| /* |
| * 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/CLDepthwiseConvolutionLayer3x3NHWCKernel.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::F16, DataType::F32, DataType::QASYMM8); |
| 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"); //COMPMID-1317 add fused activation for F32 |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); |
| ARM_COMPUTE_RETURN_ERROR_ON(depth_multiplier > 1); // COMPMID-1071 Add depth multiplier support for NHWC |
| ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != 3 || weights->dimension(2) != 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(0)); |
| 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<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, |
| const PadStrideInfo &conv_info) |
| { |
| // Get convolved dimensions |
| const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, 1 /* depth_multiplier */); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output, |
| output_shape, |
| 1, |
| input->data_type(), |
| input->quantization_info()); |
| |
| const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); |
| const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); |
| |
| const unsigned int num_rows_processed_per_iteration = is_stride_1 ? 2 : 1; |
| const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->element_size()); |
| const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2; |
| const unsigned int num_rows_written_per_iteration = std::ceil(num_rows_processed_per_iteration / static_cast<float>(conv_info.stride().first)); |
| |
| BorderSize border_size; |
| border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); |
| |
| // Configure kernel window |
| Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration)); |
| |
| AccessWindowStatic input_access(input, 0, -border_size.top, ceil_to_multiple(input->dimension(0), num_elems_accessed_per_iteration), |
| ceil_to_multiple(input->dimension(1) + border_size.bottom, num_rows_read_per_iteration)); |
| AccessWindowRectangle output_access(output, 0, 0, num_elems_accessed_per_iteration, num_rows_written_per_iteration); |
| AccessWindowHorizontal weights_access(weights, 0, num_elems_accessed_per_iteration); |
| |
| bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access); |
| |
| if(bias != nullptr) |
| { |
| AccessWindowHorizontal bias_access(bias, 0, num_elems_accessed_per_iteration); |
| window_changed = window_changed || update_window_and_padding(win, bias_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 |
| |
| CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel() |
| : _num_rows_processed_per_iteration(1), _num_planes_processed_per_iteration(1) |
| { |
| } |
| |
| BorderSize CLDepthwiseConvolutionLayer3x3NHWCKernel::border_size() const |
| { |
| return _border_size; |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3NHWCKernel::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); |
| |
| // Get convolved dimensions |
| const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier); |
| |
| // Output auto inizialitation if not yet initialized |
| auto_init_if_empty(*output->info(), |
| output_shape, |
| 1, |
| input->info()->data_type(), |
| input->info()->quantization_info()); |
| |
| ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info)); |
| |
| const unsigned int conv_stride_x = conv_info.stride().first; |
| ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 2); |
| ARM_COMPUTE_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 1); |
| |
| const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); |
| const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); |
| |
| _input = input; |
| _output = output; |
| _weights = weights; |
| _biases = biases; |
| _conv_stride_y = conv_info.stride().second; |
| _num_rows_processed_per_iteration = is_stride_1 ? 2 : 1; |
| _num_planes_processed_per_iteration = is_stride_1 ? 2 : 1; |
| _border_size = BorderSize(conv_info.pad_left(), 0, std::max(std::max(conv_info.pad_right(), conv_info.pad_bottom()), conv_info.pad_top()), 0); |
| |
| const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->info()->element_size()); |
| ; |
| |
| CLBuildOptions build_opts; |
| build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); |
| build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration)); |
| build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2))); |
| build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); |
| build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); |
| |
| 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("-DSRC_DIM_1=" + support::cpp11::to_string(_input->info()->dimension(1))); |
| 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; |
| |
| if(o1 != o2 || s1 != s2) |
| { |
| build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1)); |
| build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2)); |
| build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1)); |
| build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2)); |
| } |
| } |
| } |
| } |
| else |
| { |
| build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); |
| } |
| |
| if(is_stride_1) |
| { |
| build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(_num_rows_processed_per_iteration)); |
| build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration)); |
| build_opts.add_option("-DDST_DIM_2=" + support::cpp11::to_string(_output->info()->dimension(2))); |
| } |
| else |
| { |
| build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(conv_stride_x)); |
| build_opts.add_option("-DCONV_STRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)); |
| } |
| |
| // Create kernel |
| const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); |
| std::string kernel_name = std::string("depthwise_convolution_3x3") + (is_qasymm ? std::string("_quantized") + ((is_dot8_supported |
| && is_stride_1 /* FIXME (COMPMID-1424) */) ? "_dot8" : "") : "") + "_nhwc" + (is_stride_1 ? "_stride1" : ""); |
| |
| _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options())); |
| |
| // Configure kernel window |
| auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info); |
| ARM_COMPUTE_ERROR_THROW_ON(win_config.first); |
| ICLKernel::configure_internal(win_config.second); |
| |
| // Set config_id for enabling LWS tuning |
| _config_id = kernel_name; |
| _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)); |
| _config_id += "_"; |
| _config_id += string_from_data_type(input->info()->data_type()); |
| } |
| |
| Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, |
| unsigned int depth_multiplier, |
| ActivationLayerInfo act_info) |
| { |
| 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(), |
| biases != nullptr ? biases->clone().get() : nullptr, |
| output->clone().get(), conv_info) |
| .first); |
| |
| return Status{}; |
| } |
| |
| void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue) |
| { |
| ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); |
| ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); |
| |
| Window win = window; |
| win.set(Window::DimZ, Window::Dimension(0, std::ceil(_output->info()->dimension(2) / static_cast<float>(_num_planes_processed_per_iteration)), 1)); |
| |
| // Create input window and adjust |
| Window win_in = win; |
| win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration); |
| win_in.set_dimension_step(Window::DimZ, _conv_stride_y); |
| |
| ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) || (win_in.z().step() < window.z().step())); |
| |
| Window slice_in = win_in.first_slice_window_3D(); |
| Window slice_out = win.first_slice_window_3D(); |
| |
| unsigned int idx = 3 * num_arguments_per_3D_tensor(); |
| |
| if(_biases != nullptr) |
| { |
| Window win_biases; |
| win_biases.use_tensor_dimensions(_biases->info()->tensor_shape()); |
| win_biases.set_dimension_step(Window::DimX, window.x().step()); |
| add_1D_tensor_argument(idx, _biases, win_biases); |
| } |
| |
| const int max_offset = _input->info()->strides_in_bytes().z() * _input->info()->dimension(2) - (_input->info()->padding().bottom + _input->info()->padding().top) * |
| _input->info()->strides_in_bytes().y(); |
| _kernel.setArg(idx, max_offset); |
| |
| 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_out); |
| |
| enqueue(queue, *this, slice_out, lws_hint()); |
| } |
| while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in)); |
| } |