EVOLUTION-MANAGER

Edit File: conv_grad_ops.h

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==============================================================================*/

// This is the common header for the input and filter backprop kernels.
//
// The operation to compute Conv2D gradients.
//
// To compute the gradients for Conv2D, we need three input tensors:
//    input, filter, and backprop for output.
// And we need to compute two backprops: one for input and one for filter. We
// compute them in two different kernels.
//
// Both backprops can be computed as straightforward conv2d.
//
// Consider a case where the input is 3x3 and the filter is 2x1:
//
// INPUT = [ A  B  C ]
//         [ D  E  F ]
//         [ G  H  I ]
//
// where each "A", "B", etc is batch x in_depth
//
// FILTER = [ X  Y ]
//
// where both "X" and "Y" are in_depth x out_depth
//
// With VALID padding, the output is 3x2:
//
// OUTPUT = [ a  b ]
//          [ c  d ]
//          [ e  f ]
//
// where each "a", "b", etc is batch x out_depth
//
// So we have:
//
//   a = A * X + B * Y
//   b = B * X + C * Y
//   c = D * X + E * Y
//   d = E * X + F * Y
//   e = G * X + H * Y
//   f = H * X + I * Y
//
// So when we have backprops for the outputs (we denote them by
// a', b', ... ):
//
// The backprops for the input are:
//
//   A' = a' * X^t
//   B' = a' * Y^t + b' * X^t
//   C' = b' * Y^t
//   ...
//
// This is essentially computing a 2d conv of
//
// INPUT = [ 0  a'  b'  0 ]
//         [ 0  c'  d'  0 ]
//         [ 0  e'  f'  0 ]
// and
//
// FILTER = [ Y^t X^t ]
//
// The backprops for the filter are:
//
//   X' = A^t * a' + B^t * b' + D^t * c' + E^t * d' + G^t * e' + H^t * f'
//   Y' = B^t * a' + C^t * b' + E^t + c' + F^t * d' + H^t * e' + I^t * f'
//
// This is essentially computing a 2d conv of
//
// INPUT = [ A^t  B^t  C^t ]
//         [ D^t  E^t  F^t ]
//         [ G^t  H^t  I^t ]
//
// and
//
// FILTER = [ a'  b' ]
//          [ c'  d' ]
//          [ e'  f' ]
//
//
//////////////////////////////////////////////////////////
//
// With stride more than one, it's a bit more complicated (we will need to
// create holes to the backprop).
//
// Consider the case where
//
// INPUT = [ A B C D E ]
//         [ F G H I J ]
//         [ K L M N O ]
// and
//
// FILTER = [ X Y Z ]
//
// with stride 2.
//
// The output will be
//
// OUTPUT = [ a b ]
//          [ c d ]
//
// where:
//
//   a = A * X + B * Y + C * Z
//   b = C * X + D * Y + E * Z
//   c = K * X + L * Y + M * Z
//   d = M * X + N * Y + O * Z
//
//
// To compute the backprop for INPUT, we need to convolve
//
// INPUT = [ 0  0  a' 0  b' 0  0 ]
//         [ 0  0  0  0  0  0  0 ]
//         [ 0  0  c' 0  d' 0  0 ]
//
// (notice the holes in INPUT)
//
// and
//
// FILTER = [ Z^t  Y^t  X^t ]
//
// with stride 1.
//
// To compute the backprop for FILTER, we need to convolve

//
// INPUT = [ A^t  B^t  C^t  D^t  E^t ]
//         [ F^t  G^t  H^t  I^t  J^t ]
//         [ K^t  L^t  M^t  N^t  O^t ]
// and
//
// FILTER = [ a' 0  b' ]
//          [ 0  0  0  ]
//          [ c' 0  d' ]
//
// (notice the holes in FILTER)
//
//
// with stride 1
//
//////////////////////////////////////////////////////////
//
//
// The case for SAME padding is in fact very similar to VALID -- we just
// need to pad the input tensor a bit when computing the filter_backprop.

#ifndef TENSORFLOW_CORE_KERNELS_CONV_GRAD_OPS_H_
#define TENSORFLOW_CORE_KERNELS_CONV_GRAD_OPS_H_

#include <vector>

#include "tensorflow/core/util/padding.h"
#include "tensorflow/core/util/tensor_format.h"

namespace tensorflow {

// Forward declaration.
class OpKernelContext;

template <typename Device, typename T>
struct LaunchConv2DBackpropInputOp {
  void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune,
                  const Tensor& out_backprop, const Tensor& filter,
                  int row_dilation, int col_dilation, int row_stride,
                  int col_stride, const Padding& padding,
                  const std::vector<int64>& explicit_paddings,
                  Tensor* in_backprop, TensorFormat data_format);
};

template <typename Device, typename T>
struct LaunchConv2DBackpropFilterOp {
  void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune,
                  const Tensor& out_backprop, const Tensor& input,
                  int row_dilation, int col_dilation, int row_stride,
                  int col_stride, const Padding& padding,
                  const std::vector<int64>& explicit_paddings,
                  Tensor* filter_backprop, TensorFormat data_format);
};

#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM
template <typename T>
struct LaunchConv2DBackpropInputOp<Eigen::GpuDevice, T> {
  void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune,
                  const Tensor& input, const Tensor& filter, int row_dilation,
                  int col_dilation, int row_stride, int col_stride,
                  const Padding& padding,
                  const std::vector<int64>& explicit_paddings, Tensor* output,
                  TensorFormat data_format);
};

template <typename T>
struct LaunchConv2DBackpropFilterOp<Eigen::GpuDevice, T> {
  void operator()(OpKernelContext* ctx, bool use_cudnn, bool cudnn_use_autotune,
                  const Tensor& out_backprop, const Tensor& input,
                  int row_dilation, int col_dilation, int row_stride,
                  int col_stride, const Padding& padding,
                  const std::vector<int64>& explicit_paddings,
                  Tensor* filter_backprop, TensorFormat data_format);
};
#endif  // GOOGLE_CUDA || TENSORFLOW_USE_ROCM
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_CONV_GRAD_OPS_H_