PyTorch Bug: Tensor Corruption On Failed Resize

In the world of deep learning, PyTorch is a powerhouse, enabling researchers and developers to build and train complex neural networks. However, like any sophisticated software, it can occasionally encounter bugs. One such issue, which we'll explore in this article, relates to tensor operations and storage management, specifically when resizing tensors that have unresizable storage. This bug can lead to corrupted tensor states, often manifesting as segmentation faults or internal runtime errors, making it a critical issue for anyone relying on these specific tensor functionalities.

Understanding the Core Problem: Unresizable Storage and resize_()

PyTorch tensors are fundamental data structures. They are essentially a wrapper around a storage object, which holds the actual numerical data, and metadata that describes the tensor's shape, stride, and data type. The resize_() operation is designed to change the shape of a tensor. Normally, this operation is safe and efficient. However, problems arise when a tensor's storage is explicitly marked as non-resizable. This can happen, for instance, when a tensor is created from a NumPy array using torch.from_numpy() and then its storage is later directly manipulated or when a tensor is created with an underlying storage that cannot be resized, such as when using set_() with a pre-allocated, fixed-size storage.

When resize_() is called on a tensor with such unresizable storage, PyTorch correctly identifies the issue and raises a RuntimeError. The error message, "Trying to resize storage that is not resizable," is clear and indicates the problem. The expected behavior here is that if an error occurs during an operation, the state of the affected object should remain unchanged, a principle known as the Strong Exception Guarantee. This means that even if an exception is thrown, the program should be left in a valid, consistent state as if the operation never happened.

The "Zombie Tensor" Phenomenon

Unfortunately, this is not precisely what happens with the bug in question. Before the RuntimeError is actually thrown and caught, PyTorch's internal mechanisms update the tensor's shape and stride metadata to reflect the new target size specified in the resize_() call. This means that even though the underlying storage remains untouched (and is still of its original, potentially zero-byte, size), the tensor's metadata now points to a shape that implies a much larger data buffer. This creates a dangerous inconsistency, leading to what can be described as a "Zombie Tensor." This "zombie" tensor appears to have a valid, even significant, shape (e.g., torch.Size([5, 5, 5])), but its actual storage is empty or too small to accommodate the declared shape.

Accessing or attempting to print such a corrupted tensor after the exception has been handled (or even if it's not handled and the program continues) can lead to severe issues. The program might crash with a Segmentation Fault, a low-level error indicating that the program tried to access memory it shouldn't have. Alternatively, it might trigger another, more internal RuntimeError, as PyTorch's internal checks detect the blatant inconsistency between the tensor's declared shape and its actual data buffer size. This makes debugging particularly tricky, as the initial error might be misleading, and the subsequent crashes occur due to the consequences of the initial faulty state.

Minimal Reproduction of the Bug

To help illustrate and diagnose this problem, a minimal reproducible example is crucial. The provided example effectively demonstrates the bug:

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

In this code snippet, we first create an empty NumPy array, convert it to a PyTorch tensor, and then extract its untyped_storage(). This storage is inherently non-resizable because it originates from an empty NumPy array. We then create a new, empty PyTorch tensor t and use t.set_(locked_storage) to make it share this empty, locked storage. The critical part is the t.resize_((5, 5, 5)) call within a try-except block. As expected, this raises a RuntimeError because the storage is not resizable. However, as the bug report details, before the exception is fully processed, t.shape is incorrectly updated to torch.Size([5, 5, 5]).

When we print t.shape, we see this incorrect shape. Similarly, t.untyped_storage().nbytes() still correctly reports 0, highlighting the disconnect. The final print(t) attempts to access the data based on the erroneous shape and the empty storage, leading to the inevitable crash, either a segmentation fault or another runtime error.

The Impact of Corrupted Tensor Metadata

This bug, while specific, can have significant downstream effects. In complex machine learning pipelines, tensors are passed between numerous functions and operations. If a tensor becomes corrupted in this manner, the error might not be immediately apparent. It could propagate through the system, leading to unpredictable behavior or crashes much later in the execution, making it exceedingly difficult to pinpoint the original source of the problem. For instance, if this happens during data preprocessing or within a custom layer of a neural network, the entire training process could be compromised. The "Zombie Tensor" state violates fundamental assumptions about tensor integrity, which are crucial for the reliability of any numerical computation framework like PyTorch.

Strong Exception Guarantee: What It Means for PyTorch

The Strong Exception Guarantee is a vital concept in software engineering. It promises that if a function fails (throws an exception), the object it operated on will remain in a valid, consistent state. For a library like PyTorch, adhering to this guarantee is paramount for user trust and code stability. In the context of resize_(), this means that if the operation fails due to unresizable storage, the tensor's shape, strides, and size should revert to their state before the call, or at the very least, not be updated to an invalid configuration. The current behavior, where metadata is updated before the exception is fully handled, breaks this guarantee.

Potential Fixes and Mitigation Strategies

Addressing this bug requires ensuring that the checks for resizable storage happen before any metadata is modified. The modification of shape and stride should be an atomic operation that only occurs if the storage is confirmed to be resizable. If the storage check fails, no metadata should be changed. This would involve reordering the internal logic of the resize_() function.

For users encountering this issue, the immediate mitigation is to be aware of operations that might lead to unresizable storage and to handle potential RuntimeError exceptions carefully. If you are using torch.from_numpy() with NumPy arrays that might be manipulated or have fixed sizes, or if you are directly setting tensor storage, be cautious with subsequent resize_() calls. The try-except block shown in the reproduction is a good practice, but it doesn't solve the underlying corruption. The ideal solution is a fix within PyTorch itself.

Versions and Environment Details

Understanding the specific environment where a bug occurs is crucial for its diagnosis and resolution. The provided details indicate the following:

• PyTorch version: 2.9.0+cu126
• CUDA build: 12.6
• OS: Ubuntu 22.04.4 LTS (x86_64)
• Python version: 3.12.12
• Compiler: GCC 11.4.0

These details are essential for PyTorch developers to reproduce the bug in their testing environments and to verify any proposed fixes. The presence of CUDA in the build suggests that the issue might also be relevant in GPU-accelerated computations, although the minimal reproduction example does not utilize CUDA.

Conclusion: The Importance of Robust Tensor Operations

The bug where PyTorch updates tensor shape metadata even when storage resize fails highlights a critical need for robust exception handling and adherence to strong exception guarantees in fundamental operations. While PyTorch is a mature and powerful library, issues like this underscore the ongoing development and refinement required to ensure its reliability. By understanding the mechanics of tensor storage, metadata, and exception safety, developers can better navigate such challenges and contribute to the improvement of the library.

For those interested in the internals of PyTorch and tensor operations, exploring the official PyTorch documentation can provide valuable insights. Additionally, community forums and issue trackers are excellent resources for staying updated on bug fixes and best practices.

Learn more about PyTorch:

• Visit the Official PyTorch Website for the latest news, tutorials, and documentation.
• Explore the PyTorch GitHub Repository to view the source code, report issues, and contribute to the project.