# picotron

**Repository Path**: Time-has-wings/picotron

## Basic Information

- **Project Name**: picotron
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2025-03-21
- **Last Updated**: 2025-03-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# picotron
In the spirit of [NanoGPT](https://github.com/karpathy/nanoGPT), we created Picotron: The minimalist & most-hackable repository for pre-training Llama-like models with [4D Parallelism](https://arxiv.org/abs/2407.21783) (Data, Tensor, Pipeline, Context parallel). It is designed with simplicity and **educational** purposes in mind, making it an excellent tool for learning and experimentation.

![](assets/banière.png)
- The code itself is simple and readable: `train.py`, `model.py` and `[data|tensor|pipeline|context]_parallel.py` are all under **300** lines of code.

- Performance is not the best but still under active development. We observed 38% MFU on a LLaMA-2-7B model using 64 H100 GPUs and nearly 50% MFU on the SmolLM-1.7B model with 8 H100 GPUs. Benchmarks will come soon
- Compared to [Nanotron](https://github.com/huggingface/nanotron/tree/main), Picotron is primarily for educational purposes, helping people quickly get familiar with all the techniques in distributed training

# Tutorial videos

- A step by step tutorial on how to build Picotron distributed training framework form scratch:
    - [Picotron tutorial (playlist)](https://www.youtube.com/playlist?list=PL-_armZiJvAnhcRr6yTJ0__f3Oi-LLi9S) 🎬
    - [Picotron tutorial (codebase)](https://github.com/huggingface/picotron_tutorial) 👷🏻‍♂️

# Install

```
pip install -e .
```

# Quick start
- Get a HF token [here](https://huggingface.co/settings/tokens) to download models from HuggingFace

- GPU
    ```sh
    # To create a config file in json format under tmp by default
    python create_config.py --out_dir tmp --exp_name llama-1B --dp 8 --model_name HuggingFaceTB/SmolLM-1.7B --num_hidden_layers 15  --grad_acc_steps 32 --mbs 4 --seq_len 1024 --hf_token <HF_TOKEN>

    # Locally
    torchrun --nproc_per_node 8 train.py --config tmp/llama-1B/config.json 

    # 3D Parallelism
    python create_config.py --out_dir tmp --dp 4 --tp 2 --pp 2 --pp_engine 1f1b --exp_name llama-7B --model_name meta-llama/Llama-2-7b-hf  --grad_acc_steps 32 --mbs 4 --seq_len 1024 --hf_token <HF_TOKEN>

    # Slurm
    python submit_slurm_jobs.py --inp_dir tmp/llama-7B --qos high --hf_token <HF_TOKEN>
    ```

-  CPU (expect it to be slow)
    ```sh
    # 3D Parallelism on CPU
    python create_config.py --out_dir tmp --exp_name llama-1B-cpu --dp 2 --tp 2 --pp 2 --pp_engine 1f1b --model_name HuggingFaceTB/SmolLM-1.7B --num_hidden_layers 5  --grad_acc_steps 2 --mbs 4 --seq_len 128 --hf_token <HF_TOKEN> --use_cpu

    # Locally
    torchrun --nproc_per_node 8 train.py --config tmp/llama-1B-cpu/config.json
    ```

# Citation
If you use Picotron, please cite it as:

```bibtex
@misc{zhao2025picotron,
  author = {Haojun Zhao and Ferdinand Mom},
  title = {Picotron: Distributed training framework for education and research experimentation},
  year = {2025},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/huggingface/picotron}}
}
```

# Acknowledgements

- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
- [FairScale](https://github.com/facebookresearch/fairscale)
- [LitGPT](https://github.com/Lightning-AI/lit-gpt)