Colossal-AI

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An integrated large-scale model training system with efficient parallelization techniques.

Installation

Install From Source (Recommended)

We recommend you to install from source as the Colossal-AI is updating frequently in the early versions. The documentation will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)

git clone https://github.com/hpcaitech/ColossalAI.git
cd ColossalAI
# install dependency
pip install -r requirements/requirements.txt

# install colossalai
pip install .

Install and enable CUDA kernel fusion (compulsory installation when using fused optimizer)

pip install -v --no-cache-dir --global-option="--cuda_ext" .

PyPI

pip install colossalai

Use Docker

Run the following command to build a docker image from Dockerfile provided.

cd ColossalAI
docker build -t colossalai ./docker

Run the following command to start the docker container in interactive mode.

docker run -ti --gpus all --rm --ipc=host colossalai bash

Quick View

Start Distributed Training in Lines

import colossalai
from colossalai.utils import get_dataloader


# my_config can be path to config file or a dictionary obj
# 'localhost' is only for single node, you need to specify
# the node name if using multiple nodes
colossalai.launch(
    config=my_config,
    rank=rank,
    world_size=world_size,
    backend='nccl',
    port=29500,
    host='localhost'
)

# build your model
model = ...

# build you dataset, the dataloader will have distributed data
# sampler by default
train_dataset = ...
train_dataloader = get_dataloader(dataset=dataset,
                                shuffle=True
                                )


# build your
optimizer = ...

# build your loss function
criterion = ...

# build your lr_scheduler
engine, train_dataloader, _, _ = colossalai.initialize(
    model=model,
    optimizer=optimizer,
    criterion=criterion,
    train_dataloader=train_dataloader
)

# start training
engine.train()
for epoch in range(NUM_EPOCHS):
    for data, label in train_dataloader:
        engine.zero_grad()
        output = engine(data)
        loss = engine.criterion(output, label)
        engine.backward(loss)
        engine.step()

Write a Simple 2D Parallel Model

Let's say we have a huge MLP model and its very large hidden size makes it difficult to fit into a single GPU. We can then distribute the model weights across GPUs in a 2D mesh while you still write your model in a familiar way.

from colossalai.nn import Linear2D
import torch.nn as nn


class MLP_2D(nn.Module):

    def __init__(self):
        super().__init__()
        self.linear_1 = Linear2D(in_features=1024, out_features=16384)
        self.linear_2 = Linear2D(in_features=16384, out_features=1024)

    def forward(self, x):
        x = self.linear_1(x)
        x = self.linear_2(x)
        return x

Features

Colossal-AI provides a collection of parallel training components for you. We aim to support you to write your distributed deep learning models just like how you write your single-GPU model. We provide friendly tools to kickstart distributed training in a few lines.

Data Parallelism
Pipeline Parallelism
1D, 2D, 2.5D, 3D and sequence parallelism
Friendly trainer and engine
Extensible for new parallelism
Mixed Precision Training
Zero Redundancy Optimizer (ZeRO)

Please visit our documentation and tutorials for more details.

Cite Us

@article{bian2021colossal,
  title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
  author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
  journal={arXiv preprint arXiv:2110.14883},
  year={2021}
}

NUS-HPC-AI-Lab/ColossalAI