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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 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)} seconds per call")
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