Fast MoE
===

## Introduction

An easy-to-use but efficient implementation of the Mixture of Experts (MoE) 
model for PyTorch. 

## Installation

### Prerequisites

PyTorch with CUDA is required. The repository is currently tested with PyTorch
v1.6.0 and CUDA 10, with designed compatibility to other versions.

If distributed version is enabled, NCCL with P2P communication support,
typically versions >= 2.7.5 is needed. 

### Installing

Fast MoE contains a set of PyTorch customized opearators, including both C and
Python components. Use `python setup.py install` to easily install and enjoy
using Fast MoE for training.

## Usage 

### FMoEfy a transformer model

Transformer is currently the most popular model to be extended by MoE. Using
Fast MoE, a transformer-based model can be extended as MoE by an one-key plugin
shown as follow.

Assume that there is a PyTorch model `model` with MLP layers located at
`model.language_model.transformer.layers[<idx>].mlp`, use the following two
lines to easily scale up the MLP layers to multiple experts.

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

### Using Fast MoE as a PyTorch module

Examples can be seen in [examples](examples/). The easist way is to replace the
feed forward layer by the `FMoE` layer.

### Using Fast MoE in Parallel

For data parallel, nothing else is needed.

For expert parallel, in which experts are located separately across workers,
NCCL backend is required to be built with PyTorch. Use environment variable
`USE_NCCL=1` to `setup.py` to enable distributing experts across workers. Note
that the arguments of the MoE layers should then be excluded from the data
parallel parameter synchronization list.
E