PyTorch Bug: Corrupted Tensors And Segmentation Faults

Have you ever encountered a situation in your PyTorch workflow where things just break unexpectedly? Maybe a Segmentation Fault or a cryptic RuntimeError that seems to come out of nowhere? If so, you might have stumbled upon a subtle yet critical bug in how PyTorch handles tensor metadata, particularly when operations like resize_() encounter issues with non-resizable storage. This issue can leave your tensors in a corrupted, zombie-like state, leading to unpredictable crashes and data integrity problems. Let's dive deep into what's happening, why it's a problem, and what it means for your machine learning projects.

The Heart of the Problem: Unsafe Resizing and Metadata Mismatch

The core of this bug lies in the interaction between PyTorch's tensor resizing operations (resize_()) and tensors that are backed by immutable or non-resizable storage. When you try to resize a tensor that's linked to a NumPy array (using set_(), for instance), which typically has fixed storage, PyTorch should ideally prevent the operation or, at the very least, ensure the system remains in a consistent state. The expected behavior is that if resize_() encounters an error because the underlying storage cannot be resized, it should simply fail without altering the tensor's shape or stride metadata. In this scenario, the tensor would remain unchanged, maintaining its original, valid state. However, this is precisely where the bug manifests. Instead of rolling back or maintaining the original state upon failure, PyTorch updates the tensor's shape and stride metadata first. It's only after this metadata has been modified to reflect the new, intended size that the check for resizable storage fails, throwing a RuntimeError.

This sequence of events creates a dangerous inconsistency. The tensor's shape metadata now advertises a new, larger size (e.g., torch.Size([5, 5, 5])), but its actual underlying storage remains untouched and, crucially, empty (0 bytes). This creates what we can only describe as a "zombie tensor" – it looks like it has data and a specific shape, but the data simply isn't there in the storage. Accessing or printing such a corrupted tensor after the exception has been caught often leads to a Segmentation Fault or another internal RuntimeError because the program attempts to read data from a non-existent memory location or in a way that violates internal consistency checks. The original report highlights this by showing that after the RuntimeError from resize_(), printing the tensor results in a crash. This is a serious issue because it breaks the strong exception guarantee, which states that a function should either succeed completely or leave the system in the state it was before the call.

Reproducing the Bug: A Minimal Example

To truly understand the impact of a bug, it's essential to be able to reproduce it reliably. The provided example code demonstrates this issue with remarkable clarity. It begins by creating a tensor with an empty, zero-byte storage. This is achieved by first creating an empty NumPy array and then converting its storage into an untyped PyTorch storage. This locked_storage is then assigned to a new PyTorch tensor using t.set_(locked_storage). At this point, the tensor t is valid, with an empty shape (torch.Size([0])) and zero storage bytes.

The critical step is the attempt to resize this tensor using t.resize_((5, 5, 5)). As expected, because the underlying storage is not resizable (it's a fixed, zero-byte NumPy array storage), this operation should fail. The code anticipates this by wrapping the resize_() call in a try...except RuntimeError block. Indeed, a RuntimeError is caught, with the message "Trying to resize storage that is not resizable." This is the expected part of the failure. However, the bug occurs immediately before this exception is raised. Internally, the resize_() function first updates the tensor's shape metadata to torch.Size([5, 5, 5]). It's only after this update that it discovers the storage issue and throws the exception.

Consequently, even though the RuntimeError is caught and execution continues after the try...except block, the tensor t is left in a corrupted state. The print(f"Shape: {t.shape}") statement correctly outputs torch.Size([5, 5, 5]), showing the altered metadata. But the subsequent print(f"Storage: {t.untyped_storage().nbytes()}") reveals the underlying problem: 0, indicating that the storage size hasn't changed and remains empty. The final print(t) statement is where the actual crash often occurs, as PyTorch attempts to access data based on the torch.Size([5, 5, 5]) metadata, but finds no data in the 0-byte storage. This discrepancy between advertised shape and actual storage is the direct cause of segmentation faults and other runtime errors encountered when the corrupted tensor is used later in the program. The provided gist further suggests that in more complex scenarios, the crash might manifest as a segmentation fault rather than a caught RuntimeError during the print operation, underscoring the severity of this internal state corruption.

Why This Bug Matters: Implications for Your Projects

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