PyTorch Bug: Corrupted Tensors On Failed Resizes

by Alex Johnson 49 views

Hey there, PyTorch users! Today, we're diving into a rather sneaky bug that can cause some serious headaches if you're not careful. It involves how PyTorch handles tensor resizing, especially when dealing with storage that can't be resized. This issue can lead to what we'll affectionately call "zombie tensors" – tensors that appear to have one shape but are actually holding onto nothing, which can cause your programs to crash unexpectedly. Let's break down what's happening, why it's a problem, and what you can do about it.

Understanding the Core Problem: When Storage Says No

At its heart, this bug is about a mismatch between a tensor's metadata and its underlying data storage. PyTorch, like many deep learning frameworks, uses tensors to represent multi-dimensional arrays of data. These tensors have characteristics like shape (the dimensions of the array) and stride (how to move through memory to access elements), and they point to a storage which is the actual block of memory holding the numerical data. Normally, when you resize a tensor, you're also resizing its storage to accommodate the new dimensions.

However, there are situations where the storage itself is fixed and cannot be resized. A common scenario for this is when a PyTorch tensor is created from a NumPy array. NumPy arrays, once created, generally have a fixed memory layout. When you use torch.from_numpy() to create a PyTorch tensor that shares this NumPy array's memory, PyTorch's storage is essentially linked to that non-resizable NumPy buffer. This is often done for efficiency, to avoid unnecessary data copying.

PyTorch is smart enough to know when it's dealing with such non-resizable storage. If you try to call a method like resize_() on a tensor whose storage isn't resizable, PyTorch will correctly throw a RuntimeError. The error message is quite informative: "Trying to resize storage that is not resizable." This is exactly what we'd hope for – the system recognizes the impossibility of the operation and stops us before things go wrong.

The Unsafe Part: Metadata Gets Updated, But Storage Doesn't

Here's where the bug creeps in. While PyTorch does detect that the storage cannot be resized and does raise a RuntimeError, it's not doing so in an exception-safe manner. The problem is that before PyTorch checks if the storage is resizable, it updates the tensor's metadata – specifically, its shape and stride information. It prepares the tensor as if the resize operation was successful, setting the new target shape.

Imagine you have a tensor that's supposed to be empty (shape torch.Size([0])) and has 0 bytes of storage. You then try to resize it to a large, multi-dimensional shape, like (5, 5, 5). PyTorch starts the process, updates the tensor's internal pointers and shape information to reflect (5, 5, 5), and then it checks the storage. It finds the storage is not resizable, and it throws that RuntimeError.

At this point, the RuntimeError is caught (or propagates up), and your program might continue. However, the tensor is now in a truly corrupted state. Its shape metadata claims it's a (5, 5, 5) tensor, which would normally require a significant amount of memory. But its underlying storage() is still the original, empty, 0-byte storage. This is the "zombie tensor" – it looks like it has data and a shape, but it has no actual data to back it up.

The Consequences: Crashes and Segfaults

The real danger of these corrupted "zombie tensors" emerges when you try to interact with them later. If your code, unaware of this internal corruption, attempts to access the tensor's data – perhaps by printing it, performing a calculation, or even just accessing an element – PyTorch will try to use the outdated shape and stride metadata. Since there's no actual data in the storage corresponding to that shape, the program will likely run into trouble.

This can manifest in a couple of ways:

  • Segmentation Faults (Segfaults): This is the most severe outcome. A segfault usually means your program tried to access a memory location it wasn't supposed to, often because the pointers and sizes are inconsistent. In this case, PyTorch's internal machinery, expecting data that isn't there, might read invalid memory addresses, leading to a hard crash.
  • Internal RuntimeErrors: Sometimes, instead of a direct segfault, PyTorch might detect the inconsistency deeper within its operations and raise another RuntimeError. This might be a more specific error about tensor dimensions not matching storage size, or some other internal consistency check failure.

The provided minimal reproduction code demonstrates this. When t.resize_((5, 5, 5)) is called on a tensor t that has 0-byte storage, a RuntimeError is caught. However, if you were to then print(t), it would either crash with a segfault (as reported in some cases) or raise another RuntimeError related to the shape-storage mismatch, because the shape is now torch.Size([5, 5, 5]) while the storage().nbytes() is still 0.

Why This Matters for Your Code

This bug highlights a critical principle in software development: exception safety. When an operation is expected to potentially fail and throw an exception, it should ideally leave the system in a state as if the operation never happened. This is known as a strong exception guarantee. In this PyTorch bug, we're getting something much weaker – the exception is thrown, but the object's state is left corrupted, leading to potential failures after the exception has been handled.

For developers, this means that simply catching the RuntimeError for non-resizable storage might not be enough. The tensor object itself can become permanently damaged, and any subsequent use of that tensor, even in seemingly unrelated operations, could lead to crashes. This can be incredibly difficult to debug, especially in large codebases where tensors are passed around frequently.

Consider a scenario where you're processing data in batches. If one batch operation triggers this bug, the corrupted tensor might be passed to subsequent stages of your pipeline. By the time the crash occurs, it might be far removed from the original resize_() call, making the root cause obscure.

Reproducing the Bug: A Minimal Example

The provided code snippet offers a clear and concise way to see this bug in action. Let's walk through it:

import torch
import numpy as np

# Create non-resizable storage (0 bytes)
# We start with an empty NumPy array and get its untyped storage.
# This storage is inherently non-resizable.
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Inject into a fresh tensor
# We create a new, empty PyTorch tensor and then replace its internal storage
# with our non-resizable 'locked_storage'.
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# At this point, t.shape is torch.Size([0]) and t.storage().nbytes() is 0.

# Attempt to resize (Expected: Fail, maintain original shape)
# We try to change the shape to (5, 5, 5). Since the storage is locked,
# this operation *should* fail cleanly without changing the tensor's state.
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    # PyTorch correctly raises a RuntimeError here: "Trying to resize storage that is not resizable."
    pass

# Verify corruption
# After the exception is caught, we inspect the tensor.
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 (This part is consistent)
print(t) # This line will likely cause a crash (Segfault or RuntimeError)

As the comments indicate, the shape is incorrectly updated to torch.Size([5, 5, 5]) even though the storage().nbytes() remains 0. The subsequent print(t) attempts to interpret this (5, 5, 5) shape using the empty storage, leading to the crash.

Versions and Environment

The issue was reported with the following environment:

  • PyTorch version: 2.9.0+cu126
  • Python version: 3.12.12
  • OS: Ubuntu 22.04.4 LTS

While the specific version numbers might vary, the underlying mechanism of exception handling during storage resizing is likely consistent across recent PyTorch versions. It's always a good practice to be aware of your library versions when debugging issues.

What Can You Do?

Given that this is a bug within PyTorch itself, the most direct solution is for the PyTorch developers to fix it. The ideal fix would involve ensuring that the tensor's metadata is only updated after the storage operation has been confirmed to be successful. This would provide the strong exception guarantee and prevent the creation of corrupted tensors.

In the meantime, here are some strategies for mitigating this problem in your own code:

  1. Avoid Resizing Tensors with Non-Resizable Storage: The most straightforward approach is to avoid operations that might trigger this bug. If you know a tensor is derived from a NumPy array or has some other form of fixed storage, be cautious about calling resize_() or similar methods on it. If you need to change the shape, consider creating a new tensor with the desired shape and copying the data over, rather than trying to resize in place.

  2. Careful Error Handling: If you absolutely must perform operations that could lead to this error, ensure your error handling is robust. Catching the RuntimeError is the first step, but you also need a strategy for dealing with the potentially corrupted tensor object. This might involve:

    • Discarding the tensor entirely if an error occurs.
    • Re-initializing the tensor from scratch.
    • Implementing checks before using the tensor to ensure its shape is consistent with its storage().nbytes() (though this can be complex).

  3. Update PyTorch: Keep an eye on PyTorch releases. Bugs like this are often discovered and fixed by the community. Upgrading to the latest stable version of PyTorch might resolve the issue.

  4. Report and Contribute: If you encounter this bug, consider reporting it on the official PyTorch GitHub issues page. Providing a minimal reproduction case, as done here, is incredibly helpful for developers to pinpoint and fix the problem. If you're comfortable with C++ and PyTorch's internals, you could even attempt to fix it yourself and contribute a pull request!

Conclusion

This bug in PyTorch, where tensor metadata is updated even when storage resizing fails, can lead to insidious "zombie tensors" that ultimately cause program crashes. Understanding the interaction between tensor metadata and storage, and the importance of exception safety, is key to navigating such issues. By being aware of this problem and adopting careful coding practices, you can help prevent unexpected crashes in your PyTorch applications. Remember, robust error handling and staying updated with library versions are your best allies in the world of software development.

For more information on PyTorch's internals and tensor operations, you might find the following resources helpful: