I finally figured out why my custom CUDA kernels were hitting

I spent most of yesterday staring at a dimension mismatch error that shouldn't have existed, and it forced me to stop treating PyTorch tensors like magic black boxes. We all do it—we just call .view(), .reshape(), or .transpose() and assume the underlying memory stays organized. But if you're trying to optimize an AI workflow or write high-performance training loops, you cannot ignore how strides and memory layouts actually function under the hood.

Most people visualize a tensor as a multi-dimensional grid, like a 3D cube of numbers. That's fine for a mental model, but it's a lie when it comes to hardware. In reality, your RAM is a flat, one-dimensional line of addresses. The "shape" is just an interpretation of how we jump through that line.

The difference between shape and strides

When you define a tensor, you have the shape (the dimensions) and the stride (the number of steps to skip in memory to reach the next element in a specific dimension). This is where the real bugs hide.

If I have a 2x3 tensor:

import torch
x = torch.tensor([[1, 2, 3], [4, 5, 6]])
print(x.shape)   # torch.Size([2, 3])
print(x.stride()) # (3, 1)

The stride (3, 1) tells PyTorch: "To move down one row, skip 3 elements. To move one column over, skip 1 element." This works perfectly because the data is contiguous.

The "Contiguous" trap

The headache started when I used .transpose(). When you transpose a tensor, PyTorch doesn't actually move the data around in memory—it's too slow to do that every time. Instead, it just swaps the strides.

y = x.t()
print(y.shape)   # torch.Size([3, 2])
print(y.stride()) # (1, 3)
print(y.is_contiguous()) # False

Now the tensor is "non-contiguous." The elements are logically in a new order, but physically, they are still sitting in the old order in your RAM.

This is a huge problem when you try to use certain operations like .view(). I kept getting this specific error:
RuntimeError: view size is not compatible with input tensor's size and stride (at least one dimension spans much more than 1 stride)

The error happens because .view() requires the tensor to be contiguous. It wants to re-interpret the memory layout without copying anything, but if the strides are scrambled from a transpose, the math doesn't line up.

How to fix it (and when not to)

If you hit that error, the quick fix is usually:

z = y.contiguous().view(new_shape)

Calling .contiguous() forces PyTorch to allocate a new block of memory and copy the elements into the correct physical order.

But here is the deep dive takeaway: .contiguous() is not free. It’s a memory copy operation. If you are doing this inside a tight training loop for every single batch, you are effectively killing your GPU throughput.

Instead of constantly calling .contiguous(), try to design your AI agent or model architecture to minimize transpositions, or use .reshape() instead of .view(). While .view() is strict about memory, .reshape() is smarter—it will return a view if possible, but if the tensor is non-contiguous, it will automatically handle the copy for you. It’s a bit more "beginner-friendly," but for real-world deployment, knowing exactly when a copy is happening is the difference between a model that runs in 10ms and one that drags at 50ms.