PyTorch Bug: Corrupted Tensors Due To Failed Resize Operations
Have you ever encountered a situation in PyTorch where a tensor suddenly becomes unusable, leading to cryptic errors or even segmentation faults? It turns out there's a subtle, yet critical, bug in how PyTorch handles certain resize operations. Eghabs (the placeholder name for the component in question) updates tensor shape metadata even when storage resize fails, leading to corrupted tensors that can cause significant headaches for developers. This article delves into the nature of this bug, explains why it happens, and discusses its implications.
Understanding the Problem: The "Zombie Tensor"
Let's break down what's happening. When you call the resize_() method on a PyTorch tensor, the library attempts to modify the underlying storage that holds the tensor's data. However, there are scenarios where this storage cannot be resized. A common example is when a tensor shares its storage with a non-resizable buffer, such as a NumPy array that you might have integrated into your PyTorch workflow using set_(). In such cases, PyTorch is designed to throw a RuntimeError, specifically: "Trying to resize storage that is not resizable."
This error message is informative, but the problem lies in the exception-safety of the operation. Before PyTorch even checks if the storage is resizable, it proceeds to update the tensor's shape and stride metadata to reflect the new, target size. If the storage resize then fails, the tensor is left in a highly unstable and inconsistent state. We can call this a "Zombie Tensor". Its shape attribute might indicate a large, intended size (e.g., torch.Size([5, 5, 5])), but its actual storage() remains empty, holding 0 bytes of data. This stark mismatch between what the tensor thinks it is and what it actually contains is the root cause of the corruption.
When you try to interact with this "Zombie Tensor" after the exception has been caught, you're likely to encounter serious issues. Accessing its data or even attempting to print it can trigger Segmentation Faults or internal RuntimeErrors. This is because the program is trying to read data from a tensor that claims to have a specific shape and dimensions, but there's no actual data in its storage to fulfill that claim. The results are unpredictable and can be incredibly difficult to debug, especially in complex codebases where the corrupted tensor might have been created much earlier in the execution flow.
This behavior violates a fundamental principle of robust software design: the Strong Exception Guarantee. This guarantee states that if a function call throws an exception, the system should remain in a state as if the function call never happened. In this case, the resize_() operation, upon failing, does modify the tensor's metadata, failing to uphold this guarantee. The expected outcome is that if resize_() fails, the tensor's metadata should remain entirely unchanged, preserving its original shape (e.g., torch.Size([0]) in the minimal reproduction example).
The Minimal Reproduction Case
To illustrate this bug clearly, a minimal reproduction example has been provided. It highlights the exact sequence of events that leads to the corrupted tensor:
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
In this snippet, we first create an empty, non-resizable storage object using NumPy. This storage is then directly assigned to a new PyTorch tensor t. When t.resize_((5, 5, 5)) is called, PyTorch attempts to resize this storage. Since the storage is based on a NumPy array and is effectively locked, the resize operation fails, and a RuntimeError is correctly raised. However, as the bug describes, the tensor's shape metadata has already been updated to torch.Size([5, 5, 5]) before the error occurred.
Consequently, when we try to print the tensor t, the program attempts to interpret t as a 5x5x5 tensor. But because its underlying storage is only 0 bytes, it cannot retrieve the necessary data, leading to the described crash (either a RuntimeError in some environments or a more severe Segmentation Fault in others). The print(t) line is where the corruption becomes evident and the program falters.
Implications for Developers
The consequences of this bug can range from inconvenient to catastrophic for machine learning projects. In simpler scripts, encountering a RuntimeError might be manageable if developers are vigilant. However, in larger, more complex applications, such as training deep neural networks or performing extensive data preprocessing, these errors can manifest much later in the process. A corrupted tensor might be silently passed through several functions or layers before its presence causes a crash. This delayed failure makes debugging extremely challenging, as developers might spend hours tracing the issue back to its origin, which could be a seemingly innocuous tensor manipulation step.
The primary risk is data corruption and unpredictable behavior. When a tensor's metadata is out of sync with its storage, any operation that relies on that metadata (which is almost all tensor operations) can produce incorrect results or crash the program entirely. This can lead to incorrect model training, flawed predictions, and unreliable results. For production systems, such bugs can be disastrous, leading to downtime and loss of confidence in the application.
Furthermore, the nature of the bug means it's not immediately obvious. If a program crashes with a segmentation fault, diagnosing the cause can be a time-consuming process. Developers might initially suspect memory leaks, hardware issues, or other low-level problems, overlooking a subtle bug in tensor manipulation logic. The fact that the RuntimeError is caught but the tensor remains in a corrupted state means that the program might continue running for a while before hitting a more critical failure point, making it harder to pinpoint the exact line of code responsible.
Addressing the Bug: Towards Robust Tensor Operations
Fixing this bug requires ensuring that PyTorch operations adhere to strong exception guarantees. When an operation like resize_() fails, it must not leave the system in an intermediate, corrupted state. The metadata of the tensor should be atomic with the storage operation; either both succeed, or neither changes.
Ideally, the resize_() function should perform all necessary checks before making any modifications to the tensor's metadata. If the storage is found to be non-resizable, the RuntimeError should be raised immediately, with no changes made to the tensor's shape or stride. This would prevent the creation of "Zombie Tensors" and ensure that the tensor remains in its original, valid state even after a failed operation.
In the meantime, developers can mitigate this risk by being extra cautious when resizing tensors, especially those that might originate from or share storage with external libraries like NumPy. Explicitly checking tensor properties before and after resize operations, or employing defensive programming techniques, can help catch these issues earlier. However, the most robust solution lies in a fix within the PyTorch library itself, ensuring that such operations are inherently safe.
This bug underscores the importance of careful error handling and the need for libraries to provide strong guarantees, especially when dealing with low-level memory management and data structures. By addressing this issue, PyTorch can further enhance its reliability and become an even more dependable tool for the machine learning community.
For more information on PyTorch's internal workings and best practices, you can refer to the official PyTorch documentation. Understanding memory management and tensor operations is crucial for advanced usage, and their guides offer valuable insights. Additionally, for broader context on software robustness and exception safety, resources on computer science principles can provide a solid foundation.
