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LoicGrobol / zeldarose

Train transformer-based models.
https://zeldarose.readthedocs.io
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Update lightning requirement from <2.3.0,>=2.0.0 to >=2.0.0,<2.4.0 #89

Closed dependabot[bot] closed 4 months ago

dependabot[bot] commented 5 months ago

Updates the requirements on lightning to permit the latest version.

Release notes

Sourced from lightning's releases.

Lightning v2.3: Tensor Parallelism and 2D Parallelism

Lightning AI is excited to announce the release of Lightning 2.3 :zap:

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

This release introduces experimental support for Tensor Parallelism and 2D Parallelism, PyTorch 2.3 support, and several bugfixes and stability improvements.

Highlights

Tensor Parallelism (beta)

Tensor parallelism (TP) is a technique that splits up the computation of selected layers across GPUs to save memory and speed up distributed models. To enable TP as well as other forms of parallelism, we introduce a ModelParallelStrategy for both Lightning Trainer and Fabric. Under the hood, TP is enabled through new experimental PyTorch APIs like DTensor and torch.distributed.tensor.parallel.

PyTorch Lightning

Enabling TP in a model with PyTorch Lightning requires you to implement the LightningModule.configure_model() method where you convert selected layers of a model to paralellized layers. This is an advanced feature, because it requires a deep understanding of the model architecture. Open the tutorial Studio to learn the basics of Tensor Parallelism.

 

import lightning as L
from lightning.pytorch.strategies import ModelParallelStrategy
from torch.distributed.tensor.parallel import ColwiseParallel, RowwiseParallel
from torch.distributed.tensor.parallel import parallelize_module

1. Implement the configure_model() method in LightningModule


class LitModel(L.LightningModule):
def init(self):
super().init()
self.model = FeedForward(8192, 8192)
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dependabot[bot] commented 4 months ago

Superseded by #91.