experiments/STT-tensorflow
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

fix MKL_Complex cast problem

Browse Source 
error : argument of type "" is incompatible with parameter of type ""
This commit is contained in:
fo40225 2018-02-16 00:19:29 +08:00
parent 023d47d0f1
commit 24e343b18c
3 changed files with 47 additions and 33 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
tensorflow/core/kernels/mkl_batch_matmul_op.cc
View File

@ -29,7 +29,6 @@ limitations under the License.
#include <vector>
#include "mkl_cblas.h"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/numeric_types.h"
#include "tensorflow/core/framework/op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
@ -41,9 +40,6 @@ limitations under the License.
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#define MKL_Complex8 tensorflow::complex64
#define MKL_Complex16 tensorflow::complex128
namespace tensorflow {
typedef Eigen::ThreadPoolDevice CPUDevice;
@ -180,16 +176,16 @@ class BatchMatMulMkl : public OpKernel {
void MklCblasGemmBatch(const CBLAS_LAYOUT Layout, const bool TransA,
const bool TransB, const MKL_INT *M_Array,
const MKL_INT *N_Array, const MKL_INT *K_Array,
const MKL_Complex8 **A_Array, const MKL_INT *lda_Array,
const MKL_Complex8 **B_Array, const MKL_INT *ldb_Array,
MKL_Complex8 **C_Array, const MKL_INT *ldc_Array,
const complex64 **A_Array, const MKL_INT *lda_Array,
const complex64 **B_Array, const MKL_INT *ldb_Array,
complex64 **C_Array, const MKL_INT *ldc_Array,
const MKL_INT group_count, const MKL_INT *group_size) {
std::vector<CBLAS_TRANSPOSE> TransA_array(
group_size[0], TransA ? CblasConjTrans : CblasNoTrans);
std::vector<CBLAS_TRANSPOSE> TransB_array(
group_size[0], TransB ? CblasConjTrans : CblasNoTrans);
std::vector<MKL_Complex8> alpha_Array(group_size[0], {1.0f, 0.0f});
std::vector<MKL_Complex8> beta_Array(group_size[0], {0.0f, 0.0f});
std::vector<complex64> alpha_Array(group_size[0], {1.0f, 0.0f});
std::vector<complex64> beta_Array(group_size[0], {0.0f, 0.0f});
cblas_cgemm_batch(
Layout, &TransA_array[0], &TransB_array[0], M_Array, N_Array, K_Array,
static_cast<const void *>(&alpha_Array[0]),
@ -202,18 +198,18 @@ class BatchMatMulMkl : public OpKernel {
void MklCblasGemmBatch(const CBLAS_LAYOUT Layout, const bool TransA,
const bool TransB, const MKL_INT *M_Array,
const MKL_INT *N_Array, const MKL_INT *K_Array,
const MKL_Complex16 **A_Array,
const complex128 **A_Array,
const MKL_INT *lda_Array,
const MKL_Complex16 **B_Array,
const MKL_INT *ldb_Array, MKL_Complex16 **C_Array,
const complex128 **B_Array,
const MKL_INT *ldb_Array, complex128 **C_Array,
const MKL_INT *ldc_Array, const MKL_INT group_count,
const MKL_INT *group_size) {
std::vector<CBLAS_TRANSPOSE> TransA_array(
group_size[0], TransA ? CblasConjTrans : CblasNoTrans);
std::vector<CBLAS_TRANSPOSE> TransB_array(
group_size[0], TransB ? CblasConjTrans : CblasNoTrans);
std::vector<MKL_Complex16> alpha_Array(group_size[0], {1.0f, 0.0f});
std::vector<MKL_Complex16> beta_Array(group_size[0], {0.0f, 0.0f});
std::vector<complex128> alpha_Array(group_size[0], {1.0f, 0.0f});
std::vector<complex128> beta_Array(group_size[0], {0.0f, 0.0f});
cblas_zgemm_batch(
Layout, &TransA_array[0], &TransB_array[0], M_Array, N_Array, K_Array,
static_cast<const void *>(&alpha_Array[0]),

28
tensorflow/core/kernels/mkl_matmul_op.cc
View File

@ -170,32 +170,32 @@ class MklMatMulOp : public OpKernel {
// Matrix-Matrix Multiplication with Complex64 (std::complex<float>) tensors.
// For detailed info about parameters, look at FP32 function description.
void MklBlasGemm(bool transa, bool transb, const int m, const int n,
const int k, const std::complex<float>* a, const int lda,
const std::complex<float>* b, const int ldb,
std::complex<float>* c, int const ldc) {
const int k, const complex64* a, const int lda,
const complex64* b, const int ldb,
complex64* c, int const ldc) {
const MKL_Complex8 alpha = {1.0f, 0.0f};
const MKL_Complex8 beta = {0.0f, 0.0f};
cblas_cgemm(CblasRowMajor, transa ? CblasTrans : CblasNoTrans,
transb ? CblasTrans : CblasNoTrans, m, n, k,
static_cast<const void*>(&alpha), static_cast<const void*>(a),
lda, static_cast<const void*>(b), ldb,
static_cast<const void*>(&beta), static_cast<void*>(c), ldc);
transb ? CblasTrans : CblasNoTrans,
m, n, k, &alpha, reinterpret_cast<const MKL_Complex8*>(a), lda,
reinterpret_cast<const MKL_Complex8*>(b), ldb, &beta,
reinterpret_cast<MKL_Complex8*>(c), ldc);
}
// Matrix-Matrix Multiplication with Complex128 (std::complex<double>)
// tensors. For detailed info about parameters, look at FP32 function
// description.
void MklBlasGemm(bool transa, bool transb, const int m, const int n,
const int k, const std::complex<double>* a, const int lda,
const std::complex<double>* b, const int ldb,
std::complex<double>* c, const int ldc) {
const int k, const complex128* a, const int lda,
const complex128* b, const int ldb,
complex128* c, const int ldc) {
const MKL_Complex16 alpha = {1.0, 0.0};
const MKL_Complex16 beta = {0.0, 0.0};
cblas_zgemm(CblasRowMajor, transa ? CblasTrans : CblasNoTrans,
transb ? CblasTrans : CblasNoTrans, m, n, k,
static_cast<const void*>(&alpha), static_cast<const void*>(a),
lda, static_cast<const void*>(b), ldb,
static_cast<const void*>(&beta), static_cast<void*>(c), ldc);
transb ? CblasTrans : CblasNoTrans,
m, n, k, &alpha, reinterpret_cast<const MKL_Complex16*>(a), lda,
reinterpret_cast<const MKL_Complex16*>(b), ldb, &beta,
reinterpret_cast<MKL_Complex16*>(c), ldc);
}
};

28
tensorflow/core/kernels/mkl_transpose_op.cc
View File

@ -18,9 +18,6 @@ limitations under the License.
#ifdef INTEL_MKL
#define EIGEN_USE_THREADS
#include "tensorflow/core/framework/numeric_types.h"
#define MKL_Complex8 tensorflow::complex64
#define MKL_Complex16 tensorflow::complex128
#include "mkl_trans.h"
#include "tensorflow/core/kernels/transpose_functor.h"
#include "tensorflow/core/kernels/transpose_op.h"
@ -62,10 +59,31 @@ Status MKLTranspose2D(const char trans, const Tensor& in, Tensor* out);
INSTANTIATE(float, s)
INSTANTIATE(double, d)
INSTANTIATE(complex64, c)
INSTANTIATE(complex128, z)
#undef INSTANTIATE
template <>
Status MKLTranspose2D<complex64>(const char trans, const Tensor& in, Tensor* out) {
const MKL_Complex8 alpha = { 1.0f, 0.0f };
mkl_comatcopy('R', trans, in.dim_size(0), in.dim_size(1), alpha,
reinterpret_cast<const MKL_Complex8*>(in.flat<complex64>().data()),
in.dim_size(1),
reinterpret_cast<MKL_Complex8*>(const_cast<complex64*>(out->flat<complex64>().data())),
in.dim_size(0));
return Status::OK();
}
template <>
Status MKLTranspose2D<complex128>(const char trans, const Tensor& in, Tensor* out) {
const MKL_Complex16 alpha = { 1.0, 0.0 };
mkl_zomatcopy('R', trans, in.dim_size(0), in.dim_size(1), alpha,
reinterpret_cast<const MKL_Complex16*>(in.flat<complex128>().data()),
in.dim_size(1),
reinterpret_cast<MKL_Complex16*>(const_cast<complex128*>(out->flat<complex128>().data())),
in.dim_size(0));
return Status::OK();
}
static const char kMKLTranspose = 'T';
static const char kMKLConjugateTranspose = 'C';