Deferring Executions

When you run your usual script, instructions are executed in order. Using 🤗 Accelerate to deploy your script on several GPUs at the same time introduces a complication: while each process executes all instructions in order, some may be faster than others.

You might need to wait for all processes to have reached a certain point before executing a given instruction. For instance, you shouldn’t save a model before being sure every process is done with training, and you wouldn’t want to continue training before all the model weights have been loaded in. To do this, just write the following line in your code:

accelerator.wait_for_everyone()

This instruction will block all the processes that arrive first until all the other processes have reached that point (if you run your script on just one GPU or CPU, this won’t do anything).

A few example cases for when to use this utility are listed below:

Downloading a Dataset

When downloading a dataset, you should download it first on the main process and then loading the cached dataset in afterwards

with accelerator.main_process_first():
    datasets = load_dataset("glue", "mrpc")

Under the hood this is the same as calling:

# First do something on the main process
if accelerator.is_main_process:
    datasets = load_dataset("glue", "mrpc")
else:
    accelerator.wait_for_everyone()

# And then send it to the rest of them
if not accelerator.is_main_process:
    datasets = load_dataset("glue", "mrpc")
else:
    accelerator.wait_for_everyone()

Saving the state_dict

When saving the state_dict of the model, since you would normally save one file on just the main process you should specify that:

if accelerator.is_main_process:
    model = accelerator.unwrap_model(model)
    torch.save(model.state_dict(), "weights.pth")

Loading in the state_dict

When loading in the state_dict to a model, optimizer, or scheduler, you should wait for all workers to have the weights loaded in before moving on to training

with accelerator.main_process_first():
    state = torch.load("weights.pth")
    model.load_state_dict(state)

Applying a multi-worker CPU operation

Applying a map() operation on multiple workers, such as tokenizing should be done on the main process first, and then propagated to each one.

datasets = load_dataset("glue", "mrpc")

with accelerator.main_process_first():
    tokenized_datasets = datasets.map(
        tokenize_function,
        batched=True,
        remove_columns=["idx", "sentence1", "sentence2"],
    )