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Support multiple contraction kernel types in TensorContractionThreadPool

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This commit is contained in:
Eugene Zhulenev 2018-09-26 11:08:47 -07:00
parent 0a3356f4ec
commit 71cd3fbd6a
6 changed files with 484 additions and 20 deletions
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18
cmake/FindMkldnn.cmake Normal file
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@ -0,0 +1,18 @@
# Intel mkl-dnn support.
# Link: https://github.com/intel/mkl-dnn
if (MKLDNN)
set(MKLDNN_FIND_QUIETLY TRUE)
set(MKLDNN_INCLUDES ${MKLDNN}/include)
set(MKLDNN_LIBRARIES ${MKLDNN}/lib)
endif (MKLDNN)
find_path(MKLDNN
NAMES
mkldnn.h
PATHS
$ENV{MKLDNNDIR}/include
${INCLUDE_INSTALL_DIR}
)
include(FindPackageHandleStandardArgs)
find_package_handle_standard_args(MKLDNN DEFAULT_MSG
MKLDNN)
mark_as_advanced(MKLDNN)

5
unsupported/Eigen/CXX11/Tensor
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@ -75,6 +75,10 @@ typedef unsigned __int64 uint64_t;
#include "libxsmm.h"
#endif
#if defined(EIGEN_USE_MKLDNN)
#include "mkldnn.h"
#endif
#ifdef EIGEN_USE_THREADS
#include "ThreadPool"
#endif
@ -121,6 +125,7 @@ typedef unsigned __int64 uint64_t;
#include "src/Tensor/TensorArgMax.h"
#include "src/Tensor/TensorConcatenation.h"
#include "src/Tensor/TensorContractionMapper.h"
#include "src/Tensor/TensorContractionMkldnn.h"
#include "src/Tensor/TensorContractionBlocking.h"
#include "src/Tensor/TensorContraction.h"
#include "src/Tensor/TensorContractionThreadPool.h"

116
unsupported/Eigen/CXX11/src/Tensor/TensorContractionMkldnn.h Normal file
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@ -0,0 +1,116 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MKLDNN_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MKLDNN_H
#if defined(EIGEN_USE_MKLDNN)
// Support for MklDnn sgemm kernel in Tensor contractions:
//
// 1. Prepare packed Lhs/Rhs blocks from tensor expressions using
// DataMapper (see TensorContractionInputMapper).
// 2. Invoke gemm kernel with packed blocks (replacement for default
// gebp_kernel).
namespace Eigen {
namespace internal {
template <typename Scalar, typename StorageIndex, typename DataMapper,
int StorageOrder>
struct mkldnn_gemm_pack;
// mkl_gemm_pack for ColMajor storage order.
template <typename Scalar, typename StorageIndex, typename DataMapper>
struct mkldnn_gemm_pack<Scalar, StorageIndex, DataMapper,
/*StorageOrder*/ ColMajor> {
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename DataMapper::LinearMapper LinearMapper;
enum { PacketSize = internal::packet_traits<Scalar>::size };
EIGEN_DONT_INLINE
void operator()(Scalar *block, const DataMapper &data_mapper,
StorageIndex rows, StorageIndex cols) {
const StorageIndex unrolled_rows =
(rows / (4 * PacketSize)) * (4 * PacketSize);
const StorageIndex vectorized_rows = (rows / PacketSize) * PacketSize;
for (StorageIndex col = 0; col < cols; ++col) {
LinearMapper lm = data_mapper.getLinearMapper(0, col);
// Give compiler a strong possibility to unroll the loop.
for (StorageIndex i = 0; i < unrolled_rows; i += 4 * PacketSize) {
for (StorageIndex j = 0; j < 4; ++j) {
const Packet p = lm.template loadPacket<Packet>(i + j * PacketSize);
internal::pstoreu(block + j * PacketSize, p);
}
block += 4 * PacketSize;
}
// Process remaining rows with packets.
for (StorageIndex i = unrolled_rows; i < vectorized_rows;
i += PacketSize) {
const Packet p = lm.template loadPacket<Packet>(i);
internal::pstoreu(block, p);
block += PacketSize;
}
// Finalize with coefficients.
for (StorageIndex i = vectorized_rows; i < rows; ++i) {
*block = lm(i);
++block;
}
}
}
};
template <typename Scalar, typename StorageIndex, typename OutputMapper,
bool ConjugateLhs = false, bool ConjugateRhs = false>
struct mkldnn_gemm_kernel;
// mkldnn_gemm_kernel for floats defined as a thin layer on top of mkldnn_sgemm.
template <typename StorageIndex, typename OutputMapper, bool ConjugateLhs,
bool ConjugateRhs>
struct mkldnn_gemm_kernel</*Scalar*/ float, StorageIndex, OutputMapper,
ConjugateLhs, ConjugateRhs> {
EIGEN_DONT_INLINE
void operator()(const OutputMapper &output, const float *blockA,
const float *blockB, const StorageIndex rows,
const StorageIndex depth, const StorageIndex cols,
float alpha) {
static const int max_index = (std::numeric_limits<int>::max)();
eigen_assert(max_index > rows);
eigen_assert(max_index > cols);
eigen_assert(max_index > depth);
eigen_assert(max_index > output.stride());
const int m = static_cast<int>(rows);
const int n = static_cast<int>(cols);
const int k = static_cast<int>(depth);
const char transposeA = ConjugateLhs ? 'Y' : 'N';
const char transposeB = ConjugateRhs ? 'Y' : 'N';
const int ldA = ConjugateLhs ? k : m;
const int ldB = ConjugateRhs ? n : k;
const int ldC = static_cast<int>(output.stride());
const float beta = 1.0;
mkldnn_status_t st = mkldnn_sgemm(&transposeA, &transposeB, &m, &n, &k,
&alpha, blockA, &ldA, blockB, &ldB, &beta,
const_cast<float*>(output.data()), &ldC);
eigen_assert(st == 0);
}
};
} // namespace internal
} // namespace Eigen
#endif // EIGEN_USE_MKLDNN
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_MKLDNN_H

212
unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h
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@ -15,6 +15,177 @@
namespace Eigen {
namespace internal {
// WARNING: In this code we assume that Lhs and Rhs tensor expressions are in
// ColMajor storage order. This property is guaranteed by the
// TensorContractionOp evaluator. TensorContractionKernel specifies how we pack
// blocks of Lhs and Rhs tensor expressions, and how we invoke matrix
// multiplication for these blocks. Default tensor contraction uses
// gemm_pack_rhs, gemm_pack_lhs and gebp_kernel from Eigen Core (see
// GeneralBlocPanelKernel.h for details).
//
// By specializing contraction kernels we can use other low level libraries to
// perform matrix multiplication, and still rely on Eigen thread pool evaluator
// for scaling. Assumption is that custom gemm do not use it's own threading for
// parallelisation.
//
// - ResScalar/LhsScalar/RhsScalar - scalar type for the result of
// multiplication, lhs tensor and rhs tensor respectively.
//
// - StorageIndex - index type for the tensor expressions. In practice almost
// always is Eigen::Index.
//
// - OutputMapper provides access to the memory of the output matrix. In
// practice it's always column major blas_data_mapper (it must be of ResScalar
// type).
//
// - LhsMapper/RhsMapper similarly to blas_data_mapper provide a two dimensional
// view into the Lhs/Rhs tensor expressions. In practice it's
// TensorContractionInputMapper, or some specialization of it based on the
// type of tensor expression (e.g. TensorImagePatchOp has optimized input
// mapper).
//
// TODO(ezhulenev): Use TensorContractionKernel in default tensor contraction
// evaluator.
template<typename ResScalar, typename LhsScalar, typename RhsScalar,
typename StorageIndex, typename OutputMapper, typename LhsMapper,
typename RhsMapper>
struct TensorContractionKernel {
typedef typename internal::gebp_traits<LhsScalar, RhsScalar> Traits;
typedef internal::gemm_pack_lhs<LhsScalar, StorageIndex,
typename LhsMapper::SubMapper,
Traits::mr, Traits::LhsProgress,
typename Traits::LhsPacket4Packing, ColMajor>
LhsPacker;
typedef internal::gemm_pack_rhs<RhsScalar, StorageIndex,
typename RhsMapper::SubMapper, Traits::nr,
ColMajor>
RhsPacker;
typedef internal::gebp_kernel<LhsScalar, RhsScalar, StorageIndex,
OutputMapper, Traits::mr, Traits::nr,
/*ConjugateLhs*/ false, /*ConjugateRhs*/ false>
GebpKernel;
EIGEN_DONT_INLINE
static void packLhs(LhsScalar* lhsBlock,
const typename LhsMapper::SubMapper& data_mapper,
const StorageIndex depth, const StorageIndex rows) {
LhsPacker()(lhsBlock, data_mapper, depth, rows);
}
EIGEN_DONT_INLINE
static void packRhs(RhsScalar* rhsBlock,
const typename RhsMapper::SubMapper& data_mapper,
const StorageIndex depth, const StorageIndex cols) {
RhsPacker()(rhsBlock, data_mapper, depth, cols);
}
EIGEN_DONT_INLINE
static void invoke(const OutputMapper& output_mapper,
const LhsScalar* lhsBlock, const RhsScalar* rhsBlock,
const StorageIndex rows, const StorageIndex depth,
const StorageIndex cols, const ResScalar alpha) {
GebpKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha,
/*strideA*/ -1, /*strideB*/ -1,
/*offsetA*/ 0, /*offsetB*/ 0);
}
};
// Some tensor contraction kernels might rely on the gemm libraries that are
// optimized for a specific dimension sizes. By default Eigen picks block
// sizes to fit the working set in the L1/L2 caches, by specializing we can
// refine this choice and round up these sizes to work well with underlying gemm
// library.
// TODO(ezhulenev): Move it to TensorContractionBlocking, or keep separate?
template<typename ResScalar, typename LhsScalar, typename RhsScalar,
typename StorageIndex>
struct TensorContractionKernelBlocking {
static void refine(const StorageIndex /*m*/,
const StorageIndex /*n*/,
const StorageIndex /*k*/,
StorageIndex* /*bm*/,
StorageIndex* /*bn*/,
StorageIndex* /*bk*/) {
// By default we do nothing and stick to the block sizes picked by Eigen.
}
};
#if defined(EIGEN_USE_MKLDNN)
// If all scalar types in tensor contraction are floats, we can use mkldnn gemm
// as our low level kernel.
template<typename StorageIndex, typename OutputMapper, typename LhsMapper,
typename RhsMapper>
struct TensorContractionKernel<float, float, float, StorageIndex, OutputMapper,
LhsMapper, RhsMapper> {
// For now mkldnn has only mkldnn_sgemm (gemm for floats).
typedef float Scalar;
typedef typename internal::gebp_traits<Scalar, Scalar> Traits;
typedef internal::mkldnn_gemm_pack<Scalar, StorageIndex,
typename LhsMapper::SubMapper, ColMajor>
LhsPacker;
typedef internal::mkldnn_gemm_pack<Scalar, StorageIndex,
typename RhsMapper::SubMapper, ColMajor>
RhsPacker;
typedef internal::mkldnn_gemm_kernel<Scalar, StorageIndex, OutputMapper>
GemmKernel;
EIGEN_DONT_INLINE
static void packLhs(Scalar* lhsBlock,
const typename LhsMapper::SubMapper& data_mapper,
StorageIndex depth, StorageIndex rows) {
LhsPacker()(lhsBlock, data_mapper, rows, depth);
}
EIGEN_DONT_INLINE
static void packRhs(Scalar* rhsBlock,
const typename RhsMapper::SubMapper& data_mapper,
const StorageIndex depth, const StorageIndex cols) {
RhsPacker()(rhsBlock, data_mapper, depth, cols);
}
EIGEN_DONT_INLINE
static void invoke(const OutputMapper& output_mapper, const Scalar* lhsBlock,
const Scalar* rhsBlock, const StorageIndex rows,
const StorageIndex depth, const StorageIndex cols,
const Scalar alpha) {
GemmKernel()(output_mapper, lhsBlock, rhsBlock, rows, depth, cols, alpha);
}
};
// For mkldnn_sgemm having the right dimensions (especially for small matrices)
// is more important than fitting all the working set in L1/L2 caches.
template<typename StorageIndex>
struct TensorContractionKernelBlocking<float, float, float, StorageIndex> {
// Mkldnn Avx/Avx2/Avx512 unroll factors are: 8/16/48. We pick the largest.
static const StorageIndex kUnrollM = 48;
// Mkldnn Avx/Avx2/Avx512 unroll factors are: 6/6/8. We pick the closest
// number that divides to both of them.
static const StorageIndex kUnrollN = 24;
static void refine(const StorageIndex m,
const StorageIndex n,
const StorageIndex /*k*/,
StorageIndex* bm,
StorageIndex* bn,
StorageIndex* /*bk*/) {
// TODO(ezhulenev): There is probably a better way to pick block sizes.
*bm = (std::min)(m, Eigen::divup(*bm, kUnrollM) * kUnrollM);
*bn = (std::min)(n, Eigen::divup(*bn, kUnrollN) * kUnrollN);
// Stick with default bk.
}
};
#endif // EIGEN_USE_MKLDNN
} // namespace internal
template<typename Indices, typename LeftArgType, typename RightArgType, typename OutputKernelType>
struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> :
public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType, OutputKernelType>, ThreadPoolDevice> > {
@ -175,6 +346,10 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
bn = blocking.nc();
bk = blocking.kc();
}
// Refine blocking choice to work well with contraction kernel.
internal::TensorContractionKernelBlocking<Scalar, LhsScalar, RhsScalar,
Index>::refine(m, n, k, &bm,
&bn, &bk);
// Number of kernels for each dimension.
Index nm0 = divup(m, bm);
@ -242,17 +417,12 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
contract_t, internal::packet_traits<RhsScalar>::size,
rhs_inner_dim_contiguous, rhs_inner_dim_reordered, Unaligned>
RhsMapper;
typedef internal::gemm_pack_lhs<LhsScalar, Index,
typename LhsMapper::SubMapper, Traits::mr,
Traits::LhsProgress, typename Traits::LhsPacket4Packing, ColMajor>
LhsPacker;
typedef internal::gemm_pack_rhs<
RhsScalar, Index, typename RhsMapper::SubMapper, Traits::nr, ColMajor>
RhsPacker;
typedef internal::blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
typedef internal::gebp_kernel<LhsScalar, RhsScalar, Index, OutputMapper,
Traits::mr, Traits::nr, false, false>
GebpKernel;
typedef internal::TensorContractionKernel<
Scalar, LhsScalar, RhsScalar, Index, OutputMapper, LhsMapper, RhsMapper>
TensorContractionKernel;
Context(const Self* self, int num_threads, Scalar* buffer, Index tm, Index tn,
Index tk, Index bm, Index bn, Index bk, Index nm, Index nn, Index nk,
@ -434,8 +604,9 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
void pack_lhs(Index m, Index k) {
const Index mend = m * gm_ + gm(m);
for (Index m1 = m * gm_; m1 < mend; m1++)
LhsPacker()(packed_lhs_[k % (P - 1)][m1],
lhs_.getSubMapper(m1 * bm_, k * bk_), bk(k), bm(m1));
TensorContractionKernel::packLhs(packed_lhs_[k % (P - 1)][m1],
lhs_.getSubMapper(m1 * bm_, k * bk_),
bk(k), bm(m1));
if (!parallel_pack_ && shard_by_col_) {
signal_packing(k);
@ -458,8 +629,9 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
// deadlocks.
memset(buffer_ + n1 * bn_ * m_, 0, bn(n1) * m_ * sizeof(Scalar));
}
RhsPacker()(packed_rhs_[k % (P - 1)][n1],
rhs_.getSubMapper(k * bk_, n1 * bn_), bk(k), bn(n1));
TensorContractionKernel::packRhs(packed_rhs_[k % (P - 1)][n1],
rhs_.getSubMapper(k * bk_, n1 * bn_),
bk(k), bn(n1));
}
if (parallel_pack_ || shard_by_col_) {
@ -480,9 +652,9 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
for (Index n1 = n * gn_; n1 < nend; n1++) {
for (Index m1 = m * gm_; m1 < mend; m1++) {
const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
GebpKernel()(output_mapper, packed_lhs_[k % (P - 1)][m1],
packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1),
Scalar(1), -1, -1, 0, 0);
TensorContractionKernel::invoke(
output_mapper, packed_lhs_[k % (P - 1)][m1],
packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1), Scalar(1));
// We are done with the last task for the [m1, n1] block.
if (k + 1 == nk_) {
@ -495,9 +667,9 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT
for (Index m1 = m * gm_; m1 < mend; m1++)
for (Index n1 = n * gn_; n1 < nend; n1++) {
const auto output_mapper = output_.getSubMapper(m1 * bm_, n1 * bn_);
GebpKernel()(output_mapper, packed_lhs_[k % (P - 1)][m1],
packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1),
Scalar(1), -1, -1, 0, 0);
TensorContractionKernel::invoke(
output_mapper, packed_lhs_[k % (P - 1)][m1],
packed_rhs_[k % (P - 1)][n1], bm(m1), bk(k), bn(n1), Scalar(1));
// We are done with the last task for the [m1, n1] block.
if (k + 1 == nk_) {

12
unsupported/test/CMakeLists.txt
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@ -23,6 +23,17 @@ else(XSMM_FOUND)
ei_add_property(EIGEN_MISSING_BACKENDS "Xsmm, ")
endif(XSMM_FOUND)
find_package(Mkldnn)
if(MKLDNN_FOUND)
add_definitions("-DEIGEN_USE_MKLDNN")
include_directories(${MKLDNN_INCLUDES})
link_directories(${MKLDNN_LIBRARIES})
set(EXTERNAL_LIBS ${EXTERNAL_LIBS} mkldnn)
ei_add_property(EIGEN_TESTED_BACKENDS "Mkldd, ")
else(MKLDNN_FOUND)
ei_add_property(EIGEN_MISSING_BACKENDS "Mkldnn, ")
endif(MKLDNN_FOUND)
find_package(GoogleHash)
if(GOOGLEHASH_FOUND)
add_definitions("-DEIGEN_GOOGLEHASH_SUPPORT")
@ -190,6 +201,7 @@ if(EIGEN_TEST_CXX11)
ei_add_test(cxx11_tensor_index_list)
ei_add_test(cxx11_tensor_mixed_indices)
ei_add_test(cxx11_tensor_contraction)
ei_add_test(cxx11_tensor_contraction_mkldnn)
ei_add_test(cxx11_tensor_convolution)
ei_add_test(cxx11_tensor_expr)
ei_add_test(cxx11_tensor_fixed_size)

141
unsupported/test/cxx11_tensor_contraction_mkldnn.cpp Normal file
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@ -0,0 +1,141 @@
// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2018 Eugene Zhulenev <ezhulenev@google.com>
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
#include "main.h"
// Nothing to test here if we do not have mkldnn enabled.
#if defined(EIGEN_USE_MKLDNN)
#include <Eigen/CXX11/Tensor>
using Eigen::array;
using Eigen::ColMajor;
using Eigen::Tensor;
using Eigen::Index;
using Eigen::internal::blas_data_mapper;
using Eigen::internal::mkldnn_gemm_kernel;
using Eigen::internal::mkldnn_gemm_pack;
template <int NumDims>
static array<Index, NumDims> RandomDims(int min_dim = 1, int max_dim = 20) {
array<Index, NumDims> dims;
for (int i = 0; i < NumDims; ++i) {
dims[i] = internal::random<int>(min_dim, max_dim);
}
return dims;
}
// Packing with mkldnn_gemm_pack is the same as taking a slice of 2 dimensional
// Tensor.
template <typename Scalar>
static void test_mkldnn_gemm_pack() {
static const int Options = 0 | ColMajor;
typedef blas_data_mapper<Scalar, Index, ColMajor> DataMapper;
typedef mkldnn_gemm_pack<Scalar, Index, DataMapper, ColMajor> MkldnnGemmPack;
typedef Tensor<Scalar, 2, Options, Index> Tensor2d;
array<Index, 2> dims = RandomDims<2>(1, 500);
// Create a tensor initialized with random data.
Tensor2d src(dims);
src.setRandom();
// Pick a random slice of src tensor.
array<Index, 2> slice_start = RandomDims<2>(0, 250);
array<Index, 2> slice_size = RandomDims<2>(100, 500);
// Make sure that slice start + size do not overflow tensor dims.
for (int i = 0; i < 2; ++i) {
slice_start[i] = numext::mini(dims[i] - 1, slice_start[i]);
slice_size[i] = numext::mini(slice_size[i], dims[i] - slice_start[i]);
}
// Prepare tensors for packing and slicing results.
Tensor2d pack_dst(slice_size[0], slice_size[1]);
Tensor2d slice_dst(slice_size[0], slice_size[1]);
// Pack memory using mkldnn_gemm_pack.
DataMapper data_mapper(src.data(), dims[0]);
MkldnnGemmPack gemm_pack;
gemm_pack(pack_dst.data(),
data_mapper.getSubMapper(slice_start[0], slice_start[1]),
slice_size[0], slice_size[1]);
// Slice the source tensor.
slice_dst = src.slice(slice_start, slice_size);
// Verify that dst tensors are equal.
VERIFY_IS_EQUAL(pack_dst.dimensions().TotalSize(),
slice_dst.dimensions().TotalSize());
for (Index i = 0; i < pack_dst.dimensions().TotalSize(); ++i) {
Scalar packed = pack_dst.coeff(i);
Scalar sliced = slice_dst.coeff(i);
VERIFY_IS_EQUAL(packed, sliced);
}
}
template <typename Scalar>
static void test_mkldnn_gemm_kernel() {
static const int Options = 0 | ColMajor;
typedef Tensor<Scalar, 2, Options, Index> Tensor2d;
int m = internal::random<int>(1, 100);
int n = internal::random<int>(1, 100);
int k = internal::random<int>(1, 100);
Tensor2d lhs(m, k);
lhs.setRandom();
Tensor2d rhs(k, n);
rhs.setRandom();
// Compute matmul with mkldnn gemm kernel.
typedef blas_data_mapper<Scalar, Index, ColMajor> OutputMapper;
typedef mkldnn_gemm_kernel<Scalar, Index, OutputMapper, ColMajor>
MkldnnGemmKernel;
Tensor2d mkldnn_result(m, n);
mkldnn_result.setZero();
OutputMapper output_mapper(mkldnn_result.data(), m);
MkldnnGemmKernel gemm_kernel;
gemm_kernel(output_mapper, lhs.data(), rhs.data(), m, k, n, /*alpha*/ 1.0);
// Compute matmul with Eigen::Matrix.
typedef Eigen::Matrix<Scalar, Dynamic, Dynamic, ColMajor> Matrix;
typedef Map<Eigen::Matrix<Scalar, Dynamic, Dynamic, ColMajor> > MatrixMap;
MatrixMap lhs_mat(lhs.data(), m, k);
MatrixMap rhs_mat(rhs.data(), k, n);
Matrix matmul_result(m, n);
matmul_result.setZero();
matmul_result = lhs_mat * rhs_mat;
static const float error_threshold = 1e-4f;
// Verify that results are equal.
for (Index i = 0; i < m * n; ++i) {
Scalar gemm = mkldnn_result(i);
Scalar matmul = matmul_result(i % m, i / m);
if ((std::abs)(gemm) > error_threshold &&
(std::abs)(matmul) > error_threshold) {
if (!Eigen::internal::isApprox(gemm, matmul, error_threshold))
std::cout << "gemm=" << gemm << " matmul=" << matmul << std::endl;
VERIFY(Eigen::internal::isApprox(gemm, matmul, error_threshold));
}
}
}
EIGEN_DECLARE_TEST(cxx11_tensor_contraction_mkldnn) {
CALL_SUBTEST(test_mkldnn_gemm_pack<float>());
CALL_SUBTEST(test_mkldnn_gemm_kernel<float>());
}
#else
EIGEN_DECLARE_TEST(cxx11_tensor_contraction_mkldnn) {}
#endif // EIGEN_USE_MKLDNN