PyTorch Tensor Corruption Bug: Storage Resize Failures Explained

Ever been deep in your code, running some complex tensor operations, only to have everything crash with a cryptic error? It's a frustrating experience that can bring your development to a grinding halt. Recently, a bug in PyTorch, specifically concerning tensor metadata updates during storage resize failures, has come to light. This issue, which can lead to corrupted tensors and subsequent crashes, highlights the importance of robust error handling in deep learning frameworks. Let's dive into what's happening and why it matters for your PyTorch projects.

The Nitty-Gritty of the Nyawky Bug: Corrupted Tensors and Crashes

The Nyawky bug (as we'll refer to it for simplicity, playing on the provided keywords) occurs when you try to resize a tensor in PyTorch, but the underlying storage for that tensor can't actually be resized. This typically happens when a tensor is sharing its storage with a buffer that's not meant to be modified, like a NumPy array that you've previously injected into a PyTorch tensor using set_(). PyTorch does correctly identify this situation and throws a RuntimeError with the message: "Trying to resize storage that is not resizable." This is good – the framework recognizes the problem. However, the way the error is handled isn't exception-safe, leading to a cascading problem. Before PyTorch even realizes the storage can't be resized, it has already updated the tensor's shape and stride metadata to reflect the intended new size. So, while the error is thrown, the tensor is left in a seriously compromised state. We call this a "Zombie" tensor. It now thinks it's a certain size (e.g., a large 5x5x5 tensor), but its actual storage remains empty, holding zero bytes. If your code catches the RuntimeError and continues, or if subsequent operations attempt to access this corrupted tensor, you're very likely to encounter Segmentation Faults or other internal RuntimeErrors. This means your program crashes, often with little indication of the root cause beyond the initial resize attempt.

Imagine you're building a complex neural network, and at some point, a tensor representing your data gets into this zombie state. Your training process might run for hours, or even days, before hitting this snag. When it does, debugging can be a nightmare. The core of the issue lies in the timing: the metadata update happens before the check that confirms the storage's resizability. This temporal disconnect creates the inconsistency. The provided minimal reproduction code vividly demonstrates this. It creates an empty, non-resizable storage from a NumPy array, then sets a new tensor to use this storage. When resize_() is called, it correctly raises an error, but the t.shape is already updated to torch.Size([5, 5, 5]) while t.untyped_storage().nbytes() remains 0. Trying to print this tensor (print(t)) is what triggers the final crash, as PyTorch tries to access data that doesn't exist in the allocated (or in this case, non-existent) storage.

This bug is particularly insidious because it doesn't always manifest as an immediate crash. Sometimes, the corrupted tensor might be passed around your program for a while before an operation like printing or accessing an element finally reveals the underlying corruption. The expected behavior, in this scenario, is that if resize_() fails due to non-resizable storage, the tensor's metadata should remain exactly as it was before the operation. The shape should not change, and the storage size should remain consistent. The tensor should ideally remain torch.Size([0]) with 0 bytes of storage, and no crash should occur. The strong exception guarantee implies that operations should either succeed entirely or leave the object in its original state, which is clearly not happening here.

Why This Matters for Your PyTorch Workflow

Understanding the Nyawky bug is crucial for anyone working with PyTorch, especially when dealing with data loading, manipulation, or integration with other libraries like NumPy. The core problem is a violation of exception safety. In software engineering, particularly in systems programming and libraries like PyTorch, exception safety is a critical concept. It dictates how a program should behave when an exception occurs. There are typically three levels of exception safety:

1. Basic Guarantee: If an exception is thrown, the program remains in a valid state. No memory leaks or corruption occur, though the state might be indeterminate.
2. Strong Guarantee: If an exception is thrown, the operation is effectively undone, and the program remains in the state it was before the operation began. This is often referred to as