44 lines
1.6 KiB
Markdown
44 lines
1.6 KiB
Markdown
# picotron
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- 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](https://github.com/NVIDIA/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**.
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- Performance is not yet in okay-ish but this is under active development.
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# Install
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```
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pip install -e .
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```
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# Quick start
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- GPU
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```sh
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# DP=8
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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>
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# Locally
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torchrun --nproc_per_node 8 train.py --config tmp/llama-1B/config.json
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# 3D Parallelism
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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>
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# Slurm
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python submit_slurm_jobs.py --inp_dir tmp/llama-7B --qos high --hf_token <HF_TOKEN>
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```
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- CPU (expect it to be slow)
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```sh
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# 3D Parallelism on CPU
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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
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# Locally
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torchrun --nproc_per_node 8 train.py --config tmp/llama-1B-cpu/config.json
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```
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# Acknowledgements
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- [Megatron-LM](https://github.com/NVIDIA/Megatron-LM) |