Awesome

Nanotron is a library for pretraining transformer models. It provides a simple and flexible API to pretrain models on custom datasets. Nanotron is designed to be easy to use, fast, and scalable. It is built with the following principles in mind:

• Simplicity: Nanotron is designed to be easy to use. It provides a simple and flexible API to pretrain models on custom datasets.
• Performance: Optimized for speed and scalability, Nanotron uses the latest techniques to train models faster and more efficiently.

Installation

# Requirements: Python>=3.10
git clone https://github.com/huggingface/nanotron
cd nanotron
pip install --upgrade pip
pip install --pre torch --index-url https://download.pytorch.org/whl/nightly/cu121
pip install -e .

# Install dependencies if you want to use the example scripts
pip install datasets transformers
pip install triton "flash-attn>=2.5.0" --no-build-isolation

[!NOTE] If you get undefined symbol: ncclCommRegister error you should install torch 2.1.2 instead: pip install torch==2.1.2 --index-url https://download.pytorch.org/whl/cu121

[!TIP] We log to wandb automatically if it's installed. For that you can use pip install wandb. If you don't want to use wandb, you can run wandb disabled.

Quick Start

Training a tiny Llama model

The following command will train a tiny Llama model on a single node with 8 GPUs. The model will be saved in the checkpoints directory as specified in the config file.

CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node=8 run_train.py --config-file examples/config_tiny_llama.yaml

Run generation from your checkpoint

torchrun --nproc_per_node=1 run_generate.py --ckpt-path checkpoints/10/ --tp 1 --pp 1
# We could set a larger TP for faster generation, and a larger PP in case of very large models.

Custom examples

You can find more examples in the /examples directory:

We're working on adding more examples soon! Feel free to add a PR to add your own example. 🚀

Features

We currently support the following features:

• 3D parallelism (DP+TP+PP)
• Expert parallelism for MoEs
• AFAB and 1F1B schedules for PP
• Explicit APIs for TP and PP which enables easy debugging
• ZeRO-1 optimizer
• FP32 gradient accumulation
• Parameter tying/sharding
• Custom module checkpointing for large models
• Spectral µTransfer parametrization for scaling up neural networks
• Mamba example

And we have on our roadmap:

• FP8 training
• ZeRO-3 optimizer (a.k.a FSDP)
• torch.compile support
• Ring attention
• Interleaved 1f1b schedule

Credits

We would like to thank everyone working on LLMs, especially those sharing their work openly from which we took great inspiration: Nvidia for Megatron-LM/apex, Microsoft for DeepSpeed, HazyResearch for flash-attn..