pytorch/torch/csrc/jit/runtime/logging.h

#pragma once

#include <chrono>
#include <mutex>
#include <string>
#include <unordered_map>
#include <vector>

#include <torch/csrc/Export.h>

namespace torch::jit::logging {

class LoggerBase {
 public:
  TORCH_API virtual void addStatValue(
      const std::string& stat_name,
      int64_t val) = 0;
  virtual ~LoggerBase() = default;
};

TORCH_API LoggerBase* getLogger();
TORCH_API LoggerBase* setLogger(LoggerBase* logger);

// No-op logger. This is the default and is meant to incur almost no runtime
// overhead.

class NoopLogger : public LoggerBase {
 public:
  void addStatValue(
      const std::string& stat_name [[maybe_unused]],
      int64_t val [[maybe_unused]]) override {}
  ~NoopLogger() override = default;
};

// Trivial locking logger. Pass in an instance of this to setLogger() to use it.
// This keeps track of the sum of all statistics.
//
// NOTE: this is not written in a scalable way and should probably only be used
// in the single-threaded case or for testing.
class TORCH_API LockingLogger : public LoggerBase {
 public:
  void addStatValue(const std::string& stat_name, int64_t val) override;
  virtual int64_t getCounterValue(const std::string& name) const;
  enum class AggregationType { SUM = 0, AVG = 1 };
  void setAggregationType(const std::string& stat_name, AggregationType type);
  ~LockingLogger() override = default;

 private:
  mutable std::mutex m;
  struct RawCounter {
    RawCounter() = default;
    int64_t sum{0};
    size_t count{0};
  };
  std::unordered_map<std::string, RawCounter> raw_counters;
  std::unordered_map<std::string, AggregationType> agg_types;
};

// Make this struct so the timer internals are opaque to the user.
struct JITTimePoint {
  std::chrono::time_point<std::chrono::high_resolution_clock> point;
};

TORCH_API JITTimePoint timePoint();
TORCH_API void recordDurationSince(
    const std::string& name,
    const JITTimePoint& tp);

namespace runtime_counters {
constexpr const char* GRAPH_EXECUTORS_CONSTRUCTED =
    "pytorch_runtime.graph_executors_constructed";
constexpr const char* GRAPH_EXECUTOR_INVOCATIONS =
    "pytorch_runtime.graph_executor_invocations";
constexpr const char* EXECUTION_PLAN_CACHE_HIT =
    "pytorch_runtime.execution_plan_cache_hit";
constexpr const char* EXECUTION_PLAN_CACHE_MISS =
    "pytorch_runtime.execution_plan_cache_miss";

inline std::vector<const char*> allRuntimeCounters() {
  return {
      GRAPH_EXECUTORS_CONSTRUCTED,
      GRAPH_EXECUTOR_INVOCATIONS,
      EXECUTION_PLAN_CACHE_HIT,
      EXECUTION_PLAN_CACHE_MISS};
}

} // namespace runtime_counters

} // namespace torch::jit::logging