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/*
* Copyright (c) 2017-2021 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 "arm_compute/runtime/NEON/functions/NESoftmaxLayer.h"
#include "arm_compute/core/Validate.h"
#include "arm_compute/runtime/Tensor.h"
#include "src/core/cpu/kernels/CpuSoftmaxKernel.h"
#include "src/core/helpers/SoftmaxHelpers.h"
#include "src/runtime/cpu/operators/CpuSoftmax.h"
namespace arm_compute
{
template <bool IS_LOG>
struct NESoftmaxLayerGeneric<IS_LOG>::Impl
{
const ITensor *src{ nullptr };
ITensor *dst{ nullptr };
Tensor max{ nullptr };
Tensor tmp{ nullptr };
Tensor input_permuted{ nullptr };
Tensor output_permuted{ nullptr };
std::unique_ptr<cpu::CpuSoftmaxGeneric<IS_LOG>> op{ nullptr };
};
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _impl(std::make_unique<Impl>())
{
}
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::NESoftmaxLayerGeneric(NESoftmaxLayerGeneric &&) = default;
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG> &NESoftmaxLayerGeneric<IS_LOG>::operator=(NESoftmaxLayerGeneric &&) = default;
template <bool IS_LOG>
NESoftmaxLayerGeneric<IS_LOG>::~NESoftmaxLayerGeneric() = default;
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::configure(ITensor *input, ITensor *output, float beta, int32_t axis)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
_impl->src = input;
_impl->dst = output;
_impl->op = std::make_unique<cpu::CpuSoftmaxGeneric<IS_LOG>>();
_impl->op->configure(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())));
const bool needs_permute = actual_axis > 0;
if(needs_permute)
{
// Add to the memory manager _input_permuted
auto permute_input = std::make_unique<cpu::CpuPermute>();
_memory_group.manage(&_impl->input_permuted);
permute_input->configure(input->info(), _impl->input_permuted.info(), 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 ? &_impl->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);
_impl->max.allocator()->init(input_info.clone()->set_tensor_shape(max_sum_shape));
_impl->tmp.allocator()->init(tensor_info_tmp);
// Manage intermediate buffers
_memory_group.manage(&_impl->max);
_memory_group.manage(&_impl->tmp);
// Configure kernels
auto max_kernel = std::make_unique<cpu::kernels::CpuLogits1DMaxKernel>();
auto softmax_kernel = std::make_unique<cpu::kernels::CpuLogits1DSoftmaxKernel<IS_LOG>>();
max_kernel->configure(tmp_input->info(), _impl->max.info());
if(needs_permute)
{
auto permute_output = std::make_unique<cpu::CpuPermute>();
// Add to the memory manager _output_permuted
_memory_group.manage(&_impl->output_permuted);
// The normalization kernel stores the result in a permuted output tensor
softmax_kernel->configure(tmp_input->info(), _impl->max.info(), _impl->output_permuted.info(), beta, _impl->tmp.info());
_impl->input_permuted.allocator()->allocate();
// Re-permute the permuted output into the requested (4D) output
permute_output->configure(_impl->output_permuted.info(), output->info(), softmax_helpers::get_permutation_vector_from_softmax_axis(actual_axis));
// Allocate the intermediate permuted tensors
_impl->output_permuted.allocator()->allocate();
}
else
{
softmax_kernel->configure(tmp_input->info(), _impl->max.info(), output->info(), beta, _impl->tmp.info());
}
// Allocate intermediate buffers
_impl->max.allocator()->allocate();
_impl->tmp.allocator()->allocate();
}
template <bool IS_LOG>
Status NESoftmaxLayerGeneric<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_ON_ERROR(cpu::CpuSoftmaxGeneric<IS_LOG>::validate(input, output, beta, axis));
return Status{};
}
template <bool IS_LOG>
void NESoftmaxLayerGeneric<IS_LOG>::run()
{
MemoryGroupResourceScope scope_mg(_memory_group);
ITensorPack pack;
pack.add_tensor(TensorType::ACL_SRC, _impl->src);
pack.add_tensor(TensorType::ACL_DST, _impl->dst);
pack.add_tensor(TensorType::ACL_INT_0, &_impl->tmp);
pack.add_tensor(TensorType::ACL_INT_1, &_impl->max);
pack.add_tensor(TensorType::ACL_INT_2, &_impl->input_permuted);
pack.add_tensor(TensorType::ACL_INT_3, &_impl->output_permuted);
_impl->op->run(pack);
}
template class NESoftmaxLayerGeneric<false>;
template class NESoftmaxLayerGeneric<true>;
} // namespace arm_compute
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