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Adding tensor contraction operation backend for Sycl; adding test for contractionOp sycl backend; adding temporary solution to prevent memory leak in buffer; cleaning up cxx11_tensor_buildins_sycl.h
This commit is contained in:
Mehdi Goli
355
unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
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355
unsupported/Eigen/CXX11/src/Tensor/TensorContractionSycl.h
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// This file is part of Eigen, a lightweight C++ template library
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// for linear algebra.
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//
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// Mehdi Goli Codeplay Software Ltd.
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// Ralph Potter Codeplay Software Ltd.
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// Luke Iwanski Codeplay Software Ltd.
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// Contact: <eigen@codeplay.com>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
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/*****************************************************************
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* TensorSyclConvertToDeviceExpression.h
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*
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* \brief:
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* TensorContractionsycl
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*
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*****************************************************************/
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#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
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#define EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
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namespace Eigen {
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template <typename LhsScalar, typename RhsScalar,bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered> struct LaunchSyclKernels;
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template<typename Indices, typename LeftArgType, typename RightArgType>
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struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, const Eigen::SyclDevice> :
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public TensorContractionEvaluatorBase<TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, const Eigen::SyclDevice> > {
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typedef const Eigen::SyclDevice Device;
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typedef TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgType>, Device> Self;
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typedef TensorContractionEvaluatorBase<Self> Base;
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typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
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typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
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typedef typename XprType::Index Index;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Device>::type PacketReturnType;
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enum {
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Layout = TensorEvaluator<LeftArgType, Device>::Layout,
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};
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// Most of the code is assuming that both input tensors are ColMajor. If the
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// inputs are RowMajor, we will "cheat" by swapping the LHS and RHS:
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// If we want to compute A * B = C, where A is LHS and B is RHS, the code
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// will pretend B is LHS and A is RHS.
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typedef typename internal::conditional<
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static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
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typedef typename internal::conditional<
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static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
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static const int LDims =
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internal::array_size<typename TensorEvaluator<EvalLeftArgType, Device>::Dimensions>::value;
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static const int RDims =
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internal::array_size<typename TensorEvaluator<EvalRightArgType, Device>::Dimensions>::value;
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static const int ContractDims = internal::array_size<Indices>::value;
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typedef array<Index, LDims> left_dim_mapper_t;
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typedef array<Index, RDims> right_dim_mapper_t;
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typedef array<Index, ContractDims> contract_t;
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typedef array<Index, LDims - ContractDims> left_nocontract_t;
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typedef array<Index, RDims - ContractDims> right_nocontract_t;
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static const int NumDims = LDims + RDims - 2 * ContractDims;
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typedef DSizes<Index, NumDims> Dimensions;
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// typedefs needed in evalTo
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typedef typename internal::remove_const<typename EvalLeftArgType::Scalar>::type LhsScalar;
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typedef typename internal::remove_const<typename EvalRightArgType::Scalar>::type RhsScalar;
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typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
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typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
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typedef typename LeftEvaluator::Dimensions LeftDimensions;
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typedef typename RightEvaluator::Dimensions RightDimensions;
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EIGEN_DEVICE_FUNC TensorEvaluator(const XprType& op, const Device& device) :
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Base(op, device) {}
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// We need to redefine this method to make nvcc happy
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EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) {
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this->m_leftImpl.evalSubExprsIfNeeded(NULL);
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this->m_rightImpl.evalSubExprsIfNeeded(NULL);
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if (data) {
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evalTo(data);
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return false;
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} else {
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this->m_result = static_cast<Scalar*>(this->m_device.allocate(this->dimensions().TotalSize() * sizeof(Scalar)));
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evalTo(this->m_result);
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return true;
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}
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}
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const Eigen::SyclDevice& device() const {return this->m_device;}
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void evalTo(Scalar* buffer) const {
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// Here is the result
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if (this->m_lhs_inner_dim_contiguous) {
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if (this->m_rhs_inner_dim_contiguous) {
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if (this->m_rhs_inner_dim_reordered) {
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evalTyped<true, true, true, Unaligned>(buffer);
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}
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else {
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evalTyped<true, true, false, Unaligned>(buffer);
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}
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}
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else {
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if (this->m_rhs_inner_dim_reordered) {
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evalTyped<true, false, true, Unaligned>(buffer);
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}
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else {
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evalTyped<true, false, false, Unaligned>(buffer);
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}
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}
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}
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else {
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if (this->m_rhs_inner_dim_contiguous) {
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if (this->m_rhs_inner_dim_reordered) {
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evalTyped<false, true, true, Unaligned>(buffer);
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}
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else {
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evalTyped<false, true, false, Unaligned>(buffer);
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}
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}
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else {
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if (this->m_rhs_inner_dim_reordered) {
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evalTyped<false, false, true, Unaligned>(buffer);
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}
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else {
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evalTyped<false, false, false, Unaligned>(buffer);
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}
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}
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}
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}
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template <bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered, int Alignment>
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void evalTyped(Scalar* buffer) const {
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// columns in left side, rows in right side
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const Index k = this->m_k_size;
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EIGEN_UNUSED_VARIABLE(k)
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// rows in left side
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const Index m = this->m_i_size;
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// columns in right side
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const Index n = this->m_j_size;
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// zero out the result buffer (which must be of size at least m * n * sizeof(Scalar)
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this->m_device.memset(buffer, 0, m * n * sizeof(Scalar));
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LaunchSyclKernels<LhsScalar, RhsScalar,lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered>::Run(*this, buffer, m, n, k,
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this->m_k_strides, this->m_left_contracting_strides, this->m_right_contracting_strides,
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this->m_i_strides, this->m_j_strides, this->m_left_nocontract_strides, this->m_right_nocontract_strides);
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}
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// required by sycl to construct the expr on the device. Returns original left_impl
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const TensorEvaluator<LeftArgType, Device>& left_impl() const {
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return choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), this->m_leftImpl, this->m_rightImpl);
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}
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// required by sycl to construct the expr on the device. Returns original right_impl
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const TensorEvaluator<RightArgType, Device>& right_impl() const {
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return choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(), this->m_rightImpl, this->m_leftImpl);
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}
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// required by sycl to construct the expr on the device
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const Indices& indices() const {return this->m_expr_indices;}
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};
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/// Dummy container on the device. This is used to avoid calling the constructor of TensorEvaluator for TensorContractionOp. This makes the code much faster.
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template<typename Expr> struct TensorEvaluatorContainer;
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template<typename Indices, typename LeftArgType, typename RightArgType>
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struct TensorEvaluatorContainer<TensorContractionOp<Indices, LeftArgType, RightArgType>>{
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typedef Eigen::DefaultDevice Device;
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typedef TensorContractionOp<Indices, LeftArgType, RightArgType> XprType;
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typedef typename internal::remove_const<typename XprType::Scalar>::type Scalar;
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typedef typename XprType::Index Index;
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typedef typename XprType::CoeffReturnType CoeffReturnType;
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typedef typename PacketType<CoeffReturnType, Eigen::DefaultDevice>::type PacketReturnType;
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enum {
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Layout = TensorEvaluator<LeftArgType, Device>::Layout,
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};
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typedef typename internal::conditional<static_cast<int>(Layout) == static_cast<int>(ColMajor), LeftArgType, RightArgType>::type EvalLeftArgType;
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typedef typename internal::conditional<static_cast<int>(Layout) == static_cast<int>(ColMajor), RightArgType, LeftArgType>::type EvalRightArgType;
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typedef TensorEvaluator<EvalLeftArgType, Device> LeftEvaluator;
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typedef TensorEvaluator<EvalRightArgType, Device> RightEvaluator;
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TensorEvaluatorContainer(const XprType& op, const Eigen::DefaultDevice& device)
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: m_leftImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
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op.lhsExpression(), op.rhsExpression()), device),
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m_rightImpl(choose(Cond<static_cast<int>(Layout) == static_cast<int>(ColMajor)>(),
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op.rhsExpression(), op.lhsExpression()), device){}
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LeftEvaluator m_leftImpl;
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RightEvaluator m_rightImpl;
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};
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#define TileSizeDimM 32 // Tile size for dimension M
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#define TileSizeDimN 32 // Tile size for dimension N
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#define TileSizeDimK 16 // Tile size for dimension K
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#define WorkLoadPerThreadM 4 // Work load per thread in dimension M
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#define WorkLoadPerThreadN 4 // work load per thread in dimension N
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#define LocalThreadSizeM (TileSizeDimM/WorkLoadPerThreadM) // Local thread size for the first dimension (M here)
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#define LocalThreadSizeN (TileSizeDimN/WorkLoadPerThreadN) // Local thread size for the second dimension (N here)
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#define LoadPerThreadLhs ((TileSizeDimK*WorkLoadPerThreadM*WorkLoadPerThreadN)/(TileSizeDimN)) // workload per thread for Lhs expression
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#define LoadPerThreadRhs ((TileSizeDimK*WorkLoadPerThreadM*WorkLoadPerThreadN)/(TileSizeDimM)) // workload per thread for Rhs expression
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#define RoundUp(x,y) ((((x) + (y) - 1) / (y))*(y)) // RoundUp function to make sure that the global threadId is dividabe by local threadId
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template <typename PLEXPR, bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered> struct KernelNameConstructor;
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template <typename LhsScalar, typename RhsScalar, bool lhs_inner_dim_contiguous, bool rhs_inner_dim_contiguous, bool rhs_inner_dim_reordered> struct LaunchSyclKernels {
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template< typename Self, typename Output, typename Index, typename ContractT, typename LeftNocontractT, typename RightNocontractT>
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static void Run(const Self& self, Output* buffer, Index M, Index N, Index K,
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ContractT m_k_strides, ContractT m_left_contracting_strides, ContractT m_right_contracting_strides,
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LeftNocontractT m_i_strides, RightNocontractT m_j_strides, LeftNocontractT m_left_nocontract_strides, RightNocontractT m_right_nocontract_strides){
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// create a tuple of accessors from Evaluator
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typedef typename Eigen::TensorSycl::internal::createPlaceHolderExpression<typename Self::XprType>::Type PlaceHolderExpr;
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typedef KernelNameConstructor<PlaceHolderExpr, lhs_inner_dim_contiguous, rhs_inner_dim_contiguous, rhs_inner_dim_reordered> KernelName;
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auto functors = Eigen::TensorSycl::internal::extractFunctors(self);
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Index roundUpK = RoundUp(K, TileSizeDimK);
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Index roundUpM = RoundUp(M, TileSizeDimM);
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Index roundUpN = RoundUp(N, TileSizeDimN);
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self.device().sycl_queue().submit([&](cl::sycl::handler &cgh) {
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auto tuple_of_accessors = Eigen::TensorSycl::internal::createTupleOfAccessors<Self>(cgh, self);
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// Local memory for elements of Lhs
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cl::sycl::accessor<LhsScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> localLhs(cl::sycl::range<1>(2* TileSizeDimM * TileSizeDimK), cgh);
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// Local memory for elements of Rhs
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cl::sycl::accessor<RhsScalar, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> localRhs(cl::sycl::range<1>(2* TileSizeDimK * TileSizeDimN), cgh);
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//Output memory
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auto out_privateRes= self.device(). template get_sycl_accessor<cl::sycl::access::mode::write>(cgh, buffer);
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// sycl parallel for
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cgh.parallel_for<KernelName>( cl::sycl::nd_range<2>(cl::sycl::range<2>(roundUpM/WorkLoadPerThreadM, roundUpN/WorkLoadPerThreadN), cl::sycl::range<2>(LocalThreadSizeM, LocalThreadSizeN)), [=](cl::sycl::nd_item<2> itemID) {
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typedef typename Eigen::TensorSycl::internal::ConvertToDeviceExpression<typename Self::XprType>::Type DevExpr;
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auto device_expr =Eigen::TensorSycl::internal::createDeviceExpression<DevExpr, PlaceHolderExpr>(functors, tuple_of_accessors);
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auto device_evaluator = TensorEvaluatorContainer<DevExpr>(device_expr.expr, Eigen::DefaultDevice());
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typedef TensorEvaluatorContainer<DevExpr> DevEvaluator;
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typedef internal::TensorContractionInputMapper<LhsScalar, Index, internal::Lhs,
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typename DevEvaluator::LeftEvaluator, LeftNocontractT,
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ContractT, 1,
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lhs_inner_dim_contiguous,
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false, Unaligned, MakeGlobalPointer> LhsMapper;
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typedef internal::TensorContractionInputMapper<RhsScalar, Index, internal::Rhs,
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typename DevEvaluator::RightEvaluator, RightNocontractT,
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ContractT, 1,
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rhs_inner_dim_contiguous,
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rhs_inner_dim_reordered, Unaligned, MakeGlobalPointer> RhsMapper;
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// initialize data mappers must happen inside the kernel for device eval
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LhsMapper lhs(device_evaluator.m_leftImpl, m_left_nocontract_strides, m_i_strides, m_left_contracting_strides, m_k_strides);
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RhsMapper rhs(device_evaluator.m_rightImpl, m_right_nocontract_strides, m_j_strides, m_right_contracting_strides, m_k_strides);
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auto out_ptr = ConvertToActualTypeSycl(Output, out_privateRes);
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// Matmul Kernel
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// Thread identifiers
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const int mLocalThreadId = itemID.get_local(0); // Local ID row
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const int nLocalThreadId = itemID.get_local(1); // Local ID col
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const int mGroupId = itemID.get_group(0); // Work-group ID row
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const int nGroupId = itemID.get_group(1); // Work-group ID localCol
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const int linearLocalThreadId = nLocalThreadId*LocalThreadSizeM + mLocalThreadId; // linear local thread ID
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// Allocate register space
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float privateLhs;
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float privateRhs[WorkLoadPerThreadN];
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float privateRes[WorkLoadPerThreadM][WorkLoadPerThreadN];
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// Initialise the privateResumulation registers
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for (int wLPTM=0; wLPTM<WorkLoadPerThreadM; wLPTM++) {
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for (int wLPTN=0; wLPTN<WorkLoadPerThreadN; wLPTN++) {
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privateRes[wLPTM][wLPTN] = 0.0f;
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}
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}
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// Tile Lhs
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for (int lPTL=0; lPTL<LoadPerThreadLhs; lPTL++) {
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int
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localLhsLinearId = lPTL*LocalThreadSizeN*LocalThreadSizeM + linearLocalThreadId;
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int localLhsRow = localLhsLinearId% TileSizeDimM;
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int localLhsCol = localLhsLinearId/TileSizeDimM;
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// Load the value (wide vector load)
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int GlobalLhsColId = TileSizeDimK*0 + localLhsCol;
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localLhs[0 + ((localLhsCol*TileSizeDimM + localLhsRow)*2)] =((GlobalLhsColId < K)&& (mGroupId*(TileSizeDimM)+ localLhsRow <M))? lhs(mGroupId*(TileSizeDimM) + localLhsRow, GlobalLhsColId):static_cast<Output>(0);
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}
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// Tile Rhs
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for (int lPTR=0; lPTR<LoadPerThreadRhs; lPTR++) {
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int localRhsLinearId = lPTR*LocalThreadSizeN*LocalThreadSizeM + linearLocalThreadId;
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int localRhsRow = localRhsLinearId% TileSizeDimN;
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int localRhsCol = localRhsLinearId/TileSizeDimN;
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// Load the value (wide vector load)
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int GlobalRhsRowId = TileSizeDimK*0 + localRhsCol;
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localRhs[0 + ((localRhsCol*TileSizeDimN + localRhsRow) *2)] = ((GlobalRhsRowId < K)&& ((nGroupId*(TileSizeDimN) + localRhsRow)< N))? rhs(GlobalRhsRowId, nGroupId*(TileSizeDimN) + localRhsRow): static_cast<Output>(0);
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}
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// Loop over all tiles
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const int numTiles = roundUpK/TileSizeDimK;
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int firstHalf=0;
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do {
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// Synchronise
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itemID.barrier(cl::sycl::access::fence_space::local_space);
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// Load the next tile of Lhs and Rhs into local memory
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int nextHalf = firstHalf + 1;
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if (nextHalf < numTiles) {
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// Tile A
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for (int lPTL=0; lPTL<LoadPerThreadLhs; lPTL++) {
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int localLhsLinearId = lPTL*LocalThreadSizeN*LocalThreadSizeM + linearLocalThreadId;
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int localLhsRow = localLhsLinearId% TileSizeDimM;
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int localLhsCol = localLhsLinearId/TileSizeDimM;
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// global K id
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int GlobalLhsColId = TileSizeDimK*nextHalf + localLhsCol;
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// Store the loaded value into local memory
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localLhs[(nextHalf%2) + ((localLhsCol*TileSizeDimM + localLhsRow) *2)] = ((GlobalLhsColId < K)&& (mGroupId*(TileSizeDimM)+ localLhsRow <M))? lhs(mGroupId*(TileSizeDimM) + localLhsRow, GlobalLhsColId): static_cast<Output>(0);
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}
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// Tile B
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for (int lPTR=0; lPTR<LoadPerThreadRhs; lPTR++) {
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int localRhsLinearId = lPTR*LocalThreadSizeN*LocalThreadSizeM + linearLocalThreadId;
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int localRhsRow = localRhsLinearId% TileSizeDimN;
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int localRhsCol = localRhsLinearId/TileSizeDimN;
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// Load the value (wide vector load)
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int GlobalRhsRowId = TileSizeDimK*nextHalf + localRhsCol;
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// Store the loaded vector into local memory
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localRhs[(nextHalf%2) +((localRhsCol*TileSizeDimN + localRhsRow)*2)] = ((GlobalRhsRowId < K)&& ((nGroupId*(TileSizeDimN) + localRhsRow)< N))? rhs(GlobalRhsRowId, nGroupId*(TileSizeDimN) + localRhsRow):static_cast<Output>(0);
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}
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}
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// Loop over the values of a single tile
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for (int k=0; k<TileSizeDimK; k++) {
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// Cache the values of localRhs in registers
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for (int wLPTN=0; wLPTN<WorkLoadPerThreadN; wLPTN++) {
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int localRhsCol = nLocalThreadId + wLPTN*LocalThreadSizeN;
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privateRhs[wLPTN] = localRhs[(firstHalf%2) +((k*TileSizeDimN + localRhsCol)*2)];
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}
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// Perform the computation
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for (int wLPTM=0; wLPTM<WorkLoadPerThreadM; wLPTM++) {
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int localLhsRow = mLocalThreadId + wLPTM*LocalThreadSizeM;
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privateLhs = localLhs[(firstHalf%2)+ ((k*TileSizeDimM + localLhsRow)*2)];
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for (int wLPTN=0; wLPTN<WorkLoadPerThreadN; wLPTN++) {
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privateRes[wLPTM][wLPTN] += privateLhs * privateRhs[wLPTN];
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}
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}
|
||||
}
|
||||
// Next tile
|
||||
firstHalf++;
|
||||
} while (firstHalf<numTiles);
|
||||
|
||||
|
||||
// Store the final results in C
|
||||
for (int wLPTM=0; wLPTM<WorkLoadPerThreadM; wLPTM++) {
|
||||
int globalRow = mGroupId*TileSizeDimM + mLocalThreadId + wLPTM*LocalThreadSizeM;
|
||||
if (globalRow< M){
|
||||
for (int wLPTN=0; wLPTN<WorkLoadPerThreadN; wLPTN++) {
|
||||
int globalCol = nGroupId*TileSizeDimN + nLocalThreadId + wLPTN*LocalThreadSizeN;
|
||||
if(globalCol<N)
|
||||
out_ptr[globalCol*M + globalRow] = privateRes[wLPTM][wLPTN];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// End the kernel
|
||||
});
|
||||
});
|
||||
self.device().synchronize();
|
||||
}
|
||||
};
|
||||
|
||||
} // end namespace Eigen
|
||||
#endif // EIGEN_CXX11_TENSOR_TENSOR_CONTRACTION_SYCL_H
|
||||
Reference in New Issue
Block a user