| /* |
| * Copyright (c) 2017-2020 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/runtime/CL/functions/CLReductionOperation.h" |
| |
| #include "arm_compute/core/CL/ICLTensor.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "src/core/CL/kernels/CLFillBorderKernel.h" |
| #include "src/core/CL/kernels/CLReductionOperationKernel.h" |
| #include "src/core/helpers/AutoConfiguration.h" |
| #include "src/runtime/Utils.h" |
| |
| namespace arm_compute |
| { |
| CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _results_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _reshape(), _num_of_stages(), _reduction_axis(), _is_serial(), |
| _is_reshape_required(false) |
| { |
| } |
| |
| CLReductionOperation::~CLReductionOperation() = default; |
| |
| Status CLReductionOperation::validate(const ITensorInfo *input, const ITensorInfo *output, unsigned int axis, ReductionOperation op, bool keep_dims) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| 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"); |
| |
| const unsigned int num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->dimension(0), axis); |
| const bool is_serial = needs_serialized_reduction(op, input->data_type(), axis); |
| const bool is_reshape_required = !keep_dims; |
| |
| if(is_reshape_required && output->total_size() != 0) |
| { |
| const TensorInfo expected_output_shape = output->clone()->set_tensor_shape(arm_compute::misc::shape_calculator::compute_reduced_shape(input->tensor_shape(), axis, keep_dims)); |
| ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output_shape, output); |
| } |
| |
| auto *output_internal = output; |
| |
| TensorInfo output_before_reshape; |
| const auto input_shape = input->tensor_shape(); |
| const auto input_data_type = input->data_type(); |
| const auto input_num_channles = input->num_channels(); |
| const auto input_qinfo = input->quantization_info(); |
| const auto output_data_type = output->data_type(); |
| |
| auto initialize_tensorinfo = [](TensorInfo & ti, TensorShape shape, DataType data_type, int num_channels, QuantizationInfo qinfo) |
| { |
| ti.set_data_type(data_type).set_tensor_shape(shape).set_num_channels(num_channels).set_quantization_info(qinfo); |
| }; |
| |
| if(is_reshape_required) |
| { |
| auto shape_before_reshape = input_shape; |
| shape_before_reshape.set(axis, 1); |
| initialize_tensorinfo(output_before_reshape, shape_before_reshape, output_data_type, input_num_channles, input_qinfo); |
| output_internal = &output_before_reshape; |
| } |
| |
| if(is_serial) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, output_internal, axis, op)); |
| } |
| else |
| { |
| // Create temporary tensor infos |
| std::vector<TensorInfo> sums_vector(num_of_stages - 1); |
| |
| // Create intermediate tensor info |
| TensorShape shape{ input_shape }; |
| |
| shape.set(0, ceil(shape.x() / 128.f)); |
| |
| for(unsigned int i = 0; i < num_of_stages - 1; i++) |
| { |
| initialize_tensorinfo(sums_vector[i], shape, input_data_type, input_num_channles, input_qinfo); |
| } |
| |
| ReductionOperation first_kernel_op; |
| ReductionOperation intermediate_kernel_op; |
| ReductionOperation last_kernel_op; |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| first_kernel_op = ReductionOperation::SUM; |
| intermediate_kernel_op = ReductionOperation::SUM; |
| last_kernel_op = op; |
| break; |
| case ReductionOperation::SUM_SQUARE: |
| first_kernel_op = ReductionOperation::SUM_SQUARE; |
| intermediate_kernel_op = ReductionOperation::SUM; |
| last_kernel_op = ReductionOperation::SUM; |
| break; |
| case ReductionOperation::PROD: |
| first_kernel_op = ReductionOperation::PROD; |
| intermediate_kernel_op = ReductionOperation::PROD; |
| last_kernel_op = ReductionOperation::PROD; |
| break; |
| case ReductionOperation::MIN: |
| first_kernel_op = ReductionOperation::MIN; |
| intermediate_kernel_op = ReductionOperation::MIN; |
| last_kernel_op = ReductionOperation::MIN; |
| break; |
| case ReductionOperation::MAX: |
| first_kernel_op = ReductionOperation::MAX; |
| intermediate_kernel_op = ReductionOperation::MAX; |
| last_kernel_op = ReductionOperation::MAX; |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| |
| // Validate ReductionOperation only on first kernel |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(input, &sums_vector[0], axis, first_kernel_op)); |
| |
| // Validate ReductionOperation on intermediate stages |
| for(unsigned int i = 1; i < num_of_stages - 1; ++i) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(&sums_vector[i - 1], &sums_vector[i], axis, intermediate_kernel_op)); |
| } |
| |
| // Validate ReductionOperation on the last stage |
| const unsigned int last_stage = num_of_stages - 1; |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReductionOperationKernel::validate(&sums_vector[last_stage - 1], output_internal, axis, last_kernel_op, input->dimension(0))); |
| } |
| |
| if(is_reshape_required) |
| { |
| ARM_COMPUTE_RETURN_ON_ERROR(CLReshapeLayer::validate(output_internal, output)); |
| } |
| |
| return Status{}; |
| } |
| |
| ICLTensor *CLReductionOperation::configure_intermediate_result_vector(ICLTensor *input, ICLTensor *output) |
| { |
| if(!_is_reshape_required && _is_serial) |
| { |
| return output; |
| } |
| |
| auto intermediate_result_vector_size = _is_serial ? 1 : _num_of_stages; |
| |
| if(!_is_reshape_required) |
| { |
| --intermediate_result_vector_size; |
| } |
| |
| _results_vector.resize(intermediate_result_vector_size); |
| auto shape = input->info()->tensor_shape(); |
| |
| shape.set(_reduction_axis, _is_serial ? 1 : ceil(shape.x() / 128.f)); |
| |
| for(auto &v : _results_vector) |
| { |
| if(&v == &_results_vector.back() && _is_reshape_required) |
| { |
| shape.set(_reduction_axis, 1); |
| } |
| v.allocator()->init(input->info()->clone()->set_tensor_shape(shape)); |
| } |
| |
| return _is_reshape_required ? &_results_vector.back() : output; |
| } |
| |
| void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, output, axis, op, keep_dims); |
| } |
| |
| void CLReductionOperation::configure(const CLCompileContext &compile_context, ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op, bool keep_dims) |
| { |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| _num_of_stages = utils::calculate_number_of_stages_only_x_axis(input->info()->dimension(0), axis); |
| _reduction_axis = axis; |
| _is_serial = needs_serialized_reduction(op, input->info()->data_type(), axis); |
| _is_reshape_required = !keep_dims; |
| |
| auto *output_internal = configure_intermediate_result_vector(input, output); |
| |
| if(_is_reshape_required) |
| { |
| const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_reduced_shape(input->info()->tensor_shape(), axis, false); |
| const auto output_data_type = input->info()->data_type(); |
| auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape).set_data_type(output_data_type).reset_padding().set_is_resizable(true)); |
| } |
| |
| // Configure reduction operation kernels |
| _reduction_kernels_vector.reserve(_num_of_stages); |
| |
| // Create temporary tensors |
| if(_is_serial) |
| { |
| if(_is_reshape_required) |
| { |
| _memory_group.manage(&_results_vector.back()); |
| } |
| |
| _reduction_kernels_vector.emplace_back(std::make_unique<CLReductionOperationKernel>()); |
| _reduction_kernels_vector[0]->configure(compile_context, input, output_internal, axis, op, 0); |
| } |
| else |
| { |
| _border_handlers_vector.reserve(_num_of_stages); |
| _memory_group.manage(&_results_vector[0]); |
| |
| ReductionOperation first_kernel_op; |
| ReductionOperation intermediate_kernel_op; |
| ReductionOperation last_kernel_op; |
| PixelValue pixelValue; |
| switch(op) |
| { |
| case ReductionOperation::SUM: |
| case ReductionOperation::MEAN_SUM: |
| first_kernel_op = ReductionOperation::SUM; |
| intermediate_kernel_op = ReductionOperation::SUM; |
| last_kernel_op = op; |
| pixelValue = PixelValue(); |
| break; |
| case ReductionOperation::SUM_SQUARE: |
| first_kernel_op = ReductionOperation::SUM_SQUARE; |
| intermediate_kernel_op = ReductionOperation::SUM; |
| last_kernel_op = ReductionOperation::SUM; |
| pixelValue = PixelValue(); |
| break; |
| case ReductionOperation::PROD: |
| first_kernel_op = ReductionOperation::PROD; |
| intermediate_kernel_op = ReductionOperation::PROD; |
| last_kernel_op = ReductionOperation::PROD; |
| pixelValue = PixelValue(1, input->info()->data_type()); |
| break; |
| case ReductionOperation::MIN: |
| first_kernel_op = ReductionOperation::MIN; |
| intermediate_kernel_op = ReductionOperation::MIN; |
| last_kernel_op = ReductionOperation::MIN; |
| pixelValue = std::get<1>(get_min_max(input->info()->data_type())); |
| break; |
| case ReductionOperation::MAX: |
| first_kernel_op = ReductionOperation::MAX; |
| intermediate_kernel_op = ReductionOperation::MAX; |
| last_kernel_op = ReductionOperation::MAX; |
| pixelValue = std::get<0>(get_min_max(input->info()->data_type())); |
| break; |
| default: |
| ARM_COMPUTE_ERROR("Not supported"); |
| } |
| |
| _reduction_kernels_vector.emplace_back(std::make_unique<CLReductionOperationKernel>()); |
| _reduction_kernels_vector[0]->configure(compile_context, input, &_results_vector[0], axis, first_kernel_op); |
| |
| _border_handlers_vector.emplace_back(std::make_unique<CLFillBorderKernel>()); |
| _border_handlers_vector[0]->configure(compile_context, input, _reduction_kernels_vector[0]->border_size(), BorderMode::CONSTANT, pixelValue); |
| |
| // Apply ReductionOperation on intermediate stages |
| for(unsigned int i = 1; i < _num_of_stages - 1; ++i) |
| { |
| _memory_group.manage(&_results_vector[i]); |
| |
| _reduction_kernels_vector.emplace_back(std::make_unique<CLReductionOperationKernel>()); |
| _reduction_kernels_vector[i]->configure(compile_context, &_results_vector[i - 1], &_results_vector[i], axis, intermediate_kernel_op); |
| |
| _border_handlers_vector.emplace_back(std::make_unique<CLFillBorderKernel>()); |
| _border_handlers_vector[i]->configure(compile_context, &_results_vector[i - 1], _reduction_kernels_vector[i]->border_size(), BorderMode::CONSTANT, pixelValue); |
| |
| _results_vector[i - 1].allocator()->allocate(); |
| } |
| |
| // Apply ReductionOperation on the last stage |
| const unsigned int last_stage = _num_of_stages - 1; |
| const unsigned int input_width = input->info()->dimension(0); |
| |
| if(_is_reshape_required) |
| { |
| _memory_group.manage(&_results_vector.back()); |
| } |
| |
| _reduction_kernels_vector.emplace_back(std::make_unique<CLReductionOperationKernel>()); |
| _reduction_kernels_vector[last_stage]->configure(compile_context, &_results_vector[last_stage - 1], output_internal, axis, last_kernel_op, input_width); |
| |
| _border_handlers_vector.emplace_back(std::make_unique<CLFillBorderKernel>()); |
| _border_handlers_vector[last_stage]->configure(compile_context, &_results_vector[last_stage - 1], _reduction_kernels_vector[last_stage]->border_size(), BorderMode::CONSTANT, pixelValue); |
| |
| _results_vector[last_stage - 1].allocator()->allocate(); |
| } |
| |
| if(_is_reshape_required) |
| { |
| _reshape.configure(compile_context, &_results_vector.back(), output); |
| _results_vector.back().allocator()->allocate(); |
| } |
| } |
| |
| void CLReductionOperation::run() |
| { |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_is_serial) |
| { |
| CLScheduler::get().enqueue(*_reduction_kernels_vector[0], false); |
| } |
| else |
| { |
| for(unsigned int i = 0; i < _num_of_stages; ++i) |
| { |
| CLScheduler::get().enqueue(*_border_handlers_vector[i], false); |
| CLScheduler::get().enqueue(*_reduction_kernels_vector[i], false); |
| } |
| } |
| |
| if(_is_reshape_required) |
| { |
| _reshape.run(); |
| } |
| } |
| } // namespace arm_compute |