| /* |
| * Copyright (c) 2017 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/CL/kernels/CLReductionOperationKernel.h" |
| #include "arm_compute/core/Error.h" |
| #include "arm_compute/core/PixelValue.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "support/ToolchainSupport.h" |
| |
| using namespace arm_compute; |
| |
| CLReductionOperation::CLReductionOperation(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _sums_vector(), _reduction_kernels_vector(), _border_handlers_vector(), _num_of_stages() |
| { |
| } |
| |
| void CLReductionOperation::configure(ICLTensor *input, ICLTensor *output, unsigned int axis, ReductionOperation op) |
| { |
| // Calculate number of WGs. 16 elements per thread, 8 threads per WG |
| unsigned int num_of_wg = ceil(input->info()->dimension(0) / 128.f); |
| |
| // Calculate number of stages. First stage performs op and the rest reduction sum |
| // depending on the size of the input. Last stage should have only 1 WG. |
| _num_of_stages = num_of_wg / 128 + 2; |
| |
| // Create temporary tensors |
| _sums_vector = arm_compute::support::cpp14::make_unique<CLTensor[]>(_num_of_stages - 1); |
| |
| // Configure reduction operation kernels |
| _reduction_kernels_vector = arm_compute::support::cpp14::make_unique<CLReductionOperationKernel[]>(_num_of_stages); |
| _border_handlers_vector = arm_compute::support::cpp14::make_unique<CLFillBorderKernel[]>(_num_of_stages); |
| |
| TensorShape shape{ input->info()->tensor_shape() }; |
| for(unsigned int i = 0; i < _num_of_stages - 1; i++) |
| { |
| shape.set(0, ceil(shape.x() / 128.f)); |
| _sums_vector[i].allocator()->init(TensorInfo(shape, input->info()->num_channels(), input->info()->data_type(), input->info()->fixed_point_position())); |
| } |
| |
| // Apply ReductionOperation only on first kernel |
| _memory_group.manage(_sums_vector.get()); |
| _reduction_kernels_vector[0].configure(input, _sums_vector.get(), axis, op); |
| _border_handlers_vector[0].configure(input, _reduction_kernels_vector[0].border_size(), BorderMode::CONSTANT, PixelValue(0)); |
| |
| // Apply ReductionOperation on intermediate stages |
| for(unsigned int i = 1; i < _num_of_stages - 1; ++i) |
| { |
| _memory_group.manage(_sums_vector.get() + i); |
| _reduction_kernels_vector[i].configure(_sums_vector.get() + i - 1, _sums_vector.get() + i, axis, ReductionOperation::SUM); |
| _border_handlers_vector[i].configure(_sums_vector.get() + i - 1, _reduction_kernels_vector[i].border_size(), BorderMode::CONSTANT, PixelValue(0)); |
| _sums_vector[i - 1].allocator()->allocate(); |
| } |
| |
| // Apply ReductionOperation on the last stage |
| const unsigned int last_stage = _num_of_stages - 1; |
| _reduction_kernels_vector[last_stage].configure(_sums_vector.get() + last_stage - 1, output, axis, ReductionOperation::SUM); |
| _border_handlers_vector[last_stage].configure(_sums_vector.get() + last_stage - 1, _reduction_kernels_vector[last_stage].border_size(), BorderMode::CONSTANT, PixelValue(0)); |
| _sums_vector[last_stage - 1].allocator()->allocate(); |
| } |
| |
| void CLReductionOperation::run() |
| { |
| _memory_group.acquire(); |
| |
| 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); |
| } |
| |
| _memory_group.release(); |
| } |