PyTorch Bug: Corrupted Tensors After Failed Resize

Dec 16, 2025 by Alex Johnson 51 views

It seems we've stumbled upon a rather sneaky bug in PyTorch that can lead to some serious headaches, especially if you're working with tensors that share storage with non-resizable buffers. Let's dive into what's happening and why it's causing problems. We're talking about a situation where PyTorch thinks it's resizing a tensor, but then something goes wrong, leaving the tensor in a rather unfortunate and corrupted state, often referred to as a "Zombie" tensor. This can lead to hard-to-debug crashes, like segmentation faults, which are never fun!

Understanding the "Zombie" Tensor Problem

So, what exactly is this "zombie" tensor we're talking about? Imagine you have a tensor in PyTorch, and it's not a standalone entity. Instead, it's referencing a piece of memory, called its storage, that's also being used by something else. A common scenario for this is when you create a PyTorch tensor directly from a NumPy array using methods like set_(). NumPy arrays, by their nature, often have fixed-size storage. When you try to resize a PyTorch tensor that's linked to such fixed storage, PyTorch should recognize this limitation and gracefully refuse the operation. And indeed, it does raise a RuntimeError with a clear message: "Trying to resize storage that is not resizable." This part is good! We expect PyTorch to catch this and stop.

However, the bug lies in what happens after the error is raised. The problem is that PyTorch doesn't handle this exception perfectly. Before it checks if the underlying storage can actually be resized, it goes ahead and updates the tensor's metadata – specifically, its shape and stride. These are like the internal instructions that tell PyTorch how to interpret the data in the storage. So, even though the RuntimeError stops the storage resize, the shape and stride metadata have already been modified to reflect the new, desired size.

This creates a dangerous mismatch. You end up with a tensor that, according to its metadata (tensor.shape), is a certain size (e.g., 5x5x5), but its actual storage (tensor.storage()) is still the original, often empty or much smaller, size (e.g., 0 bytes). This inconsistency is what we call a "Zombie" tensor. It looks like it has a shape, but it has no actual data to back it up. When you then try to do something with this zombie tensor, like printing it or accessing its elements, PyTorch gets confused. It tries to read data based on the incorrect shape information from a storage that doesn't have that data, leading to crashes. The Gist linked in the discussion shows that this can manifest as a RuntimeError during printing, but in more complex scenarios, it can escalate to a dreaded segmentation fault, which is a much more severe low-level memory error. The expected behavior, according to the principle of strong exception guarantee, is that if an operation fails, the system should be left in a state as if the operation never happened. In this case, if resize_() fails, the tensor's shape should remain unchanged, reflecting its original state (e.g., torch.Size([0])), and the storage should also be unaffected. This bug violates that guarantee, leaving the tensor in a corrupted, unusable state.

The Minimal Reproduction Case

To really get a handle on this bug, a minimal reproduction case is essential. It helps developers pinpoint the exact lines of code causing the issue and ensures that the fix addresses the core problem without introducing new ones. The provided code snippet offers a perfect example of how to trigger this "zombie" tensor behavior.

Let's break it down:

Creating Non-Resizable Storage: The process starts by creating a torch.Tensor and then using .untyped_storage() to get its underlying storage. Crucially, this storage is initialized from an empty NumPy array (np.array([], dtype=np.int32)). An empty NumPy array naturally has a storage size of 0 bytes, and importantly, it's not designed to be resized. This sets the stage for the upcoming error.
```
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()
```
Injecting Storage into a Tensor: Next, a new, empty PyTorch tensor is created. This tensor is then explicitly linked to the locked_storage we just created using the .set_() method.
```
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)
```
At this point, t is a tensor with shape torch.Size([0]) and its storage is locked_storage, which has 0 bytes. Everything is consistent.
Attempting to Resize (The Trigger): The core of the bug is triggered when we attempt to resize this tensor t to a different shape, say (5, 5, 5). This is done using the t.resize_((5, 5, 5)) command. Since locked_storage is not resizable, PyTorch should raise a RuntimeError here.
```
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    pass
```
The try...except block is used to catch the expected RuntimeError and prevent the program from crashing immediately. This allows us to inspect the state of the tensor after the error occurs.
Observing the Corruption: This is where the bug manifests. After the RuntimeError is caught, the code proceeds to print the tensor's shape, its storage size, and the tensor itself.
```
print(f"Shape: {t.shape}")       # Expected: torch.Size([0]), Actual: torch.Size([5, 5, 5])
print(f"Storage: {t.untyped_storage().nbytes()}") # Expected: 0, Actual: 0
print(t) # CRASH
```
As you can see from the comments in the code, the t.shape has been updated to torch.Size([5, 5, 5]), which is the target size of the failed resize operation. However, t.untyped_storage().nbytes() still correctly reports 0, because the storage itself was never actually resized. This creates the "Zombie" state: the shape says it's a 5x5x5 tensor (which would require 125 elements, or 500 bytes for int32), but the storage is empty. The final print(t) then attempts to access this corrupted tensor, leading to a crash (either a RuntimeError or a segmentation fault, depending on the context and how the tensor is accessed).

This minimal example clearly demonstrates that PyTorch updates the tensor's shape metadata before it successfully validates that the storage can be resized. When the storage resize fails, the metadata remains in its updated, incorrect state, leading to the corruption.

Why This Matters: Impact and Implications

This bug, while seemingly specific, can have far-reaching implications for users who rely on PyTorch for complex deep learning workflows. Understanding the potential impact is key to appreciating why a fix is so important.

1. Data Corruption and Inconsistency: The most immediate consequence is that your tensors can become internally inconsistent. A tensor that reports a certain shape but has no corresponding data in its storage is fundamentally broken. This isn't just a theoretical issue; it can lead to incorrect calculations, unexpected model behavior, and subtle bugs that are incredibly difficult to track down. Imagine training a neural network, and midway through, a batch of data gets corrupted this way – the training could diverge, or worse, converge to a suboptimal solution without any obvious error messages until much later, if at all.

2. Crashing Under Load: As demonstrated by the reproduction case, accessing these corrupted tensors can lead to program crashes. In a production environment or during extensive model training, a segmentation fault can be catastrophic. It means the program terminates abruptly, potentially losing hours or even days of work. These low-level crashes are particularly frustrating because they often don't provide a clear traceback of the Python code that caused them, making debugging a painful process of elimination.

3. Unpredictable Behavior with Shared Storage: The bug is particularly pernicious because it affects tensors that share storage. This is common when interfacing with other libraries like NumPy or when using certain PyTorch operations that create views or slices of existing tensors. If you're not explicitly aware that a tensor might have non-resizable underlying storage, you might innocently try to resize it, triggering the bug without realizing the consequences. This unpredictability can erode trust in the framework.

4. Hindrance to Robust Code: Robust software engineering practices often rely on strong exception guarantees. The idea is that if an operation fails, the program should be left in a consistent state, as if the operation never occurred. This bug violates that principle. When resize_() fails, the tensor's metadata is not restored to its original state. This makes it harder to write code that defensively handles potential errors, as the error handling itself can lead to a corrupted state.

5. Debugging Nightmares: Developers and users alike can spend countless hours trying to debug issues caused by these corrupted tensors. The fact that the corruption occurs after an exception is caught makes it even more obscure. You might see the RuntimeError for the non-resizable storage, but then later, a crash occurs elsewhere in the code, with no obvious connection to the initial error. Tracing the problem back to the specific resize_() call that happened after the exception can be a daunting task. The presence of various links in the original prompt, like zhishi.xlikykx.info/Article_42250291.Htm, suggests that users might be encountering similar issues and seeking solutions or explanations, highlighting the need for a robust fix.

6. Potential for Subtle Data Loss: In scenarios where the tensor isn't immediately accessed after the failed resize, the corruption might go unnoticed for a while. If this corrupted tensor is later saved, copied, or used in a downstream computation without being detected, it could lead to subtle data loss or incorrect results that are extremely hard to detect. The integrity of the data is paramount in scientific computing and machine learning, and bugs like this undermine that integrity.

In essence, this bug creates a silent threat within PyTorch operations involving storage resizing. It’s a subtle flaw that can lead to significant stability and correctness issues, making it a critical one to address for the health and reliability of the PyTorch ecosystem.

The Path to a Fix: Ensuring Strong Exception Guarantees

Addressing this PyTorch bug requires ensuring that the resize_() operation adheres to the principle of strong exception guarantee. This means that if resize_() fails for any reason, including attempting to resize non-resizable storage, the tensor should be left in the exact same state as it was before the operation was attempted. Let's explore how this can be achieved.

1. Reordering Operations for Safety: The core of the fix lies in reordering the steps within the resize_() function. Currently, the tensor's shape and stride metadata are updated before the check for storage resizability. To implement a strong exception guarantee, this order needs to be reversed. The function should first perform all necessary checks, including verifying that the target storage can indeed be resized.

*   **Check Storage Mutability:** The very first step upon entering `resize_()` should be to query the underlying `Storage` object. If the `Storage` indicates that it is not resizable (e.g., through a flag or by its type, as is the case with storage derived from NumPy arrays), the function should immediately raise the `RuntimeError`.
*   **Metadata Update After Validation:** Only *after* confirming that the storage is mutable and the resize operation is valid should the function proceed to update the tensor's shape and stride metadata. If the storage check passes, the metadata can be updated to reflect the new dimensions. If it fails, the metadata remains untouched.

2. Implementing Robust Error Handling: While reordering is key, robust error handling within the C++ backend of PyTorch is also crucial. The resize_() operation is likely implemented in C++ for performance. This implementation needs to be carefully audited to ensure that exceptions are propagated correctly and that no partial updates occur.

*   **Atomic Operations:** Ideally, the entire process of checking storage mutability and updating metadata should be treated as an atomic operation. This means it either completes successfully or has no effect whatsoever. If the C++ implementation can guarantee atomicity, it inherently provides the strong exception guarantee.
*   **Guard Clauses:** Using guard clauses at the beginning of functions is a common programming pattern. For `resize_()`, a guard clause would check the storage's resizability right away. If the condition isn't met, the function returns or throws an exception immediately, preventing any further execution that could modify the tensor's state.

3. Testing and Verification: Once a fix is implemented, comprehensive testing is paramount. The existing minimal reproduction case should be included as a unit test to verify that the bug is resolved.

*   **Regression Tests:** A suite of regression tests should be developed to cover various scenarios: resizing tensors with resizable storage, resizing tensors with non-resizable storage (like NumPy arrays), tensors with different data types, and tensors with various dimensions. This ensures that the fix doesn't inadvertently introduce new issues.
*   **Fuzz Testing:** For complex libraries like PyTorch, fuzz testing can be invaluable. This involves feeding the `resize_()` function with a wide range of potentially invalid or unusual inputs to uncover edge cases that might have been missed.

4. Documentation and Community Communication: Clear communication about the bug and its fix is important for the PyTorch community.

*   **Release Notes:** When the fix is merged and released, it should be clearly documented in the release notes, explaining the nature of the bug and the fix applied. This helps users understand potential pitfalls they might have encountered.
*   **Best Practices:** It might also be beneficial to update documentation or provide examples illustrating best practices when dealing with tensors that might have non-resizable storage, such as emphasizing checks before attempting resize operations or using PyTorch tensors that manage their own storage where appropriate.

By focusing on reordering operations, implementing robust C++ backend logic, and ensuring thorough testing, the PyTorch developers can effectively resolve this bug. This will restore the strong exception guarantee for the resize_() operation, making PyTorch more reliable and predictable for all its users, especially those working at the intersection of PyTorch and other data manipulation libraries like NumPy.

Conclusion

The bug where PyTorch updates tensor shape metadata even when storage resize fails is a critical issue that can lead to corrupted tensors, crashes, and debugging nightmares. The minimal reproduction case clearly illustrates how attempting to resize a tensor backed by non-resizable storage (like a NumPy array) can result in a "Zombie" tensor – one with a shape indicating data that doesn't actually exist in its storage. This inconsistency violates the strong exception guarantee, a fundamental principle for robust software.

The solution lies in reordering the resize_() operation's internal logic: all checks, particularly for storage mutability, must be performed before any metadata (shape, stride) is updated. This ensures that if the resize fails, the tensor remains in its original, consistent state. Rigorous testing, including regression and fuzz testing, is essential to confirm the fix and prevent regressions.

By addressing this bug, PyTorch can maintain its reputation for reliability and provide a more stable experience for developers working with complex data structures and inter-library interoperability. For further reading on robust software design principles and exception handling, I recommend consulting resources on exception safety in C++, as many of PyTorch's underlying principles are rooted in C++ best practices.