PyTorch Tensor Corruption Bug: Resize Failures

h1: PyTorch Tensor Corruption Bug: Resize Failures

PyTorch, a powerhouse in the deep learning community, is known for its flexibility and performance. However, even the most robust libraries can sometimes present unexpected behaviors. Recently, a peculiar bug has surfaced concerning tensor operations, specifically when attempting to resize tensors that share storage with non-resizable buffers. This issue, while seemingly niche, can lead to a corrupted state in your tensors, potentially causing crashes and unpredictable behavior in your models. Let's dive deep into what's happening, why it's problematic, and what we can do about it.

h2: Understanding the Core Problem: The Unresizable Storage Conundrum

The heart of this bug lies in how PyTorch handles tensor storage and metadata. A tensor in PyTorch is essentially a wrapper around a data buffer, known as its storage. This storage holds the actual numerical data. Along with the storage, a tensor also has metadata, including its shape and strides, which dictate how the data is interpreted. Normally, when you resize a tensor, PyTorch adjusts both the metadata and the underlying storage. However, things get tricky when a tensor points to storage that cannot be resized. This commonly happens when a tensor is created from, or shares storage with, external data structures like NumPy arrays that have been explicitly injected into PyTorch using methods like set_(). In such cases, the underlying NumPy array's memory block is fixed, and PyTorch cannot simply expand or shrink it.

PyTorch is designed to be helpful, and it does detect this situation. When you attempt to resize a tensor with unresizable storage, it correctly raises a RuntimeError, stating: "Trying to resize storage that is not resizable." This is good – it tells you that the operation you're trying to perform is impossible given the current tensor's state. But here's where the bug rears its ugly head: the error handling isn't as robust as it needs to be. Before PyTorch actually checks if the storage is resizable, it proceeds to update the tensor's shape and stride metadata to reflect the new target size you requested. So, even though the RuntimeError is raised and caught, the tensor's internal shape attribute now points to a larger, hypothetical size, while its actual storage() remains empty or unchanged (with 0 bytes of data). This creates a critical inconsistency, often referred to as a "Zombie" tensor state.

This inconsistency is particularly dangerous because subsequent operations that try to access or use this tensor will operate under the assumption that the metadata (shape) is correct. Since the storage is actually empty or insufficient, this can lead to memory access violations, resulting in Segmentation Faults or other internal RuntimeError exceptions. The program might crash outright, or worse, exhibit subtle data corruption that's hard to trace back to this initial resize attempt. The minimal reproduction case provided clearly illustrates this: a tensor is created with locked storage, then resize_() is called. While the expected exception is caught, the tensor's shape is erroneously updated, leading to a state where t.shape shows a 5x5x5 dimension, but t.untyped_storage().nbytes() remains 0. Printing this tensor then causes the crash.

h2: The 'Zombie' Tensor: A Recipe for Disaster

The term "Zombie" tensor aptly describes the state of a tensor after this failed resize operation. It looks like it has a shape (e.g., torch.Size([5, 5, 5]) in the example), and your code might proceed as if that shape is valid. However, it's a hollow shell. The underlying data buffer, its storage(), is either empty or far too small to accommodate the declared shape. This disconnect between metadata and reality is a breeding ground for errors. Imagine trying to read data from a book that claims to have 500 pages, but when you open it, all the pages are blank. You'd expect problems, right? Similarly, when PyTorch tries to access elements within this "zombie" tensor – perhaps during a print operation, a mathematical calculation, or when feeding it into a neural network layer – it encounters a mismatch. It expects to find data corresponding to the specified shape, but the storage simply doesn't have it. This leads to the observed crashes, whether they manifest as a direct Segmentation Fault (a low-level memory error) or a higher-level PyTorch RuntimeError indicating an issue with the tensor's state or data access. The core issue is that the exception safety, particularly the strong exception guarantee (ensuring the program state remains unchanged if an exception occurs), is violated. In an ideal scenario, if resize_() fails due to unresizable storage, the tensor should be left in its original, valid state, with its original shape and strides. Instead, it's left in an invalid, inconsistent, and dangerous state.

h3: Why Does This Happen? A Look Under the Hood

To truly appreciate the bug, let's peek behind the curtain of PyTorch's internal workings. When you call a method like resize_() on a tensor, a sequence of operations is initiated. The tensor object holds pointers to its shape information and its storage. The resize_() operation, at a high level, first attempts to determine the new dimensions and strides based on the requested size. It then proceeds to check if the underlying storage can accommodate this new size. This check involves verifying properties of the storage, such as whether it's immutable or has a fixed capacity. Crucially, in the buggy implementation, the updating of the tensor's shape and stride metadata happens before this critical storage check is fully completed and its potential failure is handled.

So, the sequence might look something like this:

1. Request Resize: t.resize_((5, 5, 5)) is called.
2. Metadata Update: PyTorch calculates the new shape (5, 5, 5) and corresponding strides and updates the tensor object's internal metadata to reflect this. At this point, t.shape is torch.Size([5, 5, 5]).
3. Storage Check: PyTorch then checks if the storage is resizable. In this specific scenario (sharing storage with a NumPy array), it discovers that the storage is not resizable.
4. Exception Raised: A RuntimeError is raised: "Trying to resize storage that is not resizable."
5. Exception Handled (Partially): The try...except block catches this RuntimeError. However, because the metadata was already updated in step 2, the tensor remains in an inconsistent state. The storage itself (which is 0 bytes in the minimal example) was never successfully resized or allocated to match the new metadata.

The fundamental flaw is the ordering of operations and the lack of a robust rollback mechanism. The tensor's state is modified before it's confirmed that the modification can be fully completed. If the operation fails midway, the partial changes are left intact, corrupting the tensor. This violates a core principle of robust software design, particularly in C++ (which PyTorch heavily relies on), known as the Strong Exception Guarantee. This guarantee states that if an exception is thrown during an operation, the program should be left in a state as if the operation never happened. In this case, the guarantee is broken, and the tensor is left in a corrupted,