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![](docs/source/_static/images/lightning_thunder_lightmode_nobyline.png)
<div align="center">
<img alt="Thunder" src="docs/source/_static/images/lightning_thunder_lightmode_nobyline.png" width="400px" style="max-width: 100%;">
<br/>
<br/>

**Make PyTorch models Lightning fast.**

______________________________________________________________________

<p align="center">
<a href="https://lightning.ai/">Lightning.ai</a> •
<a href="#performance">Performance</a> •
<a href="#get-started">Get started</a> •
<a href="#install-thunder">Install</a> •
<a href="#hello-world">Examples</a> •
<a href="#features">Features</a> •
<a href="#documentation">Documentation</a> •
</p>

[![license](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/Lightning-AI/lightning-thunder/blob/main/LICENSE)
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[![Documentation Status](https://readthedocs.org/projects/lightning-thunder/badge/?version=latest)](https://lightning-thunder.readthedocs.io/en/latest/?badge=latest)
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</div>

# Welcome to ⚡ Lightning Thunder

Lightning Thunder is a source-to-source compiler for PyTorch.
**Thunder makes PyTorch models Lightning fast.**

It makes PyTorch programs faster both on single accelerators or in distributed settings.
Thunder is a source-to-source compiler for PyTorch. It makes PyTorch programs faster by combining and using different hardware executors at once (ie: nvFuser, torch.compile, cuDNN, and TransformerEngine FP8).

Works on single accelerators and in multi-GPU settings.
Thunder aims to be usable, understandable, and extensible.

## Performance

Thunder can achieve significant speedups over standard PyTorch eager code, through the compounding effects of optimizations and the use of best in class executors. Here is an example of the pretraining throughput for Llama 2 7B as implemented in [LitGPT](https://github.com/Lightning-AI/litgpt).

![](docs/source/_static/images/training_throughput_single.png)
<div align="center">
<img alt="Thunder" src="docs/source/_static/images/training_throughput_single.png" width="800px" style="max-width: 100%;">
</div>

We achieve a 40% speedup in training throughput compared to eager code on H100 using a combination of executors including nvFuser, torch.compile, cuDNN, and TransformerEngine FP8.
Thunder achieves a 40% speedup in training throughput compared to eager code on H100 using a combination of executors including nvFuser, torch.compile, cuDNN, and TransformerEngine FP8.

Thunder supports distributed strategies like DDP and FSDP (ZeRO2 and ZeRO3). Here is the normalized throughput measured for Llama 2 7B (this time without FP8 mixed precision, support for FSDP is underway).

![](docs/source/_static/images/normalized_training_throughput_zero2.png)
<div align="center">
<img alt="Thunder" src="docs/source/_static/images/normalized_training_throughput_zero2.png" width="800px" style="max-width: 100%;">
</div>

**NOTE: Lightning Thunder is alpha.** Feel free to get involved, expect a few bumps along the way.

## Start with Thunder
## Get started

Try Thunder without installing by using our [Zero to Thunder Tutorial Studio](https://lightning.ai/lightning-ai/studios/zero-to-thunder-tutorial).

## Install Thunder

Install [nvFuser](https://github.com/NVIDIA/Fuser) nightly, which will also install the matching PyTorch nightly:
Install [nvFuser](https://github.com/NVIDIA/Fuser) nightly, and Thunder together

```bash
# install nvFuser which installs the matching nightly PyTorch
pip install --pre 'nvfuser-cu121[torch]' --extra-index-url https://pypi.nvidia.com
```

Install Thunder:

```bash
# install thunder
pip install lightning-thunder
```

It's actually not a bad idea to install directly from `main`:
<details>
<summary>Advanced install options</summary>
<!-- following section will be skipped from PyPI description -->

### Install from main

```bash
pip install git+https://github.com/Lightning-AI/lightning-thunder.git
```

or from the local repo if you want to tinker with the internals:
### Install to tinker and contribute

Install this way to tinker with the internals and contribute:

```bash
pip install -e .
```

</details>
<!-- end skipping PyPI description -->

## Hello World

Here is a simple example of how Thunder lets you compile and run PyTorch code:
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The compiled function `jfoo` takes and returns PyTorch tensors, just like the original function, so modules and functions compiled by Thunder can be used as part of larger PyTorch programs.

## Running training
## Train models

Thunder is in its early stages, it should not be used for production runs yet.

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See [README.md](examples/lit-gpt/README.md) for details on running LitGPT with Thunder.

## What's in the box
## Features

Given a python callable or PyTorch module, Thunder can generate an optimized program that:

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Modules and functions compiled with Thunder fully interoperate with vanilla PyTorch and support PyTorch's autograd. Also, Thunder works alongside torch.compile to leverage its state-of-the-art optimizations.

## Build the documentation
## Documentation

Docs are currently not hosted publicly. However you can build them locally really quickly:

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Lightning Thunder is released under the [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) license.
See LICENSE file for details.

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[![Documentation Status](https://readthedocs.org/projects/lightning-thunder/badge/?version=latest)](https://lightning-thunder.readthedocs.io/en/latest/?badge=latest)
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