PyTorch Tensor Corruption Bug: Resize Fails, Data Corrupted

by Alex Johnson 60 views

The Nitty-Gritty of a PyTorch Tensor Problem

Let's dive into a peculiar issue that can arise when working with PyTorch, specifically concerning tensor manipulation and the unexpected consequences of failed operations. We're talking about a bug where PyTorch's tensor shape metadata gets updated even when the underlying storage resize fails. This might sound like a minor glitch, but it can lead to what we’re calling corrupted "Ulqnzk" tensors, leaving your data in a precarious, unusable state, and potentially causing your program to crash with segmentation faults or internal runtime errors. This problem occurs when you attempt to resize a tensor that shares its storage with a buffer that cannot be resized. A prime example of such a non-resizable buffer is a NumPy array that you've injected into a PyTorch tensor using set_(). In these scenarios, PyTorch does correctly identify the issue and raises a RuntimeError, stating: Trying to resize storage that is not resizable. This is good! The system recognizes the impossibility of the operation. However, the execution doesn't stop cleanly right there. Before the storage check actually fails and the exception is thrown, the tensor's internal metadata – its shape and stride information – is updated to reflect the new target size you were trying to achieve. This leaves the tensor in a rather unfortunate state, often described as a "Zombie" tensor. Imagine a tensor that tells you it has a shape of, say, (5, 5, 5), implying it should hold a significant amount of data. But, underneath, its actual storage() is empty, with 0 bytes allocated. This drastic mismatch between what the tensor reports it is and what it actually is creates a fundamental inconsistency. Consequently, any attempt to access or print this corrupted tensor after the exception has been caught can lead to serious runtime issues, ranging from Python's internal RuntimeError to the more severe segmentation faults that abruptly terminate your program. This isn't just a theoretical problem; it has real-world implications for workflows that rely on flexible tensor resizing, especially when dealing with data originating from or interacting with libraries like NumPy.

Understanding the "Zombie Tensor" Phenomenon

The core of the issue lies in the exception safety of the resize_() operation in PyTorch. When you call resize_() on a tensor, PyTorch first attempts to update the tensor's shape and stride metadata to match the requested new dimensions. This is a preparatory step. Following this, it checks if the underlying storage is actually capable of being resized. If the tensor's storage is tied to an immutable source, like a NumPy array that was directly embedded, this storage cannot be expanded or shrunk. The problem arises because the RuntimeError is raised after the metadata has already been modified. This leaves the tensor in an invalid state: its shape metadata indicates a certain size and dimensionality, but the actual storage it points to is either empty or of a different size, and crucially, it cannot be resized to match the reported shape. This is precisely the "Zombie Tensor" state. It looks like a valid tensor with specific dimensions, but it lacks the necessary backing data and cannot acquire it. The print(t) statement in the provided minimal reproduction example is a good illustration. Instead of gracefully handling the situation, it attempts to display the tensor's contents. Since the reported shape (5, 5, 5) implies a large number of elements, but the storage size is 0 bytes, this operation triggers a crash. In some environments, this manifests as a RuntimeError within Python's internal handling, while in others, it can lead to a more catastrophic segmentation fault. This happens because the memory access patterns expected by the tensor operations don't align with the reality of the zero-sized storage. The system tries to read or write data at memory locations that don't exist or aren't allocated for the tensor's apparent size, leading to memory access violations. The guarantee that an operation should either succeed completely or leave the system in its original state (a strong exception guarantee) is violated here. The operation fails, but it doesn't leave the tensor in its original state; instead, it corrupts its metadata. This inconsistency is particularly problematic because it might not be immediately obvious. You might continue processing with this "zombie" tensor, only to encounter errors much later in your computation, making debugging significantly harder. The fact that this happens even when the RuntimeError is caught and handled with a try...except block underscores the severity of the state corruption. The exception doesn't revert the changes made before the failure.

Minimal Reproduction: Unpacking the Code

To truly understand and replicate this bug, let's break down the minimal reproduction code provided. This snippet is crucial for developers to test, debug, and ultimately fix the issue.

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

First, we import the necessary libraries: torch for PyTorch tensor operations and numpy for creating the initial array. The key to this bug lies in the creation of locked_storage. Here, np.array([], dtype=np.int32) creates an empty NumPy array of 32-bit integers. When this is converted to a PyTorch tensor using torch.from_numpy() and then accessing its .untyped_storage(), we get a storage object that is intrinsically tied to the NumPy array's memory. Since the NumPy array is empty, its storage has 0 bytes. Importantly, PyTorch's storage objects derived this way from NumPy arrays often inherit the immutability or fixed-size nature of their source, making them non-resizable.

Next, we create a completely separate, fresh PyTorch tensor t initialized as empty (torch.tensor([], dtype=torch.int32)). The crucial step is t.set_(locked_storage). This operation does not copy the data; instead, it makes tensor t point to the locked_storage. So, t now has the metadata of an empty tensor (shape torch.Size([]), size 0), but it's internally referencing the locked_storage which is 0 bytes and non-resizable.

Then comes the problematic operation: t.resize_((5, 5, 5)). The intention is to change the tensor's shape to a 3-dimensional tensor with 5 elements along each dimension. Because t is referencing locked_storage, which is non-resizable and has 0 bytes, this operation should fail. And it does! PyTorch correctly detects that the underlying storage cannot accommodate the requested resize.

However, the bug lies in the sequence of events. Before the RuntimeError is raised, PyTorch updates t's internal shape and stride metadata to torch.Size([5, 5, 5]). When the RuntimeError is eventually caught (in this case, by the try...except block), the program continues, but t is now in a corrupted state. The except RuntimeError: pass block simply ignores the error, but the damage to the tensor's metadata is already done.

Finally, the verification section highlights the corruption:

  • print(f"Shape: {t.shape}") will output torch.Size([5, 5, 5]), showing the updated, but now incorrect, shape.
  • print(f"Storage: {t.untyped_storage().nbytes()}") will output 0, correctly indicating that the actual storage size hasn't changed and remains empty.
  • print(t) is the final nail in the coffin. Attempting to print the tensor's contents, which is expected to have 125 elements (555), but has 0 bytes of storage, leads to a crash – either a Python RuntimeError or a segmentation fault, depending on the environment and how the memory access is handled.

This minimal example precisely demonstrates how a failed resize_ operation on non-resizable storage can leave a tensor in an inconsistent and dangerous state.

Expected vs. Actual Behavior: Pinpointing the Flaw

When dealing with software, especially complex libraries like PyTorch, understanding the expected behavior is just as important as diagnosing the actual behavior when things go wrong. In this specific bug scenario, the discrepancy is quite stark and highlights a critical failure in how exceptions are handled during tensor resizing operations.

Expected Behavior:

Ideally, any operation that attempts to modify a PyTorch tensor should adhere to certain guarantees. For operations like resize_(), especially when dealing with potentially sensitive storage scenarios, the principle of strong exception safety should apply. This means that if an exception occurs during the operation, the object (in this case, the tensor) should be left in a state as if the operation never happened.

Specifically, when resize_() is called on a tensor whose underlying storage is immutable or cannot be resized (like our locked_storage derived from an empty NumPy array), PyTorch is expected to:

  1. Detect the impossibility: Recognize that the storage cannot be resized.
  2. Raise an exception: Throw a RuntimeError (or a similarly appropriate exception) to signal the failure.
  3. Leave the tensor unchanged: Crucially, before raising the exception, it should ensure that no modifications are made to the tensor's metadata (shape, strides, etc.). If the operation fails, the tensor should retain its original shape, which in our minimal example was torch.Size([]) (an empty tensor).

In essence, if resize_() throws a RuntimeError because the storage is locked, the tensor's shape and stride metadata should remain exactly as they were before the call. The operation either succeeds fully, or it fails cleanly without side effects on the object's state. This is the robust and predictable behavior users should be able to rely on.

Actual Behavior:

The bug, as demonstrated, deviates significantly from this expected behavior. When resize_() is called on a tensor with non-resizable storage:

  1. Metadata is updated prematurely: PyTorch does detect that the storage is non-resizable and does throw a RuntimeError. However, this happens after it has already updated the tensor's shape and stride metadata to match the new target size (e.g., torch.Size([5, 5, 5])).
  2. Tensor becomes inconsistent: This leaves the tensor in a corrupt "Zombie" state. The shape metadata now claims the tensor has a specific, non-zero size, but the actual storage remains unchanged – in our example, it's still 0 bytes and non-resizable.
  3. Subsequent operations fail: When you subsequently try to access or use this tensor (e.g., by printing it, accessing elements, or performing computations), the mismatch between the reported shape and the actual storage size leads to crashes. This can manifest as internal RuntimeErrors or, more critically, segmentation faults.

The fundamental flaw is the lack of a strong exception guarantee. The operation fails, but it leaves behind a corrupted state – the tensor's metadata is altered, but its data backing is not, and cannot be, adjusted. This inconsistency is the root cause of the crashes and unpredictable behavior observed.

Versions and Environment: Understanding the Context

Reproducing and understanding bugs often requires knowledge of the specific software versions and operating environment in which they occur. The information provided details the setup where this PyTorch tensor corruption issue was observed, offering crucial context for developers aiming to fix it.

PyTorch Version:

  • PyTorch version: 2.9.0+cu126
  • Is debug build: False
  • CUDA used to build PyTorch: 12.6

This indicates a recent version of PyTorch, built with CUDA 12.6 support. The +cu126 suffix typically denotes a build specific to a CUDA version. The fact that it's not a debug build means the performance is likely optimized, but debugging information might be less readily available compared to a debug build.

Operating System and Build Tools:

  • OS: Ubuntu 22.04.4 LTS (x86_64)
  • GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
  • Clang version: Could not collect (This might be relevant if PyTorch was built with Clang, but GCC is the primary compiler reported.)
  • CMake version: version 3.31.10
  • Libc version: glibc-2.35

This points to a standard, modern Linux environment (Ubuntu LTS) with a common GCC compiler version. The CMake version is also relatively recent. The glibc-2.35 is the standard C library for this Ubuntu version.

Python Environment:

  • Python version: 3.12.12
  • Python platform: Linux-6.6.105+-x86_64-with-glibc2.35
  • Is CUDA available: False (This is an interesting point given the PyTorch build info. It suggests that while PyTorch was built with CUDA support, the execution environment where this bug was observed might not have had access to a GPU or the CUDA runtime properly configured for that specific process).

Hardware and Drivers:

  • CUDA runtime version: 12.5.82
  • GPU models and configuration: Could not collect
  • Nvidia driver version: Could not collect
  • cuDNN version: Various versions listed (e.g., 9.2.1), suggesting cuDNN library is present.
  • Is XPU available: False
  • HIP runtime version: N/A
  • MIOpen runtime version: N/A

While PyTorch was built with CUDA 12.6, the runtime version reported is 12.5.82. The lack of collection for GPU models, driver version, and configuration might mean the bug was encountered in an environment where CUDA was not the primary focus or was not properly set up for introspection. The presence of various cuDNN versions suggests that CUDA libraries are installed, but their exact active configuration isn't easily determined from this output.

Other Libraries:

  • Is XNNPACK available: True

XNNPACK is an optimization library for neural networks, and its availability might influence performance but is less likely to be directly related to this specific tensor metadata corruption bug.

Overall Context:

This information indicates the bug was found on a Linux system using a recent Python and PyTorch version. The discrepancy between the PyTorch build's CUDA version and the reported CUDA runtime, along with Is CUDA available: False, suggests the bug might have been encountered in a CPU-only execution path, or in an environment where GPU detection was problematic. However, the core of the bug – the metadata update before exception – is a fundamental logic issue within PyTorch's C++ backend that should be reproducible regardless of the CPU/GPU execution context, as long as the resize_ operation on non-resizable storage is triggered.

Conclusion: Addressing the Tensor Anomaly

The bug we've explored, where PyTorch updates tensor metadata even when storage resize fails, leading to corrupted "Ulqnzk" tensors, is a critical issue impacting the robustness of tensor operations. The core problem is the violation of the strong exception guarantee: instead of reverting changes upon failure, the resize_ operation leaves the tensor in an inconsistent state, causing subsequent access to result in crashes. This happens because the tensor's shape and stride are updated before the check for resizable storage fails, creating a mismatch between the reported dimensions and the actual (often zero-byte) storage.

The minimal reproduction case clearly illustrates this by showing how an empty, non-resizable storage can be manipulated into a state where its reported shape is large, but its actual data capacity is nil, leading to a crash upon access. The provided version information gives a snapshot of the environment where this was observed, suggesting a recent PyTorch build on a Linux system.

The solution lies in ensuring that PyTorch operations maintain strong exception safety. Specifically, the resize_ operation should be refactored so that any metadata updates are only committed if the underlying storage operation succeeds. If the storage cannot be resized, the exception should be raised before any metadata modifications are made, thus preserving the tensor's original state.

For users encountering this issue, it's advisable to ensure you are using the latest stable version of PyTorch, as such critical bugs are often patched promptly. If you're working with NumPy arrays or other external data structures that might impose storage constraints, be extra cautious when performing in-place resize operations on PyTorch tensors that wrap them.

For further insights into PyTorch's internal workings and bug fixes, you can always refer to the official PyTorch GitHub repository. Understanding these kinds of low-level errors is key to building more stable and reliable deep learning applications. Additionally, for broader context on memory management and tensor operations in machine learning frameworks, the TensorFlow documentation on tensors, while for a different framework, often discusses similar underlying concepts and best practices.