PyTorch Bug: Corrupted Tensors On Failed Storage Resize

In the world of deep learning and data science, PyTorch is a powerhouse, enabling researchers and developers to build and train sophisticated neural networks. However, like any complex software, it's not immune to bugs. Recently, a peculiar issue surfaced concerning tensor manipulation, specifically when attempting to resize tensors that share storage with non-resizable buffers. This bug, which we'll affectionately call the "Axjunv updates tensor shape metadata even when storage resize fails, creating corrupted 'Yfxcxm' tensors" bug, can lead to corrupted tensors and, consequently, application crashes. Let's dive deep into what's happening, why it's a problem, and how it might be fixed.

The Sneaky Tensor Corruption Bug

The core of the issue lies in how PyTorch handles tensor resizing, particularly when the underlying storage cannot be resized. Imagine you have a tensor in PyTorch that's intimately linked to a NumPy array. When you try to change the shape of this PyTorch tensor using resize_(), PyTorch is supposed to check if the underlying storage can accommodate the new size. If the storage is fixed – for instance, if it's derived from a NumPy array that was directly embedded – PyTorch should throw an error and not change anything about the tensor's structure.

And indeed, it does throw an error. You'll see a RuntimeError that clearly states: "Trying to resize storage that is not resizable." This is the expected behavior, informing you that the operation cannot proceed. However, the problem is that this error handling isn't quite exception-safe. Before PyTorch even realizes the storage is unresizable, it has already gone ahead and updated the tensor's shape and stride metadata to reflect the new, target size. This creates a dangerous inconsistency: the tensor thinks it has a new shape (perhaps a large one), but its actual storage remains unchanged and, crucially, empty (0 bytes) because the resize operation ultimately failed.

This leaves the tensor in a kind of "zombie" state. It reports a size that it cannot possibly hold in its memory. When you try to access or print this corrupted tensor later on, PyTorch's internal mechanisms get confused. It tries to read data based on the reported shape, but finds no data in the storage. This often results in a catastrophic crash, manifesting as a Segmentation Fault or another internal RuntimeError. It’s a subtle bug because the error happens after the initial exception is caught, making it tricky to debug.

Minimal Reproduction of the Bug

To truly understand a bug, it's best to see it in action with a minimal example. The developers have provided a straightforward way to reproduce this issue:

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

When you run this code, you'll observe the following:

1. A tensor t is created with an empty, non-resizable storage.
2. An attempt is made to resize t to a (5, 5, 5) shape.
3. PyTorch correctly identifies that the storage is not resizable and raises a RuntimeError.
4. However, before raising the error, it updates t.shape to torch.Size([5, 5, 5]).
5. The print(t) statement then attempts to access data from a tensor that claims to be 5x5x5 but has 0 bytes of storage, leading to a crash.

The expected behavior, according to strong exception guarantees, is that if an operation fails, the object should be left in its original state. In this case, after the RuntimeError, the tensor t should still have reported a shape of torch.Size([0]), reflecting its initial empty state and the failed resize attempt. The current behavior violates this principle, leaving the tensor in a corrupted, unusable state.

Why This Bug Matters

This bug might seem niche, but it highlights a critical aspect of software robustness: exception safety. When operations fail, especially in numerical computing libraries like PyTorch, it's paramount that the state remains consistent. A corrupted tensor, even if the initial error is caught, can lead to subtle bugs down the line, making debugging a nightmare. Crashes like Segmentation Faults are particularly difficult to track because they often occur far from the root cause of the problem.

For developers integrating PyTorch into larger applications, this means that any operation involving resize_() on tensors that might share storage with external, non-resizable data structures (like NumPy arrays) carries a risk. If not handled with extreme care, it can lead to unexpected crashes, corrupting the program's state and potentially leading to data loss or incorrect results.

Understanding Tensor Storage and Metadata in PyTorch

To fully grasp the bug, let's briefly touch upon how PyTorch manages tensors. A PyTorch tensor is essentially composed of two parts: storage and metadata. The storage is the actual block of memory that holds the tensor's data elements (e.g., floating-point numbers, integers). The metadata describes how to interpret that storage. This includes:

• Shape: The dimensions of the tensor (e.g., (3, 4) for a 2D tensor with 3 rows and 4 columns).
• Strides: The number of bytes to skip in memory to move to the next element along each dimension. This is crucial for operations like slicing and transposing without copying data.
• Data type (dtype): The type of data stored (e.g., torch.float32, torch.int64).
• Device: Where the tensor resides (e.g., CPU, GPU).

The resize_() operation is intended to change the number of elements in the tensor's storage and update the metadata accordingly. However, it relies on the underlying storage object being resizable. If t.set_(locked_storage) is used, t points to locked_storage. The locked_storage itself is derived from a NumPy array and has a fixed size (0 bytes in this example). When resize_() is called, it first attempts to modify the tensor's metadata (shape, strides) to match the new requested size. Then, it checks if the storage can actually accommodate this new size. If the storage is not resizable, it raises an error. The bug occurs because the metadata is updated before the storage check, leading to the state mismatch when the error is raised.

Versions and Environment

Reproducing bugs accurately often depends on the specific versions of software used. The environment details provided are:

• PyTorch version: 2.9.0+cu126
• CUDA version: 12.6 (used to build PyTorch)
• OS: Ubuntu 22.04.4 LTS
• Python version: 3.12.12
• XNNPACK available: True

These details are essential for developers to pinpoint the exact commit or version where the bug was introduced and to test potential fixes.

Potential Fixes and Solutions

The most straightforward solution to this bug would involve ensuring that PyTorch's resize_() operation is truly exception-safe. This means the tensor's metadata (shape, strides) should only be updated after it has been confirmed that the underlying storage can be successfully resized.

One way to achieve this is to reorder the operations within the resize_() method. Instead of updating metadata first, the check for storage resizability should occur at the very beginning. If the storage is not resizable, the RuntimeError should be raised immediately, and the tensor's metadata should remain untouched. This aligns with the principle of the "Strong Exception Guarantee," where if an exception is thrown, the object remains in a valid, unchanged state.

Alternatively, if PyTorch intends to support operations that might fail without side effects, it could introduce a way to explicitly disallow resizing of tensors backed by non-resizable storage or provide a flag to control this behavior. However, the current scenario where metadata is updated before the check is a clear bug that needs addressing at the library level.

For users encountering this issue, the immediate workaround is to avoid calling resize_() on tensors that are known to share storage with non-resizable buffers, especially NumPy arrays. If you need to change the shape, consider creating a new tensor with the desired shape and copying the data, rather than attempting an in-place resize.

Conclusion

The "Axjunv updates tensor shape metadata even when storage resize fails, creating corrupted 'Yfxcxm' tensors" bug in PyTorch is a critical reminder of the importance of exception safety in complex software. While PyTorch developers are continuously working to improve the library, bugs like this can slip through. Understanding the internal workings of tensors – the separation of storage and metadata – is key to diagnosing and potentially mitigating such issues. The fix involves ensuring that metadata updates are deferred until after storage resize operations are confirmed to be successful, upholding the integrity of tensor states even when operations fail.

For those interested in the nitty-gritty of PyTorch's internals and memory management, diving into the official PyTorch documentation can provide invaluable insights. Understanding how tensors are represented and manipulated is crucial for advanced usage and debugging.

External Resources:

• PyTorch Documentation on Tensors: https://pytorch.org/docs/stable/tensors.html
• NumPy Documentation: https://numpy.org/doc/stable/