65 lines
2.5 KiB
Python
65 lines
2.5 KiB
Python
# Copyright The PyTorch Lightning 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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from typing import Union
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from pytorch_lightning.profiler import (
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AdvancedProfiler,
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BaseProfiler,
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PassThroughProfiler,
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PyTorchProfiler,
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SimpleProfiler,
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)
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from pytorch_lightning.utilities import rank_zero_warn
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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PROFILERS = {
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"simple": SimpleProfiler,
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"advanced": AdvancedProfiler,
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"pytorch": PyTorchProfiler
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}
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class ProfilerConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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def on_trainer_init(self, profiler: Union[BaseProfiler, bool, str]):
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if profiler and not isinstance(profiler, (bool, str, BaseProfiler)):
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# TODO: Update exception on removal of bool
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raise MisconfigurationException("Only None, bool, str and subclasses of `BaseProfiler`"
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" are valid values for `Trainer`'s `profiler` parameter."
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f" Received {profiler} which is of type {type(profiler)}.")
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if isinstance(profiler, bool):
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rank_zero_warn("Passing a bool value as a `profiler` argument to `Trainer` is deprecated"
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" and will be removed in v1.3. Use str ('simple' or 'advanced') instead.",
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DeprecationWarning)
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if profiler:
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profiler = SimpleProfiler()
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elif isinstance(profiler, str):
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if profiler.lower() in PROFILERS:
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profiler_class = PROFILERS[profiler.lower()]
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profiler = profiler_class()
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else:
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raise ValueError("When passing string value for the `profiler` parameter of"
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" `Trainer`, it can only be 'simple' or 'advanced'")
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self.trainer.profiler = profiler or PassThroughProfiler()
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def on_train_start(self, trainer):
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local_rank = trainer.local_rank if trainer.world_size > 1 else None
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self.trainer.profiler.on_train_start(local_rank)
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