PyTorch Bug: Corrupted Tensors After Failed Storage Resize

In the dynamic world of deep learning, PyTorch is a powerhouse, enabling researchers and developers to build and train complex neural networks with relative ease. However, like any sophisticated software, it's not immune to the occasional bug. A particularly tricky issue has emerged concerning tensor operations, specifically when attempting to resize tensors that share storage with non-resizable buffers. This bug, if left unaddressed, can lead to corrupted tensors and unexpected crashes, disrupting the development workflow. Let's dive deep into this problem and understand its implications.

Understanding the Core Issue: The resize_() Operation and Non-Resizable Buffers

The PyTorch bug surfaces when the resize_() method is invoked on a tensor whose underlying storage is intrinsically non-resizable. This scenario commonly occurs when a tensor is created from or shares memory with external data structures, such as NumPy arrays, which are often managed with fixed-size memory allocations. When PyTorch's resize_() function is called in such a context, it's expected to fail gracefully. The system should recognize that the underlying storage cannot be altered and, consequently, should leave the tensor's metadata—its shape and strides—unchanged, perhaps by raising an appropriate error. The documentation and user expectations align with a strong exception guarantee: if an operation fails, the system should be left in a state as if the operation never occurred. However, this isn't what happens.

The "Zombie Tensor" State

When resize_() is executed on a tensor with non-resizable storage, PyTorch does correctly identify the problem and raises a RuntimeError with the informative message: "Trying to resize storage that is not resizable." This is a good first step, indicating that the operation is being intercepted. However, the internal mechanics of the resize_() function are not exception-safe. Before the storage immutability check is performed and the error is raised, the tensor's shape and stride metadata are already updated to reflect the new, target size. This creates a deeply problematic situation: the tensor's metadata now describes a much larger or differently shaped data structure, but its actual underlying storage remains unchanged, often being an empty or zero-byte buffer. This creates a state often referred to as a "zombie tensor"—it looks like it has a valid, large shape, but its actual data container is non-existent or inaccessible. Subsequent attempts to interact with this "zombie tensor," such as printing its contents, accessing its elements, or performing further operations, can lead to severe issues, including segmentation faults (segfaults) or internal PyTorch RuntimeErrors. These crashes are not just inconvenient; they can be incredibly difficult to debug, especially when they occur deep within complex model architectures or data processing pipelines. The minimal reproduction code provided starkly illustrates this: a tensor that should have a shape of torch.Size([0]) and 0 bytes of storage erroneously reports a shape of torch.Size([5, 5, 5]) while still having 0 bytes of storage, leading to a crash when print(t) is called.

Reproduction and Verification of the Bug

To truly understand and address a bug, a reliable reproduction method is essential. The PyTorch bug report provides a concise Python script that perfectly encapsulates the issue. Let's break down the reproduction steps and what they reveal:

First, we need to create a scenario where a tensor's storage is explicitly non-resizable. This is achieved by leveraging NumPy arrays, which often have fixed memory allocations. The code locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage() creates an empty NumPy array and then extracts its underlying PyTorch storage. Because NumPy arrays often manage their memory in fixed blocks, this storage is marked as non-resizable.

Next, a new, empty PyTorch tensor is created: t = torch.tensor([], dtype=torch.int32). This tensor initially has a shape of torch.Size([0]) and 0 bytes of storage. The crucial step is then injecting the non-resizable storage into this tensor using t.set_(locked_storage). At this point, t is a tensor with zero elements but with storage that cannot be resized.

The resize_() Call and Its Aftermath

The core of the problem lies in the subsequent call: t.resize_((5, 5, 5)). The intention here is to change the tensor's shape to a 5x5x5 tensor. Ideally, if the storage is not resizable, this operation should fail before altering any tensor metadata and raise a RuntimeError. The provided code wraps this call in a try...except RuntimeError block to catch the expected error.

The critical flaw is that PyTorch attempts to update the tensor's shape and stride information before verifying if the underlying storage can accommodate the resize operation. When the check Trying to resize storage that is not resizable is performed and fails, a RuntimeError is raised. However, the damage is already done: t.shape has been modified to torch.Size([5, 5, 5]). The except block catches the error, preventing the program from crashing at that exact moment.

Corroborating Evidence: shape vs. storage()

The verification step is where the corrupted state becomes starkly apparent. The print statements reveal the discrepancy:

• print(f"Shape: {t.shape}") outputs Shape: torch.Size([5, 5, 5]). This reflects the attempted resize.
• print(f"Storage: {t.untyped_storage().nbytes()}") outputs Storage: 0. This shows that the actual data buffer associated with the tensor is still empty, with 0 bytes of storage.

Finally, print(t) is called. Since the tensor thinks it has 5x5x5 = 125 elements (requiring significant memory) but its actual storage has 0 bytes, accessing this non-existent data triggers a crash. In the provided environment, this manifests as a RuntimeError (likely due to internal checks in newer PyTorch versions), but in other contexts or older versions, it could very well result in a more severe segmentation fault, a low-level memory access error that terminates the program abruptly.

This minimal example is crucial because it isolates the bug, removing complexities that might obscure the root cause. It clearly demonstrates that PyTorch's resize_() operation, when applied to tensors with immutable storage, fails to uphold the strong exception guarantee, leaving the tensor in a corrupted, crash-inducing state.

Implications and Impact of the Bug

This bug, while seemingly specific to a particular sequence of operations, can have far-reaching implications for users of PyTorch, especially those working with advanced features or integrating PyTorch with other libraries like NumPy. The creation of these "zombie tensors" is a silent threat that can manifest as unpredictable crashes, making debugging a nightmare.

Stability and Reliability Concerns

At its core, this issue undermines the stability and reliability of PyTorch. When a library used for critical machine learning tasks produces crashes due to internal inconsistencies, it erodes user confidence. A segfault or a runtime error originating from accessing a tensor that should have been handled gracefully can be incredibly frustrating. Developers might spend hours, or even days, chasing down obscure bugs that are, in fact, caused by this underlying PyTorch issue. The problem is exacerbated because the crash doesn't always happen immediately after the faulty resize_() call. It might occur much later in the program's execution, perhaps when the corrupted tensor is passed to another function or used in a print statement, making the causal link difficult to trace. This lack of predictability is a hallmark of difficult-to-debug software defects.

Impact on Data Interoperability

The bug is particularly relevant for workflows that involve seamless data interoperability between PyTorch and libraries like NumPy. PyTorch's tensor.set_() method is a powerful tool for sharing data buffers, enabling efficient memory usage and avoiding unnecessary data copying. However, as this bug demonstrates, using set_() with external, potentially non-resizable buffers, and then attempting to modify the tensor's shape via resize_(), creates a direct pathway to corruption. This can deter users from employing these efficient interoperability features, leading them to opt for safer but potentially less performant alternatives, like creating entirely new tensors and copying data.

Debugging Challenges

The nature of the bug presents significant debugging challenges. The discrepancy between the tensor's reported shape and its actual storage means that standard debugging tools might provide misleading information. A debugger might show a tensor with a large torch.Size attribute, leading the developer to believe the data should be there, when in reality, it's a phantom shape pointing to empty memory. The crash itself, often a segfault, provides little context about the internal state that led to the memory access violation. Tracing the execution path back to the specific resize_() call that triggered the problem can be a laborious process, especially in large codebases.

Potential for Data Loss or Corruption

While the minimal reproduction leads to a crash, in more complex scenarios, it's conceivable that a corrupted tensor might not immediately crash the program but could lead to subtle data corruption. If the program continues to execute with a tensor that has an incorrect shape but a non-existent or improperly sized storage, subsequent operations might produce incorrect results without raising an explicit error. This could lead to models being trained on flawed data, or inference results being silently wrong, which is often far worse than an immediate crash. The integrity of the data is paramount in machine learning, and any bug that compromises it is a serious concern.

Path to Resolution: Ensuring Strong Exception Guarantees

Resolving this PyTorch bug requires ensuring that operations involving tensor metadata and storage adhere to the principle of strong exception guarantees. This means that if an operation fails, the system should be left in a state identical to its state before the operation began. For the resize_() operation on non-resizable storage, this translates to a straightforward requirement: the tensor's shape and stride metadata must remain unchanged if the storage resize fails.

Implementing a Robust resize_()

The fix lies within the implementation of the resize_() method itself. The current behavior updates metadata before checking storage mutability. A robust implementation would reverse this order. The critical steps would be:

1. Check Storage Mutability First: Before any attempt to modify the tensor's internal metadata (shape, stride), PyTorch must rigorously check if the underlying storage is indeed resizable. This check should be performed against the storage object's properties.
2. Conditional Metadata Update: Only if the storage is confirmed to be resizable should the operation proceed to update the tensor's shape and stride metadata. If the storage is found to be non-resizable, the function should immediately raise the RuntimeError without altering any metadata.
3. Atomic Operations: Ideally, operations that modify both metadata and storage should be designed to be as atomic as possible. If any part of the operation fails (e.g., storage resize fails, or even if metadata update fails for some unforeseen reason), the entire operation should be rolled back, leaving the tensor in its original state. While a full rollback might be complex, ensuring metadata is not updated upon storage failure is the most critical aspect here.

Rethinking Tensor Set Operations

While fixing resize_() is paramount, it's also worth considering how tensors are linked to external storage. The set_() method is powerful but inherently carries risks when used with memory that PyTorch cannot fully control. Perhaps future versions of PyTorch could introduce stricter checks or warnings when set_() is used with storage known to be immutable, and subsequent operations like resize_() are attempted. However, the immediate priority is to ensure that existing operations behave predictably and safely.

The Importance of Testing

This bug underscores the importance of comprehensive testing in software development, particularly for libraries that form the foundation of complex systems like deep learning frameworks. Test cases should specifically target edge cases involving tensor storage, including:

• Tensors with shared, non-resizable storage (e.g., from NumPy arrays).
• Tensors created from memory views or slices.
• Operations that resize, reshape, or modify tensor metadata.
• Scenarios where these operations are expected to fail due to storage constraints.

By including such specific test cases in the PyTorch testing suite, bugs like this can be identified and fixed early in the development cycle, before they affect a wider user base. The provided minimal reproduction is an excellent candidate for such a test case.

Conclusion: Towards More Robust Tensor Operations

The bug where PyTorch updates tensor shape metadata even when storage resize fails highlights a critical flaw in exception safety for tensor operations. The creation of "zombie tensors"—tensors with seemingly valid shapes but empty or inaccessible storage—poses a significant threat to the stability and reliability of applications built with PyTorch. This issue is particularly concerning for workflows involving NumPy interoperability and can lead to difficult-to-debug crashes and potential data corruption.

The path to resolution involves ensuring that the resize_() operation, and similar metadata-altering functions, implement a strong exception guarantee. This means verifying storage mutability before updating tensor metadata. By prioritizing safety and robustness in these fundamental operations, PyTorch can continue to be the dependable framework that the deep learning community relies on. Developers should be aware of this potential issue and the workaround of avoiding resize_() on tensors with non-resizable storage. For further insights into PyTorch's internals and bug reporting, the official PyTorch GitHub repository is an excellent resource.

For more information on tensor operations and memory management in PyTorch, you can refer to the official PyTorch documentation on tensors and the PyTorch GitHub issue tracker for ongoing discussions and bug fixes. You might also find it useful to explore resources on memory management in Python and C++, which provide broader context for understanding how such issues arise.