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// Copyright (c) 2023-2024, 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 "shape.h"
using namespace TosaReference;
using namespace Eigen;
using namespace tosa;
OpConstShape::OpConstShape(SubgraphTraverser* sgt_, uint64_t id_)
: GraphNode(sgt_, Op_CONST, id_)
{
setRequiredOperands(0, 1);
}
OpConstShape::~OpConstShape()
{}
int OpConstShape::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
return 0;
}
int OpConstShape::eval()
{
// set the shapeValue given the actual tensor value
using EigenType = typename GetEigenType<TOSA_REF_TYPE_SHAPE>::type;
auto out = dynamic_cast<TosaReference::TensorTemplate<Eigen::Tensor<EigenType, 1>>*>(this->getOutputs()[0]);
std::vector<int> shapeValue;
for (int i = 0; out != nullptr && i < out->getTensor().size(); ++i)
{
shapeValue.push_back(out->getTensor()(i));
}
this->getOutputs()[0]->setShapeValue(shapeValue);
for (auto ct : getOutputs())
{
if (!ct->getIsValid())
{
std::string err = "Constant Shape tensor " + ct->getName() + " not correctly initialized";
printNodeValidationError(err.c_str());
return 1;
}
}
// Evaluation is trivial for constants
return GraphNode::eval();
}
OpConcatShape::OpConcatShape(SubgraphTraverser* sgt_, uint64_t id_)
: GraphNode(sgt_, Op_CONCAT_SHAPE, id_)
{
setRequiredOperands(-1, 1);
setRequiredRank(1, 1);
}
OpConcatShape::~OpConcatShape()
{}
int OpConcatShape::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (inputs.empty())
{
printNodeValidationError("ConcatShape operator must have at least one input tensor");
return 1;
}
int32_t num_inputs = inputs.size();
int32_t elements_count = 0;
for (int32_t i = 0; i < num_inputs; i++)
{
if (validateRequiredRank(inputs[i]))
return 1;
ins.push_back(dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[i]));
elements_count += inputs[i]->getShape()[0];
}
ERROR_IF(elements_count != outputs[0]->getShape()[0],
"OpConcatShape: sum of input elements not equal to output number of elements");
num_dims = outputs[0]->getShape()[0];
out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
return 0;
}
int OpConcatShape::eval()
{
ETensor1<EigenType> out_tensor(num_dims);
int32_t out_idx = 0;
for (size_t i = 0; i < ins.size(); i++)
{
// all tosa.shape values are 1-d tensors
// interate in_idx in range of [0, rank of 1-d tensor]
for (int32_t in_idx = 0; in_idx < inputs[i]->getShape()[0]; in_idx++)
{
out_tensor(out_idx) = ins[i]->getTensor()(in_idx);
out_idx++;
}
}
out->getTensor() = out_tensor;
// set the shapeValue given the actual tensor value
std::vector<int> shapeValue;
for (int i = 0; i < out->getTensor().size(); ++i)
{
shapeValue.push_back(out->getTensor()(i));
}
this->getOutputs()[0]->setShapeValue(shapeValue);
return GraphNode::eval();
}
ShapeBinaryNodeBase::ShapeBinaryNodeBase(SubgraphTraverser* sgt_, const Op& op_, uint64_t id_)
: GraphNode(sgt_, op_, id_)
{
setRequiredOperands(2, 1);
setRequiredRank(1, 1);
fcn = [](EigenType a, EigenType b) -> EigenType { return EigenType(); };
}
ShapeBinaryNodeBase::~ShapeBinaryNodeBase()
{}
int ShapeBinaryNodeBase::checkTensorAttributes()
{
if (validateRequiredOperands())
return 1;
if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) || validateRequiredRank(outputs[0]))
return 1;
num_dims = outputs[0]->getShape()[0];
if (inputs[0]->getShape()[0] != num_dims)
{
std::string err = "Binary shape operators " + std::string(EnumNamesOp()[nodeType]) +
" lhs input and output rank/shape must match";
printNodeValidationError(err.c_str());
return 1;
}
if (inputs[1]->getShape()[0] != num_dims)
{
std::string err = "Binary shape operators " + std::string(EnumNamesOp()[nodeType]) +
" rhs input and output rank/shape must match";
printNodeValidationError(err.c_str());
return 1;
}
a = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
b = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]);
result = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
ASSERT_MEM(a && b && result);
return 0;
}
int ShapeBinaryNodeBase::eval()
{
auto ia = a->getTensor();
auto ib = b->getTensor();
ETensor1<EigenType> out_tens(num_dims);
for (int32_t i = 0; i < num_dims; i++)
{
EigenType lhs = ia(i);
EigenType rhs = ib(i);
out_tens(i) = (lhs < 0 || rhs < 0) ? static_cast<EigenType>(-1) : fcn(lhs, rhs);
}
result->getTensor() = out_tens;
// set the shapeValue given the actual tensor value
std::vector<int> shapeValue;
for (int i = 0; i < result->getTensor().size(); ++i)
{
shapeValue.push_back(result->getTensor()(i));
}
this->getOutputs()[0]->setShapeValue(shapeValue);
return GraphNode::eval();
}
int OpAddShape::register_fcn()
{
fcn = [](EigenType a, EigenType b) -> EigenType { return a + b; };
return 0;
}
int OpSubShape::register_fcn()
{
fcn = [](EigenType a, EigenType b) -> EigenType { return a - b; };
return 0;
}
int OpMulShape::register_fcn()
{
fcn = [](EigenType a, EigenType b) -> EigenType { return a * b; };
return 0;
}
int OpDivShape::register_fcn()
{
fcn = [](EigenType a, EigenType b) -> EigenType {
return (b == static_cast<EigenType>(0)) ? static_cast<EigenType>(-1) : (a / b);
};
return 0;
}
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