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olm.train.schedulers.base

Base learning rate scheduler for OLM.

Classes

SchedulerBase(*args, **kwargs) Base class for all OLM learning rate schedulers.

class olm.train.schedulers.base.ABC

Bases: object

Helper class that provides a standard way to create an ABC using inheritance.

class olm.train.schedulers.base.SchedulerBase(*args: Any, **kwargs: Any)

Bases: _LRScheduler, ABC

Base class for all OLM learning rate schedulers.

This class extends PyTorch’s _LRScheduler and provides a consistent interface for implementing custom learning rate schedules. All OLM schedulers should inherit from this class to maintain uniformity.

Subclasses must implement: : - get_lr(): Compute the learning rate for the current step - _get_closed_form_lr() (optional): Closed-form solution for efficiency

  • Parameters:
  • optimizer – Wrapped PyTorch optimizer.
  • last_epoch – The index of the last epoch (default: -1).
  • verbose – If True, prints a message to stdout for each update (default: False).

Example

>>> class MyScheduler(SchedulerBase):
...     def __init__(self, optimizer, param, last_epoch=-1):
...         self.param = param
...         super().__init__(optimizer, last_epoch)
...
...     def get_lr(self):
...         # Custom logic here
...         return [base_lr * self.param for base_lr in self.base_lrs]

get_last_lr() → List[float]

Return last computed learning rate by current scheduler.

  • Returns: List of last computed learning rates.

abstractmethod get_lr() → List[float]

Compute learning rate for each parameter group.

This method must be implemented by subclasses to define the learning rate schedule logic.

  • Returns: List of learning rates, one per parameter group.

load_state_dict(state_dict)

Load the scheduler state from a checkpoint.

  • Parameters: state_dict – Scheduler state returned by state_dict().

state_dict()

Returns the state of the scheduler as a dict.

Contains all non-callable attributes that are specific to the scheduler and required for checkpointing.

olm.train.schedulers.base.abstractmethod(funcobj)

A decorator indicating abstract methods.

Requires that the metaclass is ABCMeta or derived from it. A class that has a metaclass derived from ABCMeta cannot be instantiated unless all of its abstract methods are overridden. The abstract methods can be called using any of the normal ‘super’ call mechanisms. abstractmethod() may be used to declare abstract methods for properties and descriptors.

Usage:

class C(metaclass=ABCMeta): : @abstractmethod def my_abstract_method(self, arg1, arg2, argN):

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