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ColossalAI/applications/ChatGPT/examples/README.md

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Examples

Install requirements

pip install -r requirements.txt

Train with dummy prompt data

This script supports 3 strategies:

  • naive
  • ddp
  • colossalai

It uses random generated prompt data.

Naive strategy only support single GPU training:

python train_dummy.py --strategy naive
# display cli help
python train_dummy.py -h

DDP strategy and ColossalAI strategy support multi GPUs training:

# run DDP on 2 GPUs
torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy ddp
# run ColossalAI on 2 GPUs
torchrun --standalone --nproc_per_node=2 train_dummy.py --strategy colossalai

Train with real prompt data

We use awesome-chatgpt-prompts as example dataset. It is a small dataset with hundreds of prompts.

You should download prompts.csv first.

This script also supports 3 strategies.

# display cli help
python train_dummy.py -h
# run naive on 1 GPU
python train_prompts.py prompts.csv --strategy naive
# run DDP on 2 GPUs
torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy ddp
# run ColossalAI on 2 GPUs
torchrun --standalone --nproc_per_node=2 train_prompts.py prompts.csv --strategy colossalai

Train the reward model

We use rm-static as dataset to train our reward model. It is a dataset of chosen & rejected response of the same prompt.

You can download the dataset from huggingface automatically.

Use these code to train your reward model.

# Naive reward model training
python train_reward_model.py --pretrain <your model path>
# if to use LoRA
python train_reward_model.py --pretrain <your model path> --lora_rank 16

Support Model

GPT

  • GPT2-S (s)
  • GPT2-M (m)
  • GPT2-L (l)
  • GPT2-XL (xl)
  • GPT2-4B (4b)
  • GPT2-6B (6b)
  • GPT2-8B (8b)
  • GPT2-10B (10b)
  • GPT2-12B (12b)
  • GPT2-15B (15b)
  • GPT2-18B (18b)
  • GPT2-20B (20b)
  • GPT2-24B (24b)
  • GPT2-28B (28b)
  • GPT2-32B (32b)
  • GPT2-36B (36b)
  • GPT2-40B (40b)
  • GPT3 (175b)

BLOOM

OPT