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61 lines
1.7 KiB
61 lines
1.7 KiB
4 days ago
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r"""
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PyTorch Profiler is a tool that allows the collection of performance metrics during training and inference.
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Profiler's context manager API can be used to better understand what model operators are the most expensive,
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examine their input shapes and stack traces, study device kernel activity and visualize the execution trace.
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.. note::
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An earlier version of the API in :mod:`torch.autograd` module is considered legacy and will be deprecated.
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"""
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import os
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from typing import Any
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from typing_extensions import TypeVarTuple, Unpack
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from torch._C._autograd import _supported_activities, DeviceType, kineto_available
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from torch._C._profiler import _ExperimentalConfig, ProfilerActivity, RecordScope
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from torch._environment import is_fbcode
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from torch.autograd.profiler import KinetoStepTracker, record_function
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from torch.optim.optimizer import Optimizer, register_optimizer_step_post_hook
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from .profiler import (
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_KinetoProfile,
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ExecutionTraceObserver,
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profile,
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ProfilerAction,
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schedule,
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supported_activities,
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tensorboard_trace_handler,
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)
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__all__ = [
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"profile",
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"schedule",
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"supported_activities",
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"tensorboard_trace_handler",
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"ProfilerAction",
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"ProfilerActivity",
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"kineto_available",
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"DeviceType",
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"record_function",
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"ExecutionTraceObserver",
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]
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from . import itt
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_Ts = TypeVarTuple("_Ts")
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def _optimizer_post_hook(
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optimizer: Optimizer, args: tuple[Unpack[_Ts]], kwargs: dict[str, Any]
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) -> None:
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KinetoStepTracker.increment_step("Optimizer")
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if os.environ.get("KINETO_USE_DAEMON", "") or (
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is_fbcode() and os.environ.get("KINETO_FORCE_OPTIMIZER_HOOK", "")
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):
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_ = register_optimizer_step_post_hook(_optimizer_post_hook)
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