EVOLUTION-MANAGER

Edit File: costmodel.h

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you may not use this file except in compliance with the License.
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==============================================================================*/

#ifndef TENSORFLOW_CORE_GRAPH_COSTMODEL_H_
#define TENSORFLOW_CORE_GRAPH_COSTMODEL_H_

#include <unordered_map>
#include <vector>

#include "tensorflow/core/framework/cost_graph.pb.h"
#include "tensorflow/core/framework/step_stats.pb.h"
#include "tensorflow/core/framework/tensor_shape.pb.h"
#include "tensorflow/core/graph/graph.h"
#include "tensorflow/core/graph/types.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/gtl/array_slice.h"
#include "tensorflow/core/platform/macros.h"
#include "tensorflow/core/platform/protobuf.h"

namespace tensorflow {
typedef std::unordered_map<StringPiece, int32, StringPieceHasher>
    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) {
    unknown_shape_.set_unknown_rank(true);
  }

// 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_; }

inline int Id(const Node* n) const {
    if (is_global_) {
      return n->cost_id();
    } else {
      return n->id();
    }
  }

// 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 and optionally the corresponding shape of
  // the tensor generated by "output_slot" of "node". If
  void RecordMaxMemorySize(const Node* node, int output_slot, Bytes bytes,
                           const TensorShapeProto& tensor_shape,
                           const DataType& dtype);

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

// Returns the shape corresponding to the largest memory size of the tensor
  // generated by "output_slot" of "node".
  const TensorShapeProto& MaxMemoryShape(const Node* node,
                                         int output_slot) const;

// Returns the shape corresponding to the largest memory size of the tensor
  // generated by "output_slot" of "node".
  DataType MaxMemoryType(const Node* node, int output_slot) const;

// Returns the size in bytes of temporary memory consumed by "node".
  Bytes TempMemorySize(const Node* node) const;

// Returns the size of persistent memory allocated by "node".
  Bytes PersistentMemorySize(const Node* node) const;

// Records memory stats such as temp momory and persistent memory.
  void RecordMemoryStats(const Node* node, const MemoryStats& memory_stats);

// 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 RecordAllocationId(const Node* node, int output_slot, int64 alloc_id);

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

bool IsPersistentTensor(const Node* node, int64 alloc_id) 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);

// Add this CostModel into the CostGraphDef.
  void AddToCostGraphDef(const Graph* graph, CostGraphDef* cost_graph) const;

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

// Increment the times that the cost model is updated.
  void IncrementUpdateTimes();

// Get the times that the cost model is updated.
  int32 GetUpdateTimes() const;

private:
  static Bytes MinTensorMemoryUsage(const TensorShapeProto& tensor_shape,
                                    const DataType& dtype);

const bool is_global_;

// Resizes vectors so that they are large enough for "id" and id's outputs.
  void Ensure(int id, int num_outputs);

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

// The number of times the cost model is updated.
  int32 update_times_ = 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 {
    MemUsage() : temp_memory_size(0), persistent_memory_size(0) {}

// TODO(yuefengz): temp_memory_size is not being used, remove it.
    Bytes temp_memory_size;
    Bytes persistent_memory_size;

gtl::InlinedVector<Bytes, 2> output_port_mem;
    gtl::InlinedVector<TensorShapeProto, 2> output_port_shape;
    gtl::InlinedVector<DataType, 2> output_port_type;
  };
  std::vector<MemUsage> max_mem_usage_;

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

std::set<int64> persistent_alloc_ids_;
  std::map<string, std::set<int64>> persistent_alloc_ids_by_devices_;

TensorShapeProto unknown_shape_;

TF_DISALLOW_COPY_AND_ASSIGN(CostModel);
};

}  // namespace tensorflow

#endif  // TENSORFLOW_CORE_GRAPH_COSTMODEL_H_