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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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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_) {