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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;
using nncc::core::ADT::tensor::Buffer;
#include <cassert>
#include <stdexcept>
namespace
{
Index reduced_index(const Index &index, const loco::TensorAxisSet &axes)
{
Index r_index;
r_index.resize(index.rank());
for (uint32_t i = 0; i < index.rank(); ++i)
r_index.at(i) = (axes.defined(i)) ? 0 : index.at(i);
return r_index;
}
Shape reduced_shape(const Shape &shape, const loco::TensorAxisSet &axes)
{
Shape r_shape;
r_shape.resize(shape.rank());
for (uint32_t i = 0; i < shape.rank(); ++i)
r_shape.dim(i) = (axes.defined(i)) ? 1 : shape.dim(i);
return r_shape;
}
} // namespace
namespace
{
template <typename T, loco::ReduceFunc F> struct ReduceFunction
{
static void apply(Buffer<T> &lhs, const Buffer<T> &rhs, const loco::TensorAxisSet &axes)
{
throw std::runtime_error("Not supported ReduceFunc type");
}
};
template <typename T> struct ReduceFunction<T, loco::ReduceFunc::Mean>
{
static void apply(Buffer<T> &lhs, const Buffer<T> &rhs, const loco::TensorAxisSet &axes)
{
for (IndexEnumerator e{rhs.shape()}; e.valid(); e.advance())
{
const auto &index = e.current();
const auto r_index = reduced_index(index, axes);
lhs.at(r_index) += rhs.at(index);
}
uint32_t r_cnt = 1;
for (uint32_t i = 0; i < rhs.shape().rank(); ++i)
if (axes.defined(i))
r_cnt *= rhs.shape().dim(i);
for (IndexEnumerator e{lhs.shape()}; e.valid(); e.advance())
{
const auto &index = e.current();
lhs.at(index) /= static_cast<T>(r_cnt);
}
}
};
template <typename T>
void apply(Buffer<T> &lhs, const Buffer<T> &rhs, const loco::TensorReduce &node)
{
switch (node.func())
{
case loco::ReduceFunc::Mean:
ReduceFunction<T, loco::ReduceFunc::Mean>::apply(lhs, rhs, *node.axes());
break;
// TODO Support more ReduceFunc type
default:
break;
}
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::TensorReduce *node)
{
auto input_data = annot_data(node->input());
auto input_shape = input_data->shape();
validate(input_data, "Input not ready");
validate(annot_domain(node->input()) == loco::Domain::Tensor,
"Input domain of TensorReduce is not Tensor");
std::unique_ptr<NodeData> reduce_data = nullptr;
Shape r_shape = reduced_shape(*input_shape, *node->axes());
switch (input_data->dtype())
{
case loco::DataType::FLOAT32:
{
auto input_bufptr = input_data->as_f32_bufptr();
auto reduce_buf = make_buffer<float, LexicalLayout>(r_shape);
apply(reduce_buf, *input_bufptr, *node);
reduce_data = make_data(reduce_buf);
break;
}
default:
throw std::runtime_error("NYI for this DataType");
}
assert(reduce_data != nullptr);
annot_data(node, std::move(reduce_data));
annot_domain(node, annot_domain(node->input()));
}
} // namespace locomotiv
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