PyTorch Tensor Corruption: Fixing Failed Storage Resizes

by Alex Johnson 57 views

Have you ever encountered a mysterious crash in your PyTorch code, leaving you scratching your head? Sometimes, these issues stem from subtle bugs in how PyTorch handles tensor operations, especially when things don't go as planned. One such tricky situation arises when you try to resize a tensor that has underlying storage that cannot be resized. In this article, we'll dive deep into this specific problem, understand why it happens, and explore how to avoid it, ensuring your PyTorch programs run smoothly and reliably.

The Heart of the Problem: Non-Resizable Storage and resize_()

In the world of PyTorch, tensors are the fundamental building blocks for numerical computations. They are essentially multi-dimensional arrays. A tensor has two key components: its shape and stride metadata, which define how the data is organized and accessed, and its underlying storage, which holds the actual data elements. Normally, when you perform operations like resizing a tensor using resize_(), PyTorch adjusts both the metadata and the storage to accommodate the new dimensions.

However, issues can arise when a tensor's storage is fixed or non-resizable. This can happen in specific scenarios, such as when a tensor is created from a NumPy array using set_(). NumPy arrays, once created, generally have fixed storage. When you then attempt to use resize_() on such a PyTorch tensor, expecting it to expand or shrink its data buffer, PyTorch runs into a fundamental conflict. It should detect that the storage cannot be resized and raise an error to prevent further issues.

And indeed, PyTorch does try to do this. If you call resize_() on a tensor that shares storage with a non-resizable buffer, PyTorch will correctly raise a RuntimeError with a message like: "Trying to resize storage that is not resizable". This is a good thing; it's PyTorch informing you about an impossible operation.

But here's where the bug creeps in, and it's a crucial one for stability. Even though the RuntimeError is raised, the operation is not exception-safe. This means that before PyTorch checks if the storage is resizable and raises the error, it has already updated the tensor's shape and stride metadata to reflect the target size you requested in resize_(). So, even though the RuntimeError stops the operation from proceeding further with the storage, the tensor's metadata is left in a modified state. This creates an inconsistent or "Zombie" tensor state. Your tensor might report a new, larger shape (e.g., torch.Size([5, 5, 5])), but its actual underlying storage remains unchanged and, importantly, empty (0 bytes in the example provided). This jarring mismatch between what the tensor thinks its shape is and the reality of its empty storage is a recipe for disaster.

The Devastating Consequences: Crashes and Corrupted Data

The real trouble begins when you try to interact with this "Zombie" tensor after the RuntimeError has been caught and handled (or perhaps ignored). Because the tensor's shape metadata suggests it contains data, but the storage is empty, any attempt to access this data—whether through printing the tensor, performing calculations, or even just inspecting its contents—leads to unpredictable and often severe errors. The most common outcomes are Segmentation Faults or internal PyTorch RuntimeErrors. A segmentation fault, in particular, is a critical system error that happens when a program tries to access a memory location that it's not allowed to access. In this case, the program is trying to read data from an empty memory buffer as if it contained valid elements according to the tensor's shape.

Let's look at a minimal reproduction case to see this in action. We start by creating a tensor with an empty, untyped storage. This storage is derived from a NumPy array with no elements, ensuring it's 0 bytes and, crucially, non-resizable. We then inject this locked_storage into a fresh PyTorch tensor t using t.set_(locked_storage).

Now, we attempt the problematic operation: t.resize_((5, 5, 5)). As expected, PyTorch should fail here because the storage is locked. And it does throw a RuntimeError. However, as we discussed, the damage is already done. The tensor t now believes its shape is torch.Size([5, 5, 5]), even though its untyped_storage().nbytes() is still 0.

The consequence? When you try to print(t), the program crashes. In the provided example, it might manifest as a RuntimeError when trying to interpret the shape and empty storage, but in more complex scenarios or different environments, this can easily escalate to a full-blown segmentation fault. The expected behavior is that if resize_() fails due to locked storage, the tensor's metadata should remain unchanged. The shape should stay at its original torch.Size([0]), and no crash should occur. Instead, the actual behavior is this dangerous corruption of the tensor's internal state.

Why Does This Happen? Understanding the Internal Mechanics

To truly grasp why this bug occurs, we need to peek under the hood at how PyTorch manages tensor operations. The core issue lies in the ordering of operations within the resize_() function when dealing with tensors that might have non-resizable storage.

When you call tensor.resize_(new_shape), PyTorch's internal implementation typically performs the following sequence of steps:

  1. Calculate New Strides and Size: Based on the new_shape provided, PyTorch computes what the new strides and total size (number of elements) would be.
  2. Update Tensor Metadata: It then updates the tensor's internal metadata (like shape and stride attributes) to reflect these newly calculated values. This step happens before any checks related to the underlying storage.
  3. Check Storage Resizability: After updating the metadata, PyTorch checks if the tensor's underlying storage is actually capable of being resized to accommodate the new size. This check involves looking at properties of the storage, such as whether it's owned by another object (like a NumPy array) or if it has been explicitly marked as non-resizable.
  4. Attempt Storage Resize/Reallocation: If the storage is resizable, PyTorch proceeds to resize or reallocate it to match the new dimensions and copies data if necessary.
  5. Handle Errors: If the storage is not resizable (as in our problematic case), PyTorch is supposed to raise a RuntimeError.

The bug arises because the update of the tensor's metadata (step 2) occurs before the check for storage resizability (step 3). In a non-resizable storage scenario, step 3 fails, and PyTorch correctly raises an exception. However, the tensor is already in a corrupted state because step 2 has already modified its shape and stride attributes. The operation essentially updates the description of the tensor (its shape) before verifying if the actual data buffer can match that description. This leaves the tensor in a paradoxical state: its metadata claims it has a certain number of elements, but its storage buffer is unchanged and incapable of holding those elements.

This specific bug has been observed in certain versions of PyTorch (as indicated by the version information provided, potentially 2.9.0+cu126 on Ubuntu 22.04.4 LTS with Python 3.12.12). Such issues highlight the importance of strong exception guarantees in software development. A strong exception guarantee means that if an operation fails, the program's state remains unchanged, as if the operation never happened. In this case, PyTorch offers at best a