EVOLUTION-MANAGER

Edit File: reduction_ops_common.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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==============================================================================*/

// This is an internal header file intended to only be included as the
// front-matter in the implementation files of various reduction ops.  It
// is a header file because we split the various reduction ops into their
// own compilation units to get more parallelism in compilation.

#ifndef TENSORFLOW_CORE_KERNELS_REDUCTION_OPS_COMMON_H_
#define TENSORFLOW_CORE_KERNELS_REDUCTION_OPS_COMMON_H_

#define EIGEN_USE_THREADS

#include "third_party/eigen3/Eigen/Core"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"

#include "tensorflow/core/framework/numeric_op.h"
#include "tensorflow/core/framework/op_kernel.h"
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/kernels/reduction_ops.h"
#include "tensorflow/core/kernels/transpose_functor.h"
#include "tensorflow/core/lib/core/status.h"
#include "tensorflow/core/lib/gtl/inlined_vector.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

typedef Eigen::ThreadPoolDevice CPUDevice;
typedef Eigen::GpuDevice GPUDevice;

template <typename Device>
struct Constants {
  // Derive Index type. int (32-bit) or long (64-bit) depending on the
  // compile-time configuration. "float" here is not relevant.
  // TODO(zhifengc): Moves the definition to TTypes.
  typedef TTypes<float>::Tensor::Index Index;
  Eigen::array<Index, 1> kZero;
  Eigen::array<Index, 1> kOne;
  Eigen::array<Index, 2> kZeroTwo;

Constants() {
    kZero[0] = 0;
    kOne[0] = 1;
    kZeroTwo[0] = 0;
    kZeroTwo[1] = 2;
  }
};

#if defined(EIGEN_HAS_INDEX_LIST)
struct ConstantsBase {
  const Eigen::IndexList<Eigen::type2index<0>> kZero;
  const Eigen::IndexList<Eigen::type2index<1>> kOne;
  const Eigen::IndexList<Eigen::type2index<0>, Eigen::type2index<2>> kZeroTwo;
};
template <>
struct Constants<CPUDevice> : ConstantsBase {};
#endif  // EIGEN_HAS_INDEX_LIST

class ReductionHelper {
 public:
  ReductionHelper() : reduce_first_axis_(false) {}

Status Simplify(const Tensor& data, const Tensor& axis, const bool keep_dims);

// We need to do roughly:
  //   tmp_out = allocate(out_reshape())
  //   tmp_out.reshape(out_reshape) = data.reshape(data_reshape).reduce(axes)
  //   out = tmp_out.reshape(out_shape)

// The reduction result must be allocated with this shape.
  TensorShape out_reshape() const;

// The final output shape must be allocated with this shape.
  TensorShape out_shape() const;

// The reduction is on a reshaped tensor of this rank.
  int ndims() const { return data_reshape_.size(); }

// True if need to reduce the 0-th dimension.
  bool reduce_first_axis() const { return reduce_first_axis_; }

// The output is reshaped.
  template <typename T, int N>
  typename TTypes<T, N>::Tensor out(Tensor* out) {
    return out->shaped<T, N>(out_reshape_);
  }

// The input is reshaped.
  template <typename T, int N>
  typename TTypes<T, N>::ConstTensor in(const Tensor& data) {
    return data.shaped<T, N>(data_reshape_);
  }

// Shape of shuffled input
  TensorShape data_reshape() const {
    TensorShape shape;
    for (auto s : data_reshape_) shape.AddDim(s);
    return shape;
  }

// Shape with all reduction dimensions at the end
  TensorShape shuffled_shape();

// Permutation of reduced dims needed to put reduction dimensions at the end
  gtl::InlinedVector<int32, 8> permutation();

private:
  bool reduce_first_axis_;  // True if need to reduce the 0-th dimension.
  gtl::InlinedVector<int64, 4> data_reshape_;  // Reshape data before reduction.
  gtl::InlinedVector<int64, 4> out_shape_;     // The final output shape.
  gtl::InlinedVector<int64, 4> out_reshape_;   // Reshape output for reduction.
};

// For operations where the output is a reduction function along some
// dimensions of the input.
template <typename Device, class T, typename Tperm, typename Reducer>
class ReductionOp : public OpKernel {
 public:
  explicit ReductionOp(OpKernelConstruction* ctx) : OpKernel(ctx) {
    const DataType dt = DataTypeToEnum<T>::v();
    const DataType pt = DataTypeToEnum<Tperm>::v();
    OP_REQUIRES_OK(ctx, ctx->MatchSignature({dt, pt}, {dt}));

OP_REQUIRES_OK(ctx, ctx->GetAttr("keep_dims", &keep_dims_));
  }

void Compute(OpKernelContext* ctx) override {
    const Tensor& data = ctx->input(0);
    const Tensor& axes = ctx->input(1);
    VLOG(1) << "data shape: " << data.shape().DebugString();
    VLOG(1) << "axes      : " << axes.SummarizeValue(10);

ReductionHelper helper;
    OP_REQUIRES_OK(ctx, helper.Simplify(data, axes, keep_dims_));
    CHECK_GE(helper.ndims(), 0);

bool is_scalar_identity = functor::ReducerTraits<Reducer>::IsScalarIdentity;
    bool is_trivial = helper.ndims() == 0 ||
                      (helper.ndims() == 1 && !helper.reduce_first_axis());
    if (is_scalar_identity && is_trivial) {
      Tensor out;
      // Special case. Reduces nothing and does not alter the input values.
      if (!out.CopyFrom(data, helper.out_shape())) {
        ctx->SetStatus(errors::Internal("Error during reduction copy."));
      }
      ctx->set_output(0, out);
      return;
    }

// We must allocate temp tensors using the same alloc attr as
    // output(0) because it is returned as output(0) in the end.
    const AllocatorAttributes alloc_attr = ctx->output_alloc_attr(0);

Tensor tmp_out;
    typedef functor::ReduceFunctor<Device, Reducer> Functor;
    Constants<Device> constants;
    const Device& d = ctx->eigen_device<Device>();
    Reducer reducer;

if (data.NumElements() > 0 && is_trivial && !is_scalar_identity) {
      OP_REQUIRES_OK(ctx, ctx->allocate_temp(ctx->expected_output_dtype(0),
                                             TensorShape({data.NumElements()}),
                                             &tmp_out, alloc_attr));
      Functor::Reduce(ctx, tmp_out.flat<T>(),
                      data.shaped<T, 2>({1, data.NumElements()}),
                      constants.kZero, reducer);
    } else {
      // A temporary tensor whose size matches the size of the reduced
      // output.
      OP_REQUIRES_OK(
          ctx, ctx->allocate_temp(ctx->expected_output_dtype(0),
                                  helper.out_reshape(), &tmp_out, alloc_attr));

if (tmp_out.NumElements() == 0) {
        // Nothing to do, fall through to final reshaping.
      } else if (data.NumElements() == 0) {
        // Degenerate reduction where the input is empty but the output is
        // nonempty (thus tmp_out.NumElements() > 0), and we must fill the
        // output with identity elements.  Example: tf.reduce_sum(tf.zeros((0,
        // 3)), [0]). Eigen sometimes crashes in this case, so we do it
        // manually.
        Functor::FillIdentity(d, tmp_out.flat<T>(), reducer);
      } else if ((helper.ndims() == 1) && helper.reduce_first_axis()) {
        // Reduce to a scalar.
        Functor::Reduce(ctx, helper.out<T, 0>(&tmp_out), helper.in<T, 1>(data),
                        constants.kZero, reducer);
      } else if ((helper.ndims() == 2) && helper.reduce_first_axis()) {
        // Can be viewed as a reduction of a matrix along 1st dimension.
        Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
                        constants.kZero, reducer);
      } else if ((helper.ndims() == 2) && !helper.reduce_first_axis()) {
        // Can be viewed as a reduction of a matrix along 2nd dimension.
        Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 2>(data),
                        constants.kOne, reducer);
      } else if ((helper.ndims() == 3) && helper.reduce_first_axis()) {
        // Can be viewed as a reduction of a 3D tensor along 1st and 3rd
        // dimensions.
        Functor::Reduce(ctx, helper.out<T, 1>(&tmp_out), helper.in<T, 3>(data),
                        constants.kZeroTwo, reducer);
      } else if ((helper.ndims() == 3) && !helper.reduce_first_axis()) {
        // Can be viewed as a reduction of a 3D tensor along 2nd dimension.
        Functor::Reduce(ctx, helper.out<T, 2>(&tmp_out), helper.in<T, 3>(data),
                        constants.kOne, reducer);
      } else {
        // If we don't hit one of the cases above, transpose the data so that
        // all reduced dimensions are last and reuse the 2-D -> 1-D case.
        Tensor data_reshaped;
        OP_REQUIRES(ctx, data_reshaped.CopyFrom(data, helper.data_reshape()),
                    errors::Internal("Error during reduction copy."));
        Tensor shuffled;
        OP_REQUIRES_OK(ctx, ctx->allocate_temp(DataTypeToEnum<T>::value,
                                               helper.shuffled_shape(),
                                               &shuffled, alloc_attr));
        OP_REQUIRES_OK(ctx, DoTranspose(d, data_reshaped, helper.permutation(),
                                        &shuffled));
        const int64 unreduced = tmp_out.NumElements();
        const int64 reduced = shuffled.NumElements() / unreduced;
        const Tensor& const_shuffled = shuffled;
        Functor::Reduce(ctx, tmp_out.flat<T>(),
                        const_shuffled.shaped<T, 2>({unreduced, reduced}),
                        constants.kOne, reducer);
      }
    }

// Set the real output using the contents of the reduction but the
    // real expected output shape.  The number of elements should
    // match between the two shapes.
    Tensor out;
    OP_REQUIRES(ctx, out.CopyFrom(tmp_out, helper.out_shape()),
                errors::Internal("Error during reduction copy."));
    ctx->set_output(0, out);
  }

private:
  // True if the number of dimensions should be maintained.
  bool keep_dims_;
};

namespace functor {

template <typename Device, typename Reducer>
struct ReduceFunctorBase {
  template <typename OUT_T, typename IN_T, typename ReductionAxes>
  static void Reduce(OpKernelContext* ctx, OUT_T out, IN_T in,
                     const ReductionAxes& reduction_axes,
                     const Reducer& reducer) {
    const Device& d = ctx->eigen_device<Device>();
    ReduceEigenImpl<Device, OUT_T, IN_T, ReductionAxes, Reducer> reducer_impl;
    reducer_impl(d, out, in, reduction_axes, reducer);
  }

template <typename OUT_T>
  static void FillIdentity(const Device& d, OUT_T out, const Reducer& reducer) {
    FillIdentityEigenImpl(d, out, reducer);
  }
};

template <typename Reducer>
struct ReduceFunctor<CPUDevice, Reducer>
    : ReduceFunctorBase<CPUDevice, Reducer> {};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_REDUCTION_OPS_COMMON_H_