diff options
Diffstat (limited to 'src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp')
| -rw-r--r-- | src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp | 262 |
1 files changed, 73 insertions, 189 deletions
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp index 132c3ee926..645f817030 100644 --- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018-2020 ARM Limited. + * Copyright (c) 2018-2021 Arm Limited. * * SPDX-License-Identifier: MIT * @@ -23,221 +23,105 @@ */ #include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h" +#include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/CL/CLScheduler.h" +#include "arm_compute/core/KernelDescriptors.h" -using namespace arm_compute; +#include "src/core/CL/ICLKernel.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/operators/ClWinogradConv2d.h" +#include "support/Cast.h" -namespace +namespace arm_compute { -Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataLayout data_layout) +struct CLWinogradConvolutionLayer::Impl { - Size2D output_tile = Size2D{}; - - const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height); - - // Check if the input spatial dimensions are smaller than 4 - const bool is_input_lt4_nchw = (input_dims.width <= 4 && input_dims.height <= 4) && (data_layout == DataLayout::NCHW); - - if(kernel_max_dim == 3U) - { - if(kernel_dims == Size2D(3U, 3U)) - { - output_tile = is_input_lt4_nchw ? Size2D(2U, 2U) : Size2D(4U, 4U); - } - else if(kernel_dims == Size2D(3U, 1U)) - { - output_tile = is_input_lt4_nchw ? Size2D(2U, 1U) : Size2D(4U, 1U); - } - else - { - output_tile = is_input_lt4_nchw ? Size2D(1U, 2U) : Size2D(1U, 4U); - } - } - else if(kernel_max_dim == 5U) - { - output_tile = Size2D(kernel_dims.width == 1 ? 1U : 4U, - kernel_dims.height == 1 ? 1U : 4U); - } - else if(kernel_max_dim == 7U) - { - output_tile = Size2D(kernel_dims.width == 1 ? 1U : 2U, - kernel_dims.height == 1 ? 1U : 2U); - } - - return output_tile; -} - -bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size) -{ - // Check if we want to configure a Winograd configuration which requires fast math - using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>; - - std::vector<WinogradConfiguration> fast_math_winograd = - { - WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5)), - WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(7, 7)) - }; - - auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height), - std::pair<int, int>(kernel_size.width, kernel_size.height)); - - return std::find(fast_math_winograd.begin(), fast_math_winograd.end(), p) != fast_math_winograd.end(); -} -} // namespace + const ICLTensor *src{nullptr}; + const ICLTensor *weights{nullptr}; + const ICLTensor *biases{nullptr}; + ICLTensor *dst{nullptr}; + std::unique_ptr<opencl::ClWinogradConv2d> op{nullptr}; + ITensorPack run_pack{}; + MemoryGroup memory_group{}; + WorkspaceData<CLTensor> workspace_tensors{}; + bool is_prepared{false}; +}; CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager) - : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr), - _is_prepared(false) + : _impl(std::make_unique<Impl>()) { + _impl->memory_group = MemoryGroup(memory_manager); } -void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, - bool enable_fast_math) +CLWinogradConvolutionLayer::~CLWinogradConvolutionLayer() = default; + +void CLWinogradConvolutionLayer::configure(ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { - configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, act_info, enable_fast_math); + configure(CLKernelLibrary::get().get_compile_context(), input, weights, biases, output, conv_info, act_info, + enable_fast_math); } -void CLWinogradConvolutionLayer::configure(const CLCompileContext &compile_context, ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, - const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info, bool enable_fast_math) +void CLWinogradConvolutionLayer::configure(const CLCompileContext &compile_context, + ICLTensor *input, + const ICLTensor *weights, + const ICLTensor *biases, + ICLTensor *output, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { - // Get indices for the width and height - const size_t idx_width = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::HEIGHT); - - // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(input->info()->tensor_shape()[idx_width], input->info()->tensor_shape()[idx_height]); - const Size2D kernel_size = Size2D(weights->info()->tensor_shape()[idx_width], weights->info()->tensor_shape()[idx_height]); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, input->info()->data_layout()); - - // Check if the Winograd configuration requires fast math - if(!enable_fast_math) - { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false. - ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); - } - const WinogradInfo winograd_info = WinogradInfo(output_tile, - kernel_size, - input_dims, - conv_info, - input->info()->data_layout()); - - _is_prepared = false; - _original_weights = weights; - - // Manage intermediate tensors - _memory_group.manage(&_input0); - _memory_group.manage(&_batched_mm_output); - - // Do not manage _input1 as it contains the weights - - // Configure input transform - _input_transform.configure(compile_context, input, &_input0, winograd_info); - - // Configure filter transform - _filter_transform.configure(compile_context, weights, &_input1, winograd_info); - - // Configure batched matrix multiply - _batched_mm.configure(compile_context, &_input0, &_input1, nullptr, &_batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, - GEMMLowpOutputStageInfo(), - (input->info()->data_type() == DataType::F16))); - - // Configure output transform - _output_transform.configure(compile_context, &_batched_mm_output, biases, output, winograd_info, act_info); - - // Allocate temporary tensors - _input0.allocator()->allocate(); - _batched_mm_output.allocator()->allocate(); + _impl->src = input; + _impl->weights = weights; + _impl->biases = biases; + _impl->dst = output; + + _impl->op = std::make_unique<opencl::ClWinogradConv2d>(); + _impl->op->configure(compile_context, input->info(), weights->info(), + (biases != nullptr ? biases->info() : nullptr), output->info(), conv_info, act_info, + enable_fast_math); + + _impl->run_pack = {{TensorType::ACL_SRC_0, _impl->src}, + {TensorType::ACL_SRC_1, _impl->weights}, + {TensorType::ACL_SRC_2, _impl->biases}, + {TensorType::ACL_DST, _impl->dst}}; + _impl->workspace_tensors = + manage_workspace<CLTensor>(_impl->op->workspace(), _impl->memory_group, _impl->run_pack, _impl->run_pack); } -Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info, bool enable_fast_math) +Status CLWinogradConvolutionLayer::validate(const ITensorInfo *input, + const ITensorInfo *weights, + const ITensorInfo *biases, + const ITensorInfo *output, + const PadStrideInfo &conv_info, + const ActivationLayerInfo &act_info, + bool enable_fast_math) { - // Get indeces for the width and height - const size_t idx_width = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); - - // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(input->tensor_shape()[idx_width], input->tensor_shape()[idx_height]); - const Size2D kernel_size = Size2D(weights->tensor_shape()[idx_width], weights->tensor_shape()[idx_height]); - const Size2D output_tile = winograd_output_tile(input_dims, kernel_size, input->data_layout()); - - ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_left() > (kernel_size.x() / 2u)) || (conv_info.pad_right() > (kernel_size.x() / 2u))), "Winograd only supports padding up to half kernel size"); - ARM_COMPUTE_RETURN_ERROR_ON_MSG(((conv_info.pad_top() > (kernel_size.y() / 2u)) || (conv_info.pad_bottom() > (kernel_size.y() / 2u))), "Winograd only supports padding up to half kernel size"); - - // Check if the Winograd configuration requires fast math - if(!enable_fast_math) - { - ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32); //disable winograd for fp16 if fast math is false. - ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size), "This Winograd configuration requires enable_fast_math=true"); - } - - const WinogradInfo winograd_info = WinogradInfo(output_tile, - kernel_size, - input_dims, - conv_info, - input->data_layout()); - - // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); - const TensorInfo input0 = input->clone()->set_tensor_shape(input0_shape); - ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradInputTransform::validate(input, &input0, winograd_info)); - - // Validate filter transform - const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info); - const TensorInfo input1 = weights->clone()->set_tensor_shape(input1_shape); - ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradFilterTransformKernel::validate(weights, &input1, winograd_info)); - - // Validate batched matrix multiply - TensorShape batched_mm_output_shape = input0.tensor_shape(); - batched_mm_output_shape[0] = input1.tensor_shape()[0]; - const TensorInfo batched_mm_output = input0.clone()->set_tensor_shape(batched_mm_output_shape); - ARM_COMPUTE_RETURN_ON_ERROR(CLGEMM::validate(&input0, &input1, nullptr, &batched_mm_output, 1.0f, 0.0f, GEMMInfo(false, false, true /* Reshape weights only for the first run*/, 0, false, false, - GEMMLowpOutputStageInfo(), (input->data_type() == DataType::F16)))); - - // Configure output transform - ARM_COMPUTE_RETURN_ON_ERROR(CLWinogradOutputTransformKernel::validate(&batched_mm_output, biases, output, winograd_info, act_info)); - - return Status{}; + return opencl::ClWinogradConv2d::validate(input, weights, biases, output, conv_info, act_info, enable_fast_math); } void CLWinogradConvolutionLayer::run() { + MemoryGroupResourceScope scope_mg(_impl->memory_group); prepare(); - - MemoryGroupResourceScope scope_mg(_memory_group); - - // Run input transform - _input_transform.run(); - - // Run batched matrix multiplication - _batched_mm.run(); - - // Run output transform - CLScheduler::get().enqueue(_output_transform); + _impl->op->run(_impl->run_pack); } void CLWinogradConvolutionLayer::prepare() { - if(!_is_prepared) + if (!_impl->is_prepared) { - // Run filter transform and mark original weights as unused - _input1.allocator()->allocate(); - CLScheduler::get().enqueue(_filter_transform, false); - _original_weights->mark_as_unused(); - - // Prepare GEMM and release reshaped weights if marked unused by CLGEMM - _batched_mm.prepare(); - if(!_input1.is_used()) - { - _input1.allocator()->free(); - } + _impl->op->prepare(_impl->run_pack); - CLScheduler::get().queue().finish(); - _is_prepared = true; + // Release Preparation tensors + release_prepare_tensors(_impl->workspace_tensors, _impl->run_pack); + _impl->run_pack.remove_tensor(TensorType::ACL_SRC_1); + _impl->is_prepared = true; } } +} // namespace arm_compute |