PyTorch Bug: Tensor Corruption After Failed Storage Resize

Unmasking the "Zombie Tensor" Bug in PyTorch

Hello, fellow PyTorch enthusiasts and developers! Today, we're diving deep into a fascinating, yet critical, bug that can leave your PyTorch tensors in a rather unsettling state – we're talking about "Zombie Tensors". Imagine a tensor that thinks it's big and powerful, ready to hold vast amounts of data, but in reality, it's completely empty. This inconsistent state is exactly what happens when PyTorch updates its tensor shape metadata even when storage resize fails. This bug, observed in PyTorch version 2.9.0+cu126 (and potentially other versions), highlights a crucial aspect of library design: exception safety. When a resize_() operation is attempted on a tensor that shares its memory with a non-resizable buffer, like a NumPy array injected via set_(), PyTorch correctly throws a RuntimeError stating that it's "Trying to resize storage that is not resizable." While catching this error might seem like a solution, the underlying problem persists: the tensor's metadata, specifically its shape and stride, are already updated to the new, intended size before the storage resize check ultimately fails. This leaves the tensor in an inconsistent, corrupted state where its tensor.shape proudly declares a large dimension (e.g., torch.Size([5, 5, 5])), but its tensor.storage() remains stubbornly empty, reporting 0 bytes.

This discrepancy is more than just a minor annoyance; it's a recipe for disaster. Accessing these corrupted "Zombie" tensors after catching the RuntimeError can lead to serious consequences, including Segmentation Faults or internal RuntimeErrors, effectively crashing your program. For data scientists and machine learning engineers, such unpredictable behavior can be a nightmare to debug. It undermines the reliability of your models and makes it incredibly difficult to trust the integrity of your data operations. We rely on PyTorch to manage memory and tensor states robustly, and a bug like this exposes a gap in what's known as the "strong exception guarantee" – the principle that if an operation fails, the program's state should revert to what it was before the operation began. In this case, the tensor's state is clearly not reverted, leading to dangerous inconsistencies. Understanding why this happens and how to safeguard against it is paramount for writing robust and reliable PyTorch code. Let's unpack the technical details and explore the implications for your deep learning projects.

Diving Deeper: How PyTorch Creates Corrupted Tensors

To truly grasp the gravity of this PyTorch tensor corruption bug, let's dissect the mechanism that leads to these problematic "Zombie Tensors." The core of the issue lies in the interaction between two fundamental PyTorch tensor operations: resize_() and set_(). The resize_() method is designed to change a tensor's shape in-place, potentially reallocating its underlying storage if necessary. The set_() method, on the other hand, allows you to assign a pre-existing storage (or another tensor) to a new tensor, effectively sharing the underlying memory. This sharing is incredibly powerful for memory efficiency but introduces complexities, especially when that shared storage is non-resizable.

Consider a scenario where you've created a PyTorch tensor that shares its storage with a NumPy array. NumPy arrays, by default, might not expose resizable storage to PyTorch. When you then attempt to call t.resize_((5, 5, 5)) on such a tensor, here’s the sequence of events that unfortunately culminates in a corrupted state:

1. Metadata Update First: PyTorch, in its internal implementation, first proceeds to update the tensor's metadata. This includes its shape and stride attributes, setting them to the (5, 5, 5) configuration you requested. This happens before it checks the underlying storage's resizability.
2. Storage Check and Failure: Only after the metadata has been updated does PyTorch attempt to perform the actual storage reallocation or validation check. At this point, it discovers that the shared untyped_storage() object (which originated from a non-resizable NumPy array) cannot be resized.
3. RuntimeError Thrown: Correctly identifying this limitation, PyTorch then throws a RuntimeError with the message: "Trying to resize storage that is not resizable."

Now, here's the critical flaw: while the RuntimeError is raised, indicating a failure, the tensor's metadata (its shape and stride) is not rolled back. It remains in the newly updated, incorrect state. So, you end up with a tensor whose shape property (torch.Size([5, 5, 5])) tells you it's a 125-element tensor, but its actual storage().nbytes() method still reports 0 bytes. This fundamental mismatch creates an inconsistent "Zombie" state. The tensor's descriptor (metadata) points to a large block of memory, but the actual memory it's supposed to manage is either non-existent or still only 0 bytes.

The consequences are immediate and severe. As demonstrated in the minimal reproduction: print(t) will likely result in a RuntimeError or, more dangerously, a Segmentation Fault (a direct memory access violation) when the PyTorch internals try to access elements within the tensor's declared shape but find no actual allocated storage behind it. This scenario is particularly insidious because a try-except block around resize_() might lead developers to believe they've safely handled the error, only to have a crash occur much later in their program when the corrupted tensor is finally accessed. This makes debugging incredibly challenging, as the point of failure (accessing the zombie tensor) is far removed from the point of corruption (the failed resize_() call). Understanding this order of operations – metadata update before storage check – is key to comprehending why this PyTorch tensor corruption arises and how it bypasses typical error handling assumptions.

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) # CRASHES or raises RuntimeError

As you can see, the output clearly shows the shape being updated to [5, 5, 5] while nbytes() remains 0. Any subsequent operation on t will then encounter this fundamental inconsistency, leading to system instability.

The Importance of Exception Safety in Tensor Operations

The PyTorch tensor corruption bug we've been discussing isn't just a quirky edge case; it underscores a fundamental principle in software engineering, especially for low-level, performance-critical libraries like PyTorch: exception safety. When we talk about exception safety, we're referring to how a program behaves when an error occurs and an exception is thrown. Ideally, operations should provide a strong exception guarantee, meaning if an operation fails and an exception is thrown, the program's state remains exactly as it was before the operation started. In other words, if resize_() fails, the tensor's shape and stride should remain unchanged, exactly as they were before the ill-fated call.

However, in the case of our "Zombie Tensor" bug, PyTorch currently offers only a weaker guarantee – perhaps a basic guarantee at best, where invariants are preserved (the program doesn't crash immediately at the point of the exception), but the state is modified in an unexpected, inconsistent way. This leaves the tensor in a partially updated and dangerous condition, directly violating the expectation of a strong guarantee. For a library as foundational as PyTorch, which is used to build complex and often mission-critical machine learning systems, strong exception guarantees are paramount. Developers rely on these guarantees to build robust applications without constantly having to worry about hidden side effects from failed operations.

When a library like PyTorch doesn't provide strong exception guarantees for critical tensor manipulations, it introduces several significant problems for developers:

• Debugging Nightmares: As we've seen, the failure (a crash or Segmentation Fault) happens after the RuntimeError is caught, often much later in the execution flow. This temporal and spatial separation between the cause of the bug and its manifestation makes debugging incredibly difficult and time-consuming. You might spend hours tracing seemingly unrelated parts of your code, only to find the root cause was a silently corrupted tensor from a previous,