EVOLUTION-MANAGER

Edit File: redux_functor.h

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

#ifndef TENSORFLOW_CORE_KERNELS_REDUX_FUNCTOR_H_
#define TENSORFLOW_CORE_KERNELS_REDUX_FUNCTOR_H_

#define EIGEN_USE_THREADS

#include "third_party/eigen3/Eigen/Core"
#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_types.h"
#include "tensorflow/core/platform/types.h"

namespace tensorflow {

using CPUDevice = Eigen::ThreadPoolDevice;

namespace functor {

// Compute reduction over outer dimensions.
// Example:
//   input: [D1, D2, ... , DN]
//   ->
//   output: [Di, ... , DN] where i belongs to set [1,N]
template <typename InputT, typename AccumT, typename OutputT,
          typename BinaryFunctor>
struct ReduceOuterDimensions {
  ReduceOuterDimensions() {}

template <int num_dims>
  void operator()(const CPUDevice& device,
                  const Eigen::DSizes<Eigen::Index, num_dims>& input_dims,
                  const Tensor& input, Tensor* output) const {
    // Compute inner and outer dim after reshaping into 2d tensor.
    const int num_output_dims = output->dims();
    auto output_dims = output->template flat<OutputT>().dimensions();

Eigen::Index inner_dim = 1, outer_dim = 1;
    for (int i = 0; i < num_dims - num_output_dims; ++i)
      outer_dim *= input_dims[i];
    for (int i = num_dims - num_output_dims; i < num_dims; ++i)
      inner_dim *= input_dims[i];

if (1 == outer_dim) {
      // Nothing to do but passing input to output.
      output->template flat<OutputT>() =
          input.template flat<InputT>().template cast<OutputT>().reshape(
              output_dims);
      return;
    }

// Get device thread num.
    const Eigen::Index num_threads = device.numThreads();

// If the inner dim parallelism is large enough
    // TODO(ezhulenev): There seems to be no benefits in going this route. Check
    // if this can be improved, or use better heuristic?
    if (inner_dim > num_threads * 32) {
      // Do not create more blocks than there are threads in a pool.
      const Eigen::Index num_blocks = num_threads;

// Block size along the outer dimension.
      const Eigen::Index inner_block_size = Eigen::divup(inner_dim, num_blocks);
      const InputT* input_data = input.template flat<InputT>().data();

// Allocate temporary buffer for partial reductions.
      Eigen::Tensor<AccumT, 1, Eigen::RowMajor, Eigen::Index> buffer(
          {inner_dim});
      buffer.setZero();
      AccumT* buffer_data = buffer.data();

using Buffer = Eigen::TensorMap<
          Eigen::Tensor<AccumT, 1, Eigen::RowMajor, Eigen::Index>,
          Eigen::Unaligned>;

using Input = Eigen::TensorMap<
          Eigen::Tensor<const InputT, 1, Eigen::RowMajor, Eigen::Index>,
          Eigen::Unaligned>;

const auto compute = [inner_dim, outer_dim, num_blocks, inner_block_size,
                            input_data, buffer_data](
                               Eigen::Index start, Eigen::Index limit) -> void {
        DCHECK(start >= 0 && limit <= num_blocks);
        Eigen::Index inner_dim_start = start * inner_block_size;
        Eigen::Index inner_dim_limit = limit * inner_block_size;
        inner_dim_limit = std::min(inner_dim, inner_dim_limit);
        Eigen::Index my_job_len = inner_dim_limit - inner_dim_start;

const InputT* my_job_start = input_data + inner_dim_start;
        Buffer buf(buffer_data + inner_dim_start, my_job_len);

for (Eigen::Index i = 0; i < outer_dim; ++i) {
          auto in = Input(my_job_start + i * inner_dim, my_job_len);
          auto cast = in.template cast<AccumT>();
          buf = Eigen::TensorCwiseBinaryOp<BinaryFunctor, const decltype(buf),
                                           const decltype(cast)>(buf, cast);
        }
      };

// Compute cost of reducing a single block.
      const Eigen::Index compute_size = outer_dim * inner_block_size;
      const Eigen::Index compute_input_bytes = compute_size * sizeof(InputT);
      const Eigen::TensorOpCost cost(
          compute_input_bytes,
          0,  // We'll be mostly writing to L1, assume store cost is 0
          compute_size * Eigen::internal::functor_traits<BinaryFunctor>::Cost);

device.parallelFor(num_blocks, cost, compute);

// Write final result to the output.
      output->template flat<OutputT>() =
          buffer.template cast<OutputT>().reshape(output_dims);
    } else {
      // Compute block size along the outer dimension for efficiency.
      const Eigen::Index parallel_cell_size = inner_dim;
      const Eigen::Index total_workload = outer_dim * inner_dim;
      const Eigen::Index max_parallelism = total_workload / parallel_cell_size;

const Eigen::Index min_block_workload = 2000;
      const Eigen::Index min_block_size =
          Eigen::divup(min_block_workload, parallel_cell_size);
      const Eigen::Index max_num_blocks = std::min(
          max_parallelism, Eigen::divup(total_workload, min_block_size));

// Do not create more blocks than there are threads in a pool.
      const Eigen::Index num_blocks = std::min(max_num_blocks, num_threads);

// Block size along the outer dimension.
      const Eigen::Index outer_block_size = Eigen::divup(outer_dim, num_blocks);

const InputT* input_data = input.template flat<InputT>().data();

// Allocate temporary buffer for partial reductions.
      Tensor buffer(DataTypeToEnum<AccumT>::v(), {num_blocks, inner_dim});
      buffer.template flat<AccumT>().setZero();
      AccumT* buffer_data = buffer.template flat<AccumT>().data();

using Buffer = Eigen::TensorMap<
          Eigen::Tensor<AccumT, 1, Eigen::RowMajor, Eigen::Index>,
          Eigen::Unaligned>;

using Input = Eigen::TensorMap<
          Eigen::Tensor<const InputT, 1, Eigen::RowMajor, Eigen::Index>,
          Eigen::Unaligned>;

const auto compute = [inner_dim, num_blocks, outer_block_size,
                            buffer_data, input_data, outer_dim](
                               Eigen::Index start, Eigen::Index limit) -> void {
        DCHECK(start >= 0 && limit <= num_blocks);
        Eigen::Index outer_dim_start = start * outer_block_size;
        Eigen::Index outer_dim_limit = limit * outer_block_size;
        outer_dim_limit = std::min(outer_dim, outer_dim_limit);

Buffer buf(buffer_data + start * inner_dim, inner_dim);
        for (Eigen::Index i = outer_dim_start; i < outer_dim_limit; ++i) {
          auto in = Input(input_data + i * inner_dim, inner_dim);
          auto cast = in.template cast<AccumT>();
          buf = Eigen::TensorCwiseBinaryOp<BinaryFunctor, const decltype(buf),
                                           const decltype(cast)>(buf, cast);
        }
      };

// Compute cost of reducing a single block.
      const Eigen::Index compute_size = outer_block_size * inner_dim;
      const Eigen::Index compute_input_bytes = compute_size * sizeof(InputT);
      const Eigen::TensorOpCost cost(
          compute_input_bytes,
          0,  // We'll be mostly writing to L1, assume store cost is 0
          compute_size * Eigen::internal::functor_traits<BinaryFunctor>::Cost);

device.parallelFor(num_blocks, cost, compute);

// Aggregate partial results from temporary buffer into first block.
      auto buf0 = Buffer(buffer_data, inner_dim);
      // Just sum the buffer up, as inner dimensions is not large in this case.
      for (int i = 1; i < num_blocks; ++i) {
        auto buf = Buffer(buffer_data + i * inner_dim, inner_dim);
        buf0 = Eigen::TensorCwiseBinaryOp<BinaryFunctor, const decltype(buf0),
                                          const decltype(buf)>(buf0, buf);
      }
      // Write final result to the output.
      output->template flat<OutputT>() =
          buf0.template cast<OutputT>().reshape(output_dims);
    }
  }
};

// Compute reduction to some serial middle dimensions (like a axis).
// Example:
//   input: [D1, D2, ... , DN]
//   ->
//   output: [Di, ... , Dj] where i & j belongs to set [1,N].
template <typename InputT, typename AccumT, typename OutputT,
          typename BinaryFunctor, typename Reducer>
struct ReduceMiddleDimensions {
  ReduceMiddleDimensions() {}

template <int num_dims>
  void operator()(const CPUDevice& device,
                  const Eigen::DSizes<Eigen::Index, num_dims>& input_dims,
                  const Tensor& input, Tensor* output,
                  const int axis_begin_dim) const {
    // Compute dims after reshaping into 3d tensor.
    const int num_output_dims = output->dims();
    auto output_dims = output->template flat<OutputT>().dimensions();

Eigen::Index inner_dim = 1, middle_dim = 1, outer_dim = 1;
    for (int i = 0; i < axis_begin_dim; ++i) outer_dim *= input_dims[i];
    for (int i = axis_begin_dim; i < axis_begin_dim + num_output_dims; ++i)
      middle_dim *= input_dims[i];
    for (int i = axis_begin_dim + num_output_dims; i < num_dims; ++i)
      inner_dim *= input_dims[i];

if ((1 == inner_dim * outer_dim)) {
      // Nothing to do.
      output->template flat<OutputT>() =
          input.template flat<InputT>().template cast<OutputT>().reshape(
              output_dims);
      return;
    } else if (1 == inner_dim) {
      // Equivalent to ReduceOuterDimensions.
      const ReduceOuterDimensions<InputT, AccumT, OutputT, BinaryFunctor> redux;
      redux(device, input_dims, input, output);
      return;
    }

// Compute block size along the outer dimension for efficiency.
    const Eigen::Index parallel_cell_size = inner_dim;
    const Eigen::Index max_parallelism = outer_dim * middle_dim;
    const Eigen::Index total_workload = max_parallelism * inner_dim;

const Eigen::Index min_block_workload = 2000;
    const Eigen::Index min_block_size =
        Eigen::divup(min_block_workload, parallel_cell_size);
    const Eigen::Index max_num_blocks =
        std::min(max_parallelism, Eigen::divup(total_workload, min_block_size));

// Do not create more blocks than there are threads in a pool.
    const Eigen::Index num_threads = device.numThreads();
    const Eigen::Index num_blocks = std::min(max_num_blocks, num_threads);

// Block size along the outer dimension.
    const Eigen::Index outer_block_size =
        Eigen::divup(total_workload, num_blocks);

const InputT* input_data = input.template flat<InputT>().data();

// Allocate temporary buffer for partial reductions.
    Eigen::Tensor<AccumT, 2> buffer(num_blocks, middle_dim);
    buffer.setZero();
    AccumT* buffer_data = buffer.data();

using Buffer = Eigen::TensorMap<Eigen::Tensor<AccumT, 1>>;
    using Input = Eigen::TensorMap<Eigen::Tensor<const InputT, 1>>;

Eigen::array<Eigen::Index, 1> reduction_axis = {0};
    Reducer reducer;
    const BinaryFunctor binary_op;

const auto compute = [inner_dim, middle_dim, input_data, buffer_data,
                          total_workload, num_blocks, outer_block_size,
                          reduction_axis, reducer, binary_op](
                             Eigen::Index start, Eigen::Index limit) -> void {
      DCHECK(start >= 0 && limit <= num_blocks);
      Eigen::Index block_start = start * outer_block_size;
      Eigen::Index block_limit = limit * outer_block_size;
      block_limit = std::min(total_workload, block_limit);
      Buffer buf(buffer_data + start * middle_dim, middle_dim);

const int align_start =
          ((block_start + inner_dim - 1) / inner_dim) * inner_dim;
      const int align_end = (block_limit / inner_dim) * inner_dim;

Eigen::Index coordinate = block_start / inner_dim % middle_dim;
      Eigen::Tensor<AccumT, 0> reduced =
          Input(&input_data[block_start], align_start - block_start)
              .reduce(reduction_axis, reducer)
              .template cast<AccumT>();

buf(coordinate) = binary_op(buf(coordinate), reduced(0));

coordinate = align_start / inner_dim % middle_dim;
      for (int i = align_start; i < align_end; i += inner_dim) {
        reduced = Input(&input_data[i], inner_dim)
                      .reduce(reduction_axis, reducer)
                      .template cast<AccumT>();
        buf(coordinate) = binary_op(buf(coordinate), reduced(0));
        ++coordinate;
        if (middle_dim == coordinate) coordinate = 0;
      }

reduced = Input(&input_data[align_end], block_limit - align_end)
                    .reduce(reduction_axis, reducer)
                    .template cast<AccumT>();
      buf(coordinate) = binary_op(buf(coordinate), reduced(0));
    };

// Compute cost of reducing a single block.
    const Eigen::Index compute_size = outer_block_size * inner_dim;
    const Eigen::Index compute_input_bytes = compute_size * sizeof(InputT);
    const Eigen::TensorOpCost cost(
        compute_input_bytes,
        0,  // We'll be mostly writing to L1, assume store cost is 0
        compute_size * Eigen::internal::functor_traits<BinaryFunctor>::Cost);

device.parallelFor(num_blocks, cost, compute);

using Output = Eigen::TensorMap<
        Eigen::Tensor<AccumT, 1, Eigen::RowMajor, Eigen::Index>,
        Eigen::Unaligned>;
    // Aggregate partial results from temporary buffer into first block.
    auto buf0 = Output(buffer_data, middle_dim);
    // TODO(ezhulenev): Parallelize this loop for large inner dimensions?
    for (int i = 1; i < num_blocks; ++i) {
      auto buf = Output(buffer_data + i * middle_dim, middle_dim);
      buf0 = Eigen::TensorCwiseBinaryOp<BinaryFunctor, const decltype(buf0),
                                        const decltype(buf)>(buf0, buf);
    }

// Write final result to the output.
    output->template flat<OutputT>() =
        buf0.template cast<OutputT>().reshape(output_dims);
  }
};

}  // namespace functor
}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_REDUX_FUNCTOR_H_