EVOLUTION-MANAGER

Edit File: gather_nd_op.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

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distributed under the License is distributed on an "AS IS" BASIS,
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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_GATHER_ND_OP_H_
#define TENSORFLOW_CORE_KERNELS_GATHER_ND_OP_H_
// Functor definition for GatherOp, must be compilable by nvcc.

#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/bounds_check.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/util/util.h"

namespace tensorflow {

class OpKernelContext;
class Status;
class Tensor;

namespace functor {

template <typename Device, typename T, typename Index, int IXDIM>
struct GatherNdSlice {
  // Performs a slice gather op on (Tparams, Tindices), writing to Tout.
  // Returns an index to Tindices if the value at that index is out of range.
  // Returns -1 if all values of Tindices are in range.
  Index operator()(const Device& d, const Index slice_size,
                   typename TTypes<int32>::Scalar Tscratch,
                   typename TTypes<T, IXDIM + 1>::ConstTensor Tparams,
                   typename TTypes<Index>::ConstMatrix Tindices,
                   typename TTypes<T>::Matrix Tout);
};

template <typename Device, typename T, typename Index>
Status DoGatherNd(OpKernelContext* c, const Tensor& params,
                  const Tensor& indices, Tensor* out) {
  if (!TensorShapeUtils::IsVectorOrHigher(params.shape())) {
    return errors::InvalidArgument("params must be at least a vector");
  }
  if (!TensorShapeUtils::IsVectorOrHigher(indices.shape())) {
    return errors::InvalidArgument("indices must be at least a vector");
  }
  if (indices.dim_size(indices.dims() - 1) > params.dims()) {
    return errors::InvalidArgument(
        "index innermost dimension length must be <= params rank; saw: ",
        indices.dim_size(indices.dims() - 1), " vs. ", params.dims());
  }

const TensorShape& indices_shape(indices.shape());
  const int64 indices_nd = indices_shape.dim_size(indices_shape.dims() - 1);

// Check that we have enough index space
  int64 N_big = 1;
  for (int i = 0; i < indices_shape.dims() - 1; ++i) {
    N_big *= indices_shape.dim_size(i);
  }
  if (N_big > std::numeric_limits<int>::max()) {
    return errors::InvalidArgument(
        "indices has too many elements for int indexing: ", N_big, " > ",
        std::numeric_limits<int>::max());
  }
  if (params.NumElements() > std::numeric_limits<Index>::max()) {
    return errors::InvalidArgument("params.NumElements() too large for ",
                                   DataTypeString(DataTypeToEnum<Index>::v()),
                                   " indexing: ", params.NumElements(), " > ",
                                   std::numeric_limits<Index>::max());
  }

// The result shape is
  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]
  Index N_result = 1;
  for (int i = 0; i < indices_shape.dims() - 1; ++i) {
    N_result *= indices_shape.dim_size(i);
  }

const TensorShape& params_shape(params.shape());
  Index total_nd = params_shape.dims();

TensorShape result_shape(indices_shape);
  result_shape.RemoveLastDims(1);

int64 slice_size_big = 1;
  for (Index i = indices_nd; i < total_nd; ++i) {
    slice_size_big *= params_shape.dim_size(i);
    result_shape.AddDim(params_shape.dim_size(i));
  }

if (slice_size_big > std::numeric_limits<Index>::max()) {
    return errors::InvalidArgument(
        "slice size is too large for indexing: ", slice_size_big, " > ",
        std::numeric_limits<Index>::max());
  }

const Index slice_size = static_cast<Index>(slice_size_big);

TF_RETURN_IF_ERROR(
      c->allocate_temp(DataTypeToEnum<T>::value, result_shape, out));

if (N_result > 0) {
    if (params_shape.num_elements() == 0) {
      return errors::InvalidArgument(
          "Requested more than 0 entries, but "
          "params is empty.  Params shape: ",
          params_shape.DebugString());
    }

auto indices_mat = indices.flat_inner_dims<Index>();

Index bad_i = -1;

// Request to copy slices / subtensors
    // Make out a matrix with the slices the col size.
    auto out_mat = out->shaped<T, 2>({N_result, slice_size});
    Tensor scratch;
    TF_RETURN_IF_ERROR(c->allocate_temp(DT_INT32, TensorShape(), &scratch));
    auto scratch_scalar = scratch.scalar<int32>();

switch (indices_nd) {
#define PARAMS_CASE(IXDIM)                                              \
  case IXDIM: {                                                         \
    functor::GatherNdSlice<Device, T, Index, IXDIM> func;               \
    auto params_flat = params.flat_outer_dims<T, IXDIM + 1>();          \
    bad_i = func(c->eigen_device<Device>(), slice_size, scratch_scalar, \
                 params_flat, indices_mat, out_mat);                    \
  } break
      PARAMS_CASE(0);
      PARAMS_CASE(1);
      PARAMS_CASE(2);
      PARAMS_CASE(3);
      PARAMS_CASE(4);
      PARAMS_CASE(5);
      PARAMS_CASE(6);
      PARAMS_CASE(7);
#undef PARAMS_CASE
      default:
        return errors::InvalidArgument(
            "Only indices.shape[-1] values between 1 and 7 "
            "are currently supported.  Requested rank: ",
            indices_nd);
    }

// bad_i will only return >= 0 on CPUs right now.
    if (bad_i >= 0) {
      auto shape = indices.shape();
      shape.RemoveLastDims(1);
      return errors::InvalidArgument(
          "indices", SliceDebugString(shape, bad_i), " = [",
          str_util::Join(
              gtl::ArraySlice<Index>(&indices_mat(bad_i, 0), indices_nd), ", "),
          "] does not index into param shape ", params.shape().DebugString());
    }
  }
  return Status::OK();
}

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_GATHER_ND_OP_H_