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// Copyright (c) 2020, ARM Limited.
//
// 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 "scatter_gather.h"
#include "quant_util.h"
using namespace TosaReference;
using namespace Eigen;
using namespace tosa;
template <int InRank, int IndexRank, DType Dtype>
OpGather<InRank, IndexRank, Dtype>::OpGather(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_)
: GraphNode(Op_GATHER, id_)
{
setRequiredOperands(2, 1);
setRequiredRank(1, 6);
INIT_ATTRIBUTE(Axis);
}
template <int InRank, int IndexRank, DType Dtype>
OpGather<InRank, IndexRank, Dtype>::~OpGather()
{
if (attribute)
delete attribute;
}
template <int InRank, int IndexRank, DType Dtype>
int OpGather<InRank, IndexRank, Dtype>::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0]))
{
return 1;
}
// output and input must be the same types
if (inputs[0]->matchType(*outputs[0]))
{
printNodeValidationError("Failure to match input and output type");
return 1;
}
in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
index = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
ASSERT_MEM(in && index && out);
return 0;
}
template <int InRank, int IndexRank, DType Dtype>
int OpGather<InRank, IndexRank, Dtype>::eval()
{
int axis = attribute->axis();
// calculate size left and right to axis
int left_size = 1;
for (int i = 0; i < axis; ++i)
{
left_size *= in->getShape()[i];
}
int right_size = 1;
for (int i = axis + 1; i < in->getRank(); ++i)
{
right_size *= in->getShape()[i];
}
InEigenType* input_data = in->getTensor().data();
int32_t* index_data = index->getTensor().data();
OutEigenType* output_data = out->getTensor().data();
int32_t axis_size = in->getShape()[axis];
int32_t index_count = index->getElementCount();
// sanity check if index is valid
// need to check until this point since index is not known until runtime
for (size_t i = 0; i < index->getElementCount(); i++)
{
if (index_data[i] >= axis_size)
{
FATAL_ERROR_NODE("OpGather: index[%lu]=%i can't exceed axis_size=%i", i, index_data[i], axis_size);
}
}
// Eigen stores tensor in column-major
// so we iterate through dimension right to axis and the index array
// do memory copy with size of left size each time
for (int right = 0; right < right_size; ++right)
{
for (int i = 0; i < index_count; ++i)
{
std::memcpy(output_data + (right * index_count + i) * left_size,
input_data + (right * axis_size + index_data[i]) * left_size, sizeof(InEigenType) * left_size);
}
}
return GraphNode::eval();
}
// template explicit instantiation
DEF_INSTANTIATE_GATHER(OpGather, AINT8);
DEF_INSTANTIATE_GATHER(OpGather, INT16);
DEF_INSTANTIATE_GATHER(OpGather, INT32);
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