EVOLUTION-MANAGER

Edit File: tensor_slice_util.h

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

#ifndef TENSORFLOW_CORE_UTIL_TENSOR_SLICE_UTIL_H_
#define TENSORFLOW_CORE_UTIL_TENSOR_SLICE_UTIL_H_

#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/framework/tensor_slice.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

namespace {

// Some hackery to invoke eigen tensor to copy over tensor slices with variable
// dimension tensors.
// TODO(yangke): get rid of that once the variable dimension tensor support is
// in.
static const int kTensorSliceMaxRank = 8;

// Create a tensor map with the given shape: we support up to 8 dimensions. If
// the shape has less than 8 dimensions, we pad the remaining dimension with 1.
template <typename T>
Eigen::TensorMap<Eigen::Tensor<T, kTensorSliceMaxRank, Eigen::RowMajor>>
GetEigenTensorMapFromTensorShape(const TensorShape& shape, T* data) {
  Eigen::DSizes<Eigen::DenseIndex, kTensorSliceMaxRank> dsizes =
      shape.AsEigenDSizesWithPadding<kTensorSliceMaxRank>();
  Eigen::TensorMap<Eigen::Tensor<T, kTensorSliceMaxRank, Eigen::RowMajor>> eig(
      data, dsizes);
  return eig;
}

// For everything except string, a standard Eigen cast and assignment works
template <typename DstT>
struct CopyThatWorksWithStringPointer {
  template <typename SrcTensor, typename DstTensor, typename Shape>
  static void Copy(const SrcTensor& s, Shape s_start, Shape len, DstTensor& d,
                   Shape d_start) {
    d.slice(d_start, len) = s.slice(s_start, len).template cast<DstT>();
  }
};

// Eigen makes it extremely difficult to dereference a tensor of string* into
// string, so we roll our own loop instead.
template <>
struct CopyThatWorksWithStringPointer<tstring> {
  template <typename SrcTensor, typename DstTensor, typename Shape>
  static void Copy(const SrcTensor& s, Shape s_start, Shape len, DstTensor& d,
                   Shape d_start) {
    typedef typename SrcTensor::Index Index;
    static_assert(kTensorSliceMaxRank == 8,
                  "If kTensorSliceMaxRank changes, modify the loop below.");
    for (Index i0 = 0; i0 < len[0]; i0++) {
      for (Index i1 = 0; i1 < len[1]; i1++) {
        for (Index i2 = 0; i2 < len[2]; i2++) {
          for (Index i3 = 0; i3 < len[3]; i3++) {
            for (Index i4 = 0; i4 < len[4]; i4++) {
              for (Index i5 = 0; i5 < len[5]; i5++) {
                for (Index i6 = 0; i6 < len[6]; i6++) {
                  for (Index i7 = 0; i7 < len[7]; i7++) {
                    d(d_start[0] + i0, d_start[1] + i1, d_start[2] + i2,
                      d_start[3] + i3, d_start[4] + i4, d_start[5] + i5,
                      d_start[6] + i6, d_start[7] + i7) =
                        *s(s_start[0] + i0, s_start[1] + i1, s_start[2] + i2,
                           s_start[3] + i3, s_start[4] + i4, s_start[5] + i5,
                           s_start[6] + i6, s_start[7] + i7);
                  }
                }
              }
            }
          }
        }
      }
    }
  }
};

// Checkpointing of half is done by storing the raw 16 bits as a signed 32bit
// integer. To restore the checkpoint we need to do the reverse operation by
// reinterpreting the integer as a 16 bit float. This prevents us from using
// the default cast operation.
template <>
struct CopyThatWorksWithStringPointer<Eigen::half> {
  template <typename SrcTensor, typename DstTensor, typename Shape>
  static void Copy(const SrcTensor& s, Shape s_start, Shape len, DstTensor& d,
                   Shape d_start) {
    typedef typename SrcTensor::Index Index;
    static_assert(kTensorSliceMaxRank == 8,
                  "If kTensorSliceMaxRank changes, modify the loop below.");
    for (Index i0 = 0; i0 < len[0]; i0++) {
      for (Index i1 = 0; i1 < len[1]; i1++) {
        for (Index i2 = 0; i2 < len[2]; i2++) {
          for (Index i3 = 0; i3 < len[3]; i3++) {
            for (Index i4 = 0; i4 < len[4]; i4++) {
              for (Index i5 = 0; i5 < len[5]; i5++) {
                for (Index i6 = 0; i6 < len[6]; i6++) {
                  for (Index i7 = 0; i7 < len[7]; i7++) {
                    d(d_start[0] + i0, d_start[1] + i1, d_start[2] + i2,
                      d_start[3] + i3, d_start[4] + i4, d_start[5] + i5,
                      d_start[6] + i6, d_start[7] + i7) =
                        Eigen::half_impl::raw_uint16_to_half(
                            s(s_start[0] + i0, s_start[1] + i1, s_start[2] + i2,
                              s_start[3] + i3, s_start[4] + i4, s_start[5] + i5,
                              s_start[6] + i6, s_start[7] + i7));
                  }
                }
              }
            }
          }
        }
      }
    }
  }
};

// Given a tensor described by "shape", two slices "slice_s" and "slice_d",
// and two pointers "ptr_s" and "ptr_d", where "ptr_s" points to a chunk of
// memory that stores the data for "slice_s" and "ptr_d" points to a chunk of
// memory that stores the data for "slice_d". This function copies the data
// that belongs to the intersection of the two slices from slice_s to
// slice_d.  Uses Tensor cast<DstT>() to convert from SrcT to DstT. Returns true
// iff the two slices share any intersection (and thus some data is copied).
// TODO(yangke): figure out if we can make it private.
template <typename SrcT, typename DstT>
static bool CopyDataFromTensorSliceToTensorSlice(const TensorShape& shape,
                                                 const TensorSlice& slice_s,
                                                 const TensorSlice& slice_d,
                                                 const SrcT* ptr_s,
                                                 DstT* ptr_d) {
  CHECK_LE(shape.dims(), kTensorSliceMaxRank)
      << "Only tensors of size up to " << kTensorSliceMaxRank
      << " are supported";
  // We need to compute the intersection of the two slices.
  TensorSlice inter;
  if (!slice_s.Intersect(slice_d, &inter)) {
    // There is no intersection: returns false.
    return false;
  } else {
    // We need to compute the applied shapes after applying slice_s and
    // slice_d.
    TensorShape shp_s, shp_d;
    Status s;
    s = slice_s.SliceTensorShape(shape, &shp_s);
    if (!s.ok()) {
      LOG(WARNING) << s;
      return false;
    }
    s = slice_d.SliceTensorShape(shape, &shp_d);
    if (!s.ok()) {
      LOG(WARNING) << s;
      return false;
    }

// We need to compute the relative slice of "inter" w.r.t. both slice_s and
    // slice_d.
    TensorSlice rel_s, rel_d;
    slice_s.ComputeRelative(inter, &rel_s);
    slice_d.ComputeRelative(inter, &rel_d);

// Get the eigen tensor maps to the data.
    auto t_s = GetEigenTensorMapFromTensorShape(shp_s, ptr_s);
    auto t_d = GetEigenTensorMapFromTensorShape(shp_d, ptr_d);

Eigen::DSizes<Eigen::DenseIndex, kTensorSliceMaxRank> s_start, s_len,
        d_start, d_len;

rel_s.FillIndicesAndSizes<kTensorSliceMaxRank>(shp_s, &s_start, &s_len);
    rel_d.FillIndicesAndSizes<kTensorSliceMaxRank>(shp_d, &d_start, &d_len);
    CopyThatWorksWithStringPointer<DstT>::Copy(t_s, s_start, s_len, t_d,
                                               d_start);
    return true;
  }
}

}  // namespace

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_UTIL_TENSOR_SLICE_UTIL_H_