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Bodhi Hu
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docs/build.md

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@@ -202,17 +202,14 @@ This provides GPU acceleration using the MUSA cores of your Moore Threads MTT GP
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- Using `CMake`:
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```bash
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# build with MUSA and using the compilers from MUSA SDK:
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cmake -B build -DGGML_MUSA=ON \
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-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
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cmake -B build -DGGML_MUSA=ON
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cmake --build build --config Release
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```
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- For static build:
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```bash
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cmake -B build -DGGML_MUSA=ON \
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-DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON \
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-DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
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-DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
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cmake --build build --config Release
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```
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@@ -222,7 +219,6 @@ The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enab
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Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
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## HIP
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This provides GPU acceleration on HIP-supported AMD GPUs.

ggml/src/ggml-cuda/common.cuh

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@@ -404,7 +404,7 @@ static __device__ __forceinline__ int ggml_cuda_dp4a(const int a, const int b, i
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#if __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
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return __dp4a(a, b, c);
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#else
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#else // __CUDA_ARCH__ >= GGML_CUDA_CC_DP4A || defined(GGML_USE_MUSA)
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const int8_t * a8 = (const int8_t *) &a;
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const int8_t * b8 = (const int8_t *) &b;
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return c + a8[0]*b8[0] + a8[1]*b8[1] + a8[2]*b8[2] + a8[3]*b8[3];

ggml/src/ggml-cuda/ggml-cuda.cu

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@@ -262,8 +262,7 @@ static ggml_cuda_device_info ggml_cuda_init() {
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id, prop.name, prop.gcnArchName, info.devices[id].cc & 0xffff,
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device_vmm ? "yes" : "no", prop.warpSize);
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#elif defined(GGML_USE_MUSA)
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// NOTE: MUSA will reserve some shared mem, and 24B should be enough,
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// we can remove the **24** when MUSA no longer reserves shared mem.
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// NOTE: MUSA will reserve some shared mem, and 24B should be enough
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info.devices[id].smpbo = prop.sharedMemPerBlockOptin - 24;
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info.devices[id].cc = 100*prop.major + 10*prop.minor;
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#else

src/llama-model.cpp

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@@ -3703,7 +3703,7 @@ void llama_model::print_info() const {
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}
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if (arch == LLM_ARCH_LLAMA) {
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LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
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LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale);
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}
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vocab.print_info();

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