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#pragma once
#include <mutex>
#include <string>
#include <unordered_map>
#include <vector>
#include <torch/csrc/WindowsTorchApiMacro.h>
namespace torch {
namespace jit {
namespace logging {
class LoggerBase {
public:
TORCH_API virtual void addStatValue(
const std::string& stat_name,
int64_t val) = 0;
virtual ~LoggerBase() {}
};
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, int64_t val) override {}
~NoopLogger() {}
};
// 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() {}
private:
mutable std::mutex m;
struct RawCounter {
RawCounter() : sum(0), count(0) {}
int64_t sum;
size_t count;
};
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, 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 logging
} // namespace jit
} // namespace torch
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