Chapter 16: `torch.storage`, `torch.memory_format`, and Low-Level Tensor Memory¶

“Because knowing how your tensors are stored can save you time, memory, and sanity.”

16.1 What is `torch.storage`?¶

At its core, every PyTorch Tensor is a view into a Storage object — a 1D, contiguous block of memory that holds all the actual data.

x = torch.tensor([[1, 2], [3, 4]])
x_storage = x.storage()
print(list(x_storage))  # Output: [1, 2, 3, 4]

x[0][1] accesses the second element, but it’s ultimately referencing an index inside this flat storage.

This abstraction is mostly invisible in modern PyTorch, but helpful for:

Tensors created from one another may share storage, meaning changes in one affect the other:

a = torch.tensor([1, 2, 3, 4])
b = a.view(2, 2)
b[0][0] = 99
print(a)  # tensor([99, 2, 3, 4]) — same storage!

✅ If you want independent memory, use .clone():
c = a.clone()

Contiguous memory layout = row-major (C-style ordering).

x = torch.randn(2, 3)
x_T = x.t()
print(x_T.is_contiguous())  # False — transposing breaks contiguity

x_T_contig = x_T.contiguous()

⚠️ Functions like .view() only work on contiguous tensors.
✅ Use .reshape() as a safer alternative.

In deep learning, layout matters — especially for performance on GPUs or specialized hardware.

x = torch.randn(1, 3, 224, 224)  # NCHW by default
x_cl = x.to(memory_format=torch.channels_last)

x.is_contiguous(memory_format=torch.channels_last)

✅ channels_last format boosts performance for 2D convolutions on modern GPUs (A100, RTX, etc.)

torch.save(tensor, 'tensor.pt')
tensor_loaded = torch.load('tensor.pt')

with open('my_tensor.bin', 'wb') as f:
    f.write(tensor.numpy().tobytes())

tensor_from_bin = torch.frombuffer(open('my_tensor.bin', 'rb').read(), dtype=torch.float32)

Useful for embedded systems, device-to-device transfer, legacy platforms.

Use Case	Why It Matters
Custom tensor manipulation	Avoid unintended memory sharing
Model performance (conv2d)	Layout format can impact speed
Multi-threading or slicing	Views can lead to hidden memory bugs
Saving large datasets	Storage-level access may be needed
Deployment to accelerators	Requires channels_last layout

Concept	Purpose
`tensor.storage()`	Inspect or share memory manually
`.clone()`	Create independent memory copy
`.is_contiguous()`	Check if memory is sequential
`.contiguous()`	Fix layout to use `.view()` safely
`memory_format`	Optimize layout for conv ops (NCHW/NHWC)

Most users won’t need to use .storage() directly — but for debugging weird behavior, it’s a lifesaver
For performance optimization (especially with CNNs), switching to channels_last is one of the easiest wins
Memory format awareness becomes essential when building custom ops, exporting to ONNX/TensorRT, or scaling models