picotron

• The minimalist & most-hackable repository for pre-training Llama-like models with 4D Parallelism (Data, Tensor, Pipeline, Context parallel). It is a rewrite of Megatron-LM for educational purpose. The code itself is plain and readable: train.py, model.py and [data|tensor|pipeline|context]_parallel.py are all < 300 LOC.

• Performance is not yet in okay-ish but this is under active development.

Install

pip install -e .

Quick start

• GPU

# DP=8
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)

# 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

Acknowledgements

• Megatron-LM