torchtitan 0.0.2

A native-PyTorch library for large scale LLM training

torchtitan

torchtitan is still in pre-release!

torchtitan is currently in a pre-release state and under extensive development.

torchtitan is a native PyTorch reference architecture showcasing some of the latest PyTorch techniques for large scale model training.

• Designed to be easy to understand, use and extend for different training purposes.
• Minimal changes to the model code when applying 1D, 2D, or (soon) 3D Parallel.
• Modular components instead of monolithic codebase.
• Get started in minutes, not hours!

Please note: torchtitan is a proof-of-concept for Large-scale LLM training using native PyTorch. It is (and will continue to be) a repo to showcase PyTorch's latest distributed training features in a clean, minimal codebase. torchtitan is complementary to and not a replacement for any of the great large-scale LLM training codebases such as Megatron, Megablocks, LLM Foundry, Deepspeed, etc. Instead, we hope that the features showcased in torchtitan will be adopted by these codebases quickly. torchtitan is unlikely to ever grow a large community around it.

Pre-Release Updates:

(4/16/2024): TorchTitan is now public but in a pre-release state and under development. Currently we showcase pre-training Llama2 models (LLMs) of various sizes from scratch.

Key features available:
1 - FSDP2 (per param sharding)
2 - Tensor Parallel (FSDP + Tensor Parallel)
3 - Selective layer and op activation checkpointing
4 - Distributed checkpointing (asynch pending)
5 - 3 datasets pre-configured (47K - 144M)
6 - GPU usage, MFU, tokens per second and other metrics all reported and displayed via TensorBoard.
7 - optional Fused RMSNorm, learning rate scheduler, meta init, and more.
8 - All options easily configured via toml files.

Coming soon features:

1 - Asynch checkpointing
2 - FP8 support
3 - Context Parallel
4 - 3D (Pipeline Parallel)
5 - Torch Compile support

Installation

Install PyTorch from source or install the latest pytorch nightly, then install requirements by

pip install -r requirements.txt

Install additional dev requirements if you want to contribute to the repo:

pip install -r dev-requirements.txt

run the llama debug model locally to verify the setup is correct:

./run_llama_train.sh

TensorBoard

To visualize TensorBoard metrics of models trained on a remote server via a local web browser:

1. Make sure metrics.enable_tensorboard option is set to true in model training (either from a .toml file or from CLI).

2. Set up SSH tunneling, by running the following from local CLI

ssh -L 6006:127.0.0.1:6006 [username]@[hostname]

3. Inside the SSH tunnel that logged into the remote server, go to the torchtitan repo, and start the TensorBoard backend

tensorboard --logdir=./outputs/tb

4. In the local web browser, go to the URL it provides OR to http://localhost:6006/.

Multi-Node Training

For training on ParallelCluster/Slurm type configurations, you can use the multinode_trainer.slurm file to submit your sbatch job.
Note that you will need to adjust the number of nodes and gpu count to your cluster configs.
To adjust total nodes:

#SBATCH --ntasks=2
#SBATCH --nodes=2

should both be set to your total node count. Then update the srun launch parameters to match:

srun torchrun --nnodes 2

where nnodes is your total node count, matching the sbatch node count above.

To adjust gpu count per node:

If your gpu count per node is not 8, adjust:

--nproc_per_node

in the torchrun command and

#SBATCH --gpus-per-task

in the SBATCH command section.