# Gradient Accumulation (Latest) Author: [Mingyan Jiang](https://github.com/jiangmingyan) **Prerequisite** - [Define Your Configuration](../basics/define_your_config.md) - [Training Booster](../basics/booster_api.md) ## 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. ```python 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](../basics/launch_colossalai.md) for other initialization methods. ```python # 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. ```python # 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. ```python 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. ```python 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) booster.backward(train_loss, optimizer) else: output = model(img) train_loss = criterion(output, label) 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: ```shell 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. ```text 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=) 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=) 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=) 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=) ```