Skip to content

GitLab

"vscode:/vscode.git/clone" did not exist on "af5551f929b1906345abd5866959c12c83c7bdbd"
Commit ee33e2e7 authored by zhouxiang's avatar zhouxiang
Browse files

support dtk23.10

parent e432dbb0
Hide whitespace changes
Inline Side-by-side
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 {
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#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;
#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 {
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#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;
#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
}
}
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 {
// 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 {
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/encoder_igemm_func.cc
View file @ ee33e2e7
......@@ -82,11 +82,11 @@ int printPerfStructure(int m, int n, int k, const customMatmulPerf_t& perf, FILE
matmulAlgo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &swizzle, sizeof(swizzle), NULL);
cublasLtMatmulAlgoConfigGetAttribute(
matmulAlgo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption), NULL);
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
#else
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
// #else
stages = 0;
#endif
// #endif
printf("algo={ Id=%d, tileIdx=%d (%s) splitK=%d reduc=%d swizzle=%d custom=%d stages=%d} status %d "
"time %f workspace=%d mathMode=%d waves=%f\n",
......@@ -148,11 +148,11 @@ int printBatchPerfStructure(
matmulAlgo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &swizzle, sizeof(swizzle), NULL);
cublasLtMatmulAlgoConfigGetAttribute(
matmulAlgo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption), NULL);
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
#else
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
// #else
stages = 0;
#endif
// #endif
printf("algo={ Id=%d, tileIdx=%d (%s) splitK=%d reduc=%d swizzle=%d custom=%d stages=%d} status %d "
"time %f workspace=%d mathMode=%d waves=%f\n",
......@@ -279,693 +279,693 @@ static cublasStatus_t customMatmulRun(cublasLtHandle_t ltHandle, //
// Sample wrapper running through multiple algo and config attributes combination for INT8 gemm using cublasLt low-level
// API
template<typename T, typename scaleT>
int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
int m,
int n,
int k,
const scaleT* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const scaleT* beta, /* host pointer */
T* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout)
{
cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
cublasLtMatmulDesc_t operationDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
cudaStream_t stream = 0;
// SplitK value that we are going to try when SplitK is supported for a given algo
const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// Let try a fixed number of combinations
#define ALGO_COMBINATIONS 50000
int AlgoCombinations = ALGO_COMBINATIONS;
int AlgoCount = 0;
int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
customMatmulPerf_t perfResults[ALGO_COMBINATIONS];
int nbAlgoIds = 0;
#define ALGO_IDS 100
int algoIdA[ALGO_IDS];
cudaDataType_t Atype, Btype, Ctype, scaleType;
Atype = CUDA_R_8I;
Btype = CUDA_R_8I;
if (std::is_same<T, int32_t>::value && std::is_same<scaleT, int>::value) {
Ctype = CUDA_R_32I;
scaleType = CUDA_R_32I;
}
else if (std::is_same<T, int8_t>::value && std::is_same<scaleT, float>::value) {
Ctype = CUDA_R_8I;
scaleType = CUDA_R_32F;
}
else {
printf("[ERROR]<T,scaleT> of igemm is invalid\n");
exit(-1);
}
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
#else
cudaDataType_t computeType = CUDA_R_32I;
#endif
cublasOperation_t opTranspose = CUBLAS_OP_T;
bool use_ORDER_COL32_2R_4R4 = false;
#if (CUDART_VERSION >= 11000)
int device{-1};
cudaGetDevice(&device);
cudaDeviceProp props;
cudaGetDeviceProperties(&props, device);
if (props.major * 10 + props.minor >= 80) {
use_ORDER_COL32_2R_4R4 = true;
}
#endif
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 {
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
#endif
int ldaTransform = 32 * m;
int ldbTransform;
if (use_ORDER_COL32_2R_4R4) {
ldbTransform = 32 * ((n + 32 - 1) / 32) * 32;
}
else {
ldbTransform = 32 * ((n + 8 - 1) / 8) * 8;
}
int ldcTransform = 32 * m;
#if (CUDART_VERSION >= 11000)
status = cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
#else
status = cublasLtMatmulDescCreate(&operationDesc, scaleType);
#endif
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
// Create matrix descriptors.
status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, ldaTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, n, k, ldbTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status =
cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_matrixB, sizeof(order_matrixB));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldcTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
// Request AlgoId available for IGEMM
status = cublasLtMatmulAlgoGetIds(
ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, ALGO_IDS, algoIdA, &nbAlgoIds);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
// Loop over the Algo IDs
for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
cublasLtMatmulAlgo_t algo;
size_t sizeWritten = 0;
/* Initialize algo structure with given Algp ID */
status =
cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIdA[idx], &algo);
if (status != CUBLAS_STATUS_SUCCESS) {
continue;
}
// Query the tiles enums supported by that algo
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
int nbTiles = int(sizeWritten / sizeof(int));
int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
if (nbTiles == 0) {
tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
nbTiles = 1;
}
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
int nbStages = int(sizeWritten / sizeof(int));
std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
if (nbStages == 0) {
stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
nbStages = 1;
}
else {
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
}
#endif
int splitkSupport, redMask, swizzlingMax, customOptionMax;
// Retrieve Algo Capabilities attributes to be able to setup loop over the different combinations
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
/* Loop over the different tiles */
for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
#if (CUDART_VERSION >= 11000)
/* Loop over different stages count */
for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
#endif
/* Loop over the different custom option if any */
for (int customOption = 0; customOption <= customOptionMax; customOption++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
/* Loop over the CTAs swizzling support */
for (int k = 0; k <= swizzlingMax; k++) {
int splitK_trial = 0;
if (splitkSupport) {
splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
}
// Loop over the splitK value over a fixed sequence splitKSequenceA in addition to the case
// where splitK is not enabled
for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
/* Setup attribute of the algo to run */
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
int splitK_val = 0;
int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
if (l > 0) { // Split-K case
splitK_val = splitKSequenceA[l - 1];
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
&splitKSequenceA[l - 1],
sizeof(splitKSequenceA[l - 1]));
/* Going over all the reduction scheme */
for (redScheme = 1;
redScheme <= (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
redScheme = redScheme << 1) {
if (redScheme & redMask) {
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
&redScheme,
sizeof(redScheme));
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Cdesc,
algo,
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[AlgoCount],
stream);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
} // end if
} // end for
}
else { // Non-splitK case
/* if user preference is ok with workspace */
if (AlgoCount < AlgoCombinations) {
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Cdesc,
algo,
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[AlgoCount],
stream);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
}
}
} // end l
} // end k
} // end customOption
#if (CUDART_VERSION >= 11000)
} // end stagesIdx
#endif
} // end tileIdx
delete[] tileA;
} // end idx
// Sort the results per run duration
std::sort(perfResults, perfResults + AlgoCount, time_compare);
// Print timing and perf details
for (int i = 0, hasPrint = 0; i < AlgoCount; i++) {
printf("result %03d : ", i);
hasPrint = printPerfStructure(m, n, k, perfResults[i], fout, hasPrint);
}
CLEANUP:
// Descriptors are no longer needed as all GPU work was already enqueued
if (Cdesc) {
cublasLtMatrixLayoutDestroy(Cdesc);
}
if (Bdesc) {
cublasLtMatrixLayoutDestroy(Bdesc);
}
if (Adesc) {
cublasLtMatrixLayoutDestroy(Adesc);
}
if (operationDesc) {
cublasLtMatmulDescDestroy(operationDesc);
}
return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
}
template int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
int m,
int n,
int k,
const int* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const int* beta, /* host pointer */
int32_t* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout);
template int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const float* beta, /* host pointer */
int8_t* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout);
template<typename T, typename scaleT>
int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
int batchCount,
int m,
int n,
int k,
const scaleT* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const scaleT* beta, /* host pointer */
T* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout)
{
cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
cublasLtMatmulDesc_t operationDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
cudaStream_t stream = 0;
// SplitK value that we are going to try when SplitK is supported for a given algo
const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// Let try a fixed number of combinations
#define ALGO_COMBINATIONS 50000
int AlgoCombinations = ALGO_COMBINATIONS;
int AlgoCount = 0;
int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
customMatmulPerf_t perfResults[ALGO_COMBINATIONS];
int nbAlgoIds = 0;
#define ALGO_IDS 100
int algoIdA[ALGO_IDS];
cudaDataType_t Atype, Btype, Ctype, scaleType;
Atype = CUDA_R_8I;
Btype = CUDA_R_8I;
if (std::is_same<T, int32_t>::value && std::is_same<scaleT, int>::value) {
Ctype = CUDA_R_32I;
scaleType = CUDA_R_32I;
}
else if (std::is_same<T, int8_t>::value && std::is_same<scaleT, float>::value) {
Ctype = CUDA_R_8I;
scaleType = CUDA_R_32F;
}
else {
printf("[ERROR]<T,scaleT> of igemm is invalid\n");
exit(-1);
}
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
#else
cudaDataType_t computeType = CUDA_R_32I;
#endif
cublasOperation_t opTranspose = CUBLAS_OP_T;
bool use_ORDER_COL32_2R_4R4 = false;
#if (CUDART_VERSION >= 11000)
int device{-1};
cudaGetDevice(&device);
cudaDeviceProp props;
cudaGetDeviceProperties(&props, device);
if (props.major * 10 + props.minor >= 80) {
use_ORDER_COL32_2R_4R4 = true;
}
#endif
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 {
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
}
#else
order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
#endif
int ldaTransform = 32 * m;
int ldbTransform;
if (use_ORDER_COL32_2R_4R4) {
ldbTransform = 32 * ((n + 32 - 1) / 32) * 32;
}
else {
ldbTransform = 32 * ((n + 8 - 1) / 8) * 8;
}
int ldcTransform = 32 * m;
int64_t stridea, strideb, stridec;
stridea = m * k;
strideb = n * k;
stridec = m * n;
#if (CUDART_VERSION >= 11000)
status = cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
#else
status = cublasLtMatmulDescCreate(&operationDesc, scaleType);
#endif
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
// Create matrix descriptors.
status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, ldaTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stridea, sizeof(stridea));
status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, n, k, ldbTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status =
cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_matrixB, sizeof(order_matrixB));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideb, sizeof(strideb));
status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldcTransform);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stridec, sizeof(stridec));
// Request AlgoId available for IGEMM
status = cublasLtMatmulAlgoGetIds(
ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, ALGO_IDS, algoIdA, &nbAlgoIds);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
// Loop over the Algo IDs
for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
cublasLtMatmulAlgo_t algo;
size_t sizeWritten = 0;
/* Initialize algo structure with given Algp ID */
status =
cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIdA[idx], &algo);
if (status != CUBLAS_STATUS_SUCCESS) {
continue;
}
// Query the tiles enums supported by that algo
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
int nbTiles = int(sizeWritten / sizeof(int));
int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
if (nbTiles == 0) {
tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
nbTiles = 1;
}
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
int nbStages = int(sizeWritten / sizeof(int));
std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
if (nbStages == 0) {
stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
nbStages = 1;
}
else {
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
}
#endif
int splitkSupport, redMask, swizzlingMax, customOptionMax;
// Retrieve Algo Capabilities attributes to be able to setup loop over the different combinations
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
/* Loop over the different tiles */
for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
#if (CUDART_VERSION >= 11000)
/* Loop over different stages count */
for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
#endif
/* Loop over the different custom option if any */
for (int customOption = 0; customOption <= customOptionMax; customOption++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
/* Loop over the CTAs swizzling support */
for (int k = 0; k <= swizzlingMax; k++) {
int splitK_trial = 0;
if (splitkSupport) {
splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
}
// Loop over the splitK value over a fixed sequence splitKSequenceA in addition to the case
// where splitK is not enabled
for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
/* Setup attribute of the algo to run */
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
int splitK_val = 0;
int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
if (l > 0) { // Split-K case
splitK_val = splitKSequenceA[l - 1];
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
&splitKSequenceA[l - 1],
sizeof(splitKSequenceA[l - 1]));
/* Going over all the reduction scheme */
for (redScheme = 1;
redScheme <= (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
redScheme = redScheme << 1) {
if (redScheme & redMask) {
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
&redScheme,
sizeof(redScheme));
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Cdesc,
algo,
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[AlgoCount],
stream);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
} // end if
} // end for
}
else { // Non-splitK case
/* if user preference is ok with workspace */
if (AlgoCount < AlgoCombinations) {
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Cdesc,
algo,
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[AlgoCount],
stream);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
}
}
} // end l
} // end k
} // end customOption
#if (CUDART_VERSION >= 11000)
} // end stagesIdx
#endif
} // end tileIdx
delete[] tileA;
} // end idx
// Sort the results per run duration
std::sort(perfResults, perfResults + AlgoCount, time_compare);
// Print timing and perf details
for (int i = 0, hasPrint = 0; i < AlgoCount; i++) {
printf("result %03d : ", i);
hasPrint = printBatchPerfStructure(batchCount, m, n, k, perfResults[i], fout, hasPrint);
}
CLEANUP:
// Descriptors are no longer needed as all GPU work was already enqueued
if (Cdesc) {
cublasLtMatrixLayoutDestroy(Cdesc);
}
if (Bdesc) {
cublasLtMatrixLayoutDestroy(Bdesc);
}
if (Adesc) {
cublasLtMatrixLayoutDestroy(Adesc);
}
if (operationDesc) {
cublasLtMatmulDescDestroy(operationDesc);
}
return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
}
template int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
int batchCount,
int m,
int n,
int k,
const int* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const int* beta, /* host pointer */
int32_t* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout);
template int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
int batchCount,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const int8_t* A,
const int8_t* B,
const float* beta, /* host pointer */
int8_t* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout);
// template<typename T, typename scaleT>
// int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
// int m,
// int n,
// int k,
// const scaleT* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const scaleT* beta, /* host pointer */
// T* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout)
// {
// cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
// cublasLtMatmulDesc_t operationDesc = NULL;
// cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
// cudaStream_t stream = 0;
// // SplitK value that we are going to try when SplitK is supported for a given algo
// const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// // Let try a fixed number of combinations
// #define ALGO_COMBINATIONS 50000
// int AlgoCombinations = ALGO_COMBINATIONS;
// int AlgoCount = 0;
// int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
// customMatmulPerf_t perfResults[ALGO_COMBINATIONS];
// int nbAlgoIds = 0;
// #define ALGO_IDS 100
// int algoIdA[ALGO_IDS];
// cudaDataType_t Atype, Btype, Ctype, scaleType;
// Atype = CUDA_R_8I;
// Btype = CUDA_R_8I;
// if (std::is_same<T, int32_t>::value && std::is_same<scaleT, int>::value) {
// Ctype = CUDA_R_32I;
// scaleType = CUDA_R_32I;
// }
// else if (std::is_same<T, int8_t>::value && std::is_same<scaleT, float>::value) {
// Ctype = CUDA_R_8I;
// scaleType = CUDA_R_32F;
// }
// else {
// printf("[ERROR]<T,scaleT> of igemm is invalid\n");
// exit(-1);
// }
// // #if (CUDART_VERSION >= 11000)
// // cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
// // #else
// cudaDataType_t computeType = CUDA_R_32I;
// // #endif
// cublasOperation_t opTranspose = CUBLAS_OP_T;
// bool use_ORDER_COL32_2R_4R4 = false;
// // #if (CUDART_VERSION >= 11000)
// // int device{-1};
// // cudaGetDevice(&device);
// // cudaDeviceProp props;
// // cudaGetDeviceProperties(&props, device);
// // if (props.major * 10 + props.minor >= 80) {
// // use_ORDER_COL32_2R_4R4 = true;
// // }
// // #endif
// 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 {
// // order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// // }
// // #else
// order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// // #endif
// int ldaTransform = 32 * m;
// int ldbTransform;
// if (use_ORDER_COL32_2R_4R4) {
// ldbTransform = 32 * ((n + 32 - 1) / 32) * 32;
// }
// else {
// ldbTransform = 32 * ((n + 8 - 1) / 8) * 8;
// }
// int ldcTransform = 32 * m;
// // #if (CUDART_VERSION >= 11000)
// // status = cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
// // #else
// status = cublasLtMatmulDescCreate(&operationDesc, scaleType);
// // #endif
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
// // Create matrix descriptors.
// status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, ldaTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, n, k, ldbTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status =
// cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_matrixB, sizeof(order_matrixB));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldcTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// // Request AlgoId available for IGEMM
// status = cublasLtMatmulAlgoGetIds(
// ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, ALGO_IDS, algoIdA, &nbAlgoIds);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// // Loop over the Algo IDs
// for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
// cublasLtMatmulAlgo_t algo;
// size_t sizeWritten = 0;
// /* Initialize algo structure with given Algp ID */
// status =
// cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIdA[idx], &algo);
// if (status != CUBLAS_STATUS_SUCCESS) {
// continue;
// }
// // Query the tiles enums supported by that algo
// cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
// int nbTiles = int(sizeWritten / sizeof(int));
// int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
// if (nbTiles == 0) {
// tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
// nbTiles = 1;
// }
// // #if (CUDART_VERSION >= 11000)
// // cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
// // int nbStages = int(sizeWritten / sizeof(int));
// // std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
// // if (nbStages == 0) {
// // stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
// // nbStages = 1;
// // }
// // else {
// // cublasLtMatmulAlgoCapGetAttribute(
// // &algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
// // }
// // #endif
// int splitkSupport, redMask, swizzlingMax, customOptionMax;
// // Retrieve Algo Capabilities attributes to be able to setup loop over the different combinations
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
// /* Loop over the different tiles */
// for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
// // #if (CUDART_VERSION >= 11000)
// // /* Loop over different stages count */
// // for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
// // cublasLtMatmulAlgoConfigSetAttribute(
// // &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
// // #endif
// /* Loop over the different custom option if any */
// for (int customOption = 0; customOption <= customOptionMax; customOption++) {
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
// /* Loop over the CTAs swizzling support */
// for (int k = 0; k <= swizzlingMax; k++) {
// int splitK_trial = 0;
// if (splitkSupport) {
// splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
// }
// // Loop over the splitK value over a fixed sequence splitKSequenceA in addition to the case
// // where splitK is not enabled
// for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
// /* Setup attribute of the algo to run */
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
// int splitK_val = 0;
// int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
// if (l > 0) { // Split-K case
// splitK_val = splitKSequenceA[l - 1];
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
// &splitKSequenceA[l - 1],
// sizeof(splitKSequenceA[l - 1]));
// /* Going over all the reduction scheme */
// for (redScheme = 1;
// redScheme <= (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
// redScheme = redScheme << 1) {
// if (redScheme & redMask) {
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
// &redScheme,
// sizeof(redScheme));
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Cdesc,
// algo,
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[AlgoCount],
// stream);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// } // end if
// } // end for
// }
// else { // Non-splitK case
// /* if user preference is ok with workspace */
// if (AlgoCount < AlgoCombinations) {
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Cdesc,
// algo,
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[AlgoCount],
// stream);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// }
// }
// } // end l
// } // end k
// } // end customOption
// // #if (CUDART_VERSION >= 11000)
// // } // end stagesIdx
// // #endif
// } // end tileIdx
// delete[] tileA;
// } // end idx
// // Sort the results per run duration
// std::sort(perfResults, perfResults + AlgoCount, time_compare);
// // Print timing and perf details
// for (int i = 0, hasPrint = 0; i < AlgoCount; i++) {
// printf("result %03d : ", i);
// hasPrint = printPerfStructure(m, n, k, perfResults[i], fout, hasPrint);
// }
// CLEANUP:
// // Descriptors are no longer needed as all GPU work was already enqueued
// if (Cdesc) {
// cublasLtMatrixLayoutDestroy(Cdesc);
// }
// if (Bdesc) {
// cublasLtMatrixLayoutDestroy(Bdesc);
// }
// if (Adesc) {
// cublasLtMatrixLayoutDestroy(Adesc);
// }
// if (operationDesc) {
// cublasLtMatmulDescDestroy(operationDesc);
// }
// return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
// }
// template int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
// int m,
// int n,
// int k,
// const int* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const int* beta, /* host pointer */
// int32_t* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout);
// template int LtIgemmCustomFind(cublasLtHandle_t ltHandle,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const float* beta, /* host pointer */
// int8_t* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout);
// template<typename T, typename scaleT>
// int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
// int batchCount,
// int m,
// int n,
// int k,
// const scaleT* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const scaleT* beta, /* host pointer */
// T* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout)
// {
// cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
// cublasLtMatmulDesc_t operationDesc = NULL;
// cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL;
// cudaStream_t stream = 0;
// // SplitK value that we are going to try when SplitK is supported for a given algo
// const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// // Let try a fixed number of combinations
// #define ALGO_COMBINATIONS 50000
// int AlgoCombinations = ALGO_COMBINATIONS;
// int AlgoCount = 0;
// int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
// customMatmulPerf_t perfResults[ALGO_COMBINATIONS];
// int nbAlgoIds = 0;
// #define ALGO_IDS 100
// int algoIdA[ALGO_IDS];
// cudaDataType_t Atype, Btype, Ctype, scaleType;
// Atype = CUDA_R_8I;
// Btype = CUDA_R_8I;
// if (std::is_same<T, int32_t>::value && std::is_same<scaleT, int>::value) {
// Ctype = CUDA_R_32I;
// scaleType = CUDA_R_32I;
// }
// else if (std::is_same<T, int8_t>::value && std::is_same<scaleT, float>::value) {
// Ctype = CUDA_R_8I;
// scaleType = CUDA_R_32F;
// }
// else {
// printf("[ERROR]<T,scaleT> of igemm is invalid\n");
// exit(-1);
// }
// // #if (CUDART_VERSION >= 11000)
// // cublasComputeType_t computeType = CUBLAS_COMPUTE_32I;
// // #else
// cudaDataType_t computeType = CUDA_R_32I;
// // #endif
// cublasOperation_t opTranspose = CUBLAS_OP_T;
// bool use_ORDER_COL32_2R_4R4 = false;
// // #if (CUDART_VERSION >= 11000)
// // int device{-1};
// // cudaGetDevice(&device);
// // cudaDeviceProp props;
// // cudaGetDeviceProperties(&props, device);
// // if (props.major * 10 + props.minor >= 80) {
// // use_ORDER_COL32_2R_4R4 = true;
// // }
// // #endif
// 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 {
// // order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// // }
// // #else
// order_matrixB = CUBLASLT_ORDER_COL4_4R2_8C;
// // #endif
// int ldaTransform = 32 * m;
// int ldbTransform;
// if (use_ORDER_COL32_2R_4R4) {
// ldbTransform = 32 * ((n + 32 - 1) / 32) * 32;
// }
// else {
// ldbTransform = 32 * ((n + 8 - 1) / 8) * 8;
// }
// int ldcTransform = 32 * m;
// int64_t stridea, strideb, stridec;
// stridea = m * k;
// strideb = n * k;
// stridec = m * n;
// // #if (CUDART_VERSION >= 11000)
// // status = cublasLtMatmulDescCreate(&operationDesc, computeType, scaleType);
// // #else
// status = cublasLtMatmulDescCreate(&operationDesc, scaleType);
// // #endif
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSB, &opTranspose, sizeof(cublasOperation_t));
// // Create matrix descriptors.
// status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, ldaTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
// cublasLtMatrixLayoutSetAttribute(Adesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stridea, sizeof(stridea));
// status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, n, k, ldbTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status =
// cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_matrixB, sizeof(order_matrixB));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
// cublasLtMatrixLayoutSetAttribute(Bdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideb, sizeof(strideb));
// status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, ldcTransform);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_ORDER, &order_COL32, sizeof(order_COL32));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount));
// cublasLtMatrixLayoutSetAttribute(Cdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &stridec, sizeof(stridec));
// // Request AlgoId available for IGEMM
// status = cublasLtMatmulAlgoGetIds(
// ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, ALGO_IDS, algoIdA, &nbAlgoIds);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// // Loop over the Algo IDs
// for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
// cublasLtMatmulAlgo_t algo;
// size_t sizeWritten = 0;
// /* Initialize algo structure with given Algp ID */
// status =
// cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Ctype, algoIdA[idx], &algo);
// if (status != CUBLAS_STATUS_SUCCESS) {
// continue;
// }
// // Query the tiles enums supported by that algo
// cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
// int nbTiles = int(sizeWritten / sizeof(int));
// int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
// if (nbTiles == 0) {
// tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
// nbTiles = 1;
// }
// // #if (CUDART_VERSION >= 11000)
// // cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
// // int nbStages = int(sizeWritten / sizeof(int));
// // std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
// // if (nbStages == 0) {
// // stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
// // nbStages = 1;
// // }
// // else {
// // cublasLtMatmulAlgoCapGetAttribute(
// // &algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
// // }
// // #endif
// int splitkSupport, redMask, swizzlingMax, customOptionMax;
// // Retrieve Algo Capabilities attributes to be able to setup loop over the different combinations
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
// /* Loop over the different tiles */
// for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
// // #if (CUDART_VERSION >= 11000)
// // /* Loop over different stages count */
// // for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
// // cublasLtMatmulAlgoConfigSetAttribute(
// // &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
// // #endif
// /* Loop over the different custom option if any */
// for (int customOption = 0; customOption <= customOptionMax; customOption++) {
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
// /* Loop over the CTAs swizzling support */
// for (int k = 0; k <= swizzlingMax; k++) {
// int splitK_trial = 0;
// if (splitkSupport) {
// splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
// }
// // Loop over the splitK value over a fixed sequence splitKSequenceA in addition to the case
// // where splitK is not enabled
// for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
// /* Setup attribute of the algo to run */
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
// int splitK_val = 0;
// int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
// if (l > 0) { // Split-K case
// splitK_val = splitKSequenceA[l - 1];
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
// &splitKSequenceA[l - 1],
// sizeof(splitKSequenceA[l - 1]));
// /* Going over all the reduction scheme */
// for (redScheme = 1;
// redScheme <= (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
// redScheme = redScheme << 1) {
// if (redScheme & redMask) {
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
// &redScheme,
// sizeof(redScheme));
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Cdesc,
// algo,
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[AlgoCount],
// stream);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// } // end if
// } // end for
// }
// else { // Non-splitK case
// /* if user preference is ok with workspace */
// if (AlgoCount < AlgoCombinations) {
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Cdesc,
// algo,
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[AlgoCount],
// stream);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// }
// }
// } // end l
// } // end k
// } // end customOption
// // #if (CUDART_VERSION >= 11000)
// // } // end stagesIdx
// // #endif
// } // end tileIdx
// delete[] tileA;
// } // end idx
// // Sort the results per run duration
// std::sort(perfResults, perfResults + AlgoCount, time_compare);
// // Print timing and perf details
// for (int i = 0, hasPrint = 0; i < AlgoCount; i++) {
// printf("result %03d : ", i);
// hasPrint = printBatchPerfStructure(batchCount, m, n, k, perfResults[i], fout, hasPrint);
// }
// CLEANUP:
// // Descriptors are no longer needed as all GPU work was already enqueued
// if (Cdesc) {
// cublasLtMatrixLayoutDestroy(Cdesc);
// }
// if (Bdesc) {
// cublasLtMatrixLayoutDestroy(Bdesc);
// }
// if (Adesc) {
// cublasLtMatrixLayoutDestroy(Adesc);
// }
// if (operationDesc) {
// cublasLtMatmulDescDestroy(operationDesc);
// }
// return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
// }
// template int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
// int batchCount,
// int m,
// int n,
// int k,
// const int* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const int* beta, /* host pointer */
// int32_t* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout);
// template int LtBatchIgemmCustomFind(cublasLtHandle_t ltHandle,
// int batchCount,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const int8_t* A,
// const int8_t* B,
// const float* beta, /* host pointer */
// int8_t* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout);
// initialize matrix in column-major
void matInit(int rows, int cols, int8_t* p, int ld)
......
src/turbomind/utils/gemm_test/gemm_func.cc
View file @ ee33e2e7
......@@ -52,11 +52,11 @@ int printPerfStructure(int batch_size,
matmulAlgo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &swizzle, sizeof(swizzle), NULL);
cublasLtMatmulAlgoConfigGetAttribute(
matmulAlgo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption), NULL);
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
#else
// #if (CUDART_VERSION >= 11000)
// cublasLtMatmulAlgoConfigGetAttribute(matmulAlgo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stages, sizeof(stages), NULL);
// #else
stages = 0;
#endif
// #endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
uint16_t inner_shapeId, cluster_shapeId;
cublasLtMatmulAlgoConfigGetAttribute(
......@@ -74,9 +74,9 @@ int printPerfStructure(int batch_size,
#endif
printf("algo={ Id=%d, tileIdx=%d (%s) splitK=%d reduc=%d swizzle=%d custom=%d "
#if (CUDART_VERSION >= 11000)
"stages=%d "
#endif
// #if (CUDART_VERSION >= 11000)
// "stages=%d "
// #endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
"inner_shapeId=%d cluster_shapeId=%d"
#elif (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH < 3)
......@@ -91,9 +91,9 @@ int printPerfStructure(int batch_size,
reductionScheme,
swizzle,
customOption,
#if (CUDART_VERSION >= 11000)
stages,
#endif
// #if (CUDART_VERSION >= 11000)
// stages,
// #endif
#if (CUBLAS_VER_MAJOR == 11 && CUBLAS_VER_MINOR == 11 && CUBLAS_VER_PATCH >= 3)
inner_shapeId,
cluster_shapeId,
......@@ -154,704 +154,704 @@ static inline bool time_compare(const customMatmulPerf_t& perf_a, const customMa
return ((perf_a.status == CUBLAS_STATUS_SUCCESS) && (perf_a.time < perf_b.time));
}
static cublasStatus_t customMatmulRun(cublasLtHandle_t ltHandle, // to get the capabilities (required a GPU)
cublasLtMatmulDesc_t operationDesc,
const void* alpha, /* host or device pointer */
const void* A,
cublasLtMatrixLayout_t Adesc,
const void* B,
cublasLtMatrixLayout_t Bdesc,
const void* beta, /* host or device pointer */
const void* C,
cublasLtMatrixLayout_t Cdesc,
void* D,
cublasLtMatrixLayout_t Ddesc,
const cublasLtMatmulAlgo_t& algo,
int kernelRepeats,
void* workSpace,
size_t workSpaceSizeInBytes,
customMatmulPerf_t& perfResults,
cudaStream_t stream,
cudaEvent_t& startEvent,
cudaEvent_t& stopEvent)
{
cublasLtMatmulHeuristicResult_t heurResult;
/* Looping over the Algo */
int repeats = kernelRepeats;
cublasStatus_t algoStatus =
cublasLtMatmulAlgoCheck(ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Ddesc, &algo, &heurResult);
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
if (heurResult.workspaceSize <= workSpaceSizeInBytes) {
cudaError_t err, err1, err2, err3;
err = cudaEventRecord(startEvent, stream);
for (int loop = 0; loop < repeats; loop++) {
cublasStatus_t oneRunStatus = cublasLtMatmul(ltHandle,
operationDesc,
alpha,
A,
Adesc,
B,
Bdesc,
beta,
C,
Cdesc,
D,
Ddesc,
&algo,
workSpace,
workSpaceSizeInBytes,
stream);
if (oneRunStatus != CUBLAS_STATUS_SUCCESS) {
algoStatus = oneRunStatus;
break;
}
}
err1 = cudaEventRecord(stopEvent, stream);
err2 = cudaEventSynchronize(stopEvent);
float time;
err3 = cudaEventElapsedTime(&time, startEvent, stopEvent);
if ((err != cudaSuccess) || (err1 != cudaSuccess) || (err2 != cudaSuccess) || (err3 != cudaSuccess)) {
algoStatus = CUBLAS_STATUS_INTERNAL_ERROR;
}
// For the moment only add successful findings
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
perfResults.algo = algo;
perfResults.time = time / repeats;
perfResults.workspaceSize = heurResult.workspaceSize;
perfResults.wavesCount = heurResult.wavesCount;
}
}
else {
// printf("not enough workspace! %ld\n", heurResult.workspaceSize);
algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not enough workspace
}
}
return algoStatus;
}
template<typename T, typename scaleT>
int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const scaleT* alpha, /* host pointer */
const T* A,
const T* B,
const scaleT* beta, /* host pointer */
T* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD)
{
cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
cudaEvent_t startEvent;
cudaEvent_t stopEvent;
CublasDataType data_type;
cublasLtMatmulDesc_t operationDesc = NULL;
cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL, Ddesc = NULL;
cudaStream_t stream = 0;
// SplitK value that we are going to try when SplitK is supported for a
// given algo
const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// Let try a fixed number of combinations
int AlgoCount = 0;
int AlgoCountRestrict = 0; // workspace == 0
const int maxNumTraversal = 50; // max number of traversal
std::vector<cublasLtMatmulAlgo_t> algos(AlgoCombinations); // 0 <= workspace <= 32MB
std::vector<cublasLtMatmulAlgo_t> algosRestrict(AlgoCombinations); // workspace == 0
const int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
int nbAlgoIds = 0; // Number of algorithms actually returned by
// cublasLtMatmulAlgoGetIds function.
#define ALGO_IDS 100 // Number of algorithms requested.
int algoIdA[ALGO_IDS]; // Array containing the algorithm IDs returned by
// cublasLtMatmulAlgoGetIds function.
cudaDataType_t Atype, Btype, Ctype, scaleType, Dtype;
#if (CUDART_VERSION >= 11000)
cublasComputeType_t computeType;
#else
cudaDataType_t computeType;
#endif
if (std::is_same<T, float>::value) {
data_type = FLOAT_DATATYPE;
Atype = CUDA_R_32F, Btype = CUDA_R_32F, Ctype = CUDA_R_32F, Dtype = CUDA_R_32F;
}
else if (std::is_same<T, half>::value) {
data_type = HALF_DATATYPE;
Atype = CUDA_R_16F, Btype = CUDA_R_16F, Ctype = CUDA_R_16F, Dtype = CUDA_R_16F;
}
#ifdef ENABLE_BF16
else if (std::is_same<T, __nv_bfloat16>::value) {
data_type = BFLOAT16_DATATYPE;
Atype = CUDA_R_16BF, Btype = CUDA_R_16BF, Ctype = CUDA_R_16BF, Dtype = CUDA_R_16BF;
}
#endif
#ifdef ENABLE_FP8
else if (std::is_same<T, __nv_fp8_e4m3>::value) {
data_type = FP8_DATATYPE;
Atype = CUDA_R_8F_E4M3, Btype = CUDA_R_8F_E4M3, Ctype = CUDA_R_16BF;
#ifdef FP8_GEMM_OUTPUT_QUANT_DISABLE
Dtype = CUDA_R_16BF;
#else
Dtype = dtype_fp8;
#endif
}
#endif
if (sizeof(scaleT) == sizeof(float)) {
scaleType = CUDA_R_32F;
#if (CUDART_VERSION >= 11000)
computeType = CUBLAS_COMPUTE_32F;
#else
computeType = CUDA_R_32F;
#endif
}
else {
scaleType = CUDA_R_16F;
#if (CUDART_VERSION >= 11000)
computeType = CUBLAS_COMPUTE_16F;
#else
computeType = CUDA_R_16F;
#endif
}
const cublasOperation_t tA = data_type == FP8_DATATYPE ? CUBLAS_OP_T : CUBLAS_OP_N;
// Create operation descriptor; see cublasLtMatmulDescAttributes_t for
// details about defaults; here we just need to set the transforms for A and
// B
#if (CUDART_VERSION >= 11000)
status = cublasLtMatmulDescCreate(&operationDesc, computeType,
scaleType); // creates a matrix multiply descriptor
#else
status = cublasLtMatmulDescCreate(&operationDesc, computeType);
#endif
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &tA, sizeof(tA));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
#ifdef ENABLE_FP8
if (data_type == FP8_DATATYPE) {
const int8_t fastAccuMode = 1; // enable fast imprecise accum
status = cublasLtMatmulDescSetAttribute(
operationDesc, CUBLASLT_MATMUL_DESC_FAST_ACCUM, &fastAccuMode, sizeof(decltype(fastAccuMode)));
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
}
#endif
// Create matrix descriptors. We are good with the details here so no need
// to set any extra attributes
if (data_type == FP8_DATATYPE) {
status = cublasLtMatrixLayoutCreate(&Adesc, Atype, k, m, k);
}
else {
status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, m);
}
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, k, n, k);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, m);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
status = cublasLtMatrixLayoutCreate(&Ddesc, Dtype, m, n, m);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
if (batchCount > 1) {
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Adesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Bdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Cdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Ddesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Adesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideA, sizeof(strideA)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Bdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideB, sizeof(strideB)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Cdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideD, sizeof(strideD)));
check_cuda_error(cublasLtMatrixLayoutSetAttribute(
Ddesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideD, sizeof(strideD)));
}
// Create CUDA event to time the execution time of each algo
if (cudaEventCreate(&startEvent, cudaEventBlockingSync) != cudaSuccess) {
goto CLEANUP;
}
if (cudaEventCreate(&stopEvent, cudaEventBlockingSync) != cudaSuccess) {
goto CLEANUP;
}
// Request the 100 first AlgoId available
status = cublasLtMatmulAlgoGetIds(
ltHandle, computeType, scaleType, Atype, Btype, Ctype, Dtype, ALGO_IDS, algoIdA, &nbAlgoIds);
if (status != CUBLAS_STATUS_SUCCESS) {
goto CLEANUP;
}
if (nbAlgoIds > ALGO_IDS) {
printf(
"Warning: the algo id count is not large enough to guarantee the best algo %d, %d\n", nbAlgoIds, ALGO_IDS);
}
// Loop over the Algo IDs
// This loop doesn't work for fp8 gemm
for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
cublasLtMatmulAlgo_t algo;
size_t sizeWritten = 0;
/* Initialize algo structure with given Algp ID */
status =
cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Dtype, algoIdA[idx], &algo);
if (status != CUBLAS_STATUS_SUCCESS) {
continue;
}
// Query the tiles enums supported by that algo
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
int nbTiles = int(sizeWritten / sizeof(int));
int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
if (nbTiles == 0) {
tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
nbTiles = 1;
}
#if (CUDART_VERSION >= 11000)
cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
int nbStages = int(sizeWritten / sizeof(int));
std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
if (nbStages == 0) {
stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
nbStages = 1;
}
else {
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
}
#endif
int splitkSupport, redMask, swizzlingMax, customOptionMax;
// Retrieve Algo Capabilities attributes to be able to setup loop over
// the different combinations
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
cublasLtMatmulAlgoCapGetAttribute(
&algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
/* Loop over the different tiles */
for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
#if (CUDART_VERSION >= 11000)
/* Loop over different stages count */
for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
#endif
/* Loop over the different custom option if any */
for (int customOption = 0; customOption <= customOptionMax; customOption++) {
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
/* Loop over the CTAs swizzling support */
for (int k = 0; k <= swizzlingMax; k++) {
int splitK_trial = 0;
if (splitkSupport) {
splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
}
// Loop over the splitK value over a fixed sequence
// splitKSequenceA in addition to the case where splitK
// is not enabled
for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
/* Setup attribute of the algo to run */
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
int splitK_val = 0;
int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
cublasLtMatmulAlgoConfigSetAttribute(
&algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
if (l > 0) { // Split-K case
splitK_val = splitKSequenceA[l - 1];
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
&splitKSequenceA[l - 1],
sizeof(splitKSequenceA[l - 1]));
/* Going over all the reduction scheme */
for (redScheme = 1;
redScheme < (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
redScheme = redScheme << 1) {
if (redScheme & redMask) {
cublasLtMatmulAlgoConfigSetAttribute(&algo,
CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
&redScheme,
sizeof(redScheme));
cublasLtMatmulHeuristicResult_t heurResult;
cublasStatus_t algoStatus = cublasLtMatmulAlgoCheck(
ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Cdesc, &algo, &heurResult);
if (heurResult.workspaceSize > workSpaceSize) {
// printf("not enough workspace!
// %ld\n",
// heurResult.workspaceSize);
algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not enough workspace
}
else if (heurResult.workspaceSize == 0) {
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
algosRestrict[AlgoCountRestrict++] = algo;
}
}
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
algos[AlgoCount++] = algo;
}
} // end if
} // end for
}
else { // Non-splitK case
/* if user preference is ok with workspace */
if (AlgoCount < AlgoCombinations) {
cublasLtMatmulHeuristicResult_t heurResult;
cublasStatus_t algoStatus = cublasLtMatmulAlgoCheck(
ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Cdesc, &algo, &heurResult);
if (heurResult.workspaceSize > workSpaceSize) {
// printf("not enough workspace! %ld\n",
// heurResult.workspaceSize);
algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not
// enough
// workspace
}
else if (heurResult.workspaceSize == 0) {
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
algosRestrict[AlgoCountRestrict++] = algo;
}
}
if (algoStatus == CUBLAS_STATUS_SUCCESS) {
algos[AlgoCount++] = algo;
}
}
}
} // end l
} // end k
} // end customOption
#if (CUDART_VERSION >= 11000)
} // end stagesIdx
#endif
} // end tileIdx
delete[] tileA;
} // end idx
printf("AlgoCount: %d\n", AlgoCount);
if (data_type == FP8_DATATYPE) {
assert(AlgoCount == 0);
}
if (AlgoCount < maxNumTraversal && data_type != FP8_DATATYPE) {
// 0 <= workspacesize <= 32MB
for (int i = 0; i < AlgoCount; i++) {
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Cdesc,
algos[i],
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[i],
stream,
startEvent,
stopEvent);
perfResults[i].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) AlgoCount++;
}
}
else {
// Heuristic + workspacesize==0
AlgoCount = 0;
nbAlgoIds = 0;
cublasLtMatmulPreference_t pref;
cublasLtMatmulPreferenceCreate(&pref);
uint64_t maxWorkSpaceSize = workSpaceSize; //(32MB)
cublasLtMatmulPreferenceSetAttribute(
pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &maxWorkSpaceSize, sizeof(maxWorkSpaceSize));
cublasLtMatmulHeuristicResult_t heuristicResultsArray[maxNumTraversal];
cublasLtMatmulAlgoGetHeuristic(ltHandle,
operationDesc,
Adesc,
Bdesc,
Cdesc,
Ddesc,
pref,
maxNumTraversal,
heuristicResultsArray,
&nbAlgoIds);
cublasLtMatmulPreferenceDestroy(pref);
printf("return %d and run heuristic algo\n", nbAlgoIds);
for (int i = 0; i < nbAlgoIds; i++) {
if (heuristicResultsArray[i].state == CUBLAS_STATUS_SUCCESS) {
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Ddesc,
heuristicResultsArray[i].algo,
kernelRepeats,
workSpace,
workSpaceSize,
perfResults[AlgoCount],
stream,
startEvent,
stopEvent);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
}
}
// workspacesize==0
printf("workspacesize==0, run %d algos\n", AlgoCountRestrict);
for (int i = 0; i < AlgoCountRestrict && i < (maxNumTraversal - nbAlgoIds); i++) {
status = customMatmulRun(ltHandle,
operationDesc,
alpha, /* host or device pointer */
A,
Adesc,
B,
Bdesc,
beta, /* host or device pointer */
C,
Cdesc,
C,
Ddesc,
algosRestrict[i],
kernelRepeats,
NULL,
0,
perfResults[AlgoCount],
stream,
startEvent,
stopEvent);
perfResults[AlgoCount].status = status;
if (status == CUBLAS_STATUS_SUCCESS) {
AlgoCount++;
}
}
}
// Sort the results per run duration
std::sort(perfResults, perfResults + AlgoCount, time_compare);
// Print timing and perf details
for (int i = 0, hasPrint = 1; i < AlgoCount; i++) {
printf("result %03d : ", i);
hasPrint = printPerfStructure(batch_size,
seq_len,
head_num,
size_per_head,
m,
n,
k,
perfResults[i],
fout,
data_type,
hasPrint,
batchCount);
}
CLEANUP:
// Descriptors are no longer needed as all GPU work was already enqueued
if (Cdesc) {
cublasLtMatrixLayoutDestroy(Cdesc);
}
if (Bdesc) {
cublasLtMatrixLayoutDestroy(Bdesc);
}
if (Adesc) {
cublasLtMatrixLayoutDestroy(Adesc);
}
if (operationDesc) {
cublasLtMatmulDescDestroy(operationDesc);
}
if (startEvent) {
cudaEventDestroy(startEvent);
}
if (stopEvent) {
cudaEventDestroy(stopEvent);
}
return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
}
template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const float* A,
const float* B,
const float* beta, /* host pointer */
float* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD);
template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const half* alpha, /* host pointer */
const half* A,
const half* B,
const half* beta, /* host pointer */
half* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD);
#ifdef ENABLE_BF16
template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const __nv_bfloat16* A,
const __nv_bfloat16* B,
const float* beta, /* host pointer */
__nv_bfloat16* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD);
#endif
#ifdef ENABLE_FP8
template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const __nv_fp8_e4m3* A,
const __nv_fp8_e4m3* B,
const float* beta, /* host pointer */
__nv_fp8_e4m3* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD);
#endif
template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
int batch_size,
int seq_len,
int head_num,
int size_per_head,
int m,
int n,
int k,
const float* alpha, /* host pointer */
const half* A,
const half* B,
const float* beta, /* host pointer */
half* C,
void* workSpace,
size_t workSpaceSize,
FILE* fout,
customMatmulPerf_t perfResults[],
int AlgoCombinations,
cudaDataType_t dtype_fp8,
int batchCount,
int64_t strideA,
int64_t strideB,
int64_t strideD);
// static cublasStatus_t customMatmulRun(cublasLtHandle_t ltHandle, // to get the capabilities (required a GPU)
// cublasLtMatmulDesc_t operationDesc,
// const void* alpha, /* host or device pointer */
// const void* A,
// cublasLtMatrixLayout_t Adesc,
// const void* B,
// cublasLtMatrixLayout_t Bdesc,
// const void* beta, /* host or device pointer */
// const void* C,
// cublasLtMatrixLayout_t Cdesc,
// void* D,
// cublasLtMatrixLayout_t Ddesc,
// const cublasLtMatmulAlgo_t& algo,
// int kernelRepeats,
// void* workSpace,
// size_t workSpaceSizeInBytes,
// customMatmulPerf_t& perfResults,
// cudaStream_t stream,
// cudaEvent_t& startEvent,
// cudaEvent_t& stopEvent)
// {
// cublasLtMatmulHeuristicResult_t heurResult;
// /* Looping over the Algo */
// int repeats = kernelRepeats;
// cublasStatus_t algoStatus =
// cublasLtMatmulAlgoCheck(ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Ddesc, &algo, &heurResult);
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// if (heurResult.workspaceSize <= workSpaceSizeInBytes) {
// cudaError_t err, err1, err2, err3;
// err = cudaEventRecord(startEvent, stream);
// for (int loop = 0; loop < repeats; loop++) {
// cublasStatus_t oneRunStatus = cublasLtMatmul(ltHandle,
// operationDesc,
// alpha,
// A,
// Adesc,
// B,
// Bdesc,
// beta,
// C,
// Cdesc,
// D,
// Ddesc,
// &algo,
// workSpace,
// workSpaceSizeInBytes,
// stream);
// if (oneRunStatus != CUBLAS_STATUS_SUCCESS) {
// algoStatus = oneRunStatus;
// break;
// }
// }
// err1 = cudaEventRecord(stopEvent, stream);
// err2 = cudaEventSynchronize(stopEvent);
// float time;
// err3 = cudaEventElapsedTime(&time, startEvent, stopEvent);
// if ((err != cudaSuccess) || (err1 != cudaSuccess) || (err2 != cudaSuccess) || (err3 != cudaSuccess)) {
// algoStatus = CUBLAS_STATUS_INTERNAL_ERROR;
// }
// // For the moment only add successful findings
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// perfResults.algo = algo;
// perfResults.time = time / repeats;
// perfResults.workspaceSize = heurResult.workspaceSize;
// perfResults.wavesCount = heurResult.wavesCount;
// }
// }
// else {
// // printf("not enough workspace! %ld\n", heurResult.workspaceSize);
// algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not enough workspace
// }
// }
// return algoStatus;
// }
// template<typename T, typename scaleT>
// int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const scaleT* alpha, /* host pointer */
// const T* A,
// const T* B,
// const scaleT* beta, /* host pointer */
// T* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD)
// {
// cublasStatus_t status = CUBLAS_STATUS_SUCCESS;
// cudaEvent_t startEvent;
// cudaEvent_t stopEvent;
// CublasDataType data_type;
// cublasLtMatmulDesc_t operationDesc = NULL;
// cublasLtMatrixLayout_t Adesc = NULL, Bdesc = NULL, Cdesc = NULL, Ddesc = NULL;
// cudaStream_t stream = 0;
// // SplitK value that we are going to try when SplitK is supported for a
// // given algo
// const int splitKSequenceA[] = {2, 3, 4, 5, 6, 8, 12, 16, 32};
// // Let try a fixed number of combinations
// int AlgoCount = 0;
// int AlgoCountRestrict = 0; // workspace == 0
// const int maxNumTraversal = 50; // max number of traversal
// std::vector<cublasLtMatmulAlgo_t> algos(AlgoCombinations); // 0 <= workspace <= 32MB
// std::vector<cublasLtMatmulAlgo_t> algosRestrict(AlgoCombinations); // workspace == 0
// const int kernelRepeats = 100; // number of time the CUDA kernels will be run back to back
// int nbAlgoIds = 0; // Number of algorithms actually returned by
// // cublasLtMatmulAlgoGetIds function.
// #define ALGO_IDS 100 // Number of algorithms requested.
// int algoIdA[ALGO_IDS]; // Array containing the algorithm IDs returned by
// // cublasLtMatmulAlgoGetIds function.
// cudaDataType_t Atype, Btype, Ctype, scaleType, Dtype;
// // #if (CUDART_VERSION >= 11000)
// // cublasComputeType_t computeType;
// // #else
// cudaDataType_t computeType;
// // #endif
// if (std::is_same<T, float>::value) {
// data_type = FLOAT_DATATYPE;
// Atype = CUDA_R_32F, Btype = CUDA_R_32F, Ctype = CUDA_R_32F, Dtype = CUDA_R_32F;
// }
// else if (std::is_same<T, half>::value) {
// data_type = HALF_DATATYPE;
// Atype = CUDA_R_16F, Btype = CUDA_R_16F, Ctype = CUDA_R_16F, Dtype = CUDA_R_16F;
// }
// #ifdef ENABLE_BF16
// else if (std::is_same<T, __nv_bfloat16>::value) {
// data_type = BFLOAT16_DATATYPE;
// Atype = CUDA_R_16BF, Btype = CUDA_R_16BF, Ctype = CUDA_R_16BF, Dtype = CUDA_R_16BF;
// }
// #endif
// #ifdef ENABLE_FP8
// else if (std::is_same<T, __nv_fp8_e4m3>::value) {
// data_type = FP8_DATATYPE;
// Atype = CUDA_R_8F_E4M3, Btype = CUDA_R_8F_E4M3, Ctype = CUDA_R_16BF;
// #ifdef FP8_GEMM_OUTPUT_QUANT_DISABLE
// Dtype = CUDA_R_16BF;
// #else
// Dtype = dtype_fp8;
// #endif
// }
// #endif
// if (sizeof(scaleT) == sizeof(float)) {
// scaleType = CUDA_R_32F;
// // #if (CUDART_VERSION >= 11000)
// // computeType = CUBLAS_COMPUTE_32F;
// // #else
// computeType = CUDA_R_32F;
// // #endif
// }
// else {
// scaleType = CUDA_R_16F;
// // #if (CUDART_VERSION >= 11000)
// // computeType = CUBLAS_COMPUTE_16F;
// // #else
// computeType = CUDA_R_16F;
// // #endif
// }
// const cublasOperation_t tA = data_type == FP8_DATATYPE ? CUBLAS_OP_T : CUBLAS_OP_N;
// // Create operation descriptor; see cublasLtMatmulDescAttributes_t for
// // details about defaults; here we just need to set the transforms for A and
// // B
// // #if (CUDART_VERSION >= 11000)
// // status = cublasLtMatmulDescCreate(&operationDesc, computeType,
// // scaleType); // creates a matrix multiply descriptor
// // #else
// status = cublasLtMatmulDescCreate(&operationDesc, computeType);
// // #endif
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatmulDescSetAttribute(operationDesc, CUBLASLT_MATMUL_DESC_TRANSA, &tA, sizeof(tA));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// #ifdef ENABLE_FP8
// if (data_type == FP8_DATATYPE) {
// const int8_t fastAccuMode = 1; // enable fast imprecise accum
// status = cublasLtMatmulDescSetAttribute(
// operationDesc, CUBLASLT_MATMUL_DESC_FAST_ACCUM, &fastAccuMode, sizeof(decltype(fastAccuMode)));
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// }
// #endif
// // Create matrix descriptors. We are good with the details here so no need
// // to set any extra attributes
// if (data_type == FP8_DATATYPE) {
// status = cublasLtMatrixLayoutCreate(&Adesc, Atype, k, m, k);
// }
// else {
// status = cublasLtMatrixLayoutCreate(&Adesc, Atype, m, k, m);
// }
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutCreate(&Bdesc, Btype, k, n, k);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutCreate(&Cdesc, Ctype, m, n, m);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// status = cublasLtMatrixLayoutCreate(&Ddesc, Dtype, m, n, m);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// if (batchCount > 1) {
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Adesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Bdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Cdesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Ddesc, CUBLASLT_MATRIX_LAYOUT_BATCH_COUNT, &batchCount, sizeof(batchCount)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Adesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideA, sizeof(strideA)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Bdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideB, sizeof(strideB)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Cdesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideD, sizeof(strideD)));
// check_cuda_error(cublasLtMatrixLayoutSetAttribute(
// Ddesc, CUBLASLT_MATRIX_LAYOUT_STRIDED_BATCH_OFFSET, &strideD, sizeof(strideD)));
// }
// // Create CUDA event to time the execution time of each algo
// if (cudaEventCreate(&startEvent, cudaEventBlockingSync) != cudaSuccess) {
// goto CLEANUP;
// }
// if (cudaEventCreate(&stopEvent, cudaEventBlockingSync) != cudaSuccess) {
// goto CLEANUP;
// }
// // Request the 100 first AlgoId available
// status = cublasLtMatmulAlgoGetIds(
// ltHandle, computeType, scaleType, Atype, Btype, Ctype, Dtype, ALGO_IDS, algoIdA, &nbAlgoIds);
// if (status != CUBLAS_STATUS_SUCCESS) {
// goto CLEANUP;
// }
// if (nbAlgoIds > ALGO_IDS) {
// printf(
// "Warning: the algo id count is not large enough to guarantee the best algo %d, %d\n", nbAlgoIds, ALGO_IDS);
// }
// // Loop over the Algo IDs
// // This loop doesn't work for fp8 gemm
// for (int idx = 0; (idx < nbAlgoIds) && (AlgoCount < AlgoCombinations); idx++) {
// cublasLtMatmulAlgo_t algo;
// size_t sizeWritten = 0;
// /* Initialize algo structure with given Algp ID */
// status =
// cublasLtMatmulAlgoInit(ltHandle, computeType, scaleType, Atype, Btype, Ctype, Dtype, algoIdA[idx], &algo);
// if (status != CUBLAS_STATUS_SUCCESS) {
// continue;
// }
// // Query the tiles enums supported by that algo
// cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_TILE_IDS, NULL, 0, &sizeWritten);
// int nbTiles = int(sizeWritten / sizeof(int));
// int* tileA = new int[nbTiles == 0 ? 1 : nbTiles];
// if (nbTiles == 0) {
// tileA[0] = CUBLASLT_MATMUL_TILE_UNDEFINED;
// nbTiles = 1;
// }
// // #if (CUDART_VERSION >= 11000)
// // cublasLtMatmulAlgoCapGetAttribute(&algo, CUBLASLT_ALGO_CAP_STAGES_IDS, NULL, 0, &sizeWritten);
// // int nbStages = int(sizeWritten / sizeof(int));
// // std::vector<int> stagesA(nbStages == 0 ? 1 : nbStages);
// // if (nbStages == 0) {
// // stagesA[0] = CUBLASLT_MATMUL_STAGES_UNDEFINED;
// // nbStages = 1;
// // }
// // else {
// // cublasLtMatmulAlgoCapGetAttribute(
// // &algo, CUBLASLT_ALGO_CAP_STAGES_IDS, stagesA.data(), sizeof(int) * nbStages, &sizeWritten);
// // }
// // #endif
// int splitkSupport, redMask, swizzlingMax, customOptionMax;
// // Retrieve Algo Capabilities attributes to be able to setup loop over
// // the different combinations
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_TILE_IDS, tileA, sizeof(int) * nbTiles, &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_SPLITK_SUPPORT, &splitkSupport, sizeof(splitkSupport), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_REDUCTION_SCHEME_MASK, &redMask, sizeof(redMask), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CTA_SWIZZLING_SUPPORT, &swizzlingMax, sizeof(swizzlingMax), &sizeWritten);
// cublasLtMatmulAlgoCapGetAttribute(
// &algo, CUBLASLT_ALGO_CAP_CUSTOM_OPTION_MAX, &customOptionMax, sizeof(customOptionMax), &sizeWritten);
// /* Loop over the different tiles */
// for (int tileIdx = 0; tileIdx < nbTiles; tileIdx++) {
// // #if (CUDART_VERSION >= 11000)make:q
// // /* Loop over different stages count */
// // for (int stagesIdx = 0; stagesIdx < nbStages; stagesIdx++) {
// // cublasLtMatmulAlgoConfigSetAttribute(
// // &algo, CUBLASLT_ALGO_CONFIG_STAGES_ID, &stagesA[stagesIdx], sizeof(stagesA[stagesIdx]));
// // #endif
// /* Loop over the different custom option if any */
// for (int customOption = 0; customOption <= customOptionMax; customOption++) {
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CUSTOM_OPTION, &customOption, sizeof(customOption));
// /* Loop over the CTAs swizzling support */
// for (int k = 0; k <= swizzlingMax; k++) {
// int splitK_trial = 0;
// if (splitkSupport) {
// splitK_trial += sizeof(splitKSequenceA) / sizeof(splitKSequenceA[0]);
// }
// // Loop over the splitK value over a fixed sequence
// // splitKSequenceA in addition to the case where splitK
// // is not enabled
// for (int l = 0; (l < (1 + splitK_trial)) && (AlgoCount < AlgoCombinations); l++) {
// /* Setup attribute of the algo to run */
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_TILE_ID, &tileA[tileIdx], sizeof(tileA[tileIdx]));
// int splitK_val = 0;
// int redScheme = CUBLASLT_REDUCTION_SCHEME_NONE;
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_SPLITK_NUM, &splitK_val, sizeof(splitK_val));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_CTA_SWIZZLING, &k, sizeof(k));
// cublasLtMatmulAlgoConfigSetAttribute(
// &algo, CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME, &redScheme, sizeof(int));
// if (l > 0) { // Split-K case
// splitK_val = splitKSequenceA[l - 1];
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_SPLITK_NUM,
// &splitKSequenceA[l - 1],
// sizeof(splitKSequenceA[l - 1]));
// /* Going over all the reduction scheme */
// for (redScheme = 1;
// redScheme < (int)CUBLASLT_REDUCTION_SCHEME_MASK && (AlgoCount < AlgoCombinations);
// redScheme = redScheme << 1) {
// if (redScheme & redMask) {
// cublasLtMatmulAlgoConfigSetAttribute(&algo,
// CUBLASLT_ALGO_CONFIG_REDUCTION_SCHEME,
// &redScheme,
// sizeof(redScheme));
// cublasLtMatmulHeuristicResult_t heurResult;
// cublasStatus_t algoStatus = cublasLtMatmulAlgoCheck(
// ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Cdesc, &algo, &heurResult);
// if (heurResult.workspaceSize > workSpaceSize) {
// // printf("not enough workspace!
// // %ld\n",
// // heurResult.workspaceSize);
// algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not enough workspace
// }
// else if (heurResult.workspaceSize == 0) {
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// algosRestrict[AlgoCountRestrict++] = algo;
// }
// }
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// algos[AlgoCount++] = algo;
// }
// } // end if
// } // end for
// }
// else { // Non-splitK case
// /* if user preference is ok with workspace */
// if (AlgoCount < AlgoCombinations) {
// cublasLtMatmulHeuristicResult_t heurResult;
// cublasStatus_t algoStatus = cublasLtMatmulAlgoCheck(
// ltHandle, operationDesc, Adesc, Bdesc, Cdesc, Cdesc, &algo, &heurResult);
// if (heurResult.workspaceSize > workSpaceSize) {
// // printf("not enough workspace! %ld\n",
// // heurResult.workspaceSize);
// algoStatus = CUBLAS_STATUS_NOT_SUPPORTED; // Not
// // enough
// // workspace
// }
// else if (heurResult.workspaceSize == 0) {
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// algosRestrict[AlgoCountRestrict++] = algo;
// }
// }
// if (algoStatus == CUBLAS_STATUS_SUCCESS) {
// algos[AlgoCount++] = algo;
// }
// }
// }
// } // end l
// } // end k
// } // end customOption
// // #if (CUDART_VERSION >= 11000)
// } // end stagesIdx
// // #endif
// } // end tileIdx
// delete[] tileA;
// } // end idx
// printf("AlgoCount: %d\n", AlgoCount);
// if (data_type == FP8_DATATYPE) {
// assert(AlgoCount == 0);
// }
// if (AlgoCount < maxNumTraversal && data_type != FP8_DATATYPE) {
// // 0 <= workspacesize <= 32MB
// for (int i = 0; i < AlgoCount; i++) {
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Cdesc,
// algos[i],
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[i],
// stream,
// startEvent,
// stopEvent);
// perfResults[i].status = status;
// // if (status == CUBLAS_STATUS_SUCCESS) AlgoCount++;
// }
// }
// else {
// // Heuristic + workspacesize==0
// AlgoCount = 0;
// nbAlgoIds = 0;
// cublasLtMatmulPreference_t pref;
// cublasLtMatmulPreferenceCreate(&pref);
// uint64_t maxWorkSpaceSize = workSpaceSize; //(32MB)
// cublasLtMatmulPreferenceSetAttribute(
// pref, CUBLASLT_MATMUL_PREF_MAX_WORKSPACE_BYTES, &maxWorkSpaceSize, sizeof(maxWorkSpaceSize));
// cublasLtMatmulHeuristicResult_t heuristicResultsArray[maxNumTraversal];
// cublasLtMatmulAlgoGetHeuristic(ltHandle,
// operationDesc,
// Adesc,
// Bdesc,
// Cdesc,
// Ddesc,
// pref,
// maxNumTraversal,
// heuristicResultsArray,
// &nbAlgoIds);
// cublasLtMatmulPreferenceDestroy(pref);
// printf("return %d and run heuristic algo\n", nbAlgoIds);
// for (int i = 0; i < nbAlgoIds; i++) {
// if (heuristicResultsArray[i].state == CUBLAS_STATUS_SUCCESS) {
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Ddesc,
// heuristicResultsArray[i].algo,
// kernelRepeats,
// workSpace,
// workSpaceSize,
// perfResults[AlgoCount],
// stream,
// startEvent,
// stopEvent);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// }
// }
// // workspacesize==0
// printf("workspacesize==0, run %d algos\n", AlgoCountRestrict);
// for (int i = 0; i < AlgoCountRestrict && i < (maxNumTraversal - nbAlgoIds); i++) {
// status = customMatmulRun(ltHandle,
// operationDesc,
// alpha, /* host or device pointer */
// A,
// Adesc,
// B,
// Bdesc,
// beta, /* host or device pointer */
// C,
// Cdesc,
// C,
// Ddesc,
// algosRestrict[i],
// kernelRepeats,
// NULL,
// 0,
// perfResults[AlgoCount],
// stream,
// startEvent,
// stopEvent);
// perfResults[AlgoCount].status = status;
// if (status == CUBLAS_STATUS_SUCCESS) {
// AlgoCount++;
// }
// }
// }
// // Sort the results per run duration
// std::sort(perfResults, perfResults + AlgoCount, time_compare);
// // Print timing and perf details
// for (int i = 0, hasPrint = 1; i < AlgoCount; i++) {
// printf("result %03d : ", i);
// hasPrint = printPerfStructure(batch_size,
// seq_len,
// head_num,
// size_per_head,
// m,
// n,
// k,
// perfResults[i],
// fout,
// data_type,
// hasPrint,
// batchCount);
// }
// CLEANUP:
// // Descriptors are no longer needed as all GPU work was already enqueued
// if (Cdesc) {
// cublasLtMatrixLayoutDestroy(Cdesc);
// }
// if (Bdesc) {
// cublasLtMatrixLayoutDestroy(Bdesc);
// }
// if (Adesc) {
// cublasLtMatrixLayoutDestroy(Adesc);
// }
// if (operationDesc) {
// cublasLtMatmulDescDestroy(operationDesc);
// }
// if (startEvent) {
// cudaEventDestroy(startEvent);
// }
// if (stopEvent) {
// cudaEventDestroy(stopEvent);
// }
// return status == CUBLAS_STATUS_SUCCESS ? 0 : 1;
// }
// template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const float* A,
// const float* B,
// const float* beta, /* host pointer */
// float* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD);
// template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const half* alpha, /* host pointer */
// const half* A,
// const half* B,
// const half* beta, /* host pointer */
// half* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD);
// #ifdef ENABLE_BF16
// template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const __nv_bfloat16* A,
// const __nv_bfloat16* B,
// const float* beta, /* host pointer */
// __nv_bfloat16* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD);
// #endif
// #ifdef ENABLE_FP8
// template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const __nv_fp8_e4m3* A,
// const __nv_fp8_e4m3* B,
// const float* beta, /* host pointer */
// __nv_fp8_e4m3* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD);
// #endif
// template int LtHgemmCustomFind(cublasLtHandle_t ltHandle,
// int batch_size,
// int seq_len,
// int head_num,
// int size_per_head,
// int m,
// int n,
// int k,
// const float* alpha, /* host pointer */
// const half* A,
// const half* B,
// const float* beta, /* host pointer */
// half* C,
// void* workSpace,
// size_t workSpaceSize,
// FILE* fout,
// customMatmulPerf_t perfResults[],
// int AlgoCombinations,
// cudaDataType_t dtype_fp8,
// int batchCount,
// int64_t strideA,
// int64_t strideB,
// int64_t strideD);
size_t calGemmTestBufSizeInByte(int batch_size,
int seq_len,
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment