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Commit ee33e2e7 authored by zhouxiang's avatar zhouxiang
Browse files

support dtk23.10

parent e432dbb0
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Showing with 1807 additions and 1806 deletions
lmdeploy/version.py
View file @ ee33e2e7
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Tuple
__dcu_version__ = '0.0.13'
__dcu_version__ = '0.0.13+gite432dbb.abi0.dtk2310.torch1.13'
__version__ = '0.0.13'
short_version = __version__
......
src/turbomind/kernels/activation_kernels.cu
View file @ ee33e2e7
......@@ -37,14 +37,14 @@ __forceinline__ __device__ float copysignf_pos(float a, float b)
__inline__ __device__ float tanh_opt(float x)
{
#if (__CUDA_ARCH__ >= 750 && CUDART_VERSION >= 11000)
float r;
asm("tanh.approx.f32 %0,%1; \n\t" : "=f"(r) : "f"(x));
return r;
#else
// #if (__CUDA_ARCH__ >= 750 && CUDART_VERSION >= 11000)
// float r;
// asm("tanh.approx.f32 %0,%1; \n\t" : "=f"(r) : "f"(x));
// return r;
// #else
const float exp_val = -1.f * fabs(2 * x);
return copysignf_pos((1.0f - __expf(exp_val)) / (__expf(exp_val) + 1.0f), x);
#endif
// #endif
}
template<typename T>
......
src/turbomind/kernels/gemm_s_f16/cta_iterator.h
View file @ ee33e2e7
......@@ -7,11 +7,11 @@
namespace turbomind {
#if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDACC_VER_MINOR__ >= 4)
#define L2_CACHEHINT(size) ".L2::" #size "B"
#else
// #if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDACC_VER_MINOR__ >= 4)
// #define L2_CACHEHINT(size) ".L2::" #size "B"
// #else
#define L2_CACHEHINT(size)
#endif
// #endif
template<typename T>
__inline__ __device__ void cp_async_cg_A(uint32_t smem_int_ptr, const T* __restrict__ src, bool mask)
......
src/turbomind/kernels/gemm_s_f16/gemm_template.h
View file @ ee33e2e7
......@@ -61,12 +61,12 @@ __inline__ __device__ uint transpose_m8n8_b16_movmatrix(uint a)
__inline__ __device__ uint transpose_m8n8_b16(uint a, int lane_id)
{
#if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDACC_VER_MINOR__ >= 8)
(void)lane_id;
return transpose_m8n8_b16_movmatrix(a);
#else
// #if (__CUDACC_VER_MAJOR__ >= 11) && (__CUDACC_VER_MINOR__ >= 8)
// (void)lane_id;
// return transpose_m8n8_b16_movmatrix(a);
// #else
return transpose_m8n8_b16_warp_shuffle(a, lane_id);
#endif
// #endif
}
namespace ops {
......
src/turbomind/kernels/reduce_kernel_utils.cuh
View file @ ee33e2e7
......@@ -16,11 +16,11 @@
#pragma once
#include <array>
#include <assert.h>
#if ((__CUDACC_VER_MAJOR__ > 11) || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))
#include <cooperative_groups/reduce.h>
#else
// #if ((__CUDACC_VER_MAJOR__ > 11) || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))
// #include <cooperative_groups/reduce.h>
// #else
#include <cooperative_groups.h>
#endif
// #endif
#include "src/turbomind/utils/cuda_bf16_wrapper.h"
#include "src/turbomind/utils/cuda_type_utils.cuh"
#include <cuda_fp16.h>
......@@ -244,15 +244,15 @@ __inline__ __device__ void cgBlockReduceSumElements(float* element_list, float*
const int tid = cta.thread_rank();
const int blockz = blockDim.x;
for (int i = 0; i < NUM; i++) {
#if ((__CUDACC_VER_MAJOR__ > 11) || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))
cgBlockReduceSumElements_shm[i * blockz + tid] = cg::reduce(tile, element_list[i], cg::plus<float>());
#else
// #if ((__CUDACC_VER_MAJOR__ > 11) || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))
// cgBlockReduceSumElements_shm[i * blockz + tid] = cg::reduce(tile, element_list[i], cg::plus<float>());
// #else
// TODO Add implementation here
if (threadIdx.x == 0 && blockIdx.x == 0) {
printf("[ERROR] Not support cgBlockReduceSumElements when CUDA < 11 \n");
assert(false);
}
#endif
// #endif
}
cg::sync(cta);
if (tid == 0) {
......
src/turbomind/utils/CMakeLists.txt
View file @ ee33e2e7
......@@ -77,11 +77,11 @@ if (BUILD_MULTI_GPU)
target_link_libraries(nccl_utils PUBLIC ${NCCL_LIBRARIES} logger)
endif()
add_library(cublasINT8MMWrapper STATIC cublasINT8MMWrapper.cc)
# add_library(cublasINT8MMWrapper STATIC cublasINT8MMWrapper.cc)
#set_property(TARGET cublasINT8MMWrapper PROPERTY POSITION_INDEPENDENT_CODE ON)
#set_property(TARGET cublasINT8MMWrapper PROPERTY CUDA_RESOLVE_DEVICE_SYMBOLS ON)
#target_link_libraries(cublasINT8MMWrapper PUBLIC cublasLt cudart curand cublasAlgoMap cublasMMWrapper cuda_utils logger)
target_link_libraries(cublasINT8MMWrapper PUBLIC cudart curand cublasAlgoMap cublasMMWrapper cuda_utils logger)
# target_link_libraries(cublasINT8MMWrapper PUBLIC cudart curand cublasAlgoMap cublasMMWrapper cuda_utils logger)
if(ENABLE_FP8)
add_library(cublasFP8MMWrapper STATIC cublasFP8MMWrapper.cu)
......@@ -108,7 +108,7 @@ if (SPARSITY_SUPPORT)
target_link_libraries(gemm PUBLIC cusparse -lcusparseLt)
endif()
add_library(cuda_fp8_utils STATIC cuda_fp8_utils.cu)
# add_library(cuda_fp8_utils STATIC cuda_fp8_utils.cu)
#set_property(TARGET cuda_fp8_utils PROPERTY POSITION_INDEPENDENT_CODE ON)
#set_property(TARGET cuda_fp8_utils PROPERTY CUDA_RESOLVE_DEVICE_SYMBOLS ON)
......
src/turbomind/utils/allocator.h
View file @ ee33e2e7
......@@ -44,9 +44,9 @@
#include "src/turbomind/utils/logger.h"
#if defined(CUDART_VERSION) && CUDART_VERSION < 11020
// #if defined(CUDART_VERSION) && CUDART_VERSION < 11020
#define CUDA_MEMORY_POOL_DISABLED
#endif
// #endif
namespace turbomind {
......
src/turbomind/utils/cublasFP8MMWrapper.cu
View file @ ee33e2e7
......@@ -237,10 +237,10 @@ void cublasFP8MMWrapper::Gemm(__nv_bfloat16* res,
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(info.reductionScheme), sizeof(info.reductionScheme));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
#endif
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
// #endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
cublasLtMatmulAlgoConfigSetAttribute(
......@@ -462,10 +462,10 @@ void cublasFP8MMWrapper::Gemm(__nv_fp8_e4m3* res,
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(info.reductionScheme), sizeof(info.reductionScheme));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
#endif
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
// #endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
cublasLtMatmulAlgoConfigSetAttribute(
......
src/turbomind/utils/cublasINT8MMWrapper.cc
View file @ ee33e2e7
......@@ -94,11 +94,11 @@ void cublasINT8MMWrapper::Gemm(int* res,
{
mu_->lock();
cublasOperation_t opTranspose = CUBLAS_OP_T;
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
#else
// #if (CUDART_VERSION >= 11000)
// cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
// #else
cudaDataType_t computeType = CUDA_R_32I;
#endif
// #endif
cublasLtMatmulDesc_t matmulDesc;
cublasLtMatrixLayout_t AtransformDesc = NULL;
cublasLtMatrixLayout_t BtransformDesc = NULL;
......@@ -106,16 +106,16 @@ void cublasINT8MMWrapper::Gemm(int* res,
cublasLtOrder_t order_COL32 = CUBLASLT_ORDER_COL32;
cublasLtOrder_t order_matrixB;
#if (CUDART_VERSION >= 11000)
if (use_ORDER_COL32_2R_4R4_) {
order_matrixB = CUBLASLT_ORDER_COL32_2R_4R4;
}
else {
// #if (CUDART_VERSION >= 11000)
// if (use_ORDER_COL32_2R_4R4_) {
// order_matrixB = CUBLASLT_ORDER_COL32_2R_4R4;
// }
// else {
// order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// }
// #else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
#endif
// #endif
int ldaTransform = 32 * m;
int ldbTransform;
......@@ -128,11 +128,11 @@ void cublasINT8MMWrapper::Gemm(int* res,
int ldcTransform = 32 * m;
// create matmulDesc
#if (CUDART_VERSION >= 11000)
cublasLtMatmulDescCreate(&matmulDesc, computeType, CUDA_R_32I);
#else
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulDescCreate(&matmulDesc, computeType, CUDA_R_32I);
// #else
cublasLtMatmulDescCreate(&matmulDesc, computeType);
#endif
// #endif
cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
cublasLtMatrixLayoutCreate(&AtransformDesc, CUDA_R_8I, m, k, ldaTransform);
cublasLtMatrixLayoutSetAttribute(AtransformDesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
......@@ -187,10 +187,10 @@ void cublasINT8MMWrapper::Gemm(int* res,
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(tmp_info.swizzle), sizeof(tmp_info.swizzle));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(tmp_info.reductionScheme), sizeof(int));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(tmp_info.stages), sizeof(tmp_info.stages));
#endif
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(tmp_info.stages), sizeof(tmp_info.stages));
// #endif
}
else {
findAlgo = 1;
......@@ -215,16 +215,16 @@ void cublasINT8MMWrapper::Gemm(int* res,
cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(swizzle), sizeof(swizzle));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(reductionScheme), sizeof(int));
#if (CUDART_VERSION >= 11000)
int stages;
if (use_ORDER_COL32_2R_4R4_) {
stages = 15;
}
else {
stages = 13;
}
cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(stages), sizeof(stages));
#endif
// #if (CUDART_VERSION >= 11000)
// int stages;
// if (use_ORDER_COL32_2R_4R4_) {
// stages = 15;
// }
// else {
// stages = 13;
// }
// cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(stages), sizeof(stages));
// #endif
}
cublasLtMatmul(cublaslt_handle_,
......@@ -273,11 +273,11 @@ void cublasINT8MMWrapper::Gemm(int8_t* res,
// int8 gemm does not support CUBLAS_POINTER_MODE_DEVICE
// cublasLtPointerMode_t pointerMode = CUBLASLT_POINTER_MODE_ALPHA_DEVICE_VECTOR_BETA_ZERO;
cudaDataType_t scaleType = CUDA_R_32F;
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
#else
// #if (CUDART_VERSION >= 11000)
// cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
// #else
cudaDataType_t computeType = CUDA_R_32I;
#endif
// #endif
cublasLtMatmulDesc_t matmulDesc;
cublasLtMatrixLayout_t AtransformDesc = NULL;
cublasLtMatrixLayout_t BtransformDesc = NULL;
......@@ -285,16 +285,16 @@ void cublasINT8MMWrapper::Gemm(int8_t* res,
cublasLtOrder_t order_COL32 = CUBLASLT_ORDER_COL32;
cublasLtOrder_t order_matrixB;
#if (CUDART_VERSION >= 11000)
if (use_ORDER_COL32_2R_4R4_) {
order_matrixB = CUBLASLT_ORDER_COL32_2R_4R4;
}
else {
// #if (CUDART_VERSION >= 11000)
// if (use_ORDER_COL32_2R_4R4_) {
// order_matrixB = CUBLASLT_ORDER_COL32_2R_4R4;
// }
// else {
// order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// }
// #else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
#endif
// #endif
int ldaTransform = 32 * m;
......@@ -309,11 +309,11 @@ void cublasINT8MMWrapper::Gemm(int8_t* res,
int ldcTransform = 32 * m;
// create matmulDesc
#if (CUDART_VERSION >= 11000)
cublasLtMatmulDescCreate(&matmulDesc, computeType, scaleType);
#else
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulDescCreate(&matmulDesc, computeType, scaleType);
// #else
cublasLtMatmulDescCreate(&matmulDesc, computeType);
#endif
// #endif
cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_SCALE_TYPE, &scaleType, sizeof(scaleType));
// cublasLtMatmulDescSetAttribute(matmulDesc, CUBLASLT_MATMUL_DESC_POINTER_MODE, &pointerMode,
......@@ -367,10 +367,10 @@ void cublasINT8MMWrapper::Gemm(int8_t* res,
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(tmp_info.swizzle), sizeof(tmp_info.swizzle));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(tmp_info.reductionScheme), sizeof(int));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(tmp_info.stages), sizeof(tmp_info.stages));
#endif
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(tmp_info.stages), sizeof(tmp_info.stages));
// #endif
}
else {
findAlgo = 1;
......@@ -395,16 +395,16 @@ void cublasINT8MMWrapper::Gemm(int8_t* res,
cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(swizzle), sizeof(swizzle));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(reductionScheme), sizeof(int));
#if (CUDART_VERSION >= 11000)
int stages;
if (use_ORDER_COL32_2R_4R4_) {
stages = 15;
}
else {
stages = 13;
}
cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(stages), sizeof(stages));
#endif
// #if (CUDART_VERSION >= 11000)
// int stages;
// if (use_ORDER_COL32_2R_4R4_) {
// stages = 15;
// }
// else {
// stages = 13;
// }
// cublasLtMatmulAlgoConfigSetAttribute(&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(stages), sizeof(stages));
// #endif
}
float beta = 0.0f;
......
src/turbomind/utils/cublasMMWrapper.cc
View file @ ee33e2e7
......@@ -192,118 +192,119 @@ void cublasMMWrapper::Gemm(cublasOperation_t transa,
}
}
// if (using_cublasLt) {
if (0) {
cublasLtMatmulDesc_t operationDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
cudaDataType_t scaleType;
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType;
#else
cudaDataType_t computeType;
#endif
if (is_fp16_computeType) {
#if (CUDART_VERSION >= 11000)
computeType = CUBLAS_COMPUTE_16F;
#else
computeType = CUDA_R_16F;
#endif
scaleType = CUDA_R_16F;
}
else {
#if (CUDART_VERSION >= 11000)
computeType = CUBLAS_COMPUTE_32F;
#else
computeType = CUDA_R_32F;
#endif
scaleType = CUDA_R_32F;
}
// --------------------------------------
// Create descriptors for the original matrices
cublasLtMatrixLayoutCreate(&Adesc, Atype_, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda);
cublasLtMatrixLayoutCreate(&Bdesc, Btype_, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb);
cublasLtMatrixLayoutCreate(&Cdesc, Ctype_, m, n, ldc);
#if (CUDART_VERSION >= 11000)
cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
#else
cublasLtMatmulDescCreate(&operationDesc, computeType);
#endif
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));
cublasLtMatmulAlgo_t algo;
void* workSpace = cublas_workspace_;
int workspaceSize = cublas_workspace_ == NULL ? 0 : CUBLAS_WORKSPACE_SIZE;
if (findAlgo) {
if (info.workspaceSize > workspaceSize) {
findAlgo = 0;
}
else {
cublasLtMatmulAlgoInit(
cublaslt_handle_, computeType, scaleType, Atype_, Btype_, Ctype_, Ctype_, info.algoId, &algo);
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &(info.customOption), sizeof(info.customOption));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &(info.tile), sizeof(info.tile));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &(info.splitK_val), sizeof(info.splitK_val));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(info.swizzle), sizeof(info.swizzle));
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
&(info.reductionScheme),
sizeof(info.reductionScheme));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
#endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_INNER_SHAPE_ID, &(info.inner_shapeId), sizeof(info.inner_shapeId));
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_CLUSTER_SHAPE_ID,
&(info.cluster_shapeId),
sizeof(info.cluster_shapeId));
#elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_MMA_SHAPE_ID, &(info.mma_shapeId), sizeof(info.mma_shapeId));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CGA_SHAPE_ID, &(info.cga_shapeId), sizeof(info.cga_shapeId));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SCHEDULING_MODE, &(info.sche_mode), sizeof(info.sche_mode));
#endif
}
}
// cublasLtMatmul(cublaslt_handle_,
// operationDesc,
// alpha,
// A,
// Adesc,
// B,
// Bdesc,
// beta,
// C,
// Cdesc,
// C,
// Cdesc,
// (findAlgo == 1 ? (&algo) : NULL),
// workSpace,
// workspaceSize,
// stream_);
cublasLtMatmulDescDestroy(operationDesc);
cublasLtMatrixLayoutDestroy(Adesc);
cublasLtMatrixLayoutDestroy(Bdesc);
cublasLtMatrixLayoutDestroy(Cdesc);
sync_check_cuda_error();
}
else {
// if (using_cublasLt) {
// if (0) {
// cublasLtMatmulDesc_t operationDesc = NULL;
// cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
// cudaDataType_t scaleType;
// #if (CUDART_VERSION >= 11000)
// cublasComputeType_t computeType;
// #else
// cudaDataType_t computeType;
// #endif
// if (is_fp16_computeType) {
// #if (CUDART_VERSION >= 11000)
// computeType = CUBLAS_COMPUTE_16F;
// #else
// computeType = CUDA_R_16F;
// #endif
// scaleType = CUDA_R_16F;
// }
// else {
// #if (CUDART_VERSION >= 11000)
// computeType = CUBLAS_COMPUTE_32F;
// #else
// computeType = CUDA_R_32F;
// #endif
// scaleType = CUDA_R_32F;
// }
// // --------------------------------------
// // Create descriptors for the original matrices
// cublasLtMatrixLayoutCreate(&Adesc, Atype_, transa == CUBLAS_OP_N ? m : k, transa == CUBLAS_OP_N ? k : m, lda);
// cublasLtMatrixLayoutCreate(&Bdesc, Btype_, transb == CUBLAS_OP_N ? k : n, transb == CUBLAS_OP_N ? n : k, ldb);
// cublasLtMatrixLayoutCreate(&Cdesc, Ctype_, m, n, ldc);
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
// #else
// cublasLtMatmulDescCreate(&operationDesc, computeType);
// #endif
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));
// cublasLtMatmulAlgo_t algo;
// void* workSpace = cublas_workspace_;
// int workspaceSize = cublas_workspace_ == NULL ? 0 : CUBLAS_WORKSPACE_SIZE;
// if (findAlgo) {
// if (info.workspaceSize > workspaceSize) {
// findAlgo = 0;
// }
// else {
// cublasLtMatmulAlgoInit(
// cublaslt_handle_, computeType, scaleType, Atype_, Btype_, Ctype_, Ctype_, info.algoId, &algo);
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &(info.customOption), sizeof(info.customOption));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &(info.tile), sizeof(info.tile));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &(info.splitK_val), sizeof(info.splitK_val));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(info.swizzle), sizeof(info.swizzle));
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
// &(info.reductionScheme),
// sizeof(info.reductionScheme));
// // #if (CUDART_VERSION >= 11000)
// // cublasLtMatmulAlgoConfigSetAttribute(
// // &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
// // #endif
// #if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_INNER_SHAPE_ID, &(info.inner_shapeId), sizeof(info.inner_shapeId));
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_CLUSTER_SHAPE_ID,
// &(info.cluster_shapeId),
// sizeof(info.cluster_shapeId));
// #elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_MMA_SHAPE_ID, &(info.mma_shapeId), sizeof(info.mma_shapeId));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CGA_SHAPE_ID, &(info.cga_shapeId), sizeof(info.cga_shapeId));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SCHEDULING_MODE, &(info.sche_mode), sizeof(info.sche_mode));
// #endif
// }
// }
// // cublasLtMatmul(cublaslt_handle_,
// // operationDesc,
// // alpha,
// // A,
// // Adesc,
// // B,
// // Bdesc,
// // beta,
// // C,
// // Cdesc,
// // C,
// // Cdesc,
// // (findAlgo == 1 ? (&algo) : NULL),
// // workSpace,
// // workspaceSize,
// // stream_);
// cublasLtMatmulDescDestroy(operationDesc);
// cublasLtMatrixLayoutDestroy(Adesc);
// cublasLtMatrixLayoutDestroy(Bdesc);
// cublasLtMatrixLayoutDestroy(Cdesc);
// sync_check_cuda_error();
// }
// else {
int cublasAlgo = info.algoId;
check_cuda_error(cublasGemmEx(cublas_handle_,
transa,
......@@ -325,7 +326,7 @@ void cublasMMWrapper::Gemm(cublasOperation_t transa,
computeType_,
static_cast<cublasGemmAlgo_t>(cublasAlgo)));
sync_check_cuda_error();
}
// }
mu_->unlock();
}
......@@ -382,81 +383,81 @@ CublasDataType cublasMMWrapper::getCublasDataType(cudaDataType_t data_type)
return FLOAT_DATATYPE;
}
#if (CUDART_VERSION >= 11000)
// input, weight, output are row-major
// only works for cublas 11.x
void cublasMMWrapper::Gemm(cublasOperation_t transa,
cublasOperation_t transb,
const int m,
const int n,
const int k,
const void* A,
const int lda,
const void* B,
const int ldb,
const void* bias,
void* C,
const int ldc)
{
TM_LOG_DEBUG(__PRETTY_FUNCTION__);
cudaDataType_t Atype, Btype, Ctype;
cublasComputeType_t computeType;
cudaDataType_t scaleType;
float alpha_float = 1.0f;
float beta_float = 0.0f;
half alpha_half = half(1.0f);
half beta_half = half(0.0f);
void * alpha, *beta;
// int is_fp16_computeType = computeType_ == CUDA_R_16F ? 1 : 0;
if (Atype_ == CUDA_R_32F) {
computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
Atype = CUDA_R_32F;
Btype = CUDA_R_32F;
Ctype = CUDA_R_32F;
scaleType = CUDA_R_32F;
alpha = &alpha_float;
beta = &beta_float;
}
else if (Atype_ == CUDA_R_16BF) {
computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
Atype = CUDA_R_16BF;
Btype = CUDA_R_16BF;
Ctype = CUDA_R_16BF;
scaleType = CUDA_R_32F;
alpha = &alpha_float;
beta = &beta_float;
}
else {
computeType = CUBLAS_COMPUTE_16F;
Atype = CUDA_R_16F;
Btype = CUDA_R_16F;
Ctype = CUDA_R_16F;
scaleType = CUDA_R_16F;
alpha = &alpha_half;
beta = &beta_half;
}
cublasLtMatmulDesc_t operationDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
cublasLtEpilogue_t epi = CUBLASLT_EPILOGUE_BIAS;
cublasLtMatrixLayoutCreate(&Adesc, Atype, (transa == CUBLAS_OP_N) ? m : k, (transa == CUBLAS_OP_N) ? k : m, lda);
cublasLtMatrixLayoutCreate(&Bdesc, Btype, (transb == CUBLAS_OP_N) ? k : n, (transb == CUBLAS_OP_N) ? n : k, ldb);
cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldc);
cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(cublasLtEpilogue_t));
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(const void*));
// check_cuda_error(cublasLtMatmul(
// cublaslt_handle_, operationDesc, alpha, A, Adesc, B, Bdesc, beta, C, Cdesc, C, Cdesc, NULL, NULL, 0, stream_));
cublasLtMatrixLayoutDestroy(Adesc);
cublasLtMatrixLayoutDestroy(Bdesc);
cublasLtMatrixLayoutDestroy(Cdesc);
cublasLtMatmulDescDestroy(operationDesc);
}
#endif
// #if (CUDART_VERSION >= 11000)
// // input, weight, output are row-major
// // only works for cublas 11.x
// void cublasMMWrapper::Gemm(cublasOperation_t transa,
// cublasOperation_t transb,
// const int m,
// const int n,
// const int k,
// const void* A,
// const int lda,
// const void* B,
// const int ldb,
// const void* bias,
// void* C,
// const int ldc)
// {
// TM_LOG_DEBUG(__PRETTY_FUNCTION__);
// cudaDataType_t Atype, Btype, Ctype;
// cublasComputeType_t computeType;
// cudaDataType_t scaleType;
// float alpha_float = 1.0f;
// float beta_float = 0.0f;
// half alpha_half = half(1.0f);
// half beta_half = half(0.0f);
// void * alpha, *beta;
// // int is_fp16_computeType = computeType_ == CUDA_R_16F ? 1 : 0;
// if (Atype_ == CUDA_R_32F) {
// computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
// Atype = CUDA_R_32F;
// Btype = CUDA_R_32F;
// Ctype = CUDA_R_32F;
// scaleType = CUDA_R_32F;
// alpha = &alpha_float;
// beta = &beta_float;
// }
// else if (Atype_ == CUDA_R_16BF) {
// computeType = CUBLAS_COMPUTE_32F_FAST_TF32;
// Atype = CUDA_R_16BF;
// Btype = CUDA_R_16BF;
// Ctype = CUDA_R_16BF;
// scaleType = CUDA_R_32F;
// alpha = &alpha_float;
// beta = &beta_float;
// }
// else {
// computeType = CUBLAS_COMPUTE_16F;
// Atype = CUDA_R_16F;
// Btype = CUDA_R_16F;
// Ctype = CUDA_R_16F;
// scaleType = CUDA_R_16F;
// alpha = &alpha_half;
// beta = &beta_half;
// }
// cublasLtMatmulDesc_t operationDesc = NULL;
// cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
// cublasLtEpilogue_t epi = CUBLASLT_EPILOGUE_BIAS;
// cublasLtMatrixLayoutCreate(&Adesc, Atype, (transa == CUBLAS_OP_N) ? m : k, (transa == CUBLAS_OP_N) ? k : m, lda);
// cublasLtMatrixLayoutCreate(&Bdesc, Btype, (transb == CUBLAS_OP_N) ? k : n, (transb == CUBLAS_OP_N) ? n : k, ldb);
// cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldc);
// cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &transa, sizeof(cublasOperation_t));
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &transb, sizeof(cublasOperation_t));
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_EPILOGUE, &epi, sizeof(cublasLtEpilogue_t));
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_BIAS_POINTER, &bias, sizeof(const void*));
// // check_cuda_error(cublasLtMatmul(
// // cublaslt_handle_, operationDesc, alpha, A, Adesc, B, Bdesc, beta, C, Cdesc, C, Cdesc, NULL, NULL, 0, stream_));
// cublasLtMatrixLayoutDestroy(Adesc);
// cublasLtMatrixLayoutDestroy(Bdesc);
// cublasLtMatrixLayoutDestroy(Cdesc);
// cublasLtMatmulDescDestroy(operationDesc);
// }
// #endif
void cublasMMWrapper::setStream(cudaStream_t stream)
{
stream_ = stream;
......
src/turbomind/utils/cublasMMWrapper.h
View file @ ee33e2e7
......@@ -207,20 +207,20 @@ public:
CublasDataType getCublasDataType(cudaDataType_t data_type);
#if (CUDART_VERSION >= 11000)
void Gemm(cublasOperation_t transa,
cublasOperation_t transb,
const int m,
const int n,
const int k,
const void* A,
const int lda,
const void* B,
const int ldb,
const void* bias,
void* C,
const int ldc);
#endif
// #if (CUDART_VERSION >= 11000)
// void Gemm(cublasOperation_t transa,
// cublasOperation_t transb,
// const int m,
// const int n,
// const int k,
// const void* A,
// const int lda,
// const void* B,
// const int ldb,
// const void* bias,
// void* C,
// const int ldc);
// #endif
void stridedBatchedGemm(cublasOperation_t transa,
cublasOperation_t transb,
......
src/turbomind/utils/custom_ar_comm.cc
View file @ ee33e2e7
......@@ -152,17 +152,17 @@ void initCustomAllReduceComm(std::vector<std::shared_ptr<AbstractCustomComm>>* c
return;
}
#if defined(CUDART_VERSION) && CUDART_VERSION >= 11020
for (size_t i = 0; i < rank_size; i++) {
custom_all_reduce_comms->push_back(std::make_shared<CustomAllReduceComm<T>>(rank_size, i));
}
custom_all_reduce_comms->at(0)->allocateAndExchangePeerAccessPointer(custom_all_reduce_comms);
#else
// #if defined(CUDART_VERSION) && CUDART_VERSION >= 11020
// for (size_t i = 0; i < rank_size; i++) {
// custom_all_reduce_comms->push_back(std::make_shared<CustomAllReduceComm<T>>(rank_size, i));
// }
// custom_all_reduce_comms->at(0)->allocateAndExchangePeerAccessPointer(custom_all_reduce_comms);
// #else
TM_LOG_WARNING("Custom All Reduce is not supported before CUDA 11.2. Using NCCL as Comm.");
for (size_t i = 0; i < rank_size; i++) {
custom_all_reduce_comms->push_back(nullptr);
}
#endif
// #endif
}
// Template instantiation
......
src/turbomind/utils/gemm.cc
View file @ ee33e2e7
......@@ -269,82 +269,82 @@ void Gemm::gemm(const GemmOp transa,
}
// if (using_cublasLt) {
if(0) {
const size_t a_rows = (a_op == getCublasOperation(GEMM_OP_N)) ? _m : k;
const size_t a_cols = (a_op == getCublasOperation(GEMM_OP_N)) ? k : _m;
const size_t b_rows = (b_op == getCublasOperation(GEMM_OP_N)) ? k : _n;
const size_t b_cols = (b_op == getCublasOperation(GEMM_OP_N)) ? _n : k;
cublasLtMatmulDesc_t matmul_desc = NULL;
cublasLtMatrixLayout_t a_desc = NULL, b_desc = NULL, c_desc = NULL;
cudaDataType_t scale_type = getCublasDataType(compute_type_);
auto compute_type = getCublasComputeType(compute_type_);
// --------------------------------------
// Create descriptors for the original matrices
cublasLtMatrixLayoutCreate(&a_desc, a_type, a_rows, a_cols, _lda);
cublasLtMatrixLayoutCreate(&b_desc, b_type, b_rows, b_cols, _ldb);
cublasLtMatrixLayoutCreate(&c_desc, c_type, _m, _n, ldc);
#if (CUDART_VERSION >= 11000)
cublasLtMatmulDescCreate(&matmul_desc, compute_type, scale_type);
#else
cublasLtMatmulDescCreate(&matmul_desc, compute_type);
#endif
cublasLtMatmulDescSetAttribute(matmul_desc, CUBLASLT_MATMUL_DESC_TRANSA, &a_op, sizeof(cublasOperation_t));
cublasLtMatmulDescSetAttribute(matmul_desc, CUBLASLT_MATMUL_DESC_TRANSB, &b_op, sizeof(cublasOperation_t));
cublasLtMatmulAlgo_t algo;
void* workspace = workspace_;
int workspace_size = workspace_ == nullptr ? 0 : CUBLAS_WORKSPACE_SIZE;
if (findAlgo) {
if (info.workspaceSize > workspace_size) {
findAlgo = 0;
}
else {
cublasLtMatmulAlgoInit(
cublaslt_handle_, compute_type, scale_type, a_type, b_type, c_type, c_type, info.algoId, &algo);
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &(info.customOption), sizeof(info.customOption));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &(info.tile), sizeof(info.tile));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &(info.splitK_val), sizeof(info.splitK_val));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(info.swizzle), sizeof(info.swizzle));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(info.reductionScheme), sizeof(int));
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
#endif
}
}
// if(0) {
// const size_t a_rows = (a_op == getCublasOperation(GEMM_OP_N)) ? _m : k;
// const size_t a_cols = (a_op == getCublasOperation(GEMM_OP_N)) ? k : _m;
// const size_t b_rows = (b_op == getCublasOperation(GEMM_OP_N)) ? k : _n;
// const size_t b_cols = (b_op == getCublasOperation(GEMM_OP_N)) ? _n : k;
// cublasLtMatmulDesc_t matmul_desc = NULL;
// cublasLtMatrixLayout_t a_desc = NULL, b_desc = NULL, c_desc = NULL;
// cudaDataType_t scale_type = getCublasDataType(compute_type_);
// auto compute_type = getCublasComputeType(compute_type_);
// // --------------------------------------
// // Create descriptors for the original matrices
// cublasLtMatrixLayoutCreate(&a_desc, a_type, a_rows, a_cols, _lda);
// cublasLtMatrixLayoutCreate(&b_desc, b_type, b_rows, b_cols, _ldb);
// cublasLtMatrixLayoutCreate(&c_desc, c_type, _m, _n, ldc);
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulDescCreate(&matmul_desc, compute_type, scale_type);
// #else
// cublasLtMatmulDescCreate(&matmul_desc, compute_type);
// #endif
// cublasLtMatmulDescSetAttribute(matmul_desc, CUBLASLT_MATMUL_DESC_TRANSA, &a_op, sizeof(cublasOperation_t));
// cublasLtMatmulDescSetAttribute(matmul_desc, CUBLASLT_MATMUL_DESC_TRANSB, &b_op, sizeof(cublasOperation_t));
// cublasLtMatmulAlgo_t algo;
// void* workspace = workspace_;
// int workspace_size = workspace_ == nullptr ? 0 : CUBLAS_WORKSPACE_SIZE;
// if (findAlgo) {
// if (info.workspaceSize > workspace_size) {
// findAlgo = 0;
// }
// else {
// cublasLtMatmulAlgoInit(
// cublaslt_handle_, compute_type, scale_type, a_type, b_type, c_type, c_type, info.algoId, &algo);
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &(info.customOption), sizeof(info.customOption));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &(info.tile), sizeof(info.tile));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &(info.splitK_val), sizeof(info.splitK_val));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &(info.swizzle), sizeof(info.swizzle));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &(info.reductionScheme), sizeof(int));
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &(info.stages), sizeof(info.stages));
// #endif
// }
// }
cublasLtMatmul(cublaslt_handle_,
matmul_desc,
alpha_ptr,
a_data_ptr,
a_desc,
b_data_ptr,
b_desc,
beta_ptr,
C,
c_desc,
C,
c_desc,
(findAlgo == 1 ? (&algo) : NULL),
workspace,
workspace_size,
stream_);
cublasLtMatmulDescDestroy(matmul_desc);
cublasLtMatrixLayoutDestroy(a_desc);
cublasLtMatrixLayoutDestroy(b_desc);
cublasLtMatrixLayoutDestroy(c_desc);
sync_check_cuda_error();
}
else {
// cublasLtMatmul(cublaslt_handle_,
// matmul_desc,
// alpha_ptr,
// a_data_ptr,
// a_desc,
// b_data_ptr,
// b_desc,
// beta_ptr,
// C,
// c_desc,
// C,
// c_desc,
// (findAlgo == 1 ? (&algo) : NULL),
// workspace,
// workspace_size,
// stream_);
// cublasLtMatmulDescDestroy(matmul_desc);
// cublasLtMatrixLayoutDestroy(a_desc);
// cublasLtMatrixLayoutDestroy(b_desc);
// cublasLtMatrixLayoutDestroy(c_desc);
// sync_check_cuda_error();
// }
// else {
cudaDataType_t compute_type = getCublasDataType(compute_type_);
int cublas_algo = info.algoId;
check_cuda_error(cublasGemmEx(cublas_handle_,
......@@ -367,7 +367,7 @@ void Gemm::gemm(const GemmOp transa,
compute_type,
static_cast<cublasGemmAlgo_t>(cublas_algo)));
sync_check_cuda_error();
}
// }
mutex_->unlock();
}
......@@ -1035,19 +1035,19 @@ cudaDataType_t getCublasDataType(DataType dtype)
}
}
#if (CUDART_VERSION >= 11000)
cublasComputeType_t getCublasComputeType(DataType ctype)
{
switch (ctype) {
case TYPE_FP16:
return CUBLAS_COMPUTE_16F;
case TYPE_FP32:
return CUBLAS_COMPUTE_32F;
default:
throw GemmNotSupportedException("Not supported cublas compute type.");
}
}
#else
// #if (CUDART_VERSION >= 11000)
// cublasComputeType_t getCublasComputeType(DataType ctype)
// {
// switch (ctype) {
// case TYPE_FP16:
// return CUBLAS_COMPUTE_16F;
// case TYPE_FP32:
// return CUBLAS_COMPUTE_32F;
// default:
// throw GemmNotSupportedException("Not supported cublas compute type.");
// }
// }
// #else
cudaDataType_t getCublasComputeType(DataType ctype)
{
switch (ctype) {
......@@ -1059,7 +1059,7 @@ cudaDataType_t getCublasComputeType(DataType ctype)
throw GemmNotSupportedException("Not supported cublas compute type.");
}
}
#endif
// #endif
cublasOperation_t getCublasOperation(GemmOp op)
{
......
src/turbomind/utils/gemm.h
View file @ ee33e2e7
......@@ -622,11 +622,11 @@ std::shared_ptr<Gemm>
createGemm(IAllocator* allocator, cudaStream_t stream, bool sparse = false, bool quantized = false);
cudaDataType_t getCublasDataType(DataType dtype);
#if (CUDART_VERSION >= 11000)
cublasComputeType_t getCublasComputeType(DataType dtype);
#else
// #if (CUDART_VERSION >= 11000)
// cublasComputeType_t getCublasComputeType(DataType dtype);
// #else
cudaDataType_t getCublasComputeType(DataType dtype);
#endif
// #endif
cublasOperation_t getCublasOperation(GemmOp op);
std::string getGemmOpString(const GemmOp& op);
......
src/turbomind/utils/gemm_test/gemm_func.cc
View file @ ee33e2e7
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