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Diffstat (limited to 'src/gpu/cl/operators/ClWinogradConv2d.cpp')
| -rw-r--r-- | src/gpu/cl/operators/ClWinogradConv2d.cpp | 328 |
1 files changed, 328 insertions, 0 deletions
diff --git a/src/gpu/cl/operators/ClWinogradConv2d.cpp b/src/gpu/cl/operators/ClWinogradConv2d.cpp new file mode 100644 index 0000000000..8ec96b247e --- /dev/null +++ b/src/gpu/cl/operators/ClWinogradConv2d.cpp @@ -0,0 +1,328 @@ +/* + * Copyright (c) 2018-2022 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/gpu/cl/operators/ClWinogradConv2d.h" + +#include "arm_compute/core/CL/ICLTensor.h" +#include "arm_compute/core/experimental/Types.h" +#include "arm_compute/core/Utils.h" +#include "arm_compute/core/utils/misc/ShapeCalculator.h" +#include "arm_compute/core/Validate.h" +#include "arm_compute/runtime/CL/CLScheduler.h" + +#include "src/common/utils/Log.h" +#include "src/core/CL/kernels/CLFillBorderKernel.h" +#include "src/core/helpers/MemoryHelpers.h" +#include "src/gpu/cl/kernels/ClWinogradFilterTransformKernel.h" +#include "src/gpu/cl/kernels/ClWinogradInputTransformKernel.h" +#include "src/gpu/cl/kernels/ClWinogradOutputTransformKernel.h" +#include "src/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)); + ARM_COMPUTE_LOG_PARAMS(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->set_target(CLScheduler::get().target()); + _output_transform->configure(compile_context, &_batched_mm_output, biases, dst, winograd_info, act_info); + + _aux_mem = _batched_mm.workspace(); + const MemoryLifetime wino_wei_lifetm = + std::any_of(std::begin(_aux_mem), std::end(_aux_mem), + [](const auto &r) { return (r.lifetime == MemoryLifetime::Persistent) && (r.size > 0); }) + ? MemoryLifetime::Prepare + : MemoryLifetime::Persistent; + _aux_mem.push_back(MemoryInfo(offset_int_vec(2), MemoryLifetime::Temporary, _input0.total_size())); + _aux_mem.push_back(MemoryInfo(offset_int_vec(3), wino_wei_lifetm, _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) +{ + const bool is_gemm_reshaped = _aux_mem[3].lifetime == MemoryLifetime::Prepare; + + 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, is_gemm_reshaped); + CLAuxTensorHandler batched_mm_output(offset_int_vec(4), _batched_mm_output, tensors, true); + + prepare(tensors); + + // Run input transform + ITensorPack pack_it{ + {TensorType::ACL_SRC, src}, + {TensorType::ACL_DST, input0.get()}, + }; + CLScheduler::get().enqueue_op(_border_handler, pack_it, false); + CLScheduler::get().enqueue_op(*_input_transform, pack_it, false); + + // Run batched matrix multiplication + ITensorPack pack_mm = tensors; + pack_mm.add_const_tensor(TensorType::ACL_SRC_0, input0.get()); + pack_mm.add_tensor(TensorType::ACL_DST, batched_mm_output.get()); + is_gemm_reshaped ? pack_mm.remove_tensor(TensorType::ACL_SRC_1) + : pack_mm.add_const_tensor(TensorType::ACL_SRC_1, input1.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(); + + // Prepare GEMM and release reshaped weights if marked unused by ClGemm + ITensorPack mm_prepare_pack = tensors; + mm_prepare_pack.add_tensor(ACL_SRC_1, input1.get()); + _batched_mm.prepare(mm_prepare_pack); + + CLScheduler::get().queue().finish(); + _is_prepared = true; + } +} + +experimental::MemoryRequirements ClWinogradConv2d::workspace() const +{ + return _aux_mem; +} +} // namespace opencl +} // namespace arm_compute |