PyTorch Tensor Corruption Bug: Resize Failures Explained

Alex Johnson

-Dec 14, 2025

PyTorch Tensor Corruption Bug: Resize Failures Explained

In the ever-evolving world of deep learning, PyTorch stands as a powerhouse, enabling researchers and developers to build sophisticated neural networks with relative ease. Its flexibility and performance are largely due to its dynamic tensor manipulation capabilities. However, even the most robust libraries can encounter unexpected issues. Recently, a critical bug has been identified in PyTorch concerning tensor updates, specifically when storage resize operations fail. This issue can lead to corrupted "Dcqoyp" tensors, creating silent data integrity problems and potentially causing hard-to-debug crashes.

Understanding the "Zombie Tensor" Phenomenon

At its core, a PyTorch tensor is a multi-dimensional array that holds data. It's comprised of two main components: the shape metadata (which describes the dimensions and strides of the tensor) and the storage (the actual contiguous block of memory where the data resides). Typically, when you resize a tensor, both its metadata and its underlying storage are adjusted accordingly. However, a peculiar scenario arises when a tensor's storage is non-resizable, for instance, when it's derived from a NumPy array using set_(). In such cases, PyTorch correctly detects that the storage cannot be resized and raises a RuntimeError with the message: "Trying to resize storage that is not resizable." This is precisely the behavior we'd expect for error handling.

The problem, however, lies in the operation's exception safety. Before the RuntimeError is thrown due to the non-resizable storage, PyTorch unintentionally updates the tensor's shape and stride metadata to reflect the intended new size. This creates a deeply problematic state where the tensor's shape metadata indicates a large, new dimension, but its actual storage remains empty, holding zero bytes. This inconsistent state has been aptly nicknamed the "Zombie Tensor" state. A tensor in this zombie state is effectively corrupted. Any subsequent attempt to access or manipulate this tensor, such as printing it or performing operations on it, can lead to severe issues, ranging from internal RuntimeErrors to outright Segmentation Faults. These crashes are particularly insidious because they might occur much later in the execution flow, far removed from the original faulty resize_() call, making the root cause incredibly difficult to pinpoint. The minimal reproduction case provided demonstrates this vividly: a tensor is created with empty storage, then resize_() is called with a target shape. While the RuntimeError is caught, the tensor's shape attribute is updated, but its storage() still reports 0 bytes. Printing this tensor then triggers the crash.

The Bug in Detail: A Preemptive Metadata Update

To truly grasp the severity and nature of this bug, let's delve deeper into the sequence of events during a failed resize_() operation on a non-resizable tensor. When resize_() is invoked, PyTorch's internal logic first calculates the new shape and strides based on the requested dimensions. It then proceeds to check if the underlying storage can accommodate this new shape. For tensors that share storage with non-resizable buffers (like NumPy arrays managed by set_()), this check will inevitably fail. The crucial flaw is that the metadata update happens before the storage resizing check. Consequently, even though the storage itself remains unchanged (and often empty or insufficient), the tensor's shape attribute is modified to reflect the hypothetical new dimensions.

Imagine you have a tensor t that was initialized with an empty NumPy array. Its storage has 0 bytes, and its shape is torch.Size([0]). If you then try to t.resize_((5, 5, 5)), PyTorch internally prepares to change the shape to (5, 5, 5). It updates t.shape to torch.Size([5, 5, 5]). Only after this metadata modification does it attempt to resize the storage. Since the storage is non-resizable (and has 0 bytes), this step fails, and a RuntimeError is raised. However, because the exception is thrown after the shape has already been altered, the tensor is left in an inconsistent state: t.shape is torch.Size([5, 5, 5]), but t.storage().nbytes() is still 0.

This inconsistency is the root of the problem. When subsequent operations try to access the tensor's data using its shape metadata, they expect a certain amount of memory to be available based on the shape. Since the storage is only 0 bytes, these operations will try to read from or write to invalid memory locations. This can manifest as a RuntimeError if PyTorch's safety checks catch the anomaly during an operation like printing, or more severely, as a Segmentation Fault if the program attempts to access memory it doesn't own. The bug essentially leaves PyTorch in a state where its internal representation of the tensor is fundamentally broken, leading to unpredictable and dangerous runtime behavior. The RuntimeError is caught, but the damage to the tensor's integrity is already done, creating a "zombie" tensor that haunts the rest of the program's execution.

Reproduction and Impact

To confirm and understand the behavior of this bug, a minimal reproduction case has been provided. This script is designed to isolate the problematic scenario, making it easier to verify the bug and test potential fixes. Let's break down the reproduction script:

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 this script is executed, the following output and behavior are observed:

locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage(): This line creates a NumPy array with no elements and then converts it into PyTorch's untyped_storage. Crucially, this storage is marked as non-resizable.
t = torch.tensor([], dtype=torch.int32): A standard, empty PyTorch tensor is initialized.
t.set_(locked_storage): The tensor t is made to point to the locked_storage. At this point, t.shape is torch.Size([0]) and t.untyped_storage().nbytes() is 0.
t.resize_((5, 5, 5)): This is the critical operation. The intention is to change the tensor's shape to (5, 5, 5). However, because the underlying storage is non-resizable and has 0 bytes, this operation should ideally fail cleanly, leaving the tensor's shape unchanged.
except RuntimeError:: The try-except block catches the expected RuntimeError that PyTorch raises when it detects the non-resizable storage. This part of the error handling works as intended.

The Problematic Outcome:

print(f"Shape: {t.shape}"): This line reveals the first symptom of the corruption. Instead of printing torch.Size([0]), it outputs torch.Size([5, 5, 5]). This clearly shows that the shape metadata was updated, despite the failure.
print(f"Storage: {t.untyped_storage().nbytes()}"): This confirms that the storage size remains 0. The tensor now has a shape that implies it should hold 5 * 5 * 5 = 125 elements, but its storage can hold nothing.
print(t): This is where the crash typically occurs. Attempting to print the tensor forces PyTorch to access its data based on the (5, 5, 5) shape. Since the storage is empty, this leads to a runtime error, often a Segmentation Fault, as the program tries to dereference invalid memory.

The impact of this bug is significant for any PyTorch application that might involve resizing tensors, especially those that could interact with external libraries like NumPy or custom C++ extensions that manage storage. In distributed training, or complex data preprocessing pipelines, such a silent corruption could propagate, leading to incorrect model behavior or outright crashes. The absence of a clear exception guarantee during storage resize failures means developers cannot rely on PyTorch to maintain a consistent tensor state when errors occur, increasing the complexity of writing robust code.

Expected vs. Actual Behavior: The Importance of Exception Guarantees

In software development, particularly in systems dealing with low-level memory management like deep learning frameworks, the concept of exception guarantees is paramount. An exception guarantee dictates the state of the program after an exception is thrown. For operations that modify data structures, there are typically three levels of guarantees:

Basic Guarantee: If an exception occurs, the program remains in a valid state, but no guarantees are made about the state of the modified object. Resources are not leaked.
Strong Guarantee: If an exception occurs, the program is rolled back to the state it was in before the operation began. The modified object remains unchanged.
Nothrow Guarantee: The operation is guaranteed not to throw an exception under any circumstances (often achieved by performing checks beforehand).

For the resize_() operation in PyTorch, especially when dealing with sensitive operations like potentially non-resizable storage, the Strong Exception Guarantee is highly desirable. This means that if resize_() fails (e.g., due to non-resizable storage), the tensor should revert to its original state, including its original shape and stride metadata. The storage might remain unchanged, but the tensor's view of that storage should be consistent.

Let's re-examine the expected behavior based on a strong guarantee:

Initial State: Tensor t has shape=torch.Size([0]) and storage.nbytes()=0.
t.resize_((5, 5, 5)) is called: PyTorch attempts to resize the storage. It fails because the storage is non-resizable.
Exception Raised: A RuntimeError is thrown.
Expected Outcome (Strong Guarantee): Because the operation failed and a strong guarantee is in place, the tensor t should remain exactly as it was before the resize_() call. Its shape should still be torch.Size([0]), and its storage.nbytes() should still be 0. No other modifications should have occurred.

Now, let's look at the actual behavior observed, which clearly violates the strong guarantee:

Initial State: Tensor t has shape=torch.Size([0]) and storage.nbytes()=0.
t.resize_((5, 5, 5)) is called: PyTorch first updates the tensor's internal metadata to reflect the new target shape torch.Size([5, 5, 5]). Then, it attempts to resize the storage, which fails.
Exception Raised: A RuntimeError is thrown.
Actual Outcome: The exception is caught, but the tensor t is left in an inconsistent state: shape=torch.Size([5, 5, 5]) and storage.nbytes()=0. This is the "Zombie Tensor" state.

The divergence between the expected strong guarantee and the actual behavior is the crux of this bug. This failure to maintain an invariant state upon error means that developers cannot safely assume that a caught exception from resize_() will leave their tensors in a predictable, usable condition. The subsequent operations that rely on the tensor's shape metadata will operate on a fundamentally broken object, leading to the observed crashes and unpredictable behavior. This highlights the critical need for PyTorch to ensure robust error handling and uphold strong exception guarantees for its core operations.

Fixing the Bug: Ensuring Robustness in Tensor Resizing

Addressing this bug requires a careful modification of the resize_() operation within PyTorch's C++ backend. The fundamental principle is to ensure that tensor metadata is only updated after the storage resizing operation is confirmed to be successful. If the storage resize fails for any reason, the metadata should remain untouched, preserving the tensor's original state. This aligns with the principle of the Strong Exception Guarantee.

Here's a conceptual approach to fixing the resize_() method:

Check Storage Resizability First: Before any modifications are made to the tensor's metadata (shape, strides, etc.), PyTorch should perform a thorough check to determine if the underlying storage can indeed be resized to the target dimensions. This check should consider the storage's current state, its allocated capacity, and any flags indicating whether it's resizable (e.g., if it's backed by a non-resizable NumPy array or a fixed-size buffer).
Attempt Storage Resize: If the storage is determined to be resizable, the operation should proceed to attempt the actual resizing of the storage. This might involve allocating new memory, copying data if necessary, and updating the internal pointer to the storage.
Update Metadata Only on Success: If and only if the storage resizing operation completes without raising an exception, then and only then should the tensor's shape and stride metadata be updated to reflect the new dimensions. This ensures that the metadata is always consistent with the actual storage.
Exception Handling: If the storage resizing attempt fails at step 2 (e.g., due to memory allocation errors, or encountering non-resizable storage that wasn't caught in step 1 due to complex internal states), a RuntimeError should be raised. Crucially, because no metadata has been updated yet, the tensor remains in its original, consistent state. The exception signifies a failed operation, but the data structure itself remains valid and unchanged.

Essentially, the order of operations needs to be reversed: check and modify storage first, then update metadata. This is a common pattern in robust software design where mutable objects are involved: perform the risky part first, and if it succeeds, then apply the changes that reflect that success.

For the specific case of tensors sharing storage with non-resizable buffers, the initial check in step 1 should reliably catch these scenarios. If t.set_(locked_storage) creates a tensor whose storage t.storage() is marked as non-resizable, any subsequent call to t.resize_() should ideally recognize this immediately and raise the RuntimeError before attempting to modify t.shape.

Implementing this fix would involve modifying the C++ source code within PyTorch's tensor manipulation modules. The goal is to achieve the Strong Exception Guarantee for the resize_() operation, preventing the creation of these "Zombie Tensors" and ensuring that PyTorch behaves predictably even when encountering error conditions related to storage management. This not only resolves the immediate crashing issue but also significantly improves the overall reliability and maintainability of code that relies on dynamic tensor operations in PyTorch.

Conclusion: Upholding Integrity in Tensor Operations

The bug where PyTorch updates tensor shape metadata even when storage resize fails is a critical issue that can lead to "Zombie Tensors" – corrupted tensor objects that cause runtime crashes. This occurs because the shape information is modified before the non-resizable storage check fails, leaving the tensor in an inconsistent state. The failure to provide a Strong Exception Guarantee for the resize_() operation means that developers cannot rely on the tensor's integrity after such errors.

By reordering operations to ensure storage is successfully resized before metadata is updated, PyTorch can prevent this corruption. This involves prioritizing the integrity of the underlying storage and only reflecting successful changes in the tensor's shape and strides. Such a fix is crucial for maintaining data consistency and preventing hard-to-debug crashes in complex machine learning workflows.

For further reading on tensor operations and memory management in deep learning frameworks, you might find resources from PyTorch's official documentation and discussions on GitHub insightful. Examining how other frameworks handle similar edge cases can also provide valuable context.