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/*
* Copyright (c) 2019 Samsung Electronics Co., Ltd. All Rights Reserved
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "NodeExecution.h"
#include "NodeDataImpl.h"
#include "NodeDomain.h"
#include "Validation.h"
#include <nncc/core/ADT/tensor/Shape.h>
#include <nncc/core/ADT/tensor/Buffer.h>
#include <nncc/core/ADT/tensor/Index.h>
#include <nncc/core/ADT/tensor/IndexEnumerator.h>
#include <nncc/core/ADT/tensor/LexicalLayout.h>
using nncc::core::ADT::tensor::Index;
using nncc::core::ADT::tensor::IndexEnumerator;
using nncc::core::ADT::tensor::LexicalLayout;
using nncc::core::ADT::tensor::make_buffer;
using nncc::core::ADT::tensor::Shape;
#include <cassert>
#include <stdexcept>
#include <cmath>
namespace
{
Index reduce_index(const Index &index, uint32_t axis)
{
Index r_index;
r_index.resize(index.rank());
for (uint32_t i = 0; i < index.rank(); ++i)
r_index.at(i) = index.at(i);
r_index.at(axis) = 0;
return r_index;
}
Shape reduce_shape(const Shape &shape, uint32_t axis)
{
Shape r_shape;
r_shape.resize(shape.rank());
for (uint32_t i = 0; i < shape.rank(); ++i)
r_shape.dim(i) = shape.dim(i);
r_shape.dim(axis) = 1;
return r_shape;
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::TensorSoftmax *softmax)
{
auto input_data = annot_data(softmax->input());
validate(input_data, "Input not ready");
validate(annot_domain(softmax->input()) == loco::Domain::Tensor,
"Input domain of TensorSoftmax is not Tensor");
std::unique_ptr<NodeData> softmax_data = nullptr;
switch (input_data->dtype())
{
case loco::DataType::FLOAT32:
{
auto axis = softmax->axis();
auto *input_shape = input_data->shape();
auto input_bufptr = input_data->as_f32_bufptr();
auto softmax_buf = make_buffer<float, LexicalLayout>(*input_data->shape());
auto reduce_sum_shape = reduce_shape(*input_shape, axis);
auto reduce_sum_bufptr = make_buffer<float, LexicalLayout>(reduce_sum_shape);
for (IndexEnumerator e{*input_shape}; e.valid(); e.advance())
{
const auto &index = e.current();
const auto r_index = reduce_index(index, axis);
reduce_sum_bufptr.at(r_index) += exp(input_bufptr->at(index));
}
for (IndexEnumerator e{*input_shape}; e.valid(); e.advance())
{
const auto &index = e.current();
const auto r_index = reduce_index(index, axis);
softmax_buf.at(index) = exp(input_bufptr->at(index)) / reduce_sum_bufptr.at(r_index);
}
softmax_data = make_data(softmax_buf);
break;
}
default:
throw std::runtime_error("NYI for this DataType");
}
assert(softmax_data != nullptr);
annot_data(softmax, std::move(softmax_data));
annot_domain(softmax, annot_domain(softmax->input()));
}
} // namespace locomotiv
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