7 +4643,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t ggml_vk_ctx_begin(ctx, subctx); ggml_vk_matmul( ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), - m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1 + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1 ); ggml_vk_ctx_end(subctx); } @@ -4803,9 +4900,7 @@ static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_ } static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_test_transfer(" << ne << ")"); // Check transfers are correct vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); @@ -4889,9 +4984,7 @@ static void ggml_vk_quantize_data(const float * from, void * to, size_t ne, ggml } static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_test_dequant(" << ne << ")"); const size_t x_sz = sizeof(float) * ne; const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); @@ -4968,9 +5061,7 @@ static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_ } static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, size_t split_k, size_t shader_size, ggml_type quant) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_test_dequant_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << ggml_type_name(quant) << ")"); const size_t x_ne = m * k * batch; const size_t y_ne = k * n * batch; const size_t d_ne = m * n * batch; @@ -5052,7 +5143,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, ggml_vk_ctx_begin(ctx, subctx); ggml_vk_matmul( ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), - m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1 + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1 ); ggml_vk_ctx_end(subctx); } @@ -5152,9 +5245,7 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))"); ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; extra->reset(); tensor->extra = extra; @@ -5162,9 +5253,7 @@ static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) } static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_preallocate_buffers_graph(" << node << ")"); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; if (extra == nullptr) { @@ -5199,7 +5288,7 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm bool mmp = (use_src0 && use_src1 && src1_type == GGML_TYPE_F32) ? ggml_vk_get_mul_mat_mat_pipeline(ctx, src0_type, y_non_contig ? GGML_TYPE_F16 : src1->type) != nullptr : false; - const bool qx_needs_dequant = use_src0 && (mmp || x_non_contig); + const bool qx_needs_dequant = use_src0 && (!mmp || x_non_contig); const bool qy_needs_dequant = use_src1 && ((src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig); int split_k; @@ -5237,12 +5326,14 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm case GGML_OP_CONT: case GGML_OP_DUP: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: break; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { @@ -5275,9 +5366,6 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; -#endif #if defined(GGML_VULKAN_RUN_TESTS) ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached, @@ -5416,6 +5504,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { #endif if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(x_size: " << ctx->prealloc_size_x << ")"); // Resize buffer if (ctx->prealloc_x != nullptr) { ggml_vk_destroy_buffer(ctx->prealloc_x); @@ -5423,6 +5512,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x); } if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(y_size: " << ctx->prealloc_size_y << ")"); // Resize buffer if (ctx->prealloc_y != nullptr) { ggml_vk_destroy_buffer(ctx->prealloc_y); @@ -5430,6 +5520,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y); } if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(split_k_size: " << ctx->prealloc_size_split_k << ")"); // Resize buffer if (ctx->prealloc_split_k != nullptr) { ggml_vk_destroy_buffer(ctx->prealloc_split_k); @@ -5437,6 +5528,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k); } if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) { + VK_LOG_MEMORY("ggml_vk_preallocate_buffers(staging_size: " << ctx->staging_size << ")"); // Resize buffer if (ctx->staging != nullptr) { ggml_vk_destroy_buffer(ctx->staging); @@ -5454,9 +5546,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")"); ctx->semaphore_idx = 0; ctx->staging_offset = 0; @@ -5465,6 +5555,13 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src2 = node->src[2]; switch (node->op) { + // Return on empty ops to avoid generating a compute_ctx and setting exit_tensor + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NONE: + return; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { case GGML_UNARY_OP_SILU: @@ -5479,16 +5576,13 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_GET_ROWS: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_DIAG_MASK_INF: @@ -5496,8 +5590,8 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_ROPE: case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - case GGML_OP_NONE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: break; default: std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; @@ -5526,6 +5620,10 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_MUL: ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_DIV: + ggml_vk_div(ctx, ctx->compute_ctx, src0, src1, node); + break; case GGML_OP_SCALE: ggml_vk_scale(ctx, ctx->compute_ctx, src0, node); @@ -5544,12 +5642,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_DUP: ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node); - break; - case GGML_OP_RESHAPE: - case GGML_OP_VIEW: - case GGML_OP_PERMUTE: - case GGML_OP_TRANSPOSE: - case GGML_OP_NONE: break; case GGML_OP_NORM: ggml_vk_norm(ctx, ctx->compute_ctx, src0, node); @@ -5584,22 +5676,24 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_ARGSORT: ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node); + + break; + case GGML_OP_SUM_ROWS: + ggml_vk_sum_rows(ctx, ctx->compute_ctx, src0, node); + break; case GGML_OP_MUL_MAT: ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL_MAT_ID: - //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node); - std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl; - GGML_ASSERT(false); + ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, src2, node); break; default: return; } - extra->ready = true; extra->ctx_idx = ctx->compute_ctx->idx; #ifdef GGML_VULKAN_CHECK_RESULTS @@ -5622,6 +5716,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_ADD: case GGML_OP_GET_ROWS: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: @@ -5639,6 +5734,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_TRANSPOSE: case GGML_OP_NONE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: extra = (ggml_tensor_extra_gpu *) tensor->extra; break; @@ -5673,16 +5769,12 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ return true; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")"); #ifdef GGML_VULKAN_CHECK_RESULTS ggml_vk_check_results_0(ctx, params, tensor); #endif - GGML_ASSERT(extra->ready); - vk_context& subctx = ctx->gc.contexts[extra->ctx_idx]; // Only run if ctx hasn't been submitted yet @@ -5707,16 +5799,12 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ subctx.out_memcpys.clear(); } - extra->ready = false; - return true; } // Clean up after graph processing is done static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_graph_cleanup()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_graph_cleanup()"); for (auto& buffer : ctx->gc.temp_buffers) { ggml_vk_pool_free(ctx, buffer); } @@ -5760,9 +5848,7 @@ static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { // Clean up on backend free static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_cleanup(" << ctx->idx << ")"); ggml_vk_graph_cleanup(ctx); ggml_vk_destroy_buffer(ctx->prealloc_x); @@ -5828,7 +5914,9 @@ struct ggml_backend_vk_buffer_context { ~ggml_backend_vk_buffer_context() { ggml_vk_destroy_buffer(dev_buffer); - delete[] temp_tensor_extras; + if (temp_tensor_extras != nullptr) { + delete[] temp_tensor_extras; + } } ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() { @@ -5855,9 +5943,7 @@ GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { } GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl; -#endif + VK_LOG_MEMORY("ggml_backend_vk_buffer_free_buffer()"); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_vk_destroy_buffer(ctx->dev_buffer); delete ctx; @@ -5870,49 +5956,41 @@ GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")"); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); - if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) { + if (tensor->view_src != nullptr) { GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); - ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra; - extra->buffer_gpu = extra_view->buffer_gpu; - extra->offset = extra_view->offset + tensor->view_offs; + GGML_ASSERT(tensor->view_src->extra != nullptr); + tensor->extra = tensor->view_src->extra; } else { + ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); extra->buffer_gpu = ctx->dev_buffer; extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; + tensor->extra = extra; } - - tensor->extra = extra; } GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size); + ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")"); ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size); + ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { @@ -5923,7 +6001,7 @@ GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t bu vk_buffer src_buf = src_extra->buffer_gpu.lock(); vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); - ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src)); + ggml_vk_buffer_copy(dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src)); return true; } @@ -5963,11 +6041,15 @@ GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buff } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; -#endif + VK_LOG_MEMORY("ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")"); ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; - vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size); + + vk_buffer dev_buffer = nullptr; + try { + dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size); + } catch (const vk::SystemError& e) { + return nullptr; + } ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name); @@ -5990,31 +6072,19 @@ GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_ UNUSED(buft); } -GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - if (!ggml_backend_is_vk(backend)) { - return false; - } - - ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; - ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - - return buft_ctx->ctx->idx == ctx->idx; -} - static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { /* .get_name = */ ggml_backend_vk_buffer_type_name, /* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment, /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size, - /* .supports_backend = */ ggml_backend_vk_buffer_type_supports_backend, /* .is_host = */ NULL, }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl; -#endif + ggml_vk_instance_init(); + + VK_LOG_DEBUG("ggml_backend_vk_buffer_type(" << dev_num << ")"); GGML_ASSERT(dev_num < vk_instance.device_indices.size()); @@ -6038,16 +6108,12 @@ GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buff } GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl; -#endif + VK_LOG_MEMORY("ggml_backend_vk_host_buffer_free_buffer()"); ggml_vk_host_free(&vk_instance.contexts[0], buffer->context); } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; -#endif + VK_LOG_MEMORY("ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")"); size += 32; // Behave like the CPU buffer type void * ptr = nullptr; try { @@ -6081,7 +6147,6 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment, /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, }, /* .context = */ nullptr, @@ -6105,9 +6170,7 @@ GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) { GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_free(" << ctx->name << ")"); size_t idx = ctx->idx; @@ -6131,9 +6194,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_t } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_set_tensor_async(" << size << ")"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -6147,13 +6208,11 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); + ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_get_tensor_async(" << size << ")"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -6167,13 +6226,11 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c vk_buffer buf = extra->buffer_gpu.lock(); - ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); + ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + tensor->view_offs + offset, data, size); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_cpy_tensor_async()"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; @@ -6188,7 +6245,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c vk_buffer src_buf = src_extra->buffer_gpu.lock(); vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); - ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src)); + ggml_vk_buffer_copy_async(ctx->transfer_ctx, dst_buf, dst_extra->offset + dst->view_offs, src_buf, src_extra->offset + src->view_offs, ggml_nbytes(src)); return true; } @@ -6196,9 +6253,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_synchronize()" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_synchronize()"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; if(ctx->transfer_ctx == nullptr) { return; @@ -6226,9 +6281,7 @@ static bool ggml_vk_is_empty(ggml_tensor * node) { } GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)"); ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { @@ -6285,13 +6338,13 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: - return true; + return ggml_is_contiguous(op->src[0]); default: return false; } break; case GGML_OP_MUL_MAT: - // case GGML_OP_MUL_MAT_ID: + case GGML_OP_MUL_MAT_ID: { switch (op->src[0]->type) { case GGML_TYPE_F32: @@ -6361,12 +6414,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const // return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; // } break; case GGML_OP_ROPE: - { - const int mode = ((const int32_t *) op->op_params)[2]; - const bool is_glm = mode & 4; - - return !is_glm; - } break; + return ggml_is_contiguous(op->src[0]); case GGML_OP_NONE: case GGML_OP_RESHAPE: case GGML_OP_VIEW: @@ -6375,6 +6423,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_NORM: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: @@ -6383,6 +6432,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: return true; default: return false; @@ -6392,17 +6442,23 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) { - const ggml_tensor * dst = op; - const int min_batch_size = 32; - if (dst->ne[1] > min_batch_size && dst->op != GGML_OP_GET_ROWS) { - return true; + return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || + (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); + + UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_vk_supports_buft(ggml_backend_t backend, ggml_backend_buffer_type_t buft) { + if (buft->iface.get_name != ggml_backend_vk_buffer_type_name) { + return false; } - return false; + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - UNUSED(backend); + return buft_ctx->ctx->idx == ctx->idx; } // TODO: enable async and synchronize @@ -6416,9 +6472,11 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, /* .graph_plan_create = */ NULL, /* .graph_plan_free = */ NULL, + /* .graph_plan_update = */ NULL, /* .graph_plan_compute = */ NULL, /* .graph_compute = */ ggml_backend_vk_graph_compute, /* .supports_op = */ ggml_backend_vk_supports_op, + /* .supports_buft = */ ggml_backend_vk_supports_buft, /* .offload_op = */ ggml_backend_vk_offload_op, /* .event_new = */ NULL, /* .event_free = */ NULL, @@ -6436,9 +6494,7 @@ GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num) { if (vk_instance.initialized[dev_num]) { return vk_instance.backends[dev_num]; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_backend_vk_init(" << dev_num << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_backend_vk_init(" << dev_num << ")"); ggml_backend_vk_context * ctx = &vk_instance.contexts[dev_num]; ggml_vk_init(ctx, dev_num); @@ -6612,7 +6668,7 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); - ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -6654,9 +6710,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_check_results_0(" << tensor->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_check_results_0(" << tensor->name << ")"); ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; @@ -6679,9 +6733,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ size_t src1_size; size_t src2_size; - void * src0_buffer; - void * src1_buffer; - void * src2_buffer; + void * src0_buffer = nullptr; + void * src1_buffer = nullptr; + void * src2_buffer = nullptr; if (src0 != nullptr) { src0_clone = ggml_dup_tensor(ggml_ctx, src0); @@ -6696,7 +6750,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (ggml_backend_buffer_is_vk(src0->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); - uint64_t offset = extra->offset; + uint64_t offset = extra->offset + src0->view_offs; if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { @@ -6738,7 +6792,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (ggml_backend_buffer_is_vk(src1->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); - uint64_t offset = extra->offset; + uint64_t offset = extra->offset + src1->view_offs; if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { @@ -6796,7 +6850,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (ggml_backend_buffer_is_vk(src2->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); - uint64_t offset = extra->offset; + uint64_t offset = extra->offset + src2->view_offs; if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { for (int i3 = 0; i3 < src2->ne[3]; i3++) { for (int i2 = 0; i2 < src2->ne[2]; i2++) { @@ -6844,8 +6898,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_MUL_MAT_ID) { + tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone); } else if (tensor->op == GGML_OP_MUL) { tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_DIV) { + tensor_clone = ggml_div(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_SCALE) { tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); } else if (tensor->op == GGML_OP_SQR) { @@ -6869,15 +6927,15 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ } else if (tensor->op == GGML_OP_ROPE) { const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ggml_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ggml_ctx = ((int32_t *) tensor->op_params)[4]; + //const int n_ctx_ggml = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig_ggml = ((int32_t *) tensor->op_params)[4]; float freq_base = ((float *) tensor->op_params)[5]; float freq_scale = ((float *) tensor->op_params)[6]; float ext_factor = ((float *) tensor->op_params)[7]; float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ctx_orig_ggml, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: @@ -6915,6 +6973,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_ARGSORT) { tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params); + } else if (tensor->op == GGML_OP_SUM_ROWS) { + tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); @@ -6956,12 +7016,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ return; } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_check_results_1(" << tensor->name << ")" << std::endl; -#endif + VK_LOG_DEBUG("ggml_vk_check_results_1(" << tensor->name << ")"); ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; void * tensor_data = tensor->data; @@ -6972,11 +7031,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); - if (extra->offset + tensor_size >= buffer_gpu->size) { - tensor_size = buffer_gpu->size - (extra->offset); + if (extra->offset + tensor->view_offs + tensor_size >= buffer_gpu->size) { + tensor_size = buffer_gpu->size - (extra->offset + tensor->view_offs); } - ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, buffer_gpu, extra->offset + tensor->view_offs, tensor_data, tensor_size); } float first_error_result = -1.0f; @@ -7020,6 +7079,9 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3); @@ -7061,6 +7123,9 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); @@ -7085,6 +7150,9 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; + } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]); diff --git a/ggml.c b/ggml.c index 9e72b7a765dba..d5d33c2ba1029 100644 --- a/ggml.c +++ b/ggml.c @@ -5,6 +5,7 @@ #include "ggml-quants.h" #include "ggml.h" + #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) @@ -28,6 +29,10 @@ #include #endif +#ifdef GGML_USE_OPENMP +#include +#endif + #ifdef GGML_USE_METAL #include #endif @@ -60,6 +65,9 @@ typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; +typedef atomic_int atomic_flag; + +#define ATOMIC_FLAG_INIT 0 static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); @@ -73,6 +81,12 @@ static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } +static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { + return InterlockedExchange(ptr, 1); +} +static void atomic_flag_clear(atomic_flag * ptr) { + InterlockedExchange(ptr, 0); +} typedef HANDLE pthread_t; @@ -283,17 +297,6 @@ inline static void * ggml_calloc(size_t num, size_t size) { #if defined(GGML_USE_ACCELERATE) #include -#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions -#include "ggml-opencl.h" -#endif -#elif defined(GGML_USE_OPENBLAS) -#if defined(GGML_BLAS_USE_MKL) -#include -#else -#include -#endif -#elif defined(GGML_USE_CLBLAST) -#include "ggml-opencl.h" #endif // floating point type used to accumulate sums @@ -1567,11 +1570,11 @@ do { \ // F16 arithmetic is not supported by AVX, so we use F32 instead -#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) -static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { float tmp[8]; for (int i = 0; i < 8; i++) { @@ -1580,13 +1583,14 @@ static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { return (__m256)__lasx_xvld(tmp, 0); } -static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { float arr[8]; __lasx_xvst(y, arr, 0); - for (int i = 0; i < 8; i++) + for (int i = 0; i < 8; i++) { x[i] = GGML_FP32_TO_FP16(arr[i]); + } } #define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) @@ -1662,7 +1666,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 -static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); @@ -1673,7 +1677,7 @@ static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { return __lsx_vld(tmp, 0); } -static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) { +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { float arr[4]; __lsx_vst(y, arr, 0); @@ -1746,7 +1750,7 @@ struct ggml_compute_state_shared { int64_t perf_node_start_cycles; int64_t perf_node_start_time_us; - const int n_threads; + int n_threads; // synchronization primitives atomic_int n_active; // num active threads @@ -2257,6 +2261,11 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } +#if __FINITE_MATH_ONLY__ +#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" +#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" +#endif + #if defined(__ARM_NEON) && defined(__aarch64__) // adapted from arm limited optimized routine @@ -2306,32 +2315,27 @@ inline static __m512 ggml_v_expf(__m512 x) { const __m512 r = _mm512_set1_ps(0x1.8p23f); const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); - const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); - const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); - if (_mm512_kortestz(c, c)) - return _mm512_fmadd_ps(j, k, k); - const __m512i g = _mm512_and_si512( - _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), - _mm512_set1_epi32(0x82000000u)); - const __m512 s1 = - _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); - const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); const __mmask16 d = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - return _mm512_mask_blend_ps( - d, _mm512_mask_blend_ps( - c, _mm512_fmadd_ps(k, j, k), - _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), - _mm512_mul_ps(s1, s1)); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); } // computes silu x/(1+exp(-x)) in single precision vector @@ -2883,24 +2887,20 @@ struct ggml_state { // global state static struct ggml_state g_state; -static atomic_int g_state_barrier = 0; +static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; // barrier via spin lock inline static void ggml_critical_section_start(void) { - int processing = atomic_fetch_add(&g_state_barrier, 1); - - while (processing > 0) { - // wait for other threads to finish - atomic_fetch_sub(&g_state_barrier, 1); - sched_yield(); // TODO: reconsider this - processing = atomic_fetch_add(&g_state_barrier, 1); + while (atomic_flag_test_and_set(&g_state_critical)) { + // spin + sched_yield(); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { - atomic_fetch_sub(&g_state_barrier, 1); + atomic_flag_clear(&g_state_critical); } #if defined(__gnu_linux__) @@ -3206,23 +3206,42 @@ GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor) { return tensor->nb[0] > tensor->nb[1]; } +static bool ggml_is_contiguous_n(const struct ggml_tensor * tensor, int n) { + size_t next_nb = ggml_type_size(tensor->type); + if (tensor->ne[0] != ggml_blck_size(tensor->type) && tensor->nb[0] != next_nb) { + return false; + } + next_nb *= tensor->ne[0]/ggml_blck_size(tensor->type); + for (int i = 1; i < GGML_MAX_DIMS; i++) { + if (tensor->ne[i] != 1) { + if (i > n) { + if (tensor->nb[i] != next_nb) { + return false; + } + next_nb *= tensor->ne[i]; + } else { + // this dimension does not need to be contiguous + next_nb = tensor->ne[i]*tensor->nb[i]; + } + } + } + return true; +} + GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + return ggml_is_contiguous_0(tensor); +} - return - tensor->nb[0] == ggml_type_size(tensor->type) && - tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) && - tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && - tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 0); } -static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); +GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 1); +} - return - tensor->nb[0] == ggml_type_size(tensor->type) && - tensor->nb[2] == tensor->nb[1]*tensor->ne[1] && - tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) { + return ggml_is_contiguous_n(tensor, 2); } GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { @@ -3254,20 +3273,20 @@ bool ggml_are_same_shape(const struct ggml_tensor * t0, const struct ggml_tensor static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return - (t0->ne[0] == t1->ne[0] ) && - (t0->ne[1] == t1->ne[1] ) && - (t0->ne[2] == t1->ne[2] ) && - (t0->ne[3] == t1->ne[3] ); + (t0->ne[0] == t1->ne[0]) && + (t0->ne[1] == t1->ne[1]) && + (t0->ne[2] == t1->ne[2]) && + (t0->ne[3] == t1->ne[3]); } bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return - (t0->nb[0] == t1->nb[0] ) && - (t0->nb[1] == t1->nb[1] ) && - (t0->nb[2] == t1->nb[2] ) && - (t0->nb[3] == t1->nb[3] ); + (t0->nb[0] == t1->nb[0]) && + (t0->nb[1] == t1->nb[1]) && + (t0->nb[2] == t1->nb[2]) && + (t0->nb[3] == t1->nb[3]); } // check if t1 can be represented as a repeatition of t0 @@ -3357,10 +3376,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } -#if defined(GGML_USE_CLBLAST) - ggml_cl_init(); -#endif - ggml_setup_op_has_task_pass(); is_first_call = false; @@ -4064,32 +4079,26 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { switch (tensor->type) { case GGML_TYPE_I8: { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); return ((int8_t *)(tensor->data))[i]; } case GGML_TYPE_I16: { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); return ((int16_t *)(tensor->data))[i]; } case GGML_TYPE_I32: { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); return ((int32_t *)(tensor->data))[i]; } case GGML_TYPE_F16: { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } case GGML_TYPE_BF16: { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); } case GGML_TYPE_F32: { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); return ((float *)(tensor->data))[i]; } default: @@ -4111,32 +4120,26 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { switch (tensor->type) { case GGML_TYPE_I8: { - GGML_ASSERT(tensor->nb[0] == sizeof(int8_t)); ((int8_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I16: { - GGML_ASSERT(tensor->nb[0] == sizeof(int16_t)); ((int16_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_I32: { - GGML_ASSERT(tensor->nb[0] == sizeof(int32_t)); ((int32_t *)(tensor->data))[i] = value; } break; case GGML_TYPE_F16: { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; case GGML_TYPE_BF16: { - GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); } break; case GGML_TYPE_F32: { - GGML_ASSERT(tensor->nb[0] == sizeof(float)); ((float *)(tensor->data))[i] = value; } break; default: @@ -4882,10 +4885,21 @@ struct ggml_tensor * ggml_repeat_back( // ggml_concat struct ggml_tensor * ggml_concat( - struct ggml_context* ctx, - struct ggml_tensor* a, - struct ggml_tensor* b) { - GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]); + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int dim) { + GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); + + int64_t ne[GGML_MAX_DIMS]; + for (int d = 0; d < GGML_MAX_DIMS; ++d) { + if (d == dim) { + ne[d] = a->ne[d] + b->ne[d]; + continue; + } + GGML_ASSERT(a->ne[d] == b->ne[d]); + ne[d] = a->ne[d]; + } bool is_node = false; @@ -4893,7 +4907,9 @@ struct ggml_tensor * ggml_concat( is_node = true; } - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]); + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); + + ggml_set_op_params_i32(result, 0, dim); result->op = GGML_OP_CONCAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -5013,6 +5029,7 @@ struct ggml_tensor * ggml_leaky_relu( } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); result->op = GGML_OP_LEAKY_RELU; @@ -6222,16 +6239,13 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, - float xpos_base, - bool xpos_down, bool inplace) { GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); @@ -6252,15 +6266,13 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig }; memcpy(params + 5, &freq_base, sizeof(float)); memcpy(params + 6, &freq_scale, sizeof(float)); memcpy(params + 7, &ext_factor, sizeof(float)); memcpy(params + 8, &attn_factor, sizeof(float)); memcpy(params + 9, &beta_fast, sizeof(float)); memcpy(params + 10, &beta_slow, sizeof(float)); - memcpy(params + 11, &xpos_base, sizeof(float)); - memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE; @@ -6277,10 +6289,9 @@ struct ggml_tensor * ggml_rope( struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - int mode, - int n_ctx) { + int mode) { return ggml_rope_impl( - ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, false ); } @@ -6289,10 +6300,9 @@ struct ggml_tensor * ggml_rope_inplace( struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - int mode, - int n_ctx) { + int mode) { return ggml_rope_impl( - ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ctx, a, b, NULL, n_dims, mode, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, true ); } @@ -6303,8 +6313,7 @@ struct ggml_tensor * ggml_rope_ext( struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -6312,8 +6321,8 @@ struct ggml_tensor * ggml_rope_ext( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, false ); } @@ -6324,8 +6333,7 @@ struct ggml_tensor * ggml_rope_ext_inplace( struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -6333,8 +6341,8 @@ struct ggml_tensor * ggml_rope_ext_inplace( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true + ctx, a, b, c, n_dims, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, true ); } @@ -6344,8 +6352,7 @@ struct ggml_tensor * ggml_rope_custom( struct ggml_tensor * b, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -6353,8 +6360,8 @@ struct ggml_tensor * ggml_rope_custom( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, false ); } @@ -6364,8 +6371,7 @@ struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_tensor * b, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -6373,8 +6379,8 @@ struct ggml_tensor * ggml_rope_custom_inplace( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true + ctx, a, b, NULL, n_dims, mode, n_ctx_orig, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, true ); } @@ -6387,16 +6393,13 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, - float beta_slow, - float xpos_base, - bool xpos_down) { + float beta_slow) { GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); @@ -6412,15 +6415,13 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - int32_t params[13] = { /*n_past*/ 0, n_dims, mode, n_ctx, n_orig_ctx }; + int32_t params[11] = { /*n_past*/ 0, n_dims, mode, /*n_ctx*/ 0, n_ctx_orig }; memcpy(params + 5, &freq_base, sizeof(float)); memcpy(params + 6, &freq_scale, sizeof(float)); memcpy(params + 7, &ext_factor, sizeof(float)); memcpy(params + 8, &attn_factor, sizeof(float)); memcpy(params + 9, &beta_fast, sizeof(float)); memcpy(params + 10, &beta_slow, sizeof(float)); - memcpy(params + 11, &xpos_base, sizeof(float)); - memcpy(params + 12, &xpos_down, sizeof(bool)); ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ROPE_BACK; @@ -7331,13 +7332,15 @@ struct ggml_tensor * ggml_add_rel_pos_inplace( return ggml_add_rel_pos_impl(ctx, a, pw, ph, true); } -// gmml_unary +// ggml_unary static struct ggml_tensor * ggml_unary_impl( struct ggml_context * ctx, struct ggml_tensor * a, enum ggml_unary_op op, bool inplace) { + GGML_ASSERT(ggml_is_contiguous_1(a)); + bool is_node = false; if (!inplace && (a->grad)) { @@ -9006,17 +9009,6 @@ static void ggml_compute_forward_add_f32( const int ith = params->ith; const int nth = params->nth; -#ifdef GGML_USE_CLBLAST - if (src1->backend == GGML_BACKEND_TYPE_GPU) { - // TODO: OpenCL kernel support full broadcast - GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); - if (ith == 0) { - ggml_cl_add(src0, src1, dst); - } - return; - } -#endif - const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS @@ -10124,17 +10116,6 @@ static void ggml_compute_forward_mul_f32( const int ith = params->ith; const int nth = params->nth; -#if defined(GGML_USE_CLBLAST) - if (src1->backend == GGML_BACKEND_TYPE_GPU) { - // TODO: OpenCL kernel support full broadcast - GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); - if (ith == 0) { - ggml_cl_mul(src0, src1, dst); - } - return; - } -#endif - const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS @@ -10967,26 +10948,29 @@ static void ggml_compute_forward_concat_f32( GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ith; i2 < ne2; i2 += nth) { - if (i2 < ne02) { // src0 - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03); - - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; - } - } - } // src1 - else { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13); - - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); } + + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; } } } @@ -10994,8 +10978,8 @@ static void ggml_compute_forward_concat_f32( } static void ggml_compute_forward_concat( - const struct ggml_compute_params* params, - struct ggml_tensor* dst) { + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; @@ -11021,6 +11005,8 @@ static void ggml_compute_forward_abs_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11030,9 +11016,6 @@ static void ggml_compute_forward_abs_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_abs_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11067,6 +11050,8 @@ static void ggml_compute_forward_sgn_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11076,9 +11061,6 @@ static void ggml_compute_forward_sgn_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_sgn_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11113,6 +11095,8 @@ static void ggml_compute_forward_neg_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11122,9 +11106,6 @@ static void ggml_compute_forward_neg_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_neg_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11159,6 +11140,8 @@ static void ggml_compute_forward_step_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11168,9 +11151,6 @@ static void ggml_compute_forward_step_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_step_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11205,6 +11185,8 @@ static void ggml_compute_forward_tanh_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11214,9 +11196,6 @@ static void ggml_compute_forward_tanh_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_tanh_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11251,6 +11230,8 @@ static void ggml_compute_forward_elu_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11260,9 +11241,6 @@ static void ggml_compute_forward_elu_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_elu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11297,6 +11275,8 @@ static void ggml_compute_forward_relu_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11306,9 +11286,6 @@ static void ggml_compute_forward_relu_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_relu_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11343,6 +11320,8 @@ static void ggml_compute_forward_sigmoid_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11352,9 +11331,6 @@ static void ggml_compute_forward_sigmoid_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_sigmoid_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11388,9 +11364,9 @@ static void ggml_compute_forward_gelu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -11451,9 +11427,9 @@ static void ggml_compute_forward_gelu_quick_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -11514,9 +11490,9 @@ static void ggml_compute_forward_silu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -11577,6 +11553,8 @@ static void ggml_compute_forward_leaky_relu_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11626,11 +11604,11 @@ static void ggml_compute_forward_silu_back_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * grad = dst->src[1]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); - GGML_ASSERT(ggml_are_same_shape(src0, dst)); - GGML_ASSERT(ggml_are_same_shape(src0, grad)); + assert(ggml_is_contiguous_1(grad)); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); + assert(ggml_are_same_shape(src0, grad)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -11692,6 +11670,8 @@ static void ggml_compute_forward_hardswish_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11701,9 +11681,6 @@ static void ggml_compute_forward_hardswish_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_hardswish_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -11735,6 +11712,8 @@ static void ggml_compute_forward_hardsigmoid_f32( const struct ggml_tensor * src0 = dst->src[0]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11744,9 +11723,6 @@ static void ggml_compute_forward_hardsigmoid_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert(dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { ggml_vec_hardsigmoid_f32(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -12197,39 +12173,6 @@ static void ggml_compute_forward_group_norm( // ggml_compute_forward_mul_mat -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) -// helper function to determine if it is better to use BLAS or not -// for large matrices, BLAS is faster -static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { - const struct ggml_tensor * src0 = dst->src[0]; - const struct ggml_tensor * src1 = dst->src[1]; - - //const int64_t ne00 = src0->ne[0]; - //const int64_t ne01 = src0->ne[1]; - - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - // NOTE: with GGML_OP_MUL_MAT_ID we don't want to go through the BLAS branch because it will dequantize (to_float) - // all the experts for each batch element and the processing would become incredibly slow - // TODO: find the optimal values for these - if (dst->op != GGML_OP_MUL_MAT_ID && - ggml_is_contiguous(src0) && - ggml_is_contiguous(src1) && - //src0->type == GGML_TYPE_F32 && - src1->type == GGML_TYPE_F32 && - (ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) { - - /*printf("BLAS: %d %d %d %d %d\n", ne0, ne1, ne10, ne00, ne01);*/ - return true; - } - - return false; -} -#endif - static void ggml_compute_forward_mul_mat_one_chunk( const struct ggml_compute_params * params, struct ggml_tensor * dst, @@ -12367,82 +12310,6 @@ static void ggml_compute_forward_mul_mat( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(dst)) { - const int64_t ne_plane = ne01*ne00; - const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); - UNUSED(desired_wsize); - - if (params->type == GGML_TASK_TYPE_INIT) { - if (type != GGML_TYPE_F32) { - assert(params->wsize >= desired_wsize); - // parallelize by src0 rows - for (int64_t i13 = 0; i13 < ne13; i13++) { - for (int64_t i12 = 0; i12 < ne12; i12++) { - // broadcast src0 into src1 across 2nd,3rd dimension - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; - - const void * x = (char *) src0->data + i02*nb02 + i03*nb03; - float * const wdata = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; - ggml_to_float_t const to_float = type_traits[type].to_float; - - for (int64_t i01 = ith; i01 < ne01; i01 += nth) { - to_float((const char *) x + i01*nb01, wdata + i01*ne00, ne00); - } - } - } - } - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // perform sgemm, parallelization controlled by blas lib - if (ith != 0) { - return; - } - - //const int64_t tgemm0 = ggml_perf_time_us(); - for (int64_t i13 = 0; i13 < ne13; i13++) { - for (int64_t i12 = 0; i12 < ne12; i12++) { - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; - - const void * x = (char *) src0->data + i02*nb02 + i03*nb03; - const float * y = (float *) ((char *) src1->data + i12*nb12 + i13*nb13); - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - - if (type != GGML_TYPE_F32) { - x = (float *) params->wdata + i13*ne12*ne_plane + i12*ne_plane; - } - - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne1, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } - //printf("cblas_sgemm = %.3f ms, %lld flops\n", (ggml_perf_time_us() - tgemm0)/1000.0, ne13*ne12*ne1*ne01*ne10*2); - - //printf("CBLAS = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); - - return; - } -#endif - #if GGML_USE_LLAMAFILE const bool src1_cont = ggml_is_contiguous(src1); @@ -12823,21 +12690,7 @@ static void ggml_compute_forward_out_prod_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_CLBLAST) - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - bool use_blas = ggml_is_matrix(src0) && - ggml_is_matrix(src1) && - ggml_is_contiguous(src0) && - (ggml_is_contiguous(src1) || ggml_is_transposed(src1)); -#endif - if (params->type == GGML_TASK_TYPE_INIT) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) // gemm beta will zero dst - if (use_blas) { - return; - } -#endif if (ith != 0) { return; } @@ -12849,50 +12702,6 @@ static void ggml_compute_forward_out_prod_f32( return; } -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (use_blas) { - if (params->ith != 0) { // All threads other than the first do no work. - return; - } - // Arguments to ggml_compute_forward_out_prod (expressed as major,minor) - // src0: (k,n) - // src1: (k,m) - // dst: (m,n) - // - // Arguments to sgemm (see https://github.com/Reference-LAPACK/lapack/blob/master/BLAS/SRC/sgemm.f) - // Also expressed as (major,minor) - // a: (m,k): so src1 transposed - // b: (k,n): so src0 - // c: (m,n) - // - // However, if ggml_is_transposed(src1) is true, then - // src1->data already contains a transposed version, so sgemm mustn't - // transpose it further. - - int n = src0->ne[0]; - int k = src0->ne[1]; - int m = src1->ne[0]; - - int transposeA, lda; - - if (!ggml_is_transposed(src1)) { - transposeA = CblasTrans; - lda = m; - } else { - transposeA = CblasNoTrans; - lda = k; - } - - float * a = (float *) ((char *) src1->data); - float * b = (float *) ((char *) src0->data); - float * c = (float *) ((char *) dst->data); - - cblas_sgemm(CblasRowMajor, transposeA, CblasNoTrans, m, n, k, 1.0, a, lda, b, n, 0.0, c, n); - - return; - } -#endif - // dst[:,:,:,:] = 0 // for i2,i3: // for i1: @@ -13022,8 +12831,6 @@ static void ggml_compute_forward_out_prod_q_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) - if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { return; @@ -13420,6 +13227,8 @@ static void ggml_compute_forward_get_rows_q( const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + assert(i01 >= 0 && i01 < ne01); + dequantize_row_q( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); @@ -13463,6 +13272,8 @@ static void ggml_compute_forward_get_rows_f16( const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + assert(i01 >= 0 && i01 < ne01); + ggml_fp16_to_fp32_row( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); @@ -13506,7 +13317,9 @@ static void ggml_compute_forward_get_rows_bf16( const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); - ggml_bf16_to_fp32_row( + assert(i01 >= 0 && i01 < ne01); + + ggml_bf16_to_fp32_row( (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); } @@ -13549,6 +13362,8 @@ static void ggml_compute_forward_get_rows_f32( const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + assert(i01 >= 0 && i01 < ne01); + ggml_vec_cpy_f32(nc, (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), (float *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03)); @@ -14219,8 +14034,7 @@ static float rope_yarn_ramp(const float low, const float high, const int i0) { // MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. static void rope_yarn( float theta_extrap, float freq_scale, float corr_dims[2], int64_t i0, float ext_factor, float mscale, - float * cos_theta, float * sin_theta -) { + float * cos_theta, float * sin_theta) { // Get n-d rotational scaling corrected for extrapolation float theta_interp = freq_scale * theta_extrap; float theta = theta_interp; @@ -14237,18 +14051,19 @@ static void rope_yarn( // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get // `corr_dim(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -static float ggml_rope_yarn_corr_dim(int n_dims, int n_orig_ctx, float n_rot, float base) { - return n_dims * logf(n_orig_ctx / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); +static float ggml_rope_yarn_corr_dim(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * logf(n_ctx_orig / (n_rot * 2 * (float)M_PI)) / (2 * logf(base)); } static void ggml_rope_cache_init( - float theta_base, float freq_scale, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, - float * cache, float sin_sign, float theta_scale -) { + float theta_base, float freq_scale, const float * freq_factors, float corr_dims[2], int64_t ne0, float ext_factor, float mscale, + float * cache, float sin_sign, float theta_scale) { + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py float theta = theta_base; for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + const float ff = freq_factors ? freq_factors[i0/2] : 1.0f; rope_yarn( - theta, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] + theta/ff, freq_scale, corr_dims, i0, ext_factor, mscale, &cache[i0 + 0], &cache[i0 + 1] ); cache[i0 + 1] *= sin_sign; @@ -14257,11 +14072,11 @@ static void ggml_rope_cache_init( } GGML_CALL void ggml_rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims - float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)); - float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)); + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base)); dims[0] = MAX(0, start); dims[1] = MIN(n_dims - 1, end); } @@ -14281,15 +14096,11 @@ static void ggml_compute_forward_rope_f32( float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; - // these two only relevant for xPos RoPE: - float xpos_base; - bool xpos_down; - //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); @@ -14297,8 +14108,6 @@ static void ggml_compute_forward_rope_f32( memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); - memcpy(&xpos_base, (int32_t *) dst->op_params + 11, sizeof(float)); - memcpy(&xpos_down, (int32_t *) dst->op_params + 12, sizeof(bool)); GGML_TENSOR_UNARY_OP_LOCALS @@ -14326,22 +14135,17 @@ static void ggml_compute_forward_rope_f32( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; - const bool is_glm = mode & 4; const float * freq_factors = NULL; - if (is_neox) { - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - } else { - GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; } // backward process uses inverse rotation by cos and sin. @@ -14356,101 +14160,50 @@ static void ggml_compute_forward_rope_f32( const int64_t p = pos[i2]; float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox - ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; - float theta_base = (float)p; - - if (is_glm) { - theta_base = MIN(p, n_ctx - 2); - float block_theta = MAX(p - (n_ctx - 2), 0); - for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base) * sin_sign; - const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta) * sin_sign; - - theta_base *= theta_scale; - block_theta *= theta_scale; - - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = src[0]; - const float x1 = src[n_dims/2]; - const float x2 = src[n_dims]; - const float x3 = src[n_dims/2*3]; - - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta; - dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta; - } - } else if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; - // zeta scaling for xPos only: - float zeta = xpos_base != 0.0f ? powf((i0 + 0.4f * ne0) / (1.4f * ne0), p / xpos_base) : 1.0f; - if (xpos_down) zeta = 1.0f / zeta; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); const float x0 = src[0]; const float x1 = src[1]; - dst_data[0] = x0*cos_theta*zeta - x1*sin_theta*zeta; - dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta; + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[1] = x0*sin_theta + x1*cos_theta; } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; - for (int64_t ic = 0; ic < ne0; ic += 2) { - if (ic < n_dims) { - const int64_t ib = 0; - - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; - float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; - float cos_theta, sin_theta; - rope_yarn( - theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, - &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; - - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - const float x0 = src[0]; - const float x1 = src[n_dims/2]; + const float x0 = src[0]; + const float x1 = src[n_dims/2]; - dst_data[0] = x0*cos_theta - x1*sin_theta; - dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; - } else { - const int64_t i0 = ic; + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + } + } - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } @@ -14476,8 +14229,8 @@ static void ggml_compute_forward_rope_f16( //const int n_past = ((int32_t *) dst->op_params)[0]; const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; - const int n_ctx = ((int32_t *) dst->op_params)[3]; - const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + //const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_ctx_orig = ((int32_t *) dst->op_params)[4]; memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); @@ -14511,22 +14264,17 @@ static void ggml_compute_forward_rope_f16( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; - ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; - const bool is_glm = mode & 4; const float * freq_factors = NULL; - if (is_neox) { - if (src2 != NULL) { - GGML_ASSERT(src2->type == GGML_TYPE_F32); - GGML_ASSERT(src2->ne[0] >= n_dims / 2); - freq_factors = (const float *) src2->data; - } - } else { - GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; } // backward process uses inverse rotation by cos and sin. @@ -14541,43 +14289,14 @@ static void ggml_compute_forward_rope_f16( const int64_t p = pos[i2]; float * cache = (float *) params->wdata + (ne0 + CACHE_LINE_SIZE_F32)*ith; - if (!is_glm && !is_neox) { // TODO: cache sin/cos for glm, neox - ggml_rope_cache_init(p, freq_scale, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); - } + ggml_rope_cache_init(p, freq_scale, freq_factors, corr_dims, ne0, ext_factor, attn_factor, cache, sin_sign, theta_scale); for (int64_t i1 = 0; i1 < ne1; i1++) { if (ir++ < ir0) continue; if (ir > ir1) break; - float theta_base = (float)p; - - if (is_glm) { - theta_base = MIN(p, n_ctx - 2); - float block_theta = MAX(p - (n_ctx - 2), 0); - for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { - const float cos_theta = cosf(theta_base); - const float sin_theta = sinf(theta_base) * sin_sign; - const float cos_block_theta = cosf(block_theta); - const float sin_block_theta = sinf(block_theta) * sin_sign; - - theta_base *= theta_scale; - block_theta *= theta_scale; - - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - const float x2 = GGML_FP16_TO_FP32(src[n_dims]); - const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]); - - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta); - dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta); - } - } else if (!is_neox) { - for (int64_t i0 = 0; i0 < ne0; i0 += 2) { + if (!is_neox) { + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { const float cos_theta = cache[i0 + 0]; const float sin_theta = cache[i0 + 1]; @@ -14591,47 +14310,29 @@ static void ggml_compute_forward_rope_f16( dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; - for (int64_t ic = 0; ic < ne0; ic += 2) { - if (ic < n_dims) { - const int64_t ib = 0; - - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; - float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; - - float cos_theta, sin_theta; - rope_yarn( - theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, - &cos_theta, &sin_theta - ); - sin_theta *= sin_sign; + for (int64_t i0 = 0; i0 < n_dims; i0 += 2) { + const int64_t ic = i0/2; - theta_base *= theta_scale; - - const int64_t i0 = ib*n_dims + ic/2; + const float cos_theta = cache[i0 + 0]; + const float sin_theta = cache[i0 + 1]; - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + ic*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + ic*nb0); - const float x0 = GGML_FP16_TO_FP32(src[0]); - const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); - dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); - dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); - } else { - const int64_t i0 = ic; + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + } + } - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); - ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + for (int64_t i0 = n_dims; i0 < ne0; i0 += 2) { + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); - dst_data[0] = src[0]; - dst_data[1] = src[1]; - } - } + dst_data[0] = src[0]; + dst_data[1] = src[1]; } } } @@ -16818,7 +16519,10 @@ static void ggml_compute_forward_map_unary_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_are_same_shape(src0, dst)); + assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(dst)); + assert(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -16827,9 +16531,6 @@ static void ggml_compute_forward_map_unary_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -16867,6 +16568,9 @@ static void ggml_compute_forward_map_binary_f32( const struct ggml_tensor * src1 = dst->src[1]; assert(params->ith == 0); + assert(ggml_is_contiguous_1(src0)); + assert(ggml_is_contiguous_1(src1)); + assert(ggml_is_contiguous_1(dst)); assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -16876,10 +16580,6 @@ static void ggml_compute_forward_map_binary_f32( const int n = ggml_nrows(src0); const int nc = src0->ne[0]; - assert( dst->nb[0] == sizeof(float)); - assert(src0->nb[0] == sizeof(float)); - assert(src1->nb[0] == sizeof(float)); - for (int i = 0; i < n; i++) { fun(nc, (float *) ((char *) dst->data + i*( dst->nb[1])), @@ -18333,9 +18033,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + //const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); @@ -18343,8 +18043,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -18354,16 +18052,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src2, n_dims, mode, - n_ctx, - n_orig_ctx, + n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, - beta_slow, - xpos_base, - xpos_down), + beta_slow), zero_table); } } break; @@ -18373,9 +18068,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor //const int n_past = ((int32_t *) tensor->op_params)[0]; const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, xpos_base, xpos_down; + //const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_ctx_orig = ((int32_t *) tensor->op_params)[4]; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; memcpy(&freq_base, (int32_t *) tensor->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) tensor->op_params + 6, sizeof(float)); @@ -18383,8 +18078,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor memcpy(&attn_factor, (int32_t *) tensor->op_params + 8, sizeof(float)); memcpy(&beta_fast, (int32_t *) tensor->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) tensor->op_params + 10, sizeof(float)); - memcpy(&xpos_base, (int32_t *) tensor->op_params + 11, sizeof(float)); - memcpy(&xpos_down, (int32_t *) tensor->op_params + 12, sizeof(bool)); src0->grad = ggml_add_or_set(ctx, src0->grad, @@ -18394,16 +18087,13 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src2, n_dims, mode, - n_ctx, - n_orig_ctx, + n_ctx_orig, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, - xpos_base, - xpos_down, false), zero_table); } @@ -19047,6 +18737,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ switch (node->op) { case GGML_OP_CPY: case GGML_OP_DUP: + case GGML_OP_CONT: case GGML_OP_ADD: case GGML_OP_ADD1: case GGML_OP_ACC: @@ -19131,7 +18822,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ } break; case GGML_OP_SCALE: case GGML_OP_SET: - case GGML_OP_CONT: case GGML_OP_RESHAPE: case GGML_OP_VIEW: case GGML_OP_PERMUTE: @@ -19291,8 +18981,11 @@ static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_comput sched_yield(); } - * node_n = atomic_load(&state->shared->node_n); - if (* node_n != last_node_n) break; + *node_n = atomic_load(&state->shared->node_n); + if (*node_n != last_node_n) { + break; + } + #if defined(__SSE3__) // Tell the processor we're spinning. It's a processor hint for spinlocks. _mm_pause(); @@ -19302,15 +18995,18 @@ static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_comput static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_compute_state * state, const bool do_yield) { // wait for other threads to finish - const int last_task_phase = * task_phase; + const int last_task_phase = *task_phase; while (true) { if (do_yield) { sched_yield(); } - * task_phase = atomic_load(&state->shared->node_task); - if (* task_phase != last_task_phase) break; + *task_phase = atomic_load(&state->shared->node_task); + if (*task_phase != last_task_phase) { + break; + } + #if defined(__SSE3__) // Tell the processor we're spinning. It's a processor hint for spinlocks. _mm_pause(); @@ -19510,22 +19206,6 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa { const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { - cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); - } else -#endif -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node)) { - if (node->src[0]->type != GGML_TYPE_F32) { - // here we need memory for fully dequantized matrix from src0 - // take into account that src0 can be broadcasted into src1[2,3] - cur = ggml_type_size(GGML_TYPE_F32) - * node->src[0]->ne[0]*node->src[0]->ne[1] - * node->src[1]->ne[2]*node->src[1]->ne[3]; - } - } else -#endif if (node->src[1]->type != vec_dot_type) { cur = ggml_row_size(vec_dot_type, ggml_nelements(node->src[1])); } @@ -19644,6 +19324,59 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa return cplan; } +static enum ggml_status ggml_graph_compute_parallel(struct ggml_compute_state * workers, int n_threads) { + enum ggml_status compute_status = GGML_STATUS_SUCCESS; + +#ifdef GGML_USE_OPENMP + if (n_threads > 1) { + #pragma omp parallel num_threads(n_threads) + { + #pragma omp single + { + // update the number of threads from the actual number of threads that we got from OpenMP + n_threads = omp_get_num_threads(); + workers[0].shared->n_threads = n_threads; + workers[0].shared->n_active = n_threads; + } + ggml_graph_compute_thread(&workers[omp_get_thread_num()]); + } + } else { + ggml_graph_compute_thread(&workers[0]); + } +#else + // create thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; ++j) { + const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); + GGML_ASSERT(rc == 0); + UNUSED(rc); + } + } + + // this is a work thread too + ggml_graph_compute_thread(&workers[0]); + + // join or kill thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; j++) { + const int rc = ggml_thread_join(workers[j].thrd, NULL); + GGML_ASSERT(rc == 0); + UNUSED(rc); + } + } +#endif + // don't leave affinity set on the main thread + clear_numa_thread_affinity(); + + for (int j = 0; j < n_threads; j++) { + if (workers[j].ec != GGML_STATUS_SUCCESS) { + compute_status = workers[j].ec; + break; + } + } + return compute_status; +} + enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); @@ -19654,7 +19387,11 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl } } - const int n_threads = cplan->n_threads; + int n_threads = cplan->n_threads; + +#if defined(GGML_USE_OPENMP) + n_threads = MIN(n_threads, omp_get_max_threads()); +#endif struct ggml_compute_state_shared state_shared = { /*.cgraph =*/ cgraph, @@ -19670,47 +19407,20 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); - - // create thread pool - if (n_threads > 1) { - for (int j = 1; j < n_threads; ++j) { - workers[j] = (struct ggml_compute_state) { - .thrd = 0, - .ith = j, - .shared = &state_shared, - .ec = GGML_STATUS_SUCCESS, - }; - - const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); - GGML_ASSERT(rc == 0); - UNUSED(rc); - } - } - - workers[0].ith = 0; - workers[0].shared = &state_shared; - workers[0].ec = GGML_STATUS_SUCCESS; - const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); - // this is a work thread too - ggml_graph_compute_thread(&workers[0]); - enum ggml_status compute_status = workers[0].ec; - - // don't leave affinity set on the main thread - clear_numa_thread_affinity(); - - // join or kill thread pool - if (n_threads > 1) { - for (int j = 1; j < n_threads; j++) { - const int rc = ggml_thread_join(workers[j].thrd, NULL); - GGML_ASSERT(rc == 0); - if (workers[j].ec != GGML_STATUS_SUCCESS) - compute_status = workers[j].ec; - } + for (int j = 0; j < n_threads; ++j) { + workers[j] = (struct ggml_compute_state) { + .thrd = 0, + .ith = j, + .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, + }; } + enum ggml_status compute_status = ggml_graph_compute_parallel(workers, n_threads); + // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; @@ -22742,6 +22452,16 @@ int ggml_cpu_has_neon(void) { #endif } +int ggml_cpu_has_sve(void) { +#if defined(__ARM_FEATURE_SVE) + // TODO: Currently, SVE 256 bit is only supported. + GGML_ASSERT(svcntb() == QK8_0); + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; @@ -22783,7 +22503,7 @@ int ggml_cpu_has_wasm_simd(void) { } int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_BLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) return 1; #else return 0; @@ -22798,14 +22518,6 @@ int ggml_cpu_has_cuda(void) { #endif } -int ggml_cpu_has_clblast(void) { -#if defined(GGML_USE_CLBLAST) - return 1; -#else - return 0; -#endif -} - int ggml_cpu_has_vulkan(void) { #if defined(GGML_USE_VULKAN) return 1; @@ -22830,9 +22542,16 @@ int ggml_cpu_has_sycl(void) { #endif } +int ggml_cpu_has_rpc(void) { +#if defined(GGML_USE_RPC) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || - ggml_cpu_has_sycl(); + return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index be81e0c52316b..13502a3622fc4 100644 --- a/ggml.h +++ b/ggml.h @@ -756,7 +756,6 @@ extern "C" { GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); - GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor); GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); @@ -765,6 +764,11 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous() + GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 + GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); @@ -1007,12 +1011,13 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); - // concat a and b on dim 2 + // concat a and b along dim // used in stable-diffusion GGML_API struct ggml_tensor * ggml_concat( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int dim); GGML_API struct ggml_tensor * ggml_abs( struct ggml_context * ctx, @@ -1460,7 +1465,6 @@ extern "C" { // rotary position embedding // if mode & 1 == 1, skip n_past elements (NOT SUPPORTED) // if mode & 2 == 1, GPT-NeoX style - // if mode & 4 == 1, ChatGLM style // // b is an int32 vector with size a->ne[2], it contains the positions // c is freq factors (e.g. phi3-128k), (optional) @@ -1469,8 +1473,7 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - int mode, - int n_ctx); + int mode); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_inplace( @@ -1478,8 +1481,7 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - int mode, - int n_ctx); + int mode); // custom RoPE GGML_API struct ggml_tensor * ggml_rope_ext( @@ -1489,8 +1491,7 @@ extern "C" { struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -1506,8 +1507,7 @@ extern "C" { struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -1521,8 +1521,7 @@ extern "C" { struct ggml_tensor * b, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -1537,8 +1536,7 @@ extern "C" { struct ggml_tensor * b, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, @@ -1549,7 +1547,7 @@ extern "C" { // compute correction dims for YaRN RoPE scaling GGML_CALL void ggml_rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy // a - dy @@ -1560,16 +1558,13 @@ extern "C" { struct ggml_tensor * c, int n_dims, int mode, - int n_ctx, - int n_orig_ctx, + int n_ctx_orig, float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, - float beta_slow, - float xpos_base, - bool xpos_down); + float beta_slow); // clamp // in-place, returns view(a) @@ -2404,6 +2399,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); + GGML_API int ggml_cpu_has_sve (void); GGML_API int ggml_cpu_has_arm_fma (void); GGML_API int ggml_cpu_has_metal (void); GGML_API int ggml_cpu_has_f16c (void); @@ -2411,13 +2407,13 @@ extern "C" { GGML_API int ggml_cpu_has_wasm_simd (void); GGML_API int ggml_cpu_has_blas (void); GGML_API int ggml_cpu_has_cuda (void); - GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_vulkan (void); GGML_API int ggml_cpu_has_kompute (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); GGML_API int ggml_cpu_has_ssse3 (void); GGML_API int ggml_cpu_has_sycl (void); + GGML_API int ggml_cpu_has_rpc (void); GGML_API int ggml_cpu_has_vsx (void); GGML_API int ggml_cpu_has_matmul_int8(void); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index a8f7373df125f..38914eedb7976 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -4,2819 +4,43 @@ import argparse import asyncio import os -import sys -from tempfile import gettempdir, NamedTemporaryFile +from tempfile import gettempdir logger = logging.getLogger("ggml-vk-generate-shaders") -shader_f32 = """ -#define FLOAT_TYPE float -""" -shader_f16 = """ -#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require -#define FLOAT_TYPE float16_t -""" -shader_int8_ext = """ -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require -""" - -# Type-specific defines -shader_f32_defines = """ -#define QUANT_K 1 -#define QUANT_R 1 - -#define A_TYPE float -""" -shader_f16_defines = """ -#define QUANT_K 1 -#define QUANT_R 1 - -#define A_TYPE float16_t -""" -shader_q4_0_defines = """ -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_0 -{ - float16_t d; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_0 -""" -shader_q4_1_defines = """ -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q4_1 -{ - float16_t d; - float16_t m; - uint8_t qs[16]; -}; - -#define A_TYPE block_q4_1 -""" -shader_q5_0_defines = """ -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_0 -{ - float16_t d; - uint16_t qh[2]; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_0 -""" -shader_q5_1_defines = """ -#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require -#define QUANT_K 32 -#define QUANT_R 2 - -struct block_q5_1 -{ - float16_t d; - float16_t m; - uint qh; - uint8_t qs[16]; -}; - -#define A_TYPE block_q5_1 -""" -shader_q8_0_defines = """ -#define QUANT_K 32 -#define QUANT_R 1 - -struct block_q8_0 -{ - float16_t d; - int8_t qs[32]; -}; - -#define A_TYPE block_q8_0 -""" - -# K-quants -shader_q2_K_defines = """ -#define QUANT_K 256 - -struct block_q2_K -{ - uint8_t scales[QUANT_K/16]; - uint8_t qs[QUANT_K/4]; - f16vec2 d; -}; - -#define A_TYPE block_q2_K -""" -shader_q3_K_defines = """ -#define QUANT_K 256 - -struct block_q3_K -{ - uint8_t hmask[QUANT_K/8]; - uint8_t qs[QUANT_K/4]; - uint8_t scales[12]; - float16_t d; -}; - -#define A_TYPE block_q3_K -""" -shader_q4_K_defines = """ -#define QUANT_K 256 - -struct block_q4_K -{ - f16vec2 d; - uint8_t scales[3*QUANT_K/64]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q4_K -""" -shader_q5_K_defines = """ -#define QUANT_K 256 - -struct block_q5_K -{ - f16vec2 d; - uint8_t scales[12]; - uint8_t qh[QUANT_K/8]; - uint8_t qs[QUANT_K/2]; -}; - -#define A_TYPE block_q5_K -""" -shader_q6_K_defines = """ -#define QUANT_K 256 - -struct block_q6_K -{ - uint8_t ql[QUANT_K/2]; - uint8_t qh[QUANT_K/4]; - int8_t scales[QUANT_K/16]; - float16_t d; -}; - -#define A_TYPE block_q6_K -""" - -# Dequant functions -shader_float_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); -} -""" - -shader_q4_0_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; -} -""" - -shader_q4_1_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2(vui & 0xF, vui >> 4) * d + m; -} -""" - -shader_q5_0_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; -} -""" - -shader_q5_1_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - const float m = float(data_a[a_offset + ib].m); - const uint uint_qh = data_a[a_offset + ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[a_offset + ib].qs[iqs]); - return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; -} -""" - -shader_q8_0_dequant_func = """ -vec2 dequantize(uint ib, uint iqs, uint a_offset) { - const float d = float(data_a[a_offset + ib].d); - return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; -} -""" - -# MULMAT - -mulmat_head = """#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#ifdef MUL_MAT_ID -#extension GL_EXT_buffer_reference2 : require -#extension GL_EXT_nonuniform_qualifier : require -#extension GL_EXT_scalar_block_layout : require -#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require - -#define EXPERT_COUNT 8 -#endif - -#ifndef LOAD_VEC_A -#define LOAD_VEC_A 1 -#endif -#ifndef LOAD_VEC_B -#define LOAD_VEC_B 1 -#endif -""" - -mulmat_body1 = """ -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -#ifdef MUL_MAT_ID -layout (binding = 3) readonly buffer IDS {int data_ids[];}; -#endif - -layout (push_constant) uniform parameter -{ - uint M; - uint N; - uint K; - uint stride_a; - uint stride_b; - uint stride_d; - uint k_split; - - uint ne02; - uint ne12; - uint broadcast2; - uint broadcast3; - - uint batch_stride_a; - uint batch_stride_b; - uint batch_stride_d; - -#ifdef MUL_MAT_ID - uint expert_stride_a; - uint expert_stride_b0; - uint expert_stride_b1; - uint expert_stride_d; - - uint ids_stride; - - uint n_as; - uint nei0; - uint nei1; - uint nbi1; - uint ne11; -#endif -} p; - -layout (constant_id = 1) const uint BM = 64; -layout (constant_id = 2) const uint BN = 64; -layout (constant_id = 3) const uint BK = 16; // Assumed to be 32 if working with a quant -layout (constant_id = 4) const uint WM = 32; -layout (constant_id = 5) const uint WN = 32; -layout (constant_id = 6) const uint WMITER = 2; -layout (constant_id = 7) const uint TM = 4; -layout (constant_id = 8) const uint TN = 2; -layout (constant_id = 9) const uint WARP = 32; - -shared FLOAT_TYPE buf_a[BM * (BK+1)]; -shared FLOAT_TYPE buf_b[BN * (BK+1)]; - -#ifdef MUL_MAT_ID -shared u8vec2 rowids[2048]; -#endif - -void main() { -#ifdef MUL_MAT_ID - const uint batch_idx = gl_GlobalInvocationID.z / p.n_as; - const uint expert_idx = gl_GlobalInvocationID.z % p.n_as; -#else - const uint batch_idx = gl_GlobalInvocationID.z; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - - const uint blocks_m = (p.M + BM - 1) / BM; - const uint ir = gl_WorkGroupID.x % blocks_m; - const uint ik = gl_WorkGroupID.x / blocks_m; - const uint ic = gl_WorkGroupID.y; - - const uint warp_i = gl_LocalInvocationID.x / WARP; - const uint warp_r = warp_i % (BM / WM); - const uint warp_c = warp_i / (BM / WM); - - const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER); - const uint WSUBM = WM / WMITER; - const uint WSUBN = WN / WNITER; - - const uint tiw = gl_LocalInvocationID.x % WARP; - const uint tiwr = tiw % (WSUBM / TM); - const uint tiwc = tiw / (WSUBM / TM); - - const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A); - const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A); - const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B); - const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B); - - const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK; - const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK; - -#ifdef MUL_MAT_ID - uint _ne1 = 0; - for (uint ii1 = 0; ii1 < p.nei1; ii1++) { - for (uint ii0 = 0; ii0 < p.nei0; ii0++) { - if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) { - rowids[_ne1] = u8vec2(ii0, ii1); - _ne1++; - } - } - } - - const u8vec2 id = rowids[ir * BN + ic]; -#endif - - const uint start_k = ik * p.k_split; - const uint end_k = min(p.K, (ik + 1) * p.k_split); - - uint pos_a = ( -#ifdef MUL_MAT_ID - expert_idx * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; - uint pos_b = ( -#ifdef MUL_MAT_ID - id.y * p.expert_stride_b1 + - (id.x % p.ne11) * p.expert_stride_b0 + -#endif - batch_idx * p.batch_stride_b + - ic * BN * p.stride_b + start_k) / LOAD_VEC_B; - - float sums[WMITER * TM * WNITER * TN]; - FLOAT_TYPE cache_a[WMITER * TM]; - FLOAT_TYPE cache_b[WNITER * TN]; - - [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { - sums[i] = 0.0f; - } - - [[unroll]] for (uint block = start_k; block < end_k; block += BK) { - [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {""" - -mulmat_load_scalar = """ -#if LOAD_VEC_A == 8 - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx][0].x); - buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y); - buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z); - buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w); - buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x); - buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y); - buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z); - buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w); -#elif LOAD_VEC_A == 4 - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - buf_a[buf_idx ] = FLOAT_TYPE(data_a[idx].x); - buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y); - buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z); - buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w); -#else - if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) { - buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]); - } else { - buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f); - } -#endif -""" - -mulmat_load_q4_0 = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" - -mulmat_load_q4_1 = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" - -mulmat_load_q5_0 = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" - -mulmat_load_q5_1 = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a; - - const uint ib = idx / 16; - const uint iqs = idx & 0xF; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint uint_qh = data_a[ib].qh; - const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); - const uint vui = uint(data_a[ib].qs[iqs]); - const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);""" - -mulmat_load_q8_0 = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 16; - const uint iqs = (idx & 0xF) * 2; - - const float d = float(data_a[ib].d); - const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d; - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);""" - - -mulmat_load_q2_K = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30 - const uint scalesi = iqs / 8; // 0..15 - const uint qsshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 - - const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]); - const uint scales = data_a[ib].scales[scalesi]; - const vec2 d = vec2(data_a[ib].d); - - const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4); - - buf_a[buf_idx ] = FLOAT_TYPE(v.x); - buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);""" - -mulmat_load_q3_K = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 64; // 0,1 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 - const uint hmi = (iqs % 16) * 2; // 0,2,4..30 - const uint j = (iqs % 64) / 4; // 0..3 - const uint is = iqs / 8; // 0..15 - const uint halfsplit = ((iqs % 64) / 16); // 0,1,2,3 - const uint qsshift = halfsplit * 2; // 0,2,4,6 - const uint m = 1 << (4 * n + halfsplit); // 1,2,4,8,16,32,64,128 - - const int8_t us = int8_t(is < 4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (data_a[ib].scales[is-8] >> 4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) : - (data_a[ib].scales[is-8] >> 4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4)); - const float dl = float(data_a[ib].d) * float(us - 32); - - buf_a[buf_idx ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi ] & m) != 0) ? 0 : 4))); - buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));""" - -mulmat_load_q4_K = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 32; // 0,1,2,3 - const uint b = (iqs % 32) / 16; // 0,1 - const uint is = 2 * n + b; // 0..7 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 - - const vec2 loadd = vec2(data_a[ib].d); - - uint8_t sc; - uint8_t mbyte; - if (is < 4) { - sc = uint8_t(data_a[ib].scales[is ] & 63); - mbyte = uint8_t(data_a[ib].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4)); - mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); - } - const float d = loadd.x * sc; - const float m = loadd.y * mbyte; - - buf_a[buf_idx ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) - m); - buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m);""" - -mulmat_load_q5_K = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 32; // 0,1,2,3 - const uint b = (iqs % 32) / 16; // 0,1 - const uint is = 2 * n + b; // 0..7 - const uint qsi = n * 32 + (iqs % 16) * 2; // 0,2,4..126 - const uint qhi = (iqs % 16) * 2; // 0,2,4..30 - - const uint8_t hm = uint8_t(1 << (iqs / 16)); - - const vec2 loadd = vec2(data_a[ib].d); - - uint8_t sc; - uint8_t mbyte; - if (is < 4) { - sc = uint8_t(data_a[ib].scales[is ] & 63); - mbyte = uint8_t(data_a[ib].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4)); - mbyte = uint8_t((data_a[ib].scales[is + 4] >> 4) | ((data_a[ib].scales[is ] >> 6) << 4)); - } - const float d = loadd.x * sc; - const float m = loadd.y * mbyte; - - buf_a[buf_idx ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi ] & hm) != 0 ? 16 : 0)) - m); - buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m);""" - -mulmat_load_q6_K = """ - const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a; - const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A; - - const uint ib = idx / 128; // 2 values per idx - const uint iqs = idx % 128; // 0..127 - - const uint n = iqs / 64; // 0,1 - const uint b = (iqs % 64) / 32; // 0,1 - const uint is_b = (iqs % 16) / 8; // 0,1 - const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 - const uint is = 8 * n + qhshift + is_b; // 0..15 - const uint qsi = n * 64 + (iqs % 32) * 2; // 0,2,4..126 - const uint qhi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 - - const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]); - - buf_a[buf_idx ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi ] >> qhshift) & 3) << 4)) - 32)); - buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));""" - -mulmat_body2 = """ - } - [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { -#if LOAD_VEC_B == 8 - const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; - const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; - buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x); - buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y); - buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z); - buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w); - buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x); - buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y); - buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z); - buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w); -#elif LOAD_VEC_B == 4 - const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; - const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; - buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); - buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); - buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); - buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); -#else - if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); - } else { - buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); - } -#endif - } - - barrier(); - - pos_a += BK / LOAD_VEC_A; - pos_b += BK / LOAD_VEC_B; - - for (uint i = 0; i < BK; i++) { - // Load from shared into cache - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - [[unroll]] for (uint j = 0; j < TM; j++) { - cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i]; - } - } - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint j = 0; j < TN; j++) { - cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i]; - } - } - - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - [[unroll]] for (uint cc = 0; cc < TN; cc++) { - [[unroll]] for (uint cr = 0; cr < TM; cr++) { - sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]); - } - } - } - } - } - - barrier(); - } - - const uint dr = ir * BM + warp_r * WM; - const uint dc = ic * BN + warp_c * WN; - - const uint offsets = -#ifdef MUL_MAT_ID - expert_idx * p.expert_stride_d + -#endif - batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; - - [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { - [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { - - const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; - const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; - [[unroll]] for (uint cc = 0; cc < TN; cc++) { - [[unroll]] for (uint cr = 0; cr < TM; cr++) { - if (dr_warp + cr < p.M && dc_warp + cc < p.N) { - data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); - } - } - } - } - } -} -""" - -mulmat_split_k_reduce_src = """#version 450 - -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {float data_d[];}; - -layout (push_constant) uniform parameter { - uint ne; - uint k_num; -} p; - -void main() { - const uint idx = gl_GlobalInvocationID.x; - - if (idx >= p.ne) { - return; - } - - float result = 0.0f; - - [[unroll]] for (uint i = 0; i < p.k_num; i++) { - result += data_a[i * p.ne + idx]; - } - - data_d[idx] = result; -} -""" - -# DEQUANT SHADER -dequant_head = """#version 450 - -#extension GL_EXT_control_flow_attributes : require -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint M; - uint K; - uint stride_a; - uint stride_b; - uint nel; -} p; -""" - -dequant_f32_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {float data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_GlobalInvocationID.x * 16; - - if (i >= p.nel) { - return; - } - - [[unroll]] for (uint l = 0; l < 16; l++) { - data_b[i + l] = D_TYPE(data_a[i + l]); - } -} -""" - -dequant_q4_0_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q4_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const float dm = -8.0f * d; - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm); - data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + dm); - } -} -""" - -dequant_q4_1_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q4_1 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - data_b[b_idx + l + 0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m); - data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >> 4) + m); - } -} -""" - -dequant_q5_0_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q5_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - const uint iqs = q_idx + l; - const uint vui = uint(data_a[ib].qs[iqs]); - data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f)); - data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f)); - } -} -""" - -dequant_q5_1_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q5_1 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 8*il; - - const float d = float(data_a[ib].d); - const float m = float(data_a[ib].m); - const uint qh = data_a[ib].qh; - - const uint q_idx = 8*il; - - [[unroll]] for (uint l = 0; l < 8; ++l) { - const uint iqs = q_idx + l; - const uint vui = uint(data_a[ib].qs[iqs]); - data_b[b_idx + l + 0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m); - data_b[b_idx + l + 16] = D_TYPE(d * (((vui >> 4) | ((qh >> (iqs + 12)) & 0x10))) + m); - } -} -""" - -dequant_q8_0_body = """ -layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {block_q8_0 data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64; - - const uint tid = gl_LocalInvocationID.x % 64; - const uint il = tid/32; - const uint ir = tid%32; - const uint ib = 32*i + ir; - if (ib >= p.nel / 32) { - return; - } - - const uint b_idx = 1024*i + 32*ir + 16*il; - - const float d = float(data_a[ib].d); - - const uint q_idx = 16*il; - - [[unroll]] for (uint l = 0; l < 16; l += 2) { - data_b[b_idx + l ] = D_TYPE(d * data_a[ib].qs[q_idx + l ]); - data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]); - } -} -""" - -# K-quants -dequant_q2_K_body = """ -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint ip = tid / 32; - const uint il = tid - 32 * ip; - const uint is = 8 * ip + il / 16; - - const uint y_idx = i * QUANT_K + 128 * ip + il; - - const uint ql_idx = 32 * ip + il; - const uint8_t qs = data_a[i].qs[32 * ip + il]; - - FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - data_b[y_idx + 0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4)); - data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4)); - data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4)); - data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4)); - } -} -""" -dequant_q3_K_body = """ -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = uint(gl_WorkGroupID.x * 256 + wgy); - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint r = gl_LocalInvocationID.x / 4; - const uint tid = r / 2; - const uint is0 = r % 2; - const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4); - const uint n = tid / 4; - const uint j = tid - 4*n; - - const uint8_t m = uint8_t(1 << (4*n + j)); - const uint is = 8*n + 2*j + is0; - const uint shift = 2*j; - - const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : - is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : - is < 12 ? (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) : - (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4)); - const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); - const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); - - const uint y_idx = i * QUANT_K + 128 * n + 32 * j; - const uint qs_idx = 32*n; - - for (uint l = l0; l < l0 + 4; ++l) { - data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); - } - } -} -""" -dequant_q4_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint il = tid / 8; - const uint ir = tid % 8; - const uint is = 2 * il; - const uint n = 4; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - - const uint y_idx = i * QUANT_K + 64 * il + n * ir; - const uint qs_idx = 32*il + n * ir; - - uint8_t sc; - uint8_t m; - if (is < 4) { - sc = uint8_t(data_a[i].scales[is] & 63); - m = uint8_t(data_a[i].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); - } - const FLOAT_TYPE d1 = dall * sc; - const FLOAT_TYPE m1 = dmin * m; - - if (is < 4) { - sc = uint8_t(data_a[i].scales[is + 1] & 63); - m = uint8_t(data_a[i].scales[is + 5] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); - } - const FLOAT_TYPE d2 = dall * sc; - const FLOAT_TYPE m2 = dmin * m; - - [[unroll]] for (uint l = 0; l < n; ++l) { - data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1); - data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >> 4) - m2); - } - } -} -""" -dequant_q5_K_body = """ -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - - const uint tid = gl_LocalInvocationID.x; - const uint il = tid / 16; - const uint ir = tid % 16; - const uint is = 2 * il; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); - - const uint y_idx = i * QUANT_K + 64 * il + 2 * ir; - const uint qs_idx = 32*il + 2 * ir; - const uint qh_idx = 2 * ir; - - uint8_t sc; - uint8_t m; - if (is < 4) { - sc = uint8_t(data_a[i].scales[is] & 63); - m = uint8_t(data_a[i].scales[is + 4] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); - } - const FLOAT_TYPE d1 = dall * sc; - const FLOAT_TYPE m1 = dmin * m; - - if (is < 4) { - sc = uint8_t(data_a[i].scales[is + 1] & 63); - m = uint8_t(data_a[i].scales[is + 5] & 63); - } else { - sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); - m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); - } - const FLOAT_TYPE d2 = dall * sc; - const FLOAT_TYPE m2 = dmin * m; - - const uint8_t hm1 = uint8_t(1 << (2 * il )); - const uint8_t hm2 = uint8_t(1 << (2 * il + 1)); - data_b[y_idx ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx ] & 0xF) + (((data_a[i].qh[qh_idx ] & hm1) != 0) ? 16 : 0)) - m1); - data_b[y_idx + 1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1); - data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx ] >> 4) + (((data_a[i].qh[qh_idx ] & hm2) != 0) ? 16 : 0)) - m2); - data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2); - } -} -""" -dequant_q6_K_body = """ -layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; - -void main() { - [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) { - const uint i = gl_WorkGroupID.x * 256 + wgy; - if (i >= p.M * p.K / QUANT_K) { - return; - } - const uint tid = gl_LocalInvocationID.x; - const uint ip = tid / 32; - const uint il = tid - 32 * ip; - const uint is = 8 * ip + il / 16; - - const uint y_idx = i * QUANT_K + 128 * ip + il; - - const uint ql_idx = 64 * ip + il; - const uint8_t qh = data_a[i].qh[32 * ip + il]; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); - - data_b[y_idx + 0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx + 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32))); - data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32))); - data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx + 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32))); - data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >> 4) | (((qh >> 6) & 3) << 4)) - 32))); - } -} -""" - -# Mul Mat Vec -mul_mat_vec_head = """#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require -#extension GL_EXT_shader_8bit_storage : require - -#ifdef MUL_MAT_ID -#define EXPERT_COUNT 8 -#endif -""" - - -mul_mat_vec_layout = """ -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; -#ifdef MUL_MAT_ID -layout (binding = 3) readonly buffer IDS {int data_ids[];}; -#endif - -layout (push_constant) uniform parameter -{ - uint ncols; - uint stride_a; - uint stride_b; - uint stride_d; - - uint ne02; - uint ne12; - uint broadcast2; - uint broadcast3; - - uint batch_stride_a; - uint batch_stride_b; - uint batch_stride_d; - -#ifdef MUL_MAT_ID - uint expert_stride_a; - uint expert_stride_b0; - uint expert_stride_b1; - uint expert_stride_d0; - uint expert_stride_d1; - - uint ne11; - uint nei0; - uint nbi1; - uint n_as; -#endif -} p; -""" - -mul_mat_vec_body = """ -layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; - -layout (constant_id = 0) const uint BLOCK_SIZE = 32; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint tid = gl_LocalInvocationID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - tmp[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) { - const uint col = i*BLOCK_SIZE + 2*tid; - const uint ib = (row*p.ncols + col)/QUANT_K; // block index - const uint iqs = (col%QUANT_K)/QUANT_R; // quant index - const uint iybs = col - col%QUANT_K; // y block start index - - vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); - - // matrix multiplication - tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) + - FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" - -# K-quants -mul_mat_vec_q2_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint s_offset = 8*v_im; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); - FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3); - sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF); - } - tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" -mul_mat_vec_q3_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - - const uint8_t m = uint8_t(1 << (4 * v_im)); - - const uint l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - const uint s_shift = 4 * v_im; - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)); - } - tmp[16 * ix + tid] += d * sum; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" -mul_mat_vec_q4_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 - - const uint il = tid/step; // 0...3 - const uint ir = tid - step*il; // 0...7 or 0...3 - const uint n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = n * (2 * ir + v_in); // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - -#if K_QUANTS_PER_ITERATION == 2 - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - - const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3); - const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7); - const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11); - const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15); - const FLOAT_TYPE smin = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7 - ); - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); -#else - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - - const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx ]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); - const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); - const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx ]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); - const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); - const FLOAT_TYPE smin = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 - ); - - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" -mul_mat_vec_q5_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 - - const uint il = tid/4; // 0...3 - const uint ir = tid - 4*il; // 0...7 or 0...3 - - const uint v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const uint v_in = il % 2; - - const uint l0 = 4*ir + 2*v_in; // 0...15 - const uint q_offset = 32*v_im + l0; - const uint y_offset = 64*v_im + l0; - - const uint8_t hm1 = uint8_t(1 << (2*v_im)); - const uint8_t hm2 = uint8_t(hm1 << 4); - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) { - const uint y1_idx = i * QUANT_K + y_offset; - const uint y2_idx = y1_idx + 128; - - const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); - const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); - - const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); - const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); - const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); - const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); - const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); - const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); - const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); - const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); - - const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); - const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); - const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf); - const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf); - const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); - const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); - const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4); - const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4); - const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); - const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); - const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf); - const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf); - const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); - const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); - const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); - - const FLOAT_TYPE sx = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx ]) * (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sy = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sz = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y2_idx ]) * (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE sw = FLOAT_TYPE( - FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)) - + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)) - ); - const FLOAT_TYPE smin = FLOAT_TYPE( - (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3 - + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7 - ); - tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" -mul_mat_vec_q6_K_body = """ -layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; - -shared FLOAT_TYPE tmp[32]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint batch_idx = gl_GlobalInvocationID.y; -#ifdef MUL_MAT_ID - const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; - const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; -#endif - - const uint i13 = batch_idx / p.ne12; - const uint i12 = batch_idx % p.ne12; - - const uint i03 = i13 / p.broadcast3; - const uint i02 = i12 / p.broadcast2; - - const uint batch_idx_a = i03 * p.ne02 + i02; - -#ifdef MUL_MAT_ID - const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; -#endif - - const uint a_offset = -#ifdef MUL_MAT_ID - expert_id * p.expert_stride_a + -#endif - batch_idx_a * p.batch_stride_a; - const uint b_offset = -#ifdef MUL_MAT_ID - (expert_idx0 % p.ne11) * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_b; - const uint d_offset = -#ifdef MUL_MAT_ID - expert_idx0 * p.expert_stride_b0 + - expert_idx1 * p.expert_stride_b1 + -#endif - batch_idx * p.batch_stride_d; - - const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; - - const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const uint step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const uint v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const uint v_in = tid - step*v_im; // 0...15 or 0...7 - -#if K_QUANTS_PER_ITERATION == 1 - const uint l0 = v_in; // 0...15 - const uint is = 0; -#else - const uint l0 = 4 * v_in; // 0, 4, 8, ..., 28 - const uint is = v_in / 4; -#endif - - const uint ql_offset = 64*v_im + l0; - const uint qh_offset = 32*v_im + l0; - const uint s_offset = 8*v_im + is; - const uint y_offset = 128*v_im + l0; - - tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp - - [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - const uint y_idx = i * QUANT_K + y_offset; - - const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); - -#if K_QUANTS_PER_ITERATION == 1 - FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32); - tmp[16 * ix + tid] += sum; -#else - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int l = 0; l < 4; ++l) { - sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32); - } - tmp[16 * ix + tid] += sum; -#endif - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (uint s = 16; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - if (tid == 0) { - data_d[d_offset + row] = D_TYPE(tmp[0]); - } -} -""" - -mul_mat_p021_src = """#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#define BLOCK_SIZE 32 -#define FLOAT_TYPE float - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - -layout (push_constant) uniform parameter -{ - uint ncols_x; - uint nrows_x; - uint nchannels_x; - uint nchannels_y; - uint b_offset; - uint d_offset; -} p; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint row_x = gl_GlobalInvocationID.y; - const uint channel = gl_GlobalInvocationID.z; - const uint channel_x = channel / (p.nchannels_y / p.nchannels_x); - - const uint nrows_y = p.ncols_x; - const uint nrows_dst = p.nrows_x; - const uint row_dst = row_x; - - tmp[tid] = FLOAT_TYPE(0.0f); - - for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { - const uint col_x = col_x0 + tid; - - if (col_x >= p.ncols_x) { - break; - } - - // x is transposed and permuted - const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x; - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); - - const uint row_y = col_x; - - // y is not transposed but permuted - const uint iy = channel*nrows_y + row_y; - - tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); - } - - // dst is not transposed and not permuted - const uint idst = channel*nrows_dst + row_dst; - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - if (tid == 0) { - dst[idst] = tmp[0]; - } -} -""" - - -mul_mat_nc_src = """#version 450 - -#extension GL_EXT_control_flow_attributes : enable -#extension GL_EXT_shader_16bit_storage : require - -#define BLOCK_SIZE 32 -#define FLOAT_TYPE float - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - -layout (push_constant) uniform parameter -{ - uint ncols_x; - uint nrows_x; - uint row_stride_x; - uint channel_stride_x; - uint channel_x_divisor; - uint b_offset; - uint d_offset; -} p; - -shared FLOAT_TYPE tmp[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint row_x = gl_GlobalInvocationID.y; - const uint channel = gl_GlobalInvocationID.z; - const uint channel_x = channel / p.channel_x_divisor; - - const uint nrows_y = p.ncols_x; - const uint nrows_dst = p.nrows_x; - const uint row_dst = row_x; - - const uint idst = channel*nrows_dst + row_dst; - - tmp[tid] = 0.0f; - - for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { - const uint col_x = col_x0 + tid; - - if (col_x >= p.ncols_x) { - break; - } - - const uint row_y = col_x; - - const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; - const uint iy = channel*nrows_y + row_y; - - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); - - tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(); - } - - if (tid == 0) { - dst[idst] = tmp[0]; - } -} -""" - -generic_head = """ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float param1; - float param2; -} p; -""" - -generic_unary_op_head = """#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint d_offset; - float param1; float param2; -} p;""" - -generic_unary_op_layout = """ -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};""" - -generic_unary_op_funcs = """ -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint dst_idx(uint idx) { - const uint i13 = idx / (p.ne12*p.ne11*p.ne10); - const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; - const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10); - const uint i12_offset = i12*p.ne11*p.ne10; - const uint i11 = (idx - i13_offset - i12_offset) / p.ne10; - const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; - return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10; -}""" - -generic_unary_op_main = """ -void main() { - if (gl_GlobalInvocationID.x >= p.ne) { - return; - } -""" - -generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_layout}\n{generic_unary_op_funcs}\n{generic_unary_op_main}" - -generic_binary_op_head = """#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ne; - uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; - uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; - uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23; - uint d_offset; - float param1; float param2; -} p;""" - -generic_binary_op_layout = """ -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};""" - -generic_binary_op_funcs = """ -uint src0_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00; -} - -uint src1_idx(uint idx) { - const uint i03 = idx / (p.ne02*p.ne01*p.ne00); - const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; - const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00); - const uint i02_offset = i02*p.ne01*p.ne00; - const uint i01 = (idx - i03_offset - i02_offset) / p.ne00; - const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00; - - return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10; -} - -uint dst_idx(uint idx) { - const uint i23 = idx / (p.ne22*p.ne21*p.ne20); - const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20; - const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20); - const uint i22_offset = i22*p.ne21*p.ne20; - const uint i21 = (idx - i23_offset - i22_offset) / p.ne20; - const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20; - return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20; -}""" - -generic_binary_op_main = """ -void main() { - if (gl_GlobalInvocationID.x >= p.ne) { - return; - } -""" - -generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_layout}\n{generic_binary_op_funcs}\n{generic_binary_op_main}" - -# MUL F32 -mul_body = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); -} -""" - -# ADD -add_body = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); -} -""" - -# SCALE -scale_body = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1)); -} -""" - -# SQR -sqr_body = """ - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val); -} -""" - -# CLAMP -clamp_body = """ - const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); -} -""" - -# CPY -cpy_end = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]); -} -""" -# Causes an optimization error otherwise -cpy_f16_f16_end = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)]; -} -""" - -# GET_ROWS -get_rows_float_body = """ -void main() { - const uint i00 = gl_GlobalInvocationID.x; - const uint i10 = gl_GlobalInvocationID.y; - const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; - const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; - - if (i00 >= p.ne00) { - return; - } - - const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; - - const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; - const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; - -#ifndef OPTIMIZATION_ERROR_WORKAROUND - data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]); -#else - data_d[d_offset + i00] = data_a[a_offset + i00]; -#endif -} -""" - -get_rows_body = """ -void main() { - const uint i00 = (gl_GlobalInvocationID.x)*2; - const uint i10 = gl_GlobalInvocationID.y; - const uint i11 = (gl_GlobalInvocationID.z)/p.ne12; - const uint i12 = (gl_GlobalInvocationID.z)%p.ne12; - - if (i00 >= p.ne00) { - return; - } - - const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12]; - - const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03; - const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23; - - const uint ib = a_offset + i00/QUANT_K; // block index - const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index - const uint iybs = i00 - i00%QUANT_K; // dst block start index - const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - - vec2 v = dequantize(ib, iqs, 0); - - data_d[d_offset + iybs + iqs ] = D_TYPE(v.x); - data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y); -} -""" - -# UNARY -gelu_body = """ -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const float GELU_COEF_A = 0.044715f; - const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; - const uint i = gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float xi = float(data_a[i]); - const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi); - data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1))); -} -""" - -silu_body = """ -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - const float xi = float(data_a[i]); - data_d[i] = D_TYPE(xi / (1.0f + exp(-xi))); -} -""" - -relu_body = """ -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint i = gl_GlobalInvocationID.x; - - if (i >= p.KX) { - return; - } - - data_d[i] = max(float(data_a[i]), 0); -} -""" - -# DIAG_MASK_INF -diag_mask_inf_head = """#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint ncols; - uint rows_per_channel; - uint n_past; -} p; -""" -diag_mask_inf_body = """ -#extension GL_EXT_control_flow_attributes : enable - -layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -void main() { - const uint col = gl_GlobalInvocationID.y; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - const uint i = row*p.ncols + col; - if (col > p.n_past + row % p.rows_per_channel) { - data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000)); - } else { - data_d[i] = D_TYPE(data_a[i]); - } -} -""" - -# NORMS -norm_body = """ -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -shared vec2 sum[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint tid = gl_LocalInvocationID.x; - - sum[tid] = vec2(0.0f, 0.0f); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - const float xi = float(data_a[row*p.KX + col]); - sum[tid].x += xi; - sum[tid].y += xi * xi; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - sum[tid] += sum[tid + s]; - } - barrier(); - } - - const float mean = sum[0].x / p.KX; - const float var = sum[0].y / p.KX - mean * mean; - const float inv_std = inversesqrt(var + p.param1); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std); - } -} -""" - -rms_norm_body = """ -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE sum[BLOCK_SIZE]; - -void main() { - const uint row = gl_WorkGroupID.x; - const uint tid = gl_LocalInvocationID.x; - - sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]); - sum[tid] += xi * xi; - } - - // sum up partial sums and write back result - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - sum[tid] += sum[tid + s]; - } - barrier(); - } - - const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX); - const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1)); - - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col])); - } -} -""" - -# SOFT_MAX -soft_max_head = """ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout (push_constant) uniform parameter -{ - uint KX; - uint KY; - float scale; - float max_bias; - float m0; - float m1; - uint n_head_log2; -} p; -""" - -soft_max_body = """ -#extension GL_EXT_control_flow_attributes : enable -#define BLOCK_SIZE 512 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; -layout (binding = 2) buffer D {D_TYPE data_d[];}; - -shared FLOAT_TYPE vals[BLOCK_SIZE]; - -void main() { - const uint tid = gl_LocalInvocationID.x; - const uint rowx = gl_WorkGroupID.x; - const uint rowy = rowx % p.KY; - - float slope = 1.0f; - - // ALiBi - if (p.max_bias > 0.0f) { - const uint h = rowx/p.KY; // head index - - const float base = h < p.n_head_log2 ? p.m0 : p.m1; - const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; - - slope = pow(base, exp); - } - - // Find max - FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); - } - vals[tid] = max_val; - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] = max(vals[tid], vals[tid + s]); - } - barrier(); - } - - max_val = vals[0]; - barrier(); - - // Sum up values - vals[tid] = FLOAT_TYPE(0.0f); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); - vals[tid] += val; - data_d[i] = D_TYPE(val); - } - - barrier(); - [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { - if (tid < s) { - vals[tid] += vals[tid + s]; - } - barrier(); - } - - const D_TYPE divisor = D_TYPE(vals[0]); - - [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { - const uint col = col0 + tid; - - if (col >= p.KX) { - break; - } - - data_d[rowx*p.KX + col] /= divisor; - } -} -""" - -# ROPE -rope_src = """ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {int data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; - -layout (push_constant) uniform parameter { - uint ncols; - float freq_scale; - uint p_delta_rows; - float freq_base; - float ext_factor; - float attn_factor; - float corr_dims[4]; -} p; - -float rope_yarn_ramp(const float low, const float high, const uint i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); -} - -void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) { - float mscale = p.attn_factor; - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = p.freq_scale * theta_extrap; - float theta = theta_interp; - if (p.ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); - } - cos_theta = cos(theta) * mscale; - sin_theta = sin(theta) * mscale; -} - -void main() { - const uint col = gl_GlobalInvocationID.y * 2; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - const uint i = row*p.ncols + col; - const uint i2 = row/p.p_delta_rows; - - const int pos = data_b[i2]; - const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols); - - float cos_theta, sin_theta; - rope_yarn(theta_base, col, cos_theta, sin_theta); - - const float x0 = float(data_a[i + 0]); - const float x1 = float(data_a[i + 1]); - - data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); - data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta); -} -""" - -rope_neox_src = """ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; - -layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer Y {int data_b[];}; -layout (binding = 2) readonly buffer Z {float data_freq_factors[];}; -layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; - -layout (push_constant) uniform parameter { - uint ncols; - uint ndims; - float freq_scale; - uint p_delta_rows; - float freq_base; - float ext_factor; - float attn_factor; - float corr_dims[4]; - float theta_scale; - float inv_ndims; - uint has_freq_facs; -} p; - -float rope_yarn_ramp(const float low, const float high, const uint i0) { - const float y = (i0 / 2 - low) / max(0.001f, high - low); - return 1.0f - min(1.0f, max(0.0f, y)); -} - -void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) { - float mscale = p.attn_factor; - // Get n-d rotational scaling corrected for extrapolation - float theta_interp = p.freq_scale * theta_extrap; - float theta = theta_interp; - if (p.ext_factor != 0.0f) { - float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; - theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; - - // Get n-d magnitude scaling corrected for interpolation - mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); - } - cos_theta = cos(theta) * mscale; - sin_theta = sin(theta) * mscale; -} - -void main() { - const uint col = gl_GlobalInvocationID.y * 2; - const uint row = gl_GlobalInvocationID.x; - - if (col >= p.ncols) { - return; - } - - const uint ib = col / p.ndims; - const uint ic = col % p.ndims; - - if (ib > 0) { - const uint i = row*p.ncols + ib*p.ndims + ic; - - data_d[i + 0] = data_a[i + 0]; - data_d[i + 1] = data_a[i + 1]; - - return; - } - - const uint i = row*p.ncols + ib*p.ndims + ic/2; - const uint i2 = row/p.p_delta_rows; - - const float cur_rot = p.inv_ndims * ic - ib; - - const int pos = data_b[i2]; - const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f; - const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor; - - float cos_theta, sin_theta; - rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); - - const float x0 = float(data_a[i + 0]); - const float x1 = float(data_a[i + p.ndims/2]); - - data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); - data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta); -} -""" - -argsort_src = """ -#version 450 - -#extension GL_EXT_shader_16bit_storage : require - -#define BLOCK_SIZE 1024 -#define ASC 0 - -layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; - -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) buffer D {int data_d[];}; - -layout (push_constant) uniform parameter { - uint ncols; - uint ncols_pad; - uint order; -} p; - -shared int dst_row[BLOCK_SIZE]; - -void swap(uint idx0, uint idx1) { - int tmp = dst_row[idx0]; - dst_row[idx0] = dst_row[idx1]; - dst_row[idx1] = tmp; -} - -void main() { - // bitonic sort - const int col = int(gl_LocalInvocationID.x); - const uint row = gl_WorkGroupID.y; - - if (col >= p.ncols_pad) { - return; - } - - const uint row_offset = row * p.ncols; - - // initialize indices - dst_row[col] = col; - barrier(); - - for (uint k = 2; k <= p.ncols_pad; k *= 2) { - for (uint j = k / 2; j > 0; j /= 2) { - const uint ixj = col ^ j; - if (ixj > col) { - if ((col & k) == 0) { - if (dst_row[col] >= p.ncols || - (dst_row[ixj] < p.ncols && (p.order == ASC ? - data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] : - data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]])) - ) { - swap(col, ixj); - } - } else { - if (dst_row[ixj] >= p.ncols || - (dst_row[col] < p.ncols && (p.order == ASC ? - data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] : - data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]])) - ) { - swap(col, ixj); - } - } - } - barrier(); - } - } - - if (col < p.ncols) { - data_d[row_offset + col] = dst_row[col]; - } -} -""" - GLSLC = "glslc" -VK_NUM_TYPES = 16 - -GGML_TYPE_F32 = 0 -GGML_TYPE_F16 = 1 -GGML_TYPE_Q4_0 = 2 -GGML_TYPE_Q4_1 = 3 -GGML_TYPE_Q5_0 = 6 -GGML_TYPE_Q5_1 = 7 -GGML_TYPE_Q8_0 = 8 -GGML_TYPE_Q8_1 = 9 -GGML_TYPE_Q2_K = 10 -GGML_TYPE_Q3_K = 11 -GGML_TYPE_Q4_K = 12 -GGML_TYPE_Q5_K = 13 -GGML_TYPE_Q6_K = 14 -GGML_TYPE_Q8_K = 15 - - -type_names = { - GGML_TYPE_F32: "f32", - GGML_TYPE_F16: "f16", - GGML_TYPE_Q4_0: "q4_0", - GGML_TYPE_Q4_1: "q4_1", - GGML_TYPE_Q5_0: "q5_0", - GGML_TYPE_Q5_1: "q5_1", - GGML_TYPE_Q8_0: "q8_0", - GGML_TYPE_Q8_1: "q8_1", - GGML_TYPE_Q2_K: "q2_K", - GGML_TYPE_Q3_K: "q3_K", - GGML_TYPE_Q4_K: "q4_K", - GGML_TYPE_Q5_K: "q5_K", - GGML_TYPE_Q6_K: "q6_K", - GGML_TYPE_Q8_K: "q8_K", -} - -K_QUANTS_PER_ITERATION = 2 +type_names = [ + "f32", + "f16", + "q4_0", + "q4_1", + "q5_0", + "q5_1", + "q8_0", + "q2_k", + "q3_k", + "q4_k", + "q5_k", + "q6_k", +] ASYNCIO_CONCURRENCY = 64 +input_dir = "vulkan-shaders" output_dir = gettempdir() lock = asyncio.Lock() shader_fnames = [] -async def string_to_spv(name, code, defines, fp16=True): - f = NamedTemporaryFile(mode="w", delete=False) - f.write(code) - f.flush() - +async def string_to_spv(name, in_fname, defines, fp16=True): name = f"{name}{'_fp32' if not fp16 else ''}" - fname = os.path.join(output_dir, f"{name}.comp") + out_fname = os.path.join(output_dir, f"{name}.spv") - cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", f.name, "-o", fname] + in_path = os.path.join(input_dir, in_fname) + + cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", in_path, "-o", out_fname] cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) @@ -2828,33 +52,46 @@ async def string_to_spv(name, code, defines, fp16=True): error = stderr.decode() if proc.returncode: - # Generate preprocessed code - cmd = [GLSLC, "-E", f.name] - cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) + cmd = " ".join(cmd) + logger.error(f"cannot compile {name}\n\n{cmd}\n\n{error}") + return - proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) + async with lock: + shader_fnames.append((name, out_fname)) - stdout, stderr = await proc.communicate() - logger.info(" ".join(cmd)) +def matmul_shaders(tasks, fp16, matmul_id): + if fp16: + load_vec = "8" + aligned_b_type_f32 = "mat2x4" + aligned_b_type_f16 = "f16mat2x4" + else: + load_vec = "4" + aligned_b_type_f32 = "vec4" + aligned_b_type_f16 = "f16vec4" - if proc.returncode: - raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}") + base_dict = {"FLOAT_TYPE": "float" if not fp16 else "float16_t"} + shader_name = "matmul" - preprocessed_code = stdout.decode() + if matmul_id: + base_dict["MUL_MAT_ID"] = "1" + shader_name = "matmul_id" - cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) - code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())]) - logger.error(f"cannot compile {name}\n\n{code_with_lines}\n\n{error}") - f.close() - os.remove(f.name) - sys.exit(proc.returncode) + if fp16: + base_dict["FLOAT16"] = "1" - f.close() - os.remove(f.name) + # Shaders with f16 B_TYPE + tasks.append(string_to_spv(f"{shader_name}_f32_f16", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv(f"{shader_name}_f32_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F32": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16)) - async with lock: - shader_fnames.append((name, fname)) + tasks.append(string_to_spv(f"{shader_name}_f16", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv(f"{shader_name}_f16_aligned", "mul_mm.comp", base_dict | {"DATA_A_F16": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f16, "D_TYPE": "float"}, fp16)) + + for tname in type_names: + data_a_key = f"DATA_A_{tname.upper()}" + load_vec_a = load_vec if tname in ("f32", "f16") else "2" + tasks.append(string_to_spv(f"{shader_name}_{tname}_f32", "mul_mm.comp", base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv(f"{shader_name}_{tname}_f32_aligned", "mul_mm.comp", base_dict | {data_a_key: "2", "LOAD_VEC_A": load_vec_a, "LOAD_VEC_B": load_vec, "B_TYPE": aligned_b_type_f32, "D_TYPE": "float"}, fp16)) async def main(): @@ -2862,285 +99,81 @@ async def main(): tasks = [] - stream = [] - for fp16 in (False, True): - # mulmat - if fp16: - shader_float_type = shader_f16 - load_vec = "8" - vec_type_f16 = "f16mat2x4" - vec_type = "mat2x4" - else: - shader_float_type = shader_f32 - load_vec = "4" - vec_type_f16 = "f16vec4" - vec_type = "vec4" - - stream.clear() - stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) - tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - tasks.append(string_to_spv("matmul_f32_f16", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - - tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - - tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) - tasks.append(string_to_spv("matmul_q4_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q4_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2)) - tasks.append(string_to_spv("matmul_q4_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q4_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2)) - tasks.append(string_to_spv("matmul_q5_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q5_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2)) - tasks.append(string_to_spv("matmul_q5_1_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q5_1_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2)) - tasks.append(string_to_spv("matmul_q8_0_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q8_0_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2)) - tasks.append(string_to_spv("matmul_q2_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q2_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2)) - tasks.append(string_to_spv("matmul_q3_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q3_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2)) - tasks.append(string_to_spv("matmul_q4_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q4_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2)) - tasks.append(string_to_spv("matmul_q5_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q5_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - stream.clear() - stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2)) - tasks.append(string_to_spv("matmul_q6_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_q6_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - + # MUL_MAT + matmul_shaders(tasks, fp16, False) # MUL_MAT_ID - # stream.clear() - # stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # tasks.append(string_to_spv("matmul_id_f16", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_f16_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) - - # tasks.append(string_to_spv("matmul_id_f16_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_f16_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q4_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q4_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q4_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q4_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q5_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q5_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q5_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q5_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q8_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q8_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q2_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q2_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q3_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q3_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q4_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q4_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q5_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q5_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # stream.clear() - # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2)) - # tasks.append(string_to_spv("matmul_id_q6_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # tasks.append(string_to_spv("matmul_id_q6_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - - # Shaders where precision is needed, so no fp16 version - - # mul mat vec - for i in range(0, VK_NUM_TYPES): - stream.clear() - stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, mul_mat_vec_layout, shader_float_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, mul_mat_vec_layout, shader_q4_0_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, mul_mat_vec_layout, shader_q4_1_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, mul_mat_vec_layout, shader_q5_0_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, mul_mat_vec_layout, shader_q5_1_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, mul_mat_vec_layout, shader_q8_0_dequant_func, mul_mat_vec_body)) - elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, mul_mat_vec_layout, mul_mat_vec_q2_K_body)) - elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, mul_mat_vec_layout, mul_mat_vec_q3_K_body)) - elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, mul_mat_vec_layout, mul_mat_vec_q4_K_body)) - elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, mul_mat_vec_layout, mul_mat_vec_q5_K_body)) - elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, mul_mat_vec_layout, mul_mat_vec_q6_K_body)) - else: - continue - - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f16_f32", "".join(stream), {"B_TYPE": "float16_t", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - - # tasks.append(string_to_spv(f"mul_mat_vec_id_{type_names[i]}_f32", "".join(stream), {"MUL_MAT_ID": "1", "B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - - # Dequant shaders - for i in range(0, VK_NUM_TYPES): - stream.clear() - - stream.extend((dequant_head, shader_int8_ext, shader_f32)) - - if i == GGML_TYPE_F32: - stream.append(dequant_f32_body) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, dequant_q4_0_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, dequant_q4_1_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, dequant_q5_0_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, dequant_q5_1_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, dequant_q8_0_body)) - elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, dequant_q2_K_body)) - elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, dequant_q3_K_body)) - elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, dequant_q4_K_body)) - elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, dequant_q5_K_body)) - elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, dequant_q6_K_body)) - else: - continue - - tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) - - # get_rows - for i in range(0, VK_NUM_TYPES): - stream.clear() - stream.extend((generic_binary_op_head, shader_int8_ext, shader_f32)) - optimization_workaround = False - - if i == GGML_TYPE_F32: - stream.extend((shader_f32_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body)) - elif i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body)) - optimization_workaround = True - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, generic_binary_op_layout, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, generic_binary_op_layout, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, generic_binary_op_layout, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, generic_binary_op_layout, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, generic_binary_op_layout, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body)) - else: - continue - - if optimization_workaround: - tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"})) - else: - tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "int", "D_TYPE": "float"})) - - tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) + matmul_shaders(tasks, fp16, True) + + for tname in type_names: + base_dict = {"FLOAT_TYPE": "float"} + + # mul mat vec + data_a_key = f"DATA_A_{tname.upper()}" + shader = f"mul_mat_vec_{tname}.comp" if tname.endswith("_k") else "mul_mat_vec.comp" + + tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f32_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv(f"mul_mat_vec_{tname}_f16_f32", shader, base_dict | {data_a_key: "1", "B_TYPE": "float16_t", "D_TYPE": "float"})) + + tasks.append(string_to_spv(f"mul_mat_vec_id_{tname}_f32", shader, base_dict | {"MUL_MAT_ID": "1", data_a_key: "1", "B_TYPE": "float", "D_TYPE": "float"})) + + # Dequant shaders + if tname != "f16": + tasks.append(string_to_spv(f"dequant_{tname}", f"dequant_{tname}.comp", base_dict | {data_a_key: "1", "D_TYPE": "float16_t"})) + + # get_rows + if not tname.endswith("_k"): + shader = "get_rows.comp" if tname in ("f32", "f16") else "get_rows_quant.comp" + + if tname == "f16": + tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"})) + else: + tasks.append(string_to_spv(f"get_rows_{tname}", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv(f"get_rows_{tname}_f32", shader, {data_a_key: "1", "B_TYPE": "int", "D_TYPE": "float"})) + + tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) # Norms - tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("norm_f32", "norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rms_norm_f32", "rms_norm.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"})) + + tasks.append(string_to_spv("cpy_f32_f32", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", "copy.comp", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", "copy.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t", "OPTIMIZATION_ERROR_WORKAROUND": "1"})) + + tasks.append(string_to_spv("add_f32", "add.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + + tasks.append(string_to_spv("split_k_reduce", "mul_mat_split_k_reduce.comp", {})) - tasks.append(string_to_spv("cpy_f32_f32", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f16", f"{generic_unary_op_combined}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("cpy_f16_f16", f"{generic_unary_op_combined}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("mul_f32", "mul.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("add_f32", f"{generic_binary_op_combined}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("div_f32", "div.comp", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) - tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_combined}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("scale_f32", "scale.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_combined}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("sqr_f32", "square.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_combined}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("clamp_f32", "clamp.comp", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_unary_op_combined}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("gelu_f32", "gelu.comp", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("silu_f32", "silu.comp", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("relu_f32", "relu.comp", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("diag_mask_inf_f32", "diag_mask_inf.comp", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("soft_max_f32", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("soft_max_f32_f16", "soft_max.comp", base_dict | {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_norm_f32", "rope_norm.comp", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_norm_f16", "rope_norm.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("rope_neox_f32", "rope_neox.comp", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_neox_f16", "rope_neox.comp", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("argsort_f32", "argsort.comp", {"A_TYPE": "float"})) - tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"})) + tasks.append(string_to_spv("sum_rows_f32", "sum_rows.comp", base_dict | {"A_TYPE": "float", "D_TYPE": "float"})) # Helper to decorate tasks with semaphore acquisition. async def withSemaphore(sem, task): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c9ae259e1d627..fb20cfabbcab5 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -33,17 +33,22 @@ class General: FILE_TYPE = "general.file_type" class LLM: - VOCAB_SIZE = "{arch}.vocab_size" - CONTEXT_LENGTH = "{arch}.context_length" - EMBEDDING_LENGTH = "{arch}.embedding_length" - BLOCK_COUNT = "{arch}.block_count" - FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" - USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" - TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - EXPERT_COUNT = "{arch}.expert_count" - EXPERT_USED_COUNT = "{arch}.expert_used_count" - POOLING_TYPE = "{arch}.pooling_type" - LOGIT_SCALE = "{arch}.logit_scale" + VOCAB_SIZE = "{arch}.vocab_size" + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" + EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + EXPERT_COUNT = "{arch}.expert_count" + EXPERT_USED_COUNT = "{arch}.expert_used_count" + EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" + EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -55,6 +60,8 @@ class Attention: LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" CAUSAL = "{arch}.attention.causal" + Q_LORA_RANK = "{arch}.attention.q_lora_rank" + KV_LORA_RANK = "{arch}.attention.kv_lora_rank" class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" @@ -64,6 +71,7 @@ class Rope: SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" @@ -140,6 +148,7 @@ class MODEL_ARCH(IntEnum): DBRX = auto() OLMO = auto() ARCTIC = auto() + DEEPSEEK2 = auto() class MODEL_TENSOR(IntEnum): @@ -185,6 +194,12 @@ class MODEL_TENSOR(IntEnum): SSM_A = auto() SSM_D = auto() SSM_OUT = auto() + ATTN_Q_A = auto() + ATTN_Q_B = auto() + ATTN_KV_A_MQA = auto() + ATTN_KV_B = auto() + ATTN_Q_A_NORM = auto() + ATTN_KV_A_NORM = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -221,6 +236,7 @@ class MODEL_TENSOR(IntEnum): MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK2: "deepseek2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -266,6 +282,12 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", + MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", + MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", + MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", + MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", + MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -394,6 +416,7 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.TOKEN_EMBD_NORM, MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.ATTN_NORM_2, MODEL_TENSOR.ATTN_OUT_NORM, MODEL_TENSOR.ATTN_Q, MODEL_TENSOR.ATTN_Q_NORM, @@ -624,6 +647,7 @@ class MODEL_TENSOR(IntEnum): ], MODEL_ARCH.MINICPM: [ MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, @@ -757,6 +781,33 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], # TODO } @@ -790,6 +841,10 @@ class MODEL_TENSOR(IntEnum): MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index c194dd5dd1e65..a697f657b9ac8 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -5,6 +5,7 @@ import shutil import struct import tempfile +from dataclasses import dataclass from enum import Enum, auto from io import BufferedWriter from typing import IO, Any, Sequence, Mapping @@ -30,17 +31,36 @@ logger = logging.getLogger(__name__) +@dataclass +class TensorInfo: + shape: Sequence[int] + dtype: GGMLQuantizationType + nbytes: int + tensor: np.ndarray[Any, Any] | None = None + + +@dataclass +class GGUFValue: + value: Any + type: GGUFValueType + + class WriterState(Enum): + NO_FILE = auto() EMPTY = auto() HEADER = auto() KV_DATA = auto() TI_DATA = auto() + WEIGHTS = auto() class GGUFWriter: - fout: BufferedWriter + fout: BufferedWriter | None + path: os.PathLike[str] | str | None temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any]] + tensors: dict[str, TensorInfo] + kv_data: dict[str, GGUFValue] + state: WriterState _simple_value_packing = { GGUFValueType.UINT8: "B", GGUFValueType.INT8: "b", @@ -56,141 +76,140 @@ class GGUFWriter: } def __init__( - self, path: os.PathLike[str] | str, arch: str, use_temp_file: bool = True, + self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE, ): - self.fout = open(path, "wb") + self.fout = None + self.path = path self.arch = arch self.endianess = endianess - self.offset_tensor = 0 self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = bytearray() - self.kv_data_count = 0 - self.ti_data = bytearray() - self.ti_data_count = 0 - self.ti_names = set() self.use_temp_file = use_temp_file self.temp_file = None - self.tensors = [] + self.tensors = dict() + self.kv_data = dict() logger.info("gguf: This GGUF file is for {0} Endian only".format( "Big" if self.endianess == GGUFEndian.BIG else "Little", )) - self.state = WriterState.EMPTY + self.state = WriterState.NO_FILE self.add_architecture() - def write_header_to_file(self) -> None: + def open_output_file(self, path: os.PathLike[str] | str | None = None) -> None: + if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path): + # allow calling this multiple times as long as the path is the same + return + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') + + if path is not None: + self.path = path + + if self.path is not None: + if self.fout is not None: + self.fout.close() + self.fout = open(self.path, "wb") + self.state = WriterState.EMPTY + + def write_header_to_file(self, path: os.PathLike[str] | str | None = None) -> None: + self.open_output_file(path) + if self.state is not WriterState.EMPTY: raise ValueError(f'Expected output file to be empty, got {self.state}') self._write_packed(" None: if self.state is not WriterState.HEADER: raise ValueError(f'Expected output file to contain the header, got {self.state}') + assert self.fout is not None + + kv_data = bytearray() - self.fout.write(self.kv_data) + for key, val in self.kv_data.items(): + kv_data += self._pack_val(key, GGUFValueType.STRING, add_vtype=False) + kv_data += self._pack_val(val.value, val.type, add_vtype=True) + + self.fout.write(kv_data) self.flush() self.state = WriterState.KV_DATA def write_ti_data_to_file(self) -> None: if self.state is not WriterState.KV_DATA: raise ValueError(f'Expected output file to contain KV data, got {self.state}') - - self.fout.write(self.ti_data) + assert self.fout is not None + + ti_data = bytearray() + offset_tensor = 0 + + for name, ti in self.tensors.items(): + ti_data += self._pack_val(name, GGUFValueType.STRING, add_vtype=False) + n_dims = len(ti.shape) + ti_data += self._pack("I", n_dims) + for i in range(n_dims): + ti_data += self._pack("Q", ti.shape[n_dims - 1 - i]) + ti_data += self._pack("I", ti.dtype) + ti_data += self._pack("Q", offset_tensor) + offset_tensor += GGUFWriter.ggml_pad(ti.nbytes, self.data_alignment) + + self.fout.write(ti_data) self.flush() self.state = WriterState.TI_DATA - def add_key(self, key: str) -> None: - self.add_val(key, GGUFValueType.STRING, add_vtype=False) + def add_key_value(self, key: str, val: Any, vtype: GGUFValueType) -> None: + if key in self.kv_data: + raise ValueError(f'Duplicated key name {key!r}') + + self.kv_data[key] = GGUFValue(value=val, type=vtype) def add_uint8(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.UINT8) + self.add_key_value(key,val, GGUFValueType.UINT8) def add_int8(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.INT8) + self.add_key_value(key, val, GGUFValueType.INT8) def add_uint16(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.UINT16) + self.add_key_value(key, val, GGUFValueType.UINT16) def add_int16(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.INT16) + self.add_key_value(key, val, GGUFValueType.INT16) def add_uint32(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.UINT32) + self.add_key_value(key, val, GGUFValueType.UINT32) def add_int32(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.INT32) + self.add_key_value(key, val, GGUFValueType.INT32) def add_float32(self, key: str, val: float) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.FLOAT32) + self.add_key_value(key, val, GGUFValueType.FLOAT32) def add_uint64(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.UINT64) + self.add_key_value(key, val, GGUFValueType.UINT64) def add_int64(self, key: str, val: int) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.INT64) + self.add_key_value(key, val, GGUFValueType.INT64) def add_float64(self, key: str, val: float) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.FLOAT64) + self.add_key_value(key, val, GGUFValueType.FLOAT64) def add_bool(self, key: str, val: bool) -> None: - self.add_key(key) - self.add_val(val, GGUFValueType.BOOL) + self.add_key_value(key, val, GGUFValueType.BOOL) def add_string(self, key: str, val: str) -> None: if not val: return - self.add_key(key) - self.add_val(val, GGUFValueType.STRING) + self.add_key_value(key, val, GGUFValueType.STRING) def add_array(self, key: str, val: Sequence[Any]) -> None: if not isinstance(val, Sequence): raise ValueError("Value must be a sequence for array type") - self.add_key(key) - self.add_val(val, GGUFValueType.ARRAY) - - def add_val(self, val: Any, vtype: GGUFValueType | None = None, add_vtype: bool = True) -> None: - if vtype is None: - vtype = GGUFValueType.get_type(val) - - if add_vtype: - self.kv_data += self._pack("I", vtype) - self.kv_data_count += 1 - - pack_fmt = self._simple_value_packing.get(vtype) - if pack_fmt is not None: - self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) - elif vtype == GGUFValueType.STRING: - encoded_val = val.encode("utf-8") if isinstance(val, str) else val - self.kv_data += self._pack("Q", len(encoded_val)) - self.kv_data += encoded_val - elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: - ltype = GGUFValueType.get_type(val[0]) - if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): - raise ValueError("All items in a GGUF array should be of the same type") - self.kv_data += self._pack("I", ltype) - self.kv_data += self._pack("Q", len(val)) - for item in val: - self.add_val(item, add_vtype=False) - else: - raise ValueError("Invalid GGUF metadata value type or value") + self.add_key_value(key, val, GGUFValueType.ARRAY) @staticmethod def ggml_pad(x: int, n: int) -> int: @@ -200,16 +219,12 @@ def add_tensor_info( self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, ) -> None: - if self.state is not WriterState.EMPTY: - raise ValueError(f'Expected output file to be empty, got {self.state}') + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') - if name in self.ti_names: - raise ValueError(f'Duplicated tensor name {name}') - self.ti_names.add(name) + if name in self.tensors: + raise ValueError(f'Duplicated tensor name {name!r}') - encoded_name = name.encode("utf-8") - self.ti_data += self._pack("Q", len(encoded_name)) - self.ti_data += encoded_name if raw_dtype is None: if tensor_dtype == np.float16: dtype = GGMLQuantizationType.F16 @@ -231,14 +246,8 @@ def add_tensor_info( dtype = raw_dtype if tensor_dtype == np.uint8: tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) - n_dims = len(tensor_shape) - self.ti_data += self._pack("I", n_dims) - for i in range(n_dims): - self.ti_data += self._pack("Q", tensor_shape[n_dims - 1 - i]) - self.ti_data += self._pack("I", dtype) - self.ti_data += self._pack("Q", self.offset_tensor) - self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) - self.ti_data_count += 1 + + self.tensors[name] = TensorInfo(shape=tensor_shape, dtype=dtype, nbytes=tensor_nbytes) def add_tensor( self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, @@ -252,10 +261,10 @@ def add_tensor( self.temp_file = fp shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape - self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype=raw_dtype) if self.temp_file is None: - self.tensors.append(tensor) + self.tensors[name].tensor = tensor return tensor.tofile(self.temp_file) @@ -267,8 +276,9 @@ def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None fp.write(bytes([0] * pad)) def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: - if self.state is not WriterState.TI_DATA: - raise ValueError(f'Expected output file to contain tensor info, got {self.state}') + if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS: + raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}') + assert self.fout is not None if self.endianess == GGUFEndian.BIG: tensor.byteswap(inplace=True) @@ -276,50 +286,51 @@ def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: tensor.tofile(self.fout) self.write_padding(self.fout, tensor.nbytes) + self.state = WriterState.WEIGHTS + def write_tensors_to_file(self, *, progress: bool = False) -> None: self.write_ti_data_to_file() + assert self.fout is not None + self.write_padding(self.fout, self.fout.tell()) if self.temp_file is None: - self.tensors.reverse() # to pop from the "beginning" in constant time + bar = None if progress: from tqdm import tqdm - total_bytes = sum(t.nbytes for t in self.tensors) + total_bytes = sum(t.nbytes for t in self.tensors.values()) bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) - while True: - try: - tensor = self.tensors.pop() - except IndexError: - break - tensor.tofile(self.fout) - bar.update(tensor.nbytes) - self.write_padding(self.fout, tensor.nbytes) - return - while True: - try: - tensor = self.tensors.pop() - except IndexError: - break - tensor.tofile(self.fout) - self.write_padding(self.fout, tensor.nbytes) - return + # relying on the fact that Python dicts preserve insertion order (since 3.7) + for ti in self.tensors.values(): + assert ti.tensor is not None # can only iterate once over the tensors + assert ti.tensor.nbytes == ti.nbytes + ti.tensor.tofile(self.fout) + if bar is not None: + bar.update(ti.nbytes) + self.write_padding(self.fout, ti.nbytes) + ti.tensor = None + else: + self.temp_file.seek(0) - self.temp_file.seek(0) + shutil.copyfileobj(self.temp_file, self.fout) + self.flush() + self.temp_file.close() - shutil.copyfileobj(self.temp_file, self.fout) - self.flush() - self.temp_file.close() + self.state = WriterState.WEIGHTS def flush(self) -> None: + assert self.fout is not None self.fout.flush() def close(self) -> None: - self.fout.close() + if self.fout is not None: + self.fout.close() + self.fout = None def add_architecture(self) -> None: self.add_string(Keys.General.ARCHITECTURE, self.arch) @@ -374,9 +385,18 @@ def add_embedding_length(self, length: int) -> None: def add_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + def add_leading_dense_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) + def add_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_expert_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_shared_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_parallel_residual(self, use: bool) -> None: self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) @@ -407,6 +427,12 @@ def add_expert_count(self, count: int) -> None: def add_expert_used_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) + def add_expert_shared_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) + + def add_expert_weights_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + def add_layer_norm_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) @@ -416,6 +442,12 @@ def add_layer_norm_rms_eps(self, value: float) -> None: def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + def add_q_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) + + def add_kv_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) + def add_pooling_type(self, value: PoolingType) -> None: self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) @@ -431,7 +463,7 @@ def add_rope_scaling_type(self, value: RopeScalingType) -> None: def add_rope_scaling_factor(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) - def add_rope_scaling_attn_factors(self, value: Sequence[float]) -> None: + def add_rope_scaling_attn_factors(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) def add_rope_scaling_orig_ctx_len(self, value: int) -> None: @@ -440,6 +472,9 @@ def add_rope_scaling_orig_ctx_len(self, value: int) -> None: def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + def add_rope_scaling_yarn_log_mul(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) + def add_ssm_conv_kernel(self, value: int) -> None: self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) @@ -550,5 +585,32 @@ def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' return struct.pack(f'{pack_prefix}{fmt}', value) + def _pack_val(self, val: Any, vtype: GGUFValueType, add_vtype: bool) -> bytes: + kv_data = bytearray() + + if add_vtype: + kv_data += self._pack("I", vtype) + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf-8") if isinstance(val, str) else val + kv_data += self._pack("Q", len(encoded_val)) + kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + kv_data += self._pack("I", ltype) + kv_data += self._pack("Q", len(val)) + for item in val: + kv_data += self._pack_val(item, ltype, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + return kv_data + def _write_packed(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> None: + assert self.fout is not None self.fout.write(self._pack(fmt, value, skip_pack_prefix)) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8b1b21d78bb09..81b4992a51eed 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -102,6 +102,7 @@ class TensorNameMap: # Attention norm 2 MODEL_TENSOR.ATTN_NORM_2: ( "transformer.h.{bid}.ln_attn", # falcon40b + "encoder.layer.{bid}.layer_norm_1", # jina-v2-code ), # Attention query-key-value @@ -256,6 +257,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_UP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2 ), # AWQ-activation gate @@ -285,6 +287,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_GATE_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2 ), # Feed-forward down @@ -309,6 +312,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.c_proj", # starcoder2 "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 "model.layers.{bid}.residual_mlp.w2", # arctic + "encoder.layer.{bid}.mlp.down_layer", # jina-bert-v2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -320,6 +324,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_DOWN_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -347,6 +352,7 @@ class TensorNameMap: "encoder.layers.{bid}.norm2", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_3", # Grok "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 + "encoder.layer.{bid}.layer_norm_2" # jina-v2-code ), MODEL_TENSOR.SSM_IN: ( @@ -383,6 +389,30 @@ class TensorNameMap: "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), + + MODEL_TENSOR.ATTN_Q_A: ( + "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_B: ( + "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_MQA: ( + "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_B: ( + "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_A_NORM: ( + "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_NORM: ( + "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 + ), } # architecture-specific block mappings @@ -415,7 +445,7 @@ def __init__(self, arch: MODEL_ARCH, n_blocks: int): if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 128 + n_experts = 160 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 3ba99be4f4489..dc574991381a8 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -1,10 +1,15 @@ from __future__ import annotations +import re import logging import json import os from pathlib import Path -from typing import Any, Callable, Sequence, Mapping, Iterable +from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable + +from sentencepiece import SentencePieceProcessor + +import gguf from .gguf_writer import GGUFWriter @@ -163,3 +168,298 @@ def _try_load_from_config_json(self, path: Path) -> bool: for typ in self.special_token_types: self._set_special_token(typ, config.get(f'{typ}_token_id')) return True + + +@runtime_checkable +class BaseVocab(Protocol): + tokenizer_model: ClassVar[str] + name: ClassVar[str] + + +@runtime_checkable +class Vocab(BaseVocab, Protocol): + vocab_size: int + added_tokens_dict: dict[str, int] + added_tokens_list: list[str] + fname_tokenizer: Path + + def __init__(self, base_path: Path): ... + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... + + +class NoVocab(BaseVocab): + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +class BpeVocab(Vocab): + tokenizer_model = "gpt2" + name = "bpe" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + + if (fname_tokenizer := base_path / 'vocab.json').exists(): + # "slow" tokenizer + with open(fname_tokenizer, encoding="utf-8") as f: + self.vocab = json.load(f) + + try: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + else: + # "fast" tokenizer + fname_tokenizer = base_path / 'tokenizer.json' + + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding="utf-8") as f: + tokenizer_json = json.load(f) + + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + if ( + tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'ByteLevel' + ): + raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') + + self.vocab = tokenizer_model["vocab"] + + if (added := tokenizer_json.get('added_tokens')) is not None: + # Added tokens here can be duplicates of the main vocabulary. + added_tokens = {item['content']: item['id'] + for item in added + if item['content'] not in self.vocab} + + vocab_size = len(self.vocab) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " + f"{vocab_size} - {expected_end_id}; got {actual_ids}") + + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_dict = added_tokens + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} + + for i, _ in enumerate(self.vocab): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab(Vocab): + tokenizer_model = "llama" + name = "spm" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + if (fname_tokenizer := base_path / 'tokenizer.model').exists(): + # normal location + try: + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): + # not found in alternate location either + raise FileNotFoundError('Cannot find tokenizer.model') + + self.sentencepiece_tokenizer = SentencePieceProcessor() + self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) + vocab_size = self.sentencepiece_tokenizer.vocab_size() + + new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") + + # Token pieces that were added to the base vocabulary. + self.added_tokens_dict = added_tokens + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(i) + text = piece.encode("utf-8") + score: float = tokenizer.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + if tokenizer.IsControl(i): + toktype = gguf.TokenType.CONTROL + + # NOTE: I think added_tokens are user defined. + # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED + + if tokenizer.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class LlamaHfVocab(Vocab): + tokenizer_model = "llama" + name = "hfft" + + def __init__(self, base_path: Path): + fname_tokenizer = base_path / 'tokenizer.json' + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding='utf-8') as f: + tokenizer_json = json.load(f) + + # pre-check so we know if we need transformers + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( + tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'Sequence' + ): + raise FileNotFoundError('Cannot find Llama BPE tokenizer') + + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "To use LlamaHfVocab, please install the `transformers` package. " + "You can install it with `pip install transformers`." + ) from e + + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + base_path, + cache_dir=base_path, + local_files_only=True, + ) + assert self.tokenizer.is_fast # assume tokenizer.json is used + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { + tok: self.tokenizer.get_vocab()[tok] + for tok in self.tokenizer.all_special_tokens + } + self.special_ids = set(self.tokenizer.all_special_ids) + + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + + self.fname_tokenizer = fname_tokenizer + + def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: + continue + + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") + + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, token_text, self.special_ids # Reuse already stored special IDs + ) + + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + + # Determine token type based on whether it's a special token + return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL + + def get_token_score(self, token_id: int) -> float: + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + if text in self.specials: + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) + score = self.get_token_score(self.specials[text]) + else: + toktype = gguf.TokenType.USER_DEFINED + score = -1000.0 + + yield text.encode("utf-8"), score, toktype + + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.hf_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py index 1a37a7b91409d..92d14d6cd0a69 100755 --- a/gguf-py/scripts/gguf-dump.py +++ b/gguf-py/scripts/gguf-dump.py @@ -14,7 +14,7 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): sys.path.insert(0, str(Path(__file__).parent.parent)) -from gguf import GGUFReader, GGUFValueType # noqa: E402 +from gguf import GGUFReader, GGUFValueType, ReaderTensor # noqa: E402 logger = logging.getLogger("gguf-dump") @@ -101,25 +101,285 @@ def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: json.dump(result, sys.stdout) +def markdown_table_with_alignment_support(header_map: list[dict[str, str]], data: list[dict[str, Any]]): + # JSON to Markdown table formatting: https://stackoverflow.com/a/72983854/2850957 + + # Alignment Utility Function + def strAlign(padding: int, alignMode: str | None, strVal: str): + if alignMode == 'center': + return strVal.center(padding) + elif alignMode == 'right': + return strVal.rjust(padding - 1) + ' ' + elif alignMode == 'left': + return ' ' + strVal.ljust(padding - 1) + else: # default left + return ' ' + strVal.ljust(padding - 1) + + def dashAlign(padding: int, alignMode: str | None): + if alignMode == 'center': + return ':' + '-' * (padding - 2) + ':' + elif alignMode == 'right': + return '-' * (padding - 1) + ':' + elif alignMode == 'left': + return ':' + '-' * (padding - 1) + else: # default left + return '-' * (padding) + + # Calculate Padding For Each Column Based On Header and Data Length + rowsPadding = {} + for index, columnEntry in enumerate(header_map): + padCount = max([len(str(v)) for d in data for k, v in d.items() if k == columnEntry['key_name']], default=0) + 2 + headerPadCount = len(columnEntry['header_name']) + 2 + rowsPadding[index] = headerPadCount if padCount <= headerPadCount else padCount + + # Render Markdown Header + rows = [] + rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(columnEntry['header_name'])) for index, columnEntry in enumerate(header_map))) + rows.append('|'.join(dashAlign(rowsPadding[index], columnEntry.get('align')) for index, columnEntry in enumerate(header_map))) + + # Render Tabular Data + for item in data: + rows.append('|'.join(strAlign(rowsPadding[index], columnEntry.get('align'), str(item[columnEntry['key_name']])) for index, columnEntry in enumerate(header_map))) + + # Convert Tabular String Rows Into String + tableString = "" + for row in rows: + tableString += f'|{row}|\n' + + return tableString + + +def element_count_rounded_notation(count: int) -> str: + if count > 1e15 : + # Quadrillion + scaled_amount = count * 1e-15 + scale_suffix = "Q" + elif count > 1e12 : + # Trillions + scaled_amount = count * 1e-12 + scale_suffix = "T" + elif count > 1e9 : + # Billions + scaled_amount = count * 1e-9 + scale_suffix = "B" + elif count > 1e6 : + # Millions + scaled_amount = count * 1e-6 + scale_suffix = "M" + elif count > 1e3 : + # Thousands + scaled_amount = count * 1e-3 + scale_suffix = "K" + else: + # Under Thousands + scaled_amount = count + scale_suffix = "" + return f"{'~' if count > 1e3 else ''}{round(scaled_amount)}{scale_suffix}" + + +def translate_tensor_name(name): + words = name.split(".") + + # Source: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#standardized-tensor-names + abbreviation_dictionary = { + 'token_embd': 'Token embedding', + 'pos_embd': 'Position embedding', + 'output_norm': 'Output normalization', + 'output': 'Output', + 'attn_norm': 'Attention normalization', + 'attn_norm_2': 'Attention normalization', + 'attn_qkv': 'Attention query-key-value', + 'attn_q': 'Attention query', + 'attn_k': 'Attention key', + 'attn_v': 'Attention value', + 'attn_output': 'Attention output', + 'ffn_norm': 'Feed-forward network normalization', + 'ffn_up': 'Feed-forward network "up"', + 'ffn_gate': 'Feed-forward network "gate"', + 'ffn_down': 'Feed-forward network "down"', + 'ffn_gate_inp': 'Expert-routing layer for the Feed-forward network in Mixture of Expert models', + 'ffn_gate_exp': 'Feed-forward network "gate" layer per expert in Mixture of Expert models', + 'ffn_down_exp': 'Feed-forward network "down" layer per expert in Mixture of Expert models', + 'ffn_up_exp': 'Feed-forward network "up" layer per expert in Mixture of Expert models', + 'ssm_in': 'State space model input projections', + 'ssm_conv1d': 'State space model rolling/shift', + 'ssm_x': 'State space model selective parametrization', + 'ssm_a': 'State space model state compression', + 'ssm_d': 'State space model skip connection', + 'ssm_dt': 'State space model time step', + 'ssm_out': 'State space model output projection', + 'blk': 'Block' + } + + expanded_words = [] + for word in words: + word_norm = word.strip().lower() + if word_norm in abbreviation_dictionary: + expanded_words.append(abbreviation_dictionary[word_norm].title()) + else: + expanded_words.append(word.title()) + + return ' '.join(expanded_words) + + +def dump_markdown_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: + host_endian, file_endian = get_file_host_endian(reader) + markdown_content = "" + markdown_content += f'# {args.model} - GGUF Internal File Dump\n\n' + markdown_content += f'- Endian: {file_endian} endian\n' + markdown_content += '\n' + markdown_content += '## Key Value Metadata Store\n\n' + markdown_content += f'There are {len(reader.fields)} key-value pairs in this file\n' + markdown_content += '\n' + + kv_dump_table: list[dict[str, str | int]] = [] + for n, field in enumerate(reader.fields.values(), 1): + if not field.types: + pretty_type = 'N/A' + elif field.types[0] == GGUFValueType.ARRAY: + nest_count = len(field.types) - 1 + pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count + else: + pretty_type = str(field.types[-1].name) + + total_elements = len(field.data) + value = "" + if len(field.types) == 1: + curr_type = field.types[0] + if curr_type == GGUFValueType.STRING: + value = repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60]) + elif curr_type in reader.gguf_scalar_to_np: + value = str(field.parts[-1][0]) + else: + if field.types[0] == GGUFValueType.ARRAY: + curr_type = field.types[1] + if curr_type == GGUFValueType.STRING: + render_element = min(5, total_elements) + for element_pos in range(render_element): + value += repr(str(bytes(field.parts[-1 - element_pos]), encoding='utf-8')[:5]) + (", " if total_elements > 1 else "") + elif curr_type in reader.gguf_scalar_to_np: + render_element = min(7, total_elements) + for element_pos in range(render_element): + value += str(field.parts[-1 - element_pos][0]) + (", " if total_elements > 1 else "") + value = f'[ {value}{" ..." if total_elements > 1 else ""} ]' + kv_dump_table.append({"n":n, "pretty_type":pretty_type, "total_elements":total_elements, "field_name":field.name, "value":value}) + + kv_dump_table_header_map = [ + {'key_name':'n', 'header_name':'POS', 'align':'right'}, + {'key_name':'pretty_type', 'header_name':'TYPE', 'align':'left'}, + {'key_name':'total_elements', 'header_name':'Count', 'align':'right'}, + {'key_name':'field_name', 'header_name':'Key', 'align':'left'}, + {'key_name':'value', 'header_name':'Value', 'align':'left'}, + ] + + markdown_content += markdown_table_with_alignment_support(kv_dump_table_header_map, kv_dump_table) + + markdown_content += "\n" + + if not args.no_tensors: + # Group tensors by their prefix and maintain order + tensor_prefix_order: list[str] = [] + tensor_name_to_key: dict[str, int] = {} + tensor_groups: dict[str, list[ReaderTensor]] = {} + total_elements = sum(tensor.n_elements for tensor in reader.tensors) + + # Parsing Tensors Record + for key, tensor in enumerate(reader.tensors): + tensor_components = tensor.name.split('.') + + # Classify Tensor Group + tensor_group_name = "base" + if tensor_components[0] == 'blk': + tensor_group_name = f"{tensor_components[0]}.{tensor_components[1]}" + + # Check if new Tensor Group + if tensor_group_name not in tensor_groups: + tensor_groups[tensor_group_name] = [] + tensor_prefix_order.append(tensor_group_name) + + # Record Tensor and Tensor Position + tensor_groups[tensor_group_name].append(tensor) + tensor_name_to_key[tensor.name] = key + + # Tensors Mapping Dump + markdown_content += f'## Tensors Overview {element_count_rounded_notation(total_elements)} Elements\n\n' + markdown_content += f'Total number of elements in all tensors: {total_elements} Elements\n' + markdown_content += '\n' + + for group in tensor_prefix_order: + tensors = tensor_groups[group] + group_elements = sum(tensor.n_elements for tensor in tensors) + markdown_content += f"- [{translate_tensor_name(group)} Tensor Group - {element_count_rounded_notation(group_elements)} Elements](#{group.replace('.', '_')})\n" + + markdown_content += "\n" + + for group in tensor_prefix_order: + tensors = tensor_groups[group] + group_elements = sum(tensor.n_elements for tensor in tensors) + group_percentage = group_elements / total_elements * 100 + markdown_content += f"### {translate_tensor_name(group)} Tensor Group : {element_count_rounded_notation(group_elements)} Elements\n\n" + + # Precalculate column sizing for visual consistency + prettify_element_est_count_size: int = 1 + prettify_element_count_size: int = 1 + prettify_dimension_max_widths: dict[int, int] = {} + for tensor in tensors: + prettify_element_est_count_size = max(prettify_element_est_count_size, len(str(element_count_rounded_notation(tensor.n_elements)))) + prettify_element_count_size = max(prettify_element_count_size, len(str(tensor.n_elements))) + for i, dimension_size in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape))): + prettify_dimension_max_widths[i] = max(prettify_dimension_max_widths.get(i,1), len(str(dimension_size))) + + # Generate Tensor Layer Table Content + tensor_dump_table: list[dict[str, str | int]] = [] + for tensor in tensors: + human_friendly_name = translate_tensor_name(tensor.name.replace(".weight", ".(W)").replace(".bias", ".(B)")) + pretty_dimension = ' x '.join(f'{str(d):>{prettify_dimension_max_widths[i]}}' for i, d in enumerate(list(tensor.shape) + [1] * (4 - len(tensor.shape)))) + element_count_est = f"({element_count_rounded_notation(tensor.n_elements):>{prettify_element_est_count_size}})" + element_count_string = f"{element_count_est} {tensor.n_elements:>{prettify_element_count_size}}" + type_name_string = f"{tensor.tensor_type.name}" + tensor_dump_table.append({"t_id":tensor_name_to_key[tensor.name], "layer_name":tensor.name, "human_layer_name":human_friendly_name, "element_count":element_count_string, "pretty_dimension":pretty_dimension, "tensor_type":type_name_string}) + + tensor_dump_table_header_map = [ + {'key_name':'t_id', 'header_name':'T_ID', 'align':'right'}, + {'key_name':'layer_name', 'header_name':'Tensor Layer Name', 'align':'left'}, + {'key_name':'human_layer_name', 'header_name':'Human Friendly Tensor Layer Name', 'align':'left'}, + {'key_name':'element_count', 'header_name':'Elements', 'align':'left'}, + {'key_name':'pretty_dimension', 'header_name':'Shape', 'align':'left'}, + {'key_name':'tensor_type', 'header_name':'Type', 'align':'left'}, + ] + + markdown_content += markdown_table_with_alignment_support(tensor_dump_table_header_map, tensor_dump_table) + + markdown_content += "\n" + markdown_content += f"- Total elements in {group}: ({element_count_rounded_notation(group_elements):>4}) {group_elements}\n" + markdown_content += f"- Percentage of total elements: {group_percentage:.2f}%\n" + markdown_content += "\n\n" + + print(markdown_content) # noqa: NP100 + + def main() -> None: parser = argparse.ArgumentParser(description="Dump GGUF file metadata") parser.add_argument("model", type=str, help="GGUF format model filename") parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") parser.add_argument("--json", action="store_true", help="Produce JSON output") parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + parser.add_argument("--markdown", action="store_true", help="Produce markdown output") parser.add_argument("--verbose", action="store_true", help="increase output verbosity") args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - if not args.json: + if not args.json and not args.markdown: logger.info(f'* Loading: {args.model}') reader = GGUFReader(args.model, 'r') if args.json: dump_metadata_json(reader, args) + elif args.markdown: + dump_markdown_metadata(reader, args) else: dump_metadata(reader, args) diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py index c9f1927f6a0be..c4b90d5810a65 100755 --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -101,8 +101,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new logger.debug(f'Copying {field.name}') if val.value is not None: - writer.add_key(field.name) - writer.add_val(val.value, val.type) + writer.add_key_value(field.name, val.value, val.type) if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: logger.debug('Adding chat template(s)') @@ -111,8 +110,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new for key, val in new_metadata.items(): logger.debug(f'Adding {key}: "{val.value}" {val.description}') - writer.add_key(key) - writer.add_val(val.value, val.type) + writer.add_key_value(key, val.value, val.type) total_bytes = 0 @@ -144,6 +142,7 @@ def main() -> None: parser.add_argument("--general-description", type=str, help="The models general.description", metavar='"Description ..."') parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."') parser.add_argument("--chat-template-config", type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json') + parser.add_argument("--pre-tokenizer", type=str, help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"') parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model", metavar='general.url') parser.add_argument("--special-token", action="append", type=str, help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '""')) parser.add_argument("--special-token-by-id", action="append", type=str, help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0')) @@ -172,6 +171,9 @@ def main() -> None: if template: new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) + if args.pre_tokenizer: + new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer) + if remove_metadata: logger.warning('*** Warning *** Warning *** Warning **') logger.warning('* Most metadata is required for a fully functional GGUF file,') diff --git a/grammars/README.md b/grammars/README.md index 2b8384d9d330e..2f685eb6de004 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -59,9 +59,13 @@ Parentheses `()` can be used to group sequences, which allows for embedding alte ## Repetition and Optional Symbols -- `*` after a symbol or sequence means that it can be repeated zero or more times. -- `+` denotes that the symbol or sequence should appear one or more times. -- `?` makes the preceding symbol or sequence optional. +- `*` after a symbol or sequence means that it can be repeated zero or more times (equivalent to `{0,}`). +- `+` denotes that the symbol or sequence should appear one or more times (equivalent to `{1,}`). +- `?` makes the preceding symbol or sequence optional (equivalent to `{0,1}`). +- `{m}` repeats the precedent symbol or sequence exactly `m` times +- `{m,}` repeats the precedent symbol or sequence at least `m` times +- `{m,n}` repeats the precedent symbol or sequence at between `m` and `n` times (included) +- `{0,n}` repeats the precedent symbol or sequence at most `n` times (included) ## Comments and newlines @@ -87,9 +91,11 @@ item ::= [^\n]+ "\n" This guide provides a brief overview. Check out the GBNF files in this directory (`grammars/`) for examples of full grammars. You can try them out with: ``` -./main -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' +./llama-cli -m --grammar-file grammars/some-grammar.gbnf -p 'Some prompt' ``` +`llama.cpp` can also convert JSON schemas to grammars either ahead of time or at each request, see below. + ## Troubleshooting Grammars currently have performance gotchas (see https://github.com/ggerganov/llama.cpp/issues/4218). @@ -98,4 +104,41 @@ Grammars currently have performance gotchas (see https://github.com/ggerganov/ll A common pattern is to allow repetitions of a pattern `x` up to N times. -While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) will result in extremely slow inference. Instead, you can write `(x (x (x ... (x)?...)?)?)?` (w/ N-deep nesting) +While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) may result in extremely slow sampling. Instead, you can write `x{0,N}` (or `(x (x (x ... (x)?...)?)?)?` w/ N-deep nesting in earlier llama.cpp versions). + +## Using GBNF grammars + +You can use GBNF grammars: + +- In [llama-server](../examples/server)'s completion endpoints, passed as the `grammar` body field +- In [llama-cli](../examples/main), passed as the `--grammar` & `--grammar-file` flags +- With [llama-gbnf-validator](../examples/gbnf-validator) tool, to test them against strings. + +## JSON Schemas → GBNF + +`llama.cpp` supports converting a subset of https://json-schema.org/ to GBNF grammars: + +- In [llama-server](../examples/server): + - For any completion endpoints, passed as the `json_schema` body field + - For the `/chat/completions` endpoint, passed inside the `result_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}`) +- In [llama-cli](../examples/main), passed as the `--json` / `-j` flag +- To convert to a grammar ahead of time: + - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) + - in JavaScript with [json-schema-to-grammar.mjs](../examples/server/public/json-schema-to-grammar.mjs) (this is used by the [server](../examples/server)'s Web UI) + +Take a look at [tests](../../tests/test-json-schema-to-grammar.cpp) to see which features are likely supported (you'll also find usage examples in https://github.com/ggerganov/llama.cpp/pull/5978, https://github.com/ggerganov/llama.cpp/pull/6659 & https://github.com/ggerganov/llama.cpp/pull/6555). + +Here is also a non-exhaustive list of **unsupported** features: + +- `additionalProperties`: to be fixed in https://github.com/ggerganov/llama.cpp/pull/7840 +- `minimum`, `exclusiveMinimum`, `maximum`, `exclusiveMaximum` + - `integer` constraints to be implemented in https://github.com/ggerganov/llama.cpp/pull/7797 +- Remote `$ref`s in the C++ version (Python & JavaScript versions fetch https refs) +- Mixing `properties` w/ `anyOf` / `oneOf` in the same type (https://github.com/ggerganov/llama.cpp/issues/7703) +- `string` formats `uri`, `email` +- [`contains`](https://json-schema.org/draft/2020-12/json-schema-core#name-contains) / `minContains` +- `uniqueItems` +- `$anchor` (cf. [dereferencing](https://json-schema.org/draft/2020-12/json-schema-core#name-dereferencing)) +- [`not`](https://json-schema.org/draft/2020-12/json-schema-core#name-not) +- [Conditionals](https://json-schema.org/draft/2020-12/json-schema-core#name-keywords-for-applying-subsche) `if` / `then` / `else` / `dependentSchemas` +- [`patternProperties`](https://json-schema.org/draft/2020-12/json-schema-core#name-patternproperties) diff --git a/grammars/json.gbnf b/grammars/json.gbnf index a8a80752e6562..b6448c87b72ec 100644 --- a/grammars/json.gbnf +++ b/grammars/json.gbnf @@ -16,10 +16,10 @@ array ::= string ::= "\"" ( [^"\\\x7F\x00-\x1F] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes )* "\"" ws -number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [0-9] [1-9]{0,15})? ws # Optional space: by convention, applied in this grammar after literal chars when allowed -ws ::= ([ \t\n] ws)? +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/grammars/json_arr.gbnf b/grammars/json_arr.gbnf index 31a3202f863c1..b3dc6f9b16277 100644 --- a/grammars/json_arr.gbnf +++ b/grammars/json_arr.gbnf @@ -25,10 +25,10 @@ array ::= string ::= "\"" ( [^"\\\x7F\x00-\x1F] | - "\\" (["\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) # escapes + "\\" (["\\bfnrt] | "u" [0-9a-fA-F]{4}) # escapes )* "\"" ws -number ::= ("-"? ([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? ws +number ::= ("-"? ([0-9] | [1-9] [0-9]{0,15})) ("." [0-9]+)? ([eE] [-+]? [1-9] [0-9]{0,15})? ws # Optional space: by convention, applied in this grammar after literal chars when allowed -ws ::= ([ \t\n] ws)? +ws ::= | " " | "\n" [ \t]{0,20} diff --git a/kompute-shaders/op_getrows_f32.comp b/kompute-shaders/op_getrows_f32.comp new file mode 100644 index 0000000000000..9d7acdaf8a8e4 --- /dev/null +++ b/kompute-shaders/op_getrows_f32.comp @@ -0,0 +1,31 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { float inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +void dequantize_row_f32(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) { + for (int j = 0; j < k; j++) { + out_[y + j] = inA[x + j]; + } +} + +void main() { + const uint i = gl_WorkGroupID.x; + const int r = inB[i + pcs.inBOff]; + + dequantize_row_f32(r*pcs.nb01/4 + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00); +} diff --git a/kompute-shaders/op_rope_f16.comp b/kompute-shaders/op_rope_f16.comp index b446225849d5f..1a4058b3f1f10 100644 --- a/kompute-shaders/op_rope_f16.comp +++ b/kompute-shaders/op_rope_f16.comp @@ -14,7 +14,7 @@ void main() { const bool is_neox = (pcs.mode & 2) != 0; float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); diff --git a/kompute-shaders/op_rope_f32.comp b/kompute-shaders/op_rope_f32.comp index 2c0235d75b6b6..65e03827a2660 100644 --- a/kompute-shaders/op_rope_f32.comp +++ b/kompute-shaders/op_rope_f32.comp @@ -14,7 +14,7 @@ void main() { const bool is_neox = (pcs.mode & 2) != 0; float corr_dims[2]; - rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + rope_yarn_corr_dims(pcs.n_dims, pcs.n_ctx_orig, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); diff --git a/kompute-shaders/rope_common.comp b/kompute-shaders/rope_common.comp index 57ba6597a7eb2..7b9394cb2fffc 100644 --- a/kompute-shaders/rope_common.comp +++ b/kompute-shaders/rope_common.comp @@ -9,7 +9,7 @@ layout (push_constant) uniform parameter { uint outOff; int n_dims; int mode; - int n_orig_ctx; + int n_ctx_orig; float freq_base; float freq_scale; float ext_factor; @@ -54,14 +54,14 @@ void rope_yarn( // Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get // `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` -float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) { - return n_dims * log(n_orig_ctx / (n_rot * TWOPI_F)) / (2 * log(base)); +float rope_yarn_corr_factor(int n_dims, int n_ctx_orig, float n_rot, float base) { + return n_dims * log(n_ctx_orig / (n_rot * TWOPI_F)) / (2 * log(base)); } void rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, out float dims[2] + int n_dims, int n_ctx_orig, float freq_base, float beta_fast, float beta_slow, out float dims[2] ) { // start and end correction dims - dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base))); - dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base))); + dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_fast, freq_base))); + dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_ctx_orig, beta_slow, freq_base))); } diff --git a/llama.cpp b/llama.cpp index 3c9fe15bb4596..e06c851ad5b6c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13,8 +13,6 @@ #ifdef GGML_USE_CUDA # include "ggml-cuda.h" -#elif defined(GGML_USE_CLBLAST) -# include "ggml-opencl.h" #elif defined(GGML_USE_VULKAN) # include "ggml-vulkan.h" #elif defined(GGML_USE_SYCL) @@ -23,6 +21,10 @@ # include "ggml-kompute.h" #endif +#ifdef GGML_USE_BLAS +# include "ggml-blas.h" +#endif + #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif @@ -103,14 +105,14 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 128 +#define LLAMA_MAX_EXPERTS 160 // // logging // LLAMA_ATTRIBUTE_FORMAT(2, 3) -static void llama_log_internal (ggml_log_level level, const char* format, ...); +static void llama_log_internal (ggml_log_level level, const char * format, ...); static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data); #define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__) @@ -222,6 +224,7 @@ enum llm_arch { LLM_ARCH_DBRX, LLM_ARCH_OLMO, LLM_ARCH_ARCTIC, + LLM_ARCH_DEEPSEEK2, LLM_ARCH_UNKNOWN, }; @@ -259,6 +262,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, { LLM_ARCH_ARCTIC, "arctic" }, + { LLM_ARCH_DEEPSEEK2, "deepseek2" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -279,11 +283,16 @@ enum llm_kv { LLM_KV_CONTEXT_LENGTH, LLM_KV_EMBEDDING_LENGTH, LLM_KV_BLOCK_COUNT, + LLM_KV_LEADING_DENSE_BLOCK_COUNT, LLM_KV_FEED_FORWARD_LENGTH, + LLM_KV_EXPERT_FEED_FORWARD_LENGTH, + LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, LLM_KV_USE_PARALLEL_RESIDUAL, LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, + LLM_KV_EXPERT_SHARED_COUNT, + LLM_KV_EXPERT_WEIGHTS_SCALE, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, @@ -296,6 +305,8 @@ enum llm_kv { LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, LLM_KV_ATTENTION_CAUSAL, + LLM_KV_ATTENTION_Q_LORA_RANK, + LLM_KV_ATTENTION_KV_LORA_RANK, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, @@ -305,6 +316,7 @@ enum llm_kv { LLM_KV_ROPE_SCALING_ATTN_FACTOR, LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_ROPE_SCALING_YARN_LOG_MUL, LLM_KV_SPLIT_NO, LLM_KV_SPLIT_COUNT, @@ -353,17 +365,22 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, - { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, - { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, - { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, - { LLM_KV_BLOCK_COUNT, "%s.block_count" }, - { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, - { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, - { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, - { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, - { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, - { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, - { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, + { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, + { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, + { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, + { LLM_KV_BLOCK_COUNT, "%s.block_count" }, + { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, + { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, + { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" }, + { LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, "%s.expert_shared_feed_forward_length" }, + { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, + { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, + { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, + { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, + { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, + { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, + { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, + { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -374,6 +391,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" }, + { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" }, + { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, @@ -383,6 +402,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" }, { LLM_KV_SPLIT_NO, "split.no" }, { LLM_KV_SPLIT_COUNT, "split.count" }, @@ -474,6 +494,12 @@ enum llm_tensor { LLM_TENSOR_SSM_A, LLM_TENSOR_SSM_D, LLM_TENSOR_SSM_OUT, + LLM_TENSOR_ATTN_Q_A, + LLM_TENSOR_ATTN_Q_B, + LLM_TENSOR_ATTN_KV_A_MQA, + LLM_TENSOR_ATTN_KV_B, + LLM_TENSOR_ATTN_Q_A_NORM, + LLM_TENSOR_ATTN_KV_A_NORM, }; static const std::map> LLM_TENSOR_NAMES = { @@ -684,6 +710,7 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_ATTN_NORM_2, "blk.%d.attn_norm_2" }, { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, @@ -1057,6 +1084,35 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_DEEPSEEK2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q_A_NORM, "blk.%d.attn_q_a_norm" }, + { LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" }, + { LLM_TENSOR_ATTN_Q_B, "blk.%d.attn_q_b" }, + { LLM_TENSOR_ATTN_KV_A_MQA, "blk.%d.attn_kv_a_mqa" }, + { LLM_TENSOR_ATTN_KV_B, "blk.%d.attn_kv_b" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1224,6 +1280,126 @@ struct no_init { }; struct llama_file { + +#if defined(_WIN32) + // use FILE * so we don't have to re-open the file to mmap + FILE * fp; + HANDLE fp_win32; + size_t size; + +private: + std::string GetErrorMessageWin32(DWORD error_code) const { + std::string ret; + LPSTR lpMsgBuf = NULL; + DWORD bufLen = FormatMessageA(FORMAT_MESSAGE_ALLOCATE_BUFFER | FORMAT_MESSAGE_FROM_SYSTEM | FORMAT_MESSAGE_IGNORE_INSERTS, + NULL, error_code, MAKELANGID(LANG_NEUTRAL, SUBLANG_DEFAULT), (LPSTR)&lpMsgBuf, 0, NULL); + if (!bufLen) { + ret = format("Win32 error code: %s", error_code); + } else { + ret = lpMsgBuf; + LocalFree(lpMsgBuf); + } + + return ret; + } + +public: + + llama_file(const char * fname, const char * mode) { + fp = ggml_fopen(fname, mode); + if (fp == NULL) { + throw std::runtime_error(format("failed to open %s: %s", fname, strerror(errno))); + } + fp_win32 = (HANDLE) _get_osfhandle(_fileno(fp)); + seek(0, SEEK_END); + size = tell(); + seek(0, SEEK_SET); + } + + size_t tell() const { + // SetFilePointerEx returns the current position when seeking relative 0 bytes + LARGE_INTEGER li; + li.QuadPart = 0; + BOOL ret = SetFilePointerEx(fp_win32, li, &li, FILE_CURRENT); + if (!ret) { + throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str())); + } + + return li.QuadPart; + } + + void seek(size_t offset, int whence) const { + // no need to convert SEEK_* to FILE_*. The enums are the same. + // Still, keep static asserts to avoid failures in the future. + static_assert(SEEK_SET == FILE_BEGIN, "SEEK_SET != FILE_BEGIN"); + static_assert(SEEK_CUR == FILE_CURRENT, "SEEK_CUR != FILE_CURRENT"); + static_assert(SEEK_END == FILE_END, "SEEK_END != FILE_END"); + + LARGE_INTEGER li; + li.QuadPart = offset; + BOOL ret = SetFilePointerEx(fp_win32, li, NULL, whence); + if (!ret) { + throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str())); + } + } + + void read_raw(void * ptr, size_t len) const { + // On Win32 ReadFile is significant faster than fread which is again significant faster than std::fstream. Thus + // use the Win32 API to do file io instead of the C/C++ library functions. + + // There are conditions under which ReadFile cannot read chunks >64MB. + // Thus split the operation into smaller chunks if len exceeds this limit. + size_t bytes_read = 0; + while (bytes_read < len) { + size_t chunk_size = std::min(len - bytes_read, 64*1024*1024); + DWORD chunk_read = 0; + BOOL result = ReadFile(fp_win32, reinterpret_cast(ptr) + bytes_read, chunk_size, &chunk_read, NULL); + if (!result) { + throw std::runtime_error(format("read error: %s", GetErrorMessageWin32(GetLastError()).c_str())); + } + if (chunk_read < chunk_size || chunk_read == 0) { + throw std::runtime_error("unexpectedly reached end of file"); + } + + bytes_read += chunk_read; + } ; + } + + uint32_t read_u32() const { + uint32_t val; + read_raw(&val, sizeof(val)); + return val; + } + + void write_raw(const void * ptr, size_t len) const { + // There are conditions under which WriteFile cannot write chunks >64MB. + // Thus split the operation into smaller chunks if len exceeds this limit. + size_t bytes_written = 0; + while (bytes_written < len) { + size_t chunk_size = std::min(len - bytes_written, 64*1024*1024); + DWORD chunk_written = 0; + BOOL result = WriteFile(fp_win32, reinterpret_cast(ptr) + bytes_written, chunk_size, &chunk_written, NULL); + if (!result) { + throw std::runtime_error(format("write error: %s", GetErrorMessageWin32(GetLastError()).c_str())); + } + if (chunk_written < chunk_size || chunk_written == 0) { + throw std::runtime_error("unexpectedly failed to write bytes"); + } + + bytes_written += chunk_written; + } + } + + void write_u32(std::uint32_t val) const { + write_raw(&val, sizeof(val)); + } + + ~llama_file() { + if (fp) { + std::fclose(fp); + } + } +#else // use FILE * so we don't have to re-open the file to mmap FILE * fp; size_t size; @@ -1244,7 +1420,10 @@ struct llama_file { #else long ret = std::ftell(fp); #endif - GGML_ASSERT(ret != -1); // this really shouldn't fail + if (ret == -1) { + throw std::runtime_error(format("ftell error: %s", strerror(errno))); + } + return (size_t) ret; } @@ -1254,7 +1433,9 @@ struct llama_file { #else int ret = std::fseek(fp, (long) offset, whence); #endif - GGML_ASSERT(ret == 0); // same + if (ret != 0) { + throw std::runtime_error(format("seek error: %s", strerror(errno))); + } } void read_raw(void * ptr, size_t len) const { @@ -1297,6 +1478,7 @@ struct llama_file { std::fclose(fp); } } +#endif }; using llama_files = std::vector>; @@ -1651,12 +1833,13 @@ struct llama_mlock { }; using llama_mlocks = std::vector>; -static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { +// NOTE: avoid ever using this except for building the token_to_piece caches +static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) { std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + int check = llama_token_to_piece(model, token, result.data(), result.size(), special); GGML_ASSERT(check == -n_tokens); } else { @@ -1741,6 +1924,7 @@ enum e_model { MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_16B, MODEL_20B, MODEL_30B, MODEL_34B, @@ -1748,6 +1932,7 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_236B, MODEL_314B, MODEL_SMALL, MODEL_MEDIUM, @@ -1783,13 +1968,22 @@ struct llama_hparams { uint32_t n_expert_used = 0; uint32_t n_vocab_type = 0; // for BERT-style token types + uint32_t n_layer_dense_lead = 0; + uint32_t n_lora_q = 0; + uint32_t n_lora_kv = 0; + uint32_t n_ff_exp = 0; + uint32_t n_ff_shexp = 0; + uint32_t n_expert_shared = 0; + float expert_weights_scale = 0.0; + float f_norm_eps; float f_norm_rms_eps; float rope_attn_factor = 1.0f; float rope_freq_base_train; float rope_freq_scale_train; - uint32_t n_yarn_orig_ctx; + uint32_t n_ctx_orig_yarn; + float rope_yarn_log_mul; // for State Space Models uint32_t ssm_d_conv = 0; @@ -1823,8 +2017,15 @@ struct llama_hparams { if (this->n_expert != other.n_expert) return true; if (this->n_expert_used != other.n_expert_used) return true; + if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; + if (this->n_lora_q != other.n_lora_q) return true; + if (this->n_lora_kv != other.n_lora_kv) return true; + if (this->n_ff_exp != other.n_ff_exp) return true; + if (this->n_ff_shexp != other.n_ff_shexp) return true; + if (this->n_expert_shared != other.n_expert_shared) return true; + if (this->rope_finetuned != other.rope_finetuned) return true; - if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; + if (this->n_ctx_orig_yarn != other.n_ctx_orig_yarn) return true; if (this->ssm_d_conv != other.ssm_d_conv) return true; if (this->ssm_d_inner != other.ssm_d_inner) return true; @@ -1838,6 +2039,8 @@ struct llama_hparams { if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; + if (!is_float_close(this->expert_weights_scale, other.expert_weights_scale, EPSILON)) return true; + if (!is_float_close(this->rope_yarn_log_mul, other.rope_yarn_log_mul, EPSILON)) return true; return false; } @@ -1881,7 +2084,7 @@ struct llama_cparams { float rope_freq_base; float rope_freq_scale; - uint32_t n_yarn_orig_ctx; + uint32_t n_ctx_orig_yarn; // These hyperparameters are not exposed in GGUF, because all // existing YaRN models use the same values for them. float yarn_ext_factor; @@ -1913,6 +2116,8 @@ struct llama_layer { struct ggml_tensor * attn_k_norm_b; struct ggml_tensor * attn_out_norm; struct ggml_tensor * attn_out_norm_b; + struct ggml_tensor * attn_q_a_norm; + struct ggml_tensor * attn_kv_a_norm; // attention struct ggml_tensor * wq; @@ -1920,6 +2125,10 @@ struct llama_layer { struct ggml_tensor * wv; struct ggml_tensor * wo; struct ggml_tensor * wqkv; + struct ggml_tensor * wq_a; + struct ggml_tensor * wq_b; + struct ggml_tensor * wkv_a_mqa; + struct ggml_tensor * wkv_b; // attention bias struct ggml_tensor * bq; @@ -1953,8 +2162,9 @@ struct llama_layer { struct ggml_tensor * ffn_up_shexp; // ff bias - struct ggml_tensor * ffn_down_b; // b2 - struct ggml_tensor * ffn_up_b; // b3 + struct ggml_tensor * ffn_gate_b = nullptr; + struct ggml_tensor * ffn_down_b = nullptr; // b2 + struct ggml_tensor * ffn_up_b = nullptr; // b3 struct ggml_tensor * ffn_act; // mamba proj @@ -2072,12 +2282,12 @@ struct llama_control_vector { struct llama_vocab { using id = int32_t; using token = std::string; - using ttype = llama_token_type; + using tattr = llama_token_attr; struct token_data { token text; float score; - ttype type; + tattr attr; }; enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM; @@ -2086,7 +2296,8 @@ struct llama_vocab { std::unordered_map token_to_id; std::vector id_to_token; - std::unordered_map special_tokens_cache; + std::vector cache_special_tokens; + std::vector cache_token_to_piece; // llama_token_to_piece(special = true); std::map, int> bpe_ranks; @@ -2222,9 +2433,13 @@ struct llama_context { std::vector backends; #ifdef GGML_USE_METAL ggml_backend_t backend_metal = nullptr; +#endif +#ifdef GGML_USE_BLAS + ggml_backend_t backend_blas = nullptr; #endif ggml_backend_t backend_cpu = nullptr; + const llama_model & model; // key + value cache for the self attention @@ -2293,13 +2508,34 @@ struct llama_context { struct llama_control_vector cvec; }; +static size_t llama_get_device_count(const llama_model & model) { + size_t count = 1; +#if defined(GGML_USE_CUDA) + count = ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + count = ggml_backend_sycl_get_device_count(); +#elif defined(GGML_USE_VULKAN) + count = ggml_backend_vk_get_device_count(); +#endif +#if defined(GGML_USE_RPC) + count += model.rpc_servers.size(); +#endif + return count; + GGML_UNUSED(model); +} + static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_RPC - std::string endpoint = model.rpc_servers[gpu]; - buft = ggml_backend_rpc_buffer_type(endpoint.c_str()); -#elif defined(GGML_USE_METAL) +#if defined(GGML_USE_RPC) + int dev_count = (int)llama_get_device_count(model); + int rpc_count = (int)model.rpc_servers.size(); + if (gpu >= dev_count - rpc_count) { + const char * endpoint = model.rpc_servers[gpu - dev_count + rpc_count].c_str(); + return ggml_backend_rpc_buffer_type(endpoint); + } +#endif +#if defined(GGML_USE_METAL) buft = ggml_backend_metal_buffer_type(); #elif defined(GGML_USE_CUDA) buft = ggml_backend_cuda_buffer_type(gpu); @@ -2307,8 +2543,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ buft = ggml_backend_vk_buffer_type(gpu); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); -#elif defined(GGML_USE_CLBLAST) - buft = ggml_backend_opencl_buffer_type(); #elif defined(GGML_USE_KOMPUTE) buft = ggml_backend_kompute_buffer_type(gpu); if (buft == nullptr) { @@ -2347,29 +2581,19 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo GGML_UNUSED(tensor_split); } -static size_t llama_get_device_count(const llama_model & model) { -#if defined(GGML_USE_RPC) - return model.rpc_servers.size(); -#elif defined(GGML_USE_CUDA) - return ggml_backend_cuda_get_device_count(); -#elif defined(GGML_USE_SYCL) - return ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - return ggml_backend_vk_get_device_count(); -#else - return 1; -#endif - GGML_UNUSED(model); -} - static size_t llama_get_device_memory(const llama_model & model, int device) { #if defined(GGML_USE_RPC) - size_t total; - size_t free; - std::string endpoint = model.rpc_servers[device]; - ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total); - return free; -#elif defined(GGML_USE_CUDA) + int dev_count = (int)llama_get_device_count(model); + int rpc_count = (int)model.rpc_servers.size(); + if (device >= dev_count - rpc_count) { + size_t total; + size_t free; + const char * endpoint = model.rpc_servers[device - dev_count + rpc_count].c_str(); + ggml_backend_rpc_get_device_memory(endpoint, &free, &total); + return free; + } +#endif +#if defined(GGML_USE_CUDA) size_t total; size_t free; ggml_backend_cuda_get_device_memory(device, &free, &total); @@ -2441,10 +2665,6 @@ static bool llama_kv_cache_init( } } -#ifdef GGML_USE_CLBLAST - offload = false; -#endif - // count used buffer types std::map buft_layer_count; if (offload) { @@ -3631,6 +3851,44 @@ struct llama_model_loader { std::vector> read_buf; std::vector>> validation_result; +#if defined(GGML_USE_CUDA) + // 4 staging buffers for async uploads, each sized 1MB seems to be a good default for single NVMe drives. + // NVMe raid configurations might require more / larger buffers. + constexpr size_t num_buffers = 4; + constexpr size_t buffer_size = 1 * 1024 * 1024; // 1MB + + std::vector host_buffers; + std::vector host_ptrs; + std::vector events; + size_t buffer_idx = 0; // buffer to use for async loads + + ggml_backend_t cuda_backend = nullptr; + if (!use_mmap && !check_tensors) { + // When not using mmaped io use async uploads from pinned memory to GPU memory. + // First determine if the CUDA backend is active, and if so, determine the device ID. + ggml_backend_buffer_t buf = bufs_mmap.count(0) ? bufs_mmap.at(0) : nullptr; + if (buf) { + ggml_backend_buffer_type_t buffer_type = ggml_backend_buffer_get_type(buf); + for (int i = 0; i < ggml_backend_cuda_get_device_count(); ++i) { + auto * cuda_buffer_type = ggml_backend_cuda_buffer_type(i); + if (buffer_type == cuda_buffer_type) { + cuda_backend = ggml_backend_cuda_init(i); + break; + } + } + } + + // If the cuda backend is active create pinned memory buffers and events for synchronisation. + if (cuda_backend) { + for (size_t idx = 0; idx < num_buffers; ++idx) { + host_buffers.emplace_back(ggml_backend_buft_alloc_buffer(llama_default_buffer_type_cpu(true), buffer_size)); + host_ptrs.emplace_back(ggml_backend_buffer_get_base(host_buffers[idx])); + events.emplace_back(ggml_backend_event_new(cuda_backend)); + } + } + } +#endif + for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur != NULL; cur = ggml_get_next_tensor(ctx, cur)) { const auto * weight = get_weight(ggml_get_name(cur)); if (weight == nullptr) { @@ -3686,12 +3944,36 @@ struct llama_model_loader { })); } } else { - read_buf.resize(n_size); - file->seek(weight->offs, SEEK_SET); - file->read_raw(read_buf.data(), n_size); - ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size); - if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) { - throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur))); +#if defined(GGML_USE_CUDA) + // If cuda_backend is valid load the tensor in chunks to pinned memory and upload the buffers asynchronously to the GPU. + if (cuda_backend) { + file->seek(weight->offs, SEEK_SET); + + size_t bytes_read = 0; + + while (bytes_read < n_size) { + size_t read_iteration = std::min(buffer_size, n_size - bytes_read); + + ggml_backend_event_synchronize(events[buffer_idx]); + file->read_raw(host_ptrs[buffer_idx], read_iteration); + ggml_backend_tensor_set_async(cuda_backend, cur, host_ptrs[buffer_idx], bytes_read, read_iteration); + ggml_backend_event_record(events[buffer_idx]); + + bytes_read += read_iteration; + ++buffer_idx; + buffer_idx %= num_buffers; + } + } + else +#endif + { + read_buf.resize(n_size); + file->seek(weight->offs, SEEK_SET); + file->read_raw(read_buf.data(), n_size); + ggml_backend_tensor_set(cur, read_buf.data(), 0, n_size); + if (check_tensors && !ggml_validate_row_data(cur->type, read_buf.data(), n_size)) { + throw std::runtime_error(format("tensor '%s' has invalid data", ggml_get_name(cur))); + } } } } @@ -3699,6 +3981,18 @@ struct llama_model_loader { size_done += n_size; } +#if defined(GGML_USE_CUDA) + // free temporary resources used for async cuda uploads + if (cuda_backend) { + for (size_t idx = 0; idx < num_buffers;++idx) { + ggml_backend_event_synchronize(events[idx]); + ggml_backend_event_free(events[idx]); + ggml_backend_buffer_free(host_buffers[idx]); + } + ggml_backend_free(cuda_backend); + } +#endif + // check validation results bool validation_failed = false; for (auto & future : validation_result) { @@ -3832,6 +4126,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; + case MODEL_16B: return "16B"; case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; @@ -3839,6 +4134,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_40B: return "40B"; case MODEL_65B: return "65B"; case MODEL_70B: return "70B"; + case MODEL_236B: return "236B"; case MODEL_314B: return "314B"; case MODEL_SMALL: return "0.1B"; case MODEL_MEDIUM: return "0.4B"; @@ -3922,8 +4218,8 @@ static void llm_load_hparams( ml.get_key(LLM_KV_ROPE_SCALING_FINETUNED, rope_finetuned, false); hparams.rope_finetuned = rope_finetuned; - hparams.n_yarn_orig_ctx = hparams.n_ctx_train; - ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_yarn_orig_ctx, false); + hparams.n_ctx_orig_yarn = hparams.n_ctx_train; + ml.get_key(LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, hparams.n_ctx_orig_yarn, false); // rope_freq_base (optional) hparams.rope_freq_base_train = 10000.0f; @@ -3981,7 +4277,9 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; + // granite uses a vocab with len 49152 + case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break; + case 36: model.type = e_model::MODEL_8B; break; // granite case 40: model.type = e_model::MODEL_13B; break; case 48: model.type = e_model::MODEL_34B; break; case 60: model.type = e_model::MODEL_30B; break; @@ -4161,6 +4459,9 @@ static void llm_load_hparams( } break; case LLM_ARCH_QWEN2MOE: { + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp, false); + ml.get_key(LLM_KV_EXPERT_SHARED_FEED_FORWARD_LENGTH, hparams.n_ff_shexp, false); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_A2_7B; break; @@ -4251,6 +4552,8 @@ static void llm_load_hparams( case 30: model.type = e_model::MODEL_3B; break; case 32: model.type = e_model::MODEL_7B; break; case 40: model.type = e_model::MODEL_15B; break; + case 52: model.type = e_model::MODEL_20B; break; // granite + case 88: model.type = e_model::MODEL_34B; break; // granite default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4384,6 +4687,26 @@ static void llm_load_hparams( model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_DEEPSEEK2: + { + bool is_lite = (hparams.n_layer == 27); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + if (!is_lite) { + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + } + ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul); + + switch (hparams.n_layer) { + case 27: model.type = e_model::MODEL_16B; break; + case 60: model.type = e_model::MODEL_236B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4445,35 +4768,6 @@ static void llm_load_vocab( vocab.special_cls_id = -1; vocab.special_mask_id = -1; - // For Fill-In-the-Middle (FIM)/infill models which where converted - // prior to support of FIM special tokens in GGUF, the following - // will allow those models to continue to work. The general names - // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and - // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once - // new versions of these models have been published. - std::string gen_name; - ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false); - - std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(), - [](unsigned char c){ return std::tolower(c); }); - - if (gen_name.find("code") != std::string::npos) { - if (model.arch == LLM_ARCH_LLAMA) { - vocab.special_prefix_id = 32007; - vocab.special_suffix_id = 32008; - vocab.special_middle_id = 32009; - vocab.special_eot_id = 32010; - } else if (model.arch == LLM_ARCH_GEMMA) { - vocab.special_prefix_id = 67; - vocab.special_suffix_id = 69; - vocab.special_middle_id = 68; - // TODO: this is not EOT, it is "file separator" token, needs fix - // https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572 - //vocab.special_eot_id = 70; - vocab.special_eot_id = 107; - } - } - const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); if (add_space_prefix_keyidx != -1) { vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); @@ -4490,15 +4784,14 @@ static void llm_load_vocab( vocab.special_cls_id = 101; vocab.special_mask_id = 103; vocab.add_space_prefix = false; - } else { - if (tokenizer_model == "gpt2") { - vocab.type = LLAMA_VOCAB_TYPE_BPE; - } else { - LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); - LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); - vocab.type = LLAMA_VOCAB_TYPE_SPM; - return; + } else if (tokenizer_model == "gpt2") { + vocab.type = LLAMA_VOCAB_TYPE_BPE; + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); } + // read bpe merges and populate bpe ranks const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str()); if (merges_keyidx == -1) { @@ -4532,6 +4825,8 @@ static void llm_load_vocab( vocab.special_pad_id = -1; vocab.special_cls_id = -1; vocab.special_mask_id = -1; + } else { + throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str())); } // for now, only BPE models have pre-tokenizers @@ -4545,8 +4840,7 @@ static void llm_load_vocab( LLAMA_LOG_WARN("%s: ************************************ \n", __func__); LLAMA_LOG_WARN("%s: \n", __func__); vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; - } else if ( - tokenizer_pre == "default") { + } else if (tokenizer_pre == "default") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DEFAULT; } else if ( tokenizer_pre == "llama3" || @@ -4573,7 +4867,8 @@ static void llm_load_vocab( tokenizer_pre == "jina-es" || tokenizer_pre == "jina-de" || tokenizer_pre == "jina-v2-es" || - tokenizer_pre == "jina-v2-de") { + tokenizer_pre == "jina-v2-de" || + tokenizer_pre == "jina-v2-code") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { @@ -4593,6 +4888,12 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "dbrx") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX; + } else if ( + tokenizer_pre == "smaug-bpe") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG; + } else if ( + tokenizer_pre == "poro-chat") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_PORO; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -4631,12 +4932,64 @@ static void llm_load_vocab( auto & token_data = vocab.id_to_token[i]; token_data.text = std::move(word); token_data.score = scores ? scores[i] : 0.0f; - token_data.type = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL; + token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; + + if (toktypes) { //TODO: remove, required until per token attributes are available from GGUF file + switch(toktypes[i]) { + case LLAMA_TOKEN_TYPE_UNKNOWN: token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN; break; + case LLAMA_TOKEN_TYPE_UNUSED: token_data.attr = LLAMA_TOKEN_ATTR_UNUSED; break; + case LLAMA_TOKEN_TYPE_NORMAL: token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; break; + case LLAMA_TOKEN_TYPE_CONTROL: token_data.attr = LLAMA_TOKEN_ATTR_CONTROL; break; + case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break; + case LLAMA_TOKEN_TYPE_BYTE: token_data.attr = LLAMA_TOKEN_ATTR_BYTE; break; + case LLAMA_TOKEN_TYPE_UNDEFINED: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break; + default: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break; + } + } } GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size()); // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { + // For Fill-In-the-Middle (FIM)/infill models which where converted + // prior to support of FIM special tokens in GGUF, the following + // will allow those models to continue to work. The general names + // of the known models are currently CodeLlama (LLM_ARCH_LLAMA) and + // CodeGemma (LLM_ARCH_GEMMA). This can potentially be removed once + // new versions of these models have been published. + std::string gen_name; + ml.get_key(LLM_KV_GENERAL_NAME, gen_name, false); + + std::transform(gen_name.begin(), gen_name.end(), gen_name.begin(), + [](unsigned char c){ return std::tolower(c); }); + + if (gen_name.find("code") != std::string::npos) { + if (model.arch == LLM_ARCH_LLAMA + && 32010 < vocab.id_to_token.size() + && vocab.id_to_token[32007].text == "
    "
+              && vocab.id_to_token[32008].text == ""
+              && vocab.id_to_token[32009].text == ""
+              && vocab.id_to_token[32010].text == "") {
+                vocab.special_prefix_id = 32007;
+                vocab.special_suffix_id = 32008;
+                vocab.special_middle_id = 32009;
+                vocab.special_eot_id    = 32010;
+            } else if (model.arch == LLM_ARCH_GEMMA
+              && 107 < vocab.id_to_token.size()
+              && vocab.id_to_token[67].text == "<|fim_prefix|>"
+              && vocab.id_to_token[69].text == "<|fim_suffix|>"
+              && vocab.id_to_token[68].text == "<|fim_middle|>"
+              && vocab.id_to_token[107].text == "") {
+                vocab.special_prefix_id = 67;
+                vocab.special_suffix_id = 69;
+                vocab.special_middle_id = 68;
+                // TODO: this is not EOT, it is "file separator" token, needs fix
+                //       https://huggingface.co/google/codegemma-7b-it/blob/9b1d9231388358c04d90bd003458f5070d97db44/tokenizer_config.json#L565-L572
+                //vocab.special_eot_id    = 70;
+                vocab.special_eot_id    = 107;
+            }
+        }
+
         try {
             vocab.linefeed_id = llama_byte_to_token(vocab, '\n');
         } catch (const std::exception & e) {
@@ -4721,96 +5074,88 @@ static void llm_load_vocab(
 
     // build special tokens cache
     {
-        // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type,
-        //  and will always be correctly labeled in 'added_tokens.json' etc.
-        // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed
-        //  to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer
-        //  are special tokens.
-        // From testing, this appears to correlate 1:1 with special tokens.
-        //
+        for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) {
+            if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) {
+                vocab.cache_special_tokens.push_back(id);
+            }
+        }
 
-        // Counting special tokens and verifying in only one direction
-        //  is sufficient to detect difference in those two sets.
-        //
-        uint32_t special_tokens_count_by_type = 0;
-        uint32_t special_tokens_count_from_verification = 0;
+        std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(),
+            [&] (const llama_vocab::id a, const llama_vocab::id b) {
+                return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size();
+            }
+        );
 
-        bool special_tokens_definition_mismatch = false;
+        LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size());
+    }
 
-        for (const auto & t : vocab.token_to_id) {
-            const auto & token = t.first;
-            const auto & id    = t.second;
+    // build token to piece cache
+    {
+        size_t size_cache = 0;
 
-            // Count all non-normal tokens in the vocab while iterating
-            if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) {
-                special_tokens_count_by_type++;
-            }
+        std::vector cache_token_to_piece(n_vocab);
 
-            // Skip single character tokens
-            if (token.length() > 1) {
-                bool is_tokenizable = false;
+        for (uint32_t id = 0; id < n_vocab; ++id) {
+            cache_token_to_piece[id] = llama_token_to_piece(&model, id, true);
 
-                // Split token string representation in two, in all possible ways
-                //  and check if both halves can be matched to a valid token
-                for (unsigned i = 1; i < token.length();) {
-                    const auto left  = token.substr(0, i);
-                    const auto right = token.substr(i);
+            size_cache += cache_token_to_piece[id].size();
+        }
 
-                    // check if we didnt partition in the middle of a utf sequence
-                    auto utf = utf8_len(left.at(left.length() - 1));
+        std::swap(vocab.cache_token_to_piece, cache_token_to_piece);
 
-                    if (utf == 1) {
-                        if (vocab.token_to_id.find(left)  != vocab.token_to_id.end() &&
-                            vocab.token_to_id.find(right) != vocab.token_to_id.end() ) {
-                            is_tokenizable = true;
-                            break;
-                        }
-                        i++;
-                    } else {
-                        // skip over the rest of multibyte utf sequence
-                        i += utf - 1;
-                    }
+        LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0);
+    }
+
+    // Handle per token attributes
+    //NOTE: Each model customizes per token attributes.
+    //NOTE: Per token attributes are missing from the GGUF file.
+    //TODO: Extract attributes from GGUF file.
+    {
+        auto _contains_any = [] (const std::string &str, const std::vector &substrs) -> bool {
+            for (auto substr : substrs) {
+                if (str.find(substr) < std::string::npos) {
+                    return true;
                 }
+            }
+            return false;
+        };
 
-                if (!is_tokenizable) {
-                    // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1
-                    //  it's faster to re-filter them here, since there are way less candidates now
+        auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) {
+            uint32_t current = vocab.id_to_token.at(id).attr;
+            current = value ? (current | attr) : (current & ~attr);
+            vocab.id_to_token[id].attr = (llama_token_attr) current;
+        };
 
-                    // Calculate a total "utf" length of a token string representation
-                    size_t utf8_str_len = 0;
-                    for (unsigned i = 0; i < token.length();) {
-                        utf8_str_len++;
-                        i += utf8_len(token.at(i));
-                    }
+        auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) {
+            _set_tokenid_attr(vocab.token_to_id.at(token), attr, value);
+        };
 
-                    // And skip the ones which are one character
-                    if (utf8_str_len > 1) {
-                        // At this point what we have left are special tokens only
-                        vocab.special_tokens_cache[token] = id;
+        std::string model_name;
+        std::string tokenizer_pre;
 
-                        // Count manually found special tokens
-                        special_tokens_count_from_verification++;
+        ml.get_key(LLM_KV_GENERAL_NAME, model_name, false);
+        ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false);
 
-                        // If this manually found special token is not marked as such, flag a mismatch
-                        if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) {
-                            special_tokens_definition_mismatch = true;
-                        }
-                    }
-                }
+        // model name to lowercase
+        std::transform(model_name.begin(), model_name.end(), model_name.begin(),
+            [] (const std::string::value_type x) {
+                return std::tolower(x);
             }
-        }
+        );
 
-        if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) {
-            LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size(),
-                special_tokens_count_by_type, vocab.id_to_token.size()
-            );
-        } else {
-            LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n",
-                __func__,
-                special_tokens_count_from_verification, vocab.id_to_token.size()
-            );
+        // set attributes by model/tokenizer name
+        if (_contains_any(tokenizer_pre, {"jina-v2-es", "jina-v2-de"})) {
+            _set_token_attr("", LLAMA_TOKEN_ATTR_LSTRIP, true);
+        } else if (_contains_any(model_name, {"phi-3", "phi3"})) {
+            for (auto id : vocab.cache_special_tokens) {
+                _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {""}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true);
+            }
+            for (auto token : {"", "", "<|endoftext|>"}) {
+                _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false);
+            }
         }
     }
 }
@@ -4852,7 +5197,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     LLAMA_LOG_INFO("%s: rope scaling     = %s\n",     __func__, rope_scaling_type);
     LLAMA_LOG_INFO("%s: freq_base_train  = %.1f\n",   __func__, hparams.rope_freq_base_train);
     LLAMA_LOG_INFO("%s: freq_scale_train = %g\n",     __func__, hparams.rope_freq_scale_train);
-    LLAMA_LOG_INFO("%s: n_yarn_orig_ctx  = %u\n",     __func__, hparams.n_yarn_orig_ctx);
+    LLAMA_LOG_INFO("%s: n_ctx_orig_yarn  = %u\n",     __func__, hparams.n_ctx_orig_yarn);
     LLAMA_LOG_INFO("%s: rope_finetuned   = %s\n",     __func__, hparams.rope_finetuned ? "yes" : "unknown");
     LLAMA_LOG_INFO("%s: ssm_d_conv       = %u\n",     __func__, hparams.ssm_d_conv);
     LLAMA_LOG_INFO("%s: ssm_d_inner      = %u\n",     __func__, hparams.ssm_d_inner);
@@ -4892,6 +5237,21 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) {
     if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token        = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); }
     if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token        = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); }
     if (vocab.special_eot_id    != -1) { LLAMA_LOG_INFO( "%s: EOT token        = %d '%s'\n", __func__, vocab.special_eot_id,    vocab.id_to_token[vocab.special_eot_id].text.c_str() );    }
+
+    if (model.arch == LLM_ARCH_DEEPSEEK2) {
+        LLAMA_LOG_INFO("%s: n_layer_dense_lead   = %d\n",     __func__, hparams.n_layer_dense_lead);
+        LLAMA_LOG_INFO("%s: n_lora_q             = %d\n",     __func__, hparams.n_lora_q);
+        LLAMA_LOG_INFO("%s: n_lora_kv            = %d\n",     __func__, hparams.n_lora_kv);
+        LLAMA_LOG_INFO("%s: n_ff_exp             = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_expert_shared      = %d\n",     __func__, hparams.n_expert_shared);
+        LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n",   __func__, hparams.expert_weights_scale);
+        LLAMA_LOG_INFO("%s: rope_yarn_log_mul    = %.4f\n",   __func__, hparams.rope_yarn_log_mul);
+    }
+
+    if (model.arch == LLM_ARCH_QWEN2MOE) {
+        LLAMA_LOG_INFO("%s: n_ff_exp         = %d\n",     __func__, hparams.n_ff_exp);
+        LLAMA_LOG_INFO("%s: n_ff_shexp       = %d\n",     __func__, hparams.n_ff_shexp);
+    }
 }
 
 // Returns false if cancelled by progress_callback
@@ -5035,7 +5395,7 @@ static bool llm_load_tensors(
     // create tensors for the weights
     {
         const int64_t n_embd       = hparams.n_embd;
-        const int64_t n_embd_head  = n_embd / hparams.n_head;
+        const int64_t n_embd_head  = (hparams.n_head == 0) ? 0 : n_embd / hparams.n_head;
         const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa();
         const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa();
         const int64_t n_embd_gqa   = n_embd_v_gqa;
@@ -5048,8 +5408,6 @@ static bool llm_load_tensors(
             throw std::runtime_error("model has expert layers but no expert layers are used");
         }
 
-        GGML_ASSERT(n_embd_gqa == n_embd_k_gqa);
-
         ggml_context * ctx_input        = ctx_map.at(model.buft_input.buft);
         ggml_context * ctx_output       = ctx_map.at(model.buft_output.buft);
         ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix);
@@ -5069,12 +5427,10 @@ static bool llm_load_tensors(
                     // output
                     {
                         model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
-                        if (model.arch != LLM_ARCH_MINICPM){
-                            model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
-                            // if output is NULL, init from the input tok embed
-                            if (model.output == NULL) {
-                                model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
-                            }
+                        model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        // if output is NULL, init from the input tok embed
+                        if (model.output == NULL) {
+                            model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED);
                         }
                     }
 
@@ -5103,6 +5459,11 @@ static bool llm_load_tensors(
                             layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
                             layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
                             layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+
+                            // optional MLP bias
+                            layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                            layer.ffn_up_b   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED);
                         } else {
                             layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
 
@@ -5388,7 +5749,7 @@ static bool llm_load_tensors(
 
                             layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i),   {n_embd});
                         } else {
-                            layer.ffn_gate   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff});
                         }
 
                         layer.layer_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd});
@@ -5429,6 +5790,9 @@ static bool llm_load_tensors(
                         layer.attn_out_norm   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm
                         layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i),   {n_embd});
 
+                        layer.attn_norm_2   = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+                        layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i),   {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
+
                         layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,        "weight", i), {n_embd, n_ff});
                         layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE,    "weight", i), {n_embd, n_ff});
 
@@ -5674,16 +6038,17 @@ static bool llm_load_tensors(
                         GGML_ASSERT(hparams.n_expert_used > 0);
 
                         // MoE branch
-                        auto n_ff_exp = n_ff / hparams.n_expert_used;
+                        auto n_ff_exp = hparams.n_ff_exp ? hparams.n_ff_exp : n_ff / hparams.n_expert_used;
                         layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
                         layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
                         layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
 
                         // Shared expert branch
+                        auto n_ff_shexp = hparams.n_ff_shexp ? hparams.n_ff_shexp : n_ff;
                         layer.ffn_gate_inp_shexp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP_SHEXP, "weight", i), {n_embd});
-                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff});
-                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff, n_embd});
-                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff});
+                        layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_shexp});
+                        layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {n_ff_shexp, n_embd});
+                        layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd, n_ff_shexp});
                     }
                 } break;
             case LLM_ARCH_PHI2:
@@ -6210,6 +6575,70 @@ static bool llm_load_tensors(
                         layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {n_embd,   n_ff, n_expert});
                     }
                 } break;
+            case LLM_ARCH_DEEPSEEK2:
+                {
+                    bool is_lite = (hparams.n_layer == 27);
+
+                    const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+                    const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+                    const uint32_t q_lora_rank = hparams.n_lora_q;
+                    const uint32_t kv_lora_rank = hparams.n_lora_kv;
+                    const uint32_t n_ff_exp = hparams.n_ff_exp;
+
+                    model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
+
+                    // output
+                    {
+                        model.output_norm = ml.create_tensor(ctx_output,       tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd});
+                        model.output      = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT,      "weight"), {n_embd, n_vocab});
+                    }
+
+                    for (int i = 0; i < n_layer; ++i) {
+                        ggml_context * ctx_layer = ctx_for_layer(i);
+                        ggml_context * ctx_split = ctx_for_layer_split(i);
+
+                        auto & layer = model.layers[i];
+
+                        layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd});
+                        if (!is_lite) {
+                            layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank});
+                        }
+                        layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank});
+
+                        if (!is_lite) {
+                            layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A,   "weight", i), {n_embd, q_lora_rank});
+                            layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B,   "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k});
+                        } else {
+                            layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q,   "weight", i), {n_embd, n_embd_k_gqa});
+                        }
+                        layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA,   "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope});
+                        layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B,   "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)});
+                        layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd});
+
+                        layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd});
+
+                        if ((uint32_t) i < hparams.n_layer_dense_lead) {
+                            layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd,   n_ff});
+                            layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {  n_ff, n_embd});
+                            layer.ffn_up   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP,   "weight", i), {n_embd,   n_ff});
+                        } else {
+                            layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert});
+
+                            GGML_ASSERT(hparams.n_expert      > 0);
+                            GGML_ASSERT(hparams.n_expert_used > 0);
+
+                            // MoE branch
+                            layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {  n_embd, n_ff_exp, n_expert});
+                            layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp,   n_embd, n_expert});
+                            layer.ffn_up_exps   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS,   "weight", i), {  n_embd, n_ff_exp, n_expert});
+
+                            // Shared expert branch
+                            layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd,   n_ff_exp * hparams.n_expert_shared});
+                            layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), {  n_ff_exp * hparams.n_expert_shared, n_embd});
+                            layer.ffn_up_shexp   = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP,   "weight", i), {n_embd,   n_ff_exp * hparams.n_expert_shared});
+                        }
+                    }
+                } break;
             default:
                 throw std::runtime_error("unknown architecture");
         }
@@ -6409,16 +6838,6 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam
         }
 #endif
 
-#ifdef GGML_USE_SYCL
-        if (params.split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_sycl_set_single_device_mode(params.main_gpu);
-            //SYCL use device index (0, 1, 2) directly, uer input device id, then convert to device index.
-            params.main_gpu = ggml_backend_sycl_get_device_index(params.main_gpu);
-        } else {
-            ggml_backend_sycl_set_mul_device_mode();
-        }
-#endif
-
         if (!llm_load_tensors(
             ml, model, params.n_gpu_layers, params.split_mode,  params.main_gpu, params.tensor_split, params.use_mlock,
             params.progress_callback, params.progress_callback_user_data
@@ -6664,6 +7083,8 @@ static struct ggml_tensor * llm_build_moe_ffn(
                     int64_t   n_expert_used,
             llm_ffn_op_type   type_op,
                        bool   norm_w,
+                       bool   scale_w,
+                      float   w_scale,
          const llm_build_cb & cb,
                         int   il) {
     int64_t n_embd = cur->ne[0];
@@ -6695,6 +7116,10 @@ static struct ggml_tensor * llm_build_moe_ffn(
 
         weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens);
     }
+    if (scale_w) {
+        weights = ggml_scale(ctx, weights, w_scale);
+        cb(weights, "ffn_moe_weights_scaled", il);
+    }
 
     cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens);
     ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens]
@@ -6937,7 +7362,7 @@ struct llm_build_context {
     const int32_t n_kv;     // size of KV cache to consider (n_kv <= kv_self.size)
     const int32_t n_outputs;
     const int32_t kv_head;  // index of where we store new KV data in the cache
-    const int32_t n_orig_ctx;
+    const int32_t n_ctx_orig;
 
     const bool flash_attn;
 
@@ -6986,7 +7411,7 @@ struct llm_build_context {
         n_kv             (worst_case ? kv_self.size : kv_self.n),
         n_outputs        (worst_case ? n_tokens : lctx.n_outputs),
         kv_head          (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head),
-        n_orig_ctx       (cparams.n_yarn_orig_ctx),
+        n_ctx_orig       (cparams.n_ctx_orig_yarn),
         flash_attn       (cparams.flash_attn),
         pooling_type     (cparams.pooling_type),
         rope_type        (hparams.rope_type),
@@ -7044,7 +7469,7 @@ struct llm_build_context {
                             ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k),
                             ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa),
                             0),
-                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        lctx.inp_K_shift, rope_factors, n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
 
             cb(tmp, "K_shifted", il);
@@ -7153,7 +7578,7 @@ struct llm_build_context {
         // choose long/short freq factors based on the context size
         const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max;
 
-        if (n_ctx_pre_seq > hparams.n_yarn_orig_ctx) {
+        if (n_ctx_pre_seq > hparams.n_ctx_orig_yarn) {
             return model.layers[il].rope_long;
         }
 
@@ -7269,14 +7694,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7305,9 +7730,9 @@ struct llm_build_context {
                 cb(cur, "ffn_norm", il);
 
                 cur = llm_build_ffn(ctx0, cur,
-                        model.layers[il].ffn_up,   NULL,
-                        model.layers[il].ffn_gate, NULL,
-                        model.layers[il].ffn_down, NULL,
+                        model.layers[il].ffn_up,   model.layers[il].ffn_up_b,
+                        model.layers[il].ffn_gate, model.layers[il].ffn_gate_b,
+                        model.layers[il].ffn_down, model.layers[il].ffn_down_b,
                         NULL,
                         LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
                 cb(cur, "ffn_out", il);
@@ -7325,6 +7750,7 @@ struct llm_build_context {
                         model.layers[il].ffn_down_exps,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, true,
+                        false, 0.0,
                         cb, il);
                 cb(cur, "ffn_moe_out", il);
             }
@@ -7399,12 +7825,12 @@ struct llm_build_context {
                     case MODEL_7B:
                         Qcur = ggml_rope_ext(
                             ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         Kcur = ggml_rope_ext(
                             ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                            n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                            n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                             ext_factor, attn_factor, beta_fast, beta_slow
                         );
                         break;
@@ -7511,14 +7937,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7631,13 +8057,13 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7755,14 +8181,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7806,6 +8232,7 @@ struct llm_build_context {
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_GELU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -7907,14 +8334,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -7949,6 +8376,7 @@ struct llm_build_context {
                     model.layers[il].ffn_down_exps,
                     n_expert, n_expert_used,
                     LLM_FFN_SILU, true,
+                    false, 0.0,
                     cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -8260,14 +8688,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8319,6 +8747,11 @@ struct llm_build_context {
             // attention layer norm
             cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_out_norm, model.layers[il].attn_out_norm_b, LLM_NORM, cb, il);
 
+            if (model.layers[il].attn_norm_2 != nullptr) {
+                cur = ggml_add(ctx0, cur, inpL); // re-add the layer input
+                cur = llm_build_norm(ctx0, cur, hparams, model.layers[il].attn_norm_2, model.layers[il].attn_norm_2_b, LLM_NORM, cb, il);
+            }
+
             struct ggml_tensor * ffn_inp = cur;
             cb(ffn_inp, "ffn_inp", il);
 
@@ -8700,14 +9133,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, Qcur, inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, Kcur, inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8819,13 +9252,13 @@ struct llm_build_context {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -8931,14 +9364,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9045,14 +9478,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9087,6 +9520,7 @@ struct llm_build_context {
                         model.layers[il].ffn_down_exps,
                         n_expert, n_expert_used,
                         LLM_FFN_SILU, false,
+                        false, 0.0,
                         cb, il);
             cb(cur, "ffn_moe_out", il);
 
@@ -9196,7 +9630,7 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9207,7 +9641,7 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9318,7 +9752,7 @@ struct llm_build_context {
                 Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
 
                 Qcur = ggml_rope_ext(
-                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
@@ -9327,7 +9761,7 @@ struct llm_build_context {
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
-                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx,
+                    ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9435,13 +9869,13 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                         ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head,    n_tokens), inp_pos, nullptr,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                         ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr,
-                        n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        n_embd_head, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
@@ -9643,14 +10077,14 @@ struct llm_build_context {
 
                 struct ggml_tensor * Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 struct ggml_tensor * Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9759,14 +10193,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -9876,14 +10310,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10006,14 +10440,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head,    n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10078,7 +10512,7 @@ struct llm_build_context {
         cb(cur, "lmhead_scaling", -1);
 
         // lm_head
-        cur = ggml_mul_mat(ctx0, model.tok_embd, cur);
+        cur = ggml_mul_mat(ctx0, model.output, cur);
         cb(cur, "result_output", -1);
 
         ggml_build_forward_expand(gf, cur);
@@ -10126,7 +10560,7 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                         ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head,    n_tokens), inp_pos, nullptr,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Qcur, "Qcur", il);
 
@@ -10135,7 +10569,7 @@ struct llm_build_context {
 
                 Kcur = ggml_rope_ext(
                         ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr,
-                        n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                        n_embd_head_k, rope_type, n_ctx_orig, freq_base, freq_scale,
                         ext_factor, attn_factor, beta_fast, beta_slow);
                 cb(Kcur, "Kcur", il);
 
@@ -10246,14 +10680,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10536,14 +10970,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10667,14 +11101,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10781,14 +11215,14 @@ struct llm_build_context {
 
                 Qcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Qcur, "Qcur", il);
 
                 Kcur = ggml_rope_ext(
                     ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
                 cb(Kcur, "Kcur", il);
@@ -10908,29 +11342,254 @@ struct llm_build_context {
                 struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
                 cb(Qcur, "Qcur", il);
 
-                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
-                cb(Kcur, "Kcur", il);
+                struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur);
+                cb(Kcur, "Kcur", il);
+
+                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
+                cb(Vcur, "Vcur", il);
+
+                Qcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Qcur, "Qcur", il);
+
+                Kcur = ggml_rope_ext(
+                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor, beta_fast, beta_slow
+                );
+                cb(Kcur, "Kcur", il);
+
+                cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
+                        model.layers[il].wo, NULL,
+                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+            }
+
+            if (il == n_layer - 1) {
+                // skip computing output for unused tokens
+                struct ggml_tensor * inp_out_ids = build_inp_out_ids();
+                n_tokens = n_outputs;
+                cur   = ggml_get_rows(ctx0,   cur, inp_out_ids);
+                inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
+            }
+
+            struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+            cb(ffn_inp, "ffn_inp", il);
+
+            // feed-forward network
+            cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                    model.layers[il].ffn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm", il);
+
+            cur = llm_build_ffn(ctx0, cur,
+                    model.layers[il].ffn_up,   NULL,
+                    model.layers[il].ffn_gate, NULL,
+                    model.layers[il].ffn_down, NULL,
+                    NULL,
+                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+            cb(cur, "ffn_out", il);
+
+            struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
+            cb(ffn_out, "ffn_out", il);
+
+            // MoE
+            cur = llm_build_norm(ctx0, inpSA, hparams,
+                    model.layers[il].ffn_norm_exps, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "ffn_norm_exps", il);
+
+            cur = llm_build_moe_ffn(ctx0, cur,
+                    model.layers[il].ffn_gate_inp,
+                    model.layers[il].ffn_up_exps,
+                    model.layers[il].ffn_gate_exps,
+                    model.layers[il].ffn_down_exps,
+                    n_expert, n_expert_used,
+                    LLM_FFN_SILU, true,
+                    false, 0.0,
+                    cb, il);
+            cb(cur, "ffn_moe_out", il);
+
+            cur = ggml_add(ctx0, cur, ffn_out);
+            cb(cur, "ffn_out", il);
+
+            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
+            if (layer_dir != nullptr) {
+                cur = ggml_add(ctx0, cur, layer_dir);
+            }
+            cb(cur, "l_out", il);
+
+            // input for next layer
+            inpL = cur;
+        }
+
+        cur = inpL;
+
+        cur = llm_build_norm(ctx0, cur, hparams,
+                model.output_norm, NULL,
+                LLM_NORM_RMS, cb, -1);
+        cb(cur, "result_norm", -1);
+
+        // lm_head
+        cur = ggml_mul_mat(ctx0, model.output, cur);
+        cb(cur, "result_output", -1);
+
+        ggml_build_forward_expand(gf, cur);
+
+        return gf;
+    }
+
+    struct ggml_cgraph * build_deepseek2() {
+        struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false);
+
+        // mutable variable, needed during the last layer of the computation to skip unused tokens
+        int32_t n_tokens = this->n_tokens;
+
+        bool is_lite = (hparams.n_layer == 27);
+
+        // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly.
+        // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation.
+        const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale));
+        const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k));
+        const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale));
+
+        const uint32_t n_embd_head_qk_rope = hparams.n_rot;
+        const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot;
+        const uint32_t kv_lora_rank = hparams.n_lora_kv;
+
+        struct ggml_tensor * cur;
+        struct ggml_tensor * inpL;
+
+        // {n_embd, n_tokens}
+        inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
+
+        // inp_pos - contains the positions
+        struct ggml_tensor * inp_pos = build_inp_pos();
+
+        // KQ_mask (mask for 1 head, it will be broadcasted to all heads)
+        struct ggml_tensor * KQ_mask = build_inp_KQ_mask();
+
+        for (int il = 0; il < n_layer; ++il) {
+            struct ggml_tensor * inpSA = inpL;
+
+            // norm
+            cur = llm_build_norm(ctx0, inpL, hparams,
+                    model.layers[il].attn_norm, NULL,
+                    LLM_NORM_RMS, cb, il);
+            cb(cur, "attn_norm", il);
+
+            // self_attention
+            {
+                struct ggml_tensor * q = NULL;
+                if (!is_lite) {
+                    // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur);
+                    cb(q, "q", il);
+
+                    q = llm_build_norm(ctx0, q, hparams,
+                            model.layers[il].attn_q_a_norm, NULL,
+                            LLM_NORM_RMS, cb, il);
+                    cb(q, "q", il);
+
+                    // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens}
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q);
+                    cb(q, "q", il);
+                } else {
+                    q = ggml_mul_mat(ctx0, model.layers[il].wq, cur);
+                    cb(q, "q", il);
+                }
 
-                struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur);
-                cb(Vcur, "Vcur", il);
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        0);
+                cb(q_nope, "q_nope", il);
+
+                // and {n_head * n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens,
+                        ggml_row_size(q->type, hparams.n_embd_head_k),
+                        ggml_row_size(q->type, hparams.n_embd_head_k * n_head),
+                        ggml_row_size(q->type, n_embd_head_qk_nope));
+                cb(q_pe, "q_pe", il);
+
+                // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur);
+                cb(kv_pe_compresseed, "kv_pe_compresseed", il);
+
+                // split into {kv_lora_rank, n_tokens}
+                struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        0);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // and {n_embd_head_qk_rope, n_tokens}
+                struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens,
+                        kv_pe_compresseed->nb[1],
+                        kv_pe_compresseed->nb[1],
+                        ggml_row_size(kv_pe_compresseed->type, kv_lora_rank));
+                cb(k_pe, "k_pe", il);
+
+                kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm
+                kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams,
+                        model.layers[il].attn_kv_a_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(kv_compressed, "kv_compressed", il);
+
+                // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens}
+                struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed);
+                cb(kv, "kv", il);
+
+                // split into {n_head * n_embd_head_qk_nope, n_tokens}
+                struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens,
+                        ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v),
+                        ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        0);
+                cb(k_nope, "k_nope", il);
+
+                // and {n_head * n_embd_head_v, n_tokens}
+                struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens,
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head),
+                        ggml_row_size(kv->type, (n_embd_head_qk_nope)));
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_cont(ctx0, v_states);
+                cb(v_states, "v_states", il);
+
+                v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens,
+                    ggml_row_size(kv->type, hparams.n_embd_head_v * n_head),
+                    0);
+                cb(v_states, "v_states", il);
 
-                Qcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
+                q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                q_pe = ggml_rope_ext(
+                    ctx0, q_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
                 );
-                cb(Qcur, "Qcur", il);
-
-                Kcur = ggml_rope_ext(
-                    ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr,
-                    n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale,
-                    ext_factor, attn_factor, beta_fast, beta_slow
+                cb(q_pe, "q_pe", il);
+
+                // shared RoPE key
+                k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE
+                k_pe = ggml_rope_ext(
+                    ctx0, k_pe, inp_pos, nullptr,
+                    n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+                    ext_factor, attn_factor_scaled, beta_fast, beta_slow
                 );
-                cb(Kcur, "Kcur", il);
+                cb(k_pe, "k_pe", il);
+
+                struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0);
+                cb(q_states, "q_states", il);
+
+                struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0);
+                cb(k_states, "k_states", il);
 
                 cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf,
                         model.layers[il].wo, NULL,
-                        Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
+                        k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il);
             }
 
             if (il == n_layer - 1) {
@@ -10944,46 +11603,54 @@ struct llm_build_context {
             struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
             cb(ffn_inp, "ffn_inp", il);
 
-            // feed-forward network
-            cur = llm_build_norm(ctx0, ffn_inp, hparams,
-                    model.layers[il].ffn_norm, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "ffn_norm", il);
-
-            cur = llm_build_ffn(ctx0, cur,
-                    model.layers[il].ffn_up,   NULL,
-                    model.layers[il].ffn_gate, NULL,
-                    model.layers[il].ffn_down, NULL,
-                    NULL,
-                    LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
-            cb(cur, "ffn_out", il);
-
-            struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp);
-            cb(ffn_out, "ffn_out", il);
-
-            // MoE
-            cur = llm_build_norm(ctx0, inpSA, hparams,
-                    model.layers[il].ffn_norm_exps, NULL,
-                    LLM_NORM_RMS, cb, il);
-            cb(cur, "ffn_norm_exps", il);
+            if ((uint32_t) il < hparams.n_layer_dense_lead) {
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
 
-            cur = llm_build_moe_ffn(ctx0, cur,
-                    model.layers[il].ffn_gate_inp,
-                    model.layers[il].ffn_up_exps,
-                    model.layers[il].ffn_gate_exps,
-                    model.layers[il].ffn_down_exps,
-                    n_expert, n_expert_used,
-                    LLM_FFN_SILU, true,
-                    cb, il);
-            cb(cur, "ffn_moe_out", il);
+                cur = llm_build_ffn(ctx0, cur,
+                        model.layers[il].ffn_up,   NULL,
+                        model.layers[il].ffn_gate, NULL,
+                        model.layers[il].ffn_down, NULL,
+                        NULL,
+                        LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                cb(cur, "ffn_out", il);
+            } else {
+                // MoE branch
+                cur = llm_build_norm(ctx0, ffn_inp, hparams,
+                        model.layers[il].ffn_norm, NULL,
+                        LLM_NORM_RMS, cb, il);
+                cb(cur, "ffn_norm", il);
 
-            cur = ggml_add(ctx0, cur, ffn_out);
-            cb(cur, "ffn_out", il);
+                ggml_tensor * moe_out =
+                        llm_build_moe_ffn(ctx0, cur,
+                            model.layers[il].ffn_gate_inp,
+                            model.layers[il].ffn_up_exps,
+                            model.layers[il].ffn_gate_exps,
+                            model.layers[il].ffn_down_exps,
+                            n_expert, n_expert_used,
+                            LLM_FFN_SILU, false,
+                            true, hparams.expert_weights_scale,
+                            cb, il);
+                cb(moe_out, "ffn_moe_out", il);
+
+                // FFN shared expert
+                {
+                    ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, cur,
+                            model.layers[il].ffn_up_shexp,   NULL,
+                            model.layers[il].ffn_gate_shexp, NULL,
+                            model.layers[il].ffn_down_shexp, NULL,
+                            NULL,
+                            LLM_FFN_SILU, LLM_FFN_PAR, cb, il);
+                    cb(ffn_shexp, "ffn_shexp", il);
 
-            ggml_tensor * layer_dir = lctx.cvec.tensor_for(il);
-            if (layer_dir != nullptr) {
-                cur = ggml_add(ctx0, cur, layer_dir);
+                    cur = ggml_add(ctx0, moe_out, ffn_shexp);
+                    cb(cur, "ffn_out", il);
+                }
             }
+
+            cur = ggml_add(ctx0, cur, ffn_inp);
             cb(cur, "l_out", il);
 
             // input for next layer
@@ -11005,6 +11672,7 @@ struct llm_build_context {
 
         return gf;
     }
+
 };
 
 static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) {
@@ -11085,7 +11753,8 @@ static struct ggml_cgraph * llama_build_graph(
         if (batch.n_tokens < 32 || full_offload) {
             if (il != -1 && strcmp(name, "norm") == 0) {
                 for (auto * backend : lctx.backends) {
-                    if (ggml_backend_buft_supports_backend(lctx.model.buft_layer[il].buft, backend)) {
+                    if (ggml_backend_supports_buft(backend, lctx.model.buft_layer[il].buft) &&
+                        (ggml_backend_supports_op(backend, cur) || ggml_backend_offload_op(backend, cur))) {
                         ggml_backend_sched_set_tensor_backend(lctx.sched, cur, backend);
                         break;
                     }
@@ -11223,6 +11892,10 @@ static struct ggml_cgraph * llama_build_graph(
             {
                 result = llm.build_arctic();
             } break;
+        case LLM_ARCH_DEEPSEEK2:
+            {
+                result = llm.build_deepseek2();
+            } break;
         default:
             GGML_ASSERT(false);
     }
@@ -11578,6 +12251,11 @@ static void llama_graph_compute(
         ggml_backend_cpu_set_n_threads(lctx.backend_cpu, n_threads);
         ggml_backend_cpu_set_abort_callback(lctx.backend_cpu, lctx.abort_callback, lctx.abort_callback_data);
     }
+#ifdef GGML_USE_BLAS
+    if (lctx.backend_blas != nullptr) {
+        ggml_backend_blas_set_n_threads(lctx.backend_blas, n_threads);
+    }
+#endif
 
     ggml_backend_sched_graph_compute_async(lctx.sched, gf);
 
@@ -11800,17 +12478,6 @@ static int llama_decode_internal(
         }
         // LLAMA_LOG_INFO("graph build time: %.3f ms (%d nodes, %d leafs)\n", (ggml_time_us() - t_start_us)/1000.0, gf->n_nodes, gf->n_leafs);
 
-        // for big prompts, if BLAS is enabled, it is better to use only one thread
-        // otherwise, the threads are spin-lock waiting for the BLAS calls and are degrading the performance
-        // TODO: this is mostly important for Apple Silicon where CBLAS is still performing very well
-        //       we still need some threads to process all non-mul_mat ops, but not too much to avoid interfering
-        //       with the BLAS calls. need a better solution
-        // MoE Special Case: This logic applies when hparams.n_expert == 0, i.e. the model is NOT an MoE model. When an MoE is
-        //                   being processed then Accelerate/BLAS will not be involved, so capping would limit performance.
-        if (n_tokens >= 32 && hparams.n_expert == 0 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas()) {
-            n_threads = std::min(4, n_threads);
-        }
-
         ggml_backend_sched_alloc_graph(lctx.sched, gf);
 
         llama_set_inputs(lctx, u_batch);
@@ -12239,27 +12906,27 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) {
 
 static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL;
 }
 
 static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN;
 }
 
 static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL;
 }
 
 static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE;
 }
 
 static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) {
     GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED;
+    return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED;
 }
 
 static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) {
@@ -12512,6 +13179,7 @@ struct llm_tokenizer_bpe {
                         });
                         break;
                     case LLAMA_VOCAB_PRE_TYPE_DBRX:
+                    case LLAMA_VOCAB_PRE_TYPE_SMAUG:
                         word_collection = unicode_regex_split(text, {
                             // same as llama3
                             "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
@@ -12576,6 +13244,11 @@ struct llm_tokenizer_bpe {
                             "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
                         });
                         break;
+                    case LLAMA_VOCAB_PRE_TYPE_PORO:
+                        word_collection = unicode_regex_split(text, {
+                            " ?[^(\\s|.,!?…。，、।۔،)]+",
+                        });
+                        break;
                     default:
                         // default regex for BPE tokenization pre-processing
                         word_collection = unicode_regex_split(text, {
@@ -12734,7 +13407,7 @@ struct llm_tokenizer_wpm {
     llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {}
 
     void tokenize(const std::string & text, std::vector & output) {
-        auto * token_map = &vocab.token_to_id;
+        const auto & token_map = vocab.token_to_id;
 
         // normalize and split by whitespace
         std::vector words = preprocess(text);
@@ -12749,108 +13422,89 @@ struct llm_tokenizer_wpm {
             }
 
             // prepend phantom space
-            std::string word1 = "\xe2\x96\x81" + word;
-            int n = word1.size();
+            const std::string word1 = "\xe2\x96\x81" + word;
+            const int n = word1.size();
 
-            // we're at the start of a new word
-            int i = 0;
-            bool match_any = false;
+            const size_t current_tokens = output.size();
 
+            // we're at the start of a new word
             // move through character position in word
-            while (i < n) {
+            for (int i = 0; i < n; ++i) {
                 // loop through possible match length
                 bool match = false;
                 for (int j = n; j > i; j--) {
-                    auto it = token_map->find(word1.substr(i, j - i));
-                    if (it != token_map->end()) {
+                    auto it = token_map.find(word1.substr(i, j - i));
+                    if (it != token_map.end()) {
                         output.push_back(it->second);
                         match = true;
-                        match_any = true;
-                        i = j;
+                        i = j - 1;
                         break;
                     }
                 }
 
-                // must be an unknown character
-                if (!match) {
-                    i++;
+                if (!match) { // discard all
+                    output.resize(current_tokens);
+                    break;  // and discard next tokens
                 }
             }
 
             // we didn't find any matches for this word
-            if (!match_any) {
+            if (current_tokens == output.size()) {
                 output.push_back(vocab.special_unk_id);
             }
         }
     }
 
     std::vector preprocess(const std::string & text) {
-        std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
-
-        // strip accents, strip control, uniformize whitespace,
-        // to lowercase, pad chinese characters, pad punctuation
-        std::string new_str = "";
-        for (uint32_t code : cpts_nfd) {
-            const codepoint_flags flags = unicode_cpt_flags(code);
-            if (flags.is_accent_mark || flags.is_control) {
+        const std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text));
+        std::vector words(1, "");
+
+        for (const uint32_t cpt : cpts_nfd) {
+            const auto flags = unicode_cpt_flags(cpt);
+
+            if (flags.is_whitespace) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
                 continue;
             }
-            code = unicode_tolower(code);
-            if (flags.is_separator || flags.is_whitespace) {  //####FIXME: is_separator ?
-                code = ' ';
-            }
-            std::string s = unicode_cpt_to_utf8(code);
-            if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) {
-                new_str += " ";
-                new_str += s;
-                new_str += " ";
-            } else {
-                new_str += s;
+
+            assert (!flags.is_separator);
+            if (cpt == 0 || cpt == 0xFFFD || flags.is_control) {
+                continue;
             }
-        }
 
-        // split by whitespace
-        uint64_t l = 0;
-        uint64_t r = 0;
-        std::vector words;
-        while (r < new_str.size()) {
-            // if is whitespace
-            if (isspace(new_str[r], std::locale::classic())) {
-                if (r > l) words.push_back(new_str.substr(l, (r - l)));
-                l = r + 1;
-                r = l;
+            const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt));
+            if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) {
+                if (words.back().size()) {  // finish previous word if any
+                    words.emplace_back();
+                }
+                words.back() = s;       // single char word
+                words.emplace_back();   // start a new word
             } else {
-                r += 1;
+                words.back() += s;  // append char to word
             }
         }
-        if (r > l) {
-            words.push_back(new_str.substr(l, (r - l)));
-        }
-        return words;
-    }
 
-    bool is_ascii_punct(uint32_t code) {
-        if (code > 0xFF) {
-            return false;
+        if (!words.back().size()) {
+            words.pop_back();
         }
-        auto c = char(static_cast(code));
-        return ispunct(c, std::locale::classic());
+
+        return words;
     }
 
-    bool is_chinese_char(uint32_t cpt) {
-        if ((cpt >= 0x4E00  && cpt <= 0x9FFF)  ||
-            (cpt >= 0x3400  && cpt <= 0x4DBF)  ||
+    static bool is_chinese_char(uint32_t cpt) {
+        return
+            (cpt >= 0x04E00 && cpt <= 0x09FFF) ||
+            (cpt >= 0x03400 && cpt <= 0x04DBF) ||
             (cpt >= 0x20000 && cpt <= 0x2A6DF) ||
             (cpt >= 0x2A700 && cpt <= 0x2B73F) ||
             (cpt >= 0x2B740 && cpt <= 0x2B81F) ||
             (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920
-            (cpt >= 0xF900  && cpt <= 0xFAFF)  ||
-            (cpt >= 0x2F800 && cpt <= 0x2FA1F) ||
-            (cpt >= 0x3000  && cpt <= 0x303F)  ||
-            (cpt >= 0xFF00  && cpt <= 0xFFEF)) {
-            return true; // NOLINT
-        }
-        return false;
+            (cpt >= 0x0F900 && cpt <= 0x0FAFF) ||
+            (cpt >= 0x2F800 && cpt <= 0x2FA1F);
+            //(cpt >= 0x3000  && cpt <= 0x303F)  ||
+            //(cpt >= 0xFF00  && cpt <= 0xFFEF);
     }
 
     const llama_vocab & vocab;
@@ -12894,9 +13548,9 @@ struct fragment_buffer_variant {
 
 static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) {
     // for each special token
-    for (const auto & st: vocab.special_tokens_cache) {
-        const auto & special_token = st.first;
-        const auto & special_id    = st.second;
+    for (const llama_vocab::id special_id : vocab.cache_special_tokens) {
+        const auto & data = vocab.id_to_token[special_id];
+        const auto & special_token = data.text;
 
         // for each text fragment
         std::forward_list::iterator it = buffer.begin();
@@ -12905,7 +13559,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
 
             // if a fragment is text ( not yet processed )
             if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                auto * raw_text = &(fragment.raw_text);
+                auto & raw_text = fragment.raw_text;
 
                 auto raw_text_base_offset = fragment.offset;
                 auto raw_text_base_length = fragment.length;
@@ -12915,7 +13569,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     // find the first occurrence of a given special token in this fragment
                     //  passing offset argument only limit the "search area" but match coordinates
                     //  are still relative to the source full raw_text
-                    auto match = raw_text->find(special_token, raw_text_base_offset);
+                    auto match = raw_text.find(special_token, raw_text_base_offset);
 
                     // no occurrences found, stop processing this fragment for a given special token
                     if (match == std::string::npos) break;
@@ -12933,13 +13587,22 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
                     if (match > raw_text_base_offset) {
                         // left
                         const int64_t left_reminder_offset = raw_text_base_offset + 0;
-                        const int64_t left_reminder_length = match - raw_text_base_offset;
-                        buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length);
+                        int64_t left_reminder_length = match - raw_text_base_offset;
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) {
+                            while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) {
+                                left_reminder_length--;
+                            }
+                        }
+
+                        if (left_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length);
+                            it++;
+                        }
 
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str());
 #endif
-                        it++;
                     }
 
                     // special token
@@ -12948,16 +13611,25 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list<
 
                     // right
                     if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) {
-                        const int64_t right_reminder_offset = match + special_token.length();
-                        const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
-                        buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length);
+                        int64_t right_reminder_offset = match + special_token.length();
+                        int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length());
+
+                        if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) {
+                            while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) {
+                                right_reminder_offset++;
+                                right_reminder_length--;
+                            }
+                        }
+
+                        if (right_reminder_length > 0) {
+                            buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length);
+                            it++;
+                        }
 
 #ifdef PRETOKENIZERDEBUG
                         LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str());
 #endif
 
-                        it++;
-
                         if (source == 0) {
                             buffer.erase_after(buffer.before_begin());
                         } else {
@@ -13003,9 +13675,7 @@ static std::vector llama_tokenize_internal(const llama_vocab &
                 // tokenizer.encode('', add_special_tokens=True)  returns [1]
                 // tokenizer.encode('', add_special_tokens=False) returns []
 
-                static const bool rtrim = true;  //TODO: as param
                 bool is_prev_special = false;
-                bool special_token_rtrim = false;
 
                 if (add_special && vocab.special_add_bos != 0) {
                     GGML_ASSERT(vocab.special_bos_id != -1);
@@ -13015,25 +13685,8 @@ static std::vector llama_tokenize_internal(const llama_vocab &
 
                 for (const auto & fragment : fragment_buffer) {
                     if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) {
-                        // without adding this leading whitespace, we do not get the same results as the original tokenizer
-
-                        // TODO: It's likely possible to get rid of this string copy entirely
-                        //  by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer
-                        //  and passing 'add space prefix' as bool argument
-                        //
                         auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length);
 
-                        if (special_token_rtrim) {
-                            size_t num_whitespaces = 0;
-                            while (isspace(raw_text[num_whitespaces])) {
-                                num_whitespaces++;
-                            }
-                            if (num_whitespaces == raw_text.size()) {
-                                continue; // skip if all whitespaces
-                            }
-                            raw_text = raw_text.substr(num_whitespaces);
-                        }
-
                         if (vocab.add_space_prefix) {
                             if (!output.size() || is_prev_special) {  // prefix with space if first token
                                 raw_text = " " + raw_text;
@@ -13049,11 +13702,6 @@ static std::vector llama_tokenize_internal(const llama_vocab &
                     } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN)
                         output.push_back(fragment.token);
                         is_prev_special = true;
-                        // phi-3 special tokens without rtrim, works fine for llama-spm too
-                        special_token_rtrim = rtrim
-                            && fragment.token != vocab.special_bos_id
-                            && fragment.token != vocab.special_unk_id
-                            && fragment.token != vocab.special_eos_id;
                     }
                 }
 
@@ -13216,7 +13864,7 @@ static std::pair llama_grammar_match_char(
         const uint32_t                chr) {
 
     bool found            = false;
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
 
     GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT); // NOLINT
 
@@ -13225,6 +13873,10 @@ static std::pair llama_grammar_match_char(
             // inclusive range, e.g. [a-z]
             found = found || (pos->value <= chr && chr <= pos[1].value);
             pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            found = true;
+            pos += 1;
         } else {
             // exact char match, e.g. [a] or "a"
             found = found || pos->value == chr;
@@ -13242,7 +13894,7 @@ static bool llama_grammar_match_partial_char(
         const llama_grammar_element * pos,
         const llama_partial_utf8      partial_utf8) {
 
-    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR;
+    bool is_positive_char = pos->type == LLAMA_GRETYPE_CHAR || pos->type == LLAMA_GRETYPE_CHAR_ANY;
     GGML_ASSERT(is_positive_char || pos->type == LLAMA_GRETYPE_CHAR_NOT);
 
     uint32_t partial_value = partial_utf8.value;
@@ -13272,6 +13924,9 @@ static bool llama_grammar_match_partial_char(
                 return is_positive_char;
             }
             pos += 2;
+        } else if (pos->type == LLAMA_GRETYPE_CHAR_ANY) {
+            // Any character matches "."
+            return true;
         } else {
             // exact char match, e.g. [a] or "a"
             if (low <= pos->value && pos->value <= high) {
@@ -13332,6 +13987,7 @@ static void llama_grammar_advance_stack(
         }
         case LLAMA_GRETYPE_CHAR:
         case LLAMA_GRETYPE_CHAR_NOT:
+        case LLAMA_GRETYPE_CHAR_ANY:
             if (std::find(new_stacks.begin(), new_stacks.end(), stack) == new_stacks.end()) {
                 // only add the stack if it's not a duplicate of one we already have
                 new_stacks.emplace_back(stack);
@@ -14054,7 +14710,7 @@ void llama_sample_repetition_penalties(
 
 void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) {
     GGML_ASSERT(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
+    int64_t t_start_sample_us = ggml_time_us();
 
     bool allow_eog = false;
     for (const auto & stack : grammar->stacks) {
@@ -14066,12 +14722,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c
 
     std::vector, llama_partial_utf8>> candidates_decoded;
     candidates_decoded.reserve(candidates->size);
-    std::vector                              candidates_grammar;
+
+    std::vector candidates_grammar;
     candidates_grammar.reserve(candidates->size);
 
     for (size_t i = 0; i < candidates->size; ++i) {
-        const llama_token id    = candidates->data[i].id;
-        const std::string piece = llama_token_to_piece(ctx, id, false);
+        const llama_token id      = candidates->data[i].id;
+        const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id);
 
         if (llama_token_is_eog(&ctx->model, id)) {
             if (!allow_eog) {
@@ -14271,7 +14928,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
         GGML_ASSERT(false);
     }
 
-    const std::string piece = llama_token_to_piece(ctx, token, false);
+    const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token);
 
     // Note terminating 0 in decoded string
     const auto   decoded     = decode_utf8(piece, grammar->partial_utf8);
@@ -14287,260 +14944,6 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar
     ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
 }
 
-//
-// Beam search
-//
-
-struct llama_beam {
-    std::vector tokens;
-    float p;  // Cumulative beam probability (renormalized relative to all beams)
-    bool eob; // Initialize end-of-beam to false. Callback sets this to true.
-    // Sort beams by probability. In case of ties, prefer beams at eob.
-    bool operator<(const llama_beam & rhs) const {
-        return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob);
-    }
-    // Shift off first n tokens and discard them.
-    void shift_tokens(const size_t n) {
-        if (n) {
-            std::copy(tokens.begin() + n, tokens.end(), tokens.begin());
-            tokens.resize(tokens.size() - n);
-        }
-    }
-    llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; }
-};
-
-// A struct for calculating logit-related info.
-struct llama_logit_info {
-    const float * const logits;
-    const int n_vocab;
-    const float max_l;
-    const float normalizer;
-    struct sum_exp {
-        float max_l;
-        float operator()(float sum, float l) const { return sum + std::exp(l - max_l); }
-    };
-    llama_logit_info(llama_context * ctx)
-      : logits(llama_get_logits(ctx))
-      , n_vocab(llama_n_vocab(llama_get_model(ctx)))
-      , max_l(*std::max_element(logits, logits + n_vocab))
-      , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l}))
-      { }
-    llama_token_data get_token_data(const llama_token token_id) const {
-        constexpr auto p = std::numeric_limits::quiet_NaN();  // never used
-        return {token_id, logits[token_id], p};
-    }
-    // Return top k token_data by logit.
-    std::vector top_k(size_t k) {
-        std::vector min_heap;  // min-heap by logit
-        const llama_token k_min = std::min(static_cast(k), n_vocab);
-        min_heap.reserve(k_min);
-        for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) {
-            min_heap.push_back(get_token_data(token_id));
-        }
-        auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; };
-        std::make_heap(min_heap.begin(), min_heap.end(), comp);
-        for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) {
-            if (min_heap.front().logit < logits[token_id]) {
-                std::pop_heap(min_heap.begin(), min_heap.end(), comp);
-                min_heap.back().id = token_id;
-                min_heap.back().logit = logits[token_id];
-                std::push_heap(min_heap.begin(), min_heap.end(), comp);
-            }
-        }
-        return min_heap;
-    }
-    float probability_from_logit(float logit) const {
-        return normalizer * std::exp(logit - max_l);
-    }
-};
-
-struct llama_beam_search_data {
-    llama_context * ctx;
-    size_t n_beams;
-    int n_past;
-    int n_predict;
-    std::vector beams;
-    std::vector next_beams;
-
-    // Re-calculated on each loop iteration
-    size_t common_prefix_length;
-
-    // Used to communicate to/from callback on beams state.
-    std::vector beam_views;
-
-    llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict)
-      : ctx(ctx)
-      , n_beams(n_beams)
-      , n_past(n_past)
-      , n_predict(n_predict)
-      , beam_views(n_beams) {
-        beams.reserve(n_beams);
-        next_beams.reserve(n_beams);
-    }
-
-    // Collapse beams to a single beam given by index.
-    void collapse_beams(const size_t beam_idx) {
-        if (0u < beam_idx) {
-            std::swap(beams[0], beams[beam_idx]);
-        }
-        beams.resize(1);
-    }
-
-    // Min-heaps are used to efficiently collect the top-k elements (k=n_beams).
-    // The repetitive patterns below reflect the 2 stages of heaps:
-    //  * Gather elements until the vector is full, then call std::make_heap() on it.
-    //  * If the heap is full and a new element is found that should be included, pop the
-    //    least element to the back(), replace it with the new, then push it into the heap.
-    void fill_next_beams_by_top_probabilities(llama_beam & beam) {
-        // Min-heaps use a greater-than comparator.
-        const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; };
-        if (beam.eob) {
-            // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough.
-            if (next_beams.size() < n_beams) {
-                next_beams.push_back(std::move(beam));
-                if (next_beams.size() == n_beams) {
-                    std::make_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            } else if (next_beams.front().p < beam.p) {
-                std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                next_beams.back() = std::move(beam);
-                std::push_heap(next_beams.begin(), next_beams.end(), comp);
-            }
-        } else {
-            // beam is not at end-of-sentence, so branch with next top_k tokens.
-            if (!beam.tokens.empty()) {
-                llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0));
-            }
-            llama_logit_info logit_info(ctx);
-            std::vector next_tokens = logit_info.top_k(n_beams);
-
-            // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode()
-            // call in loop() will conclusively fill in the kv slot once the beams converge at this position.
-            llama_kv_cache_seq_rm(ctx, 0, n_past, -1);
-
-            size_t i=0;
-            if (next_beams.size() < n_beams) {
-                for (; next_beams.size() < n_beams ; ++i) {
-                    llama_beam next_beam = beam;
-                    next_beam.tokens.push_back(next_tokens[i].id);
-                    next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    next_beams.push_back(std::move(next_beam));
-                }
-                std::make_heap(next_beams.begin(), next_beams.end(), comp);
-            } else {
-                for (; next_beams.front().p == 0.0f ; ++i) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit);
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-            for (; i < n_beams ; ++i) {
-                const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit);
-                if (next_beams.front().p < next_p) {
-                    std::pop_heap(next_beams.begin(), next_beams.end(), comp);
-                    next_beams.back() = beam;
-                    next_beams.back().tokens.push_back(next_tokens[i].id);
-                    next_beams.back().p = next_p;
-                    std::push_heap(next_beams.begin(), next_beams.end(), comp);
-                }
-            }
-        }
-    }
-
-    // Find common_prefix_length based on beams.
-    // Requires beams is not empty.
-    size_t find_common_prefix_length() {
-        size_t common_prefix_length = beams[0].tokens.size();
-        for (size_t i = 1 ; i < beams.size() ; ++i) {
-            common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size());
-            for (size_t j = 0 ; j < common_prefix_length ; ++j) {
-                if (beams[0].tokens[j] != beams[i].tokens[j]) {
-                    common_prefix_length = j;
-                    break;
-                }
-            }
-        }
-        return common_prefix_length;
-    }
-
-    // Construct beams_state to send back to caller via the callback function.
-    // Side effect: set common_prefix_length = find_common_prefix_length();
-    llama_beams_state get_beams_state(const bool last_call) {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beam_views[i] = beams[i].view();
-        }
-        common_prefix_length = find_common_prefix_length();
-        return {beam_views.data(), beams.size(), common_prefix_length, last_call};
-    }
-
-    // Loop:
-    //  * while i < n_predict, AND
-    //  * any of the beams have not yet reached end-of-beam (eob), AND
-    //  * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence
-    //    (since all other beam probabilities can only decrease)
-    void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) {
-        beams.push_back({{}, 1.0f, false});  // Start with one empty beam w/ probability = 1.0 and !eob.
-        const auto not_eob = [](const llama_beam & beam) { return !beam.eob; };
-        for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) &&
-                       !beams[top_beam_index()].eob ; ++i) {
-            callback(callback_data, get_beams_state(false));  // Sets common_prefix_length
-            update_beams_from_beam_views();   // Update values (p,eob) that callback may have changed.
-            if (common_prefix_length) {
-                llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0));
-                n_past += common_prefix_length;
-            }
-            // Zero-out next_beam probabilities to place them last in following min-heap.
-            std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; });
-            for (llama_beam & beam : beams) {
-                beam.shift_tokens(common_prefix_length);
-                fill_next_beams_by_top_probabilities(beam);
-            }
-            // next_beams become the beams of next/final iteration. Swap them to re-use memory.
-            beams.swap(next_beams);
-            renormalize_beam_probabilities(beams);
-        }
-        collapse_beams(top_beam_index());
-        callback(callback_data, get_beams_state(true));
-    }
-
-    // As beams grow, the cumulative probabilities decrease.
-    // Renormalize them to avoid floating point underflow.
-    static void renormalize_beam_probabilities(std::vector & beams) {
-        const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; };
-        const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p);
-        std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; });
-    }
-
-    // Assumes beams is non-empty.  Uses llama_beam::operator<() for ordering.
-    size_t top_beam_index() {
-        return std::max_element(beams.begin(), beams.end()) - beams.begin();
-    }
-
-    // Copy (p,eob) for each beam which may have been changed by the callback.
-    void update_beams_from_beam_views() {
-        for (size_t i = 0 ; i < beams.size() ; ++i) {
-            beams[i].p = beam_views[i].p;
-            beams[i].eob = beam_views[i].eob;
-        }
-    }
-};
-
-void llama_beam_search(llama_context * ctx,
-                       llama_beam_search_callback_fn_t callback, void * callback_data,
-                       size_t n_beams, int n_past, int n_predict) {
-    assert(ctx);
-    const int64_t t_start_sample_us = ggml_time_us();
-
-    llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict);
-
-    beam_search_data.loop(callback, callback_data);
-
-    ctx->t_sample_us += ggml_time_us() - t_start_sample_us;
-    ctx->n_sample++;
-}
-
 //
 // quantization
 //
@@ -15058,6 +15461,14 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
         if (imatrix_data) {
             LLAMA_LOG_INFO("================================ Have weights data with %d entries\n",int(imatrix_data->size()));
             qs.has_imatrix = true;
+            // check imatrix for nans or infs
+            for (const auto & kv : *imatrix_data) {
+                for (float f : kv.second) {
+                    if (!std::isfinite(f)) {
+                        throw std::runtime_error(format("imatrix contains non-finite value %f\n", f));
+                    }
+                }
+            }
         }
     }
 
@@ -15751,7 +16162,7 @@ bool llama_supports_mlock(void) {
 }
 
 bool llama_supports_gpu_offload(void) {
-#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \
+#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL)   || defined(GGML_USE_VULKAN) || \
     defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC)
     // Defined when llama.cpp is compiled with support for offloading model layers to GPU.
     return true;
@@ -15808,7 +16219,7 @@ struct llama_model * llama_load_model_from_file(
             return true;
         };
     }
-    if (params.rpc_servers != nullptr) {
+    if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') {
         // split the servers set them into model->rpc_servers
         std::string servers(params.rpc_servers);
         size_t pos = 0;
@@ -15862,6 +16273,16 @@ struct llama_context * llama_new_context_with_model(
         params.flash_attn = false;
     }
 
+    if (params.flash_attn && model->hparams.n_embd_head_k != model->hparams.n_embd_head_v) {
+        LLAMA_LOG_WARN("%s: flash_attn requires n_embd_head_k == n_embd_head_v - forcing off\n", __func__);
+        params.flash_attn = false;
+    }
+
+    if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) {
+        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
+        return nullptr;
+    }
+
     llama_context * ctx = new llama_context(*model);
 
     const auto & hparams = model->hparams;
@@ -15900,8 +16321,8 @@ struct llama_context * llama_new_context_with_model(
 
     cparams.n_ubatch         = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
 
-    cparams.n_yarn_orig_ctx  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
-                               hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx :
+    cparams.n_ctx_orig_yarn  = params.yarn_orig_ctx    != 0 ? params.yarn_orig_ctx    :
+                               hparams.n_ctx_orig_yarn != 0 ? hparams.n_ctx_orig_yarn :
                                                               hparams.n_ctx_train;
 
     cparams.cb_eval           = params.cb_eval;
@@ -15966,17 +16387,7 @@ struct llama_context * llama_new_context_with_model(
 
     if (!hparams.vocab_only) {
         // initialize backends
-#if defined(GGML_USE_RPC)
-        for (auto & server : model->rpc_servers) {
-            ggml_backend_t backend = ggml_backend_rpc_init(server.c_str());
-            if (backend == nullptr) {
-                LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str());
-                llama_free(ctx);
-                return nullptr;
-            }
-            ctx->backends.push_back(backend);
-        }
-#elif defined(GGML_USE_METAL)
+#if defined(GGML_USE_METAL)
         if (model->n_gpu_layers > 0) {
             ctx->backend_metal = ggml_backend_metal_init();
             if (ctx->backend_metal == nullptr) {
@@ -16015,7 +16426,7 @@ struct llama_context * llama_new_context_with_model(
             return nullptr;
         }
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE) {
-            ggml_backend_t backend = ggml_backend_vk_init(0);
+            ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu);
             if (backend == nullptr) {
                 LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__);
                 llama_free(ctx);
@@ -16038,8 +16449,7 @@ struct llama_context * llama_new_context_with_model(
         if (model->split_mode == LLAMA_SPLIT_MODE_NONE || model->split_mode == LLAMA_SPLIT_MODE_ROW) {
             ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu);
             if (backend == nullptr) {
-                int main_gpu_id = ggml_backend_sycl_get_device_id(model->main_gpu);
-                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d (index %d) backend\n", __func__, main_gpu_id, model->main_gpu);
+                LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu);
                 llama_free(ctx);
                 return nullptr;
             }
@@ -16069,6 +16479,29 @@ struct llama_context * llama_new_context_with_model(
             ctx->backends.push_back(backend);
         }
 #endif
+
+#ifdef GGML_USE_BLAS
+        ctx->backend_blas = ggml_backend_blas_init();
+        if (ctx->backend_blas == nullptr) {
+            LLAMA_LOG_WARN("%s: failed to initialize BLAS backend\n", __func__);
+        } else {
+            ctx->backends.push_back(ctx->backend_blas);
+        }
+#endif
+
+#if defined(GGML_USE_RPC)
+        if (model->n_gpu_layers > 0) {
+            for (const auto & endpoint : model->rpc_servers) {
+                ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str());
+                if (backend == nullptr) {
+                    LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str());
+                    llama_free(ctx);
+                    return nullptr;
+                }
+                ctx->backends.push_back(backend);
+            }
+        }
+#endif
         ctx->backend_cpu = ggml_backend_cpu_init();
         if (ctx->backend_cpu == nullptr) {
             LLAMA_LOG_ERROR("%s: failed to initialize CPU backend\n", __func__);
@@ -16235,6 +16668,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) {
         case LLM_ARCH_COMMAND_R:
         case LLM_ARCH_OLMO:
         case LLM_ARCH_ARCTIC:
+        case LLM_ARCH_DEEPSEEK2:
             return LLAMA_ROPE_TYPE_NORM;
 
         // the pairs of head values are offset by n_rot/2
@@ -17849,9 +18283,9 @@ float llama_token_get_score(const struct llama_model * model, llama_token token)
     return model->vocab.id_to_token[token].score;
 }
 
-llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) {
+llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) {
     GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE);
-    return model->vocab.id_to_token[token].type;
+    return model->vocab.id_to_token[token].attr;
 }
 
 bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
@@ -17861,6 +18295,10 @@ bool llama_token_is_eog(const struct llama_model * model, llama_token token) {
     );
 }
 
+bool llama_token_is_control(const struct llama_model * model, llama_token token) {
+    return llama_is_control_token(model->vocab, token);
+}
+
 llama_token llama_token_bos(const struct llama_model * model) {
     return model->vocab.special_bos_id;
 }
@@ -17932,7 +18370,16 @@ static std::string llama_decode_text(const std::string & text) {
 
     const auto cpts = unicode_cpts_from_utf8(text);
     for (const auto cpt : cpts) {
-        decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt));
+        const auto utf8 = unicode_cpt_to_utf8(cpt);
+        try {
+            decoded_text += unicode_utf8_to_byte(utf8);
+        } catch (const std::out_of_range & e) {
+            decoded_text += "[UNK_BYTE_0x";
+            for (const auto c : utf8) {
+                decoded_text += format("%02x", (uint8_t) c);
+            }
+            decoded_text += text + "]";
+        }
     }
 
     return decoded_text;
@@ -17940,69 +18387,88 @@ static std::string llama_decode_text(const std::string & text) {
 
 // does not write null-terminator to buf
 int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) {
+    // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843
+    if (!special && llama_is_control_token(model->vocab, token)) {
+        return 0;
+    }
+
+    // if we have a cache - use it
+    {
+        const auto & cache = model->vocab.cache_token_to_piece;
+
+        if (!cache.empty()) {
+            const auto & res = cache.at(token);
+            if (length < (int) res.size()) {
+                return -(int) res.size();
+            }
+            memcpy(buf, res.c_str(), res.size());
+            return res.size();
+        }
+    }
+
     if (0 <= token && token < llama_n_vocab(model)) {
         switch (llama_vocab_get_type(model->vocab)) {
-        case LLAMA_VOCAB_TYPE_WPM:
-        case LLAMA_VOCAB_TYPE_SPM: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                llama_unescape_whitespace(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
-                if (length < 3) {
-                    return -3;
-                }
-                memcpy(buf, "\xe2\x96\x85", 3);
-                return 3;
-            } else if (llama_is_byte_token(model->vocab, token)) {
-                if (length < 1) {
-                    return -1;
+            case LLAMA_VOCAB_TYPE_WPM:
+            case LLAMA_VOCAB_TYPE_SPM: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (llama_is_normal_token(model->vocab, token)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    llama_unescape_whitespace(result);
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (
+                        (llama_is_user_defined_token(model->vocab, token)) ||
+                        (llama_is_control_token     (model->vocab, token) && special)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT
+                    if (length < 3) {
+                        return -3;
+                    }
+                    memcpy(buf, "\xe2\x96\x85", 3);
+                    return 3;
+                } else if (llama_is_byte_token(model->vocab, token)) {
+                    if (length < 1) {
+                        return -1;
+                    }
+                    buf[0] = llama_token_to_byte(model->vocab, token);
+                    return 1;
                 }
-                buf[0] = llama_token_to_byte(model->vocab, token);
-                return 1;
+                break;
             }
-            break;
-        }
-        case LLAMA_VOCAB_TYPE_BPE: {
-            // NOTE: we accept all unsupported token types,
-            // suppressing them like CONTROL tokens.
-            if (llama_is_normal_token(model->vocab, token)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                result = llama_decode_text(result);
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
-                }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
-            } else if (
-                    (llama_is_user_defined_token(model->vocab, token)) ||
-                    (llama_is_control_token     (model->vocab, token) && special)) {
-                std::string result = model->vocab.id_to_token[token].text;
-                if (length < (int) result.length()) {
-                    return -(int) result.length();
+            case LLAMA_VOCAB_TYPE_BPE: {
+                // NOTE: we accept all unsupported token types,
+                // suppressing them like CONTROL tokens.
+                if (llama_is_normal_token(model->vocab, token)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    result = llama_decode_text(result);
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
+                } else if (
+                        (llama_is_user_defined_token(model->vocab, token)) ||
+                        (llama_is_control_token     (model->vocab, token) && special)) {
+                    std::string result = model->vocab.id_to_token[token].text;
+                    if (length < (int) result.length()) {
+                        return -(int) result.length();
+                    }
+                    memcpy(buf, result.c_str(), result.length());
+                    return result.length();
                 }
-                memcpy(buf, result.c_str(), result.length());
-                return result.length();
+                break;
             }
-            break;
-        }
-        default:
-            GGML_ASSERT(false);
+            default:
+                GGML_ASSERT(false);
         }
     }
     return 0;
@@ -18337,6 +18803,7 @@ const char * llama_print_system_info(void) {
     s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | ";
     s += "FMA = "         + std::to_string(ggml_cpu_has_fma())         + " | ";
     s += "NEON = "        + std::to_string(ggml_cpu_has_neon())        + " | ";
+    s += "SVE = "         + std::to_string(ggml_cpu_has_sve())         + " | ";
     s += "ARM_FMA = "     + std::to_string(ggml_cpu_has_arm_fma())     + " | ";
     s += "F16C = "        + std::to_string(ggml_cpu_has_f16c())        + " | ";
     s += "FP16_VA = "     + std::to_string(ggml_cpu_has_fp16_va())     + " | ";
diff --git a/llama.h b/llama.h
index 16cece5db0e78..da310ffaf9ad9 100644
--- a/llama.h
+++ b/llama.h
@@ -85,6 +85,8 @@ extern "C" {
         LLAMA_VOCAB_PRE_TYPE_QWEN2          = 11,
         LLAMA_VOCAB_PRE_TYPE_OLMO           = 12,
         LLAMA_VOCAB_PRE_TYPE_DBRX           = 13,
+        LLAMA_VOCAB_PRE_TYPE_SMAUG          = 14,
+        LLAMA_VOCAB_PRE_TYPE_PORO           = 15,
     };
 
     // note: these values should be synchronized with ggml_rope
@@ -96,7 +98,7 @@ extern "C" {
         LLAMA_ROPE_TYPE_GLM  =  4,
     };
 
-    enum llama_token_type {
+    enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file
         LLAMA_TOKEN_TYPE_UNDEFINED    = 0,
         LLAMA_TOKEN_TYPE_NORMAL       = 1,
         LLAMA_TOKEN_TYPE_UNKNOWN      = 2,
@@ -106,6 +108,20 @@ extern "C" {
         LLAMA_TOKEN_TYPE_BYTE         = 6,
     };
 
+    enum llama_token_attr {
+        LLAMA_TOKEN_ATTR_UNDEFINED    = 0,
+        LLAMA_TOKEN_ATTR_UNKNOWN      = 1 << 0,
+        LLAMA_TOKEN_ATTR_UNUSED       = 1 << 1,
+        LLAMA_TOKEN_ATTR_NORMAL       = 1 << 2,
+        LLAMA_TOKEN_ATTR_CONTROL      = 1 << 3,  // SPECIAL?
+        LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 4,
+        LLAMA_TOKEN_ATTR_BYTE         = 1 << 5,
+        LLAMA_TOKEN_ATTR_NORMALIZED   = 1 << 6,
+        LLAMA_TOKEN_ATTR_LSTRIP       = 1 << 7,
+        LLAMA_TOKEN_ATTR_RSTRIP       = 1 << 8,
+        LLAMA_TOKEN_ATTR_SINGLE_WORD  = 1 << 9,
+    };
+
     // model file types
     enum llama_ftype {
         LLAMA_FTYPE_ALL_F32              = 0,
@@ -264,6 +280,8 @@ extern "C" {
         bool check_tensors; // validate model tensor data
     };
 
+    // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations
+    //       https://github.com/ggerganov/llama.cpp/pull/7544
     struct llama_context_params {
         uint32_t seed;              // RNG seed, -1 for random
         uint32_t n_ctx;             // text context, 0 = from model
@@ -290,14 +308,14 @@ extern "C" {
         ggml_backend_sched_eval_callback cb_eval;
         void * cb_eval_user_data;
 
-        enum ggml_type type_k; // data type for K cache
-        enum ggml_type type_v; // data type for V cache
+        enum ggml_type type_k; // data type for K cache [EXPERIMENTAL]
+        enum ggml_type type_v; // data type for V cache [EXPERIMENTAL]
 
         // Keep the booleans together to avoid misalignment during copy-by-value.
         bool logits_all;  // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead)
         bool embeddings;  // if true, extract embeddings (together with logits)
         bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
-        bool flash_attn;  // whether to use flash attention
+        bool flash_attn;  // whether to use flash attention [EXPERIMENTAL]
 
         // Abort callback
         // if it returns true, execution of llama_decode() will be aborted
@@ -348,6 +366,9 @@ extern "C" {
         // modifies a preceding LLAMA_GRETYPE_CHAR or
         // LLAMA_GRETYPE_CHAR_RNG_UPPER to add an alternate char to match ([ab], [a-zA])
         LLAMA_GRETYPE_CHAR_ALT       = 6,
+
+        // any character (.)
+        LLAMA_GRETYPE_CHAR_ANY       = 7,
     };
 
     typedef struct llama_grammar_element {
@@ -421,8 +442,8 @@ extern "C" {
 
     LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx);
 
-    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model   * model);
-    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model   * model);
+    LLAMA_API enum llama_vocab_type   llama_vocab_type  (const struct llama_model * model);
+    LLAMA_API enum llama_rope_type    llama_rope_type   (const struct llama_model * model);
 
     LLAMA_API int32_t llama_n_vocab    (const struct llama_model * model);
     LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model);
@@ -818,11 +839,14 @@ extern "C" {
 
     LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token);
 
-    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token);
+    LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token);
 
     // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.)
     LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token);
 
+    // Identify if Token Id is a control token or a render-able token
+    LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token);
+
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence
     LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence
@@ -1036,49 +1060,9 @@ extern "C" {
                      llama_token   token);
 
     //
-    // Beam search
+    // Model split
     //
 
-    struct llama_beam_view {
-        const llama_token * tokens;
-
-        size_t n_tokens;
-        float  p;        // Cumulative beam probability (renormalized relative to all beams)
-        bool   eob;      // Callback should set this to true when a beam is at end-of-beam.
-    };
-
-    // Passed to beam_search_callback function.
-    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
-    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
-    // These pointers are valid only during the synchronous callback, so should not be saved.
-    struct llama_beams_state {
-        struct llama_beam_view * beam_views;
-
-        size_t n_beams;               // Number of elements in beam_views[].
-        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
-        bool   last_call;             // True iff this is the last callback invocation.
-    };
-
-    // Type of pointer to the beam_search_callback function.
-    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
-    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
-    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
-
-    /// @details Deterministically returns entire sentence constructed by a beam search.
-    /// @param ctx Pointer to the llama_context.
-    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
-    /// @param callback_data A pointer that is simply passed back to callback.
-    /// @param n_beams Number of beams to use.
-    /// @param n_past Number of tokens already evaluated.
-    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
-    LLAMA_API void llama_beam_search(
-                   struct llama_context * ctx,
-        llama_beam_search_callback_fn_t   callback,
-                                   void * callback_data,
-                                 size_t   n_beams,
-                                int32_t   n_past,
-                                int32_t   n_predict);
-
     /// @details Build a split GGUF final path for this chunk.
     ///          llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf"
     //  Returns the split_path length.
diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf
index f8022a385e4aa..745be416a798a 100644
Binary files a/models/ggml-vocab-phi-3.gguf and b/models/ggml-vocab-phi-3.gguf differ
diff --git a/pocs/vdot/CMakeLists.txt b/pocs/vdot/CMakeLists.txt
index fb89a1cd4e833..d5405ad2991d1 100644
--- a/pocs/vdot/CMakeLists.txt
+++ b/pocs/vdot/CMakeLists.txt
@@ -1,9 +1,9 @@
-set(TARGET vdot)
+set(TARGET llama-vdot)
 add_executable(${TARGET} vdot.cpp)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 
-set(TARGET q8dot)
+set(TARGET llama-q8dot)
 add_executable(${TARGET} q8dot.cpp)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
diff --git a/requirements.txt b/requirements.txt
index 43f82dc2e600d..e5cfbf10b3da5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -4,7 +4,7 @@
 # Package versions must stay compatible across all top-level python scripts.
 #
 
--r ./requirements/requirements-convert.txt
+-r ./requirements/requirements-convert-legacy-llama.txt
 
 -r ./requirements/requirements-convert-hf-to-gguf.txt
 -r ./requirements/requirements-convert-hf-to-gguf-update.txt
diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt
index 6ac4026107fbe..6eacaf4290e0a 100644
--- a/requirements/requirements-convert-hf-to-gguf-update.txt
+++ b/requirements/requirements-convert-hf-to-gguf-update.txt
@@ -1,2 +1,2 @@
--r ./requirements-convert.txt
+-r ./requirements-convert-legacy-llama.txt
 torch~=2.1.1
diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt
index 6ac4026107fbe..6eacaf4290e0a 100644
--- a/requirements/requirements-convert-hf-to-gguf.txt
+++ b/requirements/requirements-convert-hf-to-gguf.txt
@@ -1,2 +1,2 @@
--r ./requirements-convert.txt
+-r ./requirements-convert-legacy-llama.txt
 torch~=2.1.1
diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert-legacy-llama.txt
similarity index 100%
rename from requirements/requirements-convert.txt
rename to requirements/requirements-convert-legacy-llama.txt
diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt
index a0f37cd1c71e4..e80c29012a674 100644
--- a/requirements/requirements-convert-llama-ggml-to-gguf.txt
+++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt
@@ -1 +1 @@
--r ./requirements-convert.txt
+-r ./requirements-convert-legacy-llama.txt
diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in
index 92e39708b7cf2..9311055d925d4 100644
--- a/scripts/LlamaConfig.cmake.in
+++ b/scripts/LlamaConfig.cmake.in
@@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
 set(LLAMA_BLAS @LLAMA_BLAS@)
 set(LLAMA_CUDA @LLAMA_CUDA@)
 set(LLAMA_METAL @LLAMA_METAL@)
-set(LLAMA_CLBLAST @LLAMA_CLBLAST@)
 set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@)
 set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@)
 
@@ -36,10 +35,6 @@ if (LLAMA_METAL)
     find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
 endif()
 
-if (LLAMA_CLBLAST)
-    find_package(CLBlast REQUIRED)
-endif()
-
 if (LLAMA_HIPBLAS)
     find_package(hip REQUIRED)
     find_package(hipblas REQUIRED)
diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh
index 6a7400d3c3a0b..0c6afdd591aaa 100755
--- a/scripts/check-requirements.sh
+++ b/scripts/check-requirements.sh
@@ -166,7 +166,7 @@ if (( do_cleanup )); then
     rm -rf -- "$all_venv"
 fi
 
-check_convert_script convert.py
+check_convert_script examples/convert-legacy-llama.py
 for py in convert-*.py; do
     # skip convert-hf-to-gguf-update.py
     # TODO: the check is failing for some reason:
diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh
index fd0ee88b23748..a45cd3962ac0a 100755
--- a/scripts/compare-commits.sh
+++ b/scripts/compare-commits.sh
@@ -10,16 +10,18 @@ set -x
 
 bench_args="${@:3}"
 
-rm -f llama-bench.sqlite
+rm -f llama-bench.sqlite > /dev/null
 
 # to test a backend, call the script with the corresponding environment variable (e.g. LLAMA_CUDA=1 ./scripts/compare-commits.sh ...)
 
-git checkout $1
-make clean && make -j32 $make_opts llama-bench
-./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+git checkout $1 > /dev/null
+make clean > /dev/null
+make -j$(nproc) $make_opts llama-bench > /dev/null
+./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 
-git checkout $2
-make clean && make -j32 $make_opts llama-bench
-./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite
+git checkout $2 > /dev/null
+make clean > /dev/null
+make -j$(nproc) $make_opts llama-bench > /dev/null
+./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite
 
 ./scripts/compare-llama-bench.py -b $1 -c $2
diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py
index 0ede9e67c2f15..513dde5e13f93 100755
--- a/scripts/compare-llama-bench.py
+++ b/scripts/compare-llama-bench.py
@@ -19,22 +19,22 @@
 
 # Properties by which to differentiate results per commit:
 KEY_PROPERTIES = [
-    "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas",
-    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads",
-    "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen"
+    "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas",
+    "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads",
+    "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen"
 ]
 
 # Properties that are boolean and are converted to Yes/No for the table:
-BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"]
+BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"]
 
 # Header names for the table:
 PRETTY_NAMES = {
-    "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS",
-    "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
-    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters",
-    "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type",
-    "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO",
-    "tensor_split": "Tensor split"
+    "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC",
+    "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model",
+    "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size",
+    "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode",
+    "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split",
+    "use_mmap": "Use mmap", "embeddings": "Embeddings",
 }
 
 DEFAULT_SHOW = ["model_type"]  # Always show these properties by default.
diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh
index 01fda16fd7efc..8a016843290b9 100755
--- a/scripts/convert-gg.sh
+++ b/scripts/convert-gg.sh
@@ -3,20 +3,20 @@
 set -e
 
 # LLaMA v1
-python3 convert.py ../llama1/7B  --outfile models/llama-7b/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../llama1/7B  --outfile models/llama-7b/ggml-model-f16.gguf  --outtype f16
+python3 examples/convert-legacy-llama.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
 
 # LLaMA v2
-python3 convert.py ../llama2/llama-2-7b  --outfile models/llama-7b-v2/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../llama2/llama-2-7b  --outfile models/llama-7b-v2/ggml-model-f16.gguf  --outtype f16
+python3 examples/convert-legacy-llama.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
 
 # Code Llama
-python3 convert.py ../codellama/CodeLlama-7b/  --outfile models/codellama-7b/ggml-model-f16.gguf  --outtype f16
-python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
-python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-7b/  --outfile models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
+python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
 
 # Falcon
 python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b  1
diff --git a/scripts/get-hellaswag.sh b/scripts/get-hellaswag.sh
index 121979fe2b069..4e1b1cc15f01a 100755
--- a/scripts/get-hellaswag.sh
+++ b/scripts/get-hellaswag.sh
@@ -4,7 +4,7 @@ wget https://raw.githubusercontent.com/klosax/hellaswag_text_data/main/hellaswag
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
+echo "  ./llama-perplexity -m model.gguf -f hellaswag_val_full.txt --hellaswag [--hellaswag-tasks N] [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-wikitext-103.sh b/scripts/get-wikitext-103.sh
index 880dd5cbe2230..9c65fafbcc50b 100755
--- a/scripts/get-wikitext-103.sh
+++ b/scripts/get-wikitext-103.sh
@@ -4,7 +4,7 @@ wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-raw-v1.
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f wiki.test.raw [other params]"
+echo "  ./llama-perplexity -m model.gguf -f wiki.test.raw [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-wikitext-2.sh b/scripts/get-wikitext-2.sh
index b01476a46015a..5f3845ef59a9e 100755
--- a/scripts/get-wikitext-2.sh
+++ b/scripts/get-wikitext-2.sh
@@ -5,7 +5,7 @@ unzip wikitext-2-raw-v1.zip
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
+echo "  ./llama-perplexity -m model.gguf -f wikitext-2-raw/wiki.test.raw [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/get-winogrande.sh b/scripts/get-winogrande.sh
index 5f234468e1643..f1fc0e2d47adb 100755
--- a/scripts/get-winogrande.sh
+++ b/scripts/get-winogrande.sh
@@ -4,7 +4,7 @@ wget https://huggingface.co/datasets/ikawrakow/winogrande-eval-for-llama.cpp/raw
 
 echo "Usage:"
 echo ""
-echo "  ./perplexity -m model.gguf -f winogrande-debiased-eval.csv --winogrande [--winogrande-tasks N] [other params]"
+echo "  ./llama-perplexity -m model.gguf -f winogrande-debiased-eval.csv --winogrande [--winogrande-tasks N] [other params]"
 echo ""
 
 exit 0
diff --git a/scripts/hf.sh b/scripts/hf.sh
index 58f83d6fe92a5..85c2c4d9a952e 100755
--- a/scripts/hf.sh
+++ b/scripts/hf.sh
@@ -3,9 +3,9 @@
 # Shortcut for downloading HF models
 #
 # Usage:
-#   ./main -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
-#   ./main -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/resolve/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --url https://huggingface.co/TheBloke/Mixtral-8x7B-v0.1-GGUF/blob/main/mixtral-8x7b-v0.1.Q4_K_M.gguf)
+#   ./llama-cli -m $(./scripts/hf.sh --repo TheBloke/Mixtral-8x7B-v0.1-GGUF --file mixtral-8x7b-v0.1.Q4_K_M.gguf)
 #
 
 # all logs go to stderr
diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh
index 2058ceabf9730..6ba499a2a2521 100644
--- a/scripts/pod-llama.sh
+++ b/scripts/pod-llama.sh
@@ -75,11 +75,11 @@ if [ "$1" -eq "1" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/tinyllama-1b  --outfile ./models/tinyllama-1b/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "2" ]; then
@@ -90,11 +90,11 @@ if [ "$1" -eq "2" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-7b  --outfile ./models/codellama-7b/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "3" ]; then
@@ -105,11 +105,11 @@ if [ "$1" -eq "3" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "4" ]; then
@@ -120,11 +120,11 @@ if [ "$1" -eq "4" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "5" ]; then
@@ -135,11 +135,11 @@ if [ "$1" -eq "5" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-7b-instruct  --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf  --outtype f16
 
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "6" ]; then
@@ -150,11 +150,11 @@ if [ "$1" -eq "6" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "7" ]; then
@@ -165,11 +165,11 @@ if [ "$1" -eq "7" ]; then
 
     cd /workspace/llama.cpp
 
-    python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
+    python3 examples/convert-legacy-llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16
 
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
-    ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k
+    ./llama-quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q8_0.gguf q8_0
 fi
 
 if [ "$1" -eq "1" ]; then
@@ -181,22 +181,22 @@ if [ "$1" -eq "1" ]; then
     ../scripts/get-wikitext-2.sh
     unzip wikitext-2-raw-v1.zip
 
-    make -j && ./bin/perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
+    make -j && ./bin/llama-perplexity -m ../models/tinyllama-1b/ggml-model-f16.gguf -f ./wikitext-2-raw/wiki.test.raw -ngl 100 --chunks 32
 
     # batched
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
+    LLAMA_CUDA=1 make -j && ./llama-batched ./models/tinyllama-1b/ggml-model-f16.gguf "Hello, my name is" 8 128 999
 
     # batched-bench
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
+    LLAMA_CUDA=1 make -j && ./llama-batched-bench ./models/tinyllama-1b/ggml-model-f16.gguf 4608 1 99 0 512 128 1,2,3,4,5,6,7,8,16,32
 
     # parallel
     cd /workspace/llama.cpp
 
-    LLAMA_CUDA=1 make -j && ./parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
+    LLAMA_CUDA=1 make -j && ./llama-parallel -m ./models/tinyllama-1b/ggml-model-f16.gguf -t 1 -ngl 100 -c 4096 -b 512 -s 1 -np 8 -ns 128 -n 100 -cb
 
 fi
 
@@ -204,10 +204,10 @@ fi
 #if [ "$1" -eq "7" ]; then
 #    cd /workspace/llama.cpp
 #
-#    LLAMA_CUDA=1 make -j && ./speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
+#    LLAMA_CUDA=1 make -j && ./llama-speculative -m ./models/codellama-34b-instruct/ggml-model-f16.gguf -md ./models/codellama-7b-instruct/ggml-model-q4_0.gguf -p "# Dijkstra's shortest path algorithm in Python (4 spaces indentation) + complexity analysis:\n\n" -e -ngl 999 -ngld 999 -t 4 -n 512 -c 4096 -s 21 --draft 16 -np 1 --temp 0.0
 #fi
 
 # more benches
-#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
-#LLAMA_CUDA=1 make -j && ./batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
+#LLAMA_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-7b/ggml-model-q4_k.gguf  4096 1 99 1 512,3200 128,128,800 1
+#LLAMA_CUDA=1 make -j && ./llama-batched-bench ./models/codellama-13b/ggml-model-q4_k.gguf 4096 1 99 1 512,3200 128,128,800 1
 
diff --git a/scripts/qnt-all.sh b/scripts/qnt-all.sh
index b4c2a159e2bf5..bc43738a2f498 100755
--- a/scripts/qnt-all.sh
+++ b/scripts/qnt-all.sh
@@ -26,5 +26,5 @@ set -e
 mkdir -p ${out}
 
 for q in ${qnt[@]}; do
-    time ./bin/quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
+    time ./bin/llama-quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
 done
diff --git a/scripts/run-all-ppl.sh b/scripts/run-all-ppl.sh
index e04d61d7fe091..e15f74f1b666d 100755
--- a/scripts/run-all-ppl.sh
+++ b/scripts/run-all-ppl.sh
@@ -26,5 +26,5 @@ out="../tmp/results-${model}"
 mkdir -p ${out}
 
 for q in ${qnt[@]}; do
-    time ./bin/perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
+    time ./bin/llama-perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
 done
diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py
index e986a36045549..ee21eab371418 100755
--- a/scripts/run-with-preset.py
+++ b/scripts/run-with-preset.py
@@ -10,15 +10,15 @@
 
 logger = logging.getLogger("run-with-preset")
 
-CLI_ARGS_MAIN_PERPLEXITY = [
+CLI_ARGS_LLAMA_CLI_PERPLEXITY = [
     "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
     "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
-    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
+    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix",
     "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
     "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
     "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
     "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
-    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
+    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n",
     "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
     "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
     "verbose-prompt"
@@ -29,7 +29,7 @@
     "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
 ]
 
-CLI_ARGS_SERVER = [
+CLI_ARGS_LLAMA_SERVER = [
     "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
     "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
     "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
@@ -37,7 +37,7 @@
 ]
 
 description = """Run llama.cpp binaries with presets from YAML file(s).
-To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
+To specify which binary should be run, specify the "binary" property (llama-cli, llama-perplexity, llama-bench, and llama-server are supported).
 To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
 
 Formatting considerations:
@@ -77,19 +77,19 @@
 
 props = {prop.replace("_", "-"): val for prop, val in props.items()}
 
-binary = props.pop("binary", "main")
+binary = props.pop("binary", "llama-cli")
 if known_args.binary:
     binary = known_args.binary
 
 if os.path.exists(f"./{binary}"):
     binary = f"./{binary}"
 
-if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
-    cli_args = CLI_ARGS_MAIN_PERPLEXITY
+if binary.lower().endswith("llama-cli") or binary.lower().endswith("llama-perplexity"):
+    cli_args = CLI_ARGS_LLAMA_CLI_PERPLEXITY
 elif binary.lower().endswith("llama-bench"):
     cli_args = CLI_ARGS_LLAMA_BENCH
-elif binary.lower().endswith("server"):
-    cli_args = CLI_ARGS_SERVER
+elif binary.lower().endswith("llama-server"):
+    cli_args = CLI_ARGS_LLAMA_SERVER
 else:
     logger.error(f"Unknown binary: {binary}")
     sys.exit(1)
diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh
index eb6ce458e5399..199232440449b 100644
--- a/scripts/server-llm.sh
+++ b/scripts/server-llm.sh
@@ -3,7 +3,7 @@
 # Helper script for deploying llama.cpp server with a single Bash command
 #
 # - Works on Linux and macOS
-# - Supports: CPU, CUDA, Metal, OpenCL
+# - Supports: CPU, CUDA, Metal
 # - Can run all GGUF models from HuggingFace
 # - Can serve requests in parallel
 # - Always builds latest llama.cpp from GitHub
@@ -19,7 +19,7 @@
 #   --port:            port number, default is 8888
 #   --repo:            path to a repo containing GGUF model files
 #   --wtype:           weights type (f16, q8_0, q4_0, q4_1), default is user-input
-#   --backend:         cpu, cuda, metal, opencl, depends on the OS
+#   --backend:         cpu, cuda, metal, depends on the OS
 #   --gpu-id:          gpu id, default is 0
 #   --n-parallel:      number of parallel requests, default is 8
 #   --n-kv:            KV cache size, default is 4096
@@ -72,7 +72,7 @@ function print_usage {
     printf "  --port:             port number, default is 8888\n"
     printf "  --repo:             path to a repo containing GGUF model files\n"
     printf "  --wtype:            weights type (f16, q8_0, q4_0, q4_1), default is user-input\n"
-    printf "  --backend:          cpu, cuda, metal, opencl, depends on the OS\n"
+    printf "  --backend:          cpu, cuda, metal, depends on the OS\n"
     printf "  --gpu-id:           gpu id, default is 0\n"
     printf "  --n-parallel:       number of parallel requests, default is 8\n"
     printf "  --n-kv:             KV cache size, default is 4096\n"
@@ -380,16 +380,13 @@ fi
 
 if [[ "$backend" == "cuda" ]]; then
     printf "[+] Building with CUDA backend\n"
-    LLAMA_CUDA=1 make -j server $log
+    LLAMA_CUDA=1 make -j llama-server $log
 elif [[ "$backend" == "cpu" ]]; then
     printf "[+] Building with CPU backend\n"
-    make -j server $log
+    make -j llama-server $log
 elif [[ "$backend" == "metal" ]]; then
     printf "[+] Building with Metal backend\n"
-    make -j server $log
-elif [[ "$backend" == "opencl" ]]; then
-    printf "[+] Building with OpenCL backend\n"
-    LLAMA_CLBLAST=1 make -j server $log
+    make -j llama-server $log
 else
     printf "[-] Unknown backend: %s\n" "$backend"
     exit 1
@@ -407,8 +404,6 @@ elif [[ "$backend" == "cpu" ]]; then
     args="-ngl 0"
 elif [[ "$backend" == "metal" ]]; then
     args="-ngl 999"
-elif [[ "$backend" == "opencl" ]]; then
-    args="-ngl 999"
 else
     printf "[-] Unknown backend: %s\n" "$backend"
     exit 1
@@ -418,6 +413,6 @@ if [[ $verbose -eq 1 ]]; then
     args="$args --verbose"
 fi
 
-./server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args
+./llama-server -m "../$wfile" --host 0.0.0.0 --port "$port" -c $n_kv -np "$n_parallel" $args
 
 exit 0
diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh
index cf22afc41ce65..9e34dc8b9a590 100755
--- a/scripts/sync-ggml-am.sh
+++ b/scripts/sync-ggml-am.sh
@@ -106,10 +106,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
     # src/ggml-kompute.h          -> ggml-kompute.h
     # src/ggml-metal.h            -> ggml-metal.h
     # src/ggml-metal.m            -> ggml-metal.m
-    # src/ggml-mpi.h              -> ggml-mpi.h
-    # src/ggml-mpi.c              -> ggml-mpi.c
-    # src/ggml-opencl.cpp         -> ggml-opencl.cpp
-    # src/ggml-opencl.h           -> ggml-opencl.h
     # src/ggml-quants.c           -> ggml-quants.c
     # src/ggml-quants.h           -> ggml-quants.h
     # src/ggml-rpc.cpp            -> ggml-rpc.cpp
@@ -145,10 +141,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then
         -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \
         -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \
         -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \
-        -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \
-        -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \
-        -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \
-        -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \
         -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \
         -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \
         -e 's/src\/ggml-rpc\.cpp/ggml-rpc.cpp/g' \
diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last
index 57bede67b4f19..b6c57ec5e4f14 100644
--- a/scripts/sync-ggml.last
+++ b/scripts/sync-ggml.last
@@ -1 +1 @@
-126d34985705a5a2222723c145cb4e125ac689f3
+5653a195935ea3ac54652644c9daf154dbc1571b
diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh
index ec47fb27c1202..4843f8a4ae05e 100755
--- a/scripts/sync-ggml.sh
+++ b/scripts/sync-ggml.sh
@@ -14,10 +14,6 @@ cp -rpv ../ggml/src/ggml-kompute.h          ./ggml-kompute.h
 cp -rpv ../ggml/src/ggml-metal.h            ./ggml-metal.h
 cp -rpv ../ggml/src/ggml-metal.m            ./ggml-metal.m
 cp -rpv ../ggml/src/ggml-metal.metal        ./ggml-metal.metal
-cp -rpv ../ggml/src/ggml-mpi.h              ./ggml-mpi.h
-cp -rpv ../ggml/src/ggml-mpi.c              ./ggml-mpi.c
-cp -rpv ../ggml/src/ggml-opencl.cpp         ./ggml-opencl.cpp
-cp -rpv ../ggml/src/ggml-opencl.h           ./ggml-opencl.h
 cp -rpv ../ggml/src/ggml-quants.c           ./ggml-quants.c
 cp -rpv ../ggml/src/ggml-quants.h           ./ggml-quants.h
 cp -rpv ../ggml/src/ggml-rpc.cpp            ./ggml-rpc.cpp
diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt
index 766a017524237..cfa7073153486 100644
--- a/tests/CMakeLists.txt
+++ b/tests/CMakeLists.txt
@@ -129,8 +129,11 @@ llama_target_and_test(test-rope.cpp)
 llama_target_and_test(test-model-load-cancel.cpp  LABEL "model")
 llama_target_and_test(test-autorelease.cpp        LABEL "model")
 
-llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
-target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8
+if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64")
+    llama_target_and_test(test-json-schema-to-grammar.cpp   WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..)
+    target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server)
+endif()
 
 # dummy executable - not installed
 get_filename_component(TEST_TARGET test-c.c NAME_WE)
diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp
index de74585da29dd..7c504e937a851 100644
--- a/tests/test-backend-ops.cpp
+++ b/tests/test-backend-ops.cpp
@@ -642,20 +642,29 @@ struct test_case {
 struct test_unary : public test_case {
     const ggml_unary_op op;
     const ggml_type type;
-    const std::array ne;
+    const std::array ne_a;
+    int v; // view (1 : non-contiguous a)
 
     std::string vars() override {
-        return VARS_TO_STR2(type, ne);
+        return VARS_TO_STR3(type, ne_a, v);
     }
 
     test_unary(ggml_unary_op op,
             ggml_type type = GGML_TYPE_F32,
-            std::array ne = {128, 10, 10, 10})
-        : op(op), type(type), ne(ne) {}
+            std::array ne_a = {128, 10, 10, 10},
+            int v = 0)
+        : op(op), type(type), ne_a(ne_a), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * in = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * out = ggml_unary(ctx, in, op);
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * out = ggml_unary(ctx, a, op);
         return out;
     }
 
@@ -1054,6 +1063,33 @@ struct test_sqr : public test_case {
     }
 };
 
+// GGML_OP_SQRT
+struct test_sqrt : public test_case {
+    const ggml_type type;
+    const std::array ne;
+
+    std::string vars() override {
+        return VARS_TO_STR2(type, ne);
+    }
+
+    test_sqrt(ggml_type type = GGML_TYPE_F32,
+            std::array ne = {10, 10, 10, 10})
+        : type(type), ne(ne) {}
+
+    ggml_tensor * build_graph(ggml_context * ctx) override {
+        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
+        ggml_tensor * out = ggml_sqrt(ctx, a);
+        return out;
+    }
+
+    void initialize_tensors(ggml_context * ctx) override {
+        // fill with positive values
+        for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
+            init_tensor_uniform(t, 0.0f, 100.0f);
+        }
+    }
+};
+
 // GGML_OP_CLAMP
 struct test_clamp : public test_case {
     const ggml_type type;
@@ -1138,26 +1174,37 @@ struct test_soft_max : public test_case {
 // GGML_OP_ROPE
 struct test_rope : public test_case {
     const ggml_type type;
-    const std::array ne;
+    const std::array ne_a;
     int n_dims;
     int mode;
-    int n_ctx;
+    int n_ctx; // used to generate positions
+    float fs; // freq_scale
+    float ef; // ext_factor
+    float af; // attn_factor
     bool ff;
+    int v; // view (1 : non-contiguous a)
 
     std::string vars() override {
-        return VARS_TO_STR6(type, ne, n_dims, mode, n_ctx, ff);
+        return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v);
     }
 
     test_rope(ggml_type type = GGML_TYPE_F32,
-            std::array ne = {10, 10, 10, 1},
-            int n_dims = 10, int mode = 0, int n_ctx = 512, bool ff = false)
-        : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx), ff(ff) {}
+            std::array ne_a = {10, 10, 10, 1},
+            int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0)
+        : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]);
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]);
         ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr;
-        ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f);
+        ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f);
         return out;
     }
 
@@ -1165,11 +1212,11 @@ struct test_rope : public test_case {
         for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
             if (t->type == GGML_TYPE_I32) {
                 // pos
-                std::vector data(ne[2]);
-                for (int i = 0; i < ne[2]; i++) {
+                std::vector data(ne_a[2]);
+                for (int i = 0; i < ne_a[2]; i++) {
                     data[i] = rand() % n_ctx;
                 }
-                ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int));
+                ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int));
             } else {
                 if (t->ne[0] == n_dims/2) {
                     // frequency factors in the range [0.9f, 1.1f]
@@ -1259,22 +1306,41 @@ struct test_im2col : public test_case {
 // GGML_OP_CONCAT
 struct test_concat : public test_case {
     const ggml_type type;
-    const std::array ne;
-    const int64_t b_ne2;
+    const std::array ne_a;
+    const int64_t ne_b_d;
+    const int dim;
+    const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b)
 
     std::string vars() override {
-        return VARS_TO_STR3(type, ne, b_ne2);
+        return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v);
     }
 
     test_concat(ggml_type type = GGML_TYPE_F32,
-            std::array ne = {10, 10, 10, 10},
-            int64_t b_ne2 = 10)
-        : type(type), ne(ne), b_ne2(b_ne2) {}
+            std::array ne_a = {10, 10, 10, 10},
+            int64_t ne_b_d = 10,
+            int dim = 2, int v = 0)
+        : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data());
-        ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]);
-        ggml_tensor * out = ggml_concat(ctx, a, b);
+        auto ne_b = ne_a;
+        ne_b[dim] = ne_b_d;
+        ggml_tensor * a;
+        if (v & 1) {
+            auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3;
+            a = ggml_new_tensor(ctx, type, 4, ne.data());
+            a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0);
+        } else {
+            a = ggml_new_tensor(ctx, type, 4, ne_a.data());
+        }
+        ggml_tensor * b;
+        if (v & 2) {
+            auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4;
+            b = ggml_new_tensor(ctx, type, 4, ne.data());
+            b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0);
+        } else {
+            b = ggml_new_tensor(ctx, type, 4, ne_b.data());
+        }
+        ggml_tensor * out = ggml_concat(ctx, a, b, dim);
         return out;
     }
 };
@@ -1540,21 +1606,25 @@ struct test_flash_attn_ext : public test_case {
 
     const float max_bias; // ALiBi
 
+    const ggml_type type_KV;
+
     std::string vars() override {
-        return VARS_TO_STR6(hs, nh, kv, nb, mask, max_bias);
+        return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV);
     }
 
     double max_nmse_err() override {
         return 5e-4;
     }
 
-    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f)
-        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias) {}
+    test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16)
+        : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {}
 
     ggml_tensor * build_graph(ggml_context * ctx) override {
-        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1);
-        ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
-        ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1);
+        const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV));
+
+        ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1);
+        ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
+        ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV,       hs_padded, kv, nh, 1);
         ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr;
         ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias);
         return out;
@@ -1581,7 +1651,7 @@ struct llama_hparams {
 
     // cparams
     static constexpr uint32_t n_ctx = 512; // user-specified context size
-    static constexpr uint32_t n_orig_ctx = n_ctx;
+    static constexpr uint32_t n_ctx_orig = n_ctx;
 
     // batch
     int32_t n_tokens;
@@ -1772,13 +1842,13 @@ struct test_llama : public test_llm {
 
                 Qcur = ggml_rope_ext(
                     ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head,    hp.n_tokens), inp_pos, nullptr,
-                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                    hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
                 Kcur = ggml_rope_ext(
                     ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, nullptr,
-                    hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale,
+                    hp.n_rot, 0, hp.n_ctx_orig, freq_base, freq_scale,
                     ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
@@ -1897,12 +1967,12 @@ struct test_falcon : public test_llm {
 
                 // using mode = 2 for neox mode
                 Qcur = ggml_rope_ext(
-                    ctx, Qcur, inp_pos, nullptr, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                    ctx, Qcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
                 Kcur = ggml_rope_ext(
-                    ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, 0, hp.n_orig_ctx,
+                    ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, hp.n_ctx_orig,
                     freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow
                 );
 
@@ -1982,9 +2052,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     };
 
     // unary ops
-    for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op));
-        test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }));
+    for (int v : {0, 1}) {
+        for (int op = 0; op < GGML_UNARY_OP_COUNT; op++) {
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 128, 10, 10, 10 }, v));
+            test_cases.emplace_back(new test_unary((ggml_unary_op) op, GGML_TYPE_F32, { 7, 13, 19, 23 }, v));
+        }
     }
 
     test_cases.emplace_back(new test_get_rows(GGML_TYPE_F32, 1, 8, 2, 1, false));
@@ -2155,6 +2227,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     }
 
     test_cases.emplace_back(new test_sqr());
+    test_cases.emplace_back(new test_sqrt());
     test_cases.emplace_back(new test_clamp());
 
     test_cases.emplace_back(new test_diag_mask_inf(GGML_TYPE_F32, {10, 10,  1,  1}, 5));
@@ -2194,25 +2267,48 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 0.0f));
     test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true,  0.1f, 8.0f));
 
-    for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
-        // TODO: ff not supported yet for !neox
-        test_cases.emplace_back(new test_rope(type, {128,  32, 10, 1}, 128, 0, 512, false)); // llama 7B
-        test_cases.emplace_back(new test_rope(type, {128,  40, 10, 1}, 128, 0, 512, false)); // llama 13B
-        test_cases.emplace_back(new test_rope(type, {128,  52, 10, 1}, 128, 0, 512, false)); // llama 30B
-        test_cases.emplace_back(new test_rope(type, {128,  64, 10, 1}, 128, 0, 512, false)); // llama 65B
+    {
+        bool all = true;
+
+        for (float v : { 0, 1 }) {
+            for (float fs : { 1.0f, 1.4245f }) {
+                for (float ef : { 0.0f, 0.7465f }) {
+                    for (float af : { 1.0f, 1.4245f }) {
+                        for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
+                            for (bool ff : {false, true}) { // freq_factors
+                                test_cases.emplace_back(new test_rope(type, {128,  32, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 7B
+
+                                if (all) {
+                                    test_cases.emplace_back(new test_rope(type, {128,  40, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 13B
+                                    test_cases.emplace_back(new test_rope(type, {128,  52, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 30B
+                                    test_cases.emplace_back(new test_rope(type, {128,  64, 10, 1}, 128, 0, 512, fs, ef, af, ff, v)); // llama 65B
+                                }
+
+                                if (all) {
+                                    test_cases.emplace_back(new test_rope(type, { 64,   1, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,  71, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B)
+                                    test_cases.emplace_back(new test_rope(type, { 64,   8, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm)
+                                    test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2)
+                                }
+
+                                test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1},  64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B)
+                            }
+                        }
 
-        for (bool ff : {false, true}) { // freq_factors
-            test_cases.emplace_back(new test_rope(type, { 64,   1, 10, 1},  64, 2, 512, ff)); // neox (falcon 7B)
-            test_cases.emplace_back(new test_rope(type, { 64,  71, 10, 1},  64, 2, 512, ff)); // neox (falcon 7B)
-            test_cases.emplace_back(new test_rope(type, { 64,   8, 10, 1},  64, 2, 512, ff)); // neox (falcon 40B)
-            test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1},  64, 2, 512, ff)); // neox (falcon 40B)
-            test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  20, 2, 512, ff)); // neox (stablelm)
-            test_cases.emplace_back(new test_rope(type, { 80,  32, 10, 1},  32, 2, 512, ff)); // neox (phi-2)
+                        all = false;
+                    }
+                }
+            }
         }
     }
 
-    test_cases.emplace_back(new test_concat(GGML_TYPE_F32));
-    test_cases.emplace_back(new test_concat(GGML_TYPE_I32));
+    for (int v : { 0, 1, 2, 3 }) {
+        for (int dim : { 0, 1, 2, 3, }) {
+            test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v));
+            test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v));
+        }
+    }
 
     for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) {
         test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order));
@@ -2238,7 +2334,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op
                 for (int nh : { 32, }) {
                     for (int kv : { 512, 1024, }) {
                         for (int nb : { 1, 2, 4, 8, }) {
-                            test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias));
+                            for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) {
+                                test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV));
+                            }
                         }
                     }
                 }
diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp
index 21ca43be3a963..a353276459b2d 100644
--- a/tests/test-grad0.cpp
+++ b/tests/test-grad0.cpp
@@ -1465,7 +1465,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
 
                         GGML_PRINT_DEBUG("rope f32: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope f32", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
@@ -1505,7 +1505,7 @@ int main(int argc, const char ** argv) {
                             continue;
                         }
 
-                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode, 0));
+                        struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], p, n_rot, mode));
 
                         GGML_PRINT_DEBUG("rope f16: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
                         check_gradient("rope f16", ctx0, x, f, ndims, nargs, 1e-1f, 1e-1f, INFINITY);
diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp
index 01c5bb27aabb9..8787fb1ec6987 100644
--- a/tests/test-grammar-integration.cpp
+++ b/tests/test-grammar-integration.cpp
@@ -205,6 +205,33 @@ static void test_complex_grammar() {
     );
 }
 
+static void test_special_chars() {
+    // A collection of tests to exercise special characters such as "."
+    test_grammar(
+        "special characters",
+        // Grammar
+        R"""(
+            root ::= ... "abc" ...
+            )""",
+        // Passing strings
+        {
+            "abcabcabc",
+            "aaaabcccc",
+            // NOTE: Also ensures that multi-byte characters still count as a single character
+            "🔵🟠✅abc❌🟠🔵"
+        },
+        // Failing strings
+        {
+            "aaabcccc",
+            "aaaaabcccc",
+            "aaaabccc",
+            "aaaabccccc",
+            "🔵🟠✅❌abc❌✅🟠🔵"
+            "🔵🟠abc🟠🔵"
+        }
+    );
+}
+
 static void test_quantifiers() {
     // A collection of tests to exercise * + and ? quantifiers
 
@@ -292,6 +319,82 @@ static void test_quantifiers() {
             "catyyy",
         }
     );
+    test_grammar(
+        "simple exact repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{4}
+        )""",
+        // Passing strings
+        {
+            "aaaa",
+            "bbbb",
+            "abab",
+        },
+        // Failing strings
+        {
+            "a",
+            "b",
+            "aaaaa",
+        }
+    );
+    test_grammar(
+        "simple min repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{4,}
+        )""",
+        // Passing strings
+        {
+            "aaaa",
+            "aaaaab",
+            "bbbb",
+            "ababab",
+        },
+        // Failing strings
+        {
+            "",
+            "aba",
+        }
+    );
+    test_grammar(
+        "simple max repetition",
+        // Grammar
+        R"""(
+            root ::= [ab]{0,4}
+        )""",
+        // Passing strings
+        {
+            "",
+            "a",
+            "aa",
+            "aaa",
+            "aaab",
+        },
+        // Failing strings
+        {
+            "aaaaa",
+        }
+    );
+    test_grammar(
+        "min / max repetition",
+        // Grammar
+        R"""(
+            root ::= ("0x" [A-F0-9]{2} " "?){3,5}
+        )""",
+        // Passing strings
+        {
+            "0xFF 0x12 0xAB",
+            "0xFF 0x12 0xAB 0x00 0x00",
+        },
+        // Failing strings
+        {
+            "",
+            "0xFF",
+            "0xFF 0x12",
+            "0xFF 0x12 0xAB 0x00 0x00 0x00",
+        }
+    );
 }
 
 static void test_failure_missing_root() {
@@ -369,6 +472,7 @@ int main() {
     fprintf(stdout, "Running grammar integration tests...\n");
     test_simple_grammar();
     test_complex_grammar();
+    test_special_chars();
     test_quantifiers();
     test_failure_missing_root();
     test_failure_missing_reference();
diff --git a/tests/test-grammar-parser.cpp b/tests/test-grammar-parser.cpp
index 91939e276df07..5df5abb25394c 100644
--- a/tests/test-grammar-parser.cpp
+++ b/tests/test-grammar-parser.cpp
@@ -7,28 +7,79 @@
 
 #include 
 
-int main()
-{
-    grammar_parser::parse_state parsed_grammar;
+static const char * type_str(llama_gretype type) {
+    switch (type) {
+        case LLAMA_GRETYPE_CHAR: return "LLAMA_GRETYPE_CHAR";
+        case LLAMA_GRETYPE_CHAR_NOT: return "LLAMA_GRETYPE_CHAR_NOT";
+        case LLAMA_GRETYPE_CHAR_ALT: return "LLAMA_GRETYPE_CHAR_ALT";
+        case LLAMA_GRETYPE_CHAR_RNG_UPPER: return "LLAMA_GRETYPE_CHAR_RNG_UPPER";
+        case LLAMA_GRETYPE_RULE_REF: return "LLAMA_GRETYPE_RULE_REF";
+        case LLAMA_GRETYPE_ALT: return "LLAMA_GRETYPE_ALT";
+        case LLAMA_GRETYPE_END: return "LLAMA_GRETYPE_END";
+        default: return "?";
+    }
+}
 
-    const char *grammar_bytes = R"""(root  ::= (expr "=" term "\n")+
-expr  ::= term ([-+*/] term)*
-term  ::= [0-9]+)""";
+static void verify_parsing(const char *grammar_bytes, const std::vector> expected, const std::vector &expected_rules) {
+    uint32_t index = 0;
+    grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_bytes);
 
-    parsed_grammar = grammar_parser::parse(grammar_bytes);
+    std::map symbol_names;
+    for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it) {
+        symbol_names[it->second] = it->first;
+    }
 
-    std::vector> expected = {
-        {"expr", 2},
-        {"expr_5", 5},
-        {"expr_6", 6},
-        {"root", 0},
-        {"root_1", 1},
-        {"root_4", 4},
-        {"term", 3},
-        {"term_7", 7},
+    auto print_all = [&]() {
+        fprintf(stderr, "    verify_parsing(R\"\"\"(%s)\"\"\", {\n", grammar_bytes);
+        for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it) {
+            fprintf(stderr, "        {\"%s\", %u},\n", it->first.c_str(), it->second);
+        }
+        fprintf(stderr, "    }, {\n");
+        for (size_t i_rule = 0; i_rule < parsed_grammar.rules.size(); i_rule++) {
+            fprintf(stderr, "        // %s (index %zu)\n", symbol_names[i_rule].c_str(), i_rule);
+            auto & rule = parsed_grammar.rules[i_rule];
+            for (uint32_t i = 0; i < rule.size(); i++) {
+                std::string rule_str;
+                fprintf(stderr, "        {%s, ", type_str(rule[i].type));
+                if (rule[i].type == LLAMA_GRETYPE_CHAR || rule[i].type == LLAMA_GRETYPE_CHAR_ALT ||
+                    rule[i].type == LLAMA_GRETYPE_CHAR_NOT || rule[i].type == LLAMA_GRETYPE_CHAR_RNG_UPPER) {
+                    char c = rule[i].value;
+                    if (c == '\n') {
+                        fprintf(stderr, "'\\n'");
+                    } else if (c == '\t') {
+                        fprintf(stderr, "'\\t'");
+                    } else if (c == '\r') {
+                        fprintf(stderr, "'\\r'");
+                    } else if (c == '\0') {
+                        fprintf(stderr, "'\\0'");
+                    } else {
+                        fprintf(stderr, "'%c'", c);
+                    }
+                } else if (rule[i].type == LLAMA_GRETYPE_RULE_REF) {
+                    fprintf(stderr, "/* %s */ %u", symbol_names[rule[i].value].c_str(), rule[i].value);
+                } else {
+                    fprintf(stderr, "%u", rule[i].value);
+                }
+                fprintf(stderr, "},\n");
+            }
+        }
+        fprintf(stderr, "    });\n");
     };
 
-    uint32_t index = 0;
+    if (getenv("TEST_GRAMMAR_PARSER_PRINT_ALL")) {
+        print_all();
+        fprintf(stderr, "\n");
+        return;
+    }
+
+    fprintf(stderr, "Testing grammar:%s\n", grammar_bytes);
+
+    if (parsed_grammar.symbol_ids.size() != expected.size()) {
+        fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+        print_all();
+        assert(parsed_grammar.symbol_ids.size() == expected.size());
+    }
+
     for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
     {
         std::string key = it->first;
@@ -38,51 +89,18 @@ term  ::= [0-9]+)""";
         // pretty print error message before asserting
         if (expected_pair.first != key || expected_pair.second != value)
         {
+            fprintf(stderr, "index: %u\n", index);
             fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
             fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
             fprintf(stderr, "expected_pair != actual_pair\n");
+            fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+            print_all();
         }
 
         assert(expected_pair.first == key && expected_pair.second == value);
 
         index++;
     }
-    std::vector expected_rules = {
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 61},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_CHAR, 10},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 7},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 45},
-        {LLAMA_GRETYPE_CHAR_ALT, 43},
-        {LLAMA_GRETYPE_CHAR_ALT, 42},
-        {LLAMA_GRETYPE_CHAR_ALT, 47},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 5},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_RULE_REF, 7},
-        {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_END, 0},
-    };
 
     index = 0;
     for (auto rule : parsed_grammar.rules)
@@ -97,28 +115,306 @@ term  ::= [0-9]+)""";
             if (expected_element.type != element.type || expected_element.value != element.value)
             {
                 fprintf(stderr, "index: %u\n", index);
-                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
+                fprintf(stderr, "expected_element: %s, %u\n", type_str(expected_element.type), expected_element.value);
+                fprintf(stderr, "actual_element: %s, %u\n", type_str(element.type), element.value);
                 fprintf(stderr, "expected_element != actual_element\n");
+                fprintf(stderr, "all elements:\n");
+                fprintf(stderr, "Code to update expectation (set TEST_GRAMMAR_PARSER_PRINT_ALL=1 to print all):\n");
+                print_all();
             }
 
             assert(expected_element.type == element.type && expected_element.value == element.value);
             index++;
         }
     }
+}
+
+static void verify_failure(const char *grammar_bytes) {
+    fprintf(stderr, "Testing expected failure:%s\n", grammar_bytes);
+    auto result = grammar_parser::parse(grammar_bytes);
+    assert(result.rules.empty() && "should have failed");
+}
+
+int main()
+{
+    verify_failure(R"""(
+        root ::= "a"{,}"
+    )""");
+
+    verify_failure(R"""(
+        root ::= "a"{,10}"
+    )""");
 
-    const char *longer_grammar_bytes = R"""(
-    root  ::= (expr "=" ws term "\n")+
-    expr  ::= term ([-+*/] term)*
-    term  ::= ident | num | "(" ws expr ")" ws
-    ident ::= [a-z] [a-z0-9_]* ws
-    num   ::= [0-9]+ ws
-    ws    ::= [ \t\n]*
-    )""";
+    verify_parsing(R"""(
+        root  ::= "a"
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a" | [bdx-z] | [^1-3]
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_CHAR, 'b'},
+        {LLAMA_GRETYPE_CHAR_ALT, 'd'},
+        {LLAMA_GRETYPE_CHAR_ALT, 'x'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_CHAR_NOT, '1'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '3'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a+
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"+
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a?
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
 
-    parsed_grammar = grammar_parser::parse(longer_grammar_bytes);
+    verify_parsing(R"""(
+        root  ::= "a"?
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= a*
+        a     ::= "a"
+    )""", {
+        {"a", 1},
+        {"root", 0},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // a (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* a */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"*
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
 
-    expected = {
+    verify_parsing(R"""(
+        root  ::= "a"{2}
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{2,}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{ 4}
+    )""", {
+        {"root", 0},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= "a"{2,4}
+    )""", {
+        {"root", 0},
+        {"root_1", 1},
+        {"root_2", 2},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_2 */ 2},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // root_2 (index 2)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= (expr "=" term "\n")+
+        expr  ::= term ([-+*/] term)*
+        term  ::= [0-9]+
+    )""", {
+        {"expr", 2},
+        {"expr_5", 5},
+        {"expr_6", 6},
+        {"root", 0},
+        {"root_1", 1},
+        {"root_4", 4},
+        {"term", 3},
+        {"term_7", 7},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_4 */ 4},
+        {LLAMA_GRETYPE_END, 0},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, '='},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_CHAR, '\n'},
+        {LLAMA_GRETYPE_END, 0},
+        // expr (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_END, 0},
+        // term (index 3)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* term_7 */ 7},
+        {LLAMA_GRETYPE_END, 0},
+        // root_4 (index 4)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_4 */ 4},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // expr_5 (index 5)
+        {LLAMA_GRETYPE_CHAR, '-'},
+        {LLAMA_GRETYPE_CHAR_ALT, '+'},
+        {LLAMA_GRETYPE_CHAR_ALT, '*'},
+        {LLAMA_GRETYPE_CHAR_ALT, '/'},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 3},
+        {LLAMA_GRETYPE_END, 0},
+        // expr_6 (index 6)
+        {LLAMA_GRETYPE_RULE_REF, /* expr_5 */ 5},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+        // term_7 (index 7)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* term_7 */ 7},
+        {LLAMA_GRETYPE_ALT, 0},
+        {LLAMA_GRETYPE_END, 0},
+    });
+
+    verify_parsing(R"""(
+        root  ::= (expr "=" ws term "\n")+
+        expr  ::= term ([-+*/] term)*
+        term  ::= ident | num | "(" ws expr ")" ws
+        ident ::= [a-z] [a-z0-9_]* ws
+        num   ::= [0-9]+ ws
+        ws    ::= [ \t\n]*
+    )""", {
         {"expr", 2},
         {"expr_6", 6},
         {"expr_7", 7},
@@ -132,119 +428,88 @@ term  ::= [0-9]+)""";
         {"term", 4},
         {"ws", 3},
         {"ws_12", 12},
-    };
-
-    index = 0;
-    for (auto it = parsed_grammar.symbol_ids.begin(); it != parsed_grammar.symbol_ids.end(); ++it)
-    {
-        std::string key = it->first;
-        uint32_t value = it->second;
-        std::pair expected_pair = expected[index];
-
-        // pretty print error message before asserting
-        if (expected_pair.first != key || expected_pair.second != value)
-        {
-            fprintf(stderr, "expected_pair: %s, %u\n", expected_pair.first.c_str(), expected_pair.second);
-            fprintf(stderr, "actual_pair: %s, %u\n", key.c_str(), value);
-            fprintf(stderr, "expected_pair != actual_pair\n");
-        }
-
-        assert(expected_pair.first == key && expected_pair.second == value);
-
-        index++;
-    }
-    expected_rules = {
-        {LLAMA_GRETYPE_RULE_REF, 5},
+    }, {
+        // root (index 0)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_5 */ 5},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 61},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_CHAR, 10},
+        // root_1 (index 1)
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, '='},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
+        {LLAMA_GRETYPE_CHAR, '\n'},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 4},
-        {LLAMA_GRETYPE_RULE_REF, 7},
+        // expr (index 2)
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_7 */ 7},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 12},
+        // ws (index 3)
+        {LLAMA_GRETYPE_RULE_REF, /* ws_12 */ 12},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 8},
+        // term (index 4)
+        {LLAMA_GRETYPE_RULE_REF, /* ident */ 8},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 9},
+        {LLAMA_GRETYPE_RULE_REF, /* num */ 9},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 40},
-        {LLAMA_GRETYPE_RULE_REF, 3},
-        {LLAMA_GRETYPE_RULE_REF, 2},
-        {LLAMA_GRETYPE_CHAR, 41},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        {LLAMA_GRETYPE_CHAR, '('},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
+        {LLAMA_GRETYPE_RULE_REF, /* expr */ 2},
+        {LLAMA_GRETYPE_CHAR, ')'},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
-        {LLAMA_GRETYPE_RULE_REF, 5},
+        // root_5 (index 5)
+        {LLAMA_GRETYPE_RULE_REF, /* root_1 */ 1},
+        {LLAMA_GRETYPE_RULE_REF, /* root_5 */ 5},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_RULE_REF, 1},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 45},
-        {LLAMA_GRETYPE_CHAR_ALT, 43},
-        {LLAMA_GRETYPE_CHAR_ALT, 42},
-        {LLAMA_GRETYPE_CHAR_ALT, 47},
-        {LLAMA_GRETYPE_RULE_REF, 4},
+        // expr_6 (index 6)
+        {LLAMA_GRETYPE_CHAR, '-'},
+        {LLAMA_GRETYPE_CHAR_ALT, '+'},
+        {LLAMA_GRETYPE_CHAR_ALT, '*'},
+        {LLAMA_GRETYPE_CHAR_ALT, '/'},
+        {LLAMA_GRETYPE_RULE_REF, /* term */ 4},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 6},
-        {LLAMA_GRETYPE_RULE_REF, 7},
+        // expr_7 (index 7)
+        {LLAMA_GRETYPE_RULE_REF, /* expr_6 */ 6},
+        {LLAMA_GRETYPE_RULE_REF, /* expr_7 */ 7},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 97},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
-        {LLAMA_GRETYPE_RULE_REF, 10},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        // ident (index 8)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_RULE_REF, /* ident_10 */ 10},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_RULE_REF, 11},
-        {LLAMA_GRETYPE_RULE_REF, 3},
+        // num (index 9)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* num_11 */ 11},
+        {LLAMA_GRETYPE_RULE_REF, /* ws */ 3},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 97},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 122},
-        {LLAMA_GRETYPE_CHAR_ALT, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_CHAR_ALT, 95},
-        {LLAMA_GRETYPE_RULE_REF, 10},
+        // ident_10 (index 10)
+        {LLAMA_GRETYPE_CHAR, 'a'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 'z'},
+        {LLAMA_GRETYPE_CHAR_ALT, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_CHAR_ALT, '_'},
+        {LLAMA_GRETYPE_RULE_REF, /* ident_10 */ 10},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
-        {LLAMA_GRETYPE_RULE_REF, 11},
+        // num_11 (index 11)
+        {LLAMA_GRETYPE_CHAR, '0'},
+        {LLAMA_GRETYPE_CHAR_RNG_UPPER, '9'},
+        {LLAMA_GRETYPE_RULE_REF, /* num_11 */ 11},
         {LLAMA_GRETYPE_ALT, 0},
-        {LLAMA_GRETYPE_CHAR, 48},
-        {LLAMA_GRETYPE_CHAR_RNG_UPPER, 57},
         {LLAMA_GRETYPE_END, 0},
-        {LLAMA_GRETYPE_CHAR, 32},
-        {LLAMA_GRETYPE_CHAR_ALT, 9},
-        {LLAMA_GRETYPE_CHAR_ALT, 10},
-        {LLAMA_GRETYPE_RULE_REF, 12},
+        // ws_12 (index 12)
+        {LLAMA_GRETYPE_CHAR, ' '},
+        {LLAMA_GRETYPE_CHAR_ALT, '\t'},
+        {LLAMA_GRETYPE_CHAR_ALT, '\n'},
+        {LLAMA_GRETYPE_RULE_REF, /* ws_12 */ 12},
         {LLAMA_GRETYPE_ALT, 0},
         {LLAMA_GRETYPE_END, 0},
-    };
-
-    index = 0;
-    for (auto rule : parsed_grammar.rules)
-    {
-        // compare rule to expected rule
-        for (uint32_t i = 0; i < rule.size(); i++)
-        {
-            llama_grammar_element element = rule[i];
-            llama_grammar_element expected_element = expected_rules[index];
-
-            // pretty print error message before asserting
-            if (expected_element.type != element.type || expected_element.value != element.value)
-            {
-                fprintf(stderr, "index: %u\n", index);
-                fprintf(stderr, "expected_element: %d, %u\n", expected_element.type, expected_element.value);
-                fprintf(stderr, "actual_element: %d, %u\n", element.type, element.value);
-                fprintf(stderr, "expected_element != actual_element\n");
-            }
-
-            assert(expected_element.type == element.type && expected_element.value == element.value);
-            index++;
-        }
-    }
+    });
 
     return 0;
 }
diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp
index c5361b5b8912c..87bc66b691784 100755
--- a/tests/test-json-schema-to-grammar.cpp
+++ b/tests/test-json-schema-to-grammar.cpp
@@ -105,14 +105,14 @@ static void test_all(const std::string & lang, std::function grammar tests.\n\033[0m");
+        fprintf(stderr, "\033[33mWARNING: Python not found (min version required is 3.8), skipping Python JSON schema -> grammar tests.\n\033[0m");
     }
 
     if (getenv("LLAMA_NODE_AVAILABLE") || (std::system("node --version") == 0)) {
diff --git a/tests/test-rope.cpp b/tests/test-rope.cpp
index 26c1f42dc0e95..f0895ffaad6a1 100644
--- a/tests/test-rope.cpp
+++ b/tests/test-rope.cpp
@@ -162,12 +162,12 @@ int main(int /*argc*/, const char ** /*argv*/) {
         x = get_random_tensor_f32(ctx0, ndims, ne, -1.0f, 1.0f);
 
         // 100, 101, 102, ..., 172
-        struct ggml_tensor * r0 = ggml_rope(ctx0, x,  p0, n_rot, mode, 1024);
+        struct ggml_tensor * r0 = ggml_rope(ctx0, x,  p0, n_rot, mode);
         // -67, -67, -67, ..., -67
-        struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode, 1024); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
+        struct ggml_tensor * r1 = ggml_rope(ctx0, r0, p1, n_rot, mode); // "context swap", i.e. forget n_past_0 - n_past_2 tokens
 
         //  33,  34,  35, ..., 105
-        struct ggml_tensor * r2 = ggml_rope(ctx0, x,  p2, n_rot, mode, 1024);
+        struct ggml_tensor * r2 = ggml_rope(ctx0, x,  p2, n_rot, mode);
 
         ggml_cgraph * gf = ggml_new_graph(ctx0);
 
diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh
index 1fec8bbf130db..4d2b8365547df 100755
--- a/tests/test-tokenizer-0.sh
+++ b/tests/test-tokenizer-0.sh
@@ -28,6 +28,8 @@ printf "Tokenizing using (cpp) llama.cpp ...\n"
 cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in"
 cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in"
 
+set +e
+
 diff $input.tok $input.tokcpp > /dev/null 2>&1
 
 if [ $? -eq 0 ]; then
diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py
index 7e1b656e5f5fc..52f589511e470 100644
--- a/tests/test-tokenizer-random.py
+++ b/tests/test-tokenizer-random.py
@@ -156,28 +156,47 @@ def generator_custom_text_edge_cases() -> Iterator[str]:
         'a',       # Phi-3 fail
         '<|endoftext|>',  # Phi-3 fail
         'a\na',       # TODO: Bert fail
+        'a  b',   # rstrip phi-3
+        'a  b', # lstrip jina-v2
     ]
 
 
-def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]:
-    special_tokens = set(tokenizer.all_special_tokens)
-    special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "", ""])
-    special_tokens = list(sorted(special_tokens))
+def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
+    """Brute force check all vocab words"""
+    yield from vocab
+
+
+def generator_added_lr_strip(tokenizer) -> Iterator[str]:
+    WHITESPACES = ["", " ", "  ", "    "]
+    special_tokens = list(tokenizer.all_special_tokens)
+    added_tokens   = list(tokenizer.added_tokens_encoder)
+    all_tokens     = list(sorted(set(special_tokens + added_tokens)))
+    for token in all_tokens:
+        for lstrip in WHITESPACES:
+            for rstrip in WHITESPACES:
+                yield lstrip + token + rstrip
+                yield "a" + lstrip + token + rstrip
+                yield lstrip + token + rstrip + "z"
+                yield "a" + lstrip + token + rstrip + "z"
+
+
+def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]:
+    special_tokens = list(tokenizer.all_special_tokens)
+    added_tokens   = list(tokenizer.added_tokens_encoder)
+    separations    = [" ", "\n", "\t", "-", "!", "one", "1", "", ""]
+    all_tokens     = list(sorted(set(special_tokens + added_tokens + separations)))
     rand = random.Random()
     for m in range(iterations):
         rand.seed(m)
-        words = rand.choices(special_tokens, k=500)
-        if tokenizer.add_bos_token:  # skip spam warning of double BOS
-            while words and words[0] == tokenizer.bos_token:
+        words = rand.choices(all_tokens, k=500)
+        if words[0] == tokenizer.bos_token:  # skip spam warning of double BOS
+            while len(words) > 1 and words[1] == tokenizer.bos_token:  # leave one starting BOS
+                words.pop(0)
+            if tokenizer.add_bos_token:  # drop all starting BOS
                 words.pop(0)
         yield "".join(words)
 
 
-def generator_vocab_words(vocab: list[str]) -> Iterator[str]:
-    """Brute force check all vocab words"""
-    yield from vocab
-
-
 def generator_random_chars(iterations=100) -> Iterator[str]:
     """Brute force random text with simple characters"""
 
@@ -272,8 +291,8 @@ def find_first_mismatch(ids1: list[int], ids2: list[int]):
         ids2 = func_tokenize2(text)
         if ids1 != ids2:
             i = find_first_mismatch(ids1, ids2)
-            ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1]
-            ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1]
+            ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1]
+            ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1]
             logger.info(" TokenIDs: " + str(ids1))
             logger.info(" Expected: " + str(ids2))
             raise Exception()
@@ -293,20 +312,23 @@ def main(argv: list[str] = None):
     model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096))
     tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer)
 
-    tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True)
-    tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False)
-
     def func_tokenize1(text: str):
         return model.tokenize(text, add_special=True, parse_special=True)
 
     def func_tokenize2(text: str):
         return tokenizer.encode(text, add_special_tokens=True)
 
+    ids = func_tokenize2("a")
+    assert 1 <= len(ids) <= 3
+    add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0]
+    tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token)
+
     vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True)))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text())
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases())
-    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab))
+    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer))
+    test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000))
     test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000))
@@ -318,7 +340,7 @@ def func_tokenize2(text: str):
 if __name__ == "__main__":
     # main()
 
-    path_tokenizers = "./models/tokenizers/"
+    path_tokenizers   = "./models/tokenizers/"
     path_vocab_format = "./models/ggml-vocab-%s.gguf"
 
     # import os
@@ -326,6 +348,8 @@ def func_tokenize2(text: str):
     tokenizers = [
         "llama-spm",   # SPM
         "phi-3",       # SPM
+        "jina-v2-en",  # WPM
+        "bert-bge",    # WPM
     ]
 
     for tokenizer in tokenizers:
diff --git a/unicode.cpp b/unicode.cpp
index 056a4c74172c7..2f8d73832d107 100644
--- a/unicode.cpp
+++ b/unicode.cpp
@@ -226,7 +226,7 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t
         assert(offset_end <= cpts.size());
         start = offset_end;
 
-        auto _get_cpt = [&] (const size_t pos) -> char32_t {
+        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
         };
 
@@ -253,18 +253,18 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t
         };
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
-            const char32_t cpt = _get_cpt(pos);
+            const uint32_t cpt = _get_cpt(pos);
             const auto flags = _get_flags(pos);
 
             // regex: 's|'t|'re|'ve|'m|'ll|'d
             if (cpt == '\'' && pos+1 < offset_end) {
-                char32_t cpt_next = _get_cpt(pos+1);
+                uint32_t cpt_next = _get_cpt(pos+1);
                 if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
                     pos += _add_token(pos+2);
                     continue;
                 }
                 if (pos+2 < offset_end) {
-                    char32_t cpt_next_next = _get_cpt(pos+2);
+                    uint32_t cpt_next_next = _get_cpt(pos+2);
                     if ((cpt_next == 'r' && cpt_next_next == 'e') ||
                         (cpt_next == 'v' && cpt_next_next == 'e') ||
                         (cpt_next == 'l' && cpt_next_next == 'l')) {
@@ -344,7 +344,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string &
         assert(offset_end <= cpts.size());
         start = offset_end;
 
-        auto _get_cpt = [&] (const size_t pos) -> char32_t {
+        auto _get_cpt = [&] (const size_t pos) -> uint32_t {
             return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0;
         };
 
@@ -371,18 +371,18 @@ static std::vector unicode_regex_split_custom_llama3(const std::string &
         };
 
         for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) {
-            const char32_t cpt = _get_cpt(pos);
+            const uint32_t cpt = _get_cpt(pos);
             const auto flags = _get_flags(pos);
 
             // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive
             if (cpt == '\'' && pos+1 < offset_end) {
-                char32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
+                uint32_t cpt_next = unicode_tolower(_get_cpt(pos+1));
                 if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') {
                     pos += _add_token(pos+2);
                     continue;
                 }
                 if (pos+2 < offset_end) {
-                    char32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
+                    uint32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2));
                     if ((cpt_next == 'r' && cpt_next_next == 'e') ||
                         (cpt_next == 'v' && cpt_next_next == 'e') ||
                         (cpt_next == 'l' && cpt_next_next == 'l')) {
@@ -424,7 +424,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string &
                 while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) {
                     flags2 = _get_flags(++pos);
                 }
-                char32_t cpt2 = _get_cpt(pos);
+                uint32_t cpt2 = _get_cpt(pos);
                 while (cpt2 == '\r' || cpt2 == '\n') {
                     cpt2 = _get_cpt(++pos);
                 }
@@ -435,7 +435,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string &
             size_t num_whitespaces = 0;
             size_t last_end_r_or_n = 0;
             while (_get_flags(pos+num_whitespaces).is_whitespace) {
-                char32_t cpt2 = _get_cpt(pos+num_whitespaces);
+                uint32_t cpt2 = _get_cpt(pos+num_whitespaces);
                 if (cpt2 == '\r' || cpt2 == '\n') {
                     last_end_r_or_n = pos + num_whitespaces + 1;
                 }
@@ -626,7 +626,7 @@ uint8_t unicode_utf8_to_byte(const std::string & utf8) {
     return map.at(utf8);
 }
 
-char32_t unicode_tolower(char32_t cp) {
+uint32_t unicode_tolower(uint32_t cp) {
     auto it = unicode_map_lowercase.find(cp);
     return it == unicode_map_lowercase.end() ? cp : it->second;
 }
diff --git a/unicode.h b/unicode.h
index 7513be4ad0d4f..6c488970a79d6 100644
--- a/unicode.h
+++ b/unicode.h
@@ -58,6 +58,6 @@ codepoint_flags unicode_cpt_flags(const std::string & utf8);
 std::string unicode_byte_to_utf8(uint8_t byte);
 uint8_t unicode_utf8_to_byte(const std::string & utf8);
 
-char32_t unicode_tolower(char32_t cp);
+uint32_t unicode_tolower(uint32_t cp);
 
 std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs);
diff --git a/vulkan-shaders/add.comp b/vulkan-shaders/add.comp
new file mode 100644
index 0000000000000..8475b01196386
--- /dev/null
+++ b/vulkan-shaders/add.comp
@@ -0,0 +1,12 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
diff --git a/vulkan-shaders/argsort.comp b/vulkan-shaders/argsort.comp
new file mode 100644
index 0000000000000..e55414b03c519
--- /dev/null
+++ b/vulkan-shaders/argsort.comp
@@ -0,0 +1,71 @@
+#version 450
+
+#include "types.comp"
+
+#define BLOCK_SIZE 1024
+#define ASC 0
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1)          buffer D {int data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    uint ncols_pad;
+    uint order;
+} p;
+
+shared int dst_row[BLOCK_SIZE];
+
+void swap(uint idx0, uint idx1) {
+    int tmp = dst_row[idx0];
+    dst_row[idx0] = dst_row[idx1];
+    dst_row[idx1] = tmp;
+}
+
+void main() {
+    // bitonic sort
+    const int col = int(gl_LocalInvocationID.x);
+    const uint row = gl_WorkGroupID.y;
+
+    if (col >= p.ncols_pad) {
+        return;
+    }
+
+    const uint row_offset = row * p.ncols;
+
+    // initialize indices
+    dst_row[col] = col;
+    barrier();
+
+    for (uint k = 2; k <= p.ncols_pad; k *= 2) {
+        for (uint j = k / 2; j > 0; j /= 2) {
+            const uint ixj = col ^ j;
+            if (ixj > col) {
+                if ((col & k) == 0) {
+                    if (dst_row[col] >= p.ncols ||
+                        (dst_row[ixj] < p.ncols && (p.order == ASC ?
+                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] :
+                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]]))
+                    ) {
+                        swap(col, ixj);
+                    }
+                } else {
+                    if (dst_row[ixj] >= p.ncols ||
+                        (dst_row[col] < p.ncols && (p.order == ASC ?
+                            data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] :
+                            data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]]))
+                    ) {
+                        swap(col, ixj);
+                    }
+                }
+            }
+            barrier();
+        }
+    }
+
+    if (col < p.ncols) {
+        data_d[row_offset + col] = dst_row[col];
+    }
+}
diff --git a/vulkan-shaders/clamp.comp b/vulkan-shaders/clamp.comp
new file mode 100644
index 0000000000000..ca272e227fd90
--- /dev/null
+++ b/vulkan-shaders/clamp.comp
@@ -0,0 +1,13 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val));
+}
diff --git a/vulkan-shaders/copy.comp b/vulkan-shaders/copy.comp
new file mode 100644
index 0000000000000..efb55876e35c1
--- /dev/null
+++ b/vulkan-shaders/copy.comp
@@ -0,0 +1,16 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+#else
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = data_a[src0_idx(gl_GlobalInvocationID.x)];
+#endif
+}
diff --git a/vulkan-shaders/dequant_f32.comp b/vulkan-shaders/dequant_f32.comp
new file mode 100644
index 0000000000000..a4d3fca556208
--- /dev/null
+++ b/vulkan-shaders/dequant_f32.comp
@@ -0,0 +1,20 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x * 16;
+
+    if (i >= p.nel) {
+        return;
+    }
+
+    [[unroll]] for (uint l = 0; l < 16; l++) {
+        data_b[i + l] = D_TYPE(data_a[i + l]);
+    }
+}
diff --git a/vulkan-shaders/dequant_funcs.comp b/vulkan-shaders/dequant_funcs.comp
new file mode 100644
index 0000000000000..35d424d182f0c
--- /dev/null
+++ b/vulkan-shaders/dequant_funcs.comp
@@ -0,0 +1,60 @@
+#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+#endif
+
+#if defined(DATA_A_F32)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
+}
+#endif
+
+#if defined(DATA_A_F16)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]);
+}
+#endif
+
+#if defined(DATA_A_Q4_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+}
+#endif
+
+#if defined(DATA_A_Q4_1)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2(vui & 0xF, vui >> 4) * d + m;
+}
+#endif
+
+#if defined(DATA_A_Q5_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0];
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+}
+#endif
+
+#if defined(DATA_A_Q5_1)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    const float m = float(data_a[a_offset + ib].m);
+    const uint uint_qh = data_a[a_offset + ib].qh;
+    const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+    const uint vui = uint(data_a[a_offset + ib].qs[iqs]);
+    return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+}
+#endif
+
+#if defined(DATA_A_Q8_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    const float d = float(data_a[a_offset + ib].d);
+    return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d;
+}
+#endif
diff --git a/vulkan-shaders/dequant_head.comp b/vulkan-shaders/dequant_head.comp
new file mode 100644
index 0000000000000..8d806435b7163
--- /dev/null
+++ b/vulkan-shaders/dequant_head.comp
@@ -0,0 +1,13 @@
+#extension GL_EXT_control_flow_attributes : require
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint nel;
+} p;
+
+#include "types.comp"
diff --git a/vulkan-shaders/dequant_q2_k.comp b/vulkan-shaders/dequant_q2_k.comp
new file mode 100644
index 0000000000000..157154af3a328
--- /dev/null
+++ b/vulkan-shaders/dequant_q2_k.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 32 * ip + il;
+        const uint8_t qs = data_a[i].qs[32 * ip + il];
+
+        FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+        data_b[y_idx +  0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4));
+        data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4));
+        data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4));
+        data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4));
+    }
+}
diff --git a/vulkan-shaders/dequant_q3_k.comp b/vulkan-shaders/dequant_q3_k.comp
new file mode 100644
index 0000000000000..c17dd0d999116
--- /dev/null
+++ b/vulkan-shaders/dequant_q3_k.comp
@@ -0,0 +1,42 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = uint(gl_WorkGroupID.x * 256 + wgy);
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint r = gl_LocalInvocationID.x / 4;
+        const uint tid = r / 2;
+        const uint is0 = r % 2;
+        const uint l0 = 16 * is0 + 4 * (gl_LocalInvocationID.x % 4);
+        const uint n = tid / 4;
+        const uint j = tid - 4*n;
+
+        const uint8_t m = uint8_t(1 << (4*n + j));
+        const uint is = 8*n + 2*j + is0;
+        const uint shift = 2*j;
+
+        const int8_t us = int8_t(is <  4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) :
+                                 is <  8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) :
+                                 is < 12 ? (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) :
+                                           (data_a[i].scales[is-8] >>  4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4));
+        const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d);
+        const FLOAT_TYPE dl    = d_all * FLOAT_TYPE(us - 32);
+
+        const uint y_idx = i * QUANT_K + 128 * n + 32 * j;
+        const uint qs_idx = 32*n;
+
+        for (uint l = l0; l < l0 + 4; ++l) {
+            data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4)));
+        }
+    }
+}
diff --git a/vulkan-shaders/dequant_q4_0.comp b/vulkan-shaders/dequant_q4_0.comp
new file mode 100644
index 0000000000000..11e07e66bc937
--- /dev/null
+++ b/vulkan-shaders/dequant_q4_0.comp
@@ -0,0 +1,32 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float dm = -8.0f * d;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + dm);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + dm);
+    }
+}
diff --git a/vulkan-shaders/dequant_q4_1.comp b/vulkan-shaders/dequant_q4_1.comp
new file mode 100644
index 0000000000000..2f27eee686eb9
--- /dev/null
+++ b/vulkan-shaders/dequant_q4_1.comp
@@ -0,0 +1,32 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q4_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        data_b[b_idx + l +  0] = D_TYPE(d * (data_a[ib].qs[q_idx + l] & 0xF) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (data_a[ib].qs[q_idx + l] >>  4) + m);
+    }
+}
diff --git a/vulkan-shaders/dequant_q4_k.comp b/vulkan-shaders/dequant_q4_k.comp
new file mode 100644
index 0000000000000..92acb75406db3
--- /dev/null
+++ b/vulkan-shaders/dequant_q4_k.comp
@@ -0,0 +1,56 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 8;
+        const uint ir = tid % 8;
+        const uint is = 2 * il;
+        const uint n = 4;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + n * ir;
+        const uint qs_idx = 32*il + n * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        [[unroll]] for (uint l = 0; l < n; ++l) {
+            data_b[y_idx + l     ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1);
+            data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >>  4) - m2);
+        }
+    }
+}
diff --git a/vulkan-shaders/dequant_q5_0.comp b/vulkan-shaders/dequant_q5_0.comp
new file mode 100644
index 0000000000000..b20b805292174
--- /dev/null
+++ b/vulkan-shaders/dequant_q5_0.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const uint qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10)) - 16.0f));
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10)) - 16.0f));
+    }
+}
diff --git a/vulkan-shaders/dequant_q5_1.comp b/vulkan-shaders/dequant_q5_1.comp
new file mode 100644
index 0000000000000..dc59fe3b77ee3
--- /dev/null
+++ b/vulkan-shaders/dequant_q5_1.comp
@@ -0,0 +1,35 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q5_1 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 8*il;
+
+    const float d = float(data_a[ib].d);
+    const float m = float(data_a[ib].m);
+    const uint qh = data_a[ib].qh;
+
+    const uint q_idx = 8*il;
+
+    [[unroll]] for (uint l = 0; l < 8; ++l) {
+        const uint iqs = q_idx + l;
+        const uint vui = uint(data_a[ib].qs[iqs]);
+        data_b[b_idx + l +  0] = D_TYPE(d * (((vui & 0xF) | (((qh >> iqs) << 4) & 0x10))) + m);
+        data_b[b_idx + l + 16] = D_TYPE(d * (((vui >>  4) | ((qh >> (iqs + 12)) & 0x10))) + m);
+    }
+}
diff --git a/vulkan-shaders/dequant_q5_k.comp b/vulkan-shaders/dequant_q5_k.comp
new file mode 100644
index 0000000000000..f314a76d105c6
--- /dev/null
+++ b/vulkan-shaders/dequant_q5_k.comp
@@ -0,0 +1,58 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint il = tid / 16;
+        const uint ir = tid % 16;
+        const uint is = 2 * il;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y);
+
+        const uint y_idx = i * QUANT_K + 64 * il + 2 * ir;
+        const uint qs_idx = 32*il + 2 * ir;
+        const uint qh_idx = 2 * ir;
+
+        uint8_t sc;
+        uint8_t m;
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is] & 63);
+            m  = uint8_t(data_a[i].scales[is + 4] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 4] >>  4) | ((data_a[i].scales[is    ] >> 6) << 4));
+        }
+        const FLOAT_TYPE d1 = dall * sc;
+        const FLOAT_TYPE m1 = dmin * m;
+
+        if (is < 4) {
+            sc = uint8_t(data_a[i].scales[is + 1] & 63);
+            m  = uint8_t(data_a[i].scales[is + 5] & 63);
+        } else {
+            sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4));
+            m  = uint8_t((data_a[i].scales[is + 5] >>  4) | ((data_a[i].scales[is + 1] >> 6) << 4));
+        }
+        const FLOAT_TYPE d2 = dall * sc;
+        const FLOAT_TYPE m2 = dmin * m;
+
+        const uint8_t hm1 = uint8_t(1 << (2 * il    ));
+        const uint8_t hm2 = uint8_t(1 << (2 * il + 1));
+        data_b[y_idx     ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx    ] & 0xF) + (((data_a[i].qh[qh_idx    ] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx +  1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1);
+        data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx    ]  >> 4) + (((data_a[i].qh[qh_idx    ] & hm2) != 0) ? 16 : 0)) - m2);
+        data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1]  >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2);
+    }
+}
diff --git a/vulkan-shaders/dequant_q6_k.comp b/vulkan-shaders/dequant_q6_k.comp
new file mode 100644
index 0000000000000..0b91317550f97
--- /dev/null
+++ b/vulkan-shaders/dequant_q6_k.comp
@@ -0,0 +1,33 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.M * p.K / QUANT_K) {
+            return;
+        }
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;
+        const uint il = tid - 32 * ip;
+        const uint is = 8 * ip + il / 16;
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint ql_idx = 64 * ip + il;
+        const uint8_t qh = data_a[i].qh[32 * ip + il];
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
+
+        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx +  0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
+        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
+        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx +  0] >>  4) | (((qh >> 4) & 3) << 4)) - 32)));
+        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >>  4) | (((qh >> 6) & 3) << 4)) - 32)));
+    }
+}
diff --git a/vulkan-shaders/dequant_q8_0.comp b/vulkan-shaders/dequant_q8_0.comp
new file mode 100644
index 0000000000000..bd1344a88d129
--- /dev/null
+++ b/vulkan-shaders/dequant_q8_0.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "dequant_head.comp"
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {block_q8_0 data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    const uint i = gl_WorkGroupID.x * 4 + gl_LocalInvocationID.x / 64;
+
+    const uint tid = gl_LocalInvocationID.x % 64;
+    const uint il  = tid/32;
+    const uint ir  = tid%32;
+    const uint ib = 32*i + ir;
+    if (ib >= p.nel / 32) {
+        return;
+    }
+
+    const uint b_idx = 1024*i + 32*ir + 16*il;
+
+    const float d = float(data_a[ib].d);
+
+    const uint q_idx = 16*il;
+
+    [[unroll]] for (uint l = 0; l < 16; l += 2) {
+        data_b[b_idx + l    ] = D_TYPE(d * data_a[ib].qs[q_idx + l    ]);
+        data_b[b_idx + l + 1] = D_TYPE(d * data_a[ib].qs[q_idx + l + 1]);
+    }
+}
diff --git a/vulkan-shaders/diag_mask_inf.comp b/vulkan-shaders/diag_mask_inf.comp
new file mode 100644
index 0000000000000..4e68742b51671
--- /dev/null
+++ b/vulkan-shaders/diag_mask_inf.comp
@@ -0,0 +1,34 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_control_flow_attributes : enable
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint rows_per_channel;
+    uint n_past;
+} p;
+
+#include "types.comp"
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    if (col > p.n_past + row % p.rows_per_channel) {
+        data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000));
+    } else {
+        data_d[i] = D_TYPE(data_a[i]);
+    }
+}
diff --git a/vulkan-shaders/div.comp b/vulkan-shaders/div.comp
new file mode 100644
index 0000000000000..8ee4bfc738865
--- /dev/null
+++ b/vulkan-shaders/div.comp
@@ -0,0 +1,12 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) / FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
diff --git a/vulkan-shaders/gelu.comp b/vulkan-shaders/gelu.comp
new file mode 100644
index 0000000000000..9fe807cce9506
--- /dev/null
+++ b/vulkan-shaders/gelu.comp
@@ -0,0 +1,25 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const float GELU_COEF_A    = 0.044715f;
+    const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f;
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi);
+    data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1)));
+}
diff --git a/vulkan-shaders/generic_binary_head.comp b/vulkan-shaders/generic_binary_head.comp
new file mode 100644
index 0000000000000..ab45d2564aa34
--- /dev/null
+++ b/vulkan-shaders/generic_binary_head.comp
@@ -0,0 +1,48 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint ne20; uint ne21; uint ne22; uint ne23; uint nb20; uint nb21; uint nb22; uint nb23;
+    uint d_offset;
+    float param1; float param2;
+} p;
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint src1_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+
+    return (i03 % p.ne13)*p.nb13 + (i02 % p.ne12)*p.nb12 + (i01 % p.ne11)*p.nb11 + (i00 % p.ne10)*p.nb10;
+}
+
+uint dst_idx(uint idx) {
+    const uint i23 = idx / (p.ne22*p.ne21*p.ne20);
+    const uint i23_offset = i23 * p.ne22*p.ne21*p.ne20;
+    const uint i22 = (idx - i23_offset) / (p.ne21*p.ne20);
+    const uint i22_offset = i22*p.ne21*p.ne20;
+    const uint i21 = (idx - i23_offset - i22_offset) / p.ne20;
+    const uint i20 = idx - i23_offset - i22_offset - i21*p.ne20;
+    return i23*p.nb23 + i22*p.nb22 + i21*p.nb21 + i20*p.nb20;
+}
diff --git a/vulkan-shaders/generic_head.comp b/vulkan-shaders/generic_head.comp
new file mode 100644
index 0000000000000..66e46ae6796b8
--- /dev/null
+++ b/vulkan-shaders/generic_head.comp
@@ -0,0 +1,9 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    float param1;
+    float param2;
+} p;
diff --git a/vulkan-shaders/generic_unary_head.comp b/vulkan-shaders/generic_unary_head.comp
new file mode 100644
index 0000000000000..de08de7cd84fa
--- /dev/null
+++ b/vulkan-shaders/generic_unary_head.comp
@@ -0,0 +1,35 @@
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint ne;
+    uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03;
+    uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13;
+    uint d_offset;
+    float param1; float param2;
+} p;
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+uint src0_idx(uint idx) {
+    const uint i03 = idx / (p.ne02*p.ne01*p.ne00);
+    const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00;
+    const uint i02 = (idx - i03_offset) / (p.ne01*p.ne00);
+    const uint i02_offset = i02*p.ne01*p.ne00;
+    const uint i01 = (idx - i03_offset - i02_offset) / p.ne00;
+    const uint i00 = idx - i03_offset - i02_offset - i01*p.ne00;
+    return i03*p.nb03 + i02*p.nb02 + i01*p.nb01 + i00*p.nb00;
+}
+
+uint dst_idx(uint idx) {
+    const uint i13 = idx / (p.ne12*p.ne11*p.ne10);
+    const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10;
+    const uint i12 = (idx - i13_offset) / (p.ne11*p.ne10);
+    const uint i12_offset = i12*p.ne11*p.ne10;
+    const uint i11 = (idx - i13_offset - i12_offset) / p.ne10;
+    const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10;
+    return i13*p.nb13 + i12*p.nb12 + i11*p.nb11 + i10*p.nb10;
+}
diff --git a/vulkan-shaders/get_rows.comp b/vulkan-shaders/get_rows.comp
new file mode 100644
index 0000000000000..e9ff22efa9c7a
--- /dev/null
+++ b/vulkan-shaders/get_rows.comp
@@ -0,0 +1,26 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    const uint i00 = gl_GlobalInvocationID.x;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+#ifndef OPTIMIZATION_ERROR_WORKAROUND
+    data_d[d_offset + i00] = D_TYPE(data_a[a_offset + i00]);
+#else
+    data_d[d_offset + i00] = data_a[a_offset + i00];
+#endif
+}
diff --git a/vulkan-shaders/get_rows_quant.comp b/vulkan-shaders/get_rows_quant.comp
new file mode 100644
index 0000000000000..53a9a96f2360a
--- /dev/null
+++ b/vulkan-shaders/get_rows_quant.comp
@@ -0,0 +1,31 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+#include "dequant_funcs.comp"
+
+void main() {
+    const uint i00 = (gl_GlobalInvocationID.x)*2;
+    const uint i10 = gl_GlobalInvocationID.y;
+    const uint i11 = (gl_GlobalInvocationID.z)/p.ne12;
+    const uint i12 = (gl_GlobalInvocationID.z)%p.ne12;
+
+    if (i00 >= p.ne00) {
+        return;
+    }
+
+    const uint i01 = data_b[i10*p.nb10 + i11*p.nb11 + i12*p.nb12];
+
+    const uint a_offset = i01*p.nb01 + i11*p.nb02 + i12*p.nb03;
+    const uint d_offset = i10*p.nb21 + i11*p.nb22 + i12*p.nb23;
+
+    const uint ib = a_offset + i00/QUANT_K; // block index
+    const uint iqs = (i00%QUANT_K)/QUANT_R; // quant index
+    const uint iybs = i00 - i00%QUANT_K; // dst block start index
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    vec2 v = dequantize(ib, iqs, 0);
+
+    data_d[d_offset + iybs + iqs           ] = D_TYPE(v.x);
+    data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y);
+}
diff --git a/vulkan-shaders/mul.comp b/vulkan-shaders/mul.comp
new file mode 100644
index 0000000000000..bbb0aa1d26c1b
--- /dev/null
+++ b/vulkan-shaders/mul.comp
@@ -0,0 +1,12 @@
+#version 450
+
+#include "types.comp"
+#include "generic_binary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)]));
+}
diff --git a/vulkan-shaders/mul_mat_split_k_reduce.comp b/vulkan-shaders/mul_mat_split_k_reduce.comp
new file mode 100644
index 0000000000000..825b91031f569
--- /dev/null
+++ b/vulkan-shaders/mul_mat_split_k_reduce.comp
@@ -0,0 +1,29 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {float data_a[];};
+layout (binding = 1) writeonly buffer D {float data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ne;
+    uint k_num;
+} p;
+
+void main() {
+    const uint idx = gl_GlobalInvocationID.x;
+
+    if (idx >= p.ne) {
+        return;
+    }
+
+    float result = 0.0f;
+
+    [[unroll]] for (uint i = 0; i < p.k_num; i++) {
+        result += data_a[i * p.ne + idx];
+    }
+
+    data_d[idx] = result;
+}
diff --git a/vulkan-shaders/mul_mat_vec.comp b/vulkan-shaders/mul_mat_vec.comp
new file mode 100644
index 0000000000000..292ccfd749c1a
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec.comp
@@ -0,0 +1,50 @@
+#version 450
+
+#ifdef FLOAT16
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#endif
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint i = 0; i < p.ncols/BLOCK_SIZE; i += 2) {
+        const uint col = i*BLOCK_SIZE + 2*tid;
+        const uint ib = (row*p.ncols + col)/QUANT_K; // block index
+        const uint iqs = (col%QUANT_K)/QUANT_R; // quant index
+        const uint iybs = col - col%QUANT_K; // y block start index
+
+        vec2 v = dequantize(ib, iqs, a_offset / QUANT_K);
+
+        // matrix multiplication
+        tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) +
+                    FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = BLOCK_SIZE/2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_base.comp b/vulkan-shaders/mul_mat_vec_base.comp
new file mode 100644
index 0000000000000..5920bc93641c8
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_base.comp
@@ -0,0 +1,81 @@
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_8bit_storage : require
+
+#define K_QUANTS_PER_ITERATION 2
+
+#ifdef MUL_MAT_ID
+#define EXPERT_COUNT 8
+#endif
+
+#include "types.comp"
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
+
+#include "dequant_funcs.comp"
+
+layout (push_constant) uniform parameter
+{
+    uint ncols;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint nei0;
+    uint ne11;
+#else
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+#endif
+} p;
+
+void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) {
+#ifdef MUL_MAT_ID
+    const uint expert_idx = gl_GlobalInvocationID.y;
+#else
+    const uint batch_idx = gl_GlobalInvocationID.y;
+#endif
+
+#ifndef MUL_MAT_ID
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+#else
+    const uint expert_id = data_ids[expert_idx];
+#endif
+
+    a_offset =
+#ifdef MUL_MAT_ID
+            expert_id * p.batch_stride_a;
+#else
+            batch_idx_a * p.batch_stride_a;
+#endif
+    b_offset =
+#ifdef MUL_MAT_ID
+            (expert_idx % p.ne11) * p.stride_b;
+#else
+            batch_idx * p.batch_stride_b;
+#endif
+    d_offset =
+#ifdef MUL_MAT_ID
+            expert_idx * p.stride_d;
+#else
+            batch_idx * p.batch_stride_d;
+#endif
+}
diff --git a/vulkan-shaders/mul_mat_vec_nc.comp b/vulkan-shaders/mul_mat_vec_nc.comp
new file mode 100644
index 0000000000000..cb3f3c0df0801
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_nc.comp
@@ -0,0 +1,71 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint row_stride_x;
+    uint channel_stride_x;
+    uint channel_x_divisor;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid       = gl_LocalInvocationID.x;
+    const uint row_x     = gl_GlobalInvocationID.y;
+    const uint channel   = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / p.channel_x_divisor;
+
+    const uint nrows_y   = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst   = row_x;
+
+    const uint idst = channel*nrows_dst + row_dst;
+
+    tmp[tid] = 0.0f;
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        const uint row_y = col_x;
+
+        const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x;
+        const uint iy = channel*nrows_y + row_y;
+
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_p021.comp b/vulkan-shaders/mul_mat_vec_p021.comp
new file mode 100644
index 0000000000000..4b1871caaf4c2
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_p021.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#define BLOCK_SIZE 32
+#define FLOAT_TYPE float
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE dst[];};
+
+layout (push_constant) uniform parameter
+{
+    uint ncols_x;
+    uint nrows_x;
+    uint nchannels_x;
+    uint nchannels_y;
+    uint b_offset;
+    uint d_offset;
+} p;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint row_x = gl_GlobalInvocationID.y;
+    const uint channel = gl_GlobalInvocationID.z;
+    const uint channel_x = channel / (p.nchannels_y / p.nchannels_x);
+
+    const uint nrows_y = p.ncols_x;
+    const uint nrows_dst = p.nrows_x;
+    const uint row_dst = row_x;
+
+    tmp[tid] = FLOAT_TYPE(0.0f);
+
+    for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) {
+        const uint col_x = col_x0 + tid;
+
+        if (col_x >= p.ncols_x) {
+            break;
+        }
+
+        // x is transposed and permuted
+        const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x;
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]);
+
+        const uint row_y = col_x;
+
+        // y is not transposed but permuted
+        const uint iy = channel*nrows_y + row_y;
+
+        tmp[tid] += xi * FLOAT_TYPE(data_b[iy]);
+    }
+
+    // dst is not transposed and not permuted
+    const uint idst = channel*nrows_dst + row_dst;
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+
+    if (tid == 0) {
+        dst[idst] = tmp[0];
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_q2_k.comp b/vulkan-shaders/mul_mat_vec_q2_k.comp
new file mode 100644
index 0000000000000..27bf6d51ced7c
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_q2_k.comp
@@ -0,0 +1,73 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint s_offset = 8*v_im;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);
+        FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3);
+            sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF)
+                  + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF);
+        }
+        tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_q3_k.comp b/vulkan-shaders/mul_mat_vec_q3_k.comp
new file mode 100644
index 0000000000000..7c837fef20052
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_q3_k.comp
@@ -0,0 +1,66 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+    const uint8_t m = uint8_t(1 << (4 * v_im));
+
+    const uint l0 = K_QUANTS_PER_ITERATION*v_in;            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    const uint s_shift = 4 * v_im;
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
+            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l   ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l   ] & (m << 3)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16]     ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4))
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4));
+        }
+        tmp[16 * ix + tid] += d * sum;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_q4_k.comp b/vulkan-shaders/mul_mat_vec_q4_k.comp
new file mode 100644
index 0000000000000..1566f26d3faf8
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_q4_k.comp
@@ -0,0 +1,115 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 8/K_QUANTS_PER_ITERATION;             // 8 or 4
+
+    const uint il = tid/step;                               // 0...3
+    const uint ir = tid - step*il;                          // 0...7 or 0...3
+    const uint n =  2 * K_QUANTS_PER_ITERATION;             // 2 or 4
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = n * (2 * ir + v_in);            // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+#if K_QUANTS_PER_ITERATION == 2
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset +  2] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset +  3] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset +  2]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset +  3]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3);
+        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7);
+        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11);
+        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15);
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y1_idx    ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx    ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7
+        );
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
+#else
+        const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * q4_0  + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * q4_1);
+        const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2  + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3);
+        const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * q4_4  + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * q4_5);
+        const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7);
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7
+        );
+
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin);
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_q5_k.comp b/vulkan-shaders/mul_mat_vec_q5_k.comp
new file mode 100644
index 0000000000000..4add64327ea52
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_q5_k.comp
@@ -0,0 +1,111 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/2;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%2;  // 0 or 0, 1
+
+    const uint il = tid/4;                           // 0...3
+    const uint ir = tid - 4*il;                      // 0...7 or 0...3
+
+    const uint v_im = il / 2;  // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
+    const uint v_in = il % 2;
+
+    const uint l0 = 4*ir + 2*v_in;                   // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 64*v_im + l0;
+
+    const uint8_t hm1 = uint8_t(1 << (2*v_im));
+    const uint8_t hm2 = uint8_t(hm1 << 4);
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += 2) {
+        const uint y1_idx = i * QUANT_K + y_offset;
+        const uint y2_idx = y1_idx + 128;
+
+        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);
+        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);
+
+        const uint8_t sc0 = uint8_t(  data_a[ib0 + i].scales[v_im * 2    ]       & 0x3f);
+        const uint8_t sc1 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 1]       & 0x3f);
+        const uint8_t sc2 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 4]       & 0x3f);
+        const uint8_t sc3 = uint8_t(  data_a[ib0 + i].scales[v_im * 2 + 5]       & 0x3f);
+        const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2    ] & 0xc0) >> 2));
+        const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9]       & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2));
+        const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2));
+        const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2));
+
+        const uint8_t q4_0  = uint8_t(data_a[ib0 + i].qs[q_offset     ] & 0xf);
+        const uint8_t q4_1  = uint8_t(data_a[ib0 + i].qs[q_offset +  1] & 0xf);
+        const uint8_t q4_2  = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf);
+        const uint8_t q4_3  = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf);
+        const uint8_t q4_4  = uint8_t(data_a[ib0 + i].qs[q_offset     ]  >> 4);
+        const uint8_t q4_5  = uint8_t(data_a[ib0 + i].qs[q_offset +  1]  >> 4);
+        const uint8_t q4_6  = uint8_t(data_a[ib0 + i].qs[q_offset + 16]  >> 4);
+        const uint8_t q4_7  = uint8_t(data_a[ib0 + i].qs[q_offset + 17]  >> 4);
+        const uint8_t q4_8  = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf);
+        const uint8_t q4_9  = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf);
+        const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf);
+        const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf);
+        const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64]  >> 4);
+        const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65]  >> 4);
+        const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80]  >> 4);
+        const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81]  >> 4);
+
+        const FLOAT_TYPE sx = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y1_idx     ]) * (q4_0 + (((data_a[ib0 + i].qh[l0     ] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx +  1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 +  1] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sy = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0     ] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 +  1] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sz = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y2_idx     ]) * (q4_8  + (((data_a[ib0 + i].qh[l0     ] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx +  1]) * (q4_9  + (((data_a[ib0 + i].qh[l0 +  1] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE sw = FLOAT_TYPE(
+            FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0     ] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 +  1] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0))
+          + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0))
+        );
+        const FLOAT_TYPE smin = FLOAT_TYPE(
+            (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3
+          + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7
+        );
+        tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin);
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+        }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mat_vec_q6_k.comp b/vulkan-shaders/mul_mat_vec_q6_k.comp
new file mode 100644
index 0000000000000..8c550e7c37233
--- /dev/null
+++ b/vulkan-shaders/mul_mat_vec_q6_k.comp
@@ -0,0 +1,79 @@
+#version 450
+
+#include "mul_mat_vec_base.comp"
+
+layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in;
+
+shared FLOAT_TYPE tmp[32];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+    const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row;
+
+    const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION;  // 0...31 or 0...16
+    const uint ix  = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION;  // 0 or 0, 1
+
+    const uint step = 16/K_QUANTS_PER_ITERATION;            // 16 or 8
+
+    const uint v_im = tid/step;                             // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = tid - step*v_im;                      // 0...15 or 0...7
+
+#if K_QUANTS_PER_ITERATION == 1
+    const uint l0 = v_in;                                   // 0...15
+    const uint is = 0;
+#else
+    const uint l0 = 4 * v_in;                               // 0, 4, 8, ..., 28
+    const uint is = v_in / 4;
+#endif
+
+    const uint ql_offset = 64*v_im + l0;
+    const uint qh_offset = 32*v_im + l0;
+    const uint s_offset  =  8*v_im + is;
+    const uint y_offset = 128*v_im + l0;
+
+    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp
+
+    [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
+        const uint y_idx   = i * QUANT_K + y_offset;
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+#if K_QUANTS_PER_ITERATION == 1
+        FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx +  0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset +  0]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32]  >> 4) | ((data_a[ib0 + i].qh[qh_offset +  0] & 0xc0) >> 2)) - 32)
+                       + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48]  >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32);
+        tmp[16 * ix + tid] += sum;
+#else
+        FLOAT_TYPE sum = FLOAT_TYPE(0.0);
+        [[unroll]] for (int l = 0; l < 4; ++l) {
+            sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32)
+                 + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32]  >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32);
+        }
+        tmp[16 * ix + tid] += sum;
+#endif
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (uint s = 16; s > 0; s >>= 1) {
+        if (tid < s) {
+            tmp[tid] += tmp[tid + s];
+       }
+        barrier();
+    }
+    if (tid == 0) {
+        data_d[d_offset + row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/mul_mm.comp b/vulkan-shaders/mul_mm.comp
new file mode 100644
index 0000000000000..7c2b45cce44c4
--- /dev/null
+++ b/vulkan-shaders/mul_mm.comp
@@ -0,0 +1,494 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : enable
+#extension GL_EXT_shader_16bit_storage : require
+
+#ifdef FLOAT16
+#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require
+#endif
+
+#ifdef MUL_MAT_ID
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#endif
+
+#include "types.comp"
+
+#ifndef LOAD_VEC_A
+#define LOAD_VEC_A 1
+#endif
+#ifndef LOAD_VEC_B
+#define LOAD_VEC_B 1
+#endif
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer B {B_TYPE data_b[];};
+layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};
+
+#ifdef MUL_MAT_ID
+layout (binding = 3) readonly buffer IDS {int data_ids[];};
+#endif
+
+layout (push_constant) uniform parameter
+{
+    uint M;
+    uint N;
+    uint K;
+    uint stride_a;
+    uint stride_b;
+    uint stride_d;
+
+    uint batch_stride_a;
+    uint batch_stride_b;
+    uint batch_stride_d;
+
+#ifdef MUL_MAT_ID
+    uint nei0;
+    uint nei1;
+    uint nbi1;
+    uint ne11;
+#else
+    uint k_split;
+    uint ne02;
+    uint ne12;
+    uint broadcast2;
+    uint broadcast3;
+#endif
+} p;
+
+layout (constant_id = 1) const uint BM = 64;
+layout (constant_id = 2) const uint BN = 64;
+layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working with a quant
+layout (constant_id = 4) const uint WM = 32;
+layout (constant_id = 5) const uint WN = 32;
+layout (constant_id = 6) const uint WMITER = 2;
+layout (constant_id = 7) const uint TM = 4;
+layout (constant_id = 8) const uint TN = 2;
+layout (constant_id = 9) const uint WARP = 32;
+
+shared FLOAT_TYPE buf_a[BM * (BK+1)];
+shared FLOAT_TYPE buf_b[BN * (BK+1)];
+
+#ifdef MUL_MAT_ID
+shared u16vec2 row_ids[2048];
+#endif
+
+void main() {
+#ifdef MUL_MAT_ID
+    const uint expert_idx = gl_GlobalInvocationID.z;
+#else
+    const uint batch_idx = gl_GlobalInvocationID.z;
+
+    const uint i13 = batch_idx / p.ne12;
+    const uint i12 = batch_idx % p.ne12;
+
+    const uint i03 = i13 / p.broadcast3;
+    const uint i02 = i12 / p.broadcast2;
+
+    const uint batch_idx_a = i03 * p.ne02 + i02;
+#endif
+
+    const uint blocks_m = (p.M + BM - 1) / BM;
+    const uint ir = gl_WorkGroupID.x % blocks_m;
+    const uint ik = gl_WorkGroupID.x / blocks_m;
+    const uint ic = gl_WorkGroupID.y;
+
+    const uint warp_i = gl_LocalInvocationID.x / WARP;
+    const uint warp_r = warp_i % (BM / WM);
+    const uint warp_c = warp_i / (BM / WM);
+
+    const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER);
+    const uint WSUBM = WM / WMITER;
+    const uint WSUBN = WN / WNITER;
+
+    const uint tiw = gl_LocalInvocationID.x % WARP;
+    const uint tiwr = tiw % (WSUBM / TM);
+    const uint tiwc = tiw / (WSUBM / TM);
+
+    const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
+    const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
+    const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
+    const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
+
+    const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
+    const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
+
+#ifdef MUL_MAT_ID
+    uint _ne1 = 0;
+    for (uint ii1 = 0; ii1 < p.nei1; ii1++) {
+        for (uint ii0 = 0; ii0 < p.nei0; ii0++) {
+            if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) {
+                row_ids[_ne1] = u16vec2(ii0, ii1);
+                _ne1++;
+            }
+        }
+    }
+
+    barrier();
+
+    // Workgroup has no work
+    if (ic * BN >= _ne1) return;
+#endif
+
+#ifdef MUL_MAT_ID
+    const uint start_k = 0;
+    const uint end_k = p.K;
+#else
+    const uint start_k = ik * p.k_split;
+    const uint end_k = min(p.K, (ik + 1) * p.k_split);
+#endif
+
+    uint pos_a = (
+#ifdef MUL_MAT_ID
+        expert_idx * p.batch_stride_a +
+#else
+        batch_idx_a * p.batch_stride_a +
+#endif
+        ir * BM * p.stride_a + start_k) / LOAD_VEC_A;
+#ifdef MUL_MAT_ID
+    uint pos_b = 0;
+#else
+    uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B;
+#endif
+
+    float sums[WMITER * TM * WNITER * TN];
+    FLOAT_TYPE cache_a[WMITER * TM];
+    FLOAT_TYPE cache_b[WNITER * TN];
+
+    [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) {
+        sums[i] = 0.0f;
+    }
+
+    [[unroll]] for (uint block = start_k; block < end_k; block += BK) {
+        [[unroll]] for (uint l = 0; l < BM; l += loadstride_a) {
+
+#if defined(DATA_A_F32) || defined(DATA_A_F16)
+#if LOAD_VEC_A == 8
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx][0].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx][0].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx][0].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx][0].w);
+            buf_a[buf_idx + 4] = FLOAT_TYPE(data_a[idx][1].x);
+            buf_a[buf_idx + 5] = FLOAT_TYPE(data_a[idx][1].y);
+            buf_a[buf_idx + 6] = FLOAT_TYPE(data_a[idx][1].z);
+            buf_a[buf_idx + 7] = FLOAT_TYPE(data_a[idx][1].w);
+#elif LOAD_VEC_A == 4
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+            buf_a[buf_idx    ] = FLOAT_TYPE(data_a[idx].x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(data_a[idx].y);
+            buf_a[buf_idx + 2] = FLOAT_TYPE(data_a[idx].z);
+            buf_a[buf_idx + 3] = FLOAT_TYPE(data_a[idx].w);
+#else
+            if (ir * BM + loadc_a + l < p.M && block + loadr_a < end_k) {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(data_a[pos_a + (loadc_a + l) * p.stride_a + loadr_a]);
+            } else {
+                buf_a[(loadc_a + l) * (BK+1) + loadr_a] = FLOAT_TYPE(0.0f);
+            }
+#endif
+#elif defined(DATA_A_Q4_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2(vui & 0xF, vui >> 4) - 8.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q4_1)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2(vui & 0xF, vui >> 4) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q5_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0];
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q5_1)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a;
+
+            const uint ib = idx / 16;
+            const uint iqs = idx & 0xF;
+
+            const float d = float(data_a[ib].d);
+            const float m = float(data_a[ib].m);
+            const uint uint_qh = data_a[ib].qh;
+            const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10);
+            const uint vui = uint(data_a[ib].qs[iqs]);
+            const vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m;
+
+            buf_a[buf_idx     ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 16] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q8_0)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 16;
+            const uint iqs = (idx & 0xF) * 2;
+
+            const float d = float(data_a[ib].d);
+            const vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])) * d;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q2_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                         // 2 values per idx
+            const uint iqs = idx % 128;                        // 0..127
+
+            const uint qsi = (iqs / 64) * 32 + (iqs % 16) * 2; // 0,2,4..30
+            const uint scalesi = iqs / 8;                      // 0..15
+            const uint qsshift = ((iqs % 64) / 16) * 2;        // 0,2,4,6
+
+            const uvec2 qs = uvec2(data_a[ib].qs[qsi], data_a[ib].qs[qsi + 1]);
+            const uint scales = data_a[ib].scales[scalesi];
+            const vec2 d = vec2(data_a[ib].d);
+
+            const vec2 v = d.x * float(scales & 0xF) * vec2((qs >> qsshift) & 3) - d.y * float(scales >> 4);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(v.x);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(v.y);
+#elif defined(DATA_A_Q3_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                   // 2 values per idx
+            const uint iqs = idx % 128;                  // 0..127
+
+            const uint n = iqs / 64;                     // 0,1
+            const uint qsi = n * 32 + (iqs % 16) * 2;    // 0,2,4..62
+            const uint hmi =          (iqs % 16) * 2;    // 0,2,4..30
+            const uint j = (iqs % 64) / 4;               // 0..3
+            const uint is = iqs / 8;                     // 0..15
+            const uint halfsplit = ((iqs % 64) / 16);    // 0,1,2,3
+            const uint qsshift = halfsplit * 2;          // 0,2,4,6
+            const uint m = 1 << (4 * n + halfsplit);     // 1,2,4,8,16,32,64,128
+
+            const int8_t us = int8_t(is <  4 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+8] >> 0) & 3) << 4) :
+                                    is <  8 ? (data_a[ib].scales[is-0] & 0xF) | (((data_a[ib].scales[is+4] >> 2) & 3) << 4) :
+                                    is < 12 ? (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is+0] >> 4) & 3) << 4) :
+                                            (data_a[ib].scales[is-8] >>  4) | (((data_a[ib].scales[is-4] >> 6) & 3) << 4));
+            const float dl = float(data_a[ib].d) * float(us - 32);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi    ] >> qsshift) & 3) - (((data_a[ib].hmask[hmi    ] & m) != 0) ? 0 : 4)));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dl * float(int8_t((data_a[ib].qs[qsi + 1] >> qsshift) & 3) - (((data_a[ib].hmask[hmi + 1] & m) != 0) ? 0 : 4)));
+#elif defined(DATA_A_Q4_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) - m);
+#elif defined(DATA_A_Q5_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                 // 2 values per idx
+            const uint iqs = idx % 128;                // 0..127
+
+            const uint n = iqs / 32;                   // 0,1,2,3
+            const uint b = (iqs % 32) / 16;            // 0,1
+            const uint is = 2 * n + b;                 // 0..7
+            const uint qsi = n * 32 + (iqs % 16) * 2;  // 0,2,4..126
+            const uint qhi = (iqs % 16) * 2;           // 0,2,4..30
+
+            const uint8_t hm = uint8_t(1 << (iqs / 16));
+
+            const vec2 loadd = vec2(data_a[ib].d);
+
+            uint8_t sc;
+            uint8_t mbyte;
+            if (is < 4) {
+                sc    = uint8_t(data_a[ib].scales[is    ] & 63);
+                mbyte = uint8_t(data_a[ib].scales[is + 4] & 63);
+            } else {
+                sc    = uint8_t((data_a[ib].scales[is + 4] & 0xF) | ((data_a[ib].scales[is - 4] >> 6) << 4));
+                mbyte = uint8_t((data_a[ib].scales[is + 4] >>  4) | ((data_a[ib].scales[is    ] >> 6) << 4));
+            }
+            const float d = loadd.x * sc;
+            const float m = loadd.y * mbyte;
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi    ] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi    ] & hm) != 0 ? 16 : 0)) - m);
+            buf_a[buf_idx + 1] = FLOAT_TYPE(d * (float((data_a[ib].qs[qsi + 1] >> (b * 4)) & 0xF) + float((data_a[ib].qh[qhi + 1] & hm) != 0 ? 16 : 0)) - m);
+#elif defined(DATA_A_Q6_K)
+            const uint idx = pos_a + (loadc_a + l) * p.stride_a / LOAD_VEC_A + loadr_a;
+            const uint buf_idx = (loadc_a + l) * (BK+1) + loadr_a * LOAD_VEC_A;
+
+            const uint ib = idx / 128;                  // 2 values per idx
+            const uint iqs = idx % 128;                 // 0..127
+
+            const uint n = iqs / 64;                    // 0,1
+            const uint b = (iqs % 64) / 32;             // 0,1
+            const uint is_b = (iqs % 16) / 8;           // 0,1
+            const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6
+            const uint is = 8 * n + qhshift + is_b;     // 0..15
+            const uint qsi = n * 64 + (iqs % 32) * 2;   // 0,2,4..126
+            const uint qhi = n * 32 + (iqs % 16) * 2;   // 0,2,4..62
+
+            const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]);
+
+            buf_a[buf_idx    ] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi    ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi    ] >> qhshift) & 3) << 4)) - 32));
+            buf_a[buf_idx + 1] = FLOAT_TYPE(dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32));
+#endif
+        }
+        [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) {
+#if LOAD_VEC_B == 8
+#ifdef MUL_MAT_ID
+            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
+#else
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+#endif
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx][0].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx][0].w);
+            buf_b[buf_idx + 4] = FLOAT_TYPE(data_b[idx][1].x);
+            buf_b[buf_idx + 5] = FLOAT_TYPE(data_b[idx][1].y);
+            buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z);
+            buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w);
+#elif LOAD_VEC_B == 4
+#ifdef MUL_MAT_ID
+            const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l];
+            const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b;
+#else
+            const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b;
+#endif
+            const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B;
+            buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x);
+            buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y);
+            buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z);
+            buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w);
+#elif !MUL_MAT_ID
+            if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
+            }
+#else
+            const uint row_i = ic * BN + loadc_b + l;
+            if (row_i < _ne1) {
+                const u16vec2 row_idx = row_ids[row_i];
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]);
+            } else {
+                buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f);
+            }
+#endif
+        }
+
+        barrier();
+
+        pos_a += BK / LOAD_VEC_A;
+        pos_b += BK / LOAD_VEC_B;
+
+        for (uint i = 0; i < BK; i++) {
+            // Load from shared into cache
+            [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                [[unroll]] for (uint j = 0; j < TM; j++) {
+                    cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i];
+                }
+            }
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint j = 0; j < TN; j++) {
+                    cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i];
+                }
+            }
+
+            [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+                [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+                    [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+                        [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+                            sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]);
+                        }
+                    }
+                }
+            }
+        }
+
+        barrier();
+    }
+
+    const uint dr = ir * BM + warp_r * WM;
+    const uint dc = ic * BN + warp_c * WN;
+
+#ifndef MUL_MAT_ID
+    const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z;
+#endif
+
+    [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) {
+        [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) {
+
+            const uint dr_warp = dr + wsir * WSUBM + tiwr * TM;
+            const uint dc_warp = dc + wsic * WSUBN + tiwc * TN;
+            [[unroll]] for (uint cc = 0; cc < TN; cc++) {
+#ifdef MUL_MAT_ID
+                const uint row_i = dc_warp + cc;
+                if (row_i >= _ne1) break;
+
+                const u16vec2 row_idx = row_ids[row_i];
+#endif
+                [[unroll]] for (uint cr = 0; cr < TM; cr++) {
+#ifdef MUL_MAT_ID
+                    data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+#else
+                    if (dr_warp + cr < p.M && dc_warp + cc < p.N) {
+                        data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]);
+                    }
+#endif
+                }
+            }
+        }
+    }
+}
diff --git a/vulkan-shaders/norm.comp b/vulkan-shaders/norm.comp
new file mode 100644
index 0000000000000..803dbdcb3a936
--- /dev/null
+++ b/vulkan-shaders/norm.comp
@@ -0,0 +1,44 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared vec2 sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    sum[tid] = vec2(0.0f, 0.0f);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const float xi = float(data_a[row*p.KX + col]);
+        sum[tid].x += xi;
+        sum[tid].y += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const float mean = sum[0].x / p.KX;
+    const float var = sum[0].y / p.KX - mean * mean;
+    const float inv_std = inversesqrt(var + p.param1);
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std);
+    }
+}
diff --git a/vulkan-shaders/relu.comp b/vulkan-shaders/relu.comp
new file mode 100644
index 0000000000000..7e5baa5b8b5e5
--- /dev/null
+++ b/vulkan-shaders/relu.comp
@@ -0,0 +1,21 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    data_d[i] = max(float(data_a[i]), 0);
+}
diff --git a/vulkan-shaders/rms_norm.comp b/vulkan-shaders/rms_norm.comp
new file mode 100644
index 0000000000000..cfd08d345cc83
--- /dev/null
+++ b/vulkan-shaders/rms_norm.comp
@@ -0,0 +1,42 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE sum[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint tid = gl_LocalInvocationID.x;
+
+    sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]);
+        sum[tid] += xi * xi;
+    }
+
+    // sum up partial sums and write back result
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            sum[tid] += sum[tid + s];
+        }
+        barrier();
+    }
+
+    const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX);
+    const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1));
+
+    [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) {
+        data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col]));
+    }
+}
diff --git a/vulkan-shaders/rope_head.comp b/vulkan-shaders/rope_head.comp
new file mode 100644
index 0000000000000..ea89542261cc4
--- /dev/null
+++ b/vulkan-shaders/rope_head.comp
@@ -0,0 +1,44 @@
+#include "types.comp"
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {int data_pos[];};
+layout (binding = 2) readonly buffer Z {float data_ff[];};
+layout (binding = 3) writeonly buffer D {D_TYPE data_d[];};
+
+layout (push_constant) uniform parameter {
+    uint ncols;
+    uint n_dims;
+    float freq_scale;
+    uint p_delta_rows;
+    float freq_base;
+    float ext_factor;
+    float attn_factor;
+    float corr_dims[2];
+    float theta_scale;
+    uint has_ff;
+} p;
+
+float rope_yarn_ramp(const float low, const float high, const uint i0) {
+    const float y = (i0 / 2 - low) / max(0.001f, high - low);
+    return 1.0f - min(1.0f, max(0.0f, y));
+}
+
+void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) {
+    float mscale = p.attn_factor;
+    // Get n-d rotational scaling corrected for extrapolation
+    float theta_interp = p.freq_scale * theta_extrap;
+    float theta = theta_interp;
+    if (p.ext_factor != 0.0f) {
+        float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor;
+        theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix;
+
+        // Get n-d magnitude scaling corrected for interpolation
+        mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale);
+    }
+    cos_theta = cos(theta) * mscale;
+    sin_theta = sin(theta) * mscale;
+}
diff --git a/vulkan-shaders/rope_neox.comp b/vulkan-shaders/rope_neox.comp
new file mode 100644
index 0000000000000..83b46b69b2a7f
--- /dev/null
+++ b/vulkan-shaders/rope_neox.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "rope_head.comp"
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
+    const uint i  = row*p.ncols + col/2;
+    const uint i2 = row/p.p_delta_rows;
+
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + p.n_dims/2]);
+
+    data_d[i + 0]        = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + p.n_dims/2] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
diff --git a/vulkan-shaders/rope_norm.comp b/vulkan-shaders/rope_norm.comp
new file mode 100644
index 0000000000000..e416ad9389706
--- /dev/null
+++ b/vulkan-shaders/rope_norm.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "rope_head.comp"
+
+void main() {
+    const uint col = gl_GlobalInvocationID.y * 2;
+    const uint row = gl_GlobalInvocationID.x;
+
+    if (col >= p.ncols) {
+        return;
+    }
+
+    if (col >= p.n_dims) {
+        const uint i = row*p.ncols + col;
+
+        data_d[i + 0] = data_a[i + 0];
+        data_d[i + 1] = data_a[i + 1];
+
+        return;
+    }
+
+    const uint i = row*p.ncols + col;
+    const uint i2 = row/p.p_delta_rows;
+
+    const float theta_base = data_pos[i2] * pow(p.theta_scale, col/2.0f);
+
+    const float freq_factor = p.has_ff != 0 ? data_ff[col/2] : 1.0f;
+
+    float cos_theta, sin_theta;
+    rope_yarn(theta_base / freq_factor, col, cos_theta, sin_theta);
+
+    const float x0 = float(data_a[i + 0]);
+    const float x1 = float(data_a[i + 1]);
+
+    data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta);
+    data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta);
+}
diff --git a/vulkan-shaders/scale.comp b/vulkan-shaders/scale.comp
new file mode 100644
index 0000000000000..510cb7237e8a7
--- /dev/null
+++ b/vulkan-shaders/scale.comp
@@ -0,0 +1,12 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1));
+}
diff --git a/vulkan-shaders/silu.comp b/vulkan-shaders/silu.comp
new file mode 100644
index 0000000000000..15920f06e4722
--- /dev/null
+++ b/vulkan-shaders/silu.comp
@@ -0,0 +1,22 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+
+layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+void main() {
+    const uint i = gl_GlobalInvocationID.x;
+
+    if (i >= p.KX) {
+        return;
+    }
+
+    const float xi = float(data_a[i]);
+    data_d[i] = D_TYPE(xi / (1.0f + exp(-xi)));
+}
diff --git a/vulkan-shaders/soft_max.comp b/vulkan-shaders/soft_max.comp
new file mode 100644
index 0000000000000..1b8419c7cf2a3
--- /dev/null
+++ b/vulkan-shaders/soft_max.comp
@@ -0,0 +1,106 @@
+#version 450
+
+#extension GL_EXT_shader_16bit_storage : require
+
+layout (push_constant) uniform parameter
+{
+    uint KX;
+    uint KY;
+    float scale;
+    float max_bias;
+    float m0;
+    float m1;
+    uint n_head_log2;
+} p;
+
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+#define BLOCK_SIZE 512
+
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer X {A_TYPE data_a[];};
+layout (binding = 1) readonly buffer Y {B_TYPE data_b[];};
+layout (binding = 2) buffer D {D_TYPE data_d[];};
+
+shared FLOAT_TYPE vals[BLOCK_SIZE];
+
+void main() {
+    const uint tid = gl_LocalInvocationID.x;
+    const uint rowx = gl_WorkGroupID.x;
+    const uint rowy = rowx % p.KY;
+
+    float slope = 1.0f;
+
+    // ALiBi
+    if (p.max_bias > 0.0f) {
+        const uint h = rowx/p.KY; // head index
+
+        const float base = h < p.n_head_log2 ? p.m0 : p.m1;
+        const uint   exp  = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1;
+
+        slope = pow(base, exp);
+    }
+
+    // Find max
+    FLOAT_TYPE max_val = uintBitsToFloat(0xFF800000);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)));
+    }
+    vals[tid] = max_val;
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] = max(vals[tid], vals[tid + s]);
+        }
+        barrier();
+    }
+
+    max_val = vals[0];
+    barrier();
+
+    // Sum up values
+    vals[tid] = FLOAT_TYPE(0.0f);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        const uint i = rowx * p.KX + col;
+        const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val);
+        vals[tid] += val;
+        data_d[i] = D_TYPE(val);
+    }
+
+    barrier();
+    [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) {
+        if (tid < s) {
+            vals[tid] += vals[tid + s];
+        }
+        barrier();
+    }
+
+    const D_TYPE divisor = D_TYPE(vals[0]);
+
+    [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) {
+        const uint col = col0 + tid;
+
+        if (col >= p.KX) {
+            break;
+        }
+
+        data_d[rowx*p.KX + col] /= divisor;
+    }
+}
diff --git a/vulkan-shaders/square.comp b/vulkan-shaders/square.comp
new file mode 100644
index 0000000000000..8dd19333d4e32
--- /dev/null
+++ b/vulkan-shaders/square.comp
@@ -0,0 +1,13 @@
+#version 450
+
+#include "types.comp"
+#include "generic_unary_head.comp"
+
+void main() {
+    if (gl_GlobalInvocationID.x >= p.ne) {
+        return;
+    }
+
+    const FLOAT_TYPE val = FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]);
+    data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(val * val);
+}
diff --git a/vulkan-shaders/sum_rows.comp b/vulkan-shaders/sum_rows.comp
new file mode 100644
index 0000000000000..ce2f1e2f3b3e3
--- /dev/null
+++ b/vulkan-shaders/sum_rows.comp
@@ -0,0 +1,37 @@
+#version 450
+
+#include "generic_head.comp"
+#include "types.comp"
+
+#extension GL_EXT_control_flow_attributes : enable
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
+
+layout (constant_id = 0) const uint BLOCK_SIZE = 32;
+
+shared FLOAT_TYPE tmp[BLOCK_SIZE];
+
+void main() {
+    const uint row = gl_WorkGroupID.x;
+    const uint col = gl_LocalInvocationID.x;
+
+    tmp[col] = FLOAT_TYPE(0.0f);
+
+    for (uint i = col; i < p.KX; i += BLOCK_SIZE) {
+        tmp[col] += FLOAT_TYPE(data_a[row*p.KX + i]);
+    }
+
+    barrier();
+    [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) {
+        if (col < s) {
+            tmp[col] += tmp[col + s];
+        }
+        barrier();
+    }
+
+    if (col == 0) {
+        data_d[row] = D_TYPE(tmp[0]);
+    }
+}
diff --git a/vulkan-shaders/types.comp b/vulkan-shaders/types.comp
new file mode 100644
index 0000000000000..815fcbecde8ac
--- /dev/null
+++ b/vulkan-shaders/types.comp
@@ -0,0 +1,179 @@
+#if !defined(DATA_A_F32) && !defined(DATA_A_F16)
+#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require
+#endif
+
+#if defined(DATA_A_F32)
+#define QUANT_K 1
+#define QUANT_R 1
+
+#ifndef LOAD_VEC_A
+#define A_TYPE float
+#elif LOAD_VEC_A == 4
+#define A_TYPE vec4
+#elif LOAD_VEC_A == 8
+#define A_TYPE mat2x4
+#endif
+#endif
+
+#if defined(DATA_A_F16)
+#define QUANT_K 1
+#define QUANT_R 1
+
+#ifndef LOAD_VEC_A
+#define A_TYPE float16_t
+#elif LOAD_VEC_A == 4
+#define A_TYPE f16vec4
+#elif LOAD_VEC_A == 8
+#define A_TYPE f16mat2x4
+#endif
+#endif
+
+#if defined(DATA_A_Q4_0)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_0
+{
+    float16_t d;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_0
+#endif
+
+#if defined(DATA_A_Q4_1)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q4_1
+{
+    float16_t d;
+    float16_t m;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q4_1
+#endif
+
+#if defined(DATA_A_Q5_0)
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_0
+{
+    float16_t d;
+    uint16_t qh[2];
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_0
+#endif
+
+#if defined(DATA_A_Q5_1)
+#extension GL_EXT_shader_16bit_storage : require
+#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require
+#define QUANT_K 32
+#define QUANT_R 2
+
+struct block_q5_1
+{
+    float16_t d;
+    float16_t m;
+    uint qh;
+    uint8_t qs[16];
+};
+
+#define A_TYPE block_q5_1
+#endif
+
+#if defined(DATA_A_Q8_0)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 32
+#define QUANT_R 1
+
+struct block_q8_0
+{
+    float16_t d;
+    int8_t qs[32];
+};
+
+#define A_TYPE block_q8_0
+#endif
+
+// K-quants
+#if defined(DATA_A_Q2_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q2_K
+{
+    uint8_t scales[QUANT_K/16];
+    uint8_t qs[QUANT_K/4];
+    f16vec2 d;
+};
+
+#define A_TYPE block_q2_K
+#endif
+
+#if defined(DATA_A_Q3_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q3_K
+{
+    uint8_t hmask[QUANT_K/8];
+    uint8_t qs[QUANT_K/4];
+    uint8_t scales[12];
+    float16_t d;
+};
+
+#define A_TYPE block_q3_K
+#endif
+
+#if defined(DATA_A_Q4_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q4_K
+{
+    f16vec2 d;
+    uint8_t scales[3*QUANT_K/64];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q4_K
+#endif
+
+#if defined(DATA_A_Q5_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q5_K
+{
+    f16vec2 d;
+    uint8_t scales[12];
+    uint8_t qh[QUANT_K/8];
+    uint8_t qs[QUANT_K/2];
+};
+
+#define A_TYPE block_q5_K
+#endif
+
+#if defined(DATA_A_Q6_K)
+#extension GL_EXT_shader_16bit_storage : require
+#define QUANT_K 256
+
+struct block_q6_K
+{
+    uint8_t ql[QUANT_K/2];
+    uint8_t qh[QUANT_K/4];
+    int8_t scales[QUANT_K/16];
+    float16_t d;
+};
+
+#define A_TYPE block_q6_K
+#endif