Producer.hpp

/********************************************************************************
 * Copyright (c) 2023 CEA-List
 *
 * This program and the accompanying materials are made available under the
 * terms of the Eclipse Public License 2.0 which is available at
 * http://www.eclipse.org/legal/epl-2.0.
 *
 * SPDX-License-Identifier: EPL-2.0
 *
 ********************************************************************************/

#ifndef AIDGE_CORE_OPERATOR_PRODUCER_H_
#define AIDGE_CORE_OPERATOR_PRODUCER_H_

#include <array>
#include <vector>

#include "aidge/utils/Types.h"
#include "aidge/data/Tensor.hpp"
#include "aidge/graph/Node.hpp"
#include "aidge/operator/OperatorTensor.hpp"
#include "aidge/utils/StaticAttributes.hpp"
#include "aidge/utils/Registrar.hpp"

namespace Aidge {

class Producer_Op
    : public OperatorTensor,
      public Registrable<Producer_Op, std::string, std::unique_ptr<OperatorImpl>(
                                          const Producer_Op &)> {
public:
    static constexpr const char* Type = "Producer";

    template <std::size_t DIM>
    Producer_Op(const std::array<DimSize_t, DIM>& dims)
        : OperatorTensor(Type, 0, 0, 1)
    {
        mOutputs[0]->resize(dims);
    }

    Producer_Op(const std::shared_ptr<Tensor> tensor)
        : OperatorTensor(Type, 0, 0, 1)
    {
        mOutputs[0] = tensor; // copy the pointer of the Tensor
    }

    /**
     * @brief Copy-constructor. Copy the operator attributes and its output tensor(s), but not its input tensors (the new operator has no input associated).
     * @param op OperatorTensor to copy.
     */
    Producer_Op(const Producer_Op& op)
        : OperatorTensor(op)
    {
        for (std::size_t i = 0; i < static_cast<std::size_t>(nbOutputs()); ++i) {
            mOutputs[i] = std::make_shared<Tensor>(*(op.getOutput(i)));
        }
        mImpl = op.mImpl ? Registrar<Producer_Op>::create(op.mOutputs[0]->getImpl()->backend())(*this) : nullptr;
    }

    /**
     * @brief Clone the operator using its copy-constructor.
     * @see Operator::Producer_Op
     */
    std::shared_ptr<Operator> clone() const override {
        return std::make_shared<Producer_Op>(*this);
    }

    void associateInput(const IOIndex_t /*inputIdx*/, const std::shared_ptr<Data>& /*data*/) override final {
        AIDGE_THROW_OR_ABORT(std::runtime_error, "Producer operator takes no input.");
    }

    void computeOutputDims() override final {}

    bool outputDimsForwarded() const override final {return true;}


    inline const std::vector<DimSize_t> dims() const noexcept { return mOutputs[0]->dims(); }

    void setBackend(const std::string& name) override {
        mImpl = Registrar<Producer_Op>::create(name)(*this);
        mOutputs[0]->setBackend(name);
    }

    static const std::vector<std::string> getInputsName(){
        return {};
    }
    static const std::vector<std::string> getOutputsName(){
        return {"data_output"};
    }

public:
  void forward() override final {
    printf("Basic Producer forward() function.\n");
  }
  void backward() override final {
    printf("Basic Producer backward() function.\n");
  }
};

template <std::array<DimSize_t, 1>::size_type DIM>
inline std::shared_ptr<Node> Producer(const std::array<DimSize_t, DIM> &dims, const std::string& name = "") {
  static_assert(DIM<=MaxDim,"Too many tensor dimensions required by Producer, not supported");
  return std::make_shared<Node>(std::make_shared<Producer_Op>(dims), name);
}

// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
template <std::size_t DIM>
inline std::shared_ptr<Node> Producer(DimSize_t const (&dims)[DIM], const std::string& name = "") {
  return Producer(to_array(dims), name);
}

inline std::shared_ptr<Node> Producer(const std::shared_ptr<Tensor> tensor, const std::string& name = "") {
  return std::make_shared<Node>(std::make_shared<Producer_Op>(tensor), name);
}

template <std::array<DimSize_t, 1>::size_type DIM>
void addProducer(std::shared_ptr<Node>& otherNode, const IOIndex_t inputIdx, const std::array<DimSize_t, DIM>& dims, const std::string& extension) {
    assert(inputIdx != gk_IODefaultIndex);
    static_assert(DIM<=MaxDim,"Too many tensor dimensions required by addProducer, not supported");
    const std::string prodName = (otherNode->name().empty()) ? "" : (otherNode->name() + std::string("_") + extension);
    auto prod = Producer(dims, prodName);
    prod->addChild(otherNode, 0, inputIdx);
    otherNode->getOperator()->associateInput(inputIdx, prod->getOperator()->getRawOutput(0));
}

// helper with C-style array instead of std::array for kernel_dims to allow automatic template DIM deduction
template <std::size_t DIM>
void addProducer(std::shared_ptr<Node>& otherNode, const IOIndex_t inputIdx, DimSize_t const (&dims)[DIM], const std::string& extension) {
    addProducer(otherNode, inputIdx, to_array(dims), extension);
}
} // namespace Aidge

#endif /* AIDGE_CORE_OPERATOR_PRODUCER_H_ */