/* Copyright 2015 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    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,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/

#include "tensorflow/core/util/work_sharder.h"

#include "tensorflow/core/lib/core/blocking_counter.h"
#include "tensorflow/core/platform/logging.h"

namespace tensorflow {

/* ABSL_CONST_INIT */ thread_local int per_thread_max_parallism = 1000000;

void SetPerThreadMaxParallelism(int max_parallelism) {
  CHECK_LE(0, max_parallelism);
  per_thread_max_parallism = max_parallelism;
}

int GetPerThreadMaxParallelism() { return per_thread_max_parallism; }

void Shard(int max_parallelism, thread::ThreadPool* workers, int64 total,
           int64 cost_per_unit, std::function<void(int64, int64)> work) {
  CHECK_GE(total, 0);
  if (total == 0) {
    return;
  }
  max_parallelism = std::min(max_parallelism, GetPerThreadMaxParallelism());
  if (max_parallelism <= 1) {
    // Just inline the whole work since we only have 1 thread (core).
    work(0, total);
    return;
  }
  if (max_parallelism >= workers->NumThreads()) {
    workers->ParallelFor(total, cost_per_unit, work);
    return;
  }
  Sharder::Do(total, cost_per_unit, work,
              [&workers](Sharder::Closure c) { workers->Schedule(c); },
              max_parallelism);
}

// DEPRECATED: Prefer threadpool->TransformRangeConcurrently, which allows you
// to directly specify the shard size.
void Sharder::Do(int64 total, int64 cost_per_unit, const Work& work,
                 const Runner& runner, int max_parallelism) {
  cost_per_unit = std::max(int64{1}, cost_per_unit);
  // We shard [0, total) into "num_shards" shards.
  //   1 <= num_shards <= num worker threads
  //
  // If total * cost_per_unit is small, it is not worth shard too
  // much. Let us assume each cost unit is 1ns, kMinCostPerShard=10000
  // is 10us.
  static const int64 kMinCostPerShard = 10000;
  const int num_shards =
      std::max<int>(1, std::min(static_cast<int64>(max_parallelism),
                                total * cost_per_unit / kMinCostPerShard));

  // Each shard contains up to "block_size" units. [0, total) is sharded
  // into:
  //   [0, block_size), [block_size, 2*block_size), ...
  // The 1st shard is done by the caller thread and the other shards
  // are dispatched to the worker threads. The last shard may be smaller than
  // block_size.
  const int64 block_size = (total + num_shards - 1) / num_shards;
  CHECK_GT(block_size, 0);  // total > 0 guarantees this.
  if (block_size >= total) {
    work(0, total);
    return;
  }
  const int num_shards_used = (total + block_size - 1) / block_size;
  BlockingCounter counter(num_shards_used - 1);
  for (int64 start = block_size; start < total; start += block_size) {
    auto limit = std::min(start + block_size, total);
    runner([&work, &counter, start, limit]() {
      work(start, limit);        // Compute the shard.
      counter.DecrementCount();  // The shard is done.
    });
  }

  // Inline execute the 1st shard.
  work(0, std::min(block_size, total));
  counter.Wait();
}

}  // end namespace tensorflow