Accessing Internal Modules in DistributedDataParallel for Train/Eval Mode Switching

Hello everyone,

I’m currently working with a model that follows an encoder-decoder architecture. I need the encoder to be in evaluation mode while the decoder is the component being trained. Running the model on a single GPU is clear to me. However, I face some doubts when I try to use DistributedDataParallel (DDP). Here’s the relevant portion of my code:

# Set up for DDP
args.gpu = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(args.gpu)  # set default device
dist_backend = "nccl"
# ... (additional setup) ...

# Initialize the model
model = create_model().to(args.device)  # args.device = 'cuda'
model_without_ddp = model
if distributed:
    model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])  # args.gpu = 0
    model_without_ddp = model.module

# Training loop
for epoch in range(start_epoch, args.epochs):
    train_one_epoch(model, model_without_ddp, optimizer, dataloader, args)
    evaluate(model, dataloader_test, args)

You’ll notice I’m using both model and model_without_ddp. This is based on PyTorch’s reference scripts, which I’ve used to ensure consistent code and minimize condition checks for DDP usage.

def train_one_epoch(model, model_without_ddp, optimizer, dataloader, args):
    model_without_ddp.encoder.eval()  # This replaces: if distributed: model.module.encoder.eval() else model.encoder.eval()
    train_loss = 0.0
    for x, _ in dataloader:
        x =

        loss = model(x)
        torch.nn.utils.clip_grad_norm_(model_without_ddp.decoder.parameters(), 1.0)  # Replaces: if distributed: model.module.decoder.parameters() else model.decoder.parameters.

        train_loss += loss.item()
    return train_loss

def evaluate(model, dataloader_test, args):
    model.eval()  # entire model is in eval mode
    # ... (rest of the evaluation code) ...
    return metrics

My first question is: Is this the correct approach for accessing and modifying the internal modules of a model wrapped in DDP, especially when switching between the train() and eval() modes?

Additionally, I have another question. In the training loop, I use model(x) for the forward pass, which implicitly includes both the encoder and decoder. Still, I explicitly use model_without_ddp.decoder.parameters() (the component I train). I wonder if I should be using model.parameters() because of some weird stuff inside the DPP class that I don’t know about (although the coder parameters are unnecessary) or if, on the contrary, my methodology is a fine approach.


Hi there! just in case