PyTorch Tensor Corruption Bug: Resize Failure

by Alex Johnson 46 views

Hey there, fellow PyTorch enthusiasts! Let's dive into a rather peculiar bug that's been making waves in the PyTorch community. It seems that under specific circumstances, PyTorch can end up in a bit of a pickle, leading to what we can only describe as corrupted tensors. This isn't your everyday glitch; it's a situation where the framework attempts to update a tensor's shape information even when the underlying storage can't be resized, leaving the tensor in a wonky, unusable state. We're talking about a bug that can cause segmentation faults or internal runtime errors, which is definitely something we want to get to the bottom of.

Understanding the Bug: When Resizing Goes Wrong

The core of the issue lies in the resize_() operation in PyTorch. Imagine you have a tensor that's backed by storage shared with a buffer that cannot be resized. A prime example of this is a NumPy array that's been brought into PyTorch using set_(). Normally, when you try to resize_() such a tensor, PyTorch is smart enough to catch this. It throws a RuntimeError with a clear message: "Trying to resize storage that is not resizable". And honestly, that's the behavior we'd expect and want – a clear indication that the operation can't proceed as requested.

However, the problem arises because this check, while present, isn't entirely exception-safe. Here's the breakdown: before PyTorch even gets to the point of checking if the storage is resizable, it goes ahead and updates the tensor's shape and stride metadata to reflect the new target size you asked for. So, you request a resize_ to, say, a (5, 5, 5) shape, and PyTorch updates the tensor's metadata to torch.Size([5, 5, 5]). It's only after this metadata update that the check for resizable storage fails, and the RuntimeError is raised.

What this leaves us with is a tensor in what can only be called a "Zombie" state. The tensor's .shape attribute will proudly proclaim its new, larger dimensions (like torch.Size([5, 5, 5])), but its actual storage() will still be empty, holding precisely 0 bytes of data. This is a critical inconsistency. You've told the tensor it's big and capable of holding lots of data, but it has nowhere to put it. Consequently, any attempt to access this corrupted tensor after the exception has been caught will likely result in a segmentation fault or another internal RuntimeError. This is because the program is trying to read or write data to a tensor that claims to have a specific size but has no actual memory allocated to support it.

This bug was brought to light and detailed by users like jenneyuvin10182 and hwabt, highlighting a subtle but significant flaw in how PyTorch handles error conditions during tensor manipulation. The gist provided with the report offers a minimal, reproducible example, which is invaluable for debugging and understanding the exact sequence of events that leads to this corrupted state.

Minimal Reproduction Scenario

To truly grasp the severity and nature of this bug, let's walk through the minimal reproduction code. This snippet is crucial because it isolates the problem, making it easier to understand and, hopefully, fix.

First, we need to set up the problematic storage. The key here is creating a storage that is not resizable. The example achieves this by using NumPy:

import torch
import numpy as np

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

Here, np.array([], dtype=np.int32) creates an empty NumPy array. When converted to an untyped_storage() in PyTorch, this results in a storage object that has zero bytes and, importantly, cannot have its size changed later. Think of it as a locked box that's already empty.

Next, we inject this locked_storage into a PyTorch tensor. We start with a fresh, empty tensor and then use the set_() method to associate it with our locked_storage:

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

At this point, the tensor t has a shape of torch.Size([0]) and its storage has 0 bytes, consistent with our locked_storage. Everything is as expected so far.

Now, we attempt the operation that triggers the bug – resizing the tensor:

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

This try-except block is where the magic (or rather, the bug) happens. We're attempting to resize t to a (5, 5, 5) shape. As anticipated, because t is backed by locked_storage, PyTorch should raise a RuntimeError. The except block catches this error, preventing the program from crashing at this immediate step. However, the damage has already been done internally.

Finally, we check the state of the tensor after this operation:

# 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

As the comments indicate, the output is quite alarming. The print(f"Shape: {t.shape}") line will output torch.Size([5, 5, 5]). This tells us the tensor's metadata has been updated to reflect the requested 5x5x5 shape. But then, print(f"Storage: {t.untyped_storage().nbytes()}") reveals 0. The tensor claims to be large, but its underlying storage is still empty. The final print(t) is the nail in the coffin – it's at this point that the program typically crashes, either with a RuntimeError (if the exception handling is a bit more forgiving) or, more commonly and more dangerously, a segmentation fault.

Expected vs. Actual Behavior

To be absolutely clear about the discrepancy, let's summarize:

  • Expected Behavior: If resize_() encounters a RuntimeError because the underlying storage is locked or otherwise not resizable, the tensor's metadata (its shape and strides) should remain unchanged. The principle here is the Strong Exception Guarantee, meaning that if an operation fails, the system should be left in the state it was in before the operation was attempted. In this case, the shape should remain torch.Size([0]), perfectly consistent with the 0-byte storage.

  • Actual Behavior: The exception is indeed thrown, but after the tensor's shape metadata has already been modified. This leads to the inconsistent state where t.shape is torch.Size([5, 5, 5]) while t.untyped_storage().nbytes() is 0. This mismatch is the root cause of the subsequent crashes upon trying to access the tensor's data.

The Gist and Version Information

The report includes a link to a gist, which is a fantastic resource for developers trying to debug this. It provides a more detailed log or context around the bug, which can be invaluable when the minimal reproduction is just the tip of the iceberg. Sometimes, bugs manifest differently in complex scenarios, and the gist can help bridge that gap.

Furthermore, the provided environment information gives us crucial details about the setup where this bug was observed:

  • PyTorch version: 2.9.0+cu126 (This is a future version, indicating this might be a regression or a long-standing issue.
  • OS: Ubuntu 22.04.4 LTS
  • Python version: 3.12.12

Understanding these details helps in pinpointing whether the bug is specific to certain versions or operating systems, or if it's a more fundamental issue within PyTorch's tensor manipulation logic. The fact that CUDA is mentioned in the build suggests that GPU tensor operations might also be affected, although the minimal reproduction doesn't involve a GPU.

Why This Matters

This kind of bug, where an operation fails but leaves the system in a corrupted state, can be incredibly difficult to track down. Unlike a simple crash that halts execution immediately, this leaves behind a "time bomb" – a tensor that looks fine syntactically but will cause a crash later, potentially far removed from the original erroneous operation. This can lead to hours of debugging, trying to trace the source of a segmentation fault that originates from a seemingly innocuous resize_() call.

For anyone working with tensors that might share storage with non-resizable buffers (like NumPy arrays, or tensors created in specific ways), this is a critical bug to be aware of. It underscores the importance of robust error handling and the Strong Exception Guarantee in library design. When an operation is advertised to fail under certain conditions, it must do so cleanly, without corrupting the program's state.

Hopefully, with this detailed explanation and the reproducible example, the PyTorch team can address this issue swiftly. In the meantime, exercising caution with resize_() on tensors with potentially fixed storage is advised. Always ensure your tensors have mutable storage if you intend to resize them, or be prepared for potential unexpected behavior.

If you're interested in learning more about tensor operations and memory management in PyTorch, diving into the official PyTorch documentation is always a great first step. For deeper insights into C++ exceptions and memory safety in systems programming, resources like cppreference.com offer valuable information on exception guarantees and memory management practices. For understanding the intricacies of NumPy array integration, the NumPy documentation is the definitive source.