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timer.py
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timer.py
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import time
import json
from contextlib import contextmanager
from io import StringIO
import numpy as np
import torch
class Timer:
"""
Timer for PyTorch code
Comes in the form of a contextmanager:
Example:
>>> timer = Timer()
... for i in range(10):
... with timer("expensive operation"):
... x = torch.randn(100)
... print(timer.summary())
"""
def __init__(self, verbosity_level=1, skip_first=True, on_cuda=True):
self.verbosity_level = verbosity_level
self.log_fn = self.log_info
self.skip_first = skip_first
self.cuda_available = torch.cuda.is_available() and on_cuda
self.reduce_times = []
self.reset()
def reset(self):
"""Reset the timer"""
self.totals = {} # Total time per label
self.first_time = {} # First occurrence of a label (start time)
self.last_time = {} # Last occurence of a label (end time)
self.call_counts = {} # Number of times a label occurred
@contextmanager
def __call__(self, label, epoch=-1.0, verbosity=1):
# Don't measure this if the verbosity level is too high
if verbosity > self.verbosity_level:
yield
return
# Measure the time
self._cuda_sync()
start = time.time()
yield
self._cuda_sync()
end = time.time()
if label == "batch.reduce":
self.reduce_times.append(end - start)
# Update first and last occurrence of this label
if label not in self.first_time:
self.first_time[label] = start
self.last_time[label] = end
# Update the totals and call counts
if label not in self.totals and self.skip_first:
self.totals[label] = 0.0
del self.first_time[label]
self.call_counts[label] = 0
elif label not in self.totals and not self.skip_first:
self.totals[label] = end - start
self.call_counts[label] = 1
else:
self.totals[label] += end - start
self.call_counts[label] += 1
if self.call_counts[label] > 0:
# We will reduce the probability of logging a timing
# linearly with the number of time we have seen it.
# It will always be recorded in the totals, though.
if np.random.rand() < 1 / self.call_counts[label]:
self.log_fn("timer", {"epoch": epoch, "value": end - start}, {"event": label})
def summary(self):
"""
Return a summary in string-form of all the timings recorded so far
"""
if len(self.totals) > 0:
with StringIO() as buffer:
# total_avg_time = 0
total_time = 0
print(
"--- Timer summary -----------------------------------------------",
file=buffer,
)
print(
" Event | Count | Average time | Frac.",
file=buffer,
)
for event_label in sorted(self.totals):
total = self.totals[event_label]
count = self.call_counts[event_label]
if count == 0:
continue
avg_duration = total / count
total_runtime = self.last_time[event_label] - self.first_time[event_label]
runtime_percentage = 100 * total / total_runtime
# total_avg_time += avg_duration if "." not in event_label else 0
total_time += total if event_label == "batch" else 0
print(
f"- {event_label:30s} | {count:6d} | {avg_duration:11.5f}s | {runtime_percentage:5.1f}%",
file=buffer,
)
print(
"-----------------------------------------------------------------",
file=buffer,
)
event_label = "total_time"
print(
f"- {event_label:30s} | {count:6d} | {total_time:11.5f}s |",
file=buffer,
)
print(
"-----------------------------------------------------------------",
file=buffer,
)
return buffer.getvalue()
def save_summary(self, json_file_path):
data = {}
for event_label in sorted(self.totals):
total = self.totals[event_label]
count = self.call_counts[event_label]
if count == 0:
continue
avg_duration = total / count
data[event_label] = {
"label": event_label,
"average_duration": avg_duration,
"n_events": count,
"total_time": total,
}
# Uncomment to store reduce times
# data["reduce_times"] = self.reduce_times
with open(json_file_path, "w") as fp:
json.dump(data, fp)
def _cuda_sync(self):
"""Finish all asynchronous GPU computations to get correct timings"""
if torch.cuda.is_available():
torch.cuda.synchronize()
def log_info(self, name, values, tags={}):
value_list = []
for key in sorted(values.keys()):
value = values[key]
value_list.append(f"{key}:{value:7.3f}")
values = ", ".join(value_list)
tag_list = []
for key, tag in tags.items():
tag_list.append(f"{key}:{tag}")
tags = ", ".join(tag_list)
print("{name:20s} - {values} ({tags})".format(name=name, values=values, tags=tags))