| /* |
| * Copyright (c) 2021 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/runtime/cpu/operators/CpuSoftmax.h" |
| |
| #include "arm_compute/core/Helpers.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/NEON/NEScheduler.h" |
| #include "src/core/cpu/kernels/CpuSoftmaxKernel.h" |
| #include "src/core/helpers/SoftmaxHelpers.h" |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| template <bool IS_LOG> |
| CpuSoftmaxGeneric<IS_LOG>::CpuSoftmaxGeneric() |
| : _permute_input(), _permute_output(), _max_kernel(), _softmax_kernel(), _max(nullptr), _tmp(nullptr), _input_permuted(nullptr), _output_permuted(nullptr), _needs_permute(false) |
| { |
| } |
| |
| template <bool IS_LOG> |
| void CpuSoftmaxGeneric<IS_LOG>::configure(const ITensorInfo *src, ITensorInfo *dst, float beta, int32_t axis) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(CpuSoftmaxGeneric::validate(src, dst, beta, axis)); |
| |
| const unsigned int actual_axis = static_cast<unsigned int>(wrap_around(axis, static_cast<int32_t>(src->num_dimensions()))); |
| |
| _needs_permute = actual_axis > 0; |
| |
| if(_needs_permute) |
| { |
| _input_permuted = std::make_unique<TensorInfo>(); |
| _permute_input.configure(src, _input_permuted.get(), 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) |
| const ITensorInfo *tmp_input = (_needs_permute ? _input_permuted.get() : src); |
| |
| // Create intermediate tensors shapes |
| TensorShape max_sum_shape = tmp_input->tensor_shape(); |
| max_sum_shape.set(0, 1); |
| const TensorInfo input_info = tmp_input->clone()->reset_padding().set_is_resizable(true); |
| DataType tmp_data_type = is_data_type_quantized_asymmetric(tmp_input->data_type()) ? DataType::F32 : tmp_input->data_type(); |
| TensorInfo tensor_info_tmp(input_info.clone()->set_data_type(tmp_data_type)); |
| TensorInfo max_info(tmp_input->clone()->set_tensor_shape(max_sum_shape)); |
| |
| // Init intermediate tensors |
| _max = std::make_unique<TensorInfo>(max_info); |
| _tmp = std::make_unique<TensorInfo>(tensor_info_tmp); |
| |
| // Configure kernels |
| auto mk = std::make_unique<kernels::CpuLogits1DMaxKernel>(); |
| mk->configure(tmp_input, _max.get()); |
| _max_kernel = std::move(mk); |
| |
| auto sm = std::make_unique<kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>(); |
| if(_needs_permute) |
| { |
| _output_permuted = std::make_unique<TensorInfo>(); |
| |
| // The normalization kernel stores the result in a permuted output tensor |
| sm->configure(tmp_input, _max.get(), _output_permuted.get(), beta, _tmp.get()); |
| |
| // Re-permute the permuted output into the requested (4D) output |
| _permute_output.configure(_output_permuted.get(), dst, softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| } |
| else |
| { |
| // Softmax 2D case |
| sm->configure(tmp_input, _max.get(), dst, beta, _tmp.get()); |
| } |
| _softmax_kernel = std::move(sm); |
| } |
| |
| template <bool IS_LOG> |
| Status CpuSoftmaxGeneric<IS_LOG>::validate(const ITensorInfo *src, const ITensorInfo *dst, float beta, int32_t axis) |
| { |
| // Perform validation step |
| ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_RETURN_ERROR_ON_MSG(src->num_dimensions() > 4, "Only up to 4 dimensions are supported"); |
| ARM_COMPUTE_UNUSED(beta); |
| ARM_COMPUTE_RETURN_ERROR_ON(axis < static_cast<int32_t>(-src->num_dimensions()) || static_cast<int32_t>(src->num_dimensions()) <= axis); |
| |
| // Create intermediate tensor info |
| DataType tmp_data_type = src->data_type(); |
| const TensorInfo tensor_info_tmp(src->clone()->set_data_type(tmp_data_type).set_is_resizable(true)); |
| |
| TensorShape max_sum_shape = src->tensor_shape(); |
| max_sum_shape.set(0, 1); |
| const TensorInfo tensor_info_max_sum(src->clone()->set_tensor_shape(max_sum_shape).set_data_type(tmp_data_type).set_quantization_info(src->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>(src->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(*src, permutation_vector); |
| TensorInfo input_permuted(src->clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(src, &input_permuted, permutation_vector)); |
| TensorInfo output_permuted(dst->clone()->set_tensor_shape(permuted_shape)); |
| ARM_COMPUTE_RETURN_ON_ERROR(CpuPermute::validate(&output_permuted, dst, permutation_vector)); |
| } |
| |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DMaxKernel::validate(src, &tensor_info_max_sum)); |
| ARM_COMPUTE_RETURN_ON_ERROR(kernels::CpuLogits1DSoftmaxKernel<IS_LOG>::validate(&tensor_info_tmp, &tensor_info_max_sum, dst, beta, &dont_care)); |
| |
| return Status{}; |
| } |
| |
| template <bool IS_LOG> |
| void CpuSoftmaxGeneric<IS_LOG>::run(ITensorPack &tensors) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided"); |
| |
| ITensorPack max_pack; |
| ITensorPack softmax_pack; |
| |
| if(_needs_permute) |
| { |
| ITensorPack permute_in_pack; |
| permute_in_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC)); |
| permute_in_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_2)); |
| _permute_input.run(permute_in_pack); |
| |
| max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_2)); |
| |
| softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_tensor(ACL_INT_2)); |
| softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1)); |
| softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_INT_3)); |
| softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0)); |
| } |
| else |
| { |
| max_pack.add_tensor(TensorType::ACL_SRC, tensors.get_const_tensor(ACL_SRC)); |
| softmax_pack.add_tensor(TensorType::ACL_SRC_0, tensors.get_const_tensor(ACL_SRC)); |
| softmax_pack.add_tensor(TensorType::ACL_SRC_1, tensors.get_tensor(ACL_INT_1)); |
| softmax_pack.add_tensor(TensorType::ACL_DST_0, tensors.get_tensor(ACL_DST)); |
| softmax_pack.add_tensor(TensorType::ACL_DST_1, tensors.get_tensor(ACL_INT_0)); |
| } |
| |
| max_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_INT_1)); |
| |
| NEScheduler::get().schedule_op(_max_kernel.get(), Window::DimY, _max_kernel->window(), max_pack); |
| NEScheduler::get().schedule_op(_softmax_kernel.get(), Window::DimY, _softmax_kernel->window(), softmax_pack); |
| |
| if(_needs_permute) |
| { |
| ITensorPack permute_out_pack; |
| permute_out_pack.add_tensor(TensorType::ACL_SRC, tensors.get_tensor(ACL_INT_3)); |
| permute_out_pack.add_tensor(TensorType::ACL_DST, tensors.get_tensor(ACL_DST)); |
| _permute_output.run(permute_out_pack); |
| } |
| } |
| |
| template <bool IS_LOG> |
| experimental::MemoryRequirements CpuSoftmaxGeneric<IS_LOG>::workspace() const |
| { |
| experimental::MemoryRequirements req{}; |
| |
| req.push_back({ TensorType::ACL_INT_0, _tmp->total_size(), 0 }); |
| req.push_back({ TensorType::ACL_INT_1, _max->total_size(), 0 }); |
| |
| if(_needs_permute) |
| { |
| req.push_back({ TensorType::ACL_INT_2, _input_permuted->total_size(), 0 }); |
| req.push_back({ TensorType::ACL_INT_3, _output_permuted->total_size(), 0 }); |
| } |
| |
| return req; |
| } |
| |
| template class CpuSoftmaxGeneric<false>; |
| template class CpuSoftmaxGeneric<true>; |
| } // namespace cpu |
| } // namespace arm_compute |