PyTorch Tensor Bug: Metadata Corruption On Failed Resize

by Alex Johnson 57 views

Have you ever encountered a perplexing bug in PyTorch that leads to unexpected crashes or corrupted tensors, even when you're sure your code should be safe? It turns out there's a subtle issue within PyTorch's tensor handling, specifically concerning the resize_() operation and tensors that share storage with non-resizable buffers. This bug, which we'll dive deep into, can leave your tensors in a broken state, often referred to as a "Zombie" tensor, leading to segmentation faults or internal runtime errors when you least expect them. Let's unravel this mystery and understand how it happens and why it's crucial to be aware of it.

The Core of the Problem: Unsafe Resize Operations

The heart of this issue lies in how PyTorch handles the resize_() operation when it encounters a tensor whose underlying storage cannot be resized. Imagine you have a tensor that's tightly linked to a NumPy array, perhaps through set_(). This kind of shared storage often comes with limitations – it might be fixed in size, making it non-resizable. When resize_() is called on such a tensor, PyTorch should ideally detect this incompatibility and gracefully handle the error. And indeed, it does raise a RuntimeError with a clear message: "Trying to resize storage that is not resizable." This is the correct behavior for an error condition.

However, the problem arises because this error handling isn't entirely exception-safe. Before the RuntimeError is actually raised and the operation is aborted, PyTorch proceeds to update the tensor's shape and stride metadata. It essentially thinks the resize operation has succeeded and modifies the tensor's structural information to reflect the intended new size. The catch? The actual storage, which is the part that holds the tensor's data, remains untouched and, in this scenario, is effectively empty (0 bytes) because it couldn't be resized. This creates a critical inconsistency: the tensor's metadata describes a shape that suggests a large amount of data, but the underlying storage has no data at all. This is where the "Zombie" tensor state emerges – it looks like a valid tensor with a specific shape, but its data foundation is nonexistent, leading to all sorts of downstream problems.

When you try to access or print such a corrupted tensor after the exception has been caught, the system is in for a rude awakening. It attempts to read data based on the incorrect metadata, finds that the storage is empty, and often results in a segmentation fault (a low-level crash indicating an attempt to access memory that doesn't exist or isn't allowed) or another internal RuntimeError within PyTorch's deep machinery. This makes debugging incredibly frustrating, as the root cause isn't immediately obvious from the error message you see at the surface. It’s a classic case of a race condition between metadata updates and error handling, leaving the tensor in an invalid, unusable state. Understanding this subtle interaction is key to preventing these mysterious crashes in your PyTorch applications, especially when dealing with scenarios that involve shared storage or custom tensor manipulations.

Minimal Reproduction: A Clear Illustration of the Bug

To truly grasp the impact of this bug, it's essential to see it in action. The PyTorch team, through diligent reporting and debugging, has provided a minimal, yet powerful, reproduction case that clearly demonstrates this issue. This snippet of code isolates the problematic behavior, making it easy to understand and verify. Let's walk through it:

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

Step-by-Step Breakdown of the Reproduction Code:

  1. locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage(): Here, we start by creating a NumPy array that is empty (np.array([])) and has a specific data type (dtype=np.int32). We then convert this NumPy array into a PyTorch tensor (torch.from_numpy(...)) and immediately access its untyped_storage(). The key here is that an empty NumPy array, when converted, often results in a storage object that is not designed to be resized. It has 0 bytes of actual data storage.

  2. t = torch.tensor([], dtype=torch.int32) and t.set_(locked_storage): We create a brand new, empty PyTorch tensor t. Then, using the set_() method, we replace its default storage with the locked_storage we created in the previous step. This is the crucial step that links our tensor t to a non-resizable storage.

  3. try...except RuntimeError:: This block is set up to gracefully catch the expected RuntimeError. The intention is that if resize_() fails, the exception is handled, and the program continues without crashing.

  4. t.resize_((5, 5, 5)): This is where the problematic operation occurs. We attempt to resize the tensor t to a shape of (5, 5, 5). Because t is linked to locked_storage, which is not resizable, PyTorch correctly identifies this as an error condition and prepares to raise a RuntimeError.

  5. The Buggy Behavior: As described earlier, before the RuntimeError is fully raised and the operation is aborted, the tensor's metadata is updated. So, even though the resize fails, t.shape is modified to torch.Size([5, 5, 5]). The locked_storage itself remains unchanged, with t.untyped_storage().nbytes() still reporting 0.

  6. Verification and Crash: The print statements afterwards demonstrate the corruption:

    • print(f"Shape: {t.shape}") correctly shows torch.Size([5, 5, 5]). This is the new, albeit incorrect, shape.
    • print(f"Storage: {t.untyped_storage().nbytes()}") correctly shows 0. This is the actual size of the underlying storage.
    • print(t): This is the final nail in the coffin. When you try to print the tensor t, PyTorch's internals attempt to access the data based on the torch.Size([5, 5, 5]) metadata. Since the storage is empty (0 bytes), this operation leads to a crash – either a RuntimeError within PyTorch or, more commonly in complex scenarios, a Segmentation Fault. The gist mentions a RuntimeError on print, but the original context from which this issue was extracted reported a segmentation fault, highlighting the severity and varied manifestations of this bug.

This minimal reproduction effectively isolates the bug, showing how a tensor can be left in an inconsistent state where its declared shape is completely out of sync with its actual data storage capacity, leading to unavoidable runtime failures.

Expected vs. Actual Behavior: What Should Happen and What Does

Understanding the discrepancy between what we expect from a robust library like PyTorch and what actually occurs is crucial for appreciating the severity of this bug. The core principle that should govern operations like resize_() is the concept of exception safety, particularly the strong exception guarantee. This guarantee means that if an operation fails by throwing an exception, the system should be left in the same state as it was before the operation began. In simpler terms, if something goes wrong, it shouldn't leave your data structures in a half-broken or corrupted condition.

The Expected Behavior:

When resize_() is called on a tensor that is backed by non-resizable storage, such as the locked_storage derived from an empty NumPy array in our example, the following should ideally happen:

  1. Detection: PyTorch's internal checks should identify that the tensor's storage cannot be resized to the requested dimensions.
  2. Error Raising: A RuntimeError should be raised, clearly indicating the reason for the failure (e.g., "Trying to resize storage that is not resizable.").
  3. State Preservation: Crucially, no changes should be made to the tensor's metadata. This includes its shape, strides, and any other structural information. The tensor should remain exactly as it was before the resize_() call.

In our specific reproduction case, the expected outcome would be that after the try...except block, the tensor t should still have its original shape, which is torch.Size([]) (an empty tensor), and its storage should remain at 0 bytes. No corruption, no inconsistency, just a clean failure of the requested operation.

The Actual Behavior:

Unfortunately, the current implementation of resize_() in PyTorch does not adhere to the strong exception guarantee in this specific scenario. What actually happens is quite different and leads to the problematic "Zombie" tensor state:

  1. Detection: The system correctly identifies that the storage is not resizable.
  2. Partial Update: This is the critical failure point. Before the RuntimeError is thrown, PyTorch proceeds to update the tensor's metadata. The shape is changed to reflect the dimensions requested in t.resize_((5, 5, 5)), becoming torch.Size([5, 5, 5]).
  3. Error Raising: The RuntimeError is then raised, as expected, signaling that the storage resize itself failed.
  4. Inconsistent State: The tensor is left in a highly inconsistent state. Its shape attribute now reports torch.Size([5, 5, 5]), implying it should hold data corresponding to this shape (e.g., 5 * 5 * 5 = 125 elements). However, its underlying storage (t.untyped_storage()) remains unchanged and has 0 bytes, meaning there is no actual data buffer allocated or available.

This mismatch between the tensor's reported shape and its actual storage capacity is the direct cause of the subsequent crashes. When any operation tries to access the tensor's data – be it print(t), t.data, or any computation involving t – the program attempts to operate on a non-existent memory region. This leads to either a clean RuntimeError within PyTorch's data access layer or a more severe Segmentation Fault as the program tries to access memory it shouldn't.

Summary of the Discrepancy:

  • Expected: If resize_() fails, the tensor's metadata (shape, strides) should remain unchanged, and the operation should be aborted cleanly without altering the tensor's state. A strong exception guarantee is upheld.
  • Actual: If resize_() fails due to non-resizable storage, the tensor's metadata (specifically, its shape) is updated before the exception is thrown. This leaves the tensor in a corrupted, inconsistent state where its shape does not match its storage size, leading to crashes on subsequent access.

This difference highlights a fundamental bug in PyTorch's exception handling for resize_() when dealing with shared, non-resizable storage. It's a subtle but critical flaw that can affect the stability and reliability of applications relying on such tensor manipulations.

Versions and Environment Information

When debugging issues like this, understanding the specific software versions and operating environment is crucial, as bugs can sometimes be version-specific or tied to particular system configurations. The provided information details the environment where this bug was observed:

Collecting environment information...
PyTorch version: 2.9.0+cu126
Is debug build: False
CUDA used to build PyTorch: 12.6
ROCM used to build PyTorch: N/A

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
CMake version: version 3.31.10
Libc version: glibc-2.35

Python version: 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.6.105+-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: 12.5.82
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
Is XPU available: False
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit

Key Takeaways from the Environment Info:

  • PyTorch Version: The issue was observed with PyTorch version 2.9.0+cu126. It's important to note that bugs like this can persist across versions or be introduced in new ones, so checking for updates or known issues in your specific PyTorch version is always a good practice.
  • Build Configuration: The build was not a debug build, which means the issue is present in a standard, optimized build of PyTorch. The presence of CUDA build information (12.6) suggests it was built with CUDA support, even though Is CUDA available: False indicates that CUDA might not have been detected or used in the execution environment where this bug was reported. This is common in environments where PyTorch is installed but no compatible GPU is present or configured.
  • Operating System: The bug was found on Ubuntu 22.04.4 LTS, a widely used Linux distribution. This suggests the problem is not specific to a niche OS but could affect many Linux users.
  • Compiler and Python: Standard versions of GCC (11.4.0) and Python (3.12.12) are used. The Python platform being Linux with glibc-2.35 is typical for modern Linux systems.
  • CUDA/GPU Details: While PyTorch was built with CUDA, the execution environment reported no available CUDA. This is interesting because it implies the bug is in the CPU portion of the PyTorch code related to tensor storage and resizing, not necessarily tied to GPU operations.
  • cuDNN: A list of libcudnn versions suggests that the CUDA runtime might have libraries present, but their specific version and compatibility aren't definitively stated. However, since CUDA availability is reported as false, this might be less relevant to the core bug itself.

This detailed environment information is invaluable for developers attempting to reproduce, debug, and fix the issue. It provides a concrete baseline and helps rule out environment-specific conflicts, pointing towards an intrinsic problem within the PyTorch library's tensor manipulation logic.

Conclusion and Mitigation

The bug where PyTorch's resize_() operation updates tensor metadata even when storage resize fails is a serious one, leading to corrupted "Zombie" tensors and subsequent crashes. It violates the strong exception guarantee, leaving the tensor in an inconsistent state.

Why this matters: In machine learning and deep learning workflows, data integrity and program stability are paramount. A bug like this can silently corrupt your data, lead to hard-to-debug crashes during training or inference, and undermine confidence in the framework.

Mitigation Strategies:

  • Avoid resize_() on non-resizable storage: The most direct way to avoid this bug is to refrain from calling resize_() on tensors whose storage is known or suspected to be non-resizable, especially those derived directly from external sources like NumPy arrays or other libraries that might impose storage constraints.
  • Use reshape_() or view_as(): For changing the view of the data without altering the underlying storage size, reshape_() or view_as() are generally safer alternatives, provided the total number of elements remains consistent. However, these do not resize storage.
  • Re-creation: If you need to change the size of a tensor and its storage might be problematic, consider creating a new tensor with the desired size and copying the data over (if applicable) instead of attempting an in-place resize.
  • Error Handling: While the current error handling doesn't prevent corruption, ensuring you have robust try...except blocks around operations that might fail can catch the RuntimeError before it propagates too far, though the tensor will still be in a corrupted state.
  • Stay Updated: Keep your PyTorch installation up-to-date. As this is a known issue, it's likely to be fixed in future releases. Always check the release notes for bug fixes.

This bug underscores the importance of understanding the underlying mechanics of the libraries we use. For more information on PyTorch's tensor operations and storage management, you can refer to the official PyTorch Documentation.

For deep dives into memory management and tensor internals within PyTorch, the PyTorch C++ API Documentation can offer invaluable insights, though it is geared towards more advanced users.

If you encounter similar issues, reporting them with minimal reproduction cases, like the one discussed here, is crucial for the PyTorch community to identify and fix them. You can find information on reporting bugs on the PyTorch GitHub repository.