caffe2/queue/rebatching_queue.h

/**
 * Copyright (c) 2016-present, Facebook, Inc.
 *
 * 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.
 */

#pragma once

#include <atomic>
#include <condition_variable>
#include <memory>
#include <mutex>
#include <queue>

#include "caffe2/core/logging.h"
#include "caffe2/core/operator.h"
#include "caffe2/core/stats.h"
#include "caffe2/core/tensor.h"

namespace caffe2 {

// TODO: This is a very naive implementation with a single mutex. We can do the
// atomic index + circular queue optimizations or pull something more
// heavy-weight later

class RebatchingQueue {
 public:
  RebatchingQueue(size_t capacity, size_t numBlobs);

  ~RebatchingQueue();

  bool enqueueOne(
      CPUContext& context,
      const std::vector<const TensorCPU*>& inputs);

  bool enqueueMany(
      CPUContext& context,
      const std::vector<const TensorCPU*>& inputs);

  bool dequeue(
      CPUContext& context,
      size_t numElements,
      const std::vector<TensorCPU*>& outputs);

  size_t capacity() const;

  size_t numBlobs() const;

  bool isClosed() const;

  void close();

 private:
  bool enqueue(std::vector<std::vector<TensorCPU>> splittedInputs);

  bool canWrite() const;
  bool canRead() const;

  const size_t capacity_;
  const size_t numBlobs_;

  mutable std::mutex mutex_;

  bool isClosed_{false};

  uint64_t head_{0};
  uint64_t tail_{0};

  std::condition_variable cvEmpty_;
  std::condition_variable cvOverflow_;

  std::vector<std::vector<TensorCPU>> queue_;
};
} // caffe2