# Train GPT with Colossal-AI

This example shows how to use [Colossal-AI](https://github.com/hpcaitech/ColossalAI) to run huggingface GPT training in distributed manners.

## GPT

We use the [GPT-2](https://huggingface.co/gpt2) model from huggingface transformers. The key learning goal of GPT-2 is to use unsupervised pre-training models to do supervised tasks.GPT-2 has an amazing performance in text generation, and the generated text exceeds people's expectations in terms of contextual coherence and emotional expression.

## Requirements

Before you can launch training, you need to install the following requirements.

### Install PyTorch

```bash
#conda
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
#pip
pip install torch==1.12.0+cu113 torchvision==0.13.0+cu113 torchaudio==0.12.0 --extra-index-url https://download.pytorch.org/whl/cu113
```

### Install [Colossal-AI v0.1.12](https://colossalai.org/download/) From Official Website

```bash
pip install colossalai==0.1.12+torch1.12cu11.3 -f https://release.colossalai.org
```

### Install requirements

```bash
pip install -r requirements.txt
```

This is just an example that we download PyTorch=1.12.0, CUDA=11.6 and colossalai=0.1.12+torch1.12cu11.3. You can download another version of PyTorch and its corresponding ColossalAI version. Just make sure that the version of ColossalAI is at least 0.1.10, PyTorch is at least 1.8.1 and transformers is at least 4.231.
If you want to test ZeRO1 and ZeRO2 in Colossal-AI, you need to ensure Colossal-AI>=0.1.12.

## Dataset

For simplicity, the input data is randomly generated here.

## Training
We provide two stable solutions.
One utilizes the Gemini to implement hybrid parallel strategies of Gemini, DDP/ZeRO, and Tensor Parallelism for a huggingface GPT model.
The other one use [Titans](https://github.com/hpcaitech/Titans), a distributed executed model zoo maintained by ColossalAI,to implement the hybrid parallel strategies of TP + ZeRO + PP.

We recommend using Gemini to qucikly run your model in a distributed manner.
It doesn't require significant changes to the model structures, therefore you can apply it on a new model easily.
And use Titans as an advanced weapon to pursue a more extreme performance.
Titans has included the some typical models, such as Vit and GPT.
However, it requires some efforts to start if facing a new model structure.

### GeminiDPP/ZeRO + Tensor Parallelism
```bash
bash run_gemini.sh
```

The `train_gpt_demo.py` provides three distributed plans (except ones already provided by PyTorch), you can choose the plan you want in `run_gemini.sh`. The CAI_Gemini leverages Tensor Parallel and Gemini + ZeRO DDP. For their differences, you may check out the answer to issue [here](https://github.com/hpcaitech/ColossalAI/issues/2590#issuecomment-1418766581).

- ZeRO1 (CAI_ZeRO1)
- ZeRO2 (CAI_ZeRO2)
- Gemini + ZeRO DDP (CAI_Gemini)
- Pytorch DDP (Pytorch_DDP)
- Pytorch ZeRO (Pytorch_ZeRO)

### Titans (Tensor Parallelism) + ZeRO + Pipeline Parallelism

Titans provides a customized GPT model, which uses distributed operators as building blocks.
In [./titans/README.md], we provide a hybrid parallelism of ZeRO, TP and PP.
You can switch parallel strategies using a config file.

## Performance

Testbed: a cluster of 8xA100 (80GB) and 1xAMD EPYC 7543 32-Core Processor (512 GB). GPUs are connected via PCI-e.
ColossalAI version 0.1.13.

[benchmark results on google doc](https://docs.google.com/spreadsheets/d/15A2j3RwyHh-UobAPv_hJgT4W_d7CnlPm5Fp4yEzH5K4/edit#gid=0)

[benchmark results on Tencent doc (for china)](https://docs.qq.com/sheet/DUVpqeVdxS3RKRldk?tab=BB08J2)

### Experimental Features

#### [Pipeline Parallel](./experiments/pipeline_parallel/)
#### [Auto Parallel](./experiments/auto_parallel_with_gpt/)
