| /* |
| * 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/NEON/functions/NESoftmaxLayer.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| #include "src/core/NEON/kernels/NEFillBorderKernel.h" |
| #include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" |
| #include "src/core/NEON/kernels/NESoftmaxLayerKernel.h" |
| #include "src/core/helpers/SoftmaxHelpers.h" |
| #include "support/MemorySupport.h" |
| |
| namespace arm_compute |
| { |
| template <bool IS_LOG> |
| NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default; |
| |
| template <bool IS_LOG> |
| NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _fill_border_kernel(), _max(), _tmp(), _input_permuted(), _output_permuted(), |
| _needs_permute(false) |
| { |
| } |
| |
| template <bool IS_LOG> |
| void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t axis) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_THROW_ON(NESoftmaxLayerGeneric::validate(input->info(), output->info(), beta, axis)); |
| |
| const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); |
| |
| _needs_permute = actual_axis > 0; |
| |
| if(_needs_permute) |
| { |
| // Add to the memory manager _input_permuted |
| _memory_group.manage(&_input_permuted); |
| |
| _permute_input.configure(input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| } |
| |
| // We want to deal with a 2D input. Either it is the permuted version of the original input (4D case) |
| // or it is the original input case (2D case) |
| ITensor *tmp_input = (_needs_permute ? &_input_permuted : input); |
| |
| // Create intermediate tensors shapes |
| const TensorInfo input_info = tmp_input->info()->clone()->reset_padding().set_is_resizable(true); |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::F32 : tmp_input->info()->data_type(); |
| TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); |
| |
| // Init intermediate tensors |
| TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); |
| max_sum_shape.set(0, 1); |
| _max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape)); |
| _tmp.allocator()->init(tensor_info_tmp); |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_max); |
| _memory_group.manage(&_tmp); |
| |
| // Configure kernels |
| _max_kernel = arm_compute::support::cpp14::make_unique<NELogits1DMaxKernel>(); |
| _softmax_kernel = arm_compute::support::cpp14::make_unique<NELogits1DSoftmaxKernel<IS_LOG>>(); |
| _max_kernel->configure(tmp_input, &_max); |
| if(_needs_permute) |
| { |
| // Add to the memory manager _output_permuted |
| _memory_group.manage(&_output_permuted); |
| |
| // The normalization kernel stores the result in a permuted output tensor |
| _softmax_kernel->configure(tmp_input, &_max, &_output_permuted, beta, &_tmp); |
| _input_permuted.allocator()->allocate(); |
| |
| // Re-permute the permuted output into the requested (4D) output |
| _permute_output.configure(&_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| |
| // Allocate the intermediate permuted tensors |
| _output_permuted.allocator()->allocate(); |
| } |
| else |
| { |
| // Softmax 2D case |
| _fill_border_kernel = arm_compute::support::cpp14::make_unique<NEFillBorderKernel>(); |
| _fill_border_kernel->configure(tmp_input, _max_kernel->border_size(), BorderMode::REPLICATE); |
| _softmax_kernel->configure(tmp_input, &_max, output, beta, &_tmp); |
| } |
| |
| // Allocate intermediate buffers |
| _max.allocator()->allocate(); |
| _tmp.allocator()->allocate(); |
| } |
| |
| template <bool IS_LOG> |
| Status NESoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis) |
| { |
| // Perform validation step |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->num_dimensions() > 4, "Only up to 4 dimensions are supported"); |
| ARM_COMPUTE_UNUSED(beta); |
| ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-input->num_dimensions()) || static_cast<int32_t>(input->num_dimensions()) <= axis); |
| |
| // Create intermediate tensor info |
| DataType tmp_data_type = input->data_type(); |
| const TensorInfo tensor_info_tmp(input->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); |
| |
| TensorShape max_sum_shape = input->tensor_shape(); |
| max_sum_shape.set(0, 1); |
| const TensorInfo tensor_info_max_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(input->quantization_info()).set_is_resizable(true)); |
| const TensorInfo dont_care; |
| |
| const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(input->num_dimensions()))); |
| |
| const bool needs_permute = actual_axis > 0; |
| |
| if(needs_permute) |
| { |
| const PermutationVector permutation_vector = softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis); |
| const TensorShape permuted_shape = misc::shape_calculator::compute_permutation_output_shape(*input, permutation_vector); |
| TensorInfo input_permuted(input->clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(input, &input_permuted, permutation_vector)); |
| TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NEPermute::validate(&output_permuted, output, permutation_vector)); |
| } |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DMaxKernel::validate(input, &tensor_info_max_sum)); |
| ARM_COMPUTE_RETURN_ON_ERROR(NELogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, output, beta, &dont_care)); |
| |
| return Status{}; |
| } |
| |
| template <bool IS_LOG> |
| void NESoftmaxLayerGeneric<IS_LOG>::run() |
| { |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_needs_permute) |
| { |
| _permute_input.run(); |
| } |
| else |
| { |
| NEScheduler::get().schedule(_fill_border_kernel.get(), Window::DimY); |
| } |
| |
| NEScheduler::get().schedule(_max_kernel.get(), Window::DimY); |
| NEScheduler::get().schedule(_softmax_kernel.get(), Window::DimY); |
| |
| if(_needs_permute) |
| { |
| _permute_output.run(); |
| } |
| } |
| |
| template class NESoftmaxLayerGeneric<false>; |
| template class NESoftmaxLayerGeneric<true>; |
| |
| } // namespace arm_compute |