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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 <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>
namespace
{
using namespace locomotiv;
void execute_node(loco::TensorBroadcast *tensor_broadcast)
{
auto input_data = annot_data(tensor_broadcast->input());
if (input_data == nullptr)
{
throw std::runtime_error("Annotation is required for TensorBroadcast input");
}
// Calculate output shape
Shape input_shape = *(input_data->shape());
// TODO Reuse "ShapeInferenceService"
Shape output_shape;
output_shape.resize(input_shape.rank());
for (uint32_t axis = 0; axis < input_shape.rank(); ++axis)
{
if (tensor_broadcast->mapping()->defined(axis))
{
assert(input_shape.dim(axis) == 1); // Required by TensorBroadcast definition
output_shape.dim(axis) = tensor_broadcast->mapping()->dim(axis).value();
}
else
{
output_shape.dim(axis) = input_shape.dim(axis);
}
}
assert(input_shape.rank() == output_shape.rank());
uint32_t const rank = input_shape.rank();
std::unique_ptr<NodeData> output_data = nullptr;
switch (input_data->dtype())
{
// TODO Use type-generic implementation!
case loco::DataType::FLOAT32:
{
auto input_bufptr = input_data->as_f32_bufptr();
auto output_buf = make_buffer<float, LexicalLayout>(output_shape);
for (IndexEnumerator e{output_shape}; e.valid(); e.advance())
{
auto input_index = e.current();
const auto &output_index = e.current();
for (uint32_t axis = 0; axis < rank; ++axis)
{
if (tensor_broadcast->mapping()->defined(axis))
{
input_index.at(axis) = 0;
}
}
output_buf.at(output_index) = input_bufptr->at(input_index);
}
output_data = make_data(output_buf);
break;
}
default:
throw std::runtime_error("Not yet supported");
}
assert(output_data != nullptr);
annot_data(tensor_broadcast, std::move(output_data));
annot_domain(tensor_broadcast, loco::Domain::Tensor);
}
} // namespace
namespace locomotiv
{
void NodeExecution::execute(loco::TensorBroadcast *tensor_broadcast)
{
execute_node(tensor_broadcast);
}
} // namespace locomotiv
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