272 lines
8.8 KiB
Python
272 lines
8.8 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 dataclasses import asdict, dataclass, field
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from typing import Type
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@dataclass
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class BaseProgress:
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"""Mixin that implements state-loading utilities for dataclasses."""
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def state_dict(self) -> dict:
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return asdict(self)
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def load_state_dict(self, state_dict: dict) -> None:
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self.__dict__.update(state_dict)
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@classmethod
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def from_state_dict(cls, state_dict: dict) -> "BaseProgress":
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obj = cls()
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obj.load_state_dict(state_dict)
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return obj
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@dataclass
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class ReadyCompletedTracker(BaseProgress):
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"""Track an event's progress.
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Args:
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ready: Intended to track the number of events ready to start.
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completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
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These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
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"""
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ready: int = 0
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completed: int = 0
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def reset(self) -> None:
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"""Reset the state."""
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self.ready = 0
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self.completed = 0
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def reset_on_restart(self) -> None:
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"""Reset the progress on restart.
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If there is a failure before all attributes are increased, restore the attributes to the last fully completed
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value.
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"""
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self.ready = self.completed
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@dataclass
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class StartedTracker(ReadyCompletedTracker):
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"""Track an event's progress.
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Args:
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ready: Intended to track the number of events ready to start.
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started: Intended to be incremented after the event is started (e.g. after ``on_*_start`` runs).
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completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
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These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
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"""
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started: int = 0
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def reset(self) -> None:
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super().reset()
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self.started = 0
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def reset_on_restart(self) -> None:
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super().reset_on_restart()
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self.started = self.completed
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@dataclass
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class ProcessedTracker(StartedTracker):
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"""Track an event's progress.
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Args:
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ready: Intended to track the number of events ready to start.
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started: Intended to be incremented after the event is started (e.g. after ``on_*_start`` runs).
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processed: Intended to be incremented after the event is processed.
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completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
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These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
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"""
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processed: int = 0
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def reset(self) -> None:
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super().reset()
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self.processed = 0
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def reset_on_restart(self) -> None:
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super().reset_on_restart()
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self.processed = self.completed
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@dataclass
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class Progress(BaseProgress):
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"""Track aggregated and current progress.
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Args:
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total: Intended to track the total progress of an event.
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current: Intended to track the current progress of an event.
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"""
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total: ReadyCompletedTracker = field(default_factory=ProcessedTracker)
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current: ReadyCompletedTracker = field(default_factory=ProcessedTracker)
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def __post_init__(self) -> None:
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if type(self.total) is not type(self.current): # noqa: E721
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raise ValueError("The `total` and `current` instances should be of the same class")
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def increment_ready(self) -> None:
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self.total.ready += 1
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self.current.ready += 1
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def increment_started(self) -> None:
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if not isinstance(self.total, StartedTracker):
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raise TypeError(f"`{self.total.__class__.__name__}` doesn't have a `started` attribute")
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self.total.started += 1
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self.current.started += 1
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def increment_processed(self) -> None:
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if not isinstance(self.total, ProcessedTracker):
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raise TypeError(f"`{self.total.__class__.__name__}` doesn't have a `processed` attribute")
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self.total.processed += 1
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self.current.processed += 1
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def increment_completed(self) -> None:
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self.total.completed += 1
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self.current.completed += 1
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@classmethod
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def from_defaults(cls, tracker_cls: Type[ReadyCompletedTracker], **kwargs: int) -> "Progress":
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"""Utility function to easily create an instance from keyword arguments to both ``Tracker``s."""
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return cls(total=tracker_cls(**kwargs), current=tracker_cls(**kwargs))
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def reset_on_epoch(self) -> None:
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self.current.reset()
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def reset_on_run(self) -> None:
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self.current.reset()
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def reset_on_restart(self) -> None:
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self.current.reset_on_restart()
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def load_state_dict(self, state_dict: dict) -> None:
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self.total.load_state_dict(state_dict["total"])
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self.current.load_state_dict(state_dict["current"])
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@dataclass
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class DataLoaderProgress(Progress):
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"""Tracks dataloader progress.
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These counters are local to a trainer rank. By default, they are not globally synced across all ranks.
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Args:
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total: Tracks the total dataloader progress.
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current: Tracks the current dataloader progress.
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"""
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total: ReadyCompletedTracker = field(default_factory=ReadyCompletedTracker)
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current: ReadyCompletedTracker = field(default_factory=ReadyCompletedTracker)
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@dataclass
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class BatchProgress(Progress):
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"""Tracks batch progress.
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These counters are local to a trainer rank. By default, they are not globally synced across all ranks.
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Args:
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total: Tracks the total dataloader progress.
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current: Tracks the current dataloader progress.
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is_last_batch: Whether the batch is the last one. This is useful for iterable datasets.
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"""
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is_last_batch: bool = False
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def reset_on_run(self) -> None:
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super().reset_on_run()
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self.is_last_batch = False
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def load_state_dict(self, state_dict: dict) -> None:
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super().load_state_dict(state_dict)
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self.is_last_batch = state_dict["is_last_batch"]
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@dataclass
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class SchedulerProgress(Progress):
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"""Tracks scheduler progress.
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These counters are local to a trainer rank. By default, they are not globally synced across all ranks.
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Args:
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total: Tracks the total scheduler progress.
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current: Tracks the current scheduler progress.
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"""
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total: ReadyCompletedTracker = field(default_factory=ReadyCompletedTracker)
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current: ReadyCompletedTracker = field(default_factory=ReadyCompletedTracker)
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@dataclass
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class OptimizerProgress(BaseProgress):
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"""Track optimizer progress.
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Args:
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step: Tracks ``optimizer.step`` calls.
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zero_grad: Tracks ``optimizer.zero_grad`` calls.
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"""
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step: Progress = field(default_factory=lambda: Progress.from_defaults(ReadyCompletedTracker))
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zero_grad: Progress = field(default_factory=lambda: Progress.from_defaults(StartedTracker))
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def reset_on_run(self) -> None:
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self.step.reset_on_run()
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self.zero_grad.reset_on_run()
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def reset_on_restart(self) -> None:
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self.step.reset_on_restart()
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self.zero_grad.reset_on_restart()
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def load_state_dict(self, state_dict: dict) -> None:
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self.step.load_state_dict(state_dict["step"])
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self.zero_grad.load_state_dict(state_dict["zero_grad"])
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@dataclass
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class OptimizationProgress(BaseProgress):
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"""Track optimization progress.
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Args:
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optimizer: Tracks optimizer progress.
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optimizer_position: The index of the current optimizer amongst the currently active optimizers.
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Used to know which optimizer we were using when restarting.
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Since not all optimizers may be active at a given time, this index is different from the ``optimizer_idx``
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seen in the optimization loops.
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"""
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# TODO: support for multiple optimizers
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optimizer: OptimizerProgress = field(default_factory=OptimizerProgress)
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optimizer_position: int = 0
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@property
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def optimizer_steps(self) -> int:
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return self.optimizer.step.total.completed
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def reset_on_run(self) -> None:
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self.optimizer.reset_on_run()
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def reset_on_restart(self) -> None:
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self.optimizer.reset_on_restart()
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def load_state_dict(self, state_dict: dict) -> None:
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self.optimizer.load_state_dict(state_dict["optimizer"])
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self.optimizer_position = state_dict["optimizer_position"]
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