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Diffstat (limited to 'src/runtime/gpu/cl/operators/ClWinogradConv2d.cpp')
| -rw-r--r-- | src/runtime/gpu/cl/operators/ClWinogradConv2d.cpp | 299 |
1 files changed, 0 insertions, 299 deletions
diff --git a/src/runtime/gpu/cl/operators/ClWinogradConv2d.cpp b/src/runtime/gpu/cl/operators/ClWinogradConv2d.cpp deleted file mode 100644 index c8db697778..0000000000 --- a/src/runtime/gpu/cl/operators/ClWinogradConv2d.cpp +++ /dev/null @@ -1,299 +0,0 @@ -/* - * Copyright (c) 2018-2021 Arm Limited. - * - * SPDX-License-Identifier: MIT - * - * Permission is hereby granted, free of charge, to any person obtaining a copy - * of this software and associated documentation files (the "Software"), to - * deal in the Software without restriction, including without limitation the - * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or - * sell copies of the Software, and to permit persons to whom the Software is - * furnished to do so, subject to the following conditions: - * - * The above copyright notice and this permission notice shall be included in all - * copies or substantial portions of the Software. - * - * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR - * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, - * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE - * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER - * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE - * SOFTWARE. - */ -#include "src/runtime/gpu/cl/operators/ClWinogradConv2d.h" - -#include "arm_compute/core/CL/ICLTensor.h" -#include "arm_compute/core/Utils.h" -#include "arm_compute/core/Validate.h" -#include "arm_compute/core/experimental/Types.h" -#include "arm_compute/core/utils/misc/ShapeCalculator.h" -#include "arm_compute/runtime/CL/CLScheduler.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/CL/kernels/CLFillBorderKernel.h" -#include "src/core/gpu/cl/kernels/ClWinogradFilterTransformKernel.h" -#include "src/core/gpu/cl/kernels/ClWinogradInputTransformKernel.h" -#include "src/core/gpu/cl/kernels/ClWinogradOutputTransformKernel.h" -#include "src/core/helpers/MemoryHelpers.h" -#include "src/runtime/gpu/cl/utils/ClAuxTensorHandler.h" -#include "support/Cast.h" - -using namespace arm_compute::experimental; - -namespace arm_compute -{ -namespace opencl -{ -namespace -{ -Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataLayout data_layout) -{ - 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(); -} - -Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, 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(src->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); - - // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(src->tensor_shape()[idx_width], src->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, src->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(src, 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, - src->data_layout()); - - // Validate input transform - const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info); - const TensorInfo input0 = src->clone()->set_tensor_shape(input0_shape); - ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradInputTransformKernel::validate(src, &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(kernels::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(), (src->data_type() == DataType::F16)))); - - // Configure output transform - ARM_COMPUTE_RETURN_ON_ERROR(kernels::ClWinogradOutputTransformKernel::validate(&batched_mm_output, biases, dst, winograd_info, act_info)); - return Status{}; -} - -} // namespace - -ClWinogradConv2d::ClWinogradConv2d() - : _batched_mm(), - _input_transform(std::make_unique<kernels::ClWinogradInputTransformKernel>()), - _filter_transform(std::make_unique<kernels::ClWinogradFilterTransformKernel>()), - _output_transform(std::make_unique<kernels::ClWinogradOutputTransformKernel>()), - _border_handler(), - _input0(), - _input1(), - _batched_mm_output(), - _is_prepared(false), - _aux_mem() -{ -} - -ClWinogradConv2d::~ClWinogradConv2d() = default; - -void ClWinogradConv2d::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *weights, ITensorInfo *biases, ITensorInfo *dst, - const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math) -{ - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math)); - // Get indices for the width and height - const size_t idx_width = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH); - const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT); - - // Input shape, kernel size and output tile - const Size2D input_dims = Size2D(src->tensor_shape()[idx_width], src->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, src->data_layout()); - - // Check if the Winograd configuration requires fast math - if(!enable_fast_math) - { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 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, - src->data_layout()); - - _is_prepared = false; - - // Configure input transform - _input_transform->configure(compile_context, src, &_input0, winograd_info); - _border_handler.configure(compile_context, src, _input_transform->border_size(), BorderMode::CONSTANT, PixelValue()); - - // 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(), - (src->data_type() == DataType::F16))); - - // Configure output transform - _output_transform->configure(compile_context, &_batched_mm_output, biases, dst, winograd_info, act_info); - - _aux_mem = _batched_mm.workspace(); - _aux_mem.push_back(MemoryInfo(offset_int_vec(2), MemoryLifetime::Temporary, _input0.total_size())); - _aux_mem.push_back(MemoryInfo(offset_int_vec(3), MemoryLifetime::Persistent, _input1.total_size())); - _aux_mem.push_back(MemoryInfo(offset_int_vec(4), MemoryLifetime::Temporary, _batched_mm_output.total_size())); -} - -Status ClWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info, - const ActivationLayerInfo &act_info, bool enable_fast_math) -{ - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info, act_info, enable_fast_math)); - return Status{}; -} - -void ClWinogradConv2d::run(ITensorPack &tensors) -{ - prepare(tensors); - - // Run input transform - auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_0)); - auto biases = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_2)); - auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); - - CLAuxTensorHandler input0(offset_int_vec(2), _input0, tensors, true); - CLAuxTensorHandler input1(offset_int_vec(3), _input1, tensors, true); - CLAuxTensorHandler batched_mm_output(offset_int_vec(4), _batched_mm_output, tensors, true); - - ITensorPack pack_it - { - { TensorType::ACL_SRC, src }, - { TensorType::ACL_DST, input0.get() }, - }; - CLScheduler::get().enqueue_op(_border_handler, pack_it); - CLScheduler::get().enqueue_op(*_input_transform, pack_it); - - // Run batched matrix multiplication - ITensorPack pack_mm - { - { TensorType::ACL_SRC_0, input0.get() }, - { TensorType::ACL_SRC_1, input1.get() }, - { TensorType::ACL_DST, batched_mm_output.get() }, - }; - _batched_mm.run(pack_mm); - - // Run output transform - ITensorPack pack_ot - { - { TensorType::ACL_SRC_0, batched_mm_output.get() }, - { TensorType::ACL_SRC_1, biases }, - { TensorType::ACL_DST, dst }, - }; - CLScheduler::get().enqueue_op(*_output_transform, pack_ot); -} - -void ClWinogradConv2d::prepare(ITensorPack &tensors) -{ - if(!_is_prepared) - { - auto weights = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC_1)); - ICLTensor *in1_aux = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(offset_int_vec(3))); - - CLAuxTensorHandler input1(_input1, *in1_aux); - ITensorPack pack_ft - { - { TensorType::ACL_SRC, weights }, - { TensorType::ACL_DST, input1.get() }, - }; - // Run filter transform and mark original weights as unused - CLScheduler::get().enqueue_op(*_filter_transform, pack_ft, false); - weights->mark_as_unused(); - - tensors.add_tensor(ACL_SRC_1, input1.get()); - // Prepare GEMM and release reshaped weights if marked unused by ClGemm - _batched_mm.prepare(tensors); - - CLScheduler::get().queue().finish(); - _is_prepared = true; - } -} - -experimental::MemoryRequirements ClWinogradConv2d::workspace() const -{ - return _aux_mem; -} -} // namespace opencl -} // namespace arm_compute
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