| /* |
| * Copyright (c) 2018-2023 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/kernels/ClWinogradInputTransformKernel.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/CL/CLKernelLibrary.h" |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/CL/OpenCL.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "src/core/AccessWindowStatic.h" |
| #include "src/core/CL/CLValidate.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/core/helpers/WindowHelpers.h" |
| #include "support/Cast.h" |
| #include "support/StringSupport.h" |
| |
| namespace arm_compute |
| { |
| namespace opencl |
| { |
| namespace kernels |
| { |
| namespace |
| { |
| Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16); |
| ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); |
| |
| const PadStrideInfo conv_info = winograd_info.convolution_info; |
| const Size2D output_tile_size = winograd_info.output_tile_size; |
| const Size2D kernel_size = winograd_info.kernel_size; |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides"); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd input transform not supported"); |
| |
| ARM_COMPUTE_UNUSED(conv_info); |
| ARM_COMPUTE_UNUSED(output_tile_size); |
| ARM_COMPUTE_UNUSED(kernel_size); |
| |
| // Validate configured output |
| if(output->total_size() != 0) |
| { |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_UNUSED(output); |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| |
| bool window_changed = false; |
| int num_elems_processed_per_iteration = 1; |
| |
| if(input->data_layout() == DataLayout::NHWC) |
| { |
| // In the case of FP16 computation, we can perform more |
| // output feature maps in a single work-item. |
| // From experiments, num_elems_processed_per_iteration = 2 looks good for fp16 to |
| // improve the performance. However, in order to make the implementation simpler, |
| // we set num_elems_processed_per_iteration = 2 only when the OFMs are multiple of 2. |
| // Note: At the moment, only Winograd Input Transform 3x3 can support N0 != 1 |
| const DataType dt = input->data_type(); |
| const size_t dim0 = input->dimension(0); |
| const size_t k_sz = winograd_info.kernel_size.area(); |
| const bool cond = dt == DataType::F16 && ((dim0 % 2) == 0); |
| if(cond) |
| { |
| if(k_sz == 3 || k_sz == 9) |
| { |
| num_elems_processed_per_iteration = 2; |
| } |
| } |
| } |
| Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); |
| |
| if(input->data_layout() == DataLayout::NCHW) |
| { |
| const PadStrideInfo conv_info = winograd_info.convolution_info; |
| const Size2D output_tile_size = winograd_info.output_tile_size; |
| const Size2D kernel_size = winograd_info.kernel_size; |
| |
| unsigned int num_elems_read_per_iteration_x = output_tile_size.width + kernel_size.width - 1; |
| unsigned int num_elems_read_per_iteration_y = output_tile_size.height + kernel_size.height - 1; |
| |
| AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y); |
| window_changed = update_window_and_padding(win, input_access); |
| } |
| |
| Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; |
| return std::make_pair(err, win); |
| } |
| } // namespace |
| |
| ClWinogradInputTransformKernel::ClWinogradInputTransformKernel() |
| { |
| _type = CLKernelType::WINOGRAD; |
| } |
| |
| BorderSize ClWinogradInputTransformKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void ClWinogradInputTransformKernel::configure(const ClCompileContext &compile_context, ITensorInfo *src, ITensorInfo *dst, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, dst, winograd_info)); |
| |
| auto padding_info = get_padding_info({ src, dst }); |
| |
| const PadStrideInfo conv_info = winograd_info.convolution_info; |
| const Size2D output_tile_size = winograd_info.output_tile_size; |
| const Size2D kernel_size = winograd_info.kernel_size; |
| |
| _data_layout = src->data_layout(); |
| |
| const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| |
| // Compute the number of output tiles along the x and y direction of size "output_tile_size" |
| const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(src->dimension(idx_w), src->dimension(idx_h)), |
| kernel_size, |
| output_tile_size, |
| conv_info); |
| |
| _num_tiles_x = num_tiles.width; |
| _num_tiles_y = num_tiles.height; |
| |
| const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info); |
| |
| // Output auto initialization if not yet initialized |
| auto_init_if_empty(*dst, src->clone()->set_tensor_shape(output_shape)); |
| |
| ARM_COMPUTE_ERROR_ON(_num_tiles_x * _num_tiles_y != static_cast<int>(dst->dimension(1))); |
| const size_t total_batches = src->tensor_shape().total_size_upper(3); |
| |
| // Create window and update padding |
| auto win_config = validate_and_configure_window(src, dst, winograd_info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| IClKernel::configure_internal(win_config.second, cl::NDRange(1, 1, 8)); |
| |
| _src_width = src->dimension(idx_w); |
| _src_height = src->dimension(idx_h); |
| |
| CLBuildOptions build_opts; |
| if(_data_layout == DataLayout::NHWC) |
| { |
| build_opts.add_option("-DNHWC"); |
| build_opts.add_option("-DN0=" + support::cpp11::to_string(win_config.second.x().step())); |
| build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width)); |
| build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height)); |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type())); |
| build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL"); |
| build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL"); |
| build_opts.add_option_if(total_batches > 1, "-DIS_BATCHED"); |
| } |
| else |
| { |
| build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x)); |
| build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width)); |
| build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height)); |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(src->data_type())); |
| build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL"); |
| build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL"); |
| build_opts.add_option_if(total_batches > 1, "-DSRC_DEPTH=" + support::cpp11::to_string(src->dimension(2))); |
| } |
| |
| // Create kernel |
| std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string(); |
| |
| // Get the maximum dimension from the tile size |
| const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height); |
| |
| // Check optimized kernel if output_dims == 2x2 |
| if((tile_max_dim == 2) && (_data_layout == DataLayout::NCHW)) |
| { |
| _step_z = (src->dimension(2) % 2) != 0 ? 1 : 2; |
| } |
| |
| // Append stepz and data layout |
| kernel_name += "_stepz"; |
| kernel_name += support::cpp11::to_string(_step_z); |
| kernel_name += "_" + lower_string(string_from_data_layout(_data_layout)); |
| |
| // A macro guard to compile ONLY the kernel of interest |
| build_opts.add_option("-D" + upper_string(kernel_name)); |
| _kernel = create_kernel(compile_context, kernel_name, build_opts.options()); |
| |
| _border_size = BorderSize(src->padding()); |
| |
| ARM_COMPUTE_ERROR_ON((src->data_layout() == DataLayout::NHWC) && has_padding_changed(padding_info)); |
| |
| _config_id = kernel_name; |
| _config_id += support::cpp11::to_string(src->dimension(0)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src->dimension(1)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(src->dimension(2)); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_info.pad_left()); |
| _config_id += "_"; |
| _config_id += support::cpp11::to_string(conv_info.pad_top()); |
| _config_id += "_"; |
| _config_id += lower_string(string_from_data_layout(_data_layout)); |
| } |
| |
| Status ClWinogradInputTransformKernel::validate(const ITensorInfo *src, const ITensorInfo *dst, const WinogradInfo &winograd_info) |
| { |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, dst, winograd_info)); |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(src->clone().get(), dst->clone().get(), winograd_info).first); |
| return Status{}; |
| } |
| |
| void ClWinogradInputTransformKernel::run_op(ITensorPack &tensors, const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| auto src = utils::cast::polymorphic_downcast<const ICLTensor *>(tensors.get_const_tensor(TensorType::ACL_SRC)); |
| auto dst = utils::cast::polymorphic_downcast<ICLTensor *>(tensors.get_tensor(TensorType::ACL_DST)); |
| |
| const size_t idx_w = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH); |
| const size_t idx_h = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT); |
| const size_t idx_c = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL); |
| const size_t total_batches = window.shape().total_size_upper(3); |
| |
| // Collapse window |
| Window window_collapsed = window.collapse_if_possible(IClKernel::window(), Window::DimZ); |
| |
| if(_data_layout == DataLayout::NHWC) |
| { |
| Window slice = window_collapsed.first_slice_window_3D(); |
| slice.set(1, Window::Dimension(0, _num_tiles_x * _num_tiles_y, 1)); |
| slice.set(2, Window::Dimension(0, total_batches, 1)); |
| |
| unsigned int idx = 0; |
| add_4D_tensor_argument(idx, src, slice); |
| add_4D_tensor_argument(idx, dst, slice); |
| _kernel.setArg<cl_uint>(idx++, _src_width); |
| _kernel.setArg<cl_uint>(idx++, _src_height); |
| _kernel.setArg<cl_uint>(idx++, _num_tiles_x); |
| _kernel.setArg<cl_uint>(idx++, _num_tiles_y); |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| else |
| { |
| Window slice = window_collapsed.first_slice_window_3D(); |
| slice.set(idx_w, Window::Dimension(0, _num_tiles_x, 1)); |
| slice.set(idx_h, Window::Dimension(0, _num_tiles_y, 1)); |
| |
| ARM_COMPUTE_ERROR_ON(((slice[idx_c].end() - slice[idx_c].start()) % _step_z) != 0); |
| slice.set(idx_c, Window::Dimension(slice[idx_c].start(), slice[idx_c].end(), _step_z)); |
| |
| unsigned int idx = 2 * num_arguments_per_3D_tensor(); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(src->info()->strides_in_bytes()[3])); |
| _kernel.setArg<cl_uint>(idx++, static_cast<unsigned int>(dst->info()->strides_in_bytes()[3])); |
| |
| do |
| { |
| unsigned int idx = 0; |
| add_3D_tensor_argument(idx, src, slice); |
| add_3D_tensor_argument(idx, dst, slice); |
| |
| enqueue(queue, *this, slice, lws_hint()); |
| } |
| while(window_collapsed.slide_window_slice_3D(slice)); |
| } |
| } |
| } // namespace kernels |
| } // namespace opencl |
| } // namespace arm_compute |