| /* |
| * 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/CLSoftmaxLayer.h" |
| |
| #include "arm_compute/core/CL/CLHelpers.h" |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/core/Utils.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/runtime/CL/CLScheduler.h" |
| #include "src/core/CL/ICLKernel.h" |
| #include "src/core/CL/kernels/CLFillBorderKernel.h" |
| #include "src/core/CL/kernels/CLSoftmaxLayerKernel.h" |
| #include "src/core/helpers/SoftmaxHelpers.h" |
| |
| namespace arm_compute |
| { |
| template <bool IS_LOG> |
| CLSoftmaxLayerGeneric<IS_LOG>::CLSoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager) |
| : _memory_group(std::move(memory_manager)), |
| _permute_input(), |
| _permute_output(), |
| _max_shift_exp_sum_kernel(std::make_unique<CLLogits1DMaxShiftExpSumKernel>()), |
| _norm_kernel(std::make_unique<CLLogits1DNormKernel>()), |
| _max(), |
| _sum(), |
| _tmp(), |
| _input_permuted(), |
| _output_permuted(), |
| _needs_permute() |
| { |
| } |
| |
| template <bool IS_LOG> |
| CLSoftmaxLayerGeneric<IS_LOG>::~CLSoftmaxLayerGeneric() = default; |
| |
| template <bool IS_LOG> |
| void CLSoftmaxLayerGeneric<IS_LOG>::configure(const ICLTensor *input, ICLTensor *output, float beta, int32_t axis) |
| { |
| configure(CLKernelLibrary::get().get_compile_context(), input, output, beta, axis); |
| } |
| |
| template <bool IS_LOG> |
| void CLSoftmaxLayerGeneric<IS_LOG>::configure(const CLCompileContext &compile_context, const ICLTensor *input, ICLTensor *output, float beta, int32_t axis) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); |
| ARM_COMPUTE_ERROR_THROW_ON(CLSoftmaxLayerGeneric<IS_LOG>::validate(input->info(), output->info(), beta, axis)); |
| |
| const size_t actual_axis = static_cast<size_t>(wrap_around(axis, static_cast<int32_t>(input->info()->num_dimensions()))); |
| |
| _needs_permute = actual_axis != 0; |
| ICLTensor *tmp_output = output; |
| const ICLTensor *tmp_input = _needs_permute ? &_input_permuted : input; |
| if(_needs_permute) |
| { |
| _memory_group.manage(&_input_permuted); |
| _memory_group.manage(&_output_permuted); |
| _permute_input.configure(compile_context, input, &_input_permuted, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| tmp_output = &_output_permuted; |
| } |
| |
| // Create intermediate tensors |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->info()->data_type()) ? DataType::S32 : tmp_input->info()->data_type(); |
| TensorInfo tensor_info_tmp(tmp_input->info()->clone()->set_data_type(tmp_data_type)); |
| _tmp.allocator()->init(tensor_info_tmp); |
| TensorShape max_sum_shape = tmp_input->info()->tensor_shape(); |
| max_sum_shape.set(0, 1); |
| _max.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape)); |
| _sum.allocator()->init(tmp_input->info()->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type)); |
| |
| // Set GPU target to kernels |
| _max_shift_exp_sum_kernel->set_target(CLScheduler::get().target()); |
| |
| // Manage intermediate buffers |
| _memory_group.manage(&_tmp); |
| _memory_group.manage(&_max); |
| _memory_group.manage(&_sum); |
| |
| SoftmaxKernelInfo softmax_info; |
| softmax_info.beta = beta; |
| softmax_info.is_log = IS_LOG; |
| softmax_info.input_data_type = tmp_input->info()->data_type(); |
| |
| // Configure kernels |
| _max_shift_exp_sum_kernel->configure(compile_context, tmp_input, &_max, &_tmp, &_sum, softmax_info); |
| _norm_kernel->configure(compile_context, &_tmp, &_sum, tmp_output, softmax_info); |
| |
| // Allocate intermediate buffers |
| _tmp.allocator()->allocate(); |
| _max.allocator()->allocate(); |
| _sum.allocator()->allocate(); |
| if(_needs_permute) |
| { |
| _permute_output.configure(compile_context, &_output_permuted, output, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| _input_permuted.allocator()->allocate(); |
| _output_permuted.allocator()->allocate(); |
| } |
| } |
| |
| template <bool IS_LOG> |
| Status CLSoftmaxLayerGeneric<IS_LOG>::validate(const ITensorInfo *input, const ITensorInfo *output, float beta, int32_t axis) |
| { |
| 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); |
| |
| const size_t actual_axis = static_cast<size_t>(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(CLPermute::validate(input, &input_permuted, permutation_vector)); |
| TensorInfo output_permuted(output->clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLPermute::validate(&output_permuted, output, permutation_vector)); |
| } |
| |
| // Create intermediate tensor info |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(input->data_type()) ? DataType::S32 : input->data_type(); |
| 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); |
| TensorInfo tensor_info_max(input->clone()->set_tensor_shape(max_sum_shape).set_is_resizable(true)); |
| TensorInfo tensor_info_sum(input->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(QuantizationInfo()).set_is_resizable(true)); |
| |
| SoftmaxKernelInfo softmax_info; |
| softmax_info.beta = beta; |
| softmax_info.is_log = IS_LOG; |
| softmax_info.input_data_type = input->data_type(); |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DMaxShiftExpSumKernel::validate(input, &tensor_info_max, &tensor_info_tmp, &tensor_info_sum)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CLLogits1DNormKernel::validate(&tensor_info_tmp, &tensor_info_sum, output, softmax_info)); |
| |
| return Status{}; |
| } |
| |
| template <bool IS_LOG> |
| void CLSoftmaxLayerGeneric<IS_LOG>::run() |
| { |
| MemoryGroupResourceScope scope_mg(_memory_group); |
| |
| if(_needs_permute) |
| { |
| _permute_input.run(); |
| } |
| |
| CLScheduler::get().enqueue(*_max_shift_exp_sum_kernel, false); |
| CLScheduler::get().enqueue(*_norm_kernel, !_needs_permute); |
| |
| if(_needs_permute) |
| { |
| _permute_output.run(); |
| } |
| } |
| |
| template class CLSoftmaxLayerGeneric<false>; |
| template class CLSoftmaxLayerGeneric<true>; |
| |
| } // namespace arm_compute |