/* * Copyright (c) 2017-2019 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/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h" #include "arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" #include "arm_compute/core/AccessWindowStatic.h" #include "arm_compute/core/CPP/Validate.h" #include "arm_compute/core/Coordinates.h" #include "arm_compute/core/Error.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/ITensor.h" #include "arm_compute/core/NEON/INEKernel.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/TensorShape.h" #include "arm_compute/core/Types.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "support/ToolchainSupport.h" namespace arm_compute { namespace { template class convolver_3x3 { public: static void convolve(const Window &window, unsigned int num_elems_written_per_iteration, const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { const int input_offset = -input->info()->quantization_info().uniform().offset; const int weights_offset = -weights->info()->quantization_info().uniform().offset; const int input_stride_x = input->info()->strides_in_bytes().x(); const int input_stride_y = input->info()->strides_in_bytes().y(); const int input_stride_z = input->info()->strides_in_bytes().z(); const int input_stride_w = input->info()->strides_in_bytes()[3]; const int output_stride_y = output->info()->strides_in_bytes().y(); const int kernel_stride_y = weights->info()->strides_in_bytes().y(); const int kernel_stride_z = weights->info()->strides_in_bytes().z(); const int output_w = output->info()->dimension(0); const int output_h = output->info()->dimension(1); const int delta_input = detail::get_input_num_elems_processed(num_elems_written_per_iteration); const unsigned int conv_stride_y = std::get<1>(conv_info.stride()); const unsigned int conv_pad_x = conv_info.pad_left(); const unsigned int conv_pad_y = conv_info.pad_top(); // setup output window for the iterator Window window_out = window; window_out.set(Window::DimX, Window::Dimension(0, output->info()->dimension(Window::DimX), output->info()->dimension(Window::DimX))); window_out.set(Window::DimY, Window::Dimension(0, output->info()->dimension(Window::DimY), output->info()->dimension(Window::DimY))); // setup input window for the iterator Window window_in = window; // Iteration of input is taken care of in execute_window_loop window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); window_in.set(Window::DimY, Window::Dimension(0, 0, 0)); window_in.set(Window::DimZ, Window::Dimension(0, 0, 0)); Window window_k = calculate_max_window(*weights->info(), Steps(1u)); Iterator in(input, window_in); Iterator out(output, window_out); Iterator w(weights, window_k); const uint8_t *weights_ptr = w.ptr(); execute_window_loop(window_out, [&](const Coordinates & id) { int ih = 0; int oh = 0; const uint8_t *input_ptr = in.ptr() - conv_pad_x * input_stride_x - conv_pad_y * input_stride_y + (id.z() / depth_multiplier) * input_stride_z + input_stride_w * id[3]; const uint8_t *ptr_weights_base = weights_ptr + id.z() * kernel_stride_z; const auto ptr_weights_r0 = reinterpret_cast(ptr_weights_base); const auto ptr_weights_r1 = reinterpret_cast(ptr_weights_base + kernel_stride_y); const auto ptr_weights_r2 = reinterpret_cast(ptr_weights_base + kernel_stride_y * 2); const auto vw_r0 = detail::load_matrix_row(ptr_weights_r0, weights_offset); const auto vw_r1 = detail::load_matrix_row(ptr_weights_r1, weights_offset); const auto vw_r2 = detail::load_matrix_row(ptr_weights_r2, weights_offset); for(ih = 0, oh = 0; oh < output_h; ++oh, ih += conv_stride_y) { auto in_top = reinterpret_cast(input_ptr + (ih + 0) * input_stride_y); auto in_mid = reinterpret_cast(input_ptr + (ih + dilation.y()) * input_stride_y); auto in_low = reinterpret_cast(input_ptr + (ih + 2 * dilation.y()) * input_stride_y); //uint8 auto p_out = reinterpret_cast(out.ptr() + oh * output_stride_y); //int32 for(int ow = 0; ow < output_w; ow += num_elems_written_per_iteration, in_top += delta_input, in_mid += delta_input, in_low += delta_input, p_out += num_elems_written_per_iteration) { if(dilation == Size2D(1U, 1U)) { auto vres = detail::convolve_3x3(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); detail::store_results(p_out, vres); } else { auto vres = detail::convolve_3x3_dilation(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, dilation.x(), input_offset); detail::store_results(p_out, vres); } } } }, out); } }; template inline void convolve_3x3(const Window &window, unsigned int num_elems_written_per_iteration, const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { const unsigned int conv_stride_x = std::get<0>(conv_info.stride()); switch(conv_stride_x) { case 1: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; case 2: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; case 3: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier, dilation); break; default: ARM_COMPUTE_ERROR("Not implemented"); } } Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); const DataLayout data_layout = input->data_layout(); const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(width_idx) != 3 || weights->dimension(height_idx) != 3); ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); if(output->total_size() != 0) { const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); if(is_data_type_quantized_asymmetric(input->data_type())) { ARM_COMPUTE_RETURN_ERROR_ON(output->data_type() != DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); } } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { Window win; bool window_changed = false; // Get convolved dimensions const TensorShape output_shape = misc::shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt).set_quantization_info(output->quantization_info())); // Configure kernel window (generic) const unsigned int conv_stride_x = conv_info.stride().first; const unsigned int conv_stride_y = conv_info.stride().second; const unsigned int conv_pad_top = conv_info.pad_top(); const unsigned int conv_pad_left = conv_info.pad_left(); unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; unsigned int num_elems_read_per_iteration = 0; switch(input->data_type()) { case DataType::QASYMM8: num_elems_read_per_iteration = 16 + 15 * (dilation.x() - 1); break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: num_elems_written_per_iteration = 32 >> conv_stride_x; num_elems_read_per_iteration = 24 + 23 * (dilation.x() - 1); break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: num_elems_read_per_iteration = 12 + 11 * (dilation.x() - 1); break; default: ARM_COMPUTE_ERROR("Data type not supported."); } // Configure kernel window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3 + 2 * (dilation.y() - 1), conv_stride_x, conv_stride_y); AccessWindowStatic weights_access(weights, 0, 0, 3, 3); AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); window_changed = update_window_and_padding(win, input_access, weights_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_pair(err, win); } } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() : _border_size(0), _input(), _output(), _weights(), _conv_info(), _num_elems_written_per_iteration(0), _depth_multiplier(1), _dilation() { } BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const { return _border_size; } void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, dilation)); _input = input; _output = output; _weights = weights; _conv_info = conv_info; _depth_multiplier = depth_multiplier; switch(input->info()->data_type()) { case DataType::QASYMM8: case DataType::F32: _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; break; case DataType::F16: _num_elems_written_per_iteration = 32 >> _conv_info.stride().first; break; default: ARM_COMPUTE_ERROR("Data type not supported."); } _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); _dilation = dilation; auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, dilation); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, dilation)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, dilation).first); return Status{}; } void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_UNUSED(info); switch(_input->info()->data_type()) { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; case DataType::QASYMM8: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier, _dilation); break; default: ARM_COMPUTE_ERROR("Not implemented"); } } } // namespace arm_compute