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Gradient Accumulation

Prerequisite

Training Booster

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 call booster.no_sync() which returns a context manager. It accumulate gradients without synchronization, meanwhile you should not update the weights.

Hands-on Practice

We now demonstrate gradient accumulation. In this example, we let the gradient accumulation size to be 4.

Step 1. Import libraries in train.py

Create a train.py and import the necessary dependencies. The version of torch should not be lower than 1.8.1.

import os
from pathlib import Path

import torch
from torchvision import transforms
from torchvision.datasets import CIFAR10
from torchvision.models import resnet18
from torch.utils.data import DataLoader

import colossalai
from colossalai.booster import Booster
from colossalai.booster.plugin import TorchDDPPlugin
from colossalai.logging import get_dist_logger
from colossalai.cluster.dist_coordinator import priority_execution

Step 2. Initialize Distributed Environment

We then need to initialize distributed environment. For demo purpose, we uses launch_from_torch. You can refer to Launch Colossal-AI for other initialization methods.

# initialize distributed setting
parser = colossalai.get_default_parser()
args = parser.parse_args()
# launch from torch
colossalai.launch_from_torch(config=dict())

Step 3. Create training components

Build your model, optimizer, loss function, lr scheduler and dataloaders. Note that the root path of the dataset is obtained from the environment variable DATA. You may export DATA=/path/to/data or change Path(os.environ['DATA']) to a path on your machine. Data will be automatically downloaded to the root path.

# define the training hyperparameters
BATCH_SIZE = 128
GRADIENT_ACCUMULATION = 4

# build resnet
model = resnet18(num_classes=10)

# build dataloaders
with priority_execution():
    train_dataset = CIFAR10(root=Path(os.environ.get('DATA', './data')),
                            download=True,
                            transform=transforms.Compose([
                                transforms.RandomCrop(size=32, padding=4),
                                transforms.RandomHorizontalFlip(),
                                transforms.ToTensor(),
                                transforms.Normalize(mean=[0.4914, 0.4822, 0.4465], std=[0.2023, 0.1994, 0.2010]),
                            ]))

# build criterion
criterion = torch.nn.CrossEntropyLoss()

# optimizer
optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)

Step 4. Inject Feature

Create a TorchDDPPlugin object to instantiate a Booster, and boost these training components.

plugin = TorchDDPPlugin()
booster = Booster(plugin=plugin)
train_dataloader = plugin.prepare_dataloader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
model, optimizer, criterion, train_dataloader, _ = booster.boost(model=model,
                                                                    optimizer=optimizer,
                                                                    criterion=criterion,
                                                                    dataloader=train_dataloader)

Step 5. Train with Booster

Use booster in a normal training loops, and verify gradient accumulation. param_by_iter is to record the distributed training information.

optimizer.zero_grad()
for idx, (img, label) in enumerate(train_dataloader):
        sync_context = booster.no_sync(model)
        img = img.cuda()
        label = label.cuda()
        if idx % (GRADIENT_ACCUMULATION - 1) != 0:
            with sync_context:
                output = model(img)
                train_loss = criterion(output, label)
                train_loss = train_loss / GRADIENT_ACCUMULATION
                booster.backward(train_loss, optimizer)
        else:
            output = model(img)
            train_loss = criterion(output, label)
            train_loss = train_loss / GRADIENT_ACCUMULATION
            booster.backward(train_loss, optimizer)
            optimizer.step()
            optimizer.zero_grad()

        ele_1st = next(model.parameters()).flatten()[0]
        param_by_iter.append(str(ele_1st.item()))

        if idx != 0 and idx % (GRADIENT_ACCUMULATION - 1) == 0:
            break

    for iteration, val in enumerate(param_by_iter):
        print(f'iteration {iteration} - value: {val}')

    if param_by_iter[-1] != param_by_iter[0]:
        print('The parameter is only updated in the last iteration')

Step 6. Invoke Training Scripts

To verify gradient accumulation, we can just check the change of parameter values. When gradient accumulation is set, parameters are only updated in the last step. You can run the script using this command:

colossalai run --nproc_per_node 1 train.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>)

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