elementwise_op_function.h

/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.

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. */

#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/operator.h"
#include "paddle/platform/transform.h"

#ifdef __NVCC__
#include <thrust/iterator/iterator_adaptor.h>
#endif

#include "paddle/operators/math/math_function.h"

namespace paddle {
namespace operators {

/*
 * Out = X ⊙ Y
 * If Y's shape does not match X' shape, they will be reshaped.
 * For example:
 * 1. shape(X) = (2, 3, 4, 5), shape(Y) = (3, 4), with axis=1
 *    pre=2, n=3*4, post=5
 *    x.shape(2, 12, 5) * y.shape(1,12,1).broadcast(2,12,5)
 * 2. shape(X) = (2, 3, 4, 5), shape(Y) = (4,5)
 *    pre=2*3, n=4*5, post=1
 *    x.shape(2, 3, 20) * y.shape(1,1,20).broadcast(2,3,20)
 */
inline void get_mid_dims(const framework::DDim& x_dims,
                         const framework::DDim& y_dims, const int axis,
                         int& pre, int& n, int& post) {
  pre = 1;
  n = 1;
  post = 1;
  for (int i = 0; i < axis; ++i) {
    pre *= x_dims[i];
  }

  for (int i = 0; i < y_dims.size(); ++i) {
    PADDLE_ENFORCE_EQ(x_dims[i + axis], y_dims[i],
                      "Broadcast dimension mismatch.");
    n *= y_dims[i];
  }

  for (int i = axis + y_dims.size(); i < x_dims.size(); ++i) {
    post *= x_dims[i];
  }
}

template <typename T, typename DeviceContext>
class RowwiseTransformIterator;
template <typename T, typename DeviceContext>
class MidWiseTransformIterator;

template <typename T>
class RowwiseTransformIterator<T, platform::CPUDeviceContext> {
 public:
  RowwiseTransformIterator(const T* ptr, int n) : ptr_(ptr), i_(0), n_(n) {}

  RowwiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
    ++i_;
    if (UNLIKELY(i_ == n_)) {
      i_ = 0;
    }
    return *this;
  }

  bool operator==(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) == &(*rhs);
  }

  bool operator!=(const RowwiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) != &(*rhs);
  }

  const T& operator*() { return ptr_[i_]; }

 private:
  const T* ptr_;
  int i_;
  int64_t n_;
};

template <typename T>
class MidWiseTransformIterator<T, platform::CPUDeviceContext> {
 public:
  MidWiseTransformIterator(const T* ptr, int n, int post)
      : ptr_(ptr), i_(0), j_(0), n_(n), post_(post) {}

  MidWiseTransformIterator<T, platform::CPUDeviceContext>& operator++() {
    ++j_;
    if (UNLIKELY(j_ == post_)) {
      ++i_;
      j_ = 0;
      if (UNLIKELY(i_ == n_)) {
        i_ = 0;
      }
    }
    return *this;
  }

  bool operator==(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) == &(*rhs);
  }

  bool operator!=(const MidWiseTransformIterator<T, platform::CPUDeviceContext>&
                      rhs) const {
    return (ptr_ + i_) != &(*rhs);
  }

  const T& operator*() { return ptr_[i_]; }

 private:
  const T* ptr_;
  int64_t i_;
  int64_t j_;
  int64_t n_;
  int64_t post_;
};

#ifdef __NVCC__
template <typename T>
class RowwiseTransformIterator<T, platform::CUDADeviceContext>
    : public thrust::iterator_adaptor<
          RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
 public:
  typedef thrust::iterator_adaptor<
      RowwiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
      super_t;
  HOSTDEVICE RowwiseTransformIterator(const T* x, int n)
      : super_t(x), begin_(x), n_(n){};
  friend class thrust::iterator_core_access;

 private:
  unsigned int n_;
  const T* begin_;
  HOSTDEVICE typename super_t::reference dereference() const {
    return *(begin_ + (this->base() - begin_) % n_);
  }
};

template <typename T>
class MidWiseTransformIterator<T, platform::CUDADeviceContext>
    : public thrust::iterator_adaptor<
          MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*> {
 public:
  typedef thrust::iterator_adaptor<
      MidWiseTransformIterator<T, platform::CUDADeviceContext>, const T*>
      super_t;
  HOSTDEVICE MidWiseTransformIterator(const T* x, int n, int post)
      : super_t(x), begin_(x), n_(n), post_(post){};
  friend class thrust::iterator_core_access;

 private:
  unsigned int post_;
  unsigned int n_;
  const T* begin_;
  HOSTDEVICE typename super_t::reference dereference() const {
    return *(begin_ + (((this->base() - begin_) / post_) % n_));
  }
};
#endif

template <typename Functor, typename T, typename DeviceContext,
          typename OutType = T>
class TransformFunctor {
 public:
  TransformFunctor(const framework::Tensor* x, const framework::Tensor* y,
                   framework::Tensor* z, const DeviceContext& ctx, Functor func)
      : x_(x->data<T>()),
        y_(y->data<T>()),
        z_(z->mutable_data<OutType>(ctx.GetPlace())),
        nx_(x->numel()),
        ctx_(ctx),
        func_(func) {}

  inline void Run() const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_, y_, z_, func_);
  }

  inline void RunRowWise(int n, int pre) const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_, RowwiseTransformIterator<T, DeviceContext>(y_, n),
          z_, func_);
  }

  inline void RunMidWise(int n, int pre, int post) const {
    platform::Transform<DeviceContext> trans;
    trans(ctx_, x_, x_ + nx_,
          MidWiseTransformIterator<T, DeviceContext>(y_, n, post), z_, func_);
  }

 private:
  const T* x_;
  const T* y_;
  OutType* z_;
  int64_t nx_;
  const DeviceContext& ctx_;
  Functor func_;
};

#define EIGEN_FUNCTOR(name, eigen_op)                                          \
  struct Eigen##name##Functor {                                                \
    template <typename DeviceContext, typename T>                              \
    inline void Run(const framework::Tensor* x, const framework::Tensor* y,    \
                    framework::Tensor* z,                                      \
                    const framework::ExecutionContext& ctx) {                  \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_e);                                                  \
    }                                                                          \
    template <typename DeviceContext, typename T>                              \
    inline void RunBroadCast(const framework::Tensor* x,                       \
                             const framework::Tensor* y, framework::Tensor* z, \
                             const framework::ExecutionContext& ctx, int pre,  \
                             int n) {                                          \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 2>(1, n))                  \
                         .broadcast(Eigen::DSizes<int, 2>(pre, 1))             \
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_bcast);                                              \
    }                                                                          \
    template <typename DeviceContext, typename T>                              \
    inline void RunBroadCast2(const framework::Tensor* x,                      \
                              const framework::Tensor* y,                      \
                              framework::Tensor* z,                            \
                              const framework::ExecutionContext& ctx, int pre, \
                              int n, int post) {                               \
      auto x_e = framework::EigenVector<T>::Flatten(*x);                       \
      auto y_e = framework::EigenVector<T>::Flatten(*y);                       \
      auto z_e = framework::EigenVector<T>::Flatten(*z);                       \
      auto y_bcast = y_e.reshape(Eigen::DSizes<int, 3>(1, n, 1))               \
                         .broadcast(Eigen::DSizes<int, 3>(pre, 1, post))       \
                         .reshape(Eigen::DSizes<int, 1>(x_e.size()));          \
      z_e.device(                                                              \
          *ctx.template device_context<DeviceContext>().eigen_device()) =      \
          eigen_op(x_e, y_bcast);                                              \
    }                                                                          \
  }

template <class functor, typename DeviceContext, typename T>
void ElementwiseCompute(const framework::ExecutionContext& ctx) {
  using Tensor = framework::Tensor;

  auto* x = ctx.Input<Tensor>("X");
  auto* y = ctx.Input<Tensor>("Y");
  auto* z = ctx.Output<Tensor>("Out");
  z->mutable_data<T>(ctx.GetPlace());

  auto x_dims = x->dims();
  auto y_dims = y->dims();
  PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
                    "Rank of first input must >= rank of second input.");

  if (x_dims == y_dims) {
    functor f;
    f.template Run<DeviceContext, T>(x, y, z, ctx);
    return;
  }

  int axis = ctx.Attr<int>("axis");
  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
  PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
                 "Axis should be in range [0, x_dims)");

  int pre, n, post;
  get_mid_dims(x_dims, y_dims, axis, pre, n, post);
  if (post == 1) {
    functor f;
    f.template RunBroadCast<DeviceContext, T>(x, y, z, ctx, pre, n);
    return;
  } else {
    functor f;
    f.template RunBroadCast2<DeviceContext, T>(x, y, z, ctx, pre, n, post);
    return;
  }
}

#define EIGEN_ADD(x, y) ((x) + (y))
EIGEN_FUNCTOR(Add, EIGEN_ADD);

#define EIGEN_SUB(x, y) ((x) - (y))
EIGEN_FUNCTOR(Sub, EIGEN_SUB);

#define EIGEN_MUL(x, y) ((x) * (y))
EIGEN_FUNCTOR(Mul, EIGEN_MUL);

#define EIGEN_DIV(x, y) ((x) / (y))
EIGEN_FUNCTOR(Div, EIGEN_DIV);

template <typename DeviceContext, typename T, typename functor,
          typename broadcastfunctor, typename broadcast2functor>
void ElementwiseGradCompute(const framework::ExecutionContext& ctx) {
  using Tensor = framework::Tensor;

  auto* x = ctx.Input<Tensor>("X");
  auto* y = ctx.Input<Tensor>("Y");
  auto* out = ctx.Input<Tensor>("Out");
  auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));

  auto& place = *ctx.template device_context<DeviceContext>().eigen_device();

  auto x_dims = x->dims();
  auto y_dims = y->dims();

  auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
  auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
  if (dx) {
    dx->mutable_data<T>(ctx.GetPlace());
  }
  if (dy) {
    dy->mutable_data<T>(ctx.GetPlace());
  }

  if (x_dims == y_dims) {
    functor f;
    f(place, x, y, out, dx, dy, dout);
    return;
  }

  if (y_dims.size() == 1 && y_dims[0] == 1) {
    // y is a scalar
    auto extended_dims = framework::vectorize(x_dims);
    extended_dims.push_back(1);
    x_dims = framework::make_ddim(extended_dims);
  }

  int axis = ctx.Attr<int>("axis");
  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);

  int pre, n, post;
  get_mid_dims(x_dims, y_dims, axis, pre, n, post);

  if (post == 1) {
    broadcastfunctor f;
    f(place, x, y, out, dx, dy, dout, pre, n);
    return;
  } else {
    broadcast2functor f;
    f(place, x, y, out, dx, dy, dout, pre, n, post);
    return;
  }
}

template <typename Functor, typename DeviceContext, typename T,
          typename OutType = T>
void ElementwiseComputeEx(const framework::ExecutionContext& ctx) {
  using Tensor = framework::Tensor;

  auto* x = ctx.Input<Tensor>("X");
  auto* y = ctx.Input<Tensor>("Y");
  auto* z = ctx.Output<Tensor>("Out");
  z->mutable_data<OutType>(ctx.GetPlace());
  TransformFunctor<Functor, T, DeviceContext, OutType> functor(
      x, y, z, ctx.template device_context<DeviceContext>(), Functor());

  auto x_dims = x->dims();
  auto y_dims = y->dims();
  PADDLE_ENFORCE_GE(x_dims.size(), y_dims.size(),
                    "Rank of first input must >= rank of second input.");

  if (x_dims == y_dims) {
    functor.Run();
    return;
  }

  if (y_dims.size() == 1 && y_dims[0] == 1) {
    // y is a scalar
    auto extended_dims = framework::vectorize(x_dims);
    extended_dims.push_back(1);
    x_dims = framework::make_ddim(extended_dims);
  }

  int axis = ctx.Attr<int>("axis");
  axis = (axis == -1 ? x_dims.size() - y_dims.size() : axis);
  PADDLE_ENFORCE(axis >= 0 && axis < x_dims.size(),
                 "Axis should be in range [0, x_dims)");

  int pre, n, post;
  get_mid_dims(x_dims, y_dims, axis, pre, n, post);
  if (post == 1) {
    functor.RunRowWise(n, pre);
    return;
  } else {
    functor.RunMidWise(n, pre, post);
    return;
  }
}

}  // namespace operators
}  // namespace paddle