PyTorch Tensor Corruption Bug: Failed Resizes

by Alex Johnson 46 views

Ever run into a weird error in PyTorch that makes your program crash mysteriously, maybe with a segmentation fault or an internal RuntimeError? Sometimes, these issues can be quite puzzling, especially when they happen after an operation that seems like it should have just failed gracefully. We're diving into a specific bug today where PyTorch's resize_() function has a bit of a hiccup when dealing with tensors that have non-resizable storage, leading to corrupted tensors and subsequent crashes. Let's unravel this mystery together!

The Root of the Problem: When Storage Can't Be Resized

So, what exactly is going on here? PyTorch uses a concept called 'storage' to hold the actual data for a tensor. Normally, when you perform operations like resize_(), PyTorch tries to adjust this underlying storage to match the new shape you've requested. However, there are situations where this storage is fixed and cannot be resized. A common scenario for this is when you create a tensor directly from a NumPy array using set_(). NumPy arrays, by their nature, have a fixed memory footprint once created. When PyTorch tries to resize the storage of a tensor that's backed by such a fixed NumPy array, it correctly identifies the issue and raises a RuntimeError with a message like: "Trying to resize storage that is not resizable." This is good – PyTorch is telling you, "Hey, I can't do this!"

The Unexpected Behavior: Metadata Mismatch

The core of the bug, however, lies in what happens after PyTorch detects that the storage is not resizable. Instead of rolling back all changes, the resize_() operation proceeds to update the tensor's shape and stride metadata before it fails the storage check. Imagine you have a tensor with a shape of (0,) and zero bytes of storage (which is perfectly valid for an empty tensor). If you then try to resize_() it to a much larger shape, say (5, 5, 5), PyTorch will happily update the tensor's metadata to reflect this new, larger shape. But here's the kicker: because the underlying storage couldn't be resized, it remains empty – still 0 bytes! This creates a severe inconsistency. The tensor's metadata is screaming, "I'm a 5x5x5 tensor, holding 125 elements!" while its actual data storage is empty and unchanged. This is what we call a corrupted tensor, or in the terms used in the discussion, a "Zombie" tensor. It has the appearance of a larger tensor but no data to back it up.

The Consequences: Crashes and Corrupted Data

What happens when you try to use this corrupted tensor? Well, it's not pretty. When your code, or even PyTorch's debugging tools, tries to access the data of this tensor (for example, by printing it or performing a calculation), it encounters a mismatch. It expects to find data corresponding to the (5, 5, 5) shape, but all it finds is empty storage. This discrepancy often leads to immediate crashes. In the provided issue report, the minimal reproduction case resulted in a RuntimeError when trying to print the tensor. However, the original bug reported in a more complex scenario led to a Segmentation Fault, which is a much more serious type of crash indicating a memory access violation. These crashes can be hard to debug because the error message you see might not directly point to the resize_() operation that caused the corruption; it often appears later, when the corrupted tensor is actually accessed.

Key Takeaway: Even though PyTorch correctly identifies that a tensor's storage cannot be resized, it fails to maintain the tensor's integrity. The metadata is updated prematurely, leading to a state where the shape information is no longer consistent with the actual data storage. This inconsistency is the breeding ground for runtime errors and crashes, particularly when the tensor is later accessed.

Understanding the Minimal Reproduction Case

To really get a handle on this bug, let's break down the minimal code example provided. It's designed to isolate the issue and make it easy to replicate.

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 Analysis

  1. locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage() This is the crucial first step. We create a NumPy array that is empty (np.array([])) and has a data type of 32-bit integers (dtype=np.int32). We then convert this NumPy array into a PyTorch tensor and immediately access its untyped_storage(). Because the NumPy array is empty, its storage is also empty, meaning it contains 0 bytes of data. Importantly, storage created this way, especially when originating from a fixed-size NumPy array, is often marked as non-resizable.

  2. t = torch.tensor([], dtype=torch.int32) Here, we create a standard, empty PyTorch tensor with the same data type (torch.int32). At this point, t has a shape of torch.Size([0]) and 0 bytes of storage.

  3. t.set_(locked_storage) This is where the magic (and the problem) begins. We use the set_() method to replace the internal storage of our tensor t with the locked_storage we created in the first step. Now, t is linked to that 0-byte, non-resizable storage. Its shape is still torch.Size([0]), but it's backed by a fixed, empty memory block.

  4. try: t.resize_((5, 5, 5)) except RuntimeError: pass This block attempts to change the shape of our tensor t to (5, 5, 5). The resize_() operation internally checks if the storage can accommodate this new shape. Since locked_storage is non-resizable and has 0 bytes, this check fails. PyTorch correctly raises a RuntimeError explaining that the storage cannot be resized. The try...except block catches this error, preventing the program from crashing at this exact moment. However, as noted in the bug description, the damage is already done.

  5. print(f"Shape: {t.shape}") This line prints the current shape of the tensor t. The output is Shape: torch.Size([5, 5, 5]). This is unexpected! Even though the resize_() operation failed because of the storage, the tensor's shape metadata was updated before the failure was fully processed. It now thinks it's a 5x5x5 tensor.

  6. print(f"Storage: {t.untyped_storage().nbytes()}") This line prints the size of the tensor's underlying storage in bytes. The output is Storage: 0. This clearly shows the inconsistency: the shape says (5, 5, 5), but the storage has 0 bytes. For a 5x5x5 tensor of int32, you'd expect 5 * 5 * 5 * 4 bytes (125 * 4 = 500 bytes).

  7. print(t) This is the line that triggers the actual crash. When print(t) is called, Python and PyTorch try to access and display the elements of the tensor. Because the tensor's metadata indicates a size of (5, 5, 5) but the storage is empty (0 bytes), this leads to an access violation, resulting in either a RuntimeError (as seen in this specific reproduction) or a Segmentation Fault in other environments.

The Expected vs. Actual Behavior: A Matter of Guarantees

In software development, especially with systems as complex as deep learning frameworks, there are different levels of error handling guarantees. When an operation fails, we ideally want it to leave the system in a state that is either unchanged or has a strong exception guarantee. This means that if an exception occurs, the program state should be consistent, as if the operation never happened.

  • Expected Behavior: When t.resize_((5, 5, 5)) is called on a tensor with non-resizable storage, PyTorch should recognize the impossibility of resizing the storage. If it fails, it should ensure that all changes made during the operation are rolled back. This includes the update to the tensor's shape and stride metadata. Therefore, after the RuntimeError is caught, the tensor t should ideally retain its original shape, which was torch.Size([0]). The storage size would also remain 0 bytes, maintaining consistency.

  • Actual Behavior: As demonstrated by the minimal reproduction, PyTorch fails to uphold this strong guarantee. The RuntimeError is indeed raised, confirming the storage issue. However, the tensor's shape metadata is updated before the operation fully aborts. This leaves the tensor in an inconsistent state: t.shape becomes torch.Size([5, 5, 5]) while t.untyped_storage().nbytes() remains 0. This inconsistency is the critical flaw. The subsequent attempt to use or print the tensor then triggers a crash because the expected data is not present in the storage.

This bug highlights a subtle but important aspect of exception safety in tensor operations. It's not enough to just catch an error; the system must also ensure that the internal state remains valid even when errors occur.

Versions and Environment

The issue was reported with the following environment details:

  • PyTorch Version: 2.9.0+cu126
  • Build Configuration: Not a debug build.
  • CUDA: Built with CUDA 12.6.
  • Operating System: Ubuntu 22.04.4 LTS (x86_64).
  • GCC Version: 11.4.0.
  • Python Version: 3.12.12.
  • Platform: Linux-6.6.105+-x86_64-with-glibc2.35.
  • XNNPACK: Available.

While CUDA was mentioned in the build, the reported environment indicated CUDA available: False for the runtime, suggesting this issue might not be specific to GPU operations but rather a core tensor manipulation bug.

Conclusion and Potential Fixes

The bug described, where PyTorch's resize_() fails to maintain tensor integrity after a storage resizing error, is a critical issue that can lead to hard-to-debug crashes. The core problem is the inconsistent state left behind when the exception is raised. The tensor's shape metadata is updated, but the underlying storage remains unchanged, creating a mismatch that causes subsequent operations to fail.

To fix this, the resize_() operation needs to be made more exception-safe. Specifically, the tensor's shape and stride metadata should only be updated after the storage resizing is confirmed to be successful. If the storage resizing fails at any point, the operation should be aborted cleanly, ensuring that no metadata changes are committed. This would provide the strong exception guarantee that users expect.

This type of bug often requires careful attention to the internal implementation of tensor operations, ensuring that state changes are atomic or properly rolled back in the event of an error. Developers need to meticulously check the sequence of operations within methods like resize_() and ensure that metadata modifications are the last step, contingent on the success of underlying storage operations.

For users encountering this issue, the best course of action is to avoid operations that might lead to resizing non-resizable storage if possible, or to carefully handle RuntimeError exceptions, understanding that the tensor might be in an invalid state even after the exception is caught. For a deeper dive into PyTorch's internals and tensor management, you might find the official PyTorch documentation on tensors and related developer guides insightful.