| /* |
| * Copyright (c) 2018-2019 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/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, const Size2D dilation) |
| { |
| 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); |
| |
| ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); |
| |
| 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, dilation); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); |
| } |
| |
| if(is_qasymm) |
| { |
| const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = (output->total_size() != 0) ? output->quantization_info().uniform() : iq_info; |
| |
| float multiplier = iq_info.scale * wq_info.scale / oq_info.scale; |
| ARM_COMPUTE_UNUSED(multiplier); |
| ARM_COMPUTE_RETURN_ERROR_ON(multiplier > 1.0f); |
| } |
| |
| return Status{}; |
| } |
| |
| std::pair<Status, Window> 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, const Size2D dilation) |
| { |
| // Output auto inizialitation if not yet initialized |
| const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); |
| auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape).set_quantization_info(output->quantization_info())); |
| |
| 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 ? "dwc_3x3_native_qasymm8" : "depthwise_convolution_3x3"; |
| kernel_name += (is_qasymm && is_dot8_supported ? "_dot8" : ""); |
| kernel_name += (is_qasymm ? "_nchw" : ""); |
| |
| 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 && dilation.y() == 1) ? 2 : 1; |
| num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x + (conv_stride_x > 1 ? 1 : 0); |
| num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; |
| } |
| num_elems_read_per_iteration_x += (num_elems_read_per_iteration_x - 1) * (dilation.x() - 1); |
| num_elems_read_per_iteration_y += (num_elems_read_per_iteration_y - 1) * (dilation.y() - 1); |
| |
| // 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, const Size2D &dilation) |
| { |
| 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, dilation)); |
| |
| 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, dilation); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure_internal(win_config.second); |
| |
| // Set build options |
| CLBuildOptions build_opts; |
| build_opts.add_option("-DACTIVATION_TYPE=" + lower_string(string_from_activation_func(act_info.activation()))); |
| 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("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); |
| build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); |
| build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); |
| |
| if(is_qasymm) |
| { |
| const UniformQuantizationInfo iq_info = _input->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo wq_info = _weights->info()->quantization_info().uniform(); |
| const UniformQuantizationInfo oq_info = _output->info()->quantization_info().uniform(); |
| |
| float multiplier = iq_info.scale * wq_info.scale / oq_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(-iq_info.offset)); |
| build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-wq_info.offset)); |
| build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(oq_info.offset)); |
| build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * iq_info.offset * wq_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 = quantize_qasymm8(act_info.a(), oq_info); |
| const int b_val = quantize_qasymm8(act_info.b(), oq_info); |
| const int o1 = oq_info.offset; |
| |
| 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)); |
| |
| const float s1 = iq_info.scale; |
| build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); |
| build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); |
| } |
| } |
| else |
| { |
| build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a())); |
| build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b())); |
| build_opts.add_option_if(act_info.enabled(), "-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(win_config.second.x().step())); |
| } |
| |
| build_opts.add_option_if(input->info()->data_type() == DataType::F16, "-DIS_F16"); |
| build_opts.add_option_if(input->info()->data_type() == DataType::F32, "-DIS_F32"); |
| |
| _kernel = static_cast<cl::Kernel>(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, const Size2D &dilation) |
| { |
| std::string kernel_name; |
| ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); |
| 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, dilation).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)); |
| } |
