/* * Copyright (c) 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/CLDepthwiseConvolutionLayer3x3NCHWKernel.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/CLValidate.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; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, const ActivationLayerInfo &act_info) { ARM_COMPUTE_RETURN_ERROR_ON_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_MSG(act_info.enabled() && ((input->data_type() != DataType::QASYMM8) || ((act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC))), "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported"); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); if(biases != nullptr) { if(is_qasymm) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases); } ARM_COMPUTE_RETURN_ERROR_ON((biases->dimension(0) != weights->dimension(2)) && (weights->dimension(2) != 1 || biases->dimension(0) != weights->dimension(3))); ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1); } 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); } return Status{}; } std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, GPUTarget gpu_target, std::string &kernel_name) { // Output auto inizialitation if not yet initialized const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); const unsigned int conv_stride_x = conv_info.stride().first; const unsigned int conv_stride_y = conv_info.stride().second; const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); const bool is_bifrost = get_arch_from_target(gpu_target) == GPUTarget::BIFROST; // Configure kernel window 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; if(input->data_type() == DataType::F16) { kernel_name = "depthwise_convolution_3x3_f16"; num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); num_elems_written_per_iteration_y = 1; num_elems_read_per_iteration_y = 3; switch(conv_stride_x) { case 1: num_elems_read_per_iteration_x = 8; break; case 2: num_elems_read_per_iteration_x = 9; break; case 3: num_elems_read_per_iteration_x = 16; break; default: num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; break; } if(is_bifrost) { if(conv_stride_x == 1 && conv_stride_y == 1) { kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16"; num_elems_read_per_iteration_x = 8; num_elems_written_per_iteration_x = 4; num_elems_read_per_iteration_y = 6; 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_f16"; num_elems_read_per_iteration_x = 10; num_elems_written_per_iteration_x = 4; num_elems_read_per_iteration_y = 5; num_elems_written_per_iteration_y = 2; } } } else if(input->data_type() == DataType::F32 && is_bifrost) { if(conv_stride_x == 1 && conv_stride_y == 1) { kernel_name = "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32"; 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_f32"; 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->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 { const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); kernel_name = is_qasymm ? (std::string("depthwise_convolution_3x3_quantized") + (is_dot8_supported ? "_dot8" : "") + "_nchw") : "depthwise_convolution_3x3"; num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1) ? 2 : 1; num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; } // Create window and update padding Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); AccessWindowRectangle input_access(input, -conv_info.pad_left(), -conv_info.pad_top(), num_elems_read_per_iteration_x, num_elems_read_per_iteration_y, conv_stride_x, conv_stride_y); AccessWindowStatic weights_access(weights, 0, 0, 3, 3); AccessWindowRectangle output_access(output, 0, 0, num_elems_written_per_iteration_x, num_elems_written_per_iteration_y); bool 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 CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel() : _conv_stride_x(0), _conv_pad_top(0), _conv_pad_left(0) { } BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const { return _border_size; } void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info)); bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); _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); // Configure kernel window std::string kernel_name; const GPUTarget gpu_target = get_target(); auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); // Set build options CLBuildOptions build_opts; build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z())); build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); if(is_qasymm) { 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); build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset)); build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset)); build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset)); build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset)); build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier)); build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift)); if(act_info.enabled()) { const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP); const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP); const int o1 = input->info()->quantization_info().offset; build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation()))); build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val)); build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val)); build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1)); if(output != nullptr) { const float s1 = input->info()->quantization_info().scale; const float s2 = output->info()->quantization_info().scale; const int o2 = output->info()->quantization_info().offset; build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); if(o1 != o2 || s1 != s2) { build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2)); build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2)); } } } } _kernel = static_cast(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); // 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)); } Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target) { std::string kernel_name; ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name).first); return Status{}; } void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); Window collapsed = window.collapse_if_possible(ICLKernel::window(), Window::DimZ); // Create input window and adjust Window collapsed_in = collapsed; collapsed_in.adjust(Window::DimX, -_conv_pad_left, true); collapsed_in.adjust(Window::DimY, -_conv_pad_top, true); collapsed_in.set_dimension_step(Window::DimX, collapsed_in.x().step() * _conv_stride_x); collapsed_in.set_dimension_step(Window::DimY, collapsed_in.y().step() * _conv_stride_y); Window slice_in = collapsed_in.first_slice_window_3D(); Window slice_out = collapsed.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(collapsed.slide_window_slice_3D(slice_out) && collapsed_in.slide_window_slice_3D(slice_in)); }