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414 lines
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414 lines
16 KiB
<h1 align="center">
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<span>Coati - ColossalAI Talking Intelligence</span>
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<img width="auto" height="50px", src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/logo_coati.png"/>
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</h1>
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## Table of Contents
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- [Table of Contents](#table-of-contents)
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- [What is Coati ?](#what-is-coati-)
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- [Online demo](#online-demo)
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- [Install](#install)
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- [Install the environment](#install-the-environment)
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- [Install the Transformers](#install-the-transformers)
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- [How to use?](#how-to-use)
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- [Supervised datasets collection](#supervised-datasets-collection)
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- [Stage1 - Supervised instructs tuning](#stage1---supervised-instructs-tuning)
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- [Stage2 - Training reward model](#stage2---training-reward-model)
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- [Stage3 - Training model with reinforcement learning by human feedback](#stage3---training-model-with-reinforcement-learning-by-human-feedback)
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- [Inference - After Training](#inference---after-training)
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- [Coati7B examples](#coati7b-examples)
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- [Generation](#generation)
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- [Open QA](#open-qa)
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- [Limitation for LLaMA-finetuned models](#limitation-for-llama-finetuned-models)
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- [Limitation of dataset](#limitation-of-dataset)
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- [FAQ](#faq)
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- [How to save/load checkpoint](#how-to-saveload-checkpoint)
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- [The Plan](#the-plan)
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- [Real-time progress](#real-time-progress)
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- [Invitation to open-source contribution](#invitation-to-open-source-contribution)
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- [Quick Preview](#quick-preview)
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- [Authors](#authors)
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- [Citations](#citations)
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- [Licenses](#licenses)
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---
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## What is Coati ?
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Coati is a large language model developed by Colossal-AI, which is also a unified large language model framework that has implemented the following functions
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- Supports comprehensive large-model training acceleration capabilities for ColossalAI, without requiring knowledge of complex distributed training algorithms
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- Supervised datasets collection
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- Supervised insturcts fine-tuning
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- Training reward model
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- Reinforcement learning with human feedback
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- Quantization inference
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- Fast model deploying
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- Perfectly integration with the Hugging Face ecosystem, high degree of model customization
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More details can be found in the [blog](https://www.hpc-ai.tech/blog/colossal-ai-chatgpt).
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/chatgpt.png" width=700/>
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</p>
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## Online demo
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You can experience the performance of Coati7B on this page.
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[chat.colossalai.org](https://chat.colossalai.org/)
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> 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.
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## Install
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### Install the environment
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```shell
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conda create -n coati
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conda activate coati
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pip install .
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```
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### Install the Transformers
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Given Hugging Face hasn't officially supported the LLaMA models, We fork a branch of Transformers that can be compatible with our code
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```shell
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git clone https://github.com/hpcaitech/transformers
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cd transformers
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pip install .
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```
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## How to use?
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### Supervised datasets collection
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we colllected 104K bilingual dataset of Chinese and English, and you can find the datasets in this repo
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[InstructionWild](https://github.com/XueFuzhao/InstructionWild)
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Here is how we collected the data
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/data-collect.png" width=500/>
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</p>
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### Stage1 - Supervised instructs tuning
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Stage1 is supervised instructs fine-tuning, which uses the datasets mentioned earlier to fine-tune the model
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you can run the `examples/train_sft.sh` to start a supervised instructs fine-tuning
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```
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torchrun --standalone --nproc_per_node=4 train_sft.py \
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--pretrain "/path/to/LLaMa-7B/" \
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--model 'llama' \
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--strategy colossalai_zero2 \
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--log_interval 10 \
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--save_path /path/to/Coati-7B \
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--dataset /path/to/data.json \
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--batch_size 4 \
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--accimulation_steps 8 \
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--lr 2e-5 \
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--max_datasets_size 512 \
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--max_epochs 1 \
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```
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### Stage2 - Training reward model
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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
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you can run the `examples/train_rm.sh` to start a reward model training
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```
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torchrun --standalone --nproc_per_node=4 train_reward_model.py
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--pretrain "/path/to/LLaMa-7B/" \
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--model 'llama' \
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--strategy colossalai_zero2 \
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--loss_fn 'log_exp'\
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--save_path 'rmstatic.pt' \
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```
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### Stage3 - Training model with reinforcement learning by human feedback
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Stage3 uses reinforcement learning algorithm, which is the most complex part of the training process:
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<p align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chat/stage-3.jpeg" width=800/>
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</p>
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you can run the `examples/train_prompts.sh` to start training PPO with human feedback
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```
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torchrun --standalone --nproc_per_node=4 train_prompts.py \
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--pretrain "/path/to/LLaMa-7B/" \
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--model 'llama' \
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--strategy colossalai_zero2 \
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--prompt_path /path/to/your/prompt_dataset \
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--pretrain_dataset /path/to/your/pretrain_dataset \
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--rm_pretrain /your/pretrain/rm/defination \
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--rm_path /your/rm/model/path
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```
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For more details, see [`examples/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/examples).
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### Inference - After Training
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#### 8-bit setup
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8-bit quantization is originally supported by the latest [transformers](https://github.com/huggingface/transformers). Please install it from source.
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Please ensure you have downloaded HF-format model weights of LLaMA models.
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Usage:
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```python
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from transformers import LlamaForCausalLM
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USE_8BIT = True # use 8-bit quantization; otherwise, use fp16
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model = LlamaForCausalLM.from_pretrained(
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"pretrained/path",
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load_in_8bit=USE_8BIT,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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if not USE_8BIT:
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model.half() # use fp16
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model.eval()
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```
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**Troubleshooting**: if you get error indicating your CUDA-related libraries not found when loading 8-bit model, you can check whether your `LD_LIBRARY_PATH` is correct.
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E.g. you can set `export LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH`.
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#### 4-bit setup
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Please ensure you have downloaded HF-format model weights of LLaMA models first.
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Then you can follow [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa). This lib provides efficient CUDA kernels and weight convertion script.
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After installing this lib, we may convert the original HF-format LLaMA model weights to 4-bit version.
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```shell
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CUDA_VISIBLE_DEVICES=0 python llama.py /path/to/pretrained/llama-7b c4 --wbits 4 --groupsize 128 --save llama7b-4bit.pt
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```
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Run this command in your cloned `GPTQ-for-LLaMa` directory, then you will get a 4-bit weight file `llama7b-4bit-128g.pt`.
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**Troubleshooting**: if you get error about `position_ids`, you can checkout to commit `50287c3b9ae4a3b66f6b5127c643ec39b769b155`(`GPTQ-for-LLaMa` repo).
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For more details, see [`inference/`](https://github.com/hpcaitech/ColossalAI/tree/main/applications/Chat/inference).
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## Coati7B examples
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### Generation
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<details><summary><b>E-mail</b></summary>
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</details>
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<details><summary><b>coding</b></summary>
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</details>
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<details><summary><b>regex</b></summary>
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</details>
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<details><summary><b>Tex</b></summary>
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</details>
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<details><summary><b>writing</b></summary>
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</details>
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<details><summary><b>Table</b></summary>
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</details>
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### Open QA
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<details><summary><b>Game</b></summary>
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</details>
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<details><summary><b>Travel</b></summary>
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</details>
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<details><summary><b>Physical</b></summary>
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</details>
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<details><summary><b>Chemical</b></summary>
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</details>
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<details><summary><b>Economy</b></summary>
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</details>
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You can find more examples in this [repo](https://github.com/XueFuzhao/InstructionWild/blob/main/compare.md).
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### Limitation for LLaMA-finetuned models
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- Both Alpaca and ColossalChat are based on LLaMA. It is hard to compensate for the missing knowledge in the pre-training stage.
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- Lack of counting ability: Cannot count the number of items in a list.
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- Lack of Logics (reasoning and calculation)
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- Tend to repeat the last sentence (fail to produce the end token).
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- Poor multilingual results: LLaMA is mainly trained on English datasets (Generation performs better than QA).
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### Limitation of dataset
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- Lack of summarization ability: No such instructions in finetune datasets.
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- Lack of multi-turn chat: No such instructions in finetune datasets
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- Lack of self-recognition: No such instructions in finetune datasets
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- Lack of Safety:
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- When the input contains fake facts, the model makes up false facts and explanations.
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- Cannot abide by OpenAI's policy: When generating prompts from OpenAI API, it always abides by its policy. So no violation case is in the datasets.
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## FAQ
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### How to save/load checkpoint
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We have integrated the Transformers save and load pipeline, allowing users to freely call Hugging Face's language models and save them in the HF format.
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```
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from coati.models.llama import LlamaLM
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from coati.trainer import SFTTrainer
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model = LlamaLM(pretrained=args.pretrain)
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tokenizer = AutoTokenizer.from_pretrained(args.pretrain)
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trainer = SFTTrainer(model=model,
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strategy=strategy,
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optim=optim,
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train_dataloader=train_dataloader,
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eval_dataloader=eval_dataloader,
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batch_size=args.batch_size,
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max_epochs=args.max_epochs,
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accimulation_steps = args.accimulation_steps
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)
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trainer.fit()
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trainer.save_model(path=args.save_path, only_rank0=True, tokenizer=tokenizer)
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```
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## The Plan
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- [x] implement PPO fine-tuning
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- [x] implement training reward model
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- [x] support LoRA
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- [x] support inference
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- [x] support llama from [facebook](https://github.com/facebookresearch/llama)
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- [x] implement PPO-ptx fine-tuning
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- [ ] integrate with Ray
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- [ ] support more RL paradigms, like Implicit Language Q-Learning (ILQL),
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- [ ] support chain of throught by [langchain](https://github.com/hwchase17/langchain)
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### Real-time progress
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You will find our progress in github project broad
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[Coati](https://github.com/orgs/hpcaitech/projects/17/views/1)
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## Invitation to open-source contribution
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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!
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You may contact us or participate in the following ways:
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1. [Leaving a Star ⭐](https://github.com/hpcaitech/ColossalAI/stargazers) to show your like and support. Thanks!
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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).
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3. Join the Colossal-AI community on
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[Slack](https://join.slack.com/t/colossalaiworkspace/shared_invite/zt-z7b26eeb-CBp7jouvu~r0~lcFzX832w),
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and [WeChat(微信)](https://raw.githubusercontent.com/hpcaitech/public_assets/main/colossalai/img/WeChat.png "qrcode") to share your ideas.
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4. Send your official proposal to email contact@hpcaitech.com
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Thanks so much to all of our amazing contributors!
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## Quick Preview
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<p id="ChatGPT_scaling" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT%20scaling.png" width=800/>
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</p>
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- Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference
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<p id="ChatGPT-1GPU" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/ChatGPT-1GPU.jpg" width=450/>
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</p>
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- Up to 10.3x growth in model capacity on one GPU
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- A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)
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<p id="inference" align="center">
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<img src="https://raw.githubusercontent.com/hpcaitech/public_assets/main/applications/chatgpt/LoRA%20data.jpg" width=600/>
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</p>
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- Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
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- Keep in a sufficiently high running speed
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## Authors
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Coati is developed by ColossalAI Team: [Fazzie](https://fazzie-key.cool/about/index.html), [FrankLeeeee](https://github.com/FrankLeeeee), [BlueRum](https://github.com/ht-zhou), [ver217](https://github.com/ver217)
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The Phd student [Zangwei Zheng](https://github.com/zhengzangw) and [Xue Fuzhao](https://github.com/XueFuzhao) also contributed a lot to this project.
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## Citations
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```bibtex
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@article{Hu2021LoRALA,
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title = {LoRA: Low-Rank Adaptation of Large Language Models},
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author = {Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Weizhu Chen},
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journal = {ArXiv},
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year = {2021},
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volume = {abs/2106.09685}
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}
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@article{ouyang2022training,
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title={Training language models to follow instructions with human feedback},
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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},
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journal={arXiv preprint arXiv:2203.02155},
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year={2022}
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}
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@article{touvron2023llama,
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title={LLaMA: Open and Efficient Foundation Language Models},
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author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and Rodriguez, Aurelien and Joulin, Armand and Grave, Edouard and Lample, Guillaume},
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journal={arXiv preprint arXiv:2302.13971},
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year={2023}
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}
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@misc{alpaca,
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author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
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title = {Stanford Alpaca: An Instruction-following LLaMA model},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
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}
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@misc{instructionwild,
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author = {Fuzhao Xue and Zangwei Zheng and Yang You },
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title = {Instruction in the Wild: A User-based Instruction Dataset},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/XueFuzhao/InstructionWild}},
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}
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```
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## Licenses
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Coati is licensed under the [Apache 2.0 License](LICENSE).
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