3.5 KiB
Train GPT with Colossal-AI
This example shows how to use Colossal-AI to run huggingface GPT training in distributed manners.
GPT
We use the GPT-2 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
#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 From Official Website
pip install colossalai==0.1.12+torch1.12cu11.3 -f https://release.colossalai.org
Install transformers
pip install transformers
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 randonly generated here.
Training
bash run.sh
Training config
The train_gpt_demo.py provides three distributed plans, you can choose the plan you want in run.sh. The Colossal-AI leverages Tensor Parallel and Gemini + ZeRO DDP.
- Colossal-AI
- ZeRO1 (Colossal-AI)
- ZeRO2 (Colossal-AI)
- Pytorch DDP
- Pytorch ZeRO
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.
How dose Batch Size affect the efficency.
| model | #GPU | policy | TP | batch per DP | Tflops |
|---|---|---|---|---|---|
| gpt2_10b | 2 | cpu | 1 | 32 | 122.046 |
| gpt2_10b | 2 | cpu | 1 | 16 | 82.649 |
| gpt2_10b | 2 | cpu | 1 | 8 | 61.354 |
How dose the Placement Policy affect the efficency.
| model | #GPU | policy | TP | batch per DP | Tflops |
|---|---|---|---|---|---|
| gpt2_10b | 4 | auto | 1 | 8 | 88.657 |
| gpt2_10b | 4 | cuda | 1 | 8 | OOM |
| gpt2_10b | 4 | cpu | 1 | 8 | 61.354 |
| gpt2_10b | 4 | const | 1 | 8 | 82.137 |
How dose the Tensor Parallel Degree affect the efficency.
| model | #GPU | policy | TP | batch per DP | Tflops |
|---|---|---|---|---|---|
| gpt2_10b | 4 | auto | 1 | 8 | 88.657 |
| gpt2_10b | 4 | auto | 2 | 8 | 56.687 |
| gpt2_10b | 4 | auto | 4 | 8 | 29.019 |
| gpt2_10b | 4 | auto | 4 | 64 | 50.411 |
| gpt2_20b | 1 | cpu | 1 | 8 | 43.102 |
| gpt2_20b | 4 | cpu | 4 | 8 | 28.491 |
Touch the bar of model scale and batch size.
| model | #GPU | policy | TP | batch per DP | Tflops |
|---|
| gpt2_20b | 4 | cpu | 1 | 64 | CUDA OOM | | gpt2_20b | 4 | auto | 1/2 | 64 | CUDA OOM | | gpt2_20b | 4 | cpu | 2 | 64 | 121.394 | | gpt2_20b | 4 | cpu | 2 | 8 | 43.102 | | gpt2_20b | 8 | cpu | 2 | 64 | 125.170 |