# Performing gradient accumulation with 🤗 Accelerate Gradient accumulation is a technique where you can train on bigger batch sizes than your machine would normally be able to fit into memory. This is done by accumulating gradients over several batches, and only stepping the optimizer after a certain number of batches have been performed. While technically standard gradient accumulation code would work fine in a distributed setup, it is not the most efficient method for doing so and you may experience considerable slowdowns! In this tutorial you will see how to quickly setup gradient accumulation and perform it with the utilities provided in 🤗 Accelerate, which can total to adding just one new line of code! This example will use a very simplistic PyTorch training loop that performs gradient accumulation every two batches: ```python device = "cuda" model.to(device) gradient_accumulation_steps = 2 for index, batch in enumerate(training_dataloader): inputs, targets = batch inputs = inputs.to(device) targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss = loss / gradient_accumulation_steps loss.backward() if (index + 1) % gradient_accumulation_steps == 0: optimizer.step() scheduler.step() optimizer.zero_grad() ``` ## Converting it to 🤗 Accelerate First the code shown earlier will be converted to utilize 🤗 Accelerate without the special gradient accumulation helper: ```diff + from accelerate import Accelerator + accelerator = Accelerator() + model, optimizer, training_dataloader, scheduler = accelerator.prepare( + model, optimizer, training_dataloader, scheduler + ) for index, batch in enumerate(training_dataloader): inputs, targets = batch - inputs = inputs.to(device) - targets = targets.to(device) outputs = model(inputs) loss = loss_function(outputs, targets) loss = loss / gradient_accumulation_steps + accelerator.backward(loss) if (index+1) % gradient_accumulation_steps == 0: optimizer.step() scheduler.step() optimizer.zero_grad() ``` In its current state, this code is not going to perform gradient accumulation efficiently due to a process called gradient synchronization. Read more about that in the [Concepts tutorial](../concept_guides/gradient_synchronization)! ## Letting 🤗 Accelerate handle gradient accumulation All that is left now is to let 🤗 Accelerate handle the gradient accumulation for us. To do so you should pass in a `gradient_accumulation_steps` parameter to [`Accelerator`], dictating the number of steps to perform before each call to `step()` and how to automatically adjust the loss during the call to [`~Accelerator.backward`]: ```diff from accelerate import Accelerator - accelerator = Accelerator() + accelerator = Accelerator(gradient_accumulation_steps=2) ``` Alternatively, you can pass in a `gradient_accumulation_plugin` parameter to the [`Accelerator`] object's `__init__`, which will allow you to further customize the gradient accumulation behavior. Read more about that in the [GradientAccumulationPlugin](../package_reference/accelerator#accelerate.utils.GradientAccumulationPlugin) docs. From here you can use the [`~Accelerator.accumulate`] context manager from inside your training loop to automatically perform the gradient accumulation for you! You just wrap it around the entire training part of our code: ```diff - for index, batch in enumerate(training_dataloader): + for batch in training_dataloader: + with accelerator.accumulate(model): inputs, targets = batch outputs = model(inputs) ``` You can remove all the special checks for the step number and the loss adjustment: ```diff - loss = loss / gradient_accumulation_steps accelerator.backward(loss) - if (index+1) % gradient_accumulation_steps == 0: optimizer.step() scheduler.step() optimizer.zero_grad() ``` As you can see the [`Accelerator`] is able to keep track of the batch number you are on and it will automatically know whether to step through the prepared optimizer and how to adjust the loss. Typically with gradient accumulation, you would need to adjust the number of steps to reflect the change in total batches you are training on. 🤗 Accelerate automagically does this for you by default. Behind the scenes we instantiate a [`GradientAccumulationPlugin`] configured to do this. The [`state.GradientState`] is sync'd with the active dataloader being iterated upon. As such it assumes naively that when we have reached the end of the dataloader everything will sync and a step will be performed. To disable this, set `sync_with_dataloader` to be `False` in the [`GradientAccumulationPlugin`]: ```{python} from accelerate import Accelerator from accelerate.utils import GradientAccumulationPlugin plugin = GradientAccumulationPlugin(sync_with_dataloader=False) accelerator = Accelerator(..., gradient_accumulation_plugin=plugin) ``` ## The finished code Below is the finished implementation for performing gradient accumulation with 🤗 Accelerate ```python from accelerate import Accelerator accelerator = Accelerator(gradient_accumulation_steps=2) model, optimizer, training_dataloader, scheduler = accelerator.prepare( model, optimizer, training_dataloader, scheduler ) for batch in training_dataloader: with accelerator.accumulate(model): inputs, targets = batch outputs = model(inputs) loss = loss_function(outputs, targets) accelerator.backward(loss) optimizer.step() scheduler.step() optimizer.zero_grad() ``` It's important that **only one forward/backward** should be done inside the context manager `with accelerator.accumulate(model)`. To learn more about what magic this wraps around, read the [Gradient Synchronization concept guide](../concept_guides/gradient_synchronization) ## Self-contained example Here is a self-contained example that you can run to see gradient accumulation in action with 🤗 Accelerate: ```python import torch import copy from accelerate import Accelerator from accelerate.utils import set_seed from torch.utils.data import TensorDataset, DataLoader # seed set_seed(0) # define toy inputs and labels x = torch.tensor([1., 2., 3., 4., 5., 6., 7., 8.]) y = torch.tensor([2., 4., 6., 8., 10., 12., 14., 16.]) gradient_accumulation_steps = 4 batch_size = len(x) // gradient_accumulation_steps # define dataset and dataloader dataset = TensorDataset(x, y) dataloader = DataLoader(dataset, batch_size=batch_size) # define model, optimizer and loss function model = torch.zeros((1, 1), requires_grad=True) model_clone = copy.deepcopy(model) criterion = torch.nn.MSELoss() model_optimizer = torch.optim.SGD([model], lr=0.02) accelerator = Accelerator(gradient_accumulation_steps=gradient_accumulation_steps) model, model_optimizer, dataloader = accelerator.prepare(model, model_optimizer, dataloader) model_clone_optimizer = torch.optim.SGD([model_clone], lr=0.02) print(f"initial model weight is {model.mean().item():.5f}") print(f"initial model weight is {model_clone.mean().item():.5f}") for i, (inputs, labels) in enumerate(dataloader): with accelerator.accumulate(model): inputs = inputs.view(-1, 1) print(i, inputs.flatten()) labels = labels.view(-1, 1) outputs = inputs @ model loss = criterion(outputs, labels) accelerator.backward(loss) model_optimizer.step() model_optimizer.zero_grad() loss = criterion(x.view(-1, 1) @ model_clone, y.view(-1, 1)) model_clone_optimizer.zero_grad() loss.backward() model_clone_optimizer.step() print(f"w/ accumulation, the final model weight is {model.mean().item():.5f}") print(f"w/o accumulation, the final model weight is {model_clone.mean().item():.5f}") ``` ``` initial model weight is 0.00000 initial model weight is 0.00000 0 tensor([1., 2.]) 1 tensor([3., 4.]) 2 tensor([5., 6.]) 3 tensor([7., 8.]) w/ accumulation, the final model weight is 2.04000 w/o accumulation, the final model weight is 2.04000 ```