CS348Project/llama.cpp
5
0
Fork 0
mirror of https://github.com/ggml-org/llama.cpp.git synced 2025-10-31 08:51:55 +00:00
Code Issues Packages Projects Releases Wiki Activity

rocBLAS: Avoid fp32->fp16->fp32 conversion on cdna (#11356)

Browse Source
This commit is contained in:
uvos
2025-01-24 17:50:49 +01:00
committed by GitHub
parent 9755129c27
commit 9fbadaef4f
2 changed files with 40 additions and 25 deletions
Download Patch File Download Diff File Expand all files Collapse all files

40
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@@ -1082,7 +1082,9 @@ static void ggml_cuda_op_mul_mat_cublas(
const int compute_capability = ggml_cuda_info().devices[id].cc; const int compute_capability = ggml_cuda_info().devices[id].cc;
if (compute_capability >= GGML_CUDA_CC_VOLTA && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { const bool use_fp16 = (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT;
if (compute_capability >= GGML_CUDA_CC_VOLTA && use_fp16) {
// convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32
ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id)); ggml_cuda_pool_alloc<half> src0_as_f16(ctx.pool(id));
if (src0->type != GGML_TYPE_F16) { if (src0->type != GGML_TYPE_F16) {
@@ -1103,28 +1105,38 @@ static void ggml_cuda_op_mul_mat_cublas(
to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream); to_fp16_cuda(src1_ddf_i, src1_as_f16.get(), ne, stream);
} }
const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get(); const half * src1_ptr = src1->type == GGML_TYPE_F16 ? (const half *) src1_ddf_i : src1_as_f16.get();
CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
if (compute_capability == GGML_CUDA_CC_CDNA) {
const float alpha = 1.0f;
const float beta = 0.0f;
CUBLAS_CHECK(
cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10,
&alpha, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10,
&beta, dst_dd_i, CUDA_R_32F, ldc,
CUBLAS_COMPUTE_32F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP));
} else {
ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols); ggml_cuda_pool_alloc<half> dst_f16(ctx.pool(id), row_diff*src1_ncols);
const half alpha_f16 = 1.0f; const half alpha_f16 = 1.0f;
const half beta_f16 = 0.0f; const half beta_f16 = 0.0f;
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
cu_compute_type = CUBLAS_COMPUTE_32F;
}
CUBLAS_CHECK(cublasSetStream(ctx.cublas_handle(id), stream));
CUBLAS_CHECK( CUBLAS_CHECK(
cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N, cublasGemmEx(ctx.cublas_handle(id), CUBLAS_OP_T, CUBLAS_OP_N,
row_diff, src1_ncols, ne10, row_diff, src1_ncols, ne10,
&alpha_f16, src0_ptr, CUDA_R_16F, ne00, &alpha_f16, src0_ptr, CUDA_R_16F, ne00,
src1_ptr, CUDA_R_16F, ne10, src1_ptr, CUDA_R_16F, ne10,
&beta_f16, dst_f16.get(), CUDA_R_16F, ldc, &beta_f16, dst_dd_i, CUDA_R_16F, ldc,
cu_compute_type, CUBLAS_COMPUTE_16F,
CUBLAS_GEMM_DEFAULT_TENSOR_OP)); CUBLAS_GEMM_DEFAULT_TENSOR_OP));
const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16); const to_fp32_cuda_t to_fp32_cuda = ggml_get_to_fp32_cuda(GGML_TYPE_F16);
to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); to_fp32_cuda(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream);
}
} else { } else {
ggml_cuda_pool_alloc<float> src0_ddq_as_f32(ctx.pool(id)); ggml_cuda_pool_alloc<float> src0_ddq_as_f32(ctx.pool(id));
ggml_cuda_pool_alloc<float> src1_ddq_as_f32(ctx.pool(id)); ggml_cuda_pool_alloc<float> src1_ddq_as_f32(ctx.pool(id));
@@ -1613,10 +1625,6 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F; cublasComputeType_t cu_compute_type = CUBLAS_COMPUTE_16F;
cudaDataType_t cu_data_type = CUDA_R_16F; cudaDataType_t cu_data_type = CUDA_R_16F;
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
cu_compute_type = CUBLAS_COMPUTE_32F;
}
// dst strides // dst strides
size_t nbd2 = dst->nb[2]; size_t nbd2 = dst->nb[2];
size_t nbd3 = dst->nb[3]; size_t nbd3 = dst->nb[3];
@@ -1645,6 +1653,12 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co
beta = &beta_f32; beta = &beta_f32;
} }
if (ggml_cuda_info().devices[ctx.device].cc == GGML_CUDA_CC_CDNA) {
cu_compute_type = CUBLAS_COMPUTE_32F;
alpha = &alpha_f32;
beta = &beta_f32;
}
GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne12 % ne02 == 0);
GGML_ASSERT(ne13 % ne03 == 0); GGML_ASSERT(ne13 % ne03 == 0);

3
ggml/src/ggml-cuda/mmvq.cu
View File

@@ -142,7 +142,7 @@ static void mul_mat_vec_q_cuda(
int64_t nwarps = 1; int64_t nwarps = 1;
int64_t rows_per_cuda_block = 1; int64_t rows_per_cuda_block = 1;
if (ggml_cuda_info().devices[id].cc < GGML_CUDA_CC_CDNA || ggml_cuda_info().devices[id].cc == GGML_CUDA_CC_RDNA1) { // NVIDIA and AMD older than RDNA2 but not CDNA if (ggml_cuda_info().devices[id].cc < GGML_CUDA_CC_RDNA2) { // NVIDIA and AMD older than RDNA2
switch(ncols_y) { switch(ncols_y) {
case 1: case 1:
nwarps = 4; nwarps = 4;
@@ -166,6 +166,7 @@ static void mul_mat_vec_q_cuda(
break; break;
} }
} }
const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block; const int64_t nblocks = (nrows_x + rows_per_cuda_block - 1) / rows_per_cuda_block;
const dim3 block_nums(nblocks, 1, 1); const dim3 block_nums(nblocks, 1, 1);
const dim3 block_dims(WARP_SIZE, nwarps, 1); const dim3 block_dims(WARP_SIZE, nwarps, 1);