optgs/misc/benchmarker.py

import json
from collections import defaultdict
from contextlib import contextmanager
from pathlib import Path
from time import time

import numpy as np
import torch


class Benchmarker:
    def __init__(self):
        self.execution_times = defaultdict(list)

    @contextmanager
    def time(self, tag: str, num_calls: int = 1):
        try:
            start_time = time()
            yield
        finally:
            end_time = time()
            for _ in range(num_calls):
                self.execution_times[tag].append((end_time - start_time) / num_calls)

    def record(self, tag: str, elapsed_ms: float) -> None:
        """Record a pre-measured elapsed time (in milliseconds) under the given tag."""
        self.execution_times[tag].append(elapsed_ms)

    def merge(self, other: "Benchmarker") -> None:
        """Merge another benchmarker's recorded times into this one."""
        for tag, times in other.execution_times.items():
            self.execution_times[tag].extend(times)

    def dump(self, path: Path) -> None:
        path.parent.mkdir(exist_ok=True, parents=True)
        with path.open("w") as f:
            json.dump(dict(self.execution_times), f)

    def dump_memory(self, path: Path) -> None:
        path.parent.mkdir(exist_ok=True, parents=True)
        with path.open("w") as f:
            json.dump(torch.cuda.memory_stats()["allocated_bytes.all.peak"], f)

    def summarize(self) -> None:
        for tag, times in self.execution_times.items():
            print(f"{tag}: {len(times)} calls, avg {np.mean(times):.1f} ms/call, total {sum(times)/1000:.1f} s")

    def clear_history(self) -> None:
        self.execution_times = defaultdict(list)