PyTorch Bug: `resize_()` Corrupts Tensors On Storage Fail

Dec 15, 2025 by Alex Johnson 58 views

$PyTorch Bug: `resize_()` Corrupts Tensors on Storage Fail\n\nHey there, fellow developers and AI enthusiasts! Have you ever encountered a perplexing crash or unexplainable behavior in your PyTorch applications? It's a common scenario, and sometimes the most insidious bugs lurk in unexpected corners of powerful libraries. Today, we're diving deep into a particularly tricky **PyTorch tensor bug** involving the `resize_()` operation, which can lead to *serious data corruption* and baffling crashes, including dreaded _Segmentation Faults_. This isn't just a minor glitch; it's an **exception safety issue** that can leave your `PyTorch tensors` in a compromised, inconsistent state, sometimes referred to as a "Zombie tensor." Imagine your data structure telling you it holds a vast amount of information, while in reality, its underlying storage is completely empty. That's precisely the core of this `resize_()` problem. When `PyTorch` attempts to alter the size of a tensor whose underlying storage cannot be modified – for instance, when it's backed by a non-resizable buffer like a `NumPy array` that was injected via `set_()` – it rightly throws a `RuntimeError`. _However_, and this is where the plot thickens, the tensor's metadata, specifically its `shape` and `stride` information, gets updated *before* this critical storage check fails. This means that even though the operation ultimately fails and an exception is raised, your `tensor` is left with a misleading `shape` attribute that doesn't align with its actual `storage` capacity. The result? A `corrupted tensor` ready to crash your program or, even worse, produce silently incorrect results down the line. Understanding and addressing this **PyTorch `resize_()` bug** is crucial for anyone working with `deep learning` and `machine learning` models, as data integrity forms the very foundation of reliable and robust `AI development`. Let's explore why this happens and what we can do about it.\n\n## Unpacking the "Zombie Tensor" Phenomenon: Why `resize_()` Fails So Badly\n\nThe term "_Zombie tensor_" might sound dramatic, but it perfectly encapsulates the state of a `PyTorch tensor` after encountering this particular `resize_()` bug. _Essentially, the `tensor` becomes a living dead object_: it exists, it has a `shape` and `dtype`, but its internal consistency is shattered. **This `PyTorch bug` occurs because the `resize_()` method is not fully exception-safe or atomic in this specific scenario.** In computer science, an operation is considered atomic if it appears to the rest of the system as if it happens instantaneously, or not at all. If it fails, all changes are rolled back. Here, the `metadata update` for the tensor's `shape` and `stride` happens *before* the system performs the necessary checks to ensure the `underlying storage` can actually be resized. When `PyTorch` attempts to `resize` a `tensor` that is linked to an `immutable storage` – such as a `NumPy array` that was originally passed into `torch.from_numpy()` and then perhaps `set_()` on another `tensor` – it hits a wall. The `NumPy array`, by its nature, often has fixed-size memory, and `PyTorch` cannot simply reallocate it. So, a `RuntimeError` is correctly raised, stating: _"Trying to resize storage that is not resizable."_ While this error message is helpful, the damage is already done. The `tensor's metadata` has already been optimistically updated to reflect the *intended new size*, even though the memory reallocation failed. This creates a critical mismatch: `tensor.shape` now reports a large, new size (e.g., `[5, 5, 5]`), but if you inspect `tensor.untyped_storage().nbytes()`, you'll find it still reports `0 bytes`. This inconsistency is a recipe for disaster. Any subsequent operation that relies on the `tensor's metadata` matching its `actual storage` will either lead to an immediate crash, like a `Segmentation Fault`, or other unpredictable runtime errors, potentially corrupting further operations or producing incorrect computational results. This **metadata corruption** is a silent killer, making `debugging incredibly challenging` as the problem often manifests far downstream from the initial `resize_()` call. It highlights a subtle but critical flaw in the `exception handling mechanism` within `PyTorch` for this specific `tensor manipulation` context.\n\n## A Closer Look: How This PyTorch `resize_()` Bug Corrupts Your Data\n\nTo truly grasp the insidious nature of this `PyTorch bug`, let's walk through the minimal reproduction case provided, which starkly illustrates the **`tensor corruption`** issue. We begin by creating `non-resizable storage` using `NumPy`. The line `locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()` is key. Here, we're taking an _empty_ `NumPy array` of `int32` type and converting it to a `PyTorch storage` object. Crucially, because `NumPy arrays` manage their own memory, this `storage` object inherits its `non-resizable` characteristic. It has `0 bytes` initially and cannot be grown. Next, we initialize a `fresh PyTorch tensor`, `t = torch.tensor([], dtype=torch.int32)`. This `tensor` is also initially empty. The crucial step is `t.set_(locked_storage)`. This line *injects* our `non-resizable storage` into the `tensor 't'`. Now, `t` conceptually owns the `storage` from the `NumPy array`. *The stage is set for the bug to manifest.* The very next action, `t.resize_((5, 5, 5))`, is where things go awry. We're explicitly telling `PyTorch` to `resize` this `tensor` to a `5x5x5 shape`. As anticipated, because the `underlying storage` (`locked_storage`) cannot be `resized`, `PyTorch` correctly raises a `RuntimeError`. We wrap this in a `try-except` block to catch the exception, preventing the program from immediately terminating. _This is where the inconsistency becomes apparent:_\n* `print(f$