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PyTorch Bug: Corrupted Tensors From Failed Resizes

Alex Johnson
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PyTorch Bug: Corrupted Tensors From Failed Resizes

Hey there, fellow PyTorch enthusiasts! We've stumbled upon a rather pesky bug in PyTorch that can lead to some seriously corrupted tensors, causing all sorts of unexpected crashes. It all happens when you try to resize the storage of a tensor that's linked to a non-resizable buffer, like one created from a NumPy array. Let's dive deep into what's going on and how to potentially avoid this digital nightmare.

The Nitty-Gritty: When Storage Fails, But Metadata Doesn't

So, picture this: you're working with a PyTorch tensor, and for whatever reason, you need to change its shape. You call the resize_() method, expecting it to do its thing. Now, if this tensor is sharing its underlying storage with something that can't be resized – think of a NumPy array that was injected using set_() – PyTorch should gracefully tell you, "Nope, can't do that!" And indeed, it correctly raises a RuntimeError with the message: "Trying to resize storage that is not resizable."

Here's the catch, and where the trouble begins: the operation isn't what we'd call "exception-safe." Before PyTorch even gets to the point of checking if the storage is resizable, it goes ahead and updates the tensor's shape and stride metadata. So, by the time it realizes the storage can't be changed, the tensor's metadata is already reflecting the new, target size you requested. This leaves the tensor in a very unstable, almost zombie-like state. It's like having a map that says you're in a bustling city, but when you look around, you find an empty field. In PyTorch terms, tensor.shape might report a large size, but tensor.storage() is still showing zero bytes, because the actual underlying memory couldn't be allocated or modified.

Why is this a big deal? Well, after this happens, if you try to do anything with this corrupted tensor – like printing it, accessing its elements, or using it in further computations – you're very likely to run into serious issues. This can manifest as Segmentation Faults, which are the dreaded low-level crashes that often mean something went very wrong in memory management, or as more internal RuntimeErrors that are equally unhelpful. It's a state where the tensor's declared dimensions are completely out of sync with its actual data, or lack thereof.

This isn't just a theoretical problem; it can happen in real-world scenarios where you might be combining NumPy arrays with PyTorch tensors, perhaps for pre-processing data or integrating with existing libraries. The expectation is that PyTorch operations are robust and either succeed cleanly or fail cleanly, leaving your data structures in a consistent state. This bug, unfortunately, violates that expectation, leading to silent corruption that only reveals itself later, often in very dramatic ways.

Minimal Reproduction: A Sneak Peek into the Bug

To really understand the issue, it's helpful to see it in action. The team that discovered this bug provided a minimal code snippet that demonstrates precisely how this corruption occurs. It's quite straightforward and really highlights the core problem:

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

Let's break down what this code does. First, it creates a locked_storage using torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage(). This effectively gives us a zero-byte storage that PyTorch flags as non-resizable. Then, a new, empty tensor t is created and its underlying storage is set to this locked_storage using t.set_(locked_storage). At this point, the tensor t has a shape of torch.Size([0]) and 0 bytes of storage, which is consistent.

The critical part is the try...except block. We attempt to resize this tensor t to a shape of (5, 5, 5) using t.resize_((5, 5, 5)). As expected, because the underlying storage is locked and non-resizable, PyTorch throws a RuntimeError. However, as we've discussed, this exception is not handled perfectly. Before the error is actually raised, the tensor's shape and stride information gets updated to torch.Size([5, 5, 5]).

After the except block catches the RuntimeError, the code proceeds to verify the corruption. The output clearly shows the problem: Shape: torch.Size([5, 5, 5]) is printed, indicating the metadata has been altered, but Storage: 0 is printed, confirming that no actual data storage exists. The final print(t) line is where the program typically crashes. This demonstrates the tensor's corrupted state – it thinks it has a shape of 5x5x5, but it has no memory to hold that data, leading to a crash.

This minimal example is invaluable for developers looking to fix the issue, as it isolates the exact sequence of operations that triggers the bug. It’s a clear illustration of the broken promise of exception safety in this specific scenario.

Expected vs. Actual Behavior: What Should Happen and What Does

Understanding the discrepancy between expected and actual behavior is key to grasping the severity of this bug. In the world of robust software development, especially in libraries that handle complex computations like PyTorch, there's an implicit contract around how operations should behave, particularly when errors occur. This is often referred to as exception safety guarantees.

Expected Behavior: The Strong Exception Guarantee

When an operation in PyTorch (or any well-behaved library) is expected to fail due to a specific condition, like trying to resize non-resizable storage, the ideal outcome is that the original object remains completely unchanged. This is known as the Strong Exception Guarantee. In the context of our bug, if t.resize_((5, 5, 5)) encounters the RuntimeError because the storage is not resizable, the tensor t should remain exactly as it was before the call. This means its shape and stride metadata should still reflect its original state, which in the minimal reproduction case is torch.Size([0]) and its storage should remain empty (0 bytes). The operation either succeeds, or it fails cleanly, leaving everything in its original, consistent state. No side effects, no corruption.

This guarantee is crucial because it simplifies error handling for the user. If you know that a failed operation won't mess up your existing data, you can confidently wrap it in a try...except block and continue your program's execution without worrying about the integrity of your variables. Your program can roll back to a known good state.

Actual Behavior: A Broken Promise

Unfortunately, as the provided minimal reproduction clearly shows, PyTorch does not offer the Strong Exception Guarantee in this specific scenario. Instead, it exhibits a weaker form of exception safety, leading to corruption. When t.resize_((5, 5, 5)) is called on a tensor with non-resizable storage:

  1. Metadata Update: PyTorch updates the tensor's shape metadata to the target torch.Size([5, 5, 5]).
  2. Storage Check Failure: It then proceeds to check the storage and discovers it cannot be resized.
  3. RuntimeError Raised: A RuntimeError is thrown.
  4. Inconsistent State: The except block catches the error, but by this point, the tensor t is already in a corrupted state. Its shape property reports torch.Size([5, 5, 5]), but its untyped_storage().nbytes() is still 0.

This mismatch is the root cause of the problem. The tensor's internal pointers and size indicators are now pointing to a structure that doesn't exist in memory. When subsequent operations, like print(t), try to access this non-existent data based on the incorrect shape metadata, they trigger crashes. The original program that led to the bug report even resulted in a Segmentation Fault, a critical error indicating memory access violations.

This behavior is particularly problematic because the error is not immediately obvious. The RuntimeError is caught, and the program might continue, but with a fundamentally broken tensor object lurking in memory. The consequences only appear later, making debugging a nightmare. It’s a situation where a seemingly innocuous operation can lead to system instability.

Versions and Environment Details

To help diagnose and fix this issue, it's essential to know the environment where it was observed. The bug was reported on a system running:

  • PyTorch Version: 2.9.0+cu126
  • CUDA Version: 12.6 (used to build PyTorch)
  • OS: Ubuntu 22.04.4 LTS (x86_64)
  • GCC Version: 11.4.0
  • Python Version: 3.12.12
  • XNNPACK: Available

It's worth noting that while CUDA was used to build PyTorch, the provided reproduction didn't necessarily require CUDA to be available or active for the bug to manifest. The issue appears to be related to PyTorch's internal tensor and storage management logic, which can be triggered even in CPU-bound operations when interacting with non-resizable storage.

This detailed version information is invaluable for the PyTorch development team to pinpoint the exact commit or area of code responsible for this exception safety flaw. It allows them to test fixes against a known environment and ensure compatibility.

Conclusion: Towards More Robust Tensor Operations

This bug, where PyTorch updates tensor metadata even when storage resizing fails, is a critical issue that can lead to tensor corruption and subsequent crashes. The lack of a strong exception guarantee in this specific scenario means that a failed resize_() operation on non-resizable storage can leave tensors in an inconsistent, dangerous state. This can manifest as segmentation faults or other runtime errors when the corrupted tensor is later accessed.

The minimal reproduction case clearly illustrates how this happens: metadata is updated before the storage check, and when the check fails, the tensor is left with a shape that doesn't match its actual (zero-byte) storage.

What can you do?

  • Be Cautious with resize_(): If you are using resize_() on tensors that might share storage with non-resizable objects (like NumPy arrays via set_()), be extra vigilant. Ensure your operations are wrapped in robust error handling.
  • Check Tensor Integrity: After operations that could potentially fail, consider adding checks to verify the consistency between tensor.shape and tensor.storage().nbytes() if you suspect issues.
  • Stay Updated: Keep your PyTorch versions updated. Such bugs are often identified and fixed in newer releases. The PyTorch team is actively working on improving the robustness of their library.

This issue highlights the importance of exception safety in low-level tensor operations. A strong guarantee in these cases prevents silent data corruption and system instability, making the library more reliable for all users, from researchers to production engineers.

For more information on tensor manipulation and best practices in PyTorch, you can refer to the official documentation:

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