Version: v0.1.9

Author: Shenggui Li, Yongbin Li

Prerequisite

Example Code

## Introduction​

In distributed training, gradient synchronization is required at the end of each iteration. This is important because we need to make sure the parameters are updated with the same gradients in different machines so that the resulting parameters are the same. This is often seen in data parallel as the model is replicated across data parallel ranks.

In Colossal-AI, we provide an interface for users to customize how they want to handle the synchronization. This brings flexibility in cases such as implementing a new parallelism method.

When gradient handlers are used, PyTorch DistributedDataParallel will not be used as it will synchronize automatically.

To implement a customized gradient handler, you need to follow these steps.

1. inherit BaseGradientHandler in Colossal-AI.
2. register the gradient handler into the GRADIENT_HANDLER.
3. implement handle_gradient method.
from colossalai.registry import GRADIENT_HANDLERfrom colossalai.engine.gradient_handler import BaseGradientHandler@GRADIENT_HANDLER.register_moduleclass MyGradientHandler(BaseGradientHandler):    def handle_gradient(self):        do_something()    

## Usage​

To use a gradient handler, you need to specify your gradient handler in the config file. The gradient handler will be automatically built and attached to the engine.

gradient_handler = [dict(type='MyGradientHandler')]

### Hands-On Practice​

We provide a runnable example to demonstrate the use of gradient handler. In this example, we used DataParallelGradientHandler instead of PyTorch DistributedDataParallel for data parallel training.

python -m torch.distributed.launch --nproc_per_node 4 --master_addr localhost --master_port 29500  train_with_engine.py