EVOLUTION-MANAGER

Edit File: concat_lib_cpu.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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==============================================================================*/

#ifndef TENSORFLOW_CORE_KERNELS_CONCAT_LIB_CPU_H_
#define TENSORFLOW_CORE_KERNELS_CONCAT_LIB_CPU_H_

#define EIGEN_USE_THREADS

#include <vector>
#include "tensorflow/core/framework/register_types.h"
#include "tensorflow/core/kernels/concat_lib.h"
#include "tensorflow/core/util/work_sharder.h"

namespace tensorflow {

// ElementCopier must be a struct with a single Copy function, which is passed
// the output pointer, input pointer, input index, and number of elements to
// copy from input to output.
template <typename T, typename ElementCopier>
void ConcatCPUImpl(
    DeviceBase* d,
    const std::vector<std::unique_ptr<typename TTypes<T, 2>::ConstMatrix>>&
        inputs,
    int64 cost_per_unit, ElementCopier copier,
    typename TTypes<T, 2>::Matrix* output) {
  size_t num_inputs = inputs.size();

std::vector<ptrdiff_t> sizes;
  sizes.reserve(num_inputs);
  int64 row_size = 0;
  for (const auto& input : inputs) {
    sizes.push_back(input->dimension(1));
    row_size += sizes.back();
  }

// cost_per_unit is estimated bytes to copy per output array element (for
  // strings this includes an estimate of the number of bytes of the actual
  // string data, as well).
  const int64 estimated_total_cost = output->size() * cost_per_unit;

auto worker_threads = d->tensorflow_cpu_worker_threads();
  int num_threads = std::min(4, worker_threads->num_threads);
  num_threads = static_cast<int>(
      std::min<int64>(num_threads, estimated_total_cost / 16384));
  // Single threaded mode.
  // TODO(dga):  Deduplicate this code w.r.t. sharded code below.
  if (num_threads == 0) {
    T* out = &(*output)(0, 0);
    std::vector<const T*> inp;
    inp.reserve(num_inputs);
    for (const auto& input : inputs) {
      inp.push_back(&(*input)(0, 0));
    }
    const int64 dim0 = output->dimension(0);
    for (int64 i = 0; i < dim0; ++i) {
      for (int64 j = 0; j < num_inputs; ++j) {
        auto size = sizes[j];
        copier.Copy(out, inp[j], j, size);
        out += size;
        inp[j] += size;
      }
    }
    return;
  }

// Sharded mode.
  auto work = [&row_size, &sizes, &inputs, &output, &copier, &num_inputs](
                  int64 start, int64 end) {
    int64 skipped_rows = start / row_size;
    T* out = output->data() + skipped_rows * row_size;
    T* out_start = output->data() + start;
    T* out_end = output->data() + end;

// Handle partial row at start
    if (out < out_start) {
      for (size_t j = 0; j < num_inputs; ++j) {
        ptrdiff_t size = sizes[j];
        ptrdiff_t offset = out_start - out;
        if (size <= offset) {
          out += size;
          continue;
        }
        const T* inp = &(*inputs[j])(skipped_rows, 0);
        if (offset > 0) {
          out += offset;
          inp += offset;
          size -= offset;
        }
        size = std::min(size, out_end - out);
        if (size <= 0) break;
        copier.Copy(out, inp, j, size);
        out += size;
      }
      ++skipped_rows;
    }
    if (out == out_end) return;
    CHECK(out >= out_start);
    CHECK(out < out_end);

// Copy remaining data.
    std::vector<const T*> inp;
    inp.reserve(num_inputs);
    for (const auto& input : inputs) {
      inp.push_back(&(*input)(skipped_rows, 0));
    }
    const int64 dim0 = output->dimension(0);
    for (int64 i = skipped_rows; i < dim0; ++i) {
      for (int64 j = 0; j < num_inputs; ++j) {
        ptrdiff_t size = std::min(sizes[j], out_end - out);
        copier.Copy(out, inp[j], j, size);
        out += size;
        inp[j] += size;
        if (out == out_end) return;
      }
    }
  };
  Shard(worker_threads->num_threads, worker_threads->workers, output->size(),
        cost_per_unit, work);
}

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_KERNELS_CONCAT_LIB_CPU_H_