support SYCL backend windows build

CMakeLists.txt

@@ -501,7 +501,11 @@ if (LLAMA_SYCL)
     set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h)
     set(GGML_SOURCES_SYCL ggml-sycl.cpp)
 
-    set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+    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)
+    else()
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
+    endif()
 endif()
 
 function(get_flags CCID CCVER)

README_sycl.md → README-sycl.md

@@ -8,10 +8,14 @@
 
 [Linux](#linux)
 
+[Windows](#windows)
+
 [Environment Variable](#environment-variable)
 
 [Known Issue](#known-issue)
 
+[Q&A](#q&a)
+
 [Todo](#todo)
 
 ## Background
@@ -33,7 +37,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 |OS|Status|Verified|
 |-|-|-|
 |Linux|Support|Ubuntu 22.04|
-|Windows|Ongoing| |
+|Windows|Support|Windows 11|
 
 
 ## Intel GPU
@@ -42,7 +46,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 |-|-|-|
 |Intel Data Center Max Series| Support| Max 1550|
 |Intel Data Center Flex Series| Support| Flex 170|
-|Intel Arc Series| Support| Arc 770|
+|Intel Arc Series| Support| Arc 770, 730M|
 |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
 |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7|
 
@@ -131,6 +135,7 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 #build all binary
 cmake --build . --config Release -v
 
+cd ..
 ```
 
 or
@@ -195,7 +200,7 @@ GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building
 or run by script:
 
 ```
-./examples/sycl/run_llama2.sh
+./examples/sycl/run-llama2.sh
 ```
 
 Note:
@@ -205,11 +210,173 @@ Note:
 
 5. Check the device ID in output
 
-Like：
+Like:
 ```
 Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 ```
 
+## Windows
+
+### Setup Environment
+
+1. Install Intel GPU driver.
+
+Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+
+2. Install Intel® oneAPI Base toolkit.
+
+a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
+
+Recommend to install to default folder: **/opt/intel/oneapi**.
+
+Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
+
+b. Enable oneAPI running environment:
+
+- In Search, input 'oneAPI'.
+
+Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
+
+- In Run:
+
+In CMD:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+c. Check GPU
+
+In oneAPI command line:
+
+```
+sycl-ls
+```
+
+There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**.
+
+Output (example):
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO  [31.0.101.5186]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
+
+```
+
+3. Install cmake & make
+
+a. Download & install cmake for windows: https://cmake.org/download/
+
+b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/
+
+
+### Build locally:
+
+In oneAPI command line window:
+
+```
+mkdir build
+cd build
+
+#for FP16
+#cmake -G "MinGW Makefiles" .  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+
+#for FP32
+cmake -G "MinGW Makefiles" .  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+
+#build example/main only
+#make main
+
+#build all binary
+make
+
+cd ..
+```
+
+or
+
+```
+.\examples\sycl\win-build.bat
+```
+
+Note:
+
+- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+
+### Run
+
+1. Put model file to folder **models**
+
+2. Enable oneAPI running environment
+
+- In Search, input 'oneAPI'.
+
+Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
+
+- In Run:
+
+In CMD:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+3. List device ID
+
+Run without parameter:
+
+```
+build\bin\ls-sycl-device.exe
+
+or
+
+build\bin\main.exe
+```
+
+Check the ID in startup log, like:
+
+```
+found 4 SYCL devices:
+  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
+    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
+  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
+    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
+  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero running time, recommended |
+|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+
+4. Set device ID and execute llama.cpp
+
+Set device ID = 0 by **set GGML_SYCL_DEVICE=0**
+
+```
+set GGML_SYCL_DEVICE=0
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+```
+or run by script:
+
+```
+.\examples\sycl\win-run-llama2.bat
+```
+
+Note:
+
+- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
+
+
+5. Check the device ID in output
+
+Like:
+```
+Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+```
 
 ## Environment Variable
 
@@ -220,7 +387,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 |LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, LLAMA_SYCL=ON is mandatory.|
 |LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference. <br>For FP32, not set it.|
 |CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
-|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path|
+|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path|
 
 #### Running
 
@@ -232,18 +399,23 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 
 ## Known Issue
 
+- Hang during startup
+
+  llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
+
+  Solution: add **--no-mmap**.
+
+## Q&A
+
 - Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
 
   Miss to enable oneAPI running environment.
 
   Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
 
+- In Windows, no result, not error.
 
-- Hang during startup
-
-  llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
-
-  Solution: add **--no-mmap**.
+  Miss to enable oneAPI running environment.
 
 ## Todo
 

README.md

@@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 ### Hot topics
 
 - ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138
+  - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series)
 - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow
 - Collecting Apple Silicon performance stats:
   - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167
@@ -605,7 +606,7 @@ Building the program with BLAS support may lead to some performance improvements
 
   llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
 
-  For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md).
+  For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md).
 
 
 ### Prepare Data & Run