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import contextlib
import torch
__all__ = ["start", "stop", "profile"]
def start(mode: str = "interval", wait_until_completed: bool = False) -> None:
r"""Start OS Signpost tracing from MPS backend.
The generated OS Signposts could be recorded and viewed in
XCode Instruments Logging tool.
Args:
mode(str): OS Signpost tracing mode could be "interval", "event",
or both "interval,event".
The interval mode traces the duration of execution of the operations,
whereas event mode marks the completion of executions.
See document `Recording Performance Data`_ for more info.
wait_until_completed(bool): Waits until the MPS Stream complete
executing each encoded GPU operation. This helps generating single
dispatches on the trace's timeline.
Note that enabling this option would affect the performance negatively.
.. _Recording Performance Data:
https://developer.apple.com/documentation/os/logging/recording_performance_data
"""
mode_normalized = mode.lower().replace(" ", "")
torch._C._mps_profilerStartTrace(mode_normalized, wait_until_completed)
def stop():
r"""Stops generating OS Signpost tracing from MPS backend."""
torch._C._mps_profilerStopTrace()
@contextlib.contextmanager
def profile(mode: str = "interval", wait_until_completed: bool = False):
r"""Context Manager to enabling generating OS Signpost tracing from MPS backend.
Args:
mode(str): OS Signpost tracing mode could be "interval", "event",
or both "interval,event".
The interval mode traces the duration of execution of the operations,
whereas event mode marks the completion of executions.
See document `Recording Performance Data`_ for more info.
wait_until_completed(bool): Waits until the MPS Stream complete
executing each encoded GPU operation. This helps generating single
dispatches on the trace's timeline.
Note that enabling this option would affect the performance negatively.
.. _Recording Performance Data:
https://developer.apple.com/documentation/os/logging/recording_performance_data
"""
try:
start(mode, wait_until_completed)
yield
finally:
stop()
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