ColossalAI/colossalai/engine/gradient_accumulation/__init__.py

import torch.nn as nn
from typing import List
from colossalai.engine import BaseGradientHandler
from typing import Iterable
from torch.optim import Optimizer
from torch.optim.lr_scheduler import _LRScheduler
from ._gradient_accumulation import GradAccumDataloader, GradAccumOptimizer, GradAccumLrSchedulerByStep, GradAccumGradientHandler

__all__ = [
    'accumulate_gradient', 'GradAccumDataloader', 'GradAccumOptimizer', 'GradAccumLrSchedulerByStep',
    'GradAccumGradientHandler'
]


def accumulate_gradient(model: nn.Module,
                        optimizer: Optimizer,
                        dataloader: Iterable,
                        accumulate_size: int,
                        gradient_handlers: List[BaseGradientHandler] = None,
                        lr_scheduler: _LRScheduler = None):
    r"""Turning model, optimizer, dataloader into corresponding object for gradient accumulation.

    Args:
        model (:class:`torch.nn.Module`): your model object for gradient accumulation.
        optimizer (:class:`torch.optim.Optimizer`): your optimizer object for gradient accumulation.
        dataloader (:class:`torch.utils.data.DataLoader` or iterable objects):
            your dataloader object, would be called like iter(dataloader)
        accumulate_size (int): the number of steps to accumulate gradients
        gradient_handlers (List[:class:`colossalai.engine.BaseGradientHandler`]):
            list of gradient handler objects. Default is None.
        lr_scheduler (`torch.optim.lr_scheduler` or `colossalai.nn.lr_scheduler`):
            your ``lr_scheduler`` object for gradient accumulation. Defaults to None.

    More details about `gradient_handlers` could be found in
    `Gradient_handler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/engine/gradient_handler>`_.

    More details about `lr_scheduler` could be found
    `lr_scheduler <https://github.com/hpcaitech/ColossalAI/tree/main/colossalai/nn/lr_scheduler>`_. and
    `how to adjust learning rate <https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate>`_.
    """
    optimizer = GradAccumOptimizer(optimizer, accumulate_size=accumulate_size, model=model)
    dataloader = GradAccumDataloader(dataloader, accumulate_size=accumulate_size)

    if gradient_handlers is not None:
        gradient_handlers = [GradAccumGradientHandler(handler, accumulate_size) for handler in gradient_handlers]

    if lr_scheduler is not None:
        lr_scheduler = GradAccumLrSchedulerByStep(lr_scheduler, accumulate_size=accumulate_size)

    return optimizer, dataloader, gradient_handlers, lr_scheduler