/* * Copyright (c) 2017-2021, 2023 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 "src/core/CL/kernels/CLReductionOperationKernel.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/helpers/AdjustVecSize.h" #include "arm_compute/core/utils/misc/ShapeCalculator.h" #include "arm_compute/core/utils/StringUtils.h" #include "arm_compute/core/Validate.h" #include "src/core/AccessWindowStatic.h" #include "src/core/CL/CLValidate.h" #include "src/core/helpers/AutoConfiguration.h" #include "src/core/helpers/WindowHelpers.h" #include "support/StringSupport.h" namespace arm_compute { namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); if (input->num_channels() == 1) { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::QASYMM8_SIGNED, DataType::S32, DataType::F16, DataType::F32); } else { ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 2, DataType::F16, DataType::F32); ARM_COMPUTE_RETURN_ERROR_ON(axis == 0); } 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) && (input->dimension(0) == 0) && (input->data_type() != DataType::QASYMM8) && (input->data_type() != DataType::QASYMM8_SIGNED)); ARM_COMPUTE_RETURN_ERROR_ON_MSG((op == ReductionOperation::ARG_IDX_MAX) || (op == ReductionOperation::ARG_IDX_MIN), "Not supported reduction operation, use CLArgMinMaxLayer"); if (output->total_size() != 0) { ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); } return Status{}; } } // namespace CLReductionOperationKernel::CLReductionOperationKernel() : _input(nullptr), _output(nullptr), _reduction_axis(0), _op(ReductionOperation::SUM_SQUARE) { _type = CLKernelType::ELEMENTWISE; } void CLReductionOperationKernel::configure(const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op) { configure(CLKernelLibrary::get().get_compile_context(), input, output, axis, op); } void CLReductionOperationKernel::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), axis, op)); auto padding_info = get_padding_info({input, output}); _input = input; _output = output; _reduction_axis = axis; _op = op; const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, true); auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).reset_padding().set_is_resizable(true)); // Set build options CLBuildOptions build_opts; DataType data_type = input->info()->data_type(); std::string data_type_promoted{}; if (is_data_type_quantized(data_type)) { data_type_promoted = "int"; } else { data_type_promoted = get_cl_type_from_data_type(data_type); } const unsigned int width = input->info()->dimension(0) * input->info()->num_channels(); unsigned int vec_size = (is_data_type_quantized(input->info()->data_type()) && (axis == 0)) ? 1 : 16; vec_size = adjust_vec_size(vec_size, width); const unsigned int vec_size_leftover = width % vec_size; build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(data_type)); build_opts.add_option("-DDATA_TYPE_PROMOTED=" + data_type_promoted); build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(vec_size)); build_opts.add_option("-DVEC_SIZE_LEFTOVER=" + support::cpp11::to_string(vec_size_leftover)); build_opts.add_option_if(is_data_type_float(data_type), "-DFLOAT_DATA_TYPE"); 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::SUM, "-DSUM"); build_opts.add_option_if(op == ReductionOperation::PROD, "-DPROD"); build_opts.add_option_if(op == ReductionOperation::MIN, "-DMIN"); build_opts.add_option_if(op == ReductionOperation::MAX, "-DMAX"); build_opts.add_option_if(is_data_type_quantized(data_type), "-DOFFSET=" + support::cpp11::to_string(input->info()->quantization_info().uniform().offset)); build_opts.add_option_if( is_data_type_quantized(data_type), "-DSCALE=" + float_to_string_with_full_precision(input->info()->quantization_info().uniform().scale)); 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::MIN: build_opts.add_option(("-DOPERATION=min_")); break; case ReductionOperation::MAX: build_opts.add_option(("-DOPERATION=max_")); break; case ReductionOperation::PROD: build_opts.add_option(("-DOPERATION=product")); break; default: ARM_COMPUTE_ERROR("Unsupported reduction operation"); } // Create kernel std::string kernel_axis_name; const bool is_serial_op = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis); switch (axis) { case 0: { build_opts.add_option("-DWIDTH=" + support::cpp11::to_string(width)); kernel_axis_name = ((is_serial_op) ? "non_parallel_x" : "x"); } 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 = create_kernel(compile_context, "reduction_operation_" + kernel_axis_name, build_opts.options()); // Configure kernel window TensorShape actual_input_shape = input->info()->tensor_shape(); actual_input_shape[0] = width; Window win = calculate_max_window(actual_input_shape, Steps(vec_size)); ICLKernel::configure_internal(win); ARM_COMPUTE_ERROR_ON(has_padding_changed(padding_info)); } Status CLReductionOperationKernel::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op) { ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, 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 = needs_serialized_reduction(_op, _input->info()->data_type(), _reduction_axis); 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 out_window{window}; out_window.set(Window::DimX, Window::Dimension(0, 0, 0)); Window in_slice = window_in.first_slice_window_1D(); Window out_slice = out_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) && out_window.slide_window_slice_1D(out_slice)); } else { // Set out window bool has_collapsed = true; Window window_in = window.collapse_if_possible(window, 2, &has_collapsed); ARM_COMPUTE_ERROR_ON(!has_collapsed); Window window_out = window_in; window_out.set(0, Window::Dimension()); unsigned int idx = 0; add_3D_tensor_argument(idx, _input, window_in); add_3D_tensor_argument(idx, _output, window_out); enqueue(queue, *this, window_in); } } break; case 1: { bool has_collapsed = true; Window actual_window = window.collapse_if_possible(window, 2, &has_collapsed); ARM_COMPUTE_ERROR_ON(!has_collapsed); actual_window = actual_window.shift_dimensions(1, Window::DimY); const ITensorInfo *input_info = _input->info(); const Strides &input_strides = input_info->strides_in_bytes(); const ITensorInfo *output_info = _output->info(); const Strides &output_strides = output_info->strides_in_bytes(); unsigned int idx = 0; _kernel.setArg(idx++, _input->cl_buffer()); _kernel.setArg(idx++, input_strides[1]); _kernel.setArg(idx++, input_strides[2]); _kernel.setArg(idx++, input_info->offset_first_element_in_bytes()); _kernel.setArg(idx++, _output->cl_buffer()); _kernel.setArg(idx++, output_strides[2]); _kernel.setArg(idx++, output_info->offset_first_element_in_bytes()); enqueue(queue, *this, actual_window); } break; case 2: { bool has_collapsed = true; Window actual_window = window.collapse_if_possible(window, 3, &has_collapsed); ARM_COMPUTE_ERROR_ON(!has_collapsed); actual_window = actual_window.shift_dimensions(1, Window::DimZ); const ITensorInfo *input_info = _input->info(); const Strides &input_strides = input_info->strides_in_bytes(); const ITensorInfo *output_info = _output->info(); const Strides &output_strides = output_info->strides_in_bytes(); unsigned int idx = 0; _kernel.setArg(idx++, _input->cl_buffer()); _kernel.setArg(idx++, input_strides[1]); _kernel.setArg(idx++, input_strides[2]); _kernel.setArg(idx++, input_strides[3]); _kernel.setArg(idx++, input_info->offset_first_element_in_bytes()); _kernel.setArg(idx++, _output->cl_buffer()); _kernel.setArg(idx++, output_strides[1]); _kernel.setArg(idx++, output_strides[3]); _kernel.setArg(idx++, output_info->offset_first_element_in_bytes()); enqueue(queue, *this, actual_window); } break; case 3: { bool has_collapsed = true; Window actual_window = window.shift_dimensions(1, Window::DimW); actual_window = actual_window.collapse_if_possible(actual_window, 2, &has_collapsed); ARM_COMPUTE_ERROR_ON(!has_collapsed); const ITensorInfo *input_info = _input->info(); const Strides &input_strides = input_info->strides_in_bytes(); const ITensorInfo *output_info = _output->info(); const Strides &output_strides = output_info->strides_in_bytes(); unsigned int idx = 0; _kernel.setArg(idx++, _input->cl_buffer()); _kernel.setArg(idx++, input_strides[1]); _kernel.setArg(idx++, input_strides[2]); _kernel.setArg(idx++, input_strides[3]); _kernel.setArg(idx++, input_strides[4]); _kernel.setArg(idx++, input_info->offset_first_element_in_bytes()); _kernel.setArg(idx++, _output->cl_buffer()); _kernel.setArg(idx++, output_strides[1]); _kernel.setArg(idx++, output_strides[2]); _kernel.setArg(idx++, output_strides[4]); _kernel.setArg(idx++, output_info->offset_first_element_in_bytes()); enqueue(queue, *this, actual_window); } break; default: ARM_COMPUTE_ERROR("Not supported"); } } } // namespace arm_compute