PyTorch Bug: Tensor Resize Mishandizes Metadata

Dec 15, 2025 by Alex Johnson 48 views

In the world of deep learning, tensors are the fundamental building blocks for almost everything we do. They are multi-dimensional arrays that hold our data, our model parameters, and the intermediate results of computations. Libraries like PyTorch provide powerful tools to manipulate these tensors efficiently. However, sometimes, even the most robust libraries can have their quirks, and a recent discovery highlights a peculiar issue within PyTorch concerning tensor resizing when dealing with specific storage configurations. This article delves into a bug where PyTorch updates tensor shape metadata even when storage resize fails, leading to corrupted tensors and potential crashes.

Understanding the Problem: Corrupted "Lmhebl" Tensors

The issue arises when you attempt to resize a PyTorch tensor that is sharing its underlying storage with a buffer that cannot be resized. A common scenario for this is when a tensor is created using data from a NumPy array that was directly injected into PyTorch's storage mechanism. In such cases, PyTorch correctly identifies that the storage is not resizable and raises a RuntimeError with a message like: "Trying to resize storage that is not resizable." This is precisely the behavior we’d expect – an error is thrown, and the operation halts.

However, the problem lies in the fact that PyTorch's exception handling for this specific scenario isn't perfectly safe. Before it checks if the storage can actually be resized, PyTorch proceeds to update the tensor's shape and stride metadata to reflect the *new, target size*. When the subsequent check for resizable storage fails, the `RuntimeError` is raised, but the tensor's metadata is left in an inconsistent state. This results in what can be described as a "Zombie" tensor. It appears to have a new, larger shape (e.g., 5x5x5), but its underlying storage remains empty, holding zero bytes of data.

The consequences of this "Zombie" state can be quite severe. Any subsequent attempt to access or print this corrupted tensor can lead to unpredictable behavior, ranging from internal PyTorch `RuntimeError`s to more critical issues like Segmentation Faults. These crashes can be particularly disruptive during complex data processing pipelines or model training, making it hard to pinpoint the root cause.

Minimal Reproduction of the Bug

To better understand and illustrate this bug, a minimal reproduction case has been developed. It clearly shows how this inconsistency is triggered:

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 snippet, we first create an empty, non-resizable storage using `torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()`. This creates a storage object that has zero bytes and cannot be modified in terms of its size. Then, we create a new, empty PyTorch tensor `t` and assign this `locked_storage` to it using `t.set_(locked_storage)`. At this point, the tensor `t` has a shape of `torch.Size([0])` and its storage has 0 bytes, which is perfectly consistent.

The problematic step is `t.resize_((5, 5, 5))`. We expect this operation to fail because the underlying storage is not resizable, and PyTorch should ideally leave the tensor's metadata untouched. However, as the code demonstrates, PyTorch *does* raise the expected `RuntimeError`. The critical flaw is that before the error is raised, the tensor's shape metadata is updated to `torch.Size([5, 5, 5])`. When the `try...except` block catches the `RuntimeError`, the tensor `t` is left in a corrupted state: its `shape` attribute reports `torch.Size([5, 5, 5])`, but its `untyped_storage().nbytes()` still reports `0`.

The final `print(t)` line is where the crash typically occurs. Because the tensor's shape suggests it should contain data (and therefore occupy memory), but its storage is empty, any attempt to read from it results in an invalid memory access, leading to a Segmentation Fault or another internal error. The provided gist mentioned a `RuntimeError` on print, which is another manifestation of this underlying corruption, while the original scenario that led to this discovery resulted in a more severe Segmentation Fault.

Expected vs. Actual Behavior

The expected behavior in such a scenario is that if an operation fails due to fundamental constraints (like unresizable storage), the system should ideally revert to its previous state or at least not leave the data structures in an inconsistent, corrupted form. Specifically, for the `resize_()` operation:

Expected behavior: If `resize_()` throws a `RuntimeError` because the storage is locked or not resizable, the tensor's metadata (its shape and stride) should remain unchanged. It should continue to reflect its original state, which in the minimal reproduction case is `torch.Size([0])`. This adheres to the principle of a **Strong Exception Guarantee**, meaning that if an exception is thrown, no harm is done and the program state is as if the operation never happened.
Actual behavior: The `RuntimeError` is indeed thrown, and the operation fails to resize the storage. However, the tensor's shape metadata is updated to the target size (e.g., `torch.Size([5, 5, 5])`) before the failure is detected. This creates a dangerous mismatch between the tensor's reported dimensions and its actual available memory (which is 0 bytes). This inconsistency is what leads to downstream crashes when the tensor is accessed.

Impact and Implications

This bug, while perhaps niche, can have significant implications for users who employ advanced tensor manipulation techniques or integrate PyTorch with other libraries like NumPy in specific ways. When tensors become "corrupted" in this manner, debugging can become a nightmare. The error might not manifest immediately at the point of the failed `resize_()` call but could appear much later in the execution, in a completely different part of the code, possibly as a segmentation fault, which is notoriously difficult to debug. This makes it crucial for library developers to ensure that operations are exception-safe and uphold strong guarantees, especially when dealing with mutable state like tensor metadata.

The Root Cause: Exception Safety in Tensor Operations

At its core, this issue boils down to exception safety in PyTorch's tensor operations. When a function or method can throw an exception, it's essential to consider the state of the program after the exception is handled. There are typically a few levels of exception guarantees:

Basic Guarantee: If an exception is thrown, the program remains in a valid state, but the specific state might be indeterminate.
Strong Guarantee: If an exception is thrown, the program state is guaranteed to be unchanged. This is often achieved through techniques like copy-and-swap or by performing all modifications on temporary objects that are only committed if the operation succeeds.
Nothrow Guarantee: The operation is guaranteed not to throw an exception.

In the case of `tensor.resize_()`, the intended behavior when faced with an unresizable storage should ideally be the Strong Exception Guarantee. The metadata (shape and stride) should only be updated *after* it has been confirmed that the underlying storage can indeed be resized. The current implementation, however, appears to update the metadata before this confirmation, violating the strong guarantee.

The Sequence of Events

Let's break down the problematic sequence:

resize_() is called with a target shape.
PyTorch prepares to update the tensor's shape and stride metadata to the new target dimensions.
*Crucially*, the metadata is updated first.
Next, PyTorch attempts to resize the underlying storage.
It discovers that the storage is not resizable (e.g., it's backed by a NumPy array or immutable data).
A `RuntimeError` is raised.
The `catch` block handles the exception.

The problem is that by step 4, the tensor's shape metadata has already been modified. Even though the operation failed and the storage remains unchanged (0 bytes in the example), the tensor's internal representation now points to a shape that doesn't match its storage capacity. This leaves the tensor in a corrupted state.

Why It Leads to Crashes

Tensors in PyTorch are designed with a contract between their metadata (shape, strides, data pointer) and their underlying storage. When you try to access an element, PyTorch uses the shape and strides to calculate the memory address of that element within the storage. If the shape indicates a large tensor (e.g., 5x5x5, which would normally require 25 * sizeof(dtype) bytes), but the storage is actually empty (0 bytes), attempting to read from any calculated memory address will result in an invalid memory access. This typically leads to a Segmentation Fault on systems that enforce memory protection, or an internal error within PyTorch if it detects the inconsistency before attempting memory access (though this is not always guaranteed).

Version Information and Environment

To ensure reproducibility and to help developers diagnose such issues, it's vital to document the environment and versions used. The following information was provided regarding the environment where this bug was observed:

PyTorch version: 2.9.0+cu126
Build type: False (not a debug build)
CUDA version: 12.6
OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: 11.4.0
Python version: 3.12.12 (64-bit runtime)
Python platform: Linux-6.6.105+-x86_64-with-glibc2.35
XNNPACK available: True

While CUDA was specified, the environment indicated that CUDA was not available during the collection of this information. The presence of various cuDNN versions suggests that CUDA development environments are common in the testing setup. It's important to note that this bug is likely independent of CUDA, as it pertains to the fundamental tensor metadata and storage management logic which operates at a lower level.

Potential Fixes and Best Practices

Addressing this bug requires careful modification of the `resize_()` operation's exception handling. The core principle should be to ensure that metadata updates are conditional on the successful resizing of the storage.

User-Side Mitigation

Until this bug is officially fixed, users can mitigate the risk by being mindful of how they handle tensors with potentially unresizable storage. If you are converting NumPy arrays to PyTorch tensors and then attempting to resize them, be aware of this potential pitfall. It might be safer to create a new PyTorch tensor with the desired shape and copy the data over, rather than attempting to resize the tensor in-place, especially if you are unsure about the mutability of its underlying storage.

For instance, instead of:

# Potentially problematic
t_np = torch.from_numpy(np.random.rand(3, 3))
t_np.resize_((5, 5))

Consider using:

# Safer approach
t_np_original = torch.from_numpy(np.random.rand(3, 3))
t_new = torch.empty((5, 5), dtype=t_np_original.dtype, device=t_np_original.device)
t_new.copy_(t_np_original)
# Now t_new has the desired shape and valid storage

This approach avoids the problematic `resize_()` call on storage that might not support it.

Conclusion

The bug where PyTorch updates tensor shape metadata even when storage resize fails is a critical issue related to exception safety. It can lead to "Zombie" tensors that appear to have a shape but possess no underlying data, resulting in crashes and difficult-to-debug errors. Understanding the sequence of operations within `resize_()` and the importance of strong exception guarantees is key to recognizing and potentially avoiding this problem. While developers work on a fix, users can adopt safer tensor manipulation practices, especially when dealing with data originating from sources like NumPy arrays. Ensuring robust error handling and maintaining data integrity are paramount for reliable deep learning frameworks.

For more information on tensor operations in PyTorch, you can refer to the official documentation: