Paddle/paddle/math/TensorApply.h

/**
 * TensorApply.h
 *
 * Author: hedaoyuan (hedaoyuan@baidu.com)
 * Created on: 2016-06-06
 *
 * Copyright (c) Baidu.com, Inc. All Rights Reserved
 *
 */

#pragma once

namespace paddle {

/**
 * \brief The tensor evaluator classes.
 */
template<typename Derived, class T>
class TensorApply {
public:
  explicit INLINE TensorApply(const Derived& p)
    : data_(p.data_), stride_(p.stride_),
      height_(p.height_), width_(p.width_), useGpu_(p.useGpu_) {}

  INLINE T apply(int i, int j) const {
    return data_[i * stride_ + j];
  }
  INLINE T apply(int index) const {
    return data_[index];
  }
  INLINE T& applyRef(int i, int j) {
    return data_[i * stride_ + j];
  }
  INLINE T& applyRef(int index) {
    return data_[index];
  }

  INLINE size_t getWidth() const { return width_; }
  INLINE size_t getHeight() const { return height_; }
  INLINE bool isContiguous() const { return stride_ == width_ || height_ == 1; }
  INLINE bool useGpu() const { return useGpu_; }

  T* data_;
  size_t stride_;
  size_t height_;
  size_t width_;
  bool useGpu_;
};

/**
 * \brief The tensor evaluator classes.
 * 
 * evaluator for rvalues
 */
template<typename Derived, class T>
class TensorApply<const Derived, T> {
public:
  explicit INLINE TensorApply(const Derived& p)
    : data_(p.data_), stride_(p.stride_),
      height_(p.height_), width_(p.width_), useGpu_(p.useGpu_) {}

  INLINE T apply(int i, int j) const {
    return data_[i * stride_ + j];
  }
  INLINE T apply(int index) const {
    return data_[index];
  }

  INLINE size_t getWidth() const { return width_; }
  INLINE size_t getHeight() const { return height_; }
  INLINE bool isContiguous() const { return stride_ == width_ || height_ == 1; }
  INLINE bool useGpu() const { return useGpu_; }

  const T* data_;
  size_t stride_;
  size_t height_;
  size_t width_;
  bool useGpu_;
};

template<typename Derived, class T>
class TensorApply<const TensorExpression<Derived, T>, T> {
public:
  explicit TensorApply(const TensorExpression<Derived, T>& expr)
    : expr_(expr.derived()) {}

  INLINE T apply(int i, int j) const {
    return expr_.apply(i, j);
  }
  INLINE T apply(int index) const {
    return expr_.apply(index);
  }

  INLINE size_t getWidth() const { return expr_.getWidth(); }
  INLINE size_t getHeight() const { return expr_.getHeight(); }
  INLINE bool isContiguous() const { return expr_.isContiguous(); }
  INLINE bool useGpu() const { return expr_.useGpu(); }

  TensorApply<const Derived, T> expr_;
};

/**
 * \brief The unary expression evaluator classes.
 */
template<class OP, typename ArgType, class T>
class TensorApply<const TensorUnaryOp<OP, ArgType, T>, T> {
public:
  explicit INLINE TensorApply(const TensorUnaryOp<OP, ArgType, T>& expr)
    : op_(expr.op_), expr_(expr.expr_) {}

  INLINE T apply(int i, int j) const {
    return op_(expr_.apply(i, j));
  }
  INLINE T apply(int index) const {
    return op_(expr_.apply(index));
  }

  INLINE size_t getWidth() const { return expr_.getWidth(); }
  INLINE size_t getHeight() const { return expr_.getHeight(); }
  INLINE bool isContiguous() const { return expr_.isContiguous(); }
  INLINE bool useGpu() const { return expr_.useGpu(); }

  const OP op_;
  TensorApply<ArgType, T> expr_;
};

/**
 * \brief The binary expression evaluator classes.
 */
template<class OP, typename LhsType, typename RhsType, class T>
class TensorApply<const TensorBinaryOp<OP, LhsType, RhsType, T>, T> {
public:
  explicit INLINE TensorApply(
    const TensorBinaryOp<OP, LhsType, RhsType, T>& expr)
      : op_(expr.op_), lhs_(expr.lhs_), rhs_(expr.rhs_) {
    #ifndef __CUDA_ARCH__
      CHECK_EQ(lhs_.getWidth(), rhs_.getWidth());
      CHECK_EQ(lhs_.getHeight(), rhs_.getHeight());
      CHECK_EQ(lhs_.useGpu(), rhs_.useGpu());
    #endif
  }

  INLINE T apply(int i, int j) const {
    return op_(lhs_.apply(i, j), rhs_.apply(i, j));
  }
  INLINE T apply(int index) const {
    return op_(lhs_.apply(index), rhs_.apply(index));
  }

  INLINE size_t getWidth() const { return lhs_.getWidth(); }
  INLINE size_t getHeight() const { return rhs_.getHeight(); }
  INLINE bool isContiguous() const {
    return lhs_.isContiguous() && rhs_.isContiguous();
  }
  INLINE bool useGpu() const { return lhs_.useGpu(); }

  const OP op_;
  TensorApply<LhsType, T> lhs_;
  TensorApply<RhsType, T> rhs_;
};

/**
 * \brief The ternary expression evaluator classes.
 */
template<typename ArgType1, typename ArgType2, typename ArgType3, class T>
class TensorApply<const TensorTernaryOp<ArgType1, ArgType2, ArgType3, T>, T> {
public:
  explicit INLINE TensorApply(
    const TensorTernaryOp<ArgType1, ArgType2, ArgType3, T>& expr)
    : expr1_(expr.expr1_), expr2_(expr.expr2_), expr3_(expr.expr3_) {
    #ifndef __CUDA_ARCH__
     CHECK_EQ(expr1_.getWidth(), expr2_.getWidth());
     CHECK_EQ(expr1_.getWidth(), expr3_.getWidth());
     CHECK_EQ(expr1_.getHeight(), expr2_.getHeight());
     CHECK_EQ(expr1_.getHeight(), expr3_.getHeight());
     CHECK_EQ(expr1_.useGpu(), expr2_.useGpu());
     CHECK_EQ(expr1_.useGpu(), expr3_.useGpu());
    #endif
  }

  INLINE T apply(int i, int j) const {
    return expr1_.apply(i, j) ? expr2_.apply(i, j) : expr3_.apply(i, j);
  }
  INLINE T apply(int index) const {
    return expr1_.apply(index) ? expr2_.apply(index) : expr3_.apply(index);
  }

  INLINE size_t getWidth() const { return expr1_.getWidth(); }
  INLINE size_t getHeight() const { return expr1_.getHeight(); }
  INLINE bool isContiguous() const {
    return expr1_.isContiguous() &&
      expr2_.isContiguous() && expr3_.isContiguous();
  }
  INLINE bool useGpu() const { return expr1_.useGpu(); }

  TensorApply<ArgType1, T> expr1_;
  TensorApply<ArgType2, T> expr2_;
  TensorApply<ArgType3, T> expr3_;
};

/**
 * \brief The const expression evaluator classes.
 */
template<class OP, typename ArgType, class T>
class TensorApply<const TensorConstant<OP, ArgType, T>, T> {
public:
  explicit INLINE TensorApply(const TensorConstant<OP, ArgType, T>& expr)
    : op_(expr.op_), expr_(expr.expr_) {}

  INLINE T apply(int i, int j) const {
    return op_(i, j);
  }
  INLINE T apply(int index) const {
    return op_(index);
  }

  INLINE size_t getWidth() const { return expr_.getWidth(); }
  INLINE size_t getHeight() const { return expr_.getHeight(); }
  INLINE bool isContiguous() const { return true; }
  INLINE bool useGpu() const { return expr_.useGpu(); }

  const OP op_;
  TensorApply<ArgType, T> expr_;
};

}  // namespace paddle