PyTorch Bug: Corrupted Tensors After Failed Storage Resize

We're diving into a rather peculiar and potentially problematic bug that's been identified within the PyTorch library, specifically concerning how tensor shape metadata is handled when a storage resize operation fails. This issue can lead to what's described as a "corrupted" or "zombie" tensor state, which, as you might imagine, can cause all sorts of headaches, including segmentation faults and internal runtime errors. Let's break down what's happening, why it's a problem, and what the expected versus actual behavior is.

The Nitty-Gritty of the Bug: What's Actually Happening?

At its core, this bug revolves around the resize_() method in PyTorch. When you attempt to resize a tensor, PyTorch first checks if the underlying storage can actually be resized. If the tensor shares its storage with a non-resizable buffer – a common scenario when working with external data like NumPy arrays that you've injected using set_() – PyTorch does correctly identify this and raise a RuntimeError, stating: "Trying to resize storage that is not resizable." This is good! It means PyTorch is aware of the limitation.

However, the problem arises because this check, while effective in raising an error, isn't entirely exception-safe in its execution. Before the RuntimeError is actually raised, the tensor's shape and stride metadata are updated to reflect the new, target size you attempted to resize to. So, you get an error message, but in the backend, the tensor's internal pointers to its shape have already been modified. This leaves the tensor in a very strange, inconsistent state. Imagine telling your brain the room is now twice as big, but the actual walls haven't moved – that's sort of what's happening here. The tensor's shape attribute might report a much larger size (e.g., torch.Size([5, 5, 5])), but its storage() remains at its original, small size, often 0 bytes if it was initially empty or derived from an empty NumPy array.

This mismatch between what the tensor thinks its shape is and what its actual underlying data storage can accommodate is the root cause of the problem. When you subsequently try to access or even just print this "zombie" tensor, PyTorch's internal mechanisms try to reconcile this discrepancy. Since the shape implies a certain amount of data that simply doesn't exist in the storage, it leads to memory access violations. These often manifest as Segmentation Faults (a critical operating system error indicating a program tried to access memory it shouldn't have) or more specific internal RuntimeErrors within PyTorch itself, depending on the exact operation and the internal checks that are triggered.

It's a subtle bug because the initial error message seems to indicate everything is fine – the problem is caught. But the side effects of the operation preceding the error throw the tensor into an unusable state. The core issue lies in the ordering of operations within the resize_() function: updating metadata before validating the storage's resizability.

Understanding the "Zombie Tensor" State

The term "zombie tensor" is quite evocative, and it perfectly captures the state of a tensor after this bug occurs. A zombie, in folklore, is something that appears alive but is fundamentally dead or corrupted. Similarly, a "zombie tensor" in PyTorch looks like a valid tensor because its .shape attribute reports dimensions and a total number of elements. You can inspect this shape, and it will appear as if the resize operation you attempted was successful. For instance, if you tried to resize a tensor to (5, 5, 5), the t.shape attribute will indeed report torch.Size([5, 5, 5]). This suggests a tensor with 125 elements.

However, the critical problem is that the t.storage() object, which holds the actual data, has not been resized. In the specific minimal reproduction example provided, the storage was initialized from an empty NumPy array, meaning t.untyped_storage().nbytes() returns 0. So, you have a tensor that reports it contains 125 elements (each likely 4 or 8 bytes, depending on the data type), but its actual data storage has zero bytes. This is a fundamental contradiction.

When you try to interact with such a tensor – for example, by trying to print its contents (print(t)) or perform any operation that requires accessing its data based on its shape metadata – PyTorch attempts to read or write data according to the reported shape. Since the storage is empty (or vastly smaller than expected), this leads to memory errors. The program might crash immediately with a segmentation fault, indicating that it tried to access memory locations that don't belong to the program or are invalid. In some cases, PyTorch's internal error handling might catch this and raise a more specific RuntimeError, but the underlying issue remains the same: a broken invariant between the tensor's shape and its data storage.

The implications are serious, especially in complex deep learning pipelines where tensors are passed around extensively. A corrupted tensor might not cause an immediate crash but could lead to silent data corruption further down the line, producing incorrect model outputs or gradients. The fact that the bug occurs after an exception is caught means that standard try...except blocks might not fully protect against the tensor becoming corrupted, as the problematic state change happens before the exception is thrown.

Minimal Reproduction: Seeing the Problem in Action

To truly understand the issue, let's look at the provided minimal reproduction code. It's a concise demonstration of how to trigger this problematic state.

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

In this snippet, we first create an empty NumPy array and then convert it into a PyTorch untyped_storage. This storage is inherently non-resizable because it's backed by a fixed-size NumPy array (in this case, an empty one, hence 0 bytes). We then create a PyTorch tensor t and explicitly set its storage to this locked_storage using t.set_(). At this point, t has a shape of torch.Size([0]) and 0 bytes of storage.

The crucial part is the try...except block. We attempt to call t.resize_((5, 5, 5)). As expected, because the storage is not resizable, a RuntimeError is raised. The except block catches this error, preventing the program from crashing at this specific line. However, as detailed earlier, by the time the RuntimeError is raised, t.shape has already been updated to torch.Size([5, 5, 5]).

After the try...except block, we print the tensor's shape and storage size. The output clearly shows the discrepancy: Shape: torch.Size([5, 5, 5]) but Storage: 0. The final print(t) is where the crash typically occurs. Because the tensor's metadata indicates it should have 125 elements, the program attempts to access and display these elements. Since there's no actual data in the storage, this leads to the observed crash, whether it's a segmentation fault or an internal PyTorch error.

This minimal example perfectly illustrates the violation of the Strong Exception Guarantee. This guarantee states that if an operation throws an exception, the program should be left in the same state as it was before the operation. In this case, the state has been altered (shape changed) even though the operation failed. The expected behavior would be that if resize_() fails due to non-resizable storage, the tensor's metadata (shape and stride) should remain unchanged, thus preserving its original torch.Size([0]) shape and the 0-byte storage.

Expected vs. Actual Behavior: A Clear Discrepancy

Let's summarize the expected behavior versus what's actually happening, based on the bug report and the minimal reproduction.

Expected Behavior:

When resize_() is called on a tensor whose storage is not resizable (e.g., when it's tied to a NumPy array or other fixed-size buffer), the operation should fail cleanly. If an exception is thrown (like the RuntimeError for non-resizable storage), PyTorch should ensure that no internal state of the tensor is modified. This means the tensor's shape, stride, and other metadata should remain exactly as they were before the resize_() call. In the context of the provided example, where the tensor starts with torch.Size([0]) and 0 bytes of storage, if resize_((5, 5, 5)) fails, the tensor should still have torch.Size([0]) and 0 bytes of storage after the exception is caught. This adheres to the principle of the Strong Exception Guarantee, ensuring that failures don't leave the system in a corrupted or inconsistent state.

Actual Behavior:

As demonstrated by the reproduction code, the RuntimeError is indeed raised when attempting to resize a tensor with non-resizable storage. However, before the exception is thrown, the tensor's shape and stride metadata are updated to match the requested new dimensions. So, if t.resize_((5, 5, 5)) is called, and it fails because the storage is locked, the tensor's shape attribute is modified to torch.Size([5, 5, 5]). The storage itself, however, remains unchanged and still has 0 bytes. This creates a severe inconsistency: the tensor reports a shape that implies it holds a significant amount of data, but its actual storage contains none. This inconsistency is what leads to subsequent crashes, such as segmentation faults or internal PyTorch errors, when any operation attempts to access the tensor's data based on its erroneously updated shape.

The crucial point of failure is that the metadata update happens before the check that determines if the storage is resizable. If the check fails, the metadata has already been altered, leaving the tensor in this "zombie" state.

Potential Impact and Why It Matters

This bug, while seemingly specific, has the potential for widespread impact within applications that heavily utilize PyTorch, especially those involving data manipulation that might involve resizing or reshaping tensors. When a library designed for high-performance numerical computation produces corrupted states that lead to crashes or silent data corruption, it erodes trust and can lead to significant debugging challenges.

• Data Corruption: The most insidious aspect is the potential for silent data corruption. If a corrupted tensor isn't immediately accessed in a way that triggers a crash, it might be passed along in calculations. This could lead to incorrect gradients during backpropagation in deep learning models, subtly altering the training process and leading to a model that performs poorly without an obvious reason. Or, it could affect inference, leading to incorrect predictions.
• Crashes and Instability: As seen in the minimal reproduction, a direct consequence is program instability. Segmentation faults are among the worst types of errors as they indicate a fundamental problem with memory management and can be difficult to trace back to their origin, especially in complex codebases. Even if the crash is caught as a RuntimeError by PyTorch, it still halts the program's execution.
• Debugging Nightmares: Developers might spend hours, if not days, trying to pinpoint the source of a crash or incorrect behavior, only to find it stems from a subtly corrupted tensor created during a seemingly innocuous operation. The fact that the bug occurs even when an exception is caught makes standard error handling less effective.
• Interoperability Issues: When tensors are used with other libraries (like NumPy, as in the reproduction example), such internal inconsistencies can become even more pronounced and harder to debug, especially if those libraries have different assumptions about tensor validity.

Looking Ahead: The Need for Robust Exception Safety

This bug highlights the critical importance of exception safety guarantees in software development, particularly in libraries that handle complex data structures and operations like PyTorch. The Strong Exception Guarantee is the ideal, meaning that if an operation fails, the system should be exactly as it was before the operation. While achieving this can sometimes be challenging due to performance considerations, it's crucial for maintaining program stability and data integrity.

The fix for this particular bug would likely involve reordering the operations within the resize_() method. Specifically, the check for storage resizability should happen before any metadata (shape, stride, etc.) is updated. If the storage is found to be non-resizable, the RuntimeError should be raised immediately, leaving all tensor metadata untouched. This would ensure that even if an error occurs, the tensor remains in a valid, consistent state.

For users encountering similar issues, carefully reviewing how tensors are created and resized, especially when interacting with external data sources or memory buffers, is advisable. Ensuring that operations that might fail are handled within try...except blocks is a good first step, but understanding that the failure itself might leave the object in an altered state is also key.

This issue serves as a valuable reminder of the intricate details involved in building and using robust numerical computing libraries. The PyTorch team is continuously working to improve the library's stability and reliability, and issues like this, when properly reported and reproduced, are essential for that ongoing development.

For more information on tensor operations and storage management in PyTorch, you can refer to the official PyTorch documentation on Tensors.