PyTorch Tensor Bug: Corrupted Data After Failed Resizes

by Alex Johnson 56 views

When you're deep in the world of deep learning, working with tensors is a daily affair. PyTorch, a powerhouse in this domain, offers incredible flexibility. However, even the most robust libraries can sometimes stumble, and a recent discovery has highlighted a peculiar bug within PyTorch related to tensor storage and resizing. This issue, if encountered, can lead to corrupted tensors, often referred to as "Dyilor" tensors, and potentially result in hard-to-debug crashes like Segmentation Faults.

Understanding the "Dyilor" Tensor Bug in PyTorch

The core of this problem lies in how PyTorch handles tensor resizing, specifically when the underlying storage cannot be resized. Imagine you have a tensor that's directly linked to a NumPy array or another buffer that PyTorch cannot dynamically expand or shrink. When you attempt to resize such a tensor using resize_(), PyTorch correctly identifies that the storage isn't resizable and throws a RuntimeError, stating: "Trying to resize storage that is not resizable." This is the expected and safe behavior. The tensor's underlying memory block (its storage) is fixed, and PyTorch is letting you know that you can't change its dimensions directly.

However, the bug occurs in the exception handling of this scenario. Before PyTorch actually checks if the storage is resizable and throws the error, it proceeds to update the tensor's metadata. This metadata includes the tensor's shape (like [5, 5, 5]) and its strides (which dictate how to navigate the data in memory). So, even though the RuntimeError is raised and caught, the tensor's shape information has already been modified to reflect the intended new size. The actual storage, however, remains untouched and, in the case of the reproduction example, is an empty 0-byte block. This creates a severe inconsistency: the tensor thinks it has a shape of, say, 5x5x5, but its actual data storage is empty. This paradoxical state is what leads to the "Dyilor" or "Zombie" tensor.

  • The "Zombie" Tensor State

When a tensor enters this "Zombie" state, it's in a precarious condition. Its shape attribute reports a size that doesn't correspond to any actual data, and its storage() method returns an empty (0-byte) memory block. This discrepancy is a ticking time bomb. The next time you try to access or print this corrupted tensor, PyTorch's internal mechanisms will attempt to read data based on the incorrect shape and stride information from a non-existent memory location. This mismatch invariably leads to crashes. In the provided minimal reproduction, printing the tensor triggers a RuntimeError. In more complex scenarios, especially within loops or when the tensor is passed around, this can manifest as a much more alarming Segmentation Fault, indicating a critical memory access violation. The original report mentioned encountering segmentation faults in a complex loop, which underscores the severity and potential impact of this bug on larger, more intricate PyTorch applications. The fact that the shape is updated before the exception is thrown is the critical flaw. It violates the strong exception guarantee, which states that if an operation fails, the program should be left in the state it was before the operation began. In this case, the tensor is left in a corrupted, unusable state.

Minimal Reproduction of the "Dyilor" Tensor Issue

To clearly illustrate this bug, a minimal reproduction has been provided. It involves creating a tensor with an empty, non-resizable storage and then attempting to resize it. Let's break down the code:

import torch
import numpy as np

# Create non-resizable storage (0 bytes)
# We start by creating a NumPy array with no elements and then get its untyped storage.
# This storage is inherently non-resizable by PyTorch operations like resize_().
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Inject into a fresh tensor
# A new, empty tensor is created. Its storage is then replaced with the locked_storage.
# At this point, the tensor has shape torch.Size([0]) and storage nbytes() of 0.
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# Attempt to resize (Expected: Fail, maintain original shape)
# The resize_() method is called with a target shape of (5, 5, 5).
# Because the underlying storage is locked, this operation *should* fail cleanly,
# leaving the tensor's shape and storage as they were.
# However, the bug causes the shape to be updated *before* the failure is fully processed.
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    # The exception is caught here, as expected.
    pass

# Verify corruption
# This is where the consequences of the bug become apparent.
# The shape incorrectly shows the target size, while the storage size remains 0.
print(f"Shape: {t.shape}")       # Prints: torch.Size([5, 5, 5]) - Incorrect!
print(f"Storage: {t.untyped_storage().nbytes()}") # Prints: 0 - Correct, but mismatched with shape.
print(t) # CRASH - This line attempts to access data based on the incorrect shape,
         # leading to a crash (RuntimeError or Segmentation Fault).

Expected vs. Actual Behavior

Expected Behavior: When resize_() is called on a tensor with non-resizable storage, it should raise a RuntimeError. Crucially, after the exception is caught, the tensor's metadata (its shape and strides) should remain unchanged. The tensor should retain its original shape, which in this case is torch.Size([0]). This adheres to the principle of strong exception safety, ensuring that the tensor remains in a valid, albeit possibly unchanged, state.

Actual Behavior: As demonstrated by the reproduction code, PyTorch raises the RuntimeError as expected. However, before the exception fully halts the operation, the tensor's shape metadata is updated to the target shape, torch.Size([5, 5, 5]). The storage, meanwhile, remains at 0 bytes. This leads to the "Zombie" tensor state. The subsequent print(t) statement attempts to dereference memory based on the incorrect torch.Size([5, 5, 5]) shape, causing a crash. The mismatch between the reported shape and the actual, non-existent data storage is the root cause of the instability.

This bug is particularly concerning because it doesn't always manifest as an immediate, obvious error. In many cases, the corrupted tensor might be created and then used later in a computation, leading to silent data corruption or crashes that are difficult to trace back to the original resize_() call. The provided gist further elaborates on this, noting that while the minimal reproduction might show a RuntimeError on printing, the original context led to a more severe Segmentation Fault, highlighting the unpredictable nature of memory corruption bugs.

Versions and Environment

To help diagnose and fix such issues, it's crucial to know the environment in which they occur. The reported versions are:

  • PyTorch version: 2.9.0+cu126
  • CUDA: 12.6 (used to build PyTorch)
  • OS: Ubuntu 22.04.4 LTS (x86_64)
  • GCC version: 11.4.0
  • Python version: 3.12.12

While CUDA was used in the build, the reproduction itself didn't require a CUDA-enabled GPU (Is CUDA available: False). This suggests the bug is in the core CPU tensor implementation and not specific to GPU operations.

The Importance of Exception Safety in PyTorch

This "Dyilor" tensor bug is a stark reminder of the critical importance of exception safety in software development, especially in libraries that handle low-level memory operations like PyTorch. When an operation fails and throws an exception, the system should ideally be left in a state that is no worse than before the operation. This is known as the strong exception guarantee.

In this particular case, the resize_() operation fails because the underlying storage is not resizable. The strong exception guarantee would imply that the tensor's shape and stride metadata should remain exactly as they were before the resize_() call. However, the bug violates this guarantee. The metadata is changed, creating an inconsistent and dangerous "Zombie" state. This inconsistency can lead to runtime crashes, data corruption, and significant debugging headaches for developers relying on PyTorch.

  • Why it Matters for Developers:
    • Stability: Unexpected crashes like Segmentation Faults can bring applications to a halt.
    • Debugging: Tracing memory corruption bugs can be extremely time-consuming.
    • Data Integrity: Corrupted tensors can lead to incorrect model training or inference results.
    • Reliability: Developers need to trust that the tools they use will behave predictably, even when errors occur.

Towards a Solution

The fix for this issue would involve ensuring that the tensor's metadata (shape, stride) is only updated after the storage is confirmed to be resizable and the resize operation is successfully completed. If the check for resizable storage fails, the metadata should remain untouched, and the RuntimeError should be raised, preserving the tensor's integrity.

This is a fundamental aspect of robust C++ (and by extension, PyTorch) development. Operations that modify internal state should be carefully designed to ensure that either the entire operation succeeds or the state remains unchanged. This bug highlights a scenario where a partial update of the tensor's internal representation occurred before the operation was fully validated, leading to the observed corruption.

For users encountering this issue, the best course of action is to avoid operations that might trigger this bug. This includes ensuring that tensors intended for resizing do not have their storage locked by non-resizable backends like NumPy arrays when using set_(). If you need to resize, ensure you are working with tensors that PyTorch manages its own storage for.

For more information on PyTorch's internals and development, you can refer to the official PyTorch documentation and its community forums. Understanding how tensors manage their storage is key to avoiding such pitfalls.

Learn more about tensor operations and memory management in deep learning at PyTorch Documentation.

Explore the underlying principles of memory safety and exception guarantees in programming on sites like cppreference.com.