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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2014 Benoit Steiner <benoit.steiner.goog@gmail.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_BLOCKING_H
#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
namespace Eigen {
namespace internal {
enum {
ShardByRow = 0,
ShardByCol = 1
};
// Default Blocking Strategy
template <typename LhsMapper, typename RhsMapper, typename Index, int ShardingType=ShardByCol>
class TensorContractionBlocking {
public:
typedef typename LhsMapper::Scalar LhsScalar;
typedef typename RhsMapper::Scalar RhsScalar;
EIGEN_DEVICE_FUNC TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1) :
kc_(k), mc_(m), nc_(n)
{
if (ShardingType == ShardByCol) {
computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, mc_, nc_, num_threads);
}
else {
computeProductBlockingSizes<LhsScalar, RhsScalar, 1>(kc_, nc_, mc_, num_threads);
}
}
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index kc() const { return kc_; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index mc() const { return mc_; }
EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Index nc() const { return nc_; }
private:
Index kc_;
Index mc_;
Index nc_;
};
#if defined(EIGEN_USE_LIBXSMM)
template <typename LhsScalar, typename RhsScalar, typename Index>
class TensorXsmmContractionBlocking {
public:
TensorXsmmContractionBlocking(Index k, Index m, Index n,
size_t max_num_threads = 1, bool transposeA = false,
bool transposeB = false):
k_(k), m_(m), n_(n), transposeA_(transposeA),
transposeB_(transposeB), num_threads_(max_num_threads) {
#ifdef EIGEN_TEST_SPECIFIC_BLOCKING_SIZES
if (EIGEN_TEST_SPECIFIC_BLOCKING_SIZES) {
mc_ = EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_M;
kc_ = EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_K;
nc_ = EIGEN_TEST_SPECIFIC_BLOCKING_SIZE_N;
outer_m_ = EIGEN_TEST_SPECIFIC_OUTER_BLOCKING_SIZE_M;
outer_k_ = EIGEN_TEST_SPECIFIC_OUTER_BLOCKING_SIZE_K;
outer_n_ = EIGEN_TEST_SPECIFIC_OUTER_BLOCKING_SIZE_N;
copyA_ = EIGEN_TEST_SPECIFIC_BLOCKING_COPY_A;
copyB_ = EIGEN_TEST_SPECIFIC_BLOCKING_COPY_B;
outer_m_ = outer_m_ != 0 ? outer_m_ : m;
outer_k_ = outer_k_ != 0 ? outer_k_ : k;
outer_n_ = outer_n_ != 0 ? outer_n_ : n;
}
#else
// Defaults, possibly overriden per-platform.
copyA_ = true;
copyB_ = false;
// If the matrix is small enough, don't do blocking, just call single xsmm
// kernel.
if (static_cast<double>(m)*k*n <= LIBXSMM_THRESHOLD) {
mc_ = m; kc_ = k; nc_ = n;
outer_m_ = m; outer_k_ = k; outer_n_ = n;
copyA_ = false; copyB_ = false;
} else {
int arch = libxsmm_cpuid_x86();
if (arch == LIBXSMM_X86_AVX512_CORE) {
// skylake
mc_ = 64; kc_ = 64; nc_ = 24;
outer_m_ = 512; outer_k_ = 512; outer_n_ = 24*22;
// Hack to use this kernel architecture as the other one has performance
// issues (no hardware prefetching).
// TODO(nishantpatil): This should be removed if the issues are fixed,
// or this one becomes the default.
setenv("LIBXSMM_AVX512_CLASSIC_GEMM", "1", 1);
} else if (arch == LIBXSMM_X86_AVX2) {
// haswell
mc_ = 32; kc_ = 192; nc_ = 33;
outer_m_ = 512; outer_k_ = 3*192; outer_n_ = 33*16;
} else if (arch == LIBXSMM_X86_AVX) {
// ivybridge
mc_ = 32; kc_ = 192; nc_ = 48;
outer_m_ = 512; outer_k_ = 3*192; outer_n_ = 48*11;
} else {
// generic kernel size, usually performing well
mc_ = 32; kc_ = 128; nc_ = 32;
outer_m_ = 512; outer_k_ = 512; outer_n_ = 512;
}
// Only copy if it makes the stride smaller.
copyA_ = copyA_ && (m > mc_);
copyB_ = copyB_ && (k > kc_);
}
// We need to copy anyway if transposing
copyA_ = copyA_ || transposeA;
copyB_ = copyB_ || transposeB;
// See libxsmm_gemm_prefetch_type definition in libxsmm_typedefs.h
prefetch_ = LIBXSMM_PREFETCH_AL2CL2BL2_VIA_C;
#endif
mc_ = mc_ > m ? m : mc_;
nc_ = nc_ > n ? n : nc_;
kc_ = kc_ > k ? k : kc_;
size_t compute_parallelism = (m / mc_) * (n / nc_);
size_t pack_parallelism = 0;
if (copyA_) {
pack_parallelism += (m / mc_) * (k / kc_);
}
if (copyB_) {
pack_parallelism += (n / nc_) * (k / kc_);
}
size_t parallelism = numext::maxi(compute_parallelism, pack_parallelism);
num_threads_ = numext::mini<size_t>(num_threads_,
parallelism / MIN_JOBS_PER_THREAD);
num_threads_ = numext::maxi<size_t>(num_threads_, 1);
// For optimal performance outer block sizes should be multiplies of kernel
// sizes, or bigger than matrix size (=no outer blocking).
eigen_assert(outer_m_ % mc_ == 0 || outer_m_ >= m);
eigen_assert(outer_k_ % kc_ == 0 || outer_k_ >= k);
eigen_assert(outer_n_ % nc_ == 0 || outer_n_ >= n);
}
EIGEN_ALWAYS_INLINE Index kc() const { return kc_; }
EIGEN_ALWAYS_INLINE Index mc() const { return mc_; }
EIGEN_ALWAYS_INLINE Index nc() const { return nc_; }
EIGEN_ALWAYS_INLINE Index outer_k() const { return outer_k_; }
EIGEN_ALWAYS_INLINE Index outer_m() const { return outer_m_; }
EIGEN_ALWAYS_INLINE Index outer_n() const { return outer_n_; }
EIGEN_ALWAYS_INLINE bool copyA() const { return copyA_; }
EIGEN_ALWAYS_INLINE bool copyB() const { return copyB_; }
EIGEN_ALWAYS_INLINE bool transposeA() const { return transposeA_; }
EIGEN_ALWAYS_INLINE bool transposeB() const { return transposeB_; }
EIGEN_ALWAYS_INLINE int num_threads() const { return num_threads_; }
EIGEN_ALWAYS_INLINE Index blocks_m() const { return divup(m_, mc_); }
EIGEN_ALWAYS_INLINE Index blocks_k() const { return divup(k_, kc_); }
EIGEN_ALWAYS_INLINE Index blocks_n() const { return divup(n_, nc_); }
EIGEN_ALWAYS_INLINE libxsmm_gemm_prefetch_type prefetch() const {
return prefetch_;
}
private:
Index k_, m_, n_;
Index kc_, mc_, nc_;
Index outer_k_, outer_m_, outer_n_;
bool copyA_, copyB_, transposeA_, transposeB_;
size_t num_threads_;
// Threshold for m*k*n to skip blocking and just call libxsmm
const double LIBXSMM_THRESHOLD = 80*80*80;
// For computing optimal number of threads - so that each thread gets at least
// that many jobs.
const double MIN_JOBS_PER_THREAD = 3;
libxsmm_gemm_prefetch_type prefetch_;
};
#endif // EIGEN_USE_LIBXSMM
} // end namespace internal
} // end namespace Eigen
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_BLOCKING_H
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