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# Copyright The Lightning AI team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from dataclasses import asdict, dataclass, field
from typing_extensions import override
@dataclass
class _BaseProgress:
"""Mixin that implements state-loading utilities for dataclasses."""
def state_dict(self) -> dict:
return asdict(self)
def load_state_dict(self, state_dict: dict) -> None:
self.__dict__.update(state_dict)
@classmethod
def from_state_dict(cls, state_dict: dict) -> "_BaseProgress":
obj = cls()
obj.load_state_dict(state_dict)
return obj
def reset(self) -> None:
"""Reset the object's state."""
raise NotImplementedError
@dataclass
class _ReadyCompletedTracker(_BaseProgress):
"""Track an event's progress.
Args:
ready: Intended to track the number of events ready to start.
completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
"""
ready: int = 0
completed: int = 0
@override
def reset(self) -> None:
"""Reset the state."""
self.ready = 0
self.completed = 0
def reset_on_restart(self) -> None:
"""Reset the progress on restart.
If there is a failure before all attributes are increased, restore the attributes to the last fully completed
value.
"""
self.ready = self.completed
def increment_by(self, n: int) -> None:
self.ready += n
self.completed += n
@dataclass
class _StartedTracker(_ReadyCompletedTracker):
"""Track an event's progress.
Args:
ready: Intended to track the number of events ready to start.
started: Intended to be incremented after the event is started (e.g. after ``on_*_start`` runs).
completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
"""
started: int = 0
@override
def reset(self) -> None:
super().reset()
self.started = 0
@override
def reset_on_restart(self) -> None:
super().reset_on_restart()
self.started = self.completed
@override
def increment_by(self, n: int) -> None:
super().increment_by(n)
self.started += n
@dataclass
class _ProcessedTracker(_StartedTracker):
"""Track an event's progress.
Args:
ready: Intended to track the number of events ready to start.
started: Intended to be incremented after the event is started (e.g. after ``on_*_start`` runs).
processed: Intended to be incremented after the event is processed.
completed: Intended to be incremented after the event completes (e.g. after ``on_*_end`` runs).
These attributes should be increased in order, that is, :attr:`ready` first and :attr:`completed` last.
"""
processed: int = 0
@override
def reset(self) -> None:
super().reset()
self.processed = 0
@override
def reset_on_restart(self) -> None:
super().reset_on_restart()
self.processed = self.completed
@override
def increment_by(self, n: int) -> None:
super().increment_by(n)
self.processed += n
@dataclass
class _Progress(_BaseProgress):
"""Track aggregated and current progress.
Args:
total: Intended to track the total progress of an event.
current: Intended to track the current progress of an event.
"""
total: _ReadyCompletedTracker = field(default_factory=_ProcessedTracker)
current: _ReadyCompletedTracker = field(default_factory=_ProcessedTracker)
def __post_init__(self) -> None:
if self.total.__class__ is not self.current.__class__:
raise ValueError("The `total` and `current` instances should be of the same class")
def increment_ready(self) -> None:
self.total.ready += 1
self.current.ready += 1
def increment_started(self) -> None:
if not isinstance(self.total, _StartedTracker):
raise TypeError(f"`{self.total.__class__.__name__}` doesn't have a `started` attribute")
self.total.started += 1
self.current.started += 1
def increment_processed(self) -> None:
if not isinstance(self.total, _ProcessedTracker):
raise TypeError(f"`{self.total.__class__.__name__}` doesn't have a `processed` attribute")
self.total.processed += 1
self.current.processed += 1
def increment_completed(self) -> None:
self.total.completed += 1
self.current.completed += 1
@classmethod
def from_defaults(cls, tracker_cls: type[_ReadyCompletedTracker], **kwargs: int) -> "_Progress":
"""Utility function to easily create an instance from keyword arguments to both ``Tracker``s."""
return cls(total=tracker_cls(**kwargs), current=tracker_cls(**kwargs))
@override
def reset(self) -> None:
self.total.reset()
self.current.reset()
def reset_on_run(self) -> None:
self.current.reset()
def reset_on_restart(self) -> None:
self.current.reset_on_restart()
def increment_by(self, n: int) -> None:
self.total.increment_by(n)
self.current.increment_by(n)
@override
def load_state_dict(self, state_dict: dict) -> None:
self.total.load_state_dict(state_dict["total"])
self.current.load_state_dict(state_dict["current"])
@dataclass
class _BatchProgress(_Progress):
"""Tracks batch progress.
These counters are local to a trainer rank. By default, they are not globally synced across all ranks.
Args:
total: Tracks the total batch progress.
current: Tracks the current batch progress.
is_last_batch: Whether the batch is the last one. This is useful for iterable datasets.
"""
is_last_batch: bool = False
@override
def reset(self) -> None:
super().reset()
self.is_last_batch = False
@override
def reset_on_run(self) -> None:
super().reset_on_run()
self.is_last_batch = False
def increment_by(self, n: int, is_last_batch: bool = False) -> None:
super().increment_by(n)
self.is_last_batch = is_last_batch
@override
def load_state_dict(self, state_dict: dict) -> None:
super().load_state_dict(state_dict)
self.is_last_batch = state_dict["is_last_batch"]
@dataclass
class _SchedulerProgress(_Progress):
"""Tracks scheduler progress.
These counters are local to a trainer rank. By default, they are not globally synced across all ranks.
Args:
total: Tracks the total scheduler progress.
current: Tracks the current scheduler progress.
"""
total: _ReadyCompletedTracker = field(default_factory=_ReadyCompletedTracker)
current: _ReadyCompletedTracker = field(default_factory=_ReadyCompletedTracker)
@dataclass
class _OptimizerProgress(_BaseProgress):
"""Track optimizer progress.
Args:
step: Tracks ``optimizer.step`` calls.
zero_grad: Tracks ``optimizer.zero_grad`` calls.
"""
step: _Progress = field(default_factory=lambda: _Progress.from_defaults(_ReadyCompletedTracker))
zero_grad: _Progress = field(default_factory=lambda: _Progress.from_defaults(_StartedTracker))
@override
def reset(self) -> None:
self.step.reset()
self.zero_grad.reset()
def reset_on_run(self) -> None:
self.step.reset_on_run()
self.zero_grad.reset_on_run()
def reset_on_restart(self) -> None:
self.step.reset_on_restart()
self.zero_grad.reset_on_restart()
@override
def load_state_dict(self, state_dict: dict) -> None:
self.step.load_state_dict(state_dict["step"])
self.zero_grad.load_state_dict(state_dict["zero_grad"])
@dataclass
class _OptimizationProgress(_BaseProgress):
"""Track optimization progress.
Args:
optimizer: Tracks optimizer progress.
"""
optimizer: _OptimizerProgress = field(default_factory=_OptimizerProgress)
@property
def optimizer_steps(self) -> int:
return self.optimizer.step.total.completed
@override
def reset(self) -> None:
self.optimizer.reset()
def reset_on_run(self) -> None:
self.optimizer.reset_on_run()
def reset_on_restart(self) -> None:
self.optimizer.reset_on_restart()
@override
def load_state_dict(self, state_dict: dict) -> None:
self.optimizer.load_state_dict(state_dict["optimizer"])
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