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.
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.
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Commits
cf34867 [pre-commit.ci] auto fixes from pre-commit.com hooks
cc457fe enable import lightning.app if it is installed (#20059)
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Bumps pytorch-lightning from 2.1.0 to 2.3.3.
Release notes
Sourced from pytorch-lightning's releases.
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Commits
cf34867
[pre-commit.ci] auto fixes from pre-commit.com hookscc457fe
enable importlightning.app
if it is installed (#20059)249230a
Release 2.3.323bdeda
CI: replace macOS-11 with macOS-14 (#20029)3e5fbdc
build(deps): bump Lightning-AI/utilities from 0.11.2 to 0.11.3 (#20031)de3fb57
Remove the lightning app code (#20039)056bb08
Avoid overwriting thelightning_app
entry point (#20041)8b69285
Patch release 2.3.1 (#20021)a42484c
Fix failing app tests (#19971)f6fd046
Release 2.3.0 (#19954)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
.Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot show