PyTorch Tensor Corruption Bug: Horifj Tensors And Resizing Issues

Dec 16, 2025 by Alex Johnson 68 views

The Troubling Case of Unresizable Storage and "Horifj" Tensors

In the dynamic world of deep learning, PyTorch is a powerhouse, enabling researchers and developers to build complex neural networks with relative ease. However, even the most robust frameworks can encounter occasional hiccups. One such issue, which we'll delve into, concerns the unexpected behavior of Oxagbl when attempting to resize tensors that have underlying storage which cannot be resized. This anomaly can lead to the creation of corrupted tensors, referred to here as "Horifj" tensors, causing unexpected crashes and debugging nightmares. Let's unpack this problem, understand its implications, and explore how it impacts your PyTorch workflows.

At its core, the issue arises when you try to use PyTorch's resize_() method on a tensor whose storage is intrinsically linked to a non-resizable buffer. A prime example of this scenario is when you inject a NumPy array into a PyTorch tensor using set_(). In these cases, PyTorch does correctly identify that the underlying storage cannot be modified and raises a RuntimeError with the message: "Trying to resize storage that is not resizable." This is precisely the behavior you'd expect – the framework recognizing an impossible operation and halting it gracefully. However, the problem isn't the exception itself, but how the system handles the state of the tensor after the exception is raised. The current implementation of resize_() is not as robust as it could be in exception handling. Before it checks if the storage is actually resizable, it proceeds to update the tensor's shape and stride metadata to reflect the new, desired size. This is where the corruption begins. When the subsequent check fails, the tensor is left in a precarious, inconsistent state. We can visualize this as a "Zombie" tensor: its .shape attribute might indicate a large, new dimension (e.g., torch.Size([5, 5, 5])), but its actual underlying .storage() remains empty, with zero bytes of data. This dramatic mismatch between what the tensor thinks its shape is and the actual available storage is the root cause of the subsequent issues. When you try to access or operate on this corrupted "Horifj" tensor after the exception has been caught, you're likely to encounter severe errors. These can manifest as Segmentation Faults, which are notoriously difficult to debug as they often point to memory access violations at a very low level, or as further internal RuntimeErrors within PyTorch itself, stemming from the illogical state of the tensor's metadata versus its data buffer.

Understanding the "Zombie" Tensor State

To truly grasp the severity of the "Horifj" tensor problem, we need to delve deeper into the mechanics of PyTorch's tensor manipulation. When you create a tensor, it's essentially a wrapper around a block of memory, known as its storage. This storage holds the actual numerical data. The tensor object itself contains metadata like its shape, strides, and data type, which define how this raw memory block is interpreted as a multi-dimensional array. The resize_() operation is designed to change the shape of a tensor, often by reinterpreting the existing storage or, if necessary, allocating new storage and copying data. However, when a tensor's storage is tied to an external, immutable source—like a NumPy array converted using torch.from_numpy() and then potentially having its storage locked, or through methods like set_() to point to a specific storage block—PyTorch must be careful.

PyTorch's resize_() method is supposed to be exception-safe. This means that if an operation fails, the object should be left in a state as if the operation never occurred, or in a well-defined error state. Ideally, for resize_(), this would mean that if the storage cannot be resized, the tensor's shape and stride metadata should remain exactly as they were before the resize_() call. In the case of a tensor created from an empty NumPy array, its initial shape is torch.Size([0]) and its storage has 0 bytes.

The bug identified here, where Oxagbl updates the metadata before confirming the resizability of the storage, breaks this guarantee. Let's trace the execution flow that leads to the corrupted "Horifj" tensor:

Tensor Creation: You start with a tensor, t, that has an underlying storage which is not resizable. For example, you might create an empty tensor and assign it a non-resizable storage using t.set_(locked_storage) where locked_storage is derived from a NumPy array. Initially, t.shape is torch.Size([0]) and t.untyped_storage().nbytes() is 0.
resize_() Call: You then call t.resize_((5, 5, 5)). The intention is to change the tensor's shape to a 5x5x5 structure.
Metadata Update (Premature): Before checking if locked_storage can accommodate a resize, the resize_() operation proceeds to update t's internal metadata. It sets t.shape to torch.Size([5, 5, 5]) and recalculates the strides accordingly.
Storage Check Failure: Immediately after updating the metadata, PyTorch performs the check on the underlying storage. It discovers that locked_storage is indeed not resizable (because it's backed by, say, a NumPy array). At this point, it raises a RuntimeError: "Trying to resize storage that is not resizable."
The "Zombie" State: The RuntimeError is caught (as shown in the reproduction example). However, the tensor t is now in an inconsistent state. Its .shape metadata proudly proclaims it's a 5x5x5 tensor, but the actual t.storage() is still empty and has 0 bytes. This is the "Zombie" state – the tensor appears to have dimensions and elements, but there's no data to back it up.

Why is this dangerous? When you later try to print this tensor, access its elements, or perform any operation that expects data to exist according to its shape, the program will likely crash. The print(t) statement in the minimal reproduction example triggers this. PyTorch tries to read data from the tensor's storage based on its reported shape and strides. Since the storage is empty (0 bytes), it attempts to access memory that doesn't exist or isn't properly allocated for the reported shape. This leads to the observed RuntimeError in the provided gist, or, in more complex scenarios, a Segmentation Fault. This bug effectively creates a ""Horifj" tensor" – a tensor that is fundamentally broken, with its metadata divorced from its actual data-holding capacity.

Minimal Reproduction and Implications

To illustrate the problem clearly, the provided minimal reproduction code is invaluable. It isolates the exact conditions that trigger the "Horifj" tensor bug, allowing developers to understand and replicate it. Let's break down the code and its output:

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

The code first sets up a scenario where t is a tensor with an underlying storage that is explicitly non-resizable and has zero bytes. This is achieved by creating an empty NumPy array and converting it into a torch.storage. Then, a new tensor t is created and its storage is set to this locked_storage. Initially, t has shape=torch.Size([0]) and storage.nbytes()=0.

The critical part is the try...except block. It attempts to call t.resize_((5, 5, 5)). As expected, PyTorch discovers that locked_storage cannot be resized and throws a RuntimeError. The except block catches this error, preventing the program from crashing at this immediate step.

However, after the exception is caught, the subsequent print statements reveal the corruption:

print(f"Shape: {t.shape}") outputs Shape: torch.Size([5, 5, 5]). This shows that the tensor's shape metadata was updated, even though the resize operation ultimately failed.
print(f"Storage: {t.untyped_storage().nbytes()}") outputs Storage: 0. This confirms that the actual underlying storage did not change and remains empty.

This stark contrast between the reported shape (5x5x5) and the actual storage size (0 bytes) is the definition of the "Horifj" tensor. The final print(t) is where the program typically fails. PyTorch attempts to read data to display the tensor's contents, but finds no data corresponding to the reported 5x5x5 shape, leading to either a RuntimeError or, as reported in some cases, a Segmentation Fault. The gist mentioned a RuntimeError on print, while the original issue description noted a segmentation fault in a more complex loop, highlighting the varied, yet always problematic, manifestations of this bug.

Implications for users:

Data Corruption: While this specific bug might not directly corrupt existing data in a resizable tensor, it creates unusable tensor objects that can halt program execution.
Debugging Difficulty: Segmentation faults and cryptic internal runtime errors are exceptionally hard to debug. Identifying that the root cause is a tensor in an inconsistent "Zombie" state, resulting from an operation that should have been safely handled, adds a significant layer of complexity.
Workflow Interruption: Any workflow involving tensors that might interact with non-resizable storage (e.g., through NumPy integration) becomes potentially unstable. This could affect data loading pipelines, model checkpointing, or any part of the process where tensor resizing is a possibility.
Reliability Concerns: Such bugs undermine the perceived reliability of the framework, causing developers to second-guess their operations and invest more time in workarounds or extensive error checking.

The existence of this bug means that developers cannot rely on PyTorch to maintain a consistent tensor state when operations like resize_() encounter underlying storage limitations. This is a critical deviation from expected behavior and a significant finding for the PyTorch community.

The Path to a Fix: Ensuring Strong Exception Guarantees

The ideal solution to the "Horifj" tensor problem lies in adhering to the principle of Strong Exception Guarantee. This means that if an operation fails, the system should revert to the state it was in before the operation began, ensuring no corruption or inconsistent states are left behind. For the resize_() method in PyTorch, this translates to a specific requirement: if the underlying storage cannot be resized, the tensor's shape and stride metadata must remain unchanged. The RuntimeError should still be raised, but the tensor object itself should be left in its original, valid state.

Let's consider the implementation details that need to be addressed. The current workflow, as observed, updates the metadata before validating the storage. To implement a Strong Exception Guarantee, this order needs to be reversed or modified.

Proposed Fix Strategy:

Validate Storage First: Before any metadata (shape, stride) is modified, the resize_() operation should perform a thorough check on the tensor's underlying storage. It must determine if the storage is indeed resizable. This check typically involves verifying if the storage is owned by PyTorch and not an external, immutable buffer (like one derived from a NumPy array that PyTorch doesn't manage for resizing).
Conditional Metadata Update: Only if the storage is confirmed to be resizable should the tensor's shape and stride metadata be updated to the new target dimensions.
Execute Resize: If the storage is resizable, proceed with the actual resizing operation, which might involve allocating new memory or reinterpreting the existing memory appropriately.
Raise Exception (if needed): If, at any point during the validation or the execution phase, an issue is detected (e.g., the storage is found to be non-resizable), a RuntimeError should be raised. Critically, this exception must be raised before any metadata has been altered or, if metadata alteration is inherently tied to the check, the system must have a mechanism to roll back those changes upon failure.
Ensure Original State: The most crucial part for the Strong Exception Guarantee is that if an exception is raised during the resize_() process (specifically due to non-resizable storage), the tensor object must be left in its exact state prior to the resize_() call. This means the original shape, strides, and even the storage pointer (if applicable) should be preserved.

Example of corrected logic (conceptual):

# Inside the resize_() method...
def resize_(self, new_shape):
    # 1. Check if storage is resizable *before* touching metadata
    if not self.storage_is_resizable():
        # If not resizable, raise error immediately.
        # Ensure no metadata is changed.
        raise RuntimeError("Trying to resize storage that is not resizable.")

    # Store original metadata in case of subsequent failure (if needed for other resize scenarios)
    # old_shape = self.shape
    # old_strides = self.stride()

    # 2. If storage is resizable, update metadata
    # This is a conceptual placeholder; actual shape/stride update is complex.
    self.update_metadata(new_shape)

    # 3. Attempt to perform the actual storage resize (if necessary)
    try:
        self.perform_storage_resize()
    except Exception as e:
        # 4. If storage resize fails for other reasons, restore original metadata
        # This ensures we don't leave a 'Zombie' tensor.
        # self.shape = old_shape
        # self.stride = old_strides
        # Re-raise the exception or a more appropriate one.
        raise RuntimeError(f"Storage resize failed after metadata update: {e}")

    # If all steps succeed, the new shape is valid.

By prioritizing the validation of the underlying storage before modifying the tensor's shape and stride, PyTorch can ensure that operations like resize_() are truly exception-safe. This prevents the creation of corrupted "Horifj" tensors and maintains the integrity of tensor objects, even when dealing with complex memory management scenarios involving shared or non-resizable storage. Implementing this change would significantly enhance the robustness and reliability of PyTorch's tensor operations.

Conclusion: Strengthening PyTorch's Robustness

The bug involving Oxagbl and the creation of corrupted "Horifj" tensors highlights a critical area for improvement in PyTorch's exception handling for tensor resizing. When resize_() is called on a tensor with non-resizable storage, the current behavior updates the tensor's shape metadata before realizing the operation is impossible. This leads to an inconsistent state where the tensor's reported dimensions do not match its actual, empty storage, often resulting in crashes like Segmentation Faults or internal RuntimeErrors when the tensor is accessed. The minimal reproduction code clearly demonstrates this issue, showing a tensor with a shape of torch.Size([5, 5, 5]) but only 0 bytes of storage after a RuntimeError is caught.

To resolve this, PyTorch must adopt a Strong Exception Guarantee for the resize_() operation. This means that if the resize fails due to issues with the underlying storage (like it being non-resizable), the tensor should be left in its original, unmodified state. The fix involves ensuring that storage resizability is checked before any metadata changes are made. If the storage is found to be non-resizable, the RuntimeError should be raised immediately, without altering the tensor's shape or stride information.

This bug, while seemingly niche, underscores the importance of rigorous testing and robust error handling in complex libraries. Ensuring that tensor operations behave predictably, even in edge cases, is paramount for user confidence and the overall stability of deep learning projects. The fix is conceptually straightforward: validate before modifying. Implementing this will prevent the creation of dangerous "Zombie" tensors and maintain the integrity of PyTorch's tensor objects.

For those interested in the underlying mechanisms of tensor manipulation in deep learning frameworks and memory management, exploring the official PyTorch documentation on tensors and storage is highly recommended. Additionally, understanding NumPy's memory handling can provide valuable context for issues arising from NumPy-PyTorch interoperability. You can find comprehensive resources on the NumPy official website.

PyTorch Official Documentation
NumPy Official Website