# Copyright (c) Alibaba, Inc. and its affiliates.
import contextlib
import sys
import time
import torch
if sys.version_info >= (3, 7):
[docs] @contextlib.contextmanager
def profile_time(trace_name,
name,
enabled=True,
stream=None,
end_stream=None):
"""Print time spent by CPU and GPU.
Useful as a temporary context manager to find sweet spots of
code suitable for async implementation.
"""
if (not enabled) or not torch.cuda.is_available():
yield
return
stream = stream if stream else torch.cuda.current_stream()
end_stream = end_stream if end_stream else stream
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
stream.record_event(start)
try:
cpu_start = time.monotonic()
yield
finally:
cpu_end = time.monotonic()
end_stream.record_event(end)
end.synchronize()
cpu_time = (cpu_end - cpu_start) * 1000
gpu_time = start.elapsed_time(end)
msg = '{} {} cpu_time {:.2f} ms '.format(trace_name, name,
cpu_time)
msg += 'gpu_time {:.2f} ms stream {}'.format(gpu_time, stream)
print(msg, end_stream)
[docs]def benchmark_torch_function(iters, f, *args):
f(*args)
torch.cuda.synchronize()
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
for i in range(iters):
f(*args)
end_event.record()
torch.cuda.synchronize()
return (start_event.elapsed_time(end_event) * 1.0e-3) / iters
[docs]def time_synchronized():
torch.cuda.synchronize() if torch.cuda.is_available() else None
return time.time()