| /* |
| * 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/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/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 "src/common/utils/Log.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 |