| /* |
| * Copyright (c) 2021, 2023 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/cpu/operators/CpuSoftmax.h" |
| |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/TensorInfo.h" |
| #include "arm_compute/core/utils/misc/ShapeCalculator.h" |
| #include "arm_compute/core/Validate.h" |
| #include "arm_compute/runtime/NEON/NEScheduler.h" |
| |
| #include "src/common/utils/Log.h" |
| #include "src/core/helpers/MemoryHelpers.h" |
| #include "src/core/helpers/SoftmaxHelpers.h" |
| #include "src/cpu/kernels/CpuSoftmaxKernel.h" |
| #include "src/cpu/utils/CpuAuxTensorHandler.h" |
| |
| using namespace arm_compute::experimental; |
| |
| namespace arm_compute |
| { |
| namespace cpu |
| { |
| CpuSoftmaxGeneric::CpuSoftmaxGeneric() |
| : _permute_input(), |
| _permute_output(), |
| _softmax_kernel(), |
| _tmp(), |
| _input_permuted(), |
| _output_permuted(), |
| _needs_permute(false), |
| _aux_mem(InternalTensorIdx::COUNT) |
| { |
| } |
| |
| void CpuSoftmaxGeneric::configure(const ITensorInfo *src, ITensorInfo *dst, float beta, int32_t axis, bool is_log) |
| { |
| // Perform validation step |
| ARM_COMPUTE_ERROR_ON_NULLPTR(src, dst); |
| ARM_COMPUTE_ERROR_THROW_ON(CpuSoftmaxGeneric::validate(src, dst, beta, axis)); |
| ARM_COMPUTE_LOG_PARAMS(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) |
| { |
| _permute_input.configure(src, &_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) |
| const ITensorInfo *tmp_input = (_needs_permute ? &_input_permuted : src); |
| |
| TensorInfo tensor_info_tmp; |
| if (is_data_type_quantized_asymmetric(src->data_type())) |
| { |
| // Create intermediate tensors shapes |
| const TensorInfo input_info = tmp_input->clone()->reset_padding().set_is_resizable(true); |
| tensor_info_tmp = input_info.clone()->set_data_type(DataType::F32); |
| } |
| |
| // Init intermediate tensors |
| _tmp = TensorInfo(tensor_info_tmp); |
| |
| // Configure kernels |
| auto sm = std::make_unique<kernels::CpuSoftmaxKernel>(); |
| if (_needs_permute) |
| { |
| // The normalization kernel stores the result in a permuted output tensor |
| sm->configure(tmp_input, &_output_permuted, beta, is_log, &_tmp); |
| |
| // Re-permute the permuted output into the requested (4D) output |
| _permute_output.configure(&_output_permuted, dst, |
| softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis)); |
| } |
| else |
| { |
| // Softmax 2D case |
| sm->configure(tmp_input, dst, beta, is_log, &_tmp); |
| } |
| _softmax_kernel = std::move(sm); |
| |
| if (_tmp.total_size() > 0) |
| { |
| _aux_mem[InternalTensorIdx::TMP] = |
| MemoryInfo(offset_int_vec(InternalTensorIdx::TMP), MemoryLifetime::Temporary, _tmp.total_size()); |
| } |
| |
| _aux_mem[InternalTensorIdx::PERMUTED_SRC] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), |
| MemoryLifetime::Temporary, _input_permuted.total_size()); |
| _aux_mem[InternalTensorIdx::PERMUTED_DST] = MemoryInfo(offset_int_vec(InternalTensorIdx::PERMUTED_DST), |
| MemoryLifetime::Temporary, _output_permuted.total_size()); |
| } |
| |
| Status |
| CpuSoftmaxGeneric::validate(const ITensorInfo *src, const ITensorInfo *dst, float beta, int32_t axis, bool is_log) |
| { |
| // 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 |
| TensorInfo tensor_info_tmp; |
| |
| if (is_data_type_quantized_asymmetric(src->data_type())) |
| { |
| tensor_info_tmp = src->clone()->set_data_type(DataType::F32).set_is_resizable(true); |
| } |
| |
| 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::CpuSoftmaxKernel::validate(src, dst, beta, is_log, &tensor_info_tmp)); |
| |
| return Status{}; |
| } |
| |
| void CpuSoftmaxGeneric::run(ITensorPack &tensors) |
| { |
| ARM_COMPUTE_ERROR_ON_MSG(tensors.empty(), "No inputs provided"); |
| |
| auto src = tensors.get_const_tensor(TensorType::ACL_SRC); |
| auto dst = tensors.get_tensor(TensorType::ACL_DST); |
| |
| CpuAuxTensorHandler tmp(offset_int_vec(InternalTensorIdx::TMP), _tmp, tensors, true); |
| |
| CpuAuxTensorHandler input_permuted(offset_int_vec(InternalTensorIdx::PERMUTED_SRC), _input_permuted, tensors, true); |
| CpuAuxTensorHandler output_permuted(offset_int_vec(InternalTensorIdx::PERMUTED_DST), _output_permuted, tensors, |
| true); |
| |
| ITensorPack softmax_pack; |
| |
| if (_needs_permute) |
| { |
| ITensorPack permute_in_pack = {{TensorType::ACL_SRC, src}, {TensorType::ACL_DST, input_permuted.get()}}; |
| _permute_input.run(permute_in_pack); |
| |
| softmax_pack = {{TensorType::ACL_SRC_0, input_permuted.get()}, |
| {TensorType::ACL_DST_0, output_permuted.get()}, |
| {TensorType::ACL_DST_1, tmp.get()}}; |
| } |
| else |
| { |
| softmax_pack = {{TensorType::ACL_SRC_0, src}, {TensorType::ACL_DST_0, dst}, {TensorType::ACL_DST_1, tmp.get()}}; |
| } |
| |
| 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, output_permuted.get()); |
| permute_out_pack.add_tensor(TensorType::ACL_DST, dst); |
| _permute_output.run(permute_out_pack); |
| } |
| } |
| |
| experimental::MemoryRequirements CpuSoftmaxGeneric::workspace() const |
| { |
| return _aux_mem; |
| } |
| |
| } // namespace cpu |
| } // namespace arm_compute |