/* * Copyright (c) 2017-2018 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 "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CL/CLHelpers.h" #include "arm_compute/core/CL/CLKernelLibrary.h" #include "arm_compute/core/CL/ICLKernel.h" #include "arm_compute/core/CL/ICLTensor.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/quantization/AsymmHelpers.h" using namespace arm_compute; using namespace arm_compute::misc::shape_calculator; CLDepthwiseConvolutionLayer3x3Kernel::CLDepthwiseConvolutionLayer3x3Kernel() : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0) { } BorderSize CLDepthwiseConvolutionLayer3x3Kernel::border_size() const { return _border_size; } void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_ERROR_ON(weights->info()->dimension(0) != 3 || weights->info()->dimension(1) != 3); if(biases != nullptr) { if(is_data_type_quantized_asymmetric(weights->info()->data_type())) { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } else { ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); } ARM_COMPUTE_ERROR_ON(biases->info()->dimension(0) != weights->info()->dimension(2)); ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1); } // Get convolved dimensions const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type(), input->info()->fixed_point_position(), input->info()->quantization_info()); ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); _input = input; _output = output; _weights = weights; _biases = biases; _conv_stride_x = conv_info.stride().first; _conv_stride_y = conv_info.stride().second; _conv_pad_left = conv_info.pad_left(); _conv_pad_top = conv_info.pad_top(); _border_size = BorderSize(_conv_pad_top, conv_info.pad_right(), conv_info.pad_bottom(), _conv_pad_left); // Set build options ARM_COMPUTE_ERROR_ON(_conv_stride_x < 1 || _conv_stride_x > 3); CLBuildOptions build_opts; build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); // Configure the local work size for Bifrost with a value obtained // via exhaustive autotuning for the MobileNets tensor shapes. const GPUTarget gpu_target = get_arch_from_target(get_target()); // Configure kernel window const unsigned int conv_pad_left = std::max(conv_info.pad_left(), 1U); const unsigned int conv_pad_top = std::max(conv_info.pad_top(), 1U); const unsigned int conv_pad_right = std::max(conv_info.pad_right(), 1U); const unsigned int conv_pad_bottom = std::max(conv_info.pad_bottom(), 1U); unsigned int num_elems_read_per_iteration_x = 0; unsigned int num_elems_read_per_iteration_y = 0; unsigned int num_elems_written_per_iteration_x = 0; unsigned int num_elems_written_per_iteration_y = 0; // Create kernel std::string kernel_name; if(input->info()->data_type() == DataType::F32 && gpu_target == GPUTarget::BIFROST) { if(_conv_stride_x == 1 && _conv_stride_y == 1) { kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_bifrost"; num_elems_read_per_iteration_x = 4; num_elems_read_per_iteration_y = 6; num_elems_written_per_iteration_x = 2; num_elems_written_per_iteration_y = 4; } else if(_conv_stride_x == 2 && _conv_stride_y == 2) { kernel_name = "depthwise_convolution_3x3_stridex2_stridey2_bifrost"; num_elems_read_per_iteration_x = 6; num_elems_read_per_iteration_y = 5; num_elems_written_per_iteration_x = 2; num_elems_written_per_iteration_y = 2; } else { kernel_name = "depthwise_convolution_3x3"; num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type()); num_elems_written_per_iteration_y = 1; num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x; num_elems_read_per_iteration_y = 3; } } else { kernel_name = is_data_type_quantized_asymmetric(_input->info()->data_type()) ? "depthwise_convolution_3x3_quantized" : "depthwise_convolution_3x3"; num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type()); num_elems_written_per_iteration_y = 1; num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x; num_elems_read_per_iteration_y = 3; } // Calculate right and bottom border int input_width = input->info()->dimension(0) + conv_pad_left + conv_pad_right; int input_height = input->info()->dimension(1) + conv_pad_top + conv_pad_bottom; // Add padding only if necessary or it would always result in a window_changed input_width = ceil_to_multiple(input_width, num_elems_read_per_iteration_x); input_height = ceil_to_multiple(input_height, num_elems_read_per_iteration_y); // Create window and update padding Window win = calculate_max_window(*output->info(), Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); AccessWindowStatic input_access(input->info(), -conv_pad_left, -conv_pad_top, input_width, input_height); AccessWindowStatic weights_access(weights->info(), 0, 0, 3, 3); AccessWindowRectangle output_access(output->info(), 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); update_window_and_padding(win, input_access, weights_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->info()->tensor_shape())); ICLKernel::configure(win); _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // Set static arguments if(is_data_type_quantized_asymmetric(_input->info()->data_type())) { float multiplier = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale; int output_multiplier = 0; int output_shift = 0; quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); unsigned int idx = 3 * num_arguments_per_3D_tensor() + ((_biases != nullptr) ? num_arguments_per_1D_tensor() : 0); _kernel.setArg(idx++, -_input->info()->quantization_info().offset); _kernel.setArg(idx++, -_weights->info()->quantization_info().offset); _kernel.setArg(idx++, _output->info()->quantization_info().offset); _kernel.setArg(idx++, output_multiplier); _kernel.setArg(idx++, output_shift); } // Set config_id for enabling LWS tuning _config_id = kernel_name; _config_id += "_"; _config_id += lower_string(string_from_data_type(input->info()->data_type())); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(1)); _config_id += "_"; _config_id += support::cpp11::to_string(input->info()->dimension(2)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(0)); _config_id += "_"; _config_id += support::cpp11::to_string(output->info()->dimension(1)); } void CLDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); // Create input window and adjust Window win_in = window; win_in.adjust(Window::DimX, -_conv_pad_left, true); win_in.adjust(Window::DimY, -_conv_pad_top, true); win_in.set_dimension_step(Window::DimX, window.x().step() * _conv_stride_x); win_in.set_dimension_step(Window::DimY, window.y().step() * _conv_stride_y); Window slice_in = win_in.first_slice_window_3D(); Window slice_out = window.first_slice_window_3D(); Window slice_weights = window.first_slice_window_3D(); slice_weights.set_dimension_step(Window::DimX, 0); slice_weights.set_dimension_step(Window::DimY, 0); // Set biases if(_biases != nullptr) { unsigned int idx = 3 * num_arguments_per_3D_tensor(); Window slice_biases; slice_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); add_1D_tensor_argument(idx, _biases, slice_biases); } do { unsigned int idx = 0; add_3D_tensor_argument(idx, _input, slice_in); add_3D_tensor_argument(idx, _output, slice_out); add_3D_tensor_argument(idx, _weights, slice_weights); enqueue(queue, *this, slice_out, _lws_hint); } while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in)); }