diff options
Diffstat (limited to 'src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp')
| -rw-r--r-- | src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp | 320 |
1 files changed, 320 insertions, 0 deletions
diff --git a/src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp b/src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp new file mode 100644 index 0000000000..54c48986fc --- /dev/null +++ b/src/gpu/cl/kernels/ClWinogradInputTransformKernel.cpp @@ -0,0 +1,320 @@ +/* + * 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/misc/ShapeCalculator.h" +#include "arm_compute/core/utils/StringUtils.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 |