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/*
* 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
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