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[chat] add performance and tutorial (#3786)

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2023-05-19 18:03:56 +08:00
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applications/Chat/README.md
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@@ -67,13 +67,24 @@ More details can be found in the latest news.
* [2023/02] [Open Source Solution Replicates ChatGPT Training Process! Ready to go with only 1.6GB GPU Memory](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt)
## Online demo
You can experience the performance of Coati7B on this page.
<div align="center">
<a href="https://www.youtube.com/watch?v=HcTiHzApHm0">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20YouTube.png" width="700" />
</a>
</div>
[chat.colossalai.org](https://chat.colossalai.org/)
[ColossalChat](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat): An open-source solution for cloning [ChatGPT](https://openai.com/blog/chatgpt/) with a complete RLHF pipeline.
[[code]](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat)
[[blog]](https://medium.com/@yangyou_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b)
[[demo]](https://www.youtube.com/watch?v=HcTiHzApHm0)
[[tutorial]](https://www.youtube.com/watch?v=-qFBZFmOJfg)
Due to resource constraints, we will only provide this service from 29th Mar 2023 to 5 April 2023. However, we have provided the inference code in the [inference](./inference/) folder. The WebUI will be open-sourced soon as well.
<p id="ColossalChat-Speed" align="center">
<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/ColossalChat%20Speed.jpg" width=450/>
</p>
> DeepSpeedChat performance comes from its blog on 2023 April 12, ColossalChat performance can be reproduced on an AWS p4d.24xlarge node with 8 A100-40G GPUs with the following command: torchrun --standalone --nproc_per_node 8 benchmark_opt_lora_dummy.py --max_timesteps 1 --update_timesteps 1 --use_kernels --strategy colossalai_zero2 --experience_batch_size 64 --train_batch_size 32
> Warning: Due to model and dataset size limitations, Coati is just a baby model, Coati7B may output incorrect information and lack the ability for multi-turn dialogue. There is still significant room for improvement.
## Install
### Install the environment
@@ -112,12 +123,14 @@ Here is how we collected the data
Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model.
You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning.
[[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg)
### RLHF Training Stage2 - Training reward model
Stage2 trains a reward model, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model
You can run the `examples/train_rm.sh` to start a reward model training.
[[Stage2 tutorial video]](https://www.youtube.com/watch?v=gMx2CApKhuo)
### RLHF Training Stage3 - Training model with reinforcement learning by human feedback
@@ -128,6 +141,7 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of
</p>
You can run the `examples/train_prompts.sh` to start training PPO with human feedback.
[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g)
For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples).

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applications/Chat/examples/README.md
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@@ -48,6 +48,7 @@ The following pic shows how we collected the data.
## Stage1 - Supervised instructs tuning
Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model.
[[Stage1 tutorial video]](https://www.youtube.com/watch?v=-qFBZFmOJfg)
You can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning.
@@ -83,6 +84,7 @@ torchrun --standalone --nproc_per_node=4 train_sft.py \
## Stage2 - Training reward model
We train a reward model in stage 2, which obtains corresponding scores by manually ranking different outputs for the same prompt and supervises the training of the reward model.
[[Stage2 tutorial video]](https://www.youtube.com/watch?v=gMx2CApKhuo)
You can run the `examples/train_rm.sh` to start a reward model training.
@@ -141,6 +143,7 @@ Stage3 uses reinforcement learning algorithm, which is the most complex part of
You can run the `examples/train_prompts.sh` to start PPO training.
You can also use the cmd following to start PPO training.
[[Stage3 tutorial video]](https://www.youtube.com/watch?v=Z8wwSHxPL9g)
```
torchrun --standalone --nproc_per_node=4 train_prompts.py \