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refine blas gemm

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revert-12864-feature/process_lod_grad
tensor-tang 7 years ago
parent f5d5d7b2d9
commit f72ab8961e
4 changed files with 77 additions and 76 deletions
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6
CMakeLists.txt
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@ -138,12 +138,6 @@ else()
set(THIRD_PARTY_BUILD_TYPE Release)
endif()
if(WITH_MKL)
option(MKL_SPLIT_GEMM "PaddlePaddle MKL gemm would split to small ones" OFF)
if (MKL_SPLIT_GEMM)
add_definitions(-DPADDLE_MKL_SPLIT_GEMM)
endif()
endif()
set(WITH_MKLML ${WITH_MKL})
if (NOT DEFINED WITH_MKLDNN)
if (WITH_MKL AND AVX2_FOUND)

9
paddle/fluid/operators/math/blas.h
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@ -90,6 +90,11 @@ class Blas {
void GEMM(bool transA, bool transB, int M, int N, int K, T alpha, const T* A,
int lda, const T* B, int ldb, T beta, T* C, int ldc) const;
template <typename T>
void GEMM(CBLAS_TRANSPOSE transA, CBLAS_TRANSPOSE transB, int M, int N, int K,
T alpha, const T* A, int lda, const T* B, int ldb, T beta, T* C,
int ldc) const;
#ifdef PADDLE_WITH_MKLML
template <typename T>
T* GEMM_ALLOC(const CBLAS_IDENTIFIER id, const int M, const int N,
@ -109,6 +114,10 @@ class Blas {
void GEMM_FREE(T* data) const;
#endif
template <typename T>
void MatMul(const int M, const int N, const int K, const T* A, const T* B,
T* C) const;
template <typename T>
void MatMul(const framework::Tensor& mat_a, bool trans_a,
const framework::Tensor& mat_b, bool trans_b, T alpha,

116
paddle/fluid/operators/math/blas_impl.h
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@ -217,64 +217,6 @@ struct CBlas<platform::float16> {
#endif
};
template <typename T>
inline bool UseXSMM(const int &m, const int &n, const int &k, bool transa,
bool transb, const T &alpha, const T &beta) {
#ifdef PADDLE_WITH_LIBXSMM
// Refer to https://github.com/hfp/libxsmm/blob/master/README.md
// But the threshold is custom
constexpr int LIBXSMM_THRESHOLD = 20 * 20 * 20;
if (m * n * k > LIBXSMM_THRESHOLD || transa || transb ||
std::abs<T>(alpha - static_cast<T>(1) >
std::numeric_limits<T>::epsilon()) ||
std::abs<T>(beta) > std::numeric_limits<T>::epsilon()) {
return false;
} else {
return true;
}
#endif
return false;
}
template <>
inline bool UseXSMM<platform::float16>(const int &m, const int &n, const int &k,
bool transa, bool transb,
const platform::float16 &alpha,
const platform::float16 &beta) {
return false;
}
template <typename T>
inline void GEMM_WARP(CBLAS_ORDER order, CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB, int M, int N, int K, T alpha,
const T *A, int lda, const T *B, int ldb, T beta, T *C,
int ldc) {
#ifdef PADDLE_WITH_LIBXSMM
if (UseXSMM<T>(M, N, K, transA != CblasNoTrans, transB != CblasNoTrans, alpha,
beta)) {
// Note: SMM use ColMajor
const char transa = 'N';
const char transb = 'N';
CBlas<T>::SMM_GEMM(&transa, &transb, &N, &M, &K, &alpha, B, &ldb, A, &lda,
&beta, C, &ldc);
return;
}
#endif
#ifdef PADDLE_MKL_SPLIT_GEMM
constexpr int bs = 2;
if (M % bs == 0 && transA == CblasNoTrans && transB == CblasNoTrans) {
for (int off = 0; off < M; off += bs) {
CBlas<T>::GEMM(CblasRowMajor, CblasNoTrans, CblasNoTrans, bs, N, K, alpha,
A + off * lda, lda, B, ldb, beta, C + off * ldb, ldc);
}
return;
}
#endif
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
#ifdef PADDLE_WITH_MKLML
template <>
template <typename T>
@ -319,8 +261,8 @@ void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
int lda = (transA == CblasNoTrans) ? K : M;
int ldb = (transB == CblasNoTrans) ? N : K;
int ldc = N;
GEMM_WARP<T>(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <>
@ -329,9 +271,20 @@ void Blas<platform::CPUDeviceContext>::GEMM(bool transA, bool transB, int M,
int N, int K, T alpha, const T *A,
int lda, const T *B, int ldb,
T beta, T *C, int ldc) const {
GEMM_WARP<T>(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
CBlas<T>::GEMM(CblasRowMajor, transA == false ? CblasNoTrans : CblasTrans,
transB == false ? CblasNoTrans : CblasTrans, M, N, K, alpha, A,
lda, B, ldb, beta, C, ldc);
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::GEMM(CBLAS_TRANSPOSE transA,
CBLAS_TRANSPOSE transB, int M,
int N, int K, T alpha, const T *A,
int lda, const T *B, int ldb,
T beta, T *C, int ldc) const {
CBlas<T>::GEMM(CblasRowMajor, transA, transB, M, N, K, alpha, A, lda, B, ldb,
beta, C, ldc);
}
template <typename DeviceContext>
@ -440,6 +393,43 @@ void Blas<platform::CPUDeviceContext>::BatchedGEMM(
#endif
}
template <typename DeviceContext>
template <typename T>
void Blas<DeviceContext>::MatMul(const int M, const int N, const int K,
const T *A, const T *B, T *C) const {
this->template GEMM<T>(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K,
static_cast<T>(1), A, K, B, N, static_cast<T>(0), C,
N);
}
template <>
template <typename T>
void Blas<platform::CPUDeviceContext>::MatMul(const int M, const int N,
const int K, const T *A,
const T *B, T *C) const {
#ifdef PADDLE_WITH_LIBXSMM
// Refer to https://github.com/hfp/libxsmm/blob/master/README.md
// But the threshold is custom constexpr int LIBXSMM_THRESHOLD = 20 * 20 * 20;
// Since the matrix is very small,
// so the unit of calculation is already very fast,
// and the if( M*N*K < LIBXSMM_THRESHOLD) would be overhead,
// use xsmm directly.
// Note: SMM use ColMajor
const char transa = 'N';
const char transb = 'N';
const T alpha = static_cast<T>(1);
const T beta = static_cast<T>(0);
CBlas<T>::SMM_GEMM(&transa, &transb, &N, &M, &K, &alpha, B, &N, A, &K, &beta,
C, &N);
return;
#endif
CBlas<T>::GEMM(CblasRowMajor, CblasNoTrans, CblasNoTrans, M, N, K,
static_cast<T>(1), A, K, B, N, static_cast<T>(0), C, N);
}
template <typename DeviceContext>
template <typename T>
void Blas<DeviceContext>::MatMul(const framework::Tensor &mat_a,

22
paddle/fluid/operators/math/fc_compute.h
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@ -25,17 +25,25 @@ namespace math {
template <typename DeviceContext, typename T>
inline void FCCompute(const BlasT<DeviceContext, T>& blas, const int M,
const int N, const int K, const T* X, const T* W, T* Y,
const T* B = NULL) {
blas.GEMM(CblasNoTrans, CblasNoTrans, M, N, K, static_cast<T>(1), X, W,
static_cast<T>(0), Y);
if (B) {
const T* B = NULL, bool relu = false) {
blas.MatMul(M, N, K, X, W, Y);
if (B == NULL) {
return;
}
#ifdef PADDLE_WITH_MKLML
#pragma omp parallel for if (FLAGS_paddle_num_threads > 1)
#endif
for (int i = 0; i < M; i++) {
blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
}
for (int i = 0; i < M; i++) {
blas.AXPY(N, static_cast<T>(1), B, Y + i * N);
}
if (!relu) {
return;
}
// TODO(TJ): fuse relu
LOG(FATAL) << "Not implemented!";
}
} // namespace math

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