- [How can you partcipate in open source?](#invitation-to-open-source-contribution)
---
## Intro
Implementation of RLHF (Reinforcement Learning with Human Feedback) powered by Colossal-AI. It supports distributed training and offloading, which can fit extremly large models. More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt).
The main entrypoint is `Trainer`. We only support PPO trainer now. We support many training strategies:
- NaiveStrategy: simplest strategy. Train on single GPU.
- DDPStrategy: use `torch.nn.parallel.DistributedDataParallel`. Train on multi GPUs.
- ColossalAIStrategy: use Gemini and Zero of ColossalAI. It eliminates model duplication on each GPU and supports offload. It's very useful when training large models on multi GPUs.
This function must be called after `strategy.prepare()`.
For DDP strategy, model weights are replicated on all ranks. And for ColossalAI strategy, model weights may be sharded, but all-gather will be applied before returning state dict. You can set `only_rank0=True` for both of them, which only saves checkpoint on rank0, to save disk space usage. The checkpoint is float32.
For DDP strategy, optimizer states are replicated on all ranks. You can set `only_rank0=True`. But for ColossalAI strategy, optimizer states are sharded over all ranks, and no all-gather will be applied. So for ColossalAI strategy, you can only set `only_rank0=False`. That is to say, each rank will save a cehckpoint. When loading, each rank should load the corresponding part.
Note that different stategy may have different shapes of optimizer checkpoint.
Referring to the successful attempts of [BLOOM](https://bigscience.huggingface.co/) and [Stable Diffusion](https://en.wikipedia.org/wiki/Stable_Diffusion), any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models from the starting point of replicating ChatGPT!
You may contact us or participate in the following ways:
1.[Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
2. Posting an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose), or submitting a PR on GitHub follow the guideline in [Contributing](https://github.com/hpcaitech/ColossalAI/blob/main/CONTRIBUTING.md).
- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
- Keep in a sufficiently high running speed
## Citations
```bibtex
@article{Hu2021LoRALA,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Weizhu Chen},
journal={ArXiv},
year={2021},
volume={abs/2106.09685}
}
@article{ouyang2022training,
title={Training language models to follow instructions with human feedback},
author={Ouyang, Long and Wu, Jeff and Jiang, Xu and Almeida, Diogo and Wainwright, Carroll L and Mishkin, Pamela and Zhang, Chong and Agarwal, Sandhini and Slama, Katarina and Ray, Alex and others},
> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU.
In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic.
We also provide a simple shell script to run a set of benchmarks. But it only supports benchmark on single node. However, it's easy to run on multi-nodes by modifying launch command in this script.
Usage:
```shell
# run for GPUS=(1 2 4 8) x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256)
./benchmark_gpt_dummy.sh
# run for GPUS=2 x strategy=("ddp" "colossalai_zero2" "colossalai_gemini" "colossalai_zero2_cpu" "colossalai_gemini_cpu") x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256)
./benchmark_gpt_dummy.sh 2
# run for GPUS=2 x strategy=ddp x model=("s" "m" "l" "xl" "2b" "4b" "6b" "8b" "10b") x batch_size=(1 2 4 8 16 32 64 128 256)
./benchmark_gpt_dummy.sh 2 ddp
# run for GPUS=2 x strategy=ddp x model=l x batch_size=(1 2 4 8 16 32 64 128 256)
./benchmark_gpt_dummy.sh 2 ddp l
```
## Benchmark OPT with LoRA on dummy prompt data
We provide various OPT models (string in parentheses is the corresponding model name used in this script):
- OPT-125M (125m)
- OPT-350M (350m)
- OPT-700M (700m)
- OPT-1.3B (1.3b)
- OPT-2.7B (2.7b)
- OPT-3.5B (3.5b)
- OPT-5.5B (5.5b)
- OPT-6.7B (6.7b)
- OPT-10B (10b)
- OPT-13B (13b)
We only support `torchrun` to launch now. E.g.
```shell
# run OPT-125M with no lora (lora_rank=0) on single-node single-GPU with min batch size
> ⚠ Batch sizes in CLI args and outputed throughput/TFLOPS are all values of per GPU.
In this benchmark, we assume the model architectures/sizes of actor and critic are the same for simplicity. But in practice, to reduce training cost, we may use a smaller critic.
