PyTorch: Corrupted Tensors From Failed `resize_()` On Storage

Unpacking the PyTorch resize_() Bug: What's Going Wrong?

PyTorch users and developers, have you ever encountered a perplexing issue where your tensors behave unpredictably after a resize operation, leading to frustrating crashes or incorrect data? We're here to shed light on a critical bug within PyTorch related to the resize_() method that can leave your tensors in a deeply corrupted state. This isn't just a minor glitch; it's a fundamental flaw that violates expected exception safety and can lead to significant headaches in complex deep learning workflows. Imagine working with a PyTorch tensor that, despite throwing an error during a resize attempt because its underlying storage can't be modified, still pretends to have been successfully resized. That's precisely what's happening. When you try to use resize_() on a tensor whose storage is shared with a non-resizable buffer – perhaps a NumPy array that was injected using set_() – PyTorch correctly identifies that the storage cannot be resized and throws a RuntimeError. This is the expected behavior, indicating that the operation failed. However, the problem lies in what happens before the error is raised. In an unfortunate sequence of events, the tensor's metadata, specifically its shape and stride information, gets updated to reflect the new, intended size (e.g., 5x5x5 in our example) before the system checks if the actual storage resize can even happen. This means that even though the storage itself remains untouched, holding its original 0 bytes, the tensor's internal representation now believes it's much larger. This creates a critical inconsistency between the tensor's reported shape and its actual memory allocation, essentially turning your perfectly normal tensor into a "Zombie" – alive in metadata, but dead in storage. This state is incredibly dangerous because any subsequent attempt to access or operate on this "Zombie" tensor, such as simply printing it or performing computations, will lead to unpredictable outcomes. You might encounter another RuntimeError due to memory access violations, or even worse, a fatal Segmentation Fault that abruptly crashes your entire program. This bug highlights a vital aspect of robust software design: exception safety. When an operation fails and throws an exception, the system should ideally revert to its previous, valid state, ensuring that no corrupted data or inconsistent states are left behind. Unfortunately, the resize_() method, in this specific scenario, fails to uphold this principle, leaving developers to grapple with the aftermath of silently corrupted tensor states. Understanding this behavior is the first step towards mitigating its impact and advocating for a more robust PyTorch environment.

Diving Deeper: How resize_() Creates "Zombie" Tensors

Let's really peel back the layers and understand the technical details behind how resize_() manages to create these perplexing "Zombie" tensors. The core of the problem lies in the internal order of operations within PyTorch's resize_() function when dealing with non-resizable storage. Normally, when you call resize_() on a PyTorch tensor, the system first determines the new desired shape and then attempts to allocate or reallocate the underlying memory storage to accommodate this new size. If the storage operation is successful, then (and only then) the tensor's metadata – its shape (dimensions) and stride (how many elements to skip in each dimension to get to the next element) – is updated to reflect the new memory layout. This ensures a consistent state: the tensor's reported size perfectly matches the actual allocated memory. However, in the scenario where the tensor's storage is immutable or non-resizable, such as when it's been set_() from a NumPy array (like np.array([], dtype=np.int32) which creates a 0-byte buffer), this delicate sequence is disrupted. The PyTorch internal logic, before checking if the storage can actually be resized, proceeds to update the tensor's shape and stride metadata. It optimistically assumes the storage operation will succeed. Only after this metadata update does it attempt the actual storage resize check. At this point, the check correctly identifies that the locked_storage (our NumPy array's untyped_storage()) cannot be resized, and a RuntimeError is appropriately thrown. But here's the catch: the RuntimeError is thrown too late. The tensor's metadata has already been irrevocably modified. This leaves us with a tensor where tensor.shape reports the new, larger size (e.g., torch.Size([5, 5, 5])), but tensor.untyped_storage().nbytes() stubbornly returns 0 bytes, because the physical memory wasn't actually changed. This stark mismatch is what we're calling the "Zombie" state. It's a tensor that looks healthy from a shape perspective but is internally hollow. The moment you try to interact with this "Zombie" tensor – for example, by calling print(t) as shown in the reproduction, or by attempting any arithmetic operation – PyTorch tries to access memory locations that, according to its updated shape metadata, should exist but are not backed by any actual allocated storage. This leads to undefined behavior. Depending on the exact operation and the surrounding memory context, this could manifest as a RuntimeError indicating invalid memory access, or more severely, a Segmentation Fault – a complete program crash. A Segmentation Fault happens when a program attempts to access a memory location that it's not allowed to access, often leading to an immediate and unrecoverable crash of the application. This deep dive into the internal workings reveals that the bug isn't just about an error being thrown; it's about the timing of the error relative to critical state changes, leaving a lingering, dangerous inconsistency within the PyTorch tensor object.

The Impact: Why Corrupted Tensors are a Big Deal for Developers

For anyone deeply involved in PyTorch development, from researchers building novel models to engineers deploying scalable AI applications, encountering corrupted tensors isn't just an inconvenience; it can be a major roadblock that significantly hampers productivity and jeopardizes application stability. The bug where resize_() fails but still updates metadata, creating these "Zombie" tensors, has far-reaching implications that demand our attention. Firstly, debugging becomes an absolute nightmare. Imagine your complex deep learning pipeline crashing intermittently with Segmentation Faults or cryptic RuntimeErrors. When you inspect the tensor just before the crash, its shape might look perfectly fine, giving you no immediate clue that the underlying storage is empty. You'd spend countless hours tracing your code, checking data inputs, and scrutinizing model architectures, completely unaware that the real culprit is an inconsistent internal state caused by a failed resize_() operation that happened much earlier in the execution flow. This silent corruption makes isolating and fixing the root cause extremely difficult, draining valuable development time. Secondly, application stability takes a severe hit. In production environments, even a rare Segmentation Fault can lead to costly downtime, data processing failures, or unreliable model predictions. For critical applications like autonomous systems, medical imaging, or financial modeling, where PyTorch is often deployed, such instability is simply unacceptable. Developers rely on libraries like PyTorch to be robust and predictable, especially when handling error conditions. The current behavior undermines this trust, forcing developers to implement extra, often cumbersome, defensive checks around resize_() calls, adding complexity where simplicity is desired. Furthermore, this bug introduces potential data integrity issues. While the primary manifestation is a crash, in scenarios where a corrupted tensor is somehow passed through computations before crashing, it could lead to incorrect intermediate results that might not immediately manifest as errors but could subtly compromise the output of a model. Although the reproduction mainly leads to crashes, the principle of an inconsistent state is a breeding ground for unpredictable behavior, which is the antithesis of reliable scientific computing. The essence of good software design, especially in numerical libraries, is to provide strong exception guarantees. This means that if an operation fails, the system should either remain in its original valid state (the strong guarantee) or at least in a valid but potentially modified state (the basic guarantee), without leaking resources or leaving data corrupted. The resize_() bug, by leaving tensors in an internally inconsistent "Zombie" state, fails to provide even a basic exception guarantee, making it a serious concern for any developer striving to build robust and reliable PyTorch-powered solutions. Addressing this isn't just about fixing a line of code; it's about upholding the quality and trustworthiness of the PyTorch framework itself.

A Look at the Minimal Reproduction: Seeing the Bug in Action

To truly grasp the nature of this PyTorch bug, let's walk through the minimal reproduction script provided. This simple yet powerful example perfectly illustrates how a tensor can become corrupted, leading to unexpected crashes. The script begins by leveraging NumPy to create a special kind of storage that PyTorch cannot resize.

import torch
import numpy as np

# 1. Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

Here, we're creating an empty NumPy array of int32 type. By converting this NumPy array into a PyTorch untyped_storage object, we essentially create a locked buffer. This locked_storage technically holds 0 bytes because it's derived from an empty NumPy array, and NumPy arrays cannot be dynamically resized in the way PyTorch's internal storage often can. This is the crucial first step to triggering the bug.

Next, we initialize a fresh PyTorch tensor and then inject this non-resizable storage into it.

# 2. Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

At this point, t is an empty PyTorch tensor (shape torch.Size([0])) that is now backed by our 0-byte, non-resizable locked_storage. This setup is perfectly valid for representing an empty tensor whose data comes from an external source like NumPy.

Now comes the problematic part: we attempt to resize this tensor.

# 3. Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    pass

We call t.resize_((5, 5, 5)), intending to change its shape to a 5x5x5 tensor. Because we know the underlying locked_storage is non-resizable, we expect this call to fail and raise a RuntimeError. We wrap it in a try-except block to gracefully catch this expected error. The important thing to note here is what happens inside the resize_() call before the RuntimeError is raised: the tensor's metadata (its shape attribute) is updated to torch.Size([5, 5, 5]). The error, however, occurs when the system realizes it cannot actually allocate or resize the physical storage to match this new shape. The exception is caught, so our program continues, but the tensor is left in an inconsistent state.

Finally, we verify the corruption and observe the crash.

# 4. 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 output of the first two print statements starkly reveals the bug:

• Shape: torch.Size([5, 5, 5]): The tensor claims to be a 5x5x5 tensor.
• Storage: 0: Yet, its underlying storage reports 0 bytes. This is our "Zombie" tensor – a PyTorch object whose public face (its shape) completely contradicts its internal reality (its actual memory footprint).

When print(t) is called, PyTorch attempts to materialize the tensor's contents based on its reported shape. Since t.shape is torch.Size([5, 5, 5]), PyTorch expects to read 125 integer elements from memory. However, the 0-byte storage means there's no actual memory backing this claim. This attempt to read from non-existent or unallocated memory leads directly to a RuntimeError (as observed in the gist) or, in more complex scenarios, a Segmentation Fault – a complete program crash. This minimal example serves as a clear, irrefutable demonstration of how an incomplete error handling mechanism within resize_() can create deeply problematic and unstable tensor states, making it a critical bug for PyTorch developers to be aware of.

Seeking Solutions: Ensuring Exception Safety in Tensor Operations

Now that we've thoroughly explored the PyTorch resize_() bug and its disconcerting consequences, the natural question is: what can be done to fix it, and how can developers protect themselves in the interim? The fundamental problem here is a lack of strong exception guarantee within the resize_() operation. In ideal software design, especially for foundational libraries like PyTorch, an operation that fails and throws an exception should leave the system in its original, valid state. It should be as if the operation never happened from the perspective of external observers. This principle is crucial for building robust and predictable applications. For the resize_() bug, a proper fix would involve reorganizing the internal logic of the function. Instead of optimistically updating the tensor's metadata before verifying storage resizability, PyTorch should adopt a more cautious, transactional approach. This means the check for storage resizability and the actual storage resize operation should ideally occur before any permanent changes are made to the tensor's shape or stride metadata. If the storage cannot be resized, the RuntimeError should be thrown, and the tensor's metadata should remain completely untouched, preserving its original, consistent state (e.g., torch.Size([0])).

One way to implement this internally would be to:

1. Perform all checks first: Verify if the storage is resizable and if the new size is valid.
2. Attempt storage modification: If checks pass, try to reallocate or resize the underlying storage.
3. Update metadata atomically: Only if the storage modification is successful, then update the tensor's shape and stride metadata.
4. Rollback on failure (implicitly): If any step before metadata update fails, an exception is thrown, and since metadata hasn't been changed, the tensor remains in its consistent prior state.

This ensures that the metadata and the actual storage are always in sync. If the storage resize fails, the tensor's metadata remains as it was before the failed call, preventing the "Zombie" state.

In the meantime, while a fix is implemented upstream in PyTorch, developers need strategies to mitigate this risk.

• Defensive Programming: If you are working with tensors that might have external or non-resizable storage (e.g., created via torch.from_numpy() and then set_()), avoid using resize_() directly if you anticipate potential failures. Instead, consider creating a new tensor with the desired shape and copying data, if applicable.
• Check nbytes() after resize attempts: Although it's a workaround, you could add an explicit check after catching a RuntimeError from resize_():

  try:
      t.resize_((new_shape))
  except RuntimeError:
      if t.untyped_storage().nbytes() == 0 and t.numel() > 0: # Check for inconsistency
          print("Warning: Tensor metadata corrupted after failed resize!")
          # Potentially recreate the tensor or handle the error more robustly
          t = torch.tensor([], dtype=t.dtype) # Revert to empty, consistent state
      pass
  

  This manual check can help detect the inconsistent state and allow you to reset the tensor or log a critical warning.
• Contribute to PyTorch: For those with C++ and PyTorch internals knowledge, this bug represents an opportunity to contribute to the open-source project by proposing and implementing a robust, exception-safe fix.

Ensuring metadata consistency and strong exception guarantees are paramount for a powerful and reliable scientific computing library. Addressing this resize_() bug will not only prevent cryptic crashes but also strengthen the overall stability and trustworthiness of the PyTorch ecosystem for developers worldwide.

Conclusion

We've delved into a significant PyTorch bug where the resize_() method, when encountering non-resizable storage, fails to uphold exception safety. This leads to a troubling scenario where a tensor's shape metadata is updated even though its underlying memory storage remains unchanged and empty. The result is a "Zombie" tensor—a structurally inconsistent object that inevitably leads to RuntimeErrors or Segmentation Faults when accessed. This issue underscores the critical importance of robust error handling and strong exception guarantees in foundational libraries, as such inconsistencies can severely impede debugging efforts, compromise application stability, and erode developer confidence. By understanding how this "Zombie" state is created and the potential impact it has, PyTorch developers can better anticipate and, for now, mitigate its effects through careful programming practices. Ultimately, an upstream fix that ensures metadata updates are atomic and conditional on successful storage operations will greatly enhance the reliability of the PyTorch framework.

For further reading on PyTorch internals and best practices for robust tensor operations, consider exploring:

• PyTorch Documentation: The official documentation is always a great starting point for understanding how PyTorch functions and its various features.
• NumPy Documentation: Since this bug often involves interaction with NumPy arrays and their storage, understanding NumPy's array object model can provide valuable context.
• Exception Safety in C++: Although PyTorch is Python-based, its core is C++. Learning about exception safety guarantees in C++ can offer deeper insight into the principles that should ideally govern such low-level operations.