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LoicGrobol / zeldarose

Train transformer-based models.
https://zeldarose.readthedocs.io
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Update pytorch-lightning requirement from <2.1.0,>=1.8.0 to >=1.8.0,<2.2.0 #67

Closed dependabot[bot] closed 11 months ago

dependabot[bot] commented 11 months ago

Updates the requirements on pytorch-lightning to permit the latest version.

Release notes

Sourced from pytorch-lightning's releases.

Lightning 2.1: Train Bigger, Better, Faster

Lightning AI is excited to announce the release of Lightning 2.1 :zap: It's the culmination of work from 79 contributors who have worked on features, bug-fixes, and documentation for a total of over 750+ commits since v2.0.

The theme of 2.1 is "bigger, better, faster": Bigger because training large multi-billion parameter models has gotten even more efficient thanks to FSDP, efficient initialization and sharded checkpointing improvements, better because it's easier than ever to scale models without making substantial code changes or installing third-party packages and faster because it leverages the latest hardware features to speed up training in low-bit precision thanks to new precision plugins like bitsandbytes and transformer engine. And of course, as the name implies, this release fully leverages the latest features in PyTorch 2.1 :tada:

Highlights

Improvements To Large-Scale Training With FSDP

The FSDP strategy for training large billion-parameter models gets substantial improvements and new features in Lightning 2.1, both in Trainer and Fabric (in case you didn't know, Fabric is the latest addition to the Lightning family of tools to scale models without the boilerplate code). FSDP is now more user-friendly to configure, has memory management and speed improvements, and we have a brand new end-to-end user guide with best practices (Trainer, Fabric).

Efficient Saving and Loading of Large Checkpoints

When training large billion-parameter models with FSDP, saving and resuming training, or even just loading model parameters for finetuning can be challenging, as users are are often plagued by out-of-memory errors and speed bottlenecks.

In 2.1, we made several improvements. Starting with saving checkpoints, we added support for distributed/sharded checkpoints, enabled through the setting state_dict_type in the strategy (#18364, #18358):

Trainer:

import lightning as L
from lightning.pytorch.strategies import FSDPStrategy

Default used by the strategy


strategy = FSDPStrategy(state_dict_type="full")


Enable saving distributed checkpoints


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LoicGrobol commented 11 months ago

@dependabot merge please

dependabot[bot] commented 11 months ago

Dependabot tried to merge this PR, but received the following error from GitHub:

At least 1 approving review is required by reviewers with write access.
LoicGrobol commented 11 months ago

@dependabot merge please