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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>
namespace locomotiv
{
void NodeExecution::execute(loco::TensorConcat *tensor_concat)
{
auto lhs_data = annot_data(tensor_concat->lhs());
auto rhs_data = annot_data(tensor_concat->rhs());
auto axis = tensor_concat->axis();
validate(lhs_data && rhs_data, "Ingredient not ready");
validate(lhs_data->dtype() == rhs_data->dtype(), "lhs and rhs of Concat should have same dtype");
validate(annot_domain(tensor_concat->lhs()) == loco::Domain::Tensor &&
annot_domain(tensor_concat->rhs()) == loco::Domain::Tensor,
"Some ingredients of TensorConcat is not Tensor");
// Calculate output shape
Shape lhs_shape = *lhs_data->shape();
Shape rhs_shape = *rhs_data->shape();
Shape concat_shape;
assert(lhs_shape.rank() == rhs_shape.rank());
concat_shape.resize(lhs_shape.rank());
for (uint32_t index = 0; index < lhs_shape.rank(); ++index)
{
if (index == axis)
concat_shape.dim(index) = lhs_shape.dim(index) + rhs_shape.dim(index);
else
{
assert(lhs_shape.dim(index) == rhs_shape.dim(index));
concat_shape.dim(index) = lhs_shape.dim(index);
}
}
auto left_dim_size = lhs_shape.dim(axis);
// Copy data from two inputs LHS and RHS to Concat
std::unique_ptr<NodeData> concat_data = nullptr;
switch (lhs_data->dtype())
{
case loco::DataType::FLOAT32:
{
auto lhs_bufptr = lhs_data->as_f32_bufptr();
auto rhs_bufptr = rhs_data->as_f32_bufptr();
auto concat_buf = make_buffer<float, LexicalLayout>(concat_shape);
for (IndexEnumerator e{concat_shape}; e.valid(); e.advance())
{
const auto &e_index = e.current();
if (e_index.at(axis) < left_dim_size)
{
// Left index is same as output index
concat_buf.at(e_index) = lhs_bufptr->at(e_index);
}
else
{
// Adjust right index to valid range
Index r_index = e_index;
r_index.at(axis) -= left_dim_size;
concat_buf.at(e_index) = rhs_bufptr->at(r_index);
}
}
concat_data = make_data(concat_buf);
break;
}
default:
throw std::runtime_error("NYI for this DataType");
}
assert(concat_data != nullptr);
erase_annot_data(tensor_concat);
annot_data(tensor_concat, std::move(concat_data));
annot_domain(tensor_concat, loco::Domain::Tensor);
}
} // namespace locomotiv
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