Preventing "UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars" in PyTorch Data Parallel

When working with PyTorch, one of the common warnings developers come across is the UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars. This issue often arises when using Data Parallelism to speed up computations on the device. This warning may seem daunting at first, but with a bit of understanding of how PyTorch handles tensors and parallelism, it becomes straightforward to address.

Understanding the Warning

PyTorch’s Data Parallel wrapper allows a user to run their model in parallel across multiple GPUs by distributing the input data. Under the hood, it slices data along the batch dimension (dimension 0) and sends each slice to each GPU. When it gathers outputs to construct the final output, it expects the results to be concatenated along the same dimension. The warning occurs if PyTorch finds it doesn’t need to concatenate the results because each slice comprises a scalar, not a tensor along the batch dimension as expected.

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset

class DummyDataset(Dataset):
    def __init__(self, num_samples):
        self.data = torch.randn(num_samples, 1)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        return self.data[idx]

class SimpleModel(nn.Module):
    def __init__(self):
        super(SimpleModel, self).__init__()
        self.layer = nn.Linear(1, 1)

    def forward(self, x):
        # Returning a scalar instead of a tensor along dimension 0 on purpose
        return self.layer(x).sum()

# Simulate dataset and DataLoader
num_samples = 10
batch_size = 2

# Create a dataset
dataset = DummyDataset(num_samples)
dataloader = DataLoader(dataset, batch_size=batch_size)

# Instantiate the model and wrap it with DataParallel
model = SimpleModel()
if torch.cuda.device_count() > 1:
    print("Let's use", torch.cuda.device_count(), "GPUs!")
    model = nn.DataParallel(model)
model.to(torch.device('cuda'))

# Simulate the training
for data in dataloader:
    data = data.to(torch.device('cuda'))
    output = model(data)
    print(output)

Solution

To rectify this resulting UserWarning, rectify the model's outputs by ensuring they return a tensor shaped within the batch dimension even if there’s only one scalar value needed. This prevents PyTorch from getting confused. Instead of summing inside the model, the sum should be computed outside after the forward pass.

class CorrectSimpleModel(nn.Module):
    def __init__(self):
        super(CorrectSimpleModel, self).__init__()
        self.layer = nn.Linear(1, 1)

    def forward(self, x):
        # Ensure a tensor shaped along batch dimension is returned
        return self.layer(x)

# Redefine and Data Parallel wrap the corrected model
correct_model = CorrectSimpleModel()
if torch.cuda.device_count() > 1:
    correct_model = nn.DataParallel(correct_model)
correct_model.to(torch.device('cuda'))

# Simulate the training with correct model
for data in dataloader:
    data = data.to(torch.device('cuda'))
    output = correct_model(data)
    batch_sum = output.sum()
    print(batch_sum)

The above example ensures that irrespective of how the intermediate operations inside the network are computed, their outputs maintain a consistent shape that PyTorch’s Data Parallel function handles proficiently, thus getting rid of the unwanted warning. This way, you prevent unnecessary issues and maintain the flow of operations as expected.

Conclusion

When working with distributed computing in PyTorch or any data or task parallel jobs, always assure that the operations, especially those visible to PyTorch like model.forward returns, are consistent for all partitions. Converting them into batch-dimensioned tensors usually tactically maintains compatibility with PyTorch’s parallel execution model, avoiding extraneous warnings and potentially misconfiguration issues.