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