How to Fix "View Size Is Not Compatible" in PyTorch

The Error

RuntimeError: view size is not compatible with input tensor's size and stride
(at least one dimension spans across two contiguous subspaces). Use .reshape(...) instead.

Fix 1, Replace .view() with .reshape() (Recommended)

# Instead of this:
x = x.transpose(1, 2).view(batch, -1) # ERROR

# Do this:
x = x.transpose(1, 2).reshape(batch, -1) # Works ✓

.reshape() handles both contiguous and non-contiguous tensors. It returns a view when possible and copies data when necessary.

Fix 2, Make Contiguous First

# If you need .view() specifically:
x = x.transpose(1, 2).contiguous().view(batch, -1) # Works ✓

Why This Happens

These operations make tensors non-contiguous (they change how data is indexed without moving it in memory):

• .transpose() / .t()
• .permute()
• .narrow()
• .expand()
• Slicing operations like x[:, ::2]

x = torch.randn(3, 4)
print(x.is_contiguous())       # True
print(x.t().is_contiguous())     # False
print(x.t().contiguous().is_contiguous()) # True

view() vs reshape()

• .view() — requires contiguous memory, always returns a view (no data copy), fails on non-contiguous tensors
• .reshape() — works on any tensor, returns a view if possible, copies data if needed

In most cases, .reshape() is the safer choice. Use .view() only when you specifically want to guarantee no data copy.