ggml : build backends as libraries (#10256)
* ggml : build backends as libraries --------- Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: R0CKSTAR <xiaodong.ye@mthreads.com>
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@ -230,7 +230,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
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- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
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```bash
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HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
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cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
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cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
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&& cmake --build build --config Release -- -j 16
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```
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On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DGGML_HIP_UMA=ON`.
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@ -247,7 +247,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
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```bash
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HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
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HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
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cmake -S . -B build -DGGML_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
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cmake -S . -B build -DGGML_HIP=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
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&& cmake --build build -- -j 16
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```
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@ -259,7 +259,7 @@ You can download it from your Linux distro's package manager or from here: [ROCm
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- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
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```bash
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set PATH=%HIP_PATH%\bin;%PATH%
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cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
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cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
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cmake --build build
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```
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Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
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