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build(cmake): simplify instructions (cmake -B build && cmake --build build ...) (#6964)

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* readme: cmake . -B build && cmake --build build

* build: fix typo

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>

* build: drop implicit . from cmake config command

* build: remove another superfluous .

* build: update MinGW cmake commands

* Update README-sycl.md

Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>

* build: reinstate --config Release as not the default w/ some generators + document how to build Debug

* build: revert more --config Release

* build: nit / remove -H from cmake example

* build: reword debug instructions around single/multi config split

---------

Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com>
Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>
This commit is contained in:
Olivier Chafik 2024-04-29 17:02:45 +01:00 committed by GitHub
parent d2c898f746
commit b8a7a5a90f
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10 changed files with 91 additions and 110 deletions
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8
.devops/main-intel.Dockerfile
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@ -10,14 +10,12 @@ WORKDIR /app
COPY . . COPY . .
RUN mkdir build && \ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
cd build && \
if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \ echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
fi && \ fi && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
cmake --build . --config Release --target main cmake --build build --config Release --target main
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

6
.devops/main-vulkan.Dockerfile
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@ -14,10 +14,8 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
# Build it # Build it
WORKDIR /app WORKDIR /app
COPY . . COPY . .
RUN mkdir build && \ RUN cmake -B build -DLLAMA_VULKAN=1 && \
cd build && \ cmake --build build --config Release --target main
cmake .. -DLLAMA_VULKAN=1 && \
cmake --build . --config Release --target main
# Clean up # Clean up
WORKDIR / WORKDIR /

8
.devops/server-intel.Dockerfile
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@ -10,14 +10,12 @@ WORKDIR /app
COPY . . COPY . .
RUN mkdir build && \ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
cd build && \
if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
echo "LLAMA_SYCL_F16 is set" && \ echo "LLAMA_SYCL_F16 is set" && \
export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
fi && \ fi && \
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
cmake --build . --config Release --target server cmake --build build --config Release --target server
FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime

6
.devops/server-vulkan.Dockerfile
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@ -18,10 +18,8 @@ RUN apt-get update && \
# Build it # Build it
WORKDIR /app WORKDIR /app
COPY . . COPY . .
RUN mkdir build && \ RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
cd build && \ cmake --build build --config Release --target server
cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \
cmake --build . --config Release --target server
# Clean up # Clean up
WORKDIR / WORKDIR /

6
.github/workflows/bench.yml vendored
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@ -96,9 +96,7 @@ jobs:
id: cmake_build id: cmake_build
run: | run: |
set -eux set -eux
mkdir build cmake -B build \
cd build
cmake .. \
-DLLAMA_NATIVE=OFF \ -DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \ -DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \ -DLLAMA_CURL=ON \
@ -109,7 +107,7 @@ jobs:
-DLLAMA_FATAL_WARNINGS=OFF \ -DLLAMA_FATAL_WARNINGS=OFF \
-DLLAMA_ALL_WARNINGS=OFF \ -DLLAMA_ALL_WARNINGS=OFF \
-DCMAKE_BUILD_TYPE=Release; -DCMAKE_BUILD_TYPE=Release;
cmake --build . --config Release -j $(nproc) --target server cmake --build build --config Release -j $(nproc) --target server
- name: Download the dataset - name: Download the dataset
id: download_dataset id: download_dataset

12
.github/workflows/server.yml vendored
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@ -94,15 +94,13 @@ jobs:
- name: Build - name: Build
id: cmake_build id: cmake_build
run: | run: |
mkdir build cmake -B build \
cd build
cmake .. \
-DLLAMA_NATIVE=OFF \ -DLLAMA_NATIVE=OFF \
-DLLAMA_BUILD_SERVER=ON \ -DLLAMA_BUILD_SERVER=ON \
-DLLAMA_CURL=ON \ -DLLAMA_CURL=ON \
-DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server
- name: Tests - name: Tests
@ -143,10 +141,8 @@ jobs:
- name: Build - name: Build
id: cmake_build id: cmake_build
run: | run: |
mkdir build cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
cd build cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server
- name: Python setup - name: Python setup
id: setup_python id: setup_python

27
README-sycl.md
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@ -185,9 +185,8 @@ Upon a successful installation, SYCL is enabled for the available intel devices,
```sh ```sh
git clone https://github.com/oneapi-src/oneMKL git clone https://github.com/oneapi-src/oneMKL
cd oneMKL cd oneMKL
mkdir -p buildWithCublas && cd buildWithCublas cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas
cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas cmake --build buildWithCublas --config Release
make
``` ```
@ -227,16 +226,15 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic
source /opt/intel/oneapi/setvars.sh source /opt/intel/oneapi/setvars.sh
# Build LLAMA with MKL BLAS acceleration for intel GPU # Build LLAMA with MKL BLAS acceleration for intel GPU
mkdir -p build && cd build
# Option 1: Use FP32 (recommended for better performance in most cases) # Option 1: Use FP32 (recommended for better performance in most cases)
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16 # Option 2: Use FP16
cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
# build all binary # build all binary
cmake --build . --config Release -j -v cmake --build build --config Release -j -v
``` ```
#### Nvidia GPU #### Nvidia GPU
@ -248,16 +246,15 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_
export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR
# Build LLAMA with Nvidia BLAS acceleration through SYCL # Build LLAMA with Nvidia BLAS acceleration through SYCL
mkdir -p build && cd build
# Option 1: Use FP32 (recommended for better performance in most cases) # Option 1: Use FP32 (recommended for better performance in most cases)
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
# Option 2: Use FP16 # Option 2: Use FP16
cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
# build all binary # build all binary
cmake --build . --config Release -j -v cmake --build build --config Release -j -v
``` ```
@ -412,17 +409,15 @@ b. Download & install mingw-w64 make for Windows provided by w64devkit
On the oneAPI command line window, step into the llama.cpp main directory and run the following: On the oneAPI command line window, step into the llama.cpp main directory and run the following:
``` ```
mkdir -p build
cd build
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
# Option 1: Use FP32 (recommended for better performance in most cases) # Option 1: Use FP32 (recommended for better performance in most cases)
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
# Option 2: Or FP16 # Option 2: Or FP16
cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
make -j cmake --build build --config Release -j
``` ```
Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions: Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions:

95
README.md
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@ -308,6 +308,8 @@ In order to build llama.cpp you have three different options.
make make
``` ```
**Note**: for `Debug` builds, run `make LLAMA_DEBUG=1`
- On Windows: - On Windows:
1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
@ -322,10 +324,24 @@ In order to build llama.cpp you have three different options.
- Using `CMake`: - Using `CMake`:
```bash ```bash
mkdir build cmake -B build
cd build cmake --build build --config Release
cmake .. ```
cmake --build . --config Release
**Note**: for `Debug` builds, there are two cases:
- 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...):
```bash
cmake -B build -G "Xcode"
cmake --build build --config Debug
``` ```
- Using `Zig` (version 0.11 or later): - Using `Zig` (version 0.11 or later):
@ -439,10 +455,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake` on Linux: - Using `CMake` on Linux:
```bash ```bash
mkdir build cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cd build cmake --build build --config Release
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS
cmake --build . --config Release
``` ```
- #### BLIS - #### BLIS
@ -462,11 +476,9 @@ Building the program with BLAS support may lead to some performance improvements
- Using manual oneAPI installation: - Using manual oneAPI installation:
By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
```bash ```bash
mkdir build
cd build
source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON
cmake --build . --config Release cmake --build build --config Release
``` ```
- Using oneAPI docker image: - Using oneAPI docker image:
@ -487,10 +499,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake`: - Using `CMake`:
```bash ```bash
mkdir build cmake -B build -DLLAMA_CUDA=ON
cd build cmake --build build --config Release
cmake .. -DLLAMA_CUDA=ON
cmake --build . --config Release
``` ```
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
@ -517,8 +527,8 @@ Building the program with BLAS support may lead to some performance improvements
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
```bash ```bash
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \
cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
&& cmake --build build -- -j 16 && cmake --build build --config Release -- -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"`. 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 (but enables working with integrated GPUs). However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
@ -564,15 +574,14 @@ Building the program with BLAS support may lead to some performance improvements
```sh ```sh
git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git
mkdir OpenCL-SDK/build cd OpenCL-SDK
cd OpenCL-SDK/build cmake -B build -DBUILD_DOCS=OFF \
cmake .. -DBUILD_DOCS=OFF \
-DBUILD_EXAMPLES=OFF \ -DBUILD_EXAMPLES=OFF \
-DBUILD_TESTING=OFF \ -DBUILD_TESTING=OFF \
-DOPENCL_SDK_BUILD_SAMPLES=OFF \ -DOPENCL_SDK_BUILD_SAMPLES=OFF \
-DOPENCL_SDK_TEST_SAMPLES=OFF -DOPENCL_SDK_TEST_SAMPLES=OFF
cmake --build . --config Release cmake --build build
cmake --install . --prefix /some/path cmake --install build --prefix /some/path
``` ```
</details> </details>
@ -594,23 +603,23 @@ Building the program with BLAS support may lead to some performance improvements
```cmd ```cmd
set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64"
git clone https://github.com/CNugteren/CLBlast.git git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast\build cd CLBlast
cd CLBlast\build 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 .. -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 --build . --config Release cmake --install build --prefix C:/CLBlast
cmake --install . --prefix C:/CLBlast
``` ```
(note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds)
- <details> - <details>
<summary>Unix:</summary> <summary>Unix:</summary>
```sh ```sh
git clone https://github.com/CNugteren/CLBlast.git git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build cd CLBlast
cd CLBlast/build cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF cmake --build build --config Release
cmake --build . --config Release cmake --install build --prefix /some/path
cmake --install . --prefix /some/path
``` ```
Where `/some/path` is where the built library will be installed (default is `/usr/local`). Where `/some/path` is where the built library will be installed (default is `/usr/local`).
@ -624,21 +633,17 @@ Building the program with BLAS support may lead to some performance improvements
``` ```
- CMake (Unix): - CMake (Unix):
```sh ```sh
mkdir build cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cd build cmake --build build --config Release
cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path
cmake --build . --config Release
``` ```
- CMake (Windows): - CMake (Windows):
```cmd ```cmd
set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast"
git clone https://github.com/ggerganov/llama.cpp git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp cd llama.cpp
mkdir build cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64
cd build cmake --build build --config Release
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 cmake --install build --prefix C:/LlamaCPP
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
``` ```
##### Running Llama with CLBlast ##### Running Llama with CLBlast
@ -694,10 +699,8 @@ Building the program with BLAS support may lead to some performance improvements
Then, build llama.cpp using the cmake command below: Then, build llama.cpp using the cmake command below:
```bash ```bash
mkdir -p build cmake -B build -DLLAMA_VULKAN=1
cd build cmake --build build --config Release
cmake .. -DLLAMA_VULKAN=1
cmake --build . --config Release
# Test the output binary (with "-ngl 33" to offload all layers to GPU) # 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/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4

16
examples/main-cmake-pkg/README.md
View File

@ -17,11 +17,9 @@ In this case, CLBlast was already installed so the CMake package is referenced i
```cmd ```cmd
git clone https://github.com/ggerganov/llama.cpp git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp cd llama.cpp
mkdir build 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
cd build cmake --build build --config Release
cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 cmake --install build --prefix C:/LlamaCPP
cmake --build . --config Release
cmake --install . --prefix C:/LlamaCPP
``` ```
### Build main-cmake-pkg ### Build main-cmake-pkg
@ -29,9 +27,7 @@ cmake --install . --prefix C:/LlamaCPP
```cmd ```cmd
cd ..\examples\main-cmake-pkg cd ..\examples\main-cmake-pkg
mkdir build 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
cd build cmake --build build --config Release
cmake .. -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 --install build --prefix C:/MyLlamaApp
cmake --build . --config Release
cmake --install . --prefix C:/MyLlamaApp
``` ```

13
examples/server/README.md
View File

@ -74,15 +74,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
- Using `make`: - Using `make`:
```bash ```bash
make make server
``` ```
- Using `CMake`: - Using `CMake`:
```bash ```bash
cmake --build . --config Release cmake -B build
cmake --build build --config Release -t server
``` ```
Binary is at `./build/bin/server`
## Build with SSL ## Build with SSL
`server` can also be built with SSL support using OpenSSL 3 `server` can also be built with SSL support using OpenSSL 3
@ -99,10 +102,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/
- Using `CMake`: - Using `CMake`:
```bash ```bash
mkdir build cmake -B build -DLLAMA_SERVER_SSL=ON
cd build cmake --build build --config Release -t server
cmake .. -DLLAMA_SERVER_SSL=ON
make server
``` ```
## Quick Start ## Quick Start