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<img height='60px' src='doc/logo/rect.png'/>
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[Release note](doc/release-note.md)
| [中文文档](doc/readme-cn.md)
| [Slack workspace](https://join.slack.com/t/fastmoe/shared_invite/zt-mz0ai6ol-ggov75D62YsgHfzShw8KYw)
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## Introduction

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An easy-to-use and efficient system to support the Mixture of Experts (MoE) 
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model for PyTorch. 

## Installation

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### Prerequisites

PyTorch with CUDA is required. The repository is currently tested with PyTorch
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v1.10.0 and CUDA 11.3, with designed compatibility to older and newer versions.

The minimum version of supported PyTorch is `1.7.2` with CUDA `10`. However,
there are a few known issues that requires manual modification of FastMoE's
code with specific older dependents.
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If the distributed expert feature is enabled, NCCL with P2P communication
support, typically versions `>=2.7.5`, is needed. 
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### Installing

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FastMoE contains a set of PyTorch customized opearators, including both C and
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Python components. Use `python setup.py install` to easily install and enjoy
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using FastMoE for training.
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A step-by-step tutorial for the installation procedure can be found [here](doc/installation-guide.md).

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The distributed expert feature is enabled by default. If you want to disable
it, pass environment variable `USE_NCCL=0` to the setup script.
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Note that an extra NCCL developer package is needed, which has to be consistent
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with your PyTorch's NCCL version, which can be inspected by running
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`torch.cuda.nccl.version()`. The 
[official PyTorch docker image](https://hub.docker.com/r/pytorch/pytorch) is
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recommended, as the environment is well-setup there. Otherwise, you can access
the [download link of all NCCL
versions](https://developer.nvidia.com/nccl/nccl-legacy-downloads) to download
the NCCL package that is suitable for you.
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## Usage 

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### FMoEfy a Transformer model
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Transformer is currently one of the most popular models to be extended by MoE. Using
FastMoE, a Transformer-based model can be extended as MoE by an one-key plugin
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shown as follow.

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For example, when using [Megatron-LM](https://github.com/nvidia/megatron-lm),
using the following lines can help you easily scale up the MLP layers to
multiple experts.
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```python
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model = ...

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from fmoe.megatron import fmoefy
model = fmoefy(model, num_experts=<number of experts per worker>)
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train(model, ...)
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```

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A detailed tutorial to _moefy_ Megatron-LM can be found
[here](examples/megatron).

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### Using FastMoE as a PyTorch module
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An example MoE transformer model can be seen in the
[Transformer-XL](examples/transformer-xl) example. The easist way is to replace
the MLP layer by the `FMoE` layers.
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### Using FastMoE in Parallel
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FastMoE supports both data parallel and model parallel. 
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#### Data Parallel
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In FastMoE's data parallel mode, both the gate and the experts are replicated on each worker. 
The following figure shows the forward pass of a 3-expert MoE with 2-way data parallel.

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<p align="center">
<img src="doc/fastmoe_data_parallel.png" width="600">
</p>
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For data parallel, no extra coding is needed. FastMoE works seamlessly with PyTorch's `DataParallel` or `DistributedDataParallel`.
The only drawback of data parallel is that the number of experts is constrained by each worker's memory.

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#### Model Parallel
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In FastMoE's model parallel mode, the gate network is still replicated on each worker but
experts are placed separately across workers.
Thus, by introducing additional communication cost, FastMoE enjoys a large expert pool whose size is proportional to the number of workers.

The following figure shows the forward pass of a 6-expert MoE with 2-way model parallel. Note that experts 1-3 are located in worker 1 while experts 4-6 are located in worker 2.

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<p align="center">
<img src="doc/fastmoe_model_parallel.png" width="600">
</p>
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FastMoE's model parallel requires sophiscated parallel strategies that neither PyTorch nor
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Megatron-LM provides. The `fmoe.DistributedGroupedDataParallel` module is
introduced to replace PyTorch's DDP module.
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#### Faster Performance Features

From a PPoPP'22 paper, _FasterMoE: modeling and optimizing training of
large-scale dynamic pre-trained models_, we have adopted techniques to make
FastMoE's model parallel much more efficient.

These optimizations are named as **Faster Performance Features**, and can be
enabled via several environment variables. Their usage and constraints are
detailed in [a separate document](doc/fastermoe).

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## Citation

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For the core FastMoE system.

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```
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@article{he2021fastmoe,
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      title={FastMoE: A Fast Mixture-of-Expert Training System}, 
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      author={Jiaao He and Jiezhong Qiu and Aohan Zeng and Zhilin Yang and Jidong Zhai and Jie Tang},
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      journal={arXiv preprint arXiv:2103.13262},
      year={2021}
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}
```

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For the [faster performance features](doc/fastermoe).

```
@inproceedings{he2022fastermoe,
    author = {He, Jiaao and Zhai, Jidong and Antunes, Tiago and Wang, Haojie and Luo, Fuwen and Shi, Shangfeng and Li, Qin},
    title = {FasterMoE: Modeling and Optimizing Training of Large-Scale Dynamic Pre-Trained Models},
    year = {2022},
    isbn = {9781450392044},
    publisher = {Association for Computing Machinery},
    address = {New York, NY, USA},
    url = {https://doi.org/10.1145/3503221.3508418},
    doi = {10.1145/3503221.3508418},
    booktitle = {Proceedings of the 27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming},
    pages = {120–134},
    numpages = {15},
    keywords = {parallelism, distributed deep learning, performance modeling},
    location = {Seoul, Republic of Korea},
    series = {PPoPP '22}
}
```

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## Troubleshootings / Discussion
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If you have any problem using FastMoE, or you are interested in getting involved in developing FastMoE, feel free to join [our slack channel](https://join.slack.com/t/fastmoe/shared_invite/zt-mz0ai6ol-ggov75D62YsgHfzShw8KYw).