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// Copyright (c) 2020-2022, 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 <DType Dtype>
OpGather<Dtype>::OpGather(SubgraphTraverser* sgt_,
TosaAttributeBase* attribute_,
uint64_t id_)
: GraphNode(sgt_, Op_GATHER, id_)
{
setRequiredOperands(2, 1);
}
template <DType Dtype>
OpGather<Dtype>::~OpGather()
{}
template <DType Dtype>
int OpGather<Dtype>::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (inputs[0]->getRank() != 3)
{
printNodeValidationError("OpGather: values needs to be rank 3 tensor");
return 1;
}
if (inputs[1]->getRank() != 2)
{
printNodeValidationError("OpGather: indices needs to be rank 2 tensor");
return 1;
}
if (outputs[0]->getRank() != 3)
{
printNodeValidationError("OpGather: output needs to be rank 3 tensor");
return 1;
}
K = inputs[0]->getShape()[1];
N = outputs[0]->getShape()[0];
W = outputs[0]->getShape()[1];
C = outputs[0]->getShape()[2];
if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0])
{
printNodeValidationError("OpGather: dimension N mismatch");
return 1;
}
if (W != inputs[1]->getShape()[1])
{
printNodeValidationError("OpGather: dimension W mismatch");
return 1;
}
if (C != inputs[0]->getShape()[2])
{
printNodeValidationError("OpGather: dimension C mismatch");
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;
}
values = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]);
indices = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
output = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]);
ASSERT_MEM(values && indices && output);
return 0;
}
template <DType Dtype>
int OpGather<Dtype>::eval()
{
for (int32_t n = 0; n < N; n++)
{
for (int32_t w = 0; w < W; w++)
{
int32_t k = this->indices->getTensor()(n, w);
REQUIRE(k >= 0 && k < K, "OpGather: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K);
for (int32_t c = 0; c < C; c++)
{
EigenType value = this->values->getTensor()(n, k, c);
this->output->getTensor()(n, w, c) = value;
}
}
}
return GraphNode::eval();
}
template <DType Dtype>
OpScatter<Dtype>::OpScatter(SubgraphTraverser* sgt_,
TosaAttributeBase* attribute_,
uint64_t id_)
: GraphNode(sgt_, Op_SCATTER, id_)
{
setRequiredOperands(3, 1);
}
template <DType Dtype>
OpScatter<Dtype>::~OpScatter()
{}
template <DType Dtype>
int OpScatter<Dtype>::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (inputs[0]->getRank() != 3)
{
printNodeValidationError("OpGather: values_in needs to be rank 3 tensor");
return 1;
}
if (inputs[1]->getRank() != 2)
{
printNodeValidationError("OpGather: indices needs to be rank 2 tensor");
return 1;
}
if (inputs[2]->getRank() != 3)
{
printNodeValidationError("OpGather: input needs to be rank 3 tensor");
return 1;
}
if (outputs[0]->getRank() != 3)
{
printNodeValidationError("OpGather: values_out needs to be rank 3 tensor");
return 1;
}
W = inputs[2]->getShape()[1];
N = outputs[0]->getShape()[0];
K = outputs[0]->getShape()[1];
C = outputs[0]->getShape()[2];
if (N != inputs[0]->getShape()[0] || N != inputs[1]->getShape()[0] || N != inputs[2]->getShape()[0])
{
printNodeValidationError("OpScatter: dimension N mismatch");
return 1;
}
if (W != inputs[1]->getShape()[1])
{
printNodeValidationError("OpGather: dimension W mismatch");
return 1;
}
if (C != inputs[0]->getShape()[2] || C != inputs[2]->getShape()[2])
{
printNodeValidationError("OpGather: dimension C mismatch");
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;
}
values_in = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[0]);
indices = dynamic_cast<TosaReference::TensorTemplate<TIndex>*>(inputs[1]);
input = dynamic_cast<TosaReference::TensorTemplate<TValue>*>(inputs[2]);
values_out = dynamic_cast<TosaReference::TensorTemplate<TOutput>*>(outputs[0]);
ASSERT_MEM(values_in && indices && input && values_out);
return 0;
}
template <DType Dtype>
int OpScatter<Dtype>::eval()
{
// Initializes the output tensor with the input value for values that are unchanged by the scatter operation.
this->values_out->getTensor() = this->values_in->getTensor();
for (int n = 0; n < N; n++)
{
for (int w = 0; w < W; w++)
{
int32_t k = this->indices->getTensor()(n, w);
REQUIRE(k >= 0 && k < K, "OpScatter: index(%d, %d)=%d exceed valid range [0, %d]", n, w, k, K);
for (int c = 0; c < C; c++)
{
EigenType value = this->input->getTensor()(n, w, c);
this->values_out->getTensor()(n, k, c) = value;
}
}
}
return GraphNode::eval();
}
// template explicit instantiation
DEF_INSTANTIATE_ONE_TYPE(OpGather, INT8);
DEF_INSTANTIATE_ONE_TYPE(OpGather, INT16);
DEF_INSTANTIATE_ONE_TYPE(OpGather, INT32);
DEF_INSTANTIATE_ONE_TYPE(OpGather, FP16);
DEF_INSTANTIATE_ONE_TYPE(OpGather, FLOAT);
DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT8);
DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT16);
DEF_INSTANTIATE_ONE_TYPE(OpScatter, INT32);
DEF_INSTANTIATE_ONE_TYPE(OpScatter, FP16);
DEF_INSTANTIATE_ONE_TYPE(OpScatter, FLOAT);
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