PyTorch Tensor Corruption Bug: Resizing Issues Explained
Have you ever encountered a puzzling error in PyTorch that seems to come out of nowhere, leading to crashes or unexpected behavior? You're not alone! Sometimes, even with seemingly straightforward operations, subtle bugs can creep into the system, leaving developers scratching their heads. One such intriguing issue involves how PyTorch handles tensor shape metadata when storage resizing fails. This can lead to what we'll call "corrupted tensors" – tensors that have incorrect metadata, making them unstable and prone to causing segmentation faults or internal RuntimeErrors. In this article, we'll dive deep into this specific bug, understand why it happens, and explore how it can be avoided.
Understanding the "Zombie Tensor" Phenomenon
The core of this problem lies in the interaction between PyTorch tensors, their storage, and the resize_() operation. When you have a PyTorch tensor that shares its underlying storage with a buffer that cannot be resized, attempting to resize the tensor using resize_() can lead to a corrupted state. This often happens when a tensor is created from a NumPy array using torch.from_numpy() or by injecting NumPy data via tensor.set_(). NumPy arrays, by their nature, have fixed-size storage, making them non-resizable in the context of PyTorch's dynamic resizing capabilities. So, when resize_() is called on a tensor whose storage is linked to such a non-resizable object, PyTorch should detect this and raise a RuntimeError, typically with a message like: "Trying to resize storage that is not resizable." This is the expected and correct behavior, as it flags an operation that cannot be performed safely.
However, the bug we're discussing is that this RuntimeError is not always handled with the strongest exception guarantees. Before PyTorch checks if the storage is actually resizable, it proceeds to update the tensor's shape and stride metadata to reflect the intended new size. This is the critical step that leads to the corruption. When the subsequent check fails because the storage is indeed not resizable, the RuntimeError is raised. But by then, the tensor's metadata has already been altered. This leaves the tensor in an inconsistent state: tensor.shape might report a completely different, larger size (e.g., torch.Size([5, 5, 5])), while its actual storage() remains unchanged and effectively empty (0 bytes). This peculiar state is why we refer to it as a "Zombie Tensor" – it has the appearance of having data (a shape), but its underlying substance (storage) is non-existent or inaccessible in a way that matches its metadata. Consequently, any subsequent attempt to access or print this tensor, like using print(t), can trigger a segmentation fault or another internal RuntimeError, as PyTorch tries to operate on data that isn't there according to the shape information it holds.
This issue was observed in PyTorch versions like 2.9.0+cu126 running on Ubuntu 22.04.4 LTS with Python 3.12.12. While the minimal reproduction might show a RuntimeError on print, in more complex scenarios or different environments, it can manifest as a more severe segmentation fault, making debugging a real challenge. The key takeaway here is that even when an operation fails as expected (by raising an error), the internal state of the objects involved might not be properly rolled back, leading to instability. This highlights the importance of robust error handling and state management in complex software libraries.
Reproducing the Bug: A Step-by-Step Guide
To truly understand the problem, it's best to see it in action. Fortunately, the PyTorch team has provided a minimal reproduction case that clearly demonstrates the "Zombie Tensor" phenomenon. Let's walk through it.
First, we need to create a scenario where a tensor's storage is explicitly non-resizable. The easiest way to achieve this is by leveraging NumPy arrays, which have fixed storage allocations. We start by creating an empty NumPy array with a specific data type (e.g., np.int32) and then convert its underlying storage to a PyTorch untyped_storage. This locked_storage is essentially a raw memory block that PyTorch will use, and because it originates from a NumPy array, it's flagged as non-resizable.
import torch
import numpy as np
# Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()
Next, we create a fresh PyTorch tensor. This tensor will initially be empty, with a shape reflecting that emptiness (likely torch.Size([0])). We then use the tensor.set_() method to make this new tensor share the locked_storage we just created. At this point, the tensor t is associated with the non-resizable, 0-byte storage.
# Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)
Now comes the critical part: attempting to resize this tensor using t.resize_(). We'll try to resize it to a completely different shape, say (5, 5, 5), which would normally require allocating new storage or resizing the existing one. Since locked_storage is non-resizable, this operation is destined to fail.
# Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
t.resize_((5, 5, 5))
except RuntimeError:
pass
As expected, PyTorch correctly identifies that the storage cannot be resized and raises a RuntimeError. The try...except block catches this error, preventing the program from crashing at this exact moment. However, the crucial detail is what happens before the exception is raised. As mentioned, PyTorch updates the tensor's shape metadata to torch.Size([5, 5, 5]) before it discovers that the storage cannot be resized. This is where the corruption occurs.
Finally, we can inspect the state of the tensor t to observe the consequences of this incomplete error handling:
# 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 see that t.shape now reports torch.Size([5, 5, 5]), indicating a tensor that should contain 5 * 5 * 5 = 125 elements. However, t.untyped_storage().nbytes() correctly shows 0, meaning there's no actual data storage allocated for these elements. The final print(t) statement is where the crash typically occurs. PyTorch tries to display the tensor's contents based on its reported shape, but it finds no data in the underlying storage, leading to a segmentation fault or an internal error. This starkly illustrates the inconsistency: a shape that claims data exists, but a storage that is empty.
The Expected vs. Actual Behavior Explained
Let's break down the difference between what should happen and what is happening when this bug is triggered. The ideal scenario in software development, especially when dealing with operations that can fail, is to provide a Strong Exception Guarantee. This means that if an operation fails (throws an exception), the object on which the operation was performed should remain in the state it was before the operation was attempted. For the resize_() operation on a tensor with non-resizable storage, this guarantee implies that if resize_() fails due to the storage limitation, the tensor's metadata – its shape and stride – should remain completely unchanged. The tensor should revert to its original state as if resize_() was never called.
In our reproduction case, the original tensor t was initialized as empty, with a shape of torch.Size([0]) and 0 bytes of storage. If PyTorch upheld the Strong Exception Guarantee, when t.resize_((5, 5, 5)) is called and fails because the storage is not resizable, the tensor t should still have shape = torch.Size([0]) and storage().nbytes() = 0 after the exception is caught. The RuntimeError would correctly signal that the operation couldn't be performed, but the tensor itself would remain intact and consistent.
However, the actual behavior deviates significantly from this ideal. As demonstrated, when t.resize_((5, 5, 5)) is called on a tensor with non-resizable storage, PyTorch raises a RuntimeError, as expected. But before the error is thrown, the tensor's shape and stride metadata are updated. So, after the RuntimeError is caught, the tensor t incorrectly reports its shape as torch.Size([5, 5, 5]). Simultaneously, its storage remains at 0 bytes because the resize operation couldn't actually allocate or modify the underlying memory. This creates a severe mismatch: the tensor's shape dictates a large amount of data, but the storage is empty. This inconsistency is what causes subsequent operations, like printing the tensor, to fail catastrophically. The program might crash with a segmentation fault, or it might raise another internal RuntimeError because it's trying to access memory that doesn't exist or is corrupted. This failure to guarantee the original state upon error is the essence of the bug.
Why This Matters and How to Mitigate It
This bug, while seemingly niche, points to a fundamental issue in how PyTorch handles exceptions during critical tensor operations. When an operation fails, but leaves the involved data structures in a corrupted or inconsistent state, it can lead to extremely difficult-to-debug errors further down the line. A RuntimeError during resize might be caught, but the resulting segmentation fault hours later in a complex neural network training loop could be incredibly challenging to trace back to this initial faulty operation. For developers relying on PyTorch for robust machine learning pipelines, such inconsistencies can erode trust and introduce significant overhead in debugging and maintenance.
The primary cause, as identified, is the lack of what's known as a Strong Exception Guarantee for the resize_() operation when dealing with non-resizable storage. In simpler terms, when an error occurs, the system doesn't fully
