Version: v0.1.9

Author: Shenggui Li, Yongbin Li

Prerequisite

Example Code

## Introduction​

Gradient accumulation is a common way to enlarge your batch size for training. When training large-scale models, memory can easily become the bottleneck and the batch size can be very small, (e.g. 2), leading to unsatisfactory convergence. Gradient accumulation works by adding up the gradients calculated in multiple iterations, and only update the parameters in the preset iteration.

## Usage​

It is simple to use gradient accumulation in Colossal-AI. Just add this following configuration into your config file. The integer represents the number of iterations to accumulate gradients.

gradient_accumulation = <int>

## Hands-on Practice​

We provide a runnable example to demonstrate gradient accumulation. In this example, we set the gradinet accumulation size to be 4. You can run the script using this command:

python -m torch.distributed.launch --nproc_per_node 1 --master_addr localhost --master_port 29500  run_resnet_cifar10_with_engine.py

You will see output similar to the text below. This shows gradient is indeed accumulated as the parameter is not updated in the first 3 steps, but only updated in the last step.

iteration 0, first 10 elements of param: tensor([-0.0208,  0.0189,  0.0234,  0.0047,  0.0116, -0.0283,  0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=<SliceBackward0>)iteration 1, first 10 elements of param: tensor([-0.0208,  0.0189,  0.0234,  0.0047,  0.0116, -0.0283,  0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=<SliceBackward0>)iteration 2, first 10 elements of param: tensor([-0.0208,  0.0189,  0.0234,  0.0047,  0.0116, -0.0283,  0.0071, -0.0359, -0.0267, -0.0006], device='cuda:0', grad_fn=<SliceBackward0>)iteration 3, first 10 elements of param: tensor([-0.0141,  0.0464,  0.0507,  0.0321,  0.0356, -0.0150,  0.0172, -0.0118, 0.0222,  0.0473], device='cuda:0', grad_fn=<SliceBackward0>)