/* Copyright 2015 Google Inc. 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.
==============================================================================*/

#ifndef TENSORFLOW_GRAPH_COSTMODEL_H_
#define TENSORFLOW_GRAPH_COSTMODEL_H_

#include <unordered_map>
#include <vector>

#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/types.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/macros.h"

namespace tensorflow {
typedef std::unordered_map<string, int32> NodeNameToCostIdMap;

class StepStats;

// CostModel keeps track of the following runtime statistics for nodes
// of a single Graph:
//    * The total number of times a node has executed.
//    * The accumulated execution time (in microseconds) of a node.
//    * The accumulated size (in bytes) of each node's output.
//
// This class is NOT thread-safe.
class CostModel {
 public:
  // If "global" is true, maintains costs based on Node::cost_id, otherwise
  // maintains costs based on Node::id.
  explicit CostModel(bool is_global) : is_global_(is_global) {}

  // Assigns min_count_ as a function of the median count for a Node.
  // This value is then used for suppressing the time/size costs of
  // infrequent operations.
  // NOTE(tucker): Maybe this should move to a subclass of CostModel.
  void SuppressInfrequent();

  bool is_global() const { return is_global_; }

  // Initializes cost model for 'g'.
  void InitFromGraph(const Graph& g);

  // Merges costs from cm.
  // REQUIRES: is_global_ is true for this and for "cm"
  void MergeFromGlobal(const CostModel& cm);

  // Merges costs from "cm", which has been computed relative to "g".
  // REQUIRES: is_global_ is true for this, and false for "cm".
  void MergeFromLocal(const Graph& g, const CostModel& cm);

  void MergeFromStats(const NodeNameToCostIdMap& map, const StepStats& ss);

  // Sets the number of outputs of "node".
  void SetNumOutputs(const Node* node, int num_outputs);

  // Records that "node" has executed "num_count" more times.
  void RecordCount(const Node* node, int num_count);

  // Returns how many times "node" has been executed.
  int32 TotalCount(const Node* node) const;

  // Records that "output_slot" of "node" has produced tensors of
  // aggregated "bytes".
  void RecordSize(const Node* node, int output_slot, Bytes bytes);

  // Returns total bytes of tensors produced by "node"s output slot.
  Bytes TotalBytes(const Node* node, int output_slot) const;

  // Returns a prediction for the size of the tensor at the
  // output_slot produced by one execution of "node".
  Bytes SizeEstimate(const Node* node, int output_slot) const;

  // Records that Executions of "node" have taken "time" microseconds.
  void RecordTime(const Node* node, Microseconds time);

  // Returns the total execution time for "node".
  Microseconds TotalTime(const Node* node) const;

  // Returns a prediction for one execution of "node".
  Microseconds TimeEstimate(const Node* node) const;

  // Check that an estimate is available for every OP node in graph.
  void CheckInitialized(const Graph& graph) const;

  // Records the maximum size in bytes of the tensor generated by "output_slot"
  // of "node".
  void RecordMaxSize(const Node* node, int output_slot, Bytes bytes);

  // Returns the maximum size in bytes of the tensor generated by "output_slot"
  // of "node".
  Bytes MaxSize(const Node* node, int output_slot) const;

  // Records the maximum execution time (in microseconds) of "node".
  void RecordMaxExecutionTime(const Node* node, Microseconds time);

  // Returns the maximum execution time (in microseconds) of "node".
  Microseconds MaxExecutionTime(const Node* node) const;

  // Record the unique id of the tensor generated by "output_slot" of "node".
  // Any other tensor sharing the same id will be an alias, i.e. it will share
  // the same underlying memory storage area.
  void RecordAliases(const Node* node, int output_slot, int64 alias_id);

  // Return the unique id of the tensor generated by "output_slot" of "node".
  int64 Aliases(const Node* node, int output_slot) const;

  // Helper routines to encapsulate static estimation heuristics

  // Compute an estimate of the time to copy "b" bytes over the network,
  // given a fixed cost of "network_latency_millis" milliseconds and
  // an estimated bandwidth of "estimated_gbps" gigabits per second (note that
  // this value is in gigabits, not gigabytes).
  static Microseconds CopyTimeEstimate(Bytes b, double network_latency_millis,
                                       double estimated_gbps);
  static Microseconds ComputationTimeEstimate(int64 mathops);

  // Write the contents of the CostModel to the INFO log.
  void WriteToLog();

 private:
  const bool is_global_;
  inline int Id(const Node* n) const {
    if (is_global_) {
      return n->cost_id();
    } else {
      return n->id();
    }
  }
  // Resizes vectors so that they are large enough for "id".
  void Ensure(int id);

  // Nodes and Edges whose count is < this value
  // get type/byte estimates of 0.
  int32 min_count_ = 0;

  // Number of times each Node has been executed.
  std::vector<int32> count_;
  // Cumulative execution time.
  std::vector<Microseconds> time_;
  // Cumulative Bytes output on each channel.
  std::vector<gtl::InlinedVector<Bytes, 2> > slot_bytes_;

  // Maximum execution time
  std::vector<Microseconds> max_exec_time_;

  // Maximum memory usage
  struct MemUsage {
    Bytes temp_memory_size;
    gtl::InlinedVector<Bytes, 2> output_port_mem;
  };
  std::vector<MemUsage> max_mem_usage_;

  std::vector<gtl::InlinedVector<int64, 2> > output_port_alias_;

  TF_DISALLOW_COPY_AND_ASSIGN(CostModel);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_GRAPH_COSTMODEL_H_
