# Colossal-AI

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

Paper: [Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training](https://arxiv.org/abs/2110.14883)

Blog: [Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training](https://www.hpcaitech.com/blog)

## Installation

### PyPI

```bash
pip install colossalai
```

### Install From Source

```shell
git clone git@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)

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

## Documentation

- [Documentation](https://www.colossalai.org/)

## Quick View

### Start Distributed Training in Lines

```python
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.

```python
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](./docs/parallelization.md)
- [Pipeline Parallelism](./docs/parallelization.md)
- [1D, 2D, 2.5D, 3D and sequence parallelism](./docs/parallelization.md)
- [Friendly trainer and engine](./docs/trainer_engine.md)
- [Extensible for new parallelism](./docs/add_your_parallel.md)
- [Mixed Precision Training](./docs/amp.md)
- [Zero Redundancy Optimizer (ZeRO)](./docs/zero.md)

## 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}
}
```