/* * 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/CL/kernels/CLReductionOperationKernel.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/ICLTensor.h" #include "arm_compute/core/Helpers.h" #include "arm_compute/core/TensorInfo.h" #include "arm_compute/core/Utils.h" #include "arm_compute/core/Validate.h" #include "arm_compute/core/Window.h" #include "support/ToolchainSupport.h" using namespace arm_compute; namespace { // OpenCL kernel requires input width to be a power of 2 for x-axis. constexpr unsigned int border_val = 64; Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON_MSG(op == ReductionOperation::SUM_SQUARE && input->data_type() == DataType::QASYMM8, "Not supported reduction operation for QASYMM8"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis >= TensorShape::num_max_dimensions, "Reduction axis greater than max number of dimensions"); ARM_COMPUTE_RETURN_ERROR_ON_MSG(axis > 3, "Unsupported reduction axis"); ARM_COMPUTE_RETURN_ERROR_ON(op == ReductionOperation::MEAN_SUM && axis == 0 && width == 0 && input->data_type() != DataType::QASYMM8); if(output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, output); if(op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN) { ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_type() == DataType::QASYMM8, "Not supported operation for QASYMM8"); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::U32); } else { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } } return Status{}; } std::tuple validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, unsigned int axis, ReductionOperation op) { // Output tensor auto initialization if not yet initialized TensorShape output_shape{ input->tensor_shape() }; output_shape.set(axis, 1); const bool is_arg_min_max = (op == ReductionOperation::ARG_IDX_MIN || op == ReductionOperation::ARG_IDX_MAX); DataType output_data_type = is_arg_min_max ? DataType::U32 : input->data_type(); auto_init_if_empty(*output, output_shape, 1, output_data_type, input->quantization_info()); const unsigned int num_elems_processed_per_iteration = (is_data_type_quantized(input->data_type()) && (axis == 0)) ? 1 : 16; Window win = calculate_max_window(*input, Steps(num_elems_processed_per_iteration)); bool window_changed = false; const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || is_data_type_quantized(input->data_type())); switch(axis) { case 0: { if(is_serial_op) { AccessWindowHorizontal input_access(input, 0, input->dimension(0)); AccessWindowHorizontal output_access(output, 0, 1); window_changed = update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } else { const unsigned int border_width = ((input->dimension(0) % border_val) != 0) ? border_val - input->dimension(0) % border_val : 0; AccessWindowStatic input_access(input, 0, 0, input->dimension(0) + border_width, 1); AccessWindowHorizontal output_access(output, 0, 1); window_changed = update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } } break; case 1: case 2: case 3: { AccessWindowHorizontal input_access(input, 0, num_elems_processed_per_iteration); AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration); window_changed = update_window_and_padding(win, input_access, output_access); output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); } break; default: ARM_COMPUTE_ERROR("Not supported"); } Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; return std::make_tuple(err, win); } } // namespace CLReductionOperationKernel::CLReductionOperationKernel() : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE), _border_size() { } BorderSize CLReductionOperationKernel::border_size() const { return _border_size; } void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, unsigned int width) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op, width)); _input = input; _output = output; _reduction_axis = axis; _op = op; // Set build options CLBuildOptions build_opts; std::string data_type_promoted = get_cl_type_from_data_type(input->info()->data_type()); if(is_data_type_quantized(input->info()->data_type())) { data_type_promoted = "uint"; } build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())); build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted); build_opts.add_option_if(op == ReductionOperation::SUM_SQUARE, "-DSUM_SQUARE"); build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DMEAN"); build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MAX, "-DARG_MAX"); build_opts.add_option_if(op == ReductionOperation::ARG_IDX_MIN, "-DARG_MIN"); build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD"); switch(op) { case ReductionOperation::SUM_SQUARE: build_opts.add_option(("-DOPERATION=square_sum")); break; case ReductionOperation::SUM: case ReductionOperation::MEAN_SUM: build_opts.add_option(("-DOPERATION=sum")); break; case ReductionOperation::ARG_IDX_MAX: case ReductionOperation::ARG_IDX_MIN: break; case ReductionOperation::PROD: build_opts.add_option(("-DOPERATION=product")); break; default: ARM_COMPUTE_ERROR("Unsupported reduction operation"); } // Create kernel cl::NDRange lws_hint = CLKernelLibrary::get().default_ndrange(); std::string kernel_axis_name; const bool is_serial_op = (op == ReductionOperation::ARG_IDX_MAX || op == ReductionOperation::ARG_IDX_MIN || is_data_type_quantized(input->info()->data_type())); switch(axis) { case 0: { if(is_serial_op) { build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(input->info()->dimension(0))); build_opts.add_option_if_else(_input->info()->data_type() == DataType::F32, "-DCOND_DATA_TYPE=int", "-DCOND_DATA_TYPE=short"); kernel_axis_name = "non_parallel_x"; } else { build_opts.add_option_if(op == ReductionOperation::MEAN_SUM, "-DWIDTH=" + support::cpp11::to_string(width)); const unsigned int width_leftover = input->info()->dimension(0) % border_val; const unsigned int border_width = (width_leftover != 0) ? border_val - width_leftover : 0; const unsigned int num_of_threads = ((input->info()->dimension(0) + border_width) / 16); kernel_axis_name = "x"; // Set the number of WG based on the input size. If input width is < 128 // we can use fewer threads than 8. lws_hint = cl::NDRange(std::min(8U, num_of_threads)); _border_size = BorderSize(0, border_width, 0, 0); } } break; case 1: build_opts.add_option("-DHEIGHT=" + support::cpp11::to_string(input->info()->dimension(1))); kernel_axis_name = "y"; break; case 2: build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2))); kernel_axis_name = "z"; break; case 3: build_opts.add_option("-DDEPTH=" + support::cpp11::to_string(input->info()->dimension(2))); build_opts.add_option("-DBATCH=" + support::cpp11::to_string(input->info()->dimension(3))); kernel_axis_name = "w"; break; default: ARM_COMPUTE_ERROR("Not supported"); } _kernel = static_cast(CLKernelLibrary::get().create_kernel("reduction_operation_" + kernel_axis_name, build_opts.options())); // Configure kernel window auto win_config = validate_and_configure_window(_input->info(), _output->info(), axis, op); ARM_COMPUTE_ERROR_THROW_ON(std::get<0>(win_config)); ICLKernel::configure_internal(std::get<1>(win_config), lws_hint); } Status CLReductionOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, unsigned int width) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, axis, op, width)); ARM_COMPUTE_RETURN_ON_ERROR(std::get<0>(validate_and_configure_window(input->clone().get(), output->clone().get(), axis, op))); return Status{}; } void CLReductionOperationKernel::run(const Window &window, cl::CommandQueue &queue) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window); const bool is_serial_op = (_op == ReductionOperation::ARG_IDX_MAX || _op == ReductionOperation::ARG_IDX_MIN || is_data_type_quantized(_input->info()->data_type())); switch(_reduction_axis) { case 0: { // We use parallel reduction only in non quantized types if(is_serial_op) { // Get first input and output slices Window window_in{ window }; window_in.set(Window::DimX, Window::Dimension(0, _input->info()->dimension(0), _input->info()->dimension(0))); Window in_slice = window.first_slice_window_1D(); Window out_slice = window.first_slice_window_1D(); do { unsigned int idx = 0; add_1D_tensor_argument(idx, _input, in_slice); add_1D_tensor_argument(idx, _output, out_slice); enqueue(queue, *this, in_slice); } while(window_in.slide_window_slice_1D(in_slice) && window.slide_window_slice_1D(out_slice)); } else { // Set out window Window out_window(window); out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); // Get first input and output slices Window in_slice = window.first_slice_window_2D(); Window out_slice = out_window.first_slice_window_2D(); // Reshape window const unsigned int border_width = ((in_slice.x().end() % border_val) != 0) ? border_val - in_slice.x().end() % border_val : 0; in_slice.set(Window::DimX, Window::Dimension(in_slice.x().start(), in_slice.x().end() + border_width, in_slice.x().step())); // Set local sums buffer unsigned int local_res_size = lws_hint()[0] * _input->info()->element_size(); _kernel.setArg(num_arguments_per_2D_tensor() * 2, local_res_size, nullptr); do { unsigned int idx = 0; add_2D_tensor_argument(idx, _input, in_slice); add_2D_tensor_argument(idx, _output, out_slice); enqueue(queue, *this, in_slice, lws_hint()); } while(window.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice)); } } break; case 1: { // Get first input and output slices Window window_in{ window }; window_in.set(Window::DimY, Window::Dimension(0, _input->info()->dimension(1), _input->info()->dimension(1))); Window in_slice = window_in.first_slice_window_2D(); Window out_slice = window.first_slice_window_2D(); do { unsigned int idx = 0; add_2D_tensor_argument(idx, _input, in_slice); add_2D_tensor_argument(idx, _output, out_slice); enqueue(queue, *this, in_slice); } while(window_in.slide_window_slice_2D(in_slice) && window.slide_window_slice_2D(out_slice)); } break; case 2: { // Get first input and output slices Window window_in{ window }; window_in.set(Window::DimZ, Window::Dimension(0, _input->info()->dimension(2), _input->info()->dimension(2))); Window in_slice = window_in.first_slice_window_3D(); Window out_slice = window.first_slice_window_3D(); do { unsigned int idx = 0; add_3D_tensor_argument(idx, _input, in_slice); add_3D_tensor_argument(idx, _output, out_slice); enqueue(queue, *this, in_slice); } while(window_in.slide_window_slice_3D(in_slice) && window.slide_window_slice_3D(out_slice)); } break; case 3: { // Get first input and output slices Window window_in{ window }; window_in.set(3, Window::Dimension(0, 1, 1)); Window in_slice = window_in.first_slice_window_4D(); Window out_slice = window.first_slice_window_4D(); do { unsigned int idx = 0; add_4D_tensor_argument(idx, _input, in_slice); add_4D_tensor_argument(idx, _output, out_slice); enqueue(queue, *this, in_slice); } while(window_in.slide_window_slice_4D(in_slice) && window.slide_window_slice_4D(out_slice)); } break; default: ARM_COMPUTE_ERROR("Not supported"); } }