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110 lines
3.0 KiB
110 lines
3.0 KiB
# Colossal-AI
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An integrated large-scale model training system with efficient parallelization techniques.
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Paper: [Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training](https://arxiv.org/abs/2110.14883)
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Blog: [Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training](https://www.hpcaitech.com/blog)
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## Installation
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### PyPI
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```bash
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pip install colossalai
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```
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### Install From Source
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```shell
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git clone git@github.com:hpcaitech/ColossalAI.git
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cd ColossalAI
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# install dependency
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pip install -r requirements/requirements.txt
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# install colossalai
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pip install .
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```
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Install and enable CUDA kernel fusion (compulsory installation when using fused optimizer)
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```shell
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pip install -v --no-cache-dir --global-option="--cuda_ext" .
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```
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## Documentation
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- [Documentation](https://www.colossalai.org/)
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## Quick View
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### Start Distributed Training in Lines
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```python
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import colossalai
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from colossalai.trainer import Trainer
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from colossalai.core import global_context as gpc
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engine, train_dataloader, test_dataloader = colossalai.initialize()
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trainer = Trainer(engine=engine,
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verbose=True)
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trainer.fit(
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train_dataloader=train_dataloader,
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test_dataloader=test_dataloader,
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epochs=gpc.config.num_epochs,
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hooks_cfg=gpc.config.hooks,
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display_progress=True,
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test_interval=5
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)
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```
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### Write a Simple 2D Parallel Model
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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
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then distribute the model weights across GPUs in a 2D mesh while you still write your model in a familiar way.
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```python
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from colossalai.nn import Linear2D
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import torch.nn as nn
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class MLP_2D(nn.Module):
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def __init__(self):
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super().__init__()
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self.linear_1 = Linear2D(in_features=1024, out_features=16384)
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self.linear_2 = Linear2D(in_features=16384, out_features=1024)
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def forward(self, x):
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x = self.linear_1(x)
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x = self.linear_2(x)
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return x
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```
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## Features
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Colossal-AI provides a collection of parallel training components for you. We aim to support you to write your
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distributed deep learning models just like how you write your single-GPU model. We provide friendly tools to kickstart
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distributed training in a few lines.
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- [Data Parallelism](./docs/parallelization.md)
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- [Pipeline Parallelism](./docs/parallelization.md)
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- [1D, 2D, 2.5D, 3D and sequence parallelism](./docs/parallelization.md)
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- [Friendly trainer and engine](./docs/trainer_engine.md)
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- [Extensible for new parallelism](./docs/add_your_parallel.md)
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- [Mixed Precision Training](./docs/amp.md)
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- [Zero Redundancy Optimizer (ZeRO)](./docs/zero.md)
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## Cite Us
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```
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@article{bian2021colossal,
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title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
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author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
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journal={arXiv preprint arXiv:2110.14883},
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year={2021}
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}
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```
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