| // 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> |
| // Copyright (C) 2016 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_CONVOLUTION_SYCL_H |
| #define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_SYCL_H |
| |
| // IWYU pragma: private |
| #include "./InternalHeaderCheck.h" |
| |
| namespace Eigen { |
| |
| /** \class TensorConvolution |
| * \ingroup CXX11_Tensor_Module |
| * |
| * \brief Tensor convolution class. |
| * |
| * |
| */ |
| |
| enum class convolution_type { CONV1D, CONV2D, CONV3D }; |
| template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims, |
| typename Kernel_accessor, typename Buffer_accessor, convolution_type Conv_Dim> |
| struct EigenConvolutionKernel; |
| template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims, |
| typename Kernel_accessor, typename Buffer_accessor> |
| struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, |
| Buffer_accessor, convolution_type::CONV1D> { |
| typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> |
| Local_accessor; |
| Local_accessor local_acc; |
| Evaluator device_evaluator; |
| Kernel_accessor kernel_filter; |
| Buffer_accessor buffer_acc; |
| internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper; |
| const size_t kernelSize; |
| const cl::sycl::range<2> input_range; |
| EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_, |
| Buffer_accessor buffer_acc_, |
| internal::IndexMapper<Index, InputDims, 1, Evaluator::Layout> indexMapper_, |
| const size_t kernelSize_, const cl::sycl::range<2> input_range_) |
| : local_acc(local_acc_), |
| device_evaluator(device_evaluator_), |
| kernel_filter(kernel_filter_), |
| buffer_acc(buffer_acc_), |
| indexMapper(indexMapper_), |
| kernelSize(kernelSize_), |
| input_range(input_range_) {} |
| |
| template <typename BooleanDim2> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim2 boolean_check) const { |
| return (boolean_check[0] && boolean_check[1]); |
| } |
| void operator()(cl::sycl::nd_item<2> itemID) const { |
| auto buffer_ptr = buffer_acc; |
| auto kernel_ptr = kernel_filter; |
| // the required row to be calculated for the for each plane in shered memory |
| const size_t num_input = (itemID.get_local_range()[0] + kernelSize - 1); |
| const size_t plane_kernel_offset = itemID.get_local_id(1) * num_input; |
| const size_t input_offset = itemID.get_group(0) * itemID.get_local_range()[0]; |
| const size_t plane_tensor_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(1)); |
| /// fill the shared memory |
| for (size_t i = itemID.get_local_id(0); i < num_input; i += itemID.get_local_range()[0]) { |
| const size_t local_index = i + plane_kernel_offset; |
| const size_t tensor_index = |
| plane_tensor_offset + indexMapper.mapGpuInputKernelToTensorInputOffset(i + input_offset); |
| |
| local_acc[local_index] = |
| (((i + input_offset) < (input_range[0] + kernelSize - 1)) && itemID.get_global_id(1) < input_range[1]) |
| ? device_evaluator.coeff(tensor_index) |
| : CoeffReturnType(0); |
| } |
| |
| itemID.barrier(cl::sycl::access::fence_space::local_space); |
| |
| // calculate the convolution // output start x |
| const size_t first_output_start = itemID.get_group(0) * (itemID.get_local_range()[0]); |
| if (boundary_check(itemID.get_global_id() < input_range)) { |
| CoeffReturnType result = static_cast<CoeffReturnType>(0); |
| const size_t index = plane_kernel_offset + itemID.get_local_id(0); |
| for (size_t k = 0; k < kernelSize; ++k) { |
| result += (local_acc[k + index] * kernel_ptr[k]); |
| } |
| const size_t tensor_index = |
| indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(1)) + |
| indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + first_output_start); |
| buffer_ptr[tensor_index] = result; |
| } |
| } |
| }; |
| |
| template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims, |
| typename Kernel_accessor, typename Buffer_accessor> |
| struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, |
| Buffer_accessor, convolution_type::CONV2D> { |
| typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> |
| Local_accessor; |
| Local_accessor local_acc; |
| Evaluator device_evaluator; |
| Kernel_accessor kernel_filter; |
| Buffer_accessor buffer_acc; |
| internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper; |
| const cl::sycl::range<2> kernel_size; |
| const cl::sycl::range<3> input_range; |
| EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_, |
| Buffer_accessor buffer_acc_, |
| internal::IndexMapper<Index, InputDims, 2, Evaluator::Layout> indexMapper_, |
| const cl::sycl::range<2> kernel_size_, const cl::sycl::range<3> input_range_) |
| : local_acc(local_acc_), |
| device_evaluator(device_evaluator_), |
| kernel_filter(kernel_filter_), |
| buffer_acc(buffer_acc_), |
| indexMapper(indexMapper_), |
| kernel_size(kernel_size_), |
| input_range(input_range_) {} |
| template <typename BooleanDim3> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) const { |
| return (boolean_check[0] && boolean_check[1] && boolean_check[2]); |
| } |
| |
| void operator()(cl::sycl::nd_item<3> itemID) const { |
| auto buffer_ptr = buffer_acc; |
| auto kernel_ptr = kernel_filter; |
| // the required row to be calculated for the for each plane in shered memory |
| const auto num_input = cl::sycl::range<2>{ |
| (cl::sycl::range<2>(itemID.get_local_range()[0], itemID.get_local_range()[1]) + kernel_size - 1)}; |
| |
| const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(itemID.get_global_id(2)); |
| const size_t plane_kernel_offset = itemID.get_local_id(2) * num_input[1]; |
| |
| const auto input_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0], |
| itemID.get_group(1) * itemID.get_local_range()[1]}; |
| |
| // fill the local memory |
| bool in_range_dim2 = itemID.get_global_id(2) < input_range[2]; |
| for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) { |
| const size_t local_input_offset = num_input[0] * (j + plane_kernel_offset); |
| bool in_range_dim1 = ((j + input_offset[1]) < (input_range[1] + kernel_size[1] - 1)); |
| for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) { |
| const size_t local_index = i + local_input_offset; |
| const size_t tensor_index = plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset( |
| i + input_offset[0], j + input_offset[1]); |
| local_acc[local_index] = |
| (((i + input_offset[0]) < (input_range[0] + kernel_size[0] - 1)) && in_range_dim1 && in_range_dim2) |
| ? device_evaluator.coeff(tensor_index) |
| : CoeffReturnType(0); |
| } |
| } |
| |
| itemID.barrier(cl::sycl::access::fence_space::local_space); |
| |
| // output offset start for each thread |
| const auto output_offset = cl::sycl::range<2>{itemID.get_group(0) * itemID.get_local_range()[0], |
| itemID.get_group(1) * itemID.get_local_range()[1]}; |
| |
| if (boundary_check(itemID.get_global_id() < input_range)) { |
| CoeffReturnType result = static_cast<CoeffReturnType>(0); |
| |
| for (size_t j = 0; j < kernel_size[1]; j++) { |
| size_t kernel_offset = kernel_size[0] * j; |
| const size_t index = |
| (num_input[0] * (plane_kernel_offset + j + itemID.get_local_id(1))) + itemID.get_local_id(0); |
| for (size_t i = 0; i < kernel_size[0]; i++) { |
| result += (local_acc[i + index] * kernel_ptr[i + kernel_offset]); |
| } |
| } |
| const size_t tensor_index = |
| indexMapper.mapGpuOutputPlaneToTensorOutputOffset(itemID.get_global_id(2)) + |
| indexMapper.mapGpuOutputKernelToTensorOutputOffset(itemID.get_local_id(0) + output_offset[0], |
| itemID.get_local_id(1) + output_offset[1]); |
| |
| buffer_ptr[tensor_index] = result; |
| } |
| } |
| }; |
| |
| template <typename Evaluator, typename CoeffReturnType, typename KernelType, typename Index, typename InputDims, |
| typename Kernel_accessor, typename Buffer_accessor> |
| struct EigenConvolutionKernel<Evaluator, CoeffReturnType, KernelType, Index, InputDims, Kernel_accessor, |
| Buffer_accessor, convolution_type::CONV3D> { |
| typedef cl::sycl::accessor<CoeffReturnType, 1, cl::sycl::access::mode::read_write, cl::sycl::access::target::local> |
| Local_accessor; |
| Local_accessor local_acc; |
| Evaluator device_evaluator; |
| Kernel_accessor kernel_filter; |
| Buffer_accessor buffer_acc; |
| internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper; |
| const cl::sycl::range<3> kernel_size; |
| const cl::sycl::range<3> input_range; |
| const size_t numP; |
| |
| EigenConvolutionKernel(Local_accessor local_acc_, Evaluator device_evaluator_, Kernel_accessor kernel_filter_, |
| Buffer_accessor buffer_acc_, |
| internal::IndexMapper<Index, InputDims, 3, Evaluator::Layout> indexMapper_, |
| const cl::sycl::range<3> kernel_size_, const cl::sycl::range<3> input_range_, |
| const size_t numP_) |
| : local_acc(local_acc_), |
| device_evaluator(device_evaluator_), |
| kernel_filter(kernel_filter_), |
| buffer_acc(buffer_acc_), |
| indexMapper(indexMapper_), |
| kernel_size(kernel_size_), |
| input_range(input_range_), |
| numP(numP_) {} |
| template <typename BooleanDim3> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool boundary_check(const BooleanDim3 boolean_check) const { |
| return (boolean_check[0] && boolean_check[1] && boolean_check[2]); |
| } |
| void operator()(cl::sycl::nd_item<3> itemID) const { |
| auto buffer_ptr = buffer_acc; |
| auto kernel_ptr = kernel_filter; |
| const auto num_input = cl::sycl::range<3>{itemID.get_local_range() + kernel_size - 1}; |
| |
| const auto input_offset = cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range()}; |
| |
| const auto output_offset = |
| cl::sycl::range<3>{itemID.get_group().get_id() * itemID.get_local_range() + itemID.get_local_id()}; |
| |
| for (size_t p = 0; p < numP; p++) { |
| /// fill the shared memory |
| const size_t plane_input_offset = indexMapper.mapGpuInputPlaneToTensorInputOffset(p); |
| for (size_t k = itemID.get_local_id(2); k < num_input[2]; k += itemID.get_local_range()[2]) { |
| size_t local_index_dim2 = num_input[0] * num_input[1] * k; |
| bool cond_k_dim = (k + input_offset[2] < (input_range[2] + kernel_size[2] - 1)); |
| for (size_t j = itemID.get_local_id(1); j < num_input[1]; j += itemID.get_local_range()[1]) { |
| bool cond_j_dim = cond_k_dim && (j + input_offset[1] < (input_range[1] + kernel_size[1] - 1)); |
| size_t local_index_dim1 = (num_input[0] * j) + local_index_dim2; |
| for (size_t i = itemID.get_local_id(0); i < num_input[0]; i += itemID.get_local_range()[0]) { |
| bool conds = cond_j_dim && (i + input_offset[0] < (input_range[0] + kernel_size[0] - 1)); |
| const size_t local_index = local_index_dim1 + i; |
| const size_t tensor_index = |
| plane_input_offset + indexMapper.mapGpuInputKernelToTensorInputOffset( |
| i + input_offset[0], j + input_offset[1], k + input_offset[2]); |
| local_acc[local_index] = conds ? device_evaluator.coeff(tensor_index) : CoeffReturnType(0); |
| } |
| } |
| } |
| itemID.barrier(cl::sycl::access::fence_space::local_space); |
| |
| // calculate the convolution |
| |
| if (boundary_check(itemID.get_global_id() < input_range)) { |
| CoeffReturnType result = static_cast<CoeffReturnType>(0); |
| for (size_t k = 0; k < kernel_size[2]; k++) { |
| for (size_t j = 0; j < kernel_size[1]; j++) { |
| for (size_t i = 0; i < kernel_size[0]; i++) { |
| const size_t kernel_index = i + kernel_size[0] * (j + kernel_size[1] * k); |
| const size_t local_index = |
| ((i + itemID.get_local_id(0)) + |
| num_input[0] * ((j + itemID.get_local_id(1)) + num_input[1] * (k + itemID.get_local_id(2)))); |
| |
| result += (local_acc[local_index] * kernel_ptr[kernel_index]); |
| } |
| } |
| } |
| const size_t tensor_index = |
| indexMapper.mapGpuOutputPlaneToTensorOutputOffset(p) + |
| indexMapper.mapGpuOutputKernelToTensorOutputOffset(output_offset[0], output_offset[1], output_offset[2]); |
| buffer_ptr[tensor_index] = result; |
| } |
| |
| itemID.barrier(cl::sycl::access::fence_space::local_space); |
| } |
| } |
| }; |
| |
| template <typename Indices, typename InputArgType, typename KernelArgType> |
| struct TensorEvaluator<const TensorConvolutionOp<Indices, InputArgType, KernelArgType>, Eigen::SyclDevice> { |
| typedef TensorConvolutionOp<Indices, InputArgType, KernelArgType> XprType; |
| |
| static constexpr int NumDims = |
| internal::array_size<typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions>::value; |
| static constexpr int NumKernelDims = internal::array_size<Indices>::value; |
| typedef typename XprType::Index Index; |
| typedef DSizes<Index, NumDims> Dimensions; |
| typedef typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions KernelDimensions; |
| typedef const Eigen::SyclDevice Device; |
| typedef typename XprType::CoeffReturnType CoeffReturnType; |
| typedef typename PacketType<CoeffReturnType, Eigen::SyclDevice>::type PacketReturnType; |
| typedef typename InputArgType::Scalar Scalar; |
| static constexpr int PacketSize = PacketType<CoeffReturnType, Device>::size; |
| typedef StorageMemory<CoeffReturnType, Eigen::SyclDevice> Storage; |
| typedef typename Storage::Type EvaluatorPointerType; |
| typedef StorageMemory<const CoeffReturnType, Eigen::SyclDevice> KernelStorage; |
| |
| static constexpr int Layout = TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout; |
| enum { |
| IsAligned = TensorEvaluator<InputArgType, Eigen::SyclDevice>::IsAligned & |
| TensorEvaluator<KernelArgType, Eigen::SyclDevice>::IsAligned, |
| PacketAccess = false, |
| BlockAccess = false, |
| PreferBlockAccess = false, |
| CoordAccess = false, // to be implemented |
| RawAccess = false |
| }; |
| |
| //===- Tensor block evaluation strategy (see TensorBlock.h) -------------===// |
| typedef internal::TensorBlockNotImplemented TensorBlock; |
| //===--------------------------------------------------------------------===// |
| |
| TensorEvaluator(const XprType &op, const Eigen::SyclDevice &device) |
| : m_inputImpl(op.inputExpression(), device), |
| m_kernelArg(op.kernelExpression()), |
| m_kernelImpl(op.kernelExpression(), device), |
| m_indices(op.indices()), |
| m_buf(NULL), |
| m_kernel(NULL), |
| m_local_kernel(false), |
| m_device(device) { |
| EIGEN_STATIC_ASSERT((static_cast<int>(TensorEvaluator<InputArgType, Eigen::SyclDevice>::Layout) == |
| static_cast<int>(TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Layout)), |
| YOU_MADE_A_PROGRAMMING_MISTAKE); |
| |
| const typename TensorEvaluator<InputArgType, Eigen::SyclDevice>::Dimensions &input_dims = m_inputImpl.dimensions(); |
| const typename TensorEvaluator<KernelArgType, Eigen::SyclDevice>::Dimensions &kernel_dims = |
| m_kernelImpl.dimensions(); |
| |
| m_dimensions = m_inputImpl.dimensions(); |
| for (int i = 0; i < NumKernelDims; ++i) { |
| const Index index = op.indices()[i]; |
| const Index input_dim = input_dims[index]; |
| const Index kernel_dim = kernel_dims[i]; |
| const Index result_dim = input_dim - kernel_dim + 1; |
| m_dimensions[index] = result_dim; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC const Dimensions &dimensions() const { return m_dimensions; } |
| |
| EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(EvaluatorPointerType data) { |
| preloadKernel(); |
| m_inputImpl.evalSubExprsIfNeeded(NULL); |
| if (data) { |
| executeEval(data); |
| return false; |
| } else { |
| m_buf = (EvaluatorPointerType)m_device.get( |
| (Scalar *)m_device.allocate_temp(dimensions().TotalSize() * sizeof(Scalar))); |
| executeEval(m_buf); |
| return true; |
| } |
| } |
| |
| EIGEN_STRONG_INLINE void cleanup() { |
| m_inputImpl.cleanup(); |
| if (m_buf) { |
| m_device.deallocate_temp(m_buf); |
| m_buf = NULL; |
| } |
| if (m_local_kernel) { |
| m_device.deallocate_temp(m_kernel); |
| m_local_kernel = false; |
| } |
| m_kernel = NULL; |
| } |
| /// used by sycl in order to build the sycl buffer |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Device &device() const { return m_device; } |
| /// used by sycl in order to build the sycl buffer |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE EvaluatorPointerType data() const { return m_buf; } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void preloadKernel() { |
| // Don't make a local copy of the kernel unless we have to (i.e. it's an |
| // expression that needs to be evaluated) |
| typename KernelStorage::Type in_place = m_kernelImpl.data(); |
| if (in_place) { |
| m_kernel = in_place; |
| m_local_kernel = false; |
| } else { |
| ptrdiff_t kernel_sz = m_kernelImpl.dimensions().TotalSize() * sizeof(Scalar); |
| EvaluatorPointerType local = (EvaluatorPointerType)m_device.get((Scalar *)m_device.allocate_temp(kernel_sz)); |
| typedef TensorEvalToOp<const KernelArgType> EvalTo; |
| EvalTo evalToTmp(m_device.get(local), m_kernelArg); |
| const bool PacketAccess = internal::IsVectorizable<Eigen::SyclDevice, KernelArgType>::value; |
| internal::TensorExecutor<const EvalTo, Eigen::SyclDevice, PacketAccess>::run(evalToTmp, m_device); |
| m_kernel = local; |
| m_local_kernel = true; |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void executeEval(EvaluatorPointerType data) const { |
| typedef TensorEvaluator<InputArgType, Eigen::SyclDevice> InputEvaluator; |
| typedef typename InputEvaluator::Dimensions InputDims; |
| switch (NumKernelDims) { |
| case 1: { |
| const size_t numX = dimensions()[m_indices[0]]; |
| const size_t numP = dimensions().TotalSize() / numX; |
| const auto input_dim = std::array<size_t, 2>{numX, numP}; |
| auto global_range = cl::sycl::range<2>{1, 1}; |
| auto local_range = cl::sycl::range<2>{1, 1}; |
| const size_t kernel_size = m_kernelImpl.dimensions().TotalSize(); |
| |
| m_device.parallel_for_setup(input_dim, global_range, local_range); |
| const size_t local_memory_size = (local_range[0] + kernel_size - 1) * (local_range[1]); |
| gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock()); |
| const array<Index, 1> indices{{m_indices[0]}}; |
| const array<Index, 1> kernel_dims{{m_kernelImpl.dimensions()[0]}}; |
| internal::IndexMapper<Index, InputDims, 1, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices); |
| |
| typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims, |
| typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV1D> |
| ConvKernel; |
| |
| m_device |
| .template binary_kernel_launcher<CoeffReturnType, ConvKernel>( |
| m_inputImpl, m_kernel, data, cl::sycl::nd_range<2>(global_range, local_range), local_memory_size, |
| indexMapper, kernel_size, cl::sycl::range<2>(input_dim[0], input_dim[1])) |
| .wait(); |
| break; |
| } |
| |
| case 2: { |
| auto kernel_index = std::array<size_t, 2>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 1, |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 0}; |
| auto kernel_size = cl::sycl::range<2>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]], |
| (size_t)m_kernelImpl.dimensions()[kernel_index[1]]}; |
| const size_t numX = dimensions()[m_indices[kernel_index[0]]]; |
| const size_t numY = dimensions()[m_indices[kernel_index[1]]]; |
| const size_t numP = dimensions().TotalSize() / (numX * numY); |
| auto input_dim = std::array<size_t, 3>{numX, numY, numP}; |
| |
| auto global_range = cl::sycl::range<3>{1, 1, 1}; |
| auto local_range = cl::sycl::range<3>{1, 1, 1}; |
| |
| m_device.parallel_for_setup(input_dim, global_range, local_range); |
| |
| const size_t local_memory_size = |
| (local_range[0] + kernel_size[0] - 1) * (local_range[1] + kernel_size[1] - 1) * local_range[2]; |
| gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock()); |
| const array<Index, 2> indices{{m_indices[kernel_index[0]], m_indices[kernel_index[1]]}}; |
| const array<Index, 2> kernel_dims{ |
| {m_kernelImpl.dimensions()[kernel_index[0]], m_kernelImpl.dimensions()[kernel_index[1]]}}; |
| internal::IndexMapper<Index, InputDims, 2, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices); |
| typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims, |
| typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV2D> |
| ConvKernel; |
| m_device |
| .template binary_kernel_launcher<CoeffReturnType, ConvKernel>( |
| m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size, |
| indexMapper, kernel_size, cl::sycl::range<3>{input_dim[0], input_dim[1], input_dim[2]}) |
| .wait(); |
| break; |
| } |
| |
| case 3: { |
| auto kernel_index = std::array<size_t, 3>{static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 0 : 2, |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 1 : 1, |
| static_cast<int>(Layout) == static_cast<int>(ColMajor) ? 2 : 0}; |
| |
| auto kernel_size = cl::sycl::range<3>{(size_t)m_kernelImpl.dimensions()[kernel_index[0]], |
| (size_t)m_kernelImpl.dimensions()[kernel_index[1]], |
| (size_t)m_kernelImpl.dimensions()[kernel_index[2]]}; |
| |
| const size_t numX = dimensions()[m_indices[kernel_index[0]]]; |
| const size_t numY = dimensions()[m_indices[kernel_index[1]]]; |
| const size_t numZ = dimensions()[m_indices[kernel_index[2]]]; |
| auto input_dim = std::array<size_t, 3>{numX, numY, numZ}; |
| const size_t numP = dimensions().TotalSize() / (numX * numY * numZ); |
| |
| const array<Index, 3> indices{ |
| {m_indices[kernel_index[0]], m_indices[kernel_index[1]], m_indices[kernel_index[2]]}}; |
| const array<Index, 3> kernel_dims{{m_kernelImpl.dimensions()[kernel_index[0]], |
| m_kernelImpl.dimensions()[kernel_index[1]], |
| m_kernelImpl.dimensions()[kernel_index[2]]}}; |
| |
| internal::IndexMapper<Index, InputDims, 3, Layout> indexMapper(m_inputImpl.dimensions(), kernel_dims, indices); |
| |
| auto global_range = cl::sycl::range<3>{1, 1, 1}; |
| auto local_range = cl::sycl::range<3>{1, 1, 1}; |
| |
| m_device.parallel_for_setup(input_dim, global_range, local_range); |
| auto local_memory_range = (local_range + kernel_size - 1); |
| const size_t local_memory_size = local_memory_range[0] * local_memory_range[1] * local_memory_range[2]; |
| |
| gpu_assert(static_cast<unsigned long>(local_memory_size) <= m_device.sharedMemPerBlock()); |
| typedef EigenConvolutionKernel<InputEvaluator, CoeffReturnType, Scalar, Index, InputDims, |
| typename KernelStorage::Type, EvaluatorPointerType, convolution_type::CONV3D> |
| ConvKernel; |
| m_device |
| .template binary_kernel_launcher<CoeffReturnType, ConvKernel>( |
| m_inputImpl, m_kernel, data, cl::sycl::nd_range<3>(global_range, local_range), local_memory_size, |
| indexMapper, kernel_size, cl::sycl::range<3>(input_dim[0], input_dim[1], input_dim[2]), numP) |
| .wait(); |
| break; |
| } |
| |
| default: { |
| EIGEN_STATIC_ASSERT((NumKernelDims >= 1 && NumKernelDims <= 3), |
| THIS_METHOD_IS_ONLY_FOR_OBJECTS_OF_A_SPECIFIC_SIZE); |
| } |
| } |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const { |
| eigen_assert(m_buf != NULL); |
| eigen_assert(index < m_dimensions.TotalSize()); |
| return m_buf[index]; |
| } |
| |
| template <int LoadMode> |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE PacketReturnType packet(const Index index) const { |
| eigen_assert(m_buf != NULL); |
| eigen_assert(index < m_dimensions.TotalSize()); |
| return internal::ploadt<PacketReturnType, LoadMode>(m_buf + index); |
| } |
| |
| EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { |
| // TODO(rmlarsen): FIXME: For now, this is just a copy of the CPU cost |
| // model. |
| const double kernel_size = m_kernelImpl.dimensions().TotalSize(); |
| // We ignore the use of fused multiply-add. |
| const double convolve_compute_cost = TensorOpCost::AddCost<Scalar>() + TensorOpCost::MulCost<Scalar>(); |
| const double firstIndex_compute_cost = |
| NumDims * |
| (2 * TensorOpCost::AddCost<Index>() + 2 * TensorOpCost::MulCost<Index>() + TensorOpCost::DivCost<Index>()); |
| return TensorOpCost(0, 0, firstIndex_compute_cost, vectorized, PacketSize) + |
| kernel_size * (m_inputImpl.costPerCoeff(vectorized) + m_kernelImpl.costPerCoeff(vectorized) + |
| TensorOpCost(0, 0, convolve_compute_cost, vectorized, PacketSize)); |
| } |
| |
| private: |
| // No assignment (copies are needed by the kernels) |
| TensorEvaluator &operator=(const TensorEvaluator &); |
| TensorEvaluator<InputArgType, Eigen::SyclDevice> m_inputImpl; |
| KernelArgType m_kernelArg; |
| TensorEvaluator<KernelArgType, Eigen::SyclDevice> m_kernelImpl; |
| Indices m_indices; |
| Dimensions m_dimensions; |
| EvaluatorPointerType m_buf; |
| typename KernelStorage::Type m_kernel; |
| bool m_local_kernel; |
| const Eigen::SyclDevice EIGEN_DEVICE_REF m_device; |
| }; // namespace Eigen |
| |
| } // end namespace Eigen |
| |
| #endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTION_H |
