| /* |
| * 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/Validate.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 "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: |
| case ReductionOperation::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 |
| Window win = calculate_max_window(*input->info(), Steps(vec_size)); |
| win.set(Window::DimX, Window::Dimension(win.x().start(), win.x().end() * _input->info()->num_channels(), win.x().step())); |
| 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: |
| { |
| // 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"); |
| } |
| } |
| } // namespace arm_compute |