EVOLUTION-MANAGER

Edit File: transpose_functor.h

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
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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,
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limitations under the License.
==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_TRANSPOSE_FUNCTOR_H_
#define TENSORFLOW_CORE_KERNELS_TRANSPOSE_FUNCTOR_H_

#include <numeric>
#include <string>
#include <vector>
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {
// Transpose tensor 'in' into tensor 'out' according to dimension
// permutation 'perm'.
//
// REQUIRES: in.dtype() == out->dtype()
// REQUIRES: in.dims() == out->dims()
// REQUIRES: in.dims() == perm.size()
// REQUIRES: in.dim_size(perm[i]) == out->dim_size(i)
template <typename Device>
Status DoTranspose(const Device& device, const Tensor& in,
                   const gtl::ArraySlice<int32> perm, Tensor* out);

// Conjugate and transpose tensor 'in' into tensor 'out' according to dimension
// permutation 'perm'.
//
// REQUIRES: in.dtype() == out->dtype()
// REQUIRES: in.dims() == out->dims()
// REQUIRES: in.dims() == perm.size()
// REQUIRES: in.dim_size(perm[i]) == out->dim_size(i)
template <typename Device>
Status DoConjugateTranspose(const Device& device, const Tensor& in,
                            const gtl::ArraySlice<int32> perm, Tensor* out);

// Convenience versions of DoTranspose that only swap the last (inner) two
// dimensions.
template <typename Device>
Status DoMatrixTranspose(const Device& device, const Tensor& in, Tensor* out);

// Convenience versions of DoConjugateTranspose that only swap the last (inner)
// two dimensions.
template <typename Device>
Status DoConjugateMatrixTranspose(const Device& device, const Tensor& in,
                                  Tensor* out);

// Primary device specific functor to be specialized for each device and type.
template <typename Device, typename T, bool conjugate = false>
struct Transpose {
  static void run(const Device& d, const Tensor& in,
                  const gtl::ArraySlice<int32> perm, Tensor* out);
};

// Implementation details.
namespace internal {

typedef gtl::InlinedVector<int64, 8> TransposeDimsVec;
typedef gtl::InlinedVector<int32, 8> TransposePermsVec;

// Helper function that takes a tensor shape, a permutation, combines the
// neighboring shapes if their indices in the permutation are consecutive.
// The function outputs the combined shape and new permutation.
// Example: Tensor shape {2, 3, 4, 5, 120} and permutation {0, 4, 1, 2, 3} will
// produce new shape {2, 60, 120} and new permutation {0, 2, 1}.
inline void ReduceTransposeDimensions(const TensorShape& shape,
                                      gtl::ArraySlice<int32> perm,
                                      TransposePermsVec* new_perm,
                                      TransposeDimsVec* new_dims) {
  CHECK_EQ(shape.dims(), perm.size());
  if (shape.dims() == 1) {
    // If input dimension is already 1, no need to reduce dimension.
    new_perm->resize(1);
    (*new_perm)[0] = perm[0];
    (*new_dims)[0] = shape.dim_size(0);
    return;
  }
  TransposePermsVec new_dim_position(shape.dims(), -1);
  TransposeDimsVec combined_dims(shape.dims(), 0);
  int cur_head = perm[0];
  new_dim_position[cur_head] = 0;
  combined_dims[0] = shape.dim_size(cur_head);
  int dim_idx = 0;
  for (int perm_idx = 1; perm_idx < shape.dims(); ++perm_idx) {
    // If two indices in permutation are consecutive numbers, combine their
    // dimensions.
    if (cur_head + 1 == perm[perm_idx]) {
      cur_head = perm[perm_idx];
      combined_dims[dim_idx] *= shape.dim_size(cur_head);
    } else {
      // Else start a new dimension.
      cur_head = perm[perm_idx];
      dim_idx++;
      new_dim_position[cur_head] = dim_idx;
      combined_dims[dim_idx] = shape.dim_size(cur_head);
    }
  }
  // Compact the new permutations and dimension sizes.
  new_perm->resize(dim_idx + 1);
  new_dims->resize(dim_idx + 1);
  dim_idx = 0;
  for (int i = 0; i < new_dim_position.size(); ++i) {
    if (new_dim_position[i] >= 0) {
      int new_perm_idx = new_dim_position[i];
      (*new_perm)[dim_idx] = new_perm_idx;
      (*new_dims)[dim_idx] = combined_dims[new_perm_idx];
      dim_idx++;
    }
  }
}

// If all non-singleton dimensions remain in ascending order, the shuffled
// singletons can be transposed by a reshape, saving a memory allocation & copy.
// |permutation| must be a permutation of {0, .., input_shape.dims() - 1}.
// That is, for all i, 0 <= perm[i] < input_shape.dims().
// In practice, this is checked in TransposeOp::Compute prior to calling this
// function, and the function sits here to facilitate unit testing.
inline bool NonSingletonDimensionsAlign(const TensorShape& input_shape,
                                        const std::vector<int32>& permutation) {
  int last_nonsingleton_perm_dim = -1;
  for (int perm_dim : permutation) {
    if (input_shape.dim_size(perm_dim) == 1) {
      continue;
    }
    if (perm_dim < last_nonsingleton_perm_dim) {
      return false;
    }
    last_nonsingleton_perm_dim = perm_dim;
  }
  return true;
}

// Uses Eigen to transpose.
template <typename Device, typename T, int NDIMS>
void TransposeUsingEigen(const Device& d, const Tensor& in,
                         const gtl::ArraySlice<int32> perm, bool conjugate,
                         Tensor* out) {
  Eigen::array<int, NDIMS> p;
  for (int i = 0; i < NDIMS; ++i) p[i] = perm[i];
  auto x = typename TTypes<T, NDIMS>::ConstTensor(
      reinterpret_cast<const T*>(in.tensor_data().data()),
      in.shape().AsEigenDSizes<NDIMS>());
  auto y = typename TTypes<T, NDIMS>::Tensor(
      reinterpret_cast<T*>(const_cast<char*>(out->tensor_data().data())),
      out->shape().AsEigenDSizes<NDIMS>());
  if (conjugate) {
    y.device(d) = x.conjugate().shuffle(p);
  } else {
    y.device(d) = x.shuffle(p);
  }
}

template <typename Device>
Status DoTransposeImpl(const Device& d, const Tensor& in,
                       const gtl::ArraySlice<int32> perm, bool conjugate,
                       Tensor* out) {
  CHECK_GE(in.dims(), 2);
  CHECK_EQ(in.dims(), out->dims());
  CHECK_EQ(in.dims(), perm.size());
  CHECK_EQ(in.dtype(), out->dtype());
  switch (in.dtype()) {
    case DT_BOOL:
    case DT_INT8:
    case DT_QINT8:
    case DT_QUINT8:
    case DT_UINT8:
      Transpose<Device, uint8>::run(d, in, perm, out);
      break;

case DT_BFLOAT16:
    case DT_HALF:
    case DT_INT16:
    case DT_QINT16:
    case DT_QUINT16:
    case DT_UINT16:
      Transpose<Device, uint16>::run(d, in, perm, out);
      break;

case DT_FLOAT:
    case DT_INT32:
    case DT_QINT32:
    case DT_UINT32:
      Transpose<Device, uint32>::run(d, in, perm, out);
      break;

case DT_DOUBLE:
    case DT_INT64:
    case DT_UINT64:
      Transpose<Device, uint64>::run(d, in, perm, out);
      break;

case DT_COMPLEX64:
      if (conjugate) {
#if defined(__ANDROID__) and !defined(__clang__)
        // Workaround for GCC compiler bug in Android toolchain.
        return errors::Unimplemented(
            "Conjugate transpose of complex64 not supported for GCC on "
            "Android.");
#else
        Transpose<Device, complex64, /*conjugate=*/true>::run(d, in, perm, out);
#endif
      } else {
        Transpose<Device, uint64>::run(d, in, perm, out);
      }
      break;

case DT_COMPLEX128:
      if (conjugate) {
        Transpose<Device, complex128, /*conjugate=*/true>::run(d, in, perm,
                                                               out);
      } else {
        Transpose<Device, complex128, /*conjugate=*/false>::run(d, in, perm,
                                                                out);
      }
      break;

case DT_STRING:
      Transpose<Device, tstring>::run(d, in, perm, out);
      break;

default:
      return errors::Unimplemented("Unsupported dtype on CPU: ", in.dtype());
  }
  return Status::OK();
}

template <typename Device>
inline Status DoMatrixTransposeImpl(const Device& device, const Tensor& in,
                                    bool conjugate, Tensor* out) {
  const int ndims = in.dims();
  if (ndims == 0) return Status::OK();
  TransposePermsVec perm(ndims);
  std::iota(perm.begin(), perm.end(), 0);
  std::swap(perm[ndims - 2], perm[ndims - 1]);
  return DoTransposeImpl(device, in, perm, conjugate, out);
}

}  // namespace internal
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_TRANSPOSE_FUNCTOR_H_