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Merge pull request #10189 from reyoung/feature/fix_matmul_bug

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Fix batch_gemm bugs
release/0.12.0
Xin Pan 7 years ago committed by GitHub
parent 2f53cd0a76 580dad0c2c
commit 64babc9aeb
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 25 additions and 14 deletions
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16
paddle/fluid/operators/math/math_function.cc
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@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/math/math_function.h"
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/math/math_function_impl.h"
#include "paddle/fluid/platform/float16.h"
@ -161,7 +162,8 @@ void batched_gemm<platform::CPUDeviceContext, float16>(
const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const float16 alpha, const float16* A, const float16* B, const float16 beta,
float16* C, const int batchCount, const int strideA, const int strideB) {
float16* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
PADDLE_THROW("float16 batched_gemm not supported on CPU");
}
@ -172,7 +174,8 @@ void batched_gemm<platform::CPUDeviceContext, float>(
const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const float alpha, const float* A, const float* B, const float beta,
float* C, const int batchCount, const int strideA, const int strideB) {
float* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
@ -194,7 +197,8 @@ void batched_gemm<platform::CPUDeviceContext, double>(
const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const double alpha, const double* A, const double* B, const double beta,
double* C, const int batchCount, const int strideA, const int strideB) {
double* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
@ -220,7 +224,8 @@ void batched_gemm<platform::CPUDeviceContext, float>(
const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const float alpha, const float* A, const float* B, const float beta,
float* C, const int batchCount, const int strideA, const int strideB) {
float* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
for (int k = 0; k < batchCount; ++k) {
const float* Ak = &A[k * strideA];
const float* Bk = &B[k * strideB];
@ -235,7 +240,8 @@ void batched_gemm<platform::CPUDeviceContext, double>(
const platform::CPUDeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const double alpha, const double* A, const double* B, const double beta,
double* C, const int batchCount, const int strideA, const int strideB) {
double* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
for (int k = 0; k < batchCount; ++k) {
const double* Ak = &A[k * strideA];
const double* Bk = &B[k * strideB];

16
paddle/fluid/operators/math/math_function.cu
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@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#define EIGEN_USE_GPU
#include <vector>
#include "paddle/fluid/framework/data_type.h"
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/operators/math/math_function_impl.h"
@ -267,7 +268,8 @@ void batched_gemm<platform::CUDADeviceContext, float16>(
const platform::CUDADeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const float16 alpha, const float16* A, const float16* B, const float16 beta,
float16* C, const int batchCount, const int strideA, const int strideB) {
float16* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
#if CUDA_VERSION >= 8000
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
@ -278,7 +280,7 @@ void batched_gemm<platform::CUDADeviceContext, float16>(
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
const int strideC = M * N;
const int64_t strideC = M * N;
const half h_alpha = static_cast<const half>(alpha);
const half h_beta = static_cast<const half>(beta);
@ -303,7 +305,8 @@ void batched_gemm<platform::CUDADeviceContext, float>(
const platform::CUDADeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const float alpha, const float* A, const float* B, const float beta,
float* C, const int batchCount, const int strideA, const int strideB) {
float* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
#if CUDA_VERSION >= 8000
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
@ -314,7 +317,7 @@ void batched_gemm<platform::CUDADeviceContext, float>(
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
const int strideC = M * N;
const int64_t strideC = M * N;
PADDLE_ENFORCE(platform::dynload::cublasSgemmStridedBatched(
context.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B, ldb,
@ -329,7 +332,8 @@ void batched_gemm<platform::CUDADeviceContext, double>(
const platform::CUDADeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N, const int K,
const double alpha, const double* A, const double* B, const double beta,
double* C, const int batchCount, const int strideA, const int strideB) {
double* C, const int batchCount, const int64_t strideA,
const int64_t strideB) {
#if CUDA_VERSION >= 8000
// Note that cublas follows fortran order, so the order is different from
// the cblas convention.
@ -340,7 +344,7 @@ void batched_gemm<platform::CUDADeviceContext, double>(
(transA == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
cublasOperation_t cuTransB =
(transB == CblasNoTrans) ? CUBLAS_OP_N : CUBLAS_OP_T;
const int strideC = M * N;
const int64_t strideC = M * N;
PADDLE_ENFORCE(platform::dynload::cublasDgemmStridedBatched(
context.cublas_handle(), cuTransB, cuTransA, N, M, K, &alpha, B, ldb,

7
paddle/fluid/operators/math/math_function.h
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@ -26,7 +26,7 @@ limitations under the License. */
#ifndef LAPACK_FOUND
extern "C" {
#include <cblas.h>
#include <cblas.h> // NOLINT
int LAPACKE_sgetrf(int matrix_layout, int m, int n, float* a, int lda,
int* ipiv);
int LAPACKE_dgetrf(int matrix_layout, int m, int n, double* a, int lda,
@ -39,6 +39,7 @@ int LAPACKE_dgetri(int matrix_layout, int n, double* a, int lda,
#endif
#include <cmath>
#include <vector>
#include "paddle/fluid/framework/eigen.h"
#include "paddle/fluid/framework/tensor.h"
@ -78,8 +79,8 @@ template <typename DeviceContext, typename T>
void batched_gemm(const DeviceContext& context, const CBLAS_TRANSPOSE transA,
const CBLAS_TRANSPOSE transB, const int M, const int N,
const int K, const T alpha, const T* A, const T* B,
const T beta, T* C, const int batchCount, const int strideA,
const int strideB);
const T beta, T* C, const int batchCount,
const int64_t strideA, const int64_t strideB);
template <typename DeviceContext, typename T>
void gemv(const DeviceContext& context, const bool trans_a, const int M,

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