PyTorch Tensor Bug: Corrupted Metadata On Resize Failure

Hey there, PyTorch users! Today, we're diving into a rather quirky bug that's been causing some headaches, particularly when dealing with tensors that share storage with non-resizable buffers. This issue, centered around the PyTorch tensor shape metadata corruption bug, can lead to some unexpected behavior and even crashes if not handled carefully. Let's break down what's happening, why it's a problem, and how it might be affecting your deep learning workflows.

Understanding the Problem: When Resize Fails, But Metadata Doesn't

So, picture this: you're working with a PyTorch tensor, and you decide to resize it using the resize_() function. Normally, this is a straightforward operation. However, things get a bit tricky when the tensor you're trying to resize is sharing its underlying storage with something that can't be resized. A classic example of this is when you inject data from a NumPy array into a PyTorch tensor using set_(). In these scenarios, PyTorch is smart enough to recognize the limitation and will correctly throw a RuntimeError, stating something like: "Trying to resize storage that is not resizable." This is exactly the behavior we'd expect, right? It tells you, "Hey, I can't do that!"

The crux of the bug, however, lies in what happens *after* this error is raised. Even though the operation fails because the storage itself cannot be modified, PyTorch doesn't quite clean up everything properly. Before the check for resizable storage actually fails, the tensor's internal metadata – specifically, its shape and stride information – gets updated to reflect the new, desired size. This means that even though the actual data buffer (the storage) remains unchanged and empty (0 bytes in this case), the tensor's metadata now points to a much larger, non-existent structure. This creates a state that the community has dubbed a "Zombie" tensor. It looks like it has a shape (e.g., 5x5x5), but its underlying storage is effectively empty.

The consequences of this inconsistency can be severe. If you try to access or print this "Zombie" tensor after the exception has been caught, you're likely to encounter either a Segmentation Fault (a dreaded low-level memory access error) or another internal RuntimeError. This is because the program is trying to read data from or write data to a tensor whose dimensions (shape) don't match the reality of its data buffer. It's like being told to find a book in a library with a catalog entry for a massive tome, but when you go to the shelf, there's nothing there. This is a critical issue for any application relying on stable tensor operations, especially in complex model training loops where such errors can cascade and halt progress.

The "Zombie Tensor" Phenomenon: A Deep Dive

Let's really unpack the concept of this corrupted "Hzckvs" tensor state. When you perform an operation like tensor.resize_((5, 5, 5)) on a tensor that has its storage linked to a non-resizable buffer, the internal workings of PyTorch execute a series of steps. The intention is to prepare the tensor for its new dimensions. This involves updating several pieces of metadata:

• Shape: This describes the dimensions of the tensor (e.g., `torch.Size([5, 5, 5])`).
• Strides: These define how to step through the tensor's memory to access elements along each dimension.

The problem arises because the update to this metadata happens *before* the critical check that verifies whether the underlying storage can actually accommodate the new shape. In the case of a non-resizable buffer, like a NumPy array that has been `set_()` into a tensor, the storage simply cannot be expanded or changed. When PyTorch eventually performs this check, it correctly identifies the impossibility and raises a RuntimeError. However, the metadata has already been modified. This leaves the tensor in a state where tensor.shape might report torch.Size([5, 5, 5]), but tensor.storage().nbytes() reports 0.

This mismatch is the root cause of the instability. When you later attempt to interact with this tensor – for instance, by printing its contents using print(t) or by trying to access an element `t[0, 0, 0]` – the code attempts to navigate the tensor's structure based on its (corrupted) shape and strides. Since the storage is empty, these navigation attempts lead to invalid memory accesses. In a Python environment, this often manifests as a RuntimeError, as seen in the provided reproduction. However, in more complex C++ backends or when integrated into larger C++ applications, these invalid memory accesses can easily escalate into a full-blown Segmentation Fault. This is precisely what happened in the original, more complex scenario that led to the discovery of this bug. The gist shows a Python traceback, but the underlying issue can be far more severe.

The expected behavior, adhering to the Strong Exception Guarantee, is that if an operation fails, the object should be left in its original state. In this case, if resize_() fails due to a non-resizable buffer, the tensor's shape and stride metadata should remain unchanged, reflecting its initial state (e.g., `torch.Size([0])`). The current behavior violates this principle, leaving the tensor in a precarious, corrupted state that poses a significant risk to program stability and data integrity. Developers relying on PyTorch for critical applications need to be aware of this potential pitfall, especially when dealing with tensors derived from external, fixed-size data sources.

Minimal Reproduction and Analysis

To truly grasp the issue, let's look at the minimal reproduction code provided. It's a concise demonstration of the PyTorch tensor shape metadata corruption bug in action:

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

Let's break down what's happening step-by-step:

1. locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage(): Here, we create a NumPy array with zero elements. When converted to a PyTorch tensor and then accessing its `untyped_storage()`, we get a storage object that is inherently non-resizable because it's backed by a fixed-size NumPy array (even though it's empty).
2. t = torch.tensor([], dtype=torch.int32): A standard, empty PyTorch tensor is created.
3. t.set_(locked_storage): This is the critical step. We are now telling our tensor `t` to use the `locked_storage` we created earlier. From this point on, `t` is linked to this non-resizable storage.
4. try: t.resize_((5, 5, 5)) except RuntimeError: pass: We then attempt to resize the tensor `t` to a shape of (5, 5, 5). PyTorch's internal logic for `resize_()` starts by preparing the metadata for this new shape. It updates the `shape` and `stride` attributes of the tensor object `t`. Only after* this metadata update does it attempt to check if the underlying `locked_storage` can be resized. Since `locked_storage` is tied to a NumPy array, it's not resizable, and PyTorch correctly raises a `RuntimeError`. The `try...except` block catches this error, preventing the program from crashing at this exact point.
5. print(f"Shape: {t.shape}"): This line demonstrates the corruption. It prints `Shape: torch.Size([5, 5, 5])`. This is the metadata that was updated before* the resize operation failed.
6. print(f"Storage: {t.untyped_storage().nbytes()}"): This line shows the reality of the storage. It prints `0`, indicating that the actual data buffer has zero bytes, as expected since it's linked to an empty, non-resizable source.
7. print(t): This is where the program is likely to fail. When `print(t)` is called, it tries to access the tensor's data using the shape information (`torch.Size([5, 5, 5])`) but finds that there is no actual data in the storage. This inconsistency leads to the observed crash (Segmentation Fault or RuntimeError).