PyTorch Bug: Corrupted Tensors After Failed Storage Resize

Dec 13, 2025 by Alex Johnson 59 views

$PyTorch Bug: Corrupted Tensors After Failed Storage Resize\n\n## Unmasking the PyTorch Tensor Metadata Corruption Bug\n\nThe *PyTorch tensor metadata corruption bug* is a subtle yet critical flaw that can lead to significant headaches for developers working with custom storage in PyTorch. Imagine you're building a complex machine learning model, meticulously managing memory, and then *bam!* – your tensors suddenly report incorrect sizes, leading to crashes or unpredictable behavior. This insidious issue arises when `resize_()` is invoked on a tensor whose underlying storage is *non-resizable*. While PyTorch correctly identifies this limitation and raises a `RuntimeError`, the problem is that this error occurs *after* the tensor's metadata – its reported shape and stride – has already been updated to the *intended new size*. This leaves the tensor in a dangerously inconsistent state, often referred to as a \"Zombie\" tensor. A \"Zombie\" tensor, in this context, is one where `tensor.shape` proudly declares a new, larger dimension, perhaps `torch.Size([5, 5, 5])`, but `tensor.storage().nbytes()` (or similar checks on the actual allocated memory) reveals that the storage remains stubbornly at its original, non-resizable size, often 0 bytes. This stark mismatch creates a ticking time bomb within your application. Any subsequent attempt to access the tensor's data, even a seemingly innocuous operation like `print(t)`, will inevitably lead to *Segmentation Faults* (a severe crash at the operating system level due to illegal memory access) or internal *RuntimeErrors* within PyTorch itself, making debugging a nightmare. The core of the problem lies in the *lack of exception safety* in the `resize_()` operation. A robust software design principle, known as the *Strong Exception Guarantee*, dictates that if an operation fails, the state of the object should remain unchanged as if the operation never happened. In this case, PyTorch's `resize_()` fails to uphold this crucial guarantee, leaving behind a *corrupted tensor* that can derail an entire application. This isn't just an academic curiosity; it's a real-world problem that can halt your research or deployment, leading to unreliable models and lost productivity. Understanding this bug is absolutely the first step towards mitigating its risks and developing more resilient deep learning solutions, ensuring the integrity of your data and the stability of your PyTorch applications. Without proper exception handling, your tensors, which are the very foundation of your computations, become untrustworthy, making every operation a potential gamble. This makes careful management of tensor resizing crucial when dealing with external memory sources. It demands vigilance from developers to ensure their computational pipelines remain robust and reliable.\n\n## Diving Deep into the Problem: When Resizing Goes Wrong\n\nTo truly grasp the severity of this *PyTorch tensor metadata corruption bug*, let's dive deeper into the mechanics of what happens when resizing goes awry. The scenario typically begins when a PyTorch tensor is deliberately made to *share storage* with an external, non-resizable memory buffer. This is a common and powerful technique for interoperability, for example, when you use `t.set_(locked_storage)` to inject a NumPy array's memory (converted to an untyped storage) into a PyTorch tensor. The idea is brilliant: avoid unnecessary data copies and work directly with existing memory. However, this power comes with a critical caveat. While PyTorch is incredibly flexible, certain underlying storage types, especially those originating from external libraries like NumPy, are *not designed to be dynamically resized* by PyTorch's internal mechanisms. When you then try to call `t.resize_((target_shape))` on such a tensor, PyTorch's internal checks eventually detect that the storage cannot be reallocated or resized. At this point, it correctly raises a `RuntimeError` with a message like \"*Trying to resize storage that is not resizable*\".\n\nThe critical flaw, however, is that this `RuntimeError` is raised *after* a crucial step has already taken place: the tensor's metadata, specifically its `shape` and `stride`, has been updated to reflect the *new, desired size*. It's like a computer program optimistically declaring a large chunk of memory available before actually checking if it can acquire it. So, even though the actual storage reallocation *fails*, the tensor's internal records now show it as having the new dimensions. This creates the aforementioned *inconsistent state* – a \"Zombie\" tensor whose `shape` attribute suggests it holds data of a certain size (e.g., 125 elements for a 5x5x5 tensor), while its underlying `storage().nbytes()` reveals it still occupies 0 bytes. This fundamental mismatch is catastrophic. If your code, assuming the resize was successful (or handling the exception gracefully by trying to use the tensor anyway), attempts to read or write data from this *corrupted tensor*, it will be trying to access memory that either doesn't exist or is completely unallocated according to the actual storage. This leads directly to serious issues like *Segmentation Faults*, where the operating system intervenes to prevent your program from accessing unauthorized memory, or various *RuntimeErrors* within PyTorch when it tries to perform operations based on the conflicting metadata. This behavior utterly breaks the *Strong Exception Guarantee*, which is a cornerstone of robust software development. It means developers cannot rely on the tensor's state remaining consistent if `resize_()` fails, forcing them to implement complex and often error-prone manual checks or re-initializations after any failed resize operation, undermining the very trust users place in the library.\n\n## Reproducing the \"Zombie Tensor\" State: A Step-by-Step Guide\n\nExperiencing the *PyTorch tensor metadata corruption bug* firsthand is the best way to understand its severity. Fortunately, the provided minimal reproduction code offers a crystal-clear demonstration of how to create these problematic \"Zombie\" tensors. Let's walk through it step by step, illustrating precisely how `resize_()` can lead to a *corrupted tensor* state.\n\nFirst, the code initializes `locked_storage` using `torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()`. This is a crucial line because it creates a *non-resizable storage* object. We're essentially telling PyTorch to manage a memory buffer that originated from a NumPy array, which PyTorch cannot dynamically reallocate if its size needs to change. Critically, we start with an *empty* NumPy array, meaning our `locked_storage` initially has 0 bytes. Next, we create a fresh, empty PyTorch tensor: `t = torch.tensor([], dtype=torch.int32)`. Then, the magic (or rather, the problem) happens: `t.set_(locked_storage)`. This command *injects* our non-resizable, 0-byte `locked_storage` into tensor `t`. Now, `t` effectively points to the memory managed by `locked_storage`, inheriting its non-resizable characteristic.\n\nThe next step is where the `resize_()` operation is attempted: `try: t.resize_((5, 5, 5)) except RuntimeError: pass`. Here, we're trying to resize tensor `t` to a 5x5x5 shape, expecting it to occupy enough memory for 125 integers. As anticipated, because `locked_storage` is non-resizable, PyTorch correctly throws a `RuntimeError` saying \"*Trying to resize storage that is not resizable*\". The `try-except` block catches this error, allowing the program to continue. However, this is precisely where the bug manifests. The *expected behavior* after catching such a RuntimeError is that the tensor's state should revert to its original form, meaning `t.shape` should remain `torch.Size([0])`, indicating an empty tensor. This is the *Strong Exception Guarantee* in action, ensuring state consistency.\n\nBut what actually happens? The output from `print(f\$