PyTorch Bug: Tensor Corruption On Resize Failure
Hey everyone! Today, we're diving into a rather tricky bug that's been spotted in PyTorch, specifically concerning how tensor shape metadata is handled when storage resizing fails. This issue can lead to corrupted tensors, often referred to as "Zombie" tensors, which can cause unexpected crashes like Segmentation Faults or internal RuntimeErrors. Let's break down what's happening and why it's important to be aware of.
The Problem: Corrupted Tensors from Failed Resizes
So, imagine you're working with PyTorch tensors, and you decide to resize one. Normally, this is a straightforward operation. However, things get complicated when a tensor is sharing its storage with a buffer that cannot be resized. A prime example of this is when you've used set_() to inject a NumPy array into a PyTorch tensor. In these scenarios, PyTorch does correctly identify that the storage isn't resizable and throws a RuntimeError with the message: Trying to resize storage that is not resizable. This is good – the system recognizes the issue.
What Goes Wrong?
The core of the problem lies in the fact that this operation isn't exception-safe. Before PyTorch checks if the storage can actually be resized, it goes ahead and updates the tensor's shape and stride metadata to reflect the new, target size you requested. Then, when it discovers the storage is immutable, it throws that RuntimeError. At this point, the tensor is left in a very bad state: its shape attribute might indicate a large, new dimension (like torch.Size([5, 5, 5])), but its actual underlying storage() is still empty, with 0 bytes. This critical mismatch between the advertised shape and the available data is what leads to the "Zombie" tensor.
The "Zombie" Tensor State
When a tensor enters this "Zombie" state, any subsequent attempt to interact with it—whether it's printing its contents, accessing its elements, or performing other operations—can lead to serious trouble. The system expects to find data corresponding to the declared shape, but it finds nothing. This discrepancy often results in a crash. While the bug report mentions both segmentation faults and internal runtime errors, the minimal reproduction example provided shows a RuntimeError occurring when trying to print(t). This highlights how fundamental the issue is; even basic operations can fail spectacularly when they encounter these corrupted tensors. The expected behavior, as per strong exception guarantees, is that if an operation like resize_() fails, the tensor should be left exactly as it was before the operation. In this case, it should have retained its original shape (likely torch.Size([0]) in the minimal example) and its storage information.
Minimal Reproduction Case
To make this concrete, let's look at the minimal reproduction code provided:
import torch
import numpy as np
# Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()
# Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)
# Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
t.resize_((5, 5, 5))
except RuntimeError:
pass
# 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
This snippet clearly demonstrates the issue. First, it creates an empty NumPy array and converts it into an untyped_storage. This storage is inherently non-resizable. Then, a new PyTorch tensor t is created, and its storage is set to this locked_storage. When t.resize_((5, 5, 5)) is called within a try-except block, the RuntimeError is caught. However, by the time the error is caught, t.shape has already been updated to torch.Size([5, 5, 5]), while t.untyped_storage().nbytes() remains 0. The final print(t) then attempts to access data that doesn't exist according to the updated shape, leading to the observed crash.
Why is This Important?
This bug might seem niche, but it touches upon fundamental aspects of how PyTorch manages memory and tensor metadata. The principle of strong exception safety is crucial in libraries like PyTorch. Users rely on the guarantee that if an operation fails, the system will be left in a consistent, usable state. When this guarantee is broken, it can lead to subtle bugs that are hard to track down, especially in larger, more complex applications. Imagine this happening deep within a training loop or a data loading pipeline – the entire process could halt unexpectedly with cryptic errors.
Implications for Users
For users encountering mysterious crashes, especially those involving tensors that might have been derived from or interacted with NumPy arrays in specific ways, this bug could be a potential culprit. It emphasizes the importance of understanding how tensors share storage and the implications of attempting operations on tensors with immutable backing data. While PyTorch developers are working on fixing this, developers should be mindful of operations that could lead to this state. If possible, avoid operations that might resize tensors with non-resizable storage, or ensure robust error handling around such operations.
Versions and Environment
The bug was reported with the following environment details:
- PyTorch version:
2.9.0+cu126 - CUDA:
12.6(though the issue isn't CUDA-specific as it fails even without CUDA) - OS:
Ubuntu 22.04.4 LTS - Python version:
3.12.12
This information is vital for debugging and pinpointing the exact commit or version where the issue might have been introduced or could be fixed. It's always good practice to include such details when reporting bugs.
Conclusion and Next Steps
This bug highlights a critical aspect of library robustness: exception safety. When an operation fails, the system should not be left in a corrupted state. The current behavior in PyTorch, where tensor metadata is updated before a storage immutability check fails, creates these problematic "Zombie" tensors. The consequence is a tensor that looks like it has data (based on its shape) but actually has none, leading to crashes upon access.
Developers are actively working on ensuring that operations like resize_() are exception-safe, meaning they will either complete successfully or leave the tensor entirely unmodified if an error occurs. Until a fix is released and confirmed, developers should exercise caution when resizing tensors that might have non-resizable storage. Understanding tensor storage mechanisms and exception guarantees is key to writing stable and reliable deep learning code.
For more details on PyTorch's internals and tensor operations, you can refer to the official PyTorch documentation. Investigating the source code around tensor resizing and storage management will be crucial for understanding and resolving this issue.
For further reading on robust software development and exception handling principles, you can explore resources like The Hitchhiker's Guide to Python which offers insights into writing high-quality Python code:
