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HIP: enable mfma mmq on gfx908 and gfx90a for select datatypes and shapes (#14949)

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This commit is contained in:
uvos
2025-07-30 17:38:06 +02:00
committed by GitHub
parent e32a4ec60e
commit ad4a700117
3 changed files with 25 additions and 10 deletions
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7
ggml/src/ggml-cuda/common.cuh
View File

@@ -227,9 +227,9 @@ typedef float2 dfloat2;
#define FP16_MMA_AVAILABLE
#endif // defined(GGML_HIP_ROCWMMA_FATTN) && (defined(CDNA) || defined(RDNA3) || (defined(GGML_HIP_ROCWMMA_FATTN_GFX12) && defined(RDNA4)))
#if defined(GGML_USE_HIP) && defined(CDNA3) && !defined(GGML_HIP_NO_MMQ_MFMA)
#if defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
#define AMD_MFMA_AVAILABLE
#endif // defined(GGML_USE_HIP) && defined(CDNA3) && !defined(GGML_HIP_NO_MMQ_MFMA)
#endif // defined(GGML_USE_HIP) && defined(CDNA) && !defined(GGML_HIP_NO_MMQ_MFMA)
#if !defined(GGML_USE_HIP) && __CUDA_ARCH__ >= GGML_CUDA_CC_TURING
#define NEW_MMA_AVAILABLE
@@ -293,10 +293,9 @@ static bool fp32_mma_hardware_available(const int cc) {
return GGML_CUDA_CC_IS_CDNA(cc);
}
// AMD CDNA3 matrix cores.. Will add support for other CDNA generations later.
static bool amd_mfma_available(const int cc) {
#if !defined(GGML_HIP_NO_MMQ_MFMA)
return GGML_CUDA_CC_IS_CDNA3(cc);
return GGML_CUDA_CC_IS_CDNA(cc);
#else
return false;
#endif //!defined(GGML_HIP_NO_MMQ_MFMA)

24
ggml/src/ggml-cuda/mmq.cu
View File

@@ -109,8 +109,8 @@ void ggml_cuda_mul_mat_q(
const int64_t s03 = src0->nb[3] / ts_src0;
const int64_t s3 = dst->nb[3] / ts_dst;
const bool use_stream_k = ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
|| (GGML_CUDA_CC_IS_AMD(cc) && GGML_CUDA_CC_IS_CDNA3(cc)));
const bool use_stream_k = (GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
|| GGML_CUDA_CC_IS_CDNA(cc);
if (!ids) {
const size_t nbytes_src1_q8_1 = ne13*ne12 * ne11*ne10_padded * sizeof(block_q8_1)/QK8_1 +
@@ -252,7 +252,7 @@ void ggml_cuda_op_mul_mat_q(
// Also its fixup needs to allocate a temporary buffer in the memory pool.
// There are multiple parallel CUDA streams for src1_ncols != ne11 which would introduce a race condition for this buffer.
const bool use_stream_k = ((GGML_CUDA_CC_IS_NVIDIA(cc) && ggml_cuda_highest_compiled_arch(cc) >= GGML_CUDA_CC_VOLTA)
|| (GGML_CUDA_CC_IS_AMD(cc) && GGML_CUDA_CC_IS_CDNA3(cc)))
|| GGML_CUDA_CC_IS_CDNA(cc))
&& src1_ncols == ne11;
const mmq_args args = {
src0_dd_i, src0->type, (const int *) src1_ddq_i, nullptr, nullptr, dst_dd_i,
@@ -306,7 +306,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
return false;
}
if (new_mma_available(cc) || amd_mfma_available(cc)) {
if (new_mma_available(cc)) {
return true;
}
@@ -322,5 +322,21 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11) {
return !fp16_mma_hardware_available(cc) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
}
if (amd_mfma_available(cc)) {
// As of ROCM 7.0 rocblas/tensile performs very poorly on CDNA3 and hipblaslt (via ROCBLAS_USE_HIPBLASLT)
// performs better but is currently suffering from a crash on this architecture.
// TODO: Revisit when hipblaslt is fixed on CDNA3
if (GGML_CUDA_CC_IS_CDNA3(cc)) {
return true;
}
if (ne11 <= 128 || type == GGML_TYPE_Q4_0 || type == GGML_TYPE_Q4_1 || type == GGML_TYPE_Q5_0 || type == GGML_TYPE_Q5_1) {
return true;
}
if (ne11 <= 256 && (type == GGML_TYPE_Q4_K || type == GGML_TYPE_Q5_K)) {
return true;
}
return false;
}
return (!GGML_CUDA_CC_IS_RDNA4(cc) && !GGML_CUDA_CC_IS_RDNA3(cc) && !GGML_CUDA_CC_IS_CDNA(cc)) || ne11 < MMQ_DP4A_MAX_BATCH_SIZE;
}

4
ggml/src/ggml-cuda/mmq.cuh
View File

@@ -3096,8 +3096,8 @@ static __global__ void mul_mat_q(
}
__syncthreads();
// On AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
#if (defined(GGML_USE_HIP) && !defined(CDNA3)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
// On non-CDNA AMD or old CUDA the performance with stream-k was worse, use conventional tiling instead:
#if (defined(GGML_USE_HIP) && !defined(CDNA)) || __CUDA_ARCH__ < GGML_CUDA_CC_VOLTA
{
const int wt = blockIdx.z / nchannels_y;
const int zt = blockIdx.z - wt*nchannels_y;