/* * 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/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.h" #include "arm_compute/core/NEON/kernels/detail/NEDirectConvolutionDetail.h" #include "arm_compute/core/AccessWindowStatic.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" using namespace arm_compute; using namespace arm_compute::detail; using namespace arm_compute::misc::shape_calculator; using namespace depthwise; 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 int input_offset = -input->info()->quantization_info().offset; const int weights_offset = -weights->info()->quantization_info().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 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 = 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; // we just want execute_window_loop to iterate over the dimensions > 2, so we set the first 2 dimensions to 0 window_in.set(Window::DimX, Window::Dimension(0, 0, 0)); window_in.set(Window::DimY, 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() - id.z() / depth_multiplier) * input_stride_z; 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 = load_matrix_row(ptr_weights_r0, weights_offset); const auto vw_r1 = load_matrix_row(ptr_weights_r1, weights_offset); const auto vw_r2 = 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 + 1) * input_stride_y); auto in_low = reinterpret_cast(input_ptr + (ih + 2) * input_stride_y); auto p_out = reinterpret_cast(out.ptr() + oh * output_stride_y); 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) { auto vres = convolve_3x3(in_top, in_mid, in_low, vw_r0, vw_r1, vw_r2, input_offset); store_results(p_out, vres); } } }, in, 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 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); break; case 2: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); break; case 3: convolver_3x3::convolve(window, num_elems_written_per_iteration, input, weights, output, conv_info, depth_multiplier); 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, bool is_optimized) { 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); if(!is_optimized) { ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); } if(output->total_size() != 0) { const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); 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, bool is_optimized, IDepthwiseConvolution *convolver = nullptr) { Window win; bool window_changed = false; if(is_optimized) { if(convolver != nullptr) { auto win_last = convolver->get_window(); win.set(Window::DimX, Window::Dimension(0, win_last, 1)); // Auto-configure output bool same_padding = conv_info.has_padding(); TensorShape output_shape{ input->tensor_shape() }; output_shape.set(1, convolver->output_size(output_shape.y(), same_padding)); // Set width output_shape.set(2, convolver->output_size(output_shape.z(), same_padding)); // Set height 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)); // Configure window (optimised) // Set padding in channels const int num_channels = weights->dimension(0); if((num_channels >= 128) && (num_channels % 16 == 0)) { input->extend_padding(PaddingSize(0, 4, 0, 0)); weights->extend_padding(PaddingSize(0, 4, 0, 0)); output->extend_padding(PaddingSize(0, 4, 0, 0)); } } } else { // Get convolved dimensions const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); 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)); // 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; break; #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: num_elems_read_per_iteration = 24; break; #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: num_elems_read_per_iteration = 12; 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, 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(), _convolver(nullptr), _num_elems_written_per_iteration(0), _run_optimized(false), _depth_multiplier(1) { } 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, DataLayout data_layout) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); _input = input; _output = output; _weights = weights; _conv_info = conv_info; _depth_multiplier = depth_multiplier; _convolver = nullptr; _run_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->info()->tensor_shape(), conv_info, input->info()->data_type(), depth_multiplier, data_layout); (_run_optimized) ? configure_optimized() : configure_generic(); } Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); bool is_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->tensor_shape(), conv_info, input->data_type(), depth_multiplier, input->data_layout()); ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, is_optimized)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, is_optimized).first); return Status{}; } void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_UNUSED(info); (_run_optimized) ? run_optimized(window, info) : run_generic(window, info); } bool NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(TensorShape input_shape, PadStrideInfo conv_info, DataType dt, unsigned int depth_multiplier, DataLayout data_layout) { // Reshape input shape if in NHWC format TensorShape in_shape{ input_shape }; if(data_layout == DataLayout::NHWC) { in_shape.set(Window::DimX, input_shape.y()); in_shape.set(Window::DimY, input_shape.z()); in_shape.set(Window::DimZ, input_shape.x()); } // Check supported data type bool supported_datatype = is_data_type_float(dt) || is_data_type_quantized(dt); // Check for supported strides const auto &strides = conv_info.stride(); bool supported_strides = (strides.first == strides.second) && ((strides.first == 1) || (strides.first == 2)); // Check for supported padding const auto pad_top = conv_info.pad_top(); const auto pad_right = conv_info.pad_right(); const auto pad_bottom = conv_info.pad_bottom(); const auto pad_left = conv_info.pad_left(); PadStrideInfo same_pad = calculate_same_pad(in_shape, TensorShape(3U, 3U), conv_info); bool is_same_padding = (pad_top == same_pad.pad_top()) && (pad_right == same_pad.pad_right()) && (pad_bottom == same_pad.pad_bottom()) && (pad_left == same_pad.pad_left()); bool is_valid_padding = (pad_top == 0) && (pad_right == 0) && (pad_bottom == 0) && (pad_left == 0); bool supported_padding = is_same_padding || is_valid_padding; return supported_datatype && supported_strides && supported_padding && (depth_multiplier == 1); } void NEDepthwiseConvolutionLayer3x3Kernel::generate_convolver() { ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(_input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(_input, _weights); ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(1) != 3 || _weights->info()->dimension(2) != 3); _convolver = create_convolver_object(_conv_info, _weights, _input, _output, true); if(_convolver) { _convolver->set_offsets(-_input->info()->quantization_info().offset, -_weights->info()->quantization_info().offset); } } void NEDepthwiseConvolutionLayer3x3Kernel::configure_generic() { ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, false); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } void NEDepthwiseConvolutionLayer3x3Kernel::configure_optimized() { ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); _border_size = BorderSize(0, 0); _convolver = create_convolver_object(_conv_info, _weights, _input, _output); auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, true, _convolver.get()); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); INEKernel::configure(win_config.second); } void NEDepthwiseConvolutionLayer3x3Kernel::run_generic(const Window &window, const ThreadInfo &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); 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); break; case DataType::QASYMM8: convolve_3x3(window, _num_elems_written_per_iteration, _input, _weights, _output, _conv_info, _depth_multiplier); break; default: ARM_COMPUTE_ERROR("Not implemented"); } } void NEDepthwiseConvolutionLayer3x3Kernel::run_optimized(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_UNUSED(info); ARM_COMPUTE_ERROR_ON(!_convolver); const size_t start = window.x().start(); const size_t end = window.x().end(); _convolver->run(start, end); } std::unique_ptr NEDepthwiseConvolutionLayer3x3Kernel::create_convolver_object(PadStrideInfo conv_info, const ITensor *w, const ITensor *in, ITensor *out, bool setup_strides) { const DataType dt = in->info()->data_type(); const TensorShape shape = in->info()->tensor_shape(); const int in_rows = shape.z(); const int in_cols = shape.y(); const int n_batches = shape[3]; const int n_channels = shape.x(); const bool padding_same = conv_info.has_padding(); const int weight_col_stride = (setup_strides) ? w->info()->strides_in_bytes().y() / w->info()->element_size() : 0; const int weight_row_stride = (setup_strides) ? w->info()->strides_in_bytes().z() / w->info()->element_size() : 0; const int input_col_stride = (setup_strides) ? in->info()->strides_in_bytes().y() / in->info()->element_size() : 0; const int input_row_stride = (setup_strides) ? in->info()->strides_in_bytes().z() / in->info()->element_size() : 0; const int input_batch_stride = (setup_strides) ? in->info()->strides_in_bytes()[3] / in->info()->element_size() : 0; const int output_col_stride = (setup_strides) ? out->info()->strides_in_bytes().y() / out->info()->element_size() : 0; const int output_row_stride = (setup_strides) ? out->info()->strides_in_bytes().z() / out->info()->element_size() : 0; const int output_batch_stride = (setup_strides) ? out->info()->strides_in_bytes()[3] / out->info()->element_size() : 0; const auto stride_x = conv_info.stride().first; switch(dt) { case DataType::QASYMM8: { switch(stride_x) { case 1: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), in->ptr_to_element(Coordinates()), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); case 2: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), in->ptr_to_element(Coordinates()), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); default: return nullptr; } break; } #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F16: { switch(stride_x) { case 1: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), reinterpret_cast(in->ptr_to_element(Coordinates())), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); case 2: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), reinterpret_cast(in->ptr_to_element(Coordinates())), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); default: return nullptr; } break; } #endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC case DataType::F32: { switch(stride_x) { case 1: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), reinterpret_cast(in->ptr_to_element(Coordinates())), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); case 2: return arm_compute::support::cpp14::make_unique>( n_batches, in_rows, in_cols, n_channels, padding_same, reinterpret_cast(w->ptr_to_element(Coordinates())), reinterpret_cast(in->ptr_to_element(Coordinates())), reinterpret_cast(out->ptr_to_element(Coordinates())), weight_col_stride, weight_row_stride, input_col_stride, input_row_stride, input_batch_stride, output_col_stride, output_row_stride, output_batch_stride); default: return nullptr; } break; } default: return nullptr; } }