PyTorch Bug: Corrupted Tensors After Failed Resizing

by Alex Johnson 53 views

Ever experienced those head-scratching moments when your code suddenly crashes, and you have no idea why? Sometimes, it's not your logic that's flawed, but a subtle bug lurking within the libraries you rely on. Today, we're diving deep into a particularly tricky issue in PyTorch concerning tensor resizing, where a seemingly straightforward operation can lead to corrupted data structures and unexpected crashes. This isn't just a theoretical problem; it can manifest as segmentation faults or internal runtime errors, making your debugging process a real headache. Let's unpack this, understand its implications, and explore how it happens.

Understanding the Core Problem: Resizing Tensors with Shared Storage

The issue at hand revolves around the resize_() method in PyTorch and its interaction with tensors that share storage with non-resizable buffers. When you have a tensor that's backed by storage that cannot be modified (like a NumPy array that's been injected into a PyTorch tensor using set_()), PyTorch should behave gracefully. In fact, it does correctly identify this situation and raises a RuntimeError with the message: "Trying to resize storage that is not resizable." This is good; the library recognizes the problematic operation.

However, the catch is that the error handling isn't entirely exception-safe. Before PyTorch detects that the underlying storage cannot be resized, it proceeds to update the tensor's shape and stride metadata. Imagine you have a tensor that's initially empty, with a shape of torch.Size([0]) and 0 bytes of storage. If you then attempt to resize it to, say, a (5, 5, 5) shape, the metadata is updated to reflect this new shape before the check for resizable storage is performed. When this check ultimately fails, a RuntimeError is raised. But by this point, the tensor's metadata is already out of sync with its actual storage.

This leaves the tensor in a precarious and corrupted state, often referred to as a "Zombie" tensor. The tensor.shape will report the new, larger dimensions (e.g., torch.Size([5, 5, 5])), but tensor.storage().nbytes() will still report 0 bytes, indicating that no actual data has been allocated or is accessible. This fundamental mismatch between what the tensor thinks its shape is and how much data it actually has is a recipe for disaster. Any subsequent attempt to access or operate on this corrupted tensor, such as printing it or performing calculations, is highly likely to result in a segmentation fault (a crash of your program) or another internal RuntimeError because the program is trying to access memory that doesn't exist or is in an inconsistent state.

A Minimal Reproduction of the Bug

To truly grasp the problem, it's best to see it in action. The developers behind the bug report provided a concise Python snippet that demonstrates this issue. Let's walk through it:

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 Breakdown:

  1. locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage(): Here, we start by creating an empty NumPy array (np.array([])). This array has a data type of int32 and, crucially, no elements. We then convert this NumPy array into a PyTorch tensor and immediately access its untyped_storage(). This storage is intrinsically linked to the NumPy array and, because the NumPy array is empty and fixed, this storage is effectively non-resizable and contains 0 bytes of data.

  2. t = torch.tensor([], dtype=torch.int32): We create a standard, empty PyTorch tensor with the same data type (int32). At this point, t has a shape of torch.Size([]) and likely points to its own, initially empty, storage.

  3. t.set_(locked_storage): This is the critical step where we override the default storage of tensor t and make it point to the locked_storage we created earlier. Now, t is a tensor that believes it should hold int32 data, but its underlying storage is fixed, non-resizable, and has 0 bytes.

  4. try...except RuntimeError block: We attempt to resize the tensor t to a new shape of (5, 5, 5) using t.resize_((5, 5, 5)). PyTorch's internal logic begins this operation. It first updates the tensor's shape and stride information to torch.Size([5, 5, 5]). Then, it checks if the underlying storage can accommodate this change. Since locked_storage is non-resizable and has 0 bytes, this check fails, and a RuntimeError is raised.

  5. pass: We catch the RuntimeError and simply pass, meaning we don't do anything with the exception. This is where the problem manifests: the exception is caught, but the tensor t remains in its corrupted state.

  6. Verification: The subsequent print statements reveal the corruption:

    • print(f"Shape: {t.shape}") outputs Shape: torch.Size([5, 5, 5]). This shows that the shape metadata was indeed updated, even though the resize failed.
    • print(f"Storage: {t.untyped_storage().nbytes()}") outputs Storage: 0. This confirms that the actual storage size remains 0 bytes.
    • print(t): This is where the program typically crashes. Trying to print a tensor that claims to have 5*5*5 = 125 elements but has 0 bytes of storage leads to undefined behavior, often a segmentation fault or an internal error.

The Expected vs. Actual Behavior

This bug highlights a violation of the Strong Exception Guarantee. In robust software design, when an operation fails (throws an exception), the system should ideally be left in the state it was before the operation was attempted. This means that if resize_() fails because the storage isn't resizable, the tensor's metadata (its shape and stride) should remain exactly as it was before the resize_() call.

Expected Behavior:

If resize_() throws a RuntimeError due to locked storage, the tensor's metadata (shape/stride) should remain unchanged. For the example above, the shape should remain torch.Size([0]) because the operation to change it failed.

Actual Behavior:

The exception is thrown, but the tensor shape is erroneously updated to torch.Size([5, 5, 5]). This creates a critical inconsistency: the tensor's shape metadata indicates a large number of elements, but its actual storage is empty (0 bytes). This mismatch is what causes the subsequent crashes when the tensor is accessed or printed, as the program attempts to read data from non-existent memory locations.

Implications and Versions

This kind of bug can be particularly insidious. It might not cause an immediate crash if the corrupted tensor isn't accessed in a way that triggers the underlying memory issues. However, it can lead to unpredictable behavior later in the execution of a program, making it difficult to trace the root cause. The original report mentioned that while their gist resulted in a RuntimeError on print, their actual use case led to a segmentation fault, underscoring the severity and varied manifestations of this issue.

Environment Details:

The bug was observed in the following environment:

  • PyTorch Version: 2.9.0+cu126
  • CUDA Build: 12.6
  • OS: Ubuntu 22.04.4 LTS (x86_64)
  • GCC Version: 11.4.0
  • Python Version: 3.12.12
  • Python Platform: Linux-6.6.105+-x86_64-with-glibc2.35

While CUDA was available during the build, it was not used in the reproduction environment (CUDA used to build PyTorch: 12.6 vs Is CUDA available: False). The presence of specific CUDA and cuDNN versions suggests a system configured for GPU acceleration, though the bug itself is reproducible on the CPU.

Conclusion: The Importance of Robust Error Handling

The bug described, where PyTorch fails to maintain state consistency after a resize_() operation encounters non-resizable storage, is a critical flaw. It underscores the importance of strong exception guarantees in library design. When an operation fails, it should ideally leave the system in a predictable, unchanged state, preventing corrupted data structures that can lead to hard-to-debug crashes like segmentation faults. Developers relying on PyTorch should be aware of this potential issue, especially when manipulating tensors that might share storage with external, fixed-size buffers.

This type of problem highlights the complexities involved in managing memory and metadata, particularly in high-performance libraries like PyTorch. Ensuring that every operation, especially those involving potentially mutable storage, is exception-safe is paramount for maintaining the reliability and stability of deep learning frameworks. For more in-depth information on tensor operations and memory management in PyTorch, you can refer to the official PyTorch Documentation. Understanding how tensors are represented and manipulated is key to avoiding such pitfalls.

For further reading on exception safety in C++, which underlies many of these concepts, the CppReference page on Exception Safety offers valuable insights into basic, strong, and nothrow guarantees.