PyTorch Bug: Corrupted Tensors From Failed Resizes

Ever hit a snag in your PyTorch workflow that caused your program to crash unexpectedly, leaving you scratching your head? You're not alone! Sometimes, even the most robust libraries can have little hiccups. Today, we're diving deep into a specific bug that has been causing some serious headaches: PyTorch tensor shape metadata updates even when storage resize fails, leading to corrupted "Gjbcdv" tensors.

The Nitty-Gritty of the Bug: When Resizing Goes Wrong

Let's get down to the nitty-gritty of this bug. When you try to resize a tensor in PyTorch using the resize_() method, it expects to be able to modify the underlying storage. However, if this tensor is sharing its storage with a buffer that cannot be resized – for example, a NumPy array that you've injected into PyTorch using set_() – PyTorch should ideally handle this gracefully. And in one sense, it does! It correctly raises a RuntimeError with a message like, "Trying to resize storage that is not resizable." This is good; it tells you something is wrong.

But here's where the problem lies: the operation isn't exception-safe. Before PyTorch even checks if the storage can be resized, it updates the tensor's shape and stride metadata to reflect the new, target size you requested. So, even though the storage resize ultimately fails and raises an error, the tensor's metadata is already in a messed-up state. This leaves the tensor in what we can only describe as a "Zombie" state. It's like a ghost of a tensor: its .shape attribute might tell you it's a nice, large tensor (say, 5x5x5), but its .storage() is completely empty, holding 0 bytes of actual data. This fundamental mismatch between what the tensor thinks it is and what its underlying storage actually is is the root cause of the corruption.

What Happens When You Encounter This "Zombie" Tensor?

The consequences of this "Zombie" state can be quite severe. The moment you try to interact with this corrupted tensor after the RuntimeError has been caught – perhaps by trying to print it, access its elements, or perform any other operation – your program is likely to crash. These crashes can manifest as Segmentation Faults (a common C/C++ error indicating memory access violations) or internal RuntimeErrors within PyTorch itself. This is because the program is trying to operate on a tensor that claims to have a certain size and structure, but has no actual data to back it up. Imagine asking someone to read a book of 500 pages, but they only have the cover. They wouldn't know what to do, and neither does your program when faced with these corrupted tensors.

This bug was particularly highlighted in the context of the "Jcutxe" update, where a similar issue occurred with "Qkygpr" tensors. The core problem remains the same: the metadata gets updated before the critical check for resizability, leading to this dangerous inconsistency. The issue was observed on PyTorch version 2.9.0+cu126 running on Ubuntu 22.04.4 LTS, with Python 3.12.12. While the specific versions are noted, this type of subtle bug could potentially affect other versions as well, making it crucial to understand and guard against.

Minimal Reproduction: Seeing the Bug in Action

To truly understand a bug, it's best to see it in action. The researchers provided a minimal reproduction case that clearly demonstrates this problematic behavior. Let's walk through it:

import torch
import numpy as np

# Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    pass

# Verify corruption
print(f"Shape: {t.shape}")       # Prints: torch.Size([5, 5, 5])
print(f"Storage: {t.untyped_storage().nbytes()}") # Prints: 0
print(t) # CRASH

In this snippet, we first create an empty, non-resizable storage using a NumPy array with zero elements. Then, we create a PyTorch tensor t and explicitly set its storage to this locked_storage. The crucial part is the t.resize_((5, 5, 5)) call within a try...except block. As expected, this line should fail because locked_storage is not resizable. PyTorch correctly throws a RuntimeError.

However, after the exception is caught, the code proceeds to print the tensor's shape and storage size. The output is eye-opening: Shape: torch.Size([5, 5, 5]) and Storage: 0. The shape has been incorrectly updated to (5, 5, 5), while the storage size remains 0 bytes. The final print(t) line is where the crash typically occurs, leading to either a RuntimeError (as seen in the gist) or a more severe Segmentation Fault.

The Expected vs. Actual Behavior

Let's summarize the expected and actual outcomes:

• Expected Behavior: If resize_() encounters a RuntimeError because the storage is locked, the tensor's metadata (its shape and stride information) should remain unchanged. It should retain its original shape, which in this case is torch.Size([0]). This aligns with the Strong Exception Guarantee, meaning that if an operation fails, the system should be left in the state it was in before the operation began.
• Actual Behavior: The exception is thrown, which is good. However, the tensor's shape is erroneously updated to torch.Size([5, 5, 5]) before the error handling kicks in. This discrepancy between the reported shape and the actual 0-byte storage corrupts the tensor and predictably leads to crashes upon subsequent access or printing.

The Gist of the Problem: Corrupted Metadata

The core issue boils down to the order of operations within the resize_() function. The metadata update happens before the check that determines if the underlying storage can actually be resized. This means that even if the resize operation is fundamentally impossible due to locked storage, the tensor's internal pointers and size information are still modified. This leaves the tensor in an invalid state, where its shape claims it should contain data, but its storage is empty. When operations like print() try to access this data based on the incorrect shape, they run into undefined behavior, leading to crashes.

Why This Matters: Impact on Your Projects

This seemingly small bug can have significant ripple effects on your PyTorch projects, especially in complex machine learning pipelines. Here's why it's a big deal:

1. Unpredictable Crashes: The most immediate impact is random and unpredictable crashes. If your code accidentally triggers this bug, it might work fine for hours or days before suddenly failing with a segmentation fault or a cryptic runtime error. Debugging such issues can be incredibly time-consuming and frustrating.
2. Data Corruption: Although this specific bug doesn't corrupt existing data in the sense of changing values, it corrupts the state of the tensor object itself. A tensor that reports a shape but has no storage is fundamentally unusable and can lead to logical errors if not handled carefully.
3. Difficult Debugging: As mentioned, pinpointing the exact cause of a crash that stems from this issue can be challenging. The error message might not directly point to the resize_() operation, especially if it occurs deep within a library or a complex model. The minimal reproduction case helps, but identifying the trigger in a larger application can be tough.
4. Potential for Silent Failures: In less severe cases, or if error handling is not robust, a corrupted tensor might not immediately crash the program. Instead, it could lead to incorrect computations down the line, producing nonsensical results that are hard to trace back to the original cause.

Understanding this bug is crucial for anyone working extensively with PyTorch, particularly when dealing with scenarios that involve sharing data with external libraries like NumPy or when using custom storage mechanisms.

Looking Ahead: Preventing and Fixing

While this discussion is about identifying and understanding the bug, the implications point towards solutions. The ideal fix would involve ensuring that the check for storage resizability happens before any metadata updates occur. This way, if the storage is indeed not resizable, the RuntimeError is raised without altering the tensor's shape and stride, thus preserving the Strong Exception Guarantee.

Until such a fix is officially implemented and deployed in PyTorch, developers need to be mindful of these edge cases. If you're working with tensors that might have non-resizable storage (like those derived from NumPy arrays via set_()), you should exercise caution when calling resize_(). Consider alternatives or ensure that your tensor's storage is managed in a way that avoids this conflict. Robust error handling around tensor manipulation operations can also help catch and manage these situations more gracefully, preventing catastrophic crashes.

This bug serves as a good reminder of the importance of exception safety in software development. Ensuring that operations either succeed completely or leave the system unchanged is key to building reliable and robust applications. For more in-depth information on PyTorch internals and debugging, you can always refer to the official PyTorch Documentation.