PyTorch Tensor Resize Bug: Zombie Tensors And Crashes

Introduction

In the world of deep learning and tensor manipulation, PyTorch is a powerhouse. It's known for its flexibility and performance, especially when dealing with complex neural networks. However, like any sophisticated software, it can sometimes encounter unexpected issues. One such peculiar problem, which can lead to frustrating crashes and data corruption, involves the resize_() operation on tensors that share storage with non-resizable buffers. This article dives deep into this specific bug, exploring how it arises, its implications, and what it means for your PyTorch workflows. We'll be focusing on the phenomenon where PyTorch updates tensor metadata even when the underlying storage resize operation fails, creating what we'll call "zombie" tensors.

Understanding the "Zombie" Tensor Phenomenon

Let's start by laying out the scenario that triggers this bug. PyTorch allows you to create tensors that are essentially views or wrappers around existing data structures, including NumPy arrays. When you use torch.from_numpy() or similar methods, you're creating a PyTorch tensor that shares its underlying data storage with the NumPy array. This is often a performance optimization, as it avoids unnecessary data copying. Now, consider what happens when you try to resize a tensor that has this shared, non-resizable storage. PyTorch's resize_() method is designed to change the shape and size of a tensor. If the underlying storage cannot accommodate this change – for instance, if it's a fixed-size NumPy array or a memory block that's not meant to be expanded – PyTorch correctly raises a RuntimeError. The error message is quite clear: "Trying to resize storage that is not resizable." This is the expected and correct behavior. However, the bug lies in what happens after this exception is raised. The crucial issue is that PyTorch's internal state becomes inconsistent. Before the RuntimeError is actually thrown, the tensor's shape and stride metadata are updated to reflect the new target size that was requested. This means that even though the RuntimeError signals that the storage itself couldn't be resized, the tensor's metadata now points to a larger, non-existent data structure. The tensor is left in a peculiar, corrupted state – a "zombie" tensor. It has a shape that implies it contains data, but its actual storage is empty or unchanged, typically with zero bytes allocated. This mismatch between what the tensor thinks it should contain (based on its shape) and what it actually contains (its zero-byte storage) is the root cause of subsequent problems. When you try to interact with this "zombie" tensor – for example, by printing it, accessing its elements, or performing further operations – PyTorch's underlying C++ backend attempts to access data that isn't there according to the now-corrupted metadata. This often leads to a segmentation fault (a severe program crash) or another internal RuntimeError because the operations are fundamentally invalid. The original bug report mentions that in some complex scenarios, a segmentation fault can occur, while in a minimal reproduction, a RuntimeError might be seen during printing. Both point to the same underlying data corruption and inconsistency.

The Mechanics of Failure: A Deeper Dive

To truly grasp the severity of this bug, let's dissect the sequence of events. When you call tensor.resize_(new_shape), PyTorch embarks on a process that involves checking the tensor's underlying storage. If this storage is deemed resizable (e.g., a dynamically allocated PyTorch tensor buffer), the operation proceeds. The storage might be reallocated to accommodate the new size, and then the tensor's metadata (shape, strides, etc.) is updated to match this new configuration. However, the problem arises when the storage is not resizable. This commonly occurs when a tensor is created from a NumPy array using torch.from_numpy(). In this case, the tensor's storage is directly tied to the NumPy array's memory. NumPy arrays generally have fixed-size storage once created. The PyTorch resize_() operation, in its execution flow, first updates the tensor's shape and stride information to match the requested new_shape. It's only after this metadata update that it proceeds to check if the underlying storage is capable of holding data of the new size. If the storage is found to be non-resizable (which it will be in our case), a RuntimeError is raised. The critical flaw here is that the exception occurs after the metadata has already been modified. This leaves the tensor object in an inconsistent state: its shape attribute reflects the new_shape, but its storage() still points to the original, unmodified, and typically zero-byte storage. This is the "zombie tensor" state. Subsequent attempts to use this tensor are problematic. For instance, print(t) attempts to display the tensor's contents. Internally, this involves iterating through the tensor's elements based on its shape and strides. Since the shape indicates a size (e.g., (5, 5, 5)), but the storage has zero bytes, the program tries to read data from invalid memory locations. This leads to a segmentation fault, a low-level error indicating that the program tried to access memory it didn't have permission to access. Alternatively, as seen in the minimal reproduction, the error might manifest as another RuntimeError within PyTorch's error-handling mechanisms when it detects this invalid state during operations like printing. The strong exception guarantee, which dictates that a function should either succeed completely or leave the system in the state it was before the call, is violated here. The operation fails, but it leaves the tensor object in a corrupted state, not the original one. This lack of exception safety is what makes the bug particularly insidious. It's not just about the immediate failure; it's about the lingering corruption that can cause unexpected crashes much later in the program's execution, potentially in entirely different parts of the code, making debugging a nightmare.

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