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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 "reduction.h"
#include "quant_util.h"
using namespace TosaReference;
using namespace Eigen;
using namespace tosa;
template <int Rank, DType Dtype>
ReduceNode<Rank, Dtype>::ReduceNode(const Op& op_, TosaAttributeBase* attribute_, uint64_t id_)
: GraphNode(op_, id_)
{
setRequiredOperands(1, 1);
setRequiredRank(0, 4);
INIT_ATTRIBUTE(Axis);
}
template <int Rank, DType Dtype>
ReduceNode<Rank, Dtype>::~ReduceNode()
{
if (attribute)
delete attribute;
}
template <int Rank, DType Dtype>
int ReduceNode<Rank, Dtype>::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (validateRequiredRank(inputs[0]) || validateRequiredRank(outputs[0]))
{
return 1;
}
if (attribute->axis() < 0 || attribute->axis() >= inputs[0]->getRank())
{
printNodeValidationError("Reduce axis must between [0, input_rank - 1]");
return 1;
}
if (inputs[0]->matchRank(*outputs[0]))
{
printNodeValidationError("Input and output tensor ranks must match");
return 1;
}
in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
ASSERT_MEM(in && out);
dims[0] = this->attribute->axis();
return 0;
}
template <int Rank, DType Dtype>
int OpReduceAll<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().all(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
template <int Rank, DType Dtype>
int OpReduceAny<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().any(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
template <int Rank, DType Dtype>
int OpReduceMax<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().maximum(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
template <int Rank, DType Dtype>
int OpReduceMin<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().minimum(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
template <int Rank, DType Dtype>
int OpReduceProduct<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().prod(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
template <int Rank, DType Dtype>
int OpReduceSum<Rank, Dtype>::eval()
{
this->out->getTensor() = this->in->getTensor().sum(this->dims).reshape(this->out->getTensor().dimensions());
return GraphNode::eval();
}
// template explicit instantiation
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceAll, BOOL);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceAny, BOOL);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, FLOAT);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, AINT8);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, INT16);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMax, INT32);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, FLOAT);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, AINT8);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, INT16);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceMin, INT32);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceProduct, FLOAT);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceSum, FLOAT);
DEF_INSTANTIATE_RANK1_6_ONE_RANK_ONE_TYPE(OpReduceSum, INT32);
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