pytorch-lightning

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# Copyright The Lightning AI team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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#     http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Profiler to check if there are any bottlenecks in your code."""
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import cProfile
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import io
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import logging
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import pstats
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from pathlib import Path
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from typing import Dict, Optional, Tuple, Union
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from typing_extensions import override
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from lightning.pytorch.profilers.profiler import Profiler
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log = logging.getLogger(__name__)
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class AdvancedProfiler(Profiler):
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    """This profiler uses Python's cProfiler to record more detailed information about time spent in each function call
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    recorded during a given action.
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    The output is quite verbose and you should only use this if you want very detailed reports.
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    """
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    def __init__(
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        self,
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        dirpath: Optional[Union[str, Path]] = None,
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        filename: Optional[str] = None,
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        line_count_restriction: float = 1.0,
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    ) -> None:
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        """
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        Args:
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            dirpath: Directory path for the ``filename``. If ``dirpath`` is ``None`` but ``filename`` is present, the
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                ``trainer.log_dir`` (from :class:`~lightning.pytorch.loggers.tensorboard.TensorBoardLogger`)
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                will be used.
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            filename: If present, filename where the profiler results will be saved instead of printing to stdout.
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                The ``.txt`` extension will be used automatically.
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            line_count_restriction: this can be used to limit the number of functions
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                reported for each action. either an integer (to select a count of lines),
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                or a decimal fraction between 0.0 and 1.0 inclusive (to select a percentage of lines)
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        Raises:
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            ValueError:
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                If you attempt to stop recording an action which was never started.
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        """
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        super().__init__(dirpath=dirpath, filename=filename)
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        self.profiled_actions: Dict[str, cProfile.Profile] = {}
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        self.line_count_restriction = line_count_restriction
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    @override
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    def start(self, action_name: str) -> None:
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        if action_name not in self.profiled_actions:
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            self.profiled_actions[action_name] = cProfile.Profile()
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        self.profiled_actions[action_name].enable()
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    @override
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    def stop(self, action_name: str) -> None:
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        pr = self.profiled_actions.get(action_name)
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        if pr is None:
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            raise ValueError(f"Attempting to stop recording an action ({action_name}) which was never started.")
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        pr.disable()
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    @override
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    def summary(self) -> str:
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        recorded_stats = {}
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        for action_name, pr in self.profiled_actions.items():
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            s = io.StringIO()
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            ps = pstats.Stats(pr, stream=s).strip_dirs().sort_stats("cumulative")
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            ps.print_stats(self.line_count_restriction)
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            recorded_stats[action_name] = s.getvalue()
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        return self._stats_to_str(recorded_stats)
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    @override
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    def teardown(self, stage: Optional[str]) -> None:
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        super().teardown(stage=stage)
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        self.profiled_actions = {}
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    def __reduce__(self) -> Tuple:
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        # avoids `TypeError: cannot pickle 'cProfile.Profile' object`
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        return (
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            self.__class__,
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            (),
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            {"dirpath": self.dirpath, "filename": self.filename, "line_count_restriction": self.line_count_restriction},
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        )
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