What is the difference between `torch.Tensor` and `torch.autograd.Variable`?

Step 1: Understand the Historical Context

In PyTorch 0.3 and earlier, the torch.autograd.Variable class was a thin wrapper around a torch.Tensor that was used for automatic differentiation.

Step 2: The Migration Path

Here is a migration path for the existing PyTorch 0.3 code:

1. Remove Variable wrappers:

The first step is to remove all the Variable wrappers from the code.

PyTorch 0.3:

from torch.autograd import Variable
x = Variable(torch.ones(2, 2), requires_grad=True)

PyTorch 1.0:

import torch
x = torch.ones(2, 2, requires_grad=True)

2. Replace .data with .detach():

The .data attribute should be replaced with the .detach() method. The .detach() method returns a new tensor that shares the same storage as the original tensor, but is detached from the computation graph.

PyTorch 0.3:

x_data = x.data

PyTorch 1.0:

x_data = x.detach()

3. Replace volatile with torch.no_grad():

The volatile argument should be replaced with the torch.no_grad() context manager.

PyTorch 0.3:

x = Variable(torch.ones(2, 2), volatile=True)
y = model(x)

PyTorch 1.0:

with torch.no_grad():
    y = model(x)

Step 3: The Benefits of the New API

The new autograd API is simpler, more consistent, and easier to use than the old API. It also has several performance benefits, such as reduced memory usage and faster execution.

By migrating to the new API, you can take advantage of all the new features and improvements in PyTorch 1.0 and beyond.