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crasm committed Dec 27, 2023
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177 changes: 1 addition & 176 deletions .github/ISSUE_TEMPLATE/bug.md
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Expand Up @@ -6,179 +6,4 @@ 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.

# Expected Behavior

Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do.

# Current Behavior

Please provide a detailed written description of what `llama.cpp` did, instead.

# Environment and Context

Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.

* Physical (or virtual) hardware you are using, e.g. for Linux:

`$ lscpu`

* Operating System, e.g. for Linux:

`$ uname -a`

* SDK version, e.g. for Linux:

```
$ python3 --version
$ make --version
$ g++ --version
```

# Failure Information (for bugs)

Please help provide information about the failure / bug.

# Steps to Reproduce

Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.

1. step 1
2. step 2
3. step 3
4. etc.

# Failure Logs

Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.

Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability.

Example environment info:
```
llama.cpp$ git log | head -1
commit 2af23d30434a677c6416812eea52ccc0af65119c
llama.cpp$ lscpu | egrep "AMD|Flags"
Vendor ID: AuthenticAMD
Model name: AMD Ryzen Threadripper 1950X 16-Core Processor
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
Virtualization: AMD-V
llama.cpp$ python3 --version
Python 3.10.9
llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
numpy 1.24.2
numpydoc 1.5.0
sentencepiece 0.1.97
torch 1.13.1
torchvision 0.14.1
llama.cpp$ make --version | head -1
GNU Make 4.3
$ md5sum ./models/65B/ggml-model-q4_0.bin
dbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin
```

Example run with the Linux command [perf](https://www.brendangregg.com/perf.html)
```
llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
main: seed = 1679149377
llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
llama_model_load: n_vocab = 32000
llama_model_load: n_ctx = 512
llama_model_load: n_embd = 8192
llama_model_load: n_mult = 256
llama_model_load: n_head = 64
llama_model_load: n_layer = 80
llama_model_load: n_rot = 128
llama_model_load: f16 = 2
llama_model_load: n_ff = 22016
llama_model_load: n_parts = 8
llama_model_load: ggml ctx size = 41477.73 MB
llama_model_load: memory_size = 2560.00 MB, n_mem = 40960
llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
llama_model_load: .......................................................................................... done
llama_model_load: model size = 4869.09 MB / num tensors = 723
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
main: prompt: 'Please close your issue when it has been answered.'
main: number of tokens in prompt = 11
1 -> ''
12148 -> 'Please'
3802 -> ' close'
596 -> ' your'
2228 -> ' issue'
746 -> ' when'
372 -> ' it'
756 -> ' has'
1063 -> ' been'
7699 -> ' answered'
29889 -> '.'
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
Please close your issue when it has been answered.
@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
main: mem per token = 71159620 bytes
main: load time = 19309.95 ms
main: sample time = 168.62 ms
main: predict time = 223895.61 ms / 888.47 ms per token
main: total time = 246406.42 ms
Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
3636882.89 msec task-clock # 14.677 CPUs utilized
13509 context-switches # 3.714 /sec
2436 cpu-migrations # 0.670 /sec
10476679 page-faults # 2.881 K/sec
13133115082869 cycles # 3.611 GHz (16.77%)
29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%)
10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%)
23479217109614 instructions # 1.79 insn per cycle
# 0.44 stalled cycles per insn (16.76%)
2353072268027 branches # 647.002 M/sec (16.77%)
1998682780 branch-misses # 0.08% of all branches (16.76%)
247.802177522 seconds time elapsed
3618.573072000 seconds user
18.491698000 seconds sys
```
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.
15 changes: 14 additions & 1 deletion .github/workflows/docker.yml
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Expand Up @@ -69,6 +69,19 @@ jobs:
docker-images: true
swap-storage: true

- name: Determine tag name
id: tag
shell: bash
run: |
BUILD_NUMBER="$(git rev-list --count HEAD)"
SHORT_HASH="$(git rev-parse --short=7 HEAD)"
if [[ "${{ env.BRANCH_NAME }}" == "master" ]]; then
echo "name=b${BUILD_NUMBER}" >> $GITHUB_OUTPUT
else
SAFE_NAME=$(echo "${{ env.BRANCH_NAME }}" | tr '/' '-')
echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
fi
- name: Build and push Docker image (versioned)
if: github.event_name == 'push'
uses: docker/build-push-action@v4
Expand All @@ -85,5 +98,5 @@ jobs:
context: .
push: ${{ github.event_name == 'push' }}
platforms: ${{ matrix.config.platforms }}
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}"
tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
file: ${{ matrix.config.dockerfile }}
1 change: 1 addition & 0 deletions .gitignore
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Expand Up @@ -48,6 +48,7 @@ models-mnt
/llama-bench
/llava-cli
/lookahead
/lookup
/main
/metal
/perplexity
Expand Down
2 changes: 2 additions & 0 deletions CMakeLists.txt
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Expand Up @@ -302,6 +302,8 @@ if (LLAMA_CUBLAS)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()

set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver)

if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
# 52 == lowest CUDA 12 standard
# 60 == f16 CUDA intrinsics
Expand Down
34 changes: 27 additions & 7 deletions Makefile
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Expand Up @@ -2,7 +2,7 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup tests/test-c.o

# Binaries only useful for tests
TEST_TARGETS = \
Expand Down Expand Up @@ -282,8 +282,17 @@ endif
ifneq ($(filter aarch64%,$(UNAME_M)),)
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
# Nvidia Jetson
MK_CFLAGS += -mcpu=native
MK_CXXFLAGS += -mcpu=native
JETSON_RELEASE_INFO = $(shell jetson_release)
ifdef JETSON_RELEASE_INFO
ifneq ($(filter TX2%,$(JETSON_RELEASE_INFO)),)
JETSON_EOL_MODULE_DETECT = 1
CC = aarch64-unknown-linux-gnu-gcc
cxx = aarch64-unknown-linux-gnu-g++
endif
endif
endif

ifneq ($(filter armv6%,$(UNAME_M)),)
Expand Down Expand Up @@ -357,15 +366,16 @@ ifdef LLAMA_BLIS
endif # LLAMA_BLIS

ifdef LLAMA_CUBLAS
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include
MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib
OBJS += ggml-cuda.o
MK_NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math

MK_NVCCFLAGS = -use_fast_math
ifndef JETSON_EOL_MODULE_DETECT
MK_NVCCFLAGS += --forward-unknown-to-host-compiler
endif # JETSON_EOL_MODULE_DETECT
ifdef LLAMA_DEBUG
MK_NVCCFLAGS += -lineinfo
endif

endif # LLAMA_DEBUG
ifdef LLAMA_CUDA_NVCC
NVCC = $(LLAMA_CUDA_NVCC)
else
Expand Down Expand Up @@ -417,7 +427,11 @@ ifdef LLAMA_CUDA_CCBIN
MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
ifdef JETSON_EOL_MODULE_DETECT
$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
else
$(NVCC) $(BASE_CXXFLAGS) $(NVCCFLAGS) -Wno-pedantic -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
endif # JETSON_EOL_MODULE_DETECT
endif # LLAMA_CUBLAS

ifdef LLAMA_CLBLAST
Expand Down Expand Up @@ -452,6 +466,9 @@ ifdef LLAMA_HIPBLAS
LLAMA_CUDA_MMV_Y ?= 1
LLAMA_CUDA_KQUANTS_ITER ?= 2
MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
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 += -lhipblas -lamdhip64 -lrocblas
HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS))
Expand Down Expand Up @@ -645,6 +662,9 @@ parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
Expand Down
14 changes: 13 additions & 1 deletion README.md
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Expand Up @@ -102,6 +102,7 @@ as the main playground for developing new features for the [ggml](https://github
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
- [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557)

**Multimodal models:**

Expand All @@ -123,6 +124,7 @@ as the main playground for developing new features for the [ggml](https://github
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)

**UI:**

Expand All @@ -131,6 +133,7 @@ as the main playground for developing new features for the [ggml](https://github
- [withcatai/catai](https://github.com/withcatai/catai)
- [semperai/amica](https://github.com/semperai/amica)
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal)

---

Expand Down Expand Up @@ -395,6 +398,9 @@ Building the program with BLAS support may lead to some performance improvements
- #### cuBLAS
This provides BLAS acceleration using the CUDA cores of your Nvidia GPU. Make sure to have the CUDA toolkit installed. You can download it from your Linux distro's package manager (e.g. `apt install nvidia-cuda-toolkit`) or from here: [CUDA Toolkit](https://developer.nvidia.com/cuda-downloads).

For Jetson user, if you have Jetson Orin, you can try this: [Offical Support](https://www.jetson-ai-lab.com/tutorial_text-generation.html). If you are using an old model(nano/TX2), need some additional operations before compiling.

- Using `make`:
```bash
make LLAMA_CUBLAS=1
Expand Down Expand Up @@ -439,7 +445,13 @@ Building the program with BLAS support may lead to some performance improvements
&& cmake --build build -- -j 16
```
On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`.
However, this hurts performance for non-integrated GPUs.
However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
- Using `make` (example for target gfx1030, build with 16 CPU threads):
```bash
make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gxf1030
```
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
```bash
set PATH=%HIP_PATH%\bin;%PATH%
Expand Down
3 changes: 2 additions & 1 deletion common/common.h
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Expand Up @@ -51,7 +51,7 @@ struct gpt_params {
int32_t n_ctx = 512; // context size
int32_t n_batch = 512; // 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 = 16; // number of tokens to draft during speculative decoding
int32_t n_draft = 8; // 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
Expand Down Expand Up @@ -240,3 +240,4 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);

// Dump the KV cache view showing individual sequences in each cell (long output).
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);

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