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78d2329 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | 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)
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