PyTorch Resize_() Bug: Preventing Corrupted Tensors

Dec 13, 2025 by Admin 52 views

Hey there, fellow developers and AI enthusiasts! Ever been in a situation where your code should work perfectly, but then it throws a Segmentation Fault or some cryptic RuntimeError that leaves you scratching your head? Well, today, we're diving into a particularly sneaky issue within PyTorch that can lead to exactly that: corrupted tensors. Specifically, we're talking about a bug where PyTorch's resize_() operation, even when it fails gracefully, can leave your tensors in a totally inconsistent and dangerous state. This isn't just a minor glitch; it's a potential landmine for data integrity and program stability in your deep learning projects.

Understanding the Core Problem: When `resize_()` Leads to "Zombie" Tensors

Let's get straight to the point, guys. The core problem we're unraveling here is how PyTorch handles resize_() calls on tensors when their underlying storage cannot actually be resized. Imagine you have a PyTorch tensor, which is essentially a fancy wrapper around a block of memory (its storage), holding all your precious numerical data. The resize_() method is super handy because it allows you to change the shape and size of your tensor in-place, which can be really efficient. However, not all tensor storage is created equal. Sometimes, a tensor might share its storage with an external, non-resizable buffer – think a NumPy array that you've injected into PyTorch using set_(). When you try to resize_() such a tensor, PyTorch is smart enough to detect that the storage isn't flexible, and it correctly raises a RuntimeError saying, "Trying to resize storage that is not resizable." That sounds good, right? An error is thrown, so you know something went wrong. But here's the kicker, folks: the operation isn't what we call exception-safe. This means that even though an error is eventually thrown, before that error truly halts the operation, some critical parts of the tensor get updated. Specifically, the tensor's metadata – its shape and stride – gets updated to the new, target size you requested, even if the actual memory reallocation failed. The result? You're left with what we affectionately call a "Zombie" tensor. This tensor thinks it's big and full of data, showing a large shape, but its storage() remains stubbornly empty, sitting at 0 bytes. It's like having a map that tells you a treasure chest is full, but when you open it, it's completely bare! This mismatch is a recipe for disaster, and it's something we absolutely need to be aware of to ensure the robustness of our PyTorch applications. Without proper handling, this seemingly innocuous error can cascade into much larger, harder-to-debug issues.

Diving Deeper: How PyTorch Tensors Get Corrupted by Failed Resizes

Now, let's peel back another layer and really dig into how these PyTorch tensors get corrupted when a storage resize fails. This isn't just about a simple error; it's about the order of operations within PyTorch's resize_() function. When you call t.resize_((5, 5, 5)) on a tensor t, the internal machinery of PyTorch kicks into gear. First, it calculates the new required size for the tensor's data and attempts to resize the underlying storage. Before this storage resize attempt fully completes and succeeds, or even before a failure check is performed and an exception is raised, PyTorch first updates the tensor's shape and stride metadata. Think of it like this: your tensor object has an internal representation of its dimensions (its shape) and how to navigate through its data (its stride). These are just numbers, easy to change. But the actual data resides in the storage, which is the raw memory block. If that storage is linked to something external and non-resizable, like a NumPy array you brought in via set_(), the attempt to actually grow or shrink that memory block will fail. However, the crucial point is that the shape and stride attributes have already been updated to reflect the intended new size. So, by the time PyTorch realizes it can't physically resize the storage and throws that RuntimeError, the tensor's metadata is already out of sync with its physical storage. You end up with a tensor whose t.shape might proudly declare torch.Size([5, 5, 5]), suggesting it's ready for 125 elements, but if you check t.untyped_storage().nbytes(), it will still report 0 bytes, because the underlying memory never actually changed. This creates a deeply inconsistent state, a kind of cognitive dissonance within the tensor object itself. When your code later tries to access elements within this 5x5x5 tensor, PyTorch will look at the shape metadata, calculate an offset into what it believes is a large block of memory, but then crash because that memory simply doesn't exist or isn't accessible. This gap between expectation (metadata) and reality (storage) is the root cause of the crashes and undefined behavior we're seeing. It’s a classic example of an operation that isn't atomic or sufficiently guarded against partial failure, leaving behind a mess for subsequent operations to trip over. Understanding this specific sequence of events is key to both identifying and, eventually, fixing such robust issues in complex libraries like PyTorch.

The Dangers of "Zombie" Tensors: Why This Matters to Your Codebase

Alright, so we've got these "Zombie" tensors – objects that look alive on the outside (their shape metadata is updated) but are essentially dead inside (their storage is empty). But why is this such a big deal, you might ask? Well, let me tell you, folks, the dangers here are not to be underestimated. When you have a tensor in this inconsistent state, accessing it subsequent to the caught exception often leads to catastrophic failures. The most common culprits are Segmentation Faults (SegFaults) or internal RuntimeErrors. A Segmentation Fault is particularly nasty because it means your program is trying to access a memory location it doesn't have permission to, or that simply doesn't exist, leading to an immediate and ungraceful crash of your entire application. It's like trying to read a page from a book that was never printed – your brain just short-circuits! Imagine running a long training job or a complex data pipeline, only for it to suddenly SegFault without a clear traceback in your Python code, leaving you utterly baffled. That's the headache these "Zombie" tensors can cause. On the other hand, internal RuntimeErrors are also problematic, as they often signal deep inconsistencies within the library itself, indicating that PyTorch's internal assumptions about its own data structures have been violated. These errors, while perhaps less brutal than a SegFault that kills your process entirely, still signify corrupted state and can lead to incorrect computations or further unexpected behavior down the line. The real peril here is that the initial RuntimeError about non-resizable storage might be caught and handled, making you think everything is okay. But the tensor, despite the caught exception, remains in a corrupted state. If your code proceeds to use that tensor, even just to print it or perform a simple operation, you're playing with fire. Debugging these issues can be incredibly difficult and time-consuming because the initial cause (the failed resize_() and subsequent metadata update) is far removed from the eventual crash point. You might see a SegFault hours or days later, in a completely different part of your codebase, making it a true needle-in-a-haystack problem. This underscores the critical importance of exception safety in library design – ensuring that if an operation fails, it leaves the system in a consistent, known state, preventing these kinds of dangerous "Zombie" objects from haunting your programs.

Reproducing the Bug: A Step-by-Step Guide for the Curious Developer

Alright, let's get our hands a little dirty and walk through the minimal reproduction of this bug. Seeing is believing, right? This little snippet of Python code, using torch and numpy, clearly demonstrates the issue. So, grab your editor, fire up a Python environment with PyTorch installed (we're talking version 2.9.0+cu126 in this case, but the principle should hold across recent versions), and follow along. We'll break down each line of code so you understand exactly what's happening.

First up, we import our necessary libraries:

import torch
import numpy as np

Pretty standard, nothing groundbreaking here. Next, we create some non-resizable storage. This is key to triggering the bug:

locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

What's happening here? We're taking an empty NumPy array of integers (np.array([], dtype=np.int32)), converting it into a PyTorch tensor (torch.from_numpy(...)), and then extracting its raw untyped_storage(). The crucial part is that NumPy arrays, when their memory is directly exposed to PyTorch, often create torch.Storage objects that are not resizable by PyTorch because PyTorch doesn't own or manage that memory; NumPy does. This locked_storage will report 0 bytes because it's derived from an empty NumPy array.

Now, we'll create a fresh PyTorch tensor and inject this locked_storage into it:

t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

Here, t starts as an empty integer tensor. The t.set_(locked_storage) call is powerful: it tells t to discard its own storage and instead use locked_storage as its underlying memory. So now, t is directly backed by our non-resizable, 0-byte storage. Its shape should currently be torch.Size([0]).

And now for the moment of truth – attempting to resize this tensor:

try:
    t.resize_((5, 5, 5))
except RuntimeError:
    pass

We wrap this in a try-except block because we expect a RuntimeError! We're trying to resize t to a 5x5x5 shape (125 elements). Since t is backed by our locked_storage, which can't be resized, PyTorch should complain. And it does! The RuntimeError is indeed caught. Most folks would assume that after catching this error, the tensor t would revert to its original, consistent state. This is where the bug lies! The pass statement in the except block means we're silently swallowing the error, which is often a bad practice in production code but useful for demonstration here.

Finally, we verify the corruption:

print(f"Shape: {t.shape}")
print(f"Storage: {t.untyped_storage().nbytes()}")
print(t) # CRASH expected here

If you run this, you'll see something shocking:

Shape: torch.Size([5, 5, 5]) - Woah! The tensor thinks it's 5x5x5!
Storage: 0 - But its storage is still 0 bytes!

This is our "Zombie" tensor in full effect. And then, when you try to print(t) (or access any element), boom! You'll likely hit a RuntimeError (as shown in the gist) or, as noted in the original bug report, a Segmentation Fault in more complex scenarios. This print(t) line triggers a crash because PyTorch tries to access the elements based on the corrupted shape but finds no actual data in the storage. This reproduction clearly illustrates the desynchronization between a tensor's metadata and its underlying storage, making it a critical example for understanding this tricky bug. It's a fantastic way to grasp the nuances of how low-level memory management can impact high-level library behavior and why thorough testing and exception safety are paramount in robust software development, especially in performance-critical frameworks like PyTorch.

Expected vs. Actual Behavior: The "Strong Exception Guarantee" Dilemma

When we talk about robust software, especially in critical libraries like PyTorch, there's a concept called the "Strong Exception Guarantee" that's incredibly important. What does it mean? Simply put, if an operation fails and throws an exception, the system should be left in its original state. No partial changes, no inconsistent data, no half-baked results. It's like a transaction in a database: either it completes fully and successfully, or if it fails, it rolls back all changes, leaving no trace of the failed attempt. For our resize_() scenario, the expected behavior is crystal clear: If resize_() throws a RuntimeError because the storage it's trying to manipulate isn't resizable, then the tensor's metadata – its shape and stride – should remain completely unchanged. It should hold onto its original torch.Size([0]) shape, perfectly consistent with its 0-byte storage. The operation should be atomic in effect: either it fully succeeds in resizing both the storage and the metadata, or it completely fails, leaving no side effects on the tensor's state. This adheres to the principle of strong exception safety, ensuring that even when things go wrong, your program's state is predictable and safe to continue operating on, or at least to clean up in a controlled manner.

However, what we're actually observing, as demonstrated by our reproduction, is the actual behavior: The exception is thrown, which is good, but the tensor's shape metadata is updated to the new, desired size (torch.Size([5, 5, 5]) in our example), while the actual storage remains 0 bytes. This is a violation of the strong exception guarantee. The system is left in an inconsistent state. The tensor becomes a "Zombie," as we've discussed, its outward appearance (shape) lying about its internal reality (empty storage). This mismatch is not just an aesthetic problem; it's a critical flaw that breaks the contract between the user and the library. When the library doesn't guarantee a consistent state after an exception, developers are forced to implement complex, defensive programming patterns just to ensure data integrity, adding unnecessary boilerplate and cognitive load. The most significant fallout of this actual behavior is that any subsequent attempt to interact with this corrupted tensor – be it for printing, performing calculations, or iterating over its elements – will likely lead to a crash, either a RuntimeError or a hard Segmentation Fault. This means that even if you carefully try-except your resize_() calls, you still can't trust the tensor's state afterwards, making error recovery incredibly tricky and bug hunting a nightmare. It really highlights why the strong exception guarantee isn't just a theoretical concept, but a practical necessity for building reliable and resilient software, especially in high-performance computing environments like deep learning where data integrity is paramount.

Impact on Development: Real-World Scenarios and Debugging Nightmares

For us developers working with PyTorch, this resize_() bug isn't just an academic curiosity; it has significant real-world impact on our development workflows and can lead to some serious debugging nightmares. Imagine you're building a complex data loading pipeline or a custom neural network layer where you need to dynamically resize tensors. Maybe you're working with variable-length sequences, or you're pre-allocating buffers that sometimes need to be adjusted. If you're using resize_() in these scenarios, and especially if you're working with tensors whose storage is managed externally (like data coming from C++ extensions, shared memory, or memory-mapped files via NumPy), you could inadvertently be creating these "Zombie" tensors without even knowing it. The immediate RuntimeError about non-resizable storage might be caught and perhaps even logged, but if your code doesn't explicitly re-initialize or re-assign the tensor, it's still carrying that hidden corruption. Later on, when a different part of your model tries to perform a forward pass or a simple validation check, it might hit a Segmentation Fault. These aren't always easy to trace back to the original resize_() call, which could have happened much earlier in the program's execution. It's a classic case of "heisenbug" – a bug that changes its behavior or disappears when one tries to observe or debug it directly, because the corruption happened long before the crash. This makes debugging incredibly frustrating and time-consuming, wasting precious developer hours that could be spent on actual model improvements. Furthermore, the problem goes beyond just crashes. In some edge cases, if a corrupted tensor isn't accessed in a way that immediately triggers a SegFault (e.g., if only its shape is queried without reading actual data), it could potentially lead to silent data corruption or incorrect results without an obvious error. This is arguably even worse, as it could mean models are trained on bad data or inferences are made incorrectly, leading to incorrect scientific findings or flawed product decisions. The integrity of numerical computations is fundamental to deep learning, and anything that undermines that integrity, especially subtly, is a huge concern. Therefore, understanding this bug is crucial for anyone building robust PyTorch applications, pushing us to implement more defensive programming practices and be extra vigilant about tensor lifecycle management, particularly around operations that modify tensor dimensions and storage.

Mitigation Strategies and Best Practices: Keeping Your Tensors Healthy

So, given this tricky resize_() bug, what can we, as developers, do to protect our code and keep our PyTorch tensors healthy? It's all about mitigation strategies and best practices. While we wait for a potential fix in PyTorch itself (which, by the way, is a great candidate for a community contribution!), we can adopt several defensive coding techniques. First and foremost, when dealing with tensors that are backed by potentially non-resizable storage (e.g., those created via torch.from_numpy and then possibly set_()), you should avoid in-place resize_() operations if you expect a failure. Instead of modifying the tensor's shape directly, consider creating a new tensor with the desired shape and then copying data into it if necessary. This approach ensures that you're always working with a fresh, consistently allocated tensor. For instance, if you want to resize t but are unsure about its storage, you might do new_t = torch.empty((5, 5, 5), dtype=t.dtype) and then copy over relevant data from t if t was valid. Another robust strategy is to ensure that if resize_() does fail, you explicitly invalidate or re-initialize the tensor. If your try-except block catches a RuntimeError from resize_(), you should not assume the tensor t is in a usable state. Instead, you could re-assign it to an empty tensor: t = torch.tensor([], dtype=t.dtype) or t = None. This effectively cleans up the "Zombie" state, preventing future crashes. Furthermore, for situations where you need dynamic resizing with external data, it might be safer to use torch.clone() or torch.copy_() to detach the tensor from its original non-resizable storage before attempting a resize_(). If t points to locked_storage, t_copy = t.clone().detach() would create a new tensor with its own resizable storage, which you could then resize_() safely. This ensures that the storage becomes PyTorch-managed and thus fully resizable. A final piece of advice is to adopt a mindset of "assume failure leaves bad state" when dealing with operations that might modify shared or external memory. Rigorous unit testing, particularly with edge cases like empty tensors or externally managed buffers, can also help catch these subtle bugs early in the development cycle. By implementing these practices, you can significantly reduce the risk of encountering corrupted tensors and the frustrating debugging sessions that come with them, leading to more stable and predictable deep learning applications. It's all about being proactive and mindful of the underlying mechanics of how PyTorch manages memory and tensor state, especially when dealing with operations that aren't fully exception-safe.

Looking Ahead: Towards a More Robust PyTorch Ecosystem

This deep dive into the resize_() bug isn't just about pointing out a flaw; it's about contributing to a more robust PyTorch ecosystem. Every bug report, every minimal reproduction, and every discussion like this helps the community and the PyTorch core developers build a better, more reliable framework. The importance of exception safety cannot be overstated in a library as widely used and critical as PyTorch. Users expect that when an operation fails, the system state remains consistent, preventing cascading errors and intractable debugging sessions. The current behavior of resize_() violates this expectation, and addressing it would be a significant step forward in making PyTorch even more dependable. For those of us in the community, this is a fantastic opportunity to engage. Bug reports, especially those with clear, minimal reproductions, are incredibly valuable. Even better, talented developers might consider diving into the PyTorch C++ codebase to investigate the exact sequence of events in resize_() and propose a fix that ensures the metadata updates are conditional on the storage resize succeeding. This could involve a transactional approach, where metadata changes are committed only after successful storage allocation, or a rollback mechanism in case of failure. Beyond this specific bug, it serves as a powerful reminder for all of us to be mindful of library internals, understand the guarantees (or lack thereof) offered by various functions, and practice defensive programming. As PyTorch continues to evolve and push the boundaries of AI research and deployment, maintaining code quality, stability, and predictability is paramount. By understanding and addressing issues like the "Zombie" tensor bug, we collectively contribute to a future where deep learning development is not only powerful and flexible but also inherently more stable and less prone to unexpected, hard-to-diagnose crashes. Let's keep working together to make PyTorch the best it can be, fostering an environment of continuous improvement and collective problem-solving. It's this collaborative spirit that truly drives innovation and makes our tech community so incredibly vibrant and effective. This continuous vigilance and commitment to quality are what ensure that tools like PyTorch remain at the cutting edge, supporting the next generation of AI breakthroughs and making life easier for all of us building those incredible applications. Always remember: a bug found and fixed is a step towards a stronger, more reliable foundation for everyone!

PyTorch Version Information for Reference:

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