PyTorch Tensor Resize Bug: Updates Shape On Failed Resize

by Alex Johnson 58 views

In the world of deep learning, tensors are the fundamental building blocks, and PyTorch is a powerhouse for manipulating them. However, even the most robust libraries can have their quirks. Today, we're diving deep into a rather insidious bug within PyTorch that can lead to corrupted tensor states, specifically when a resize_() operation fails due to underlying storage limitations. This issue, affecting how PyTorch handles tensor metadata versus its actual storage, can leave you with tensors that appear to have a different shape than they actually do, potentially leading to cryptic errors like segmentation faults or internal runtime errors. We'll explore the problem, provide a minimal reproduction case, and discuss the expected versus actual behavior, all to help you understand and potentially avoid this tricky scenario.

Understanding the "Zombie Tensor" Phenomenon

Let's talk about the core of the problem: PyTorch updates tensor shape metadata even when storage resize fails. This might sound like a minor detail, but it has significant implications for the integrity of your tensors. When you attempt to resize a tensor using resize_(), PyTorch first tries to update the tensor's shape and stride information to reflect the new dimensions you've requested. This is a crucial step in preparing for the actual memory reallocation or adjustment. However, if the tensor's underlying storage is immutable – meaning it cannot be resized – PyTorch is supposed to raise a RuntimeError to signal this incompatibility. For instance, this often happens when a tensor shares its storage with a non-resizable buffer, such as a NumPy array that was directly injected into the tensor using set_().

The bug arises because PyTorch's exception handling isn't as robust as it could be in this specific scenario. Before it checks if the storage is actually resizable, it proceeds to update the tensor's shape and stride metadata. So, if the storage check then fails and raises a RuntimeError, the tensor is left in a peculiar and dangerous state. It's like a ghost of its former self, or as we're calling it, a "Zombie Tensor". Its shape attribute might proudly declare a new, larger size (e.g., torch.Size([5, 5, 5])), but its storage() remains stubbornly empty, holding zero bytes of data. This stark mismatch between what the tensor thinks it is (its shape) and what it actually contains (its storage) is the root cause of the subsequent problems. When you try to access or print such a corrupted tensor, the program might crash with a segmentation fault or throw another internal RuntimeError because the system is trying to operate on data that doesn't exist in the expected format or location. This makes debugging particularly challenging, as the initial error might be a RuntimeError during the resize attempt, but the actual crash could occur much later in your code, making it hard to trace back to the root cause.

A Minimal Reproduction of the Bug

To truly grasp the severity and nature of this PyTorch bug, let's walk through a minimal reproduction scenario. This code snippet is designed to be as straightforward as possible, isolating the exact conditions that trigger the corrupted tensor state. It leverages NumPy's flexibility to create a non-resizable storage that PyTorch then attempts to manipulate, revealing the underlying issue in its exception-safe handling.

First, we need to set up the scenario by creating a tensor with non-resizable storage. A common way to achieve this is by using a NumPy array that has been converted into a PyTorch tensor, and then explicitly accessing its untyped_storage(). By creating an empty NumPy array (np.array([], dtype=np.int32)) and then getting its storage, we obtain a zero-byte, non-resizable storage object. This locked_storage is the key ingredient for demonstrating the bug. We then create a fresh, empty PyTorch tensor (torch.tensor([], dtype=torch.int32)), which initially has a shape of torch.Size([0]) and an empty storage.

The critical step is injecting this locked_storage into our fresh tensor using the t.set_(locked_storage) method. At this point, t is a tensor that points to an immutable, zero-byte storage. Now, we intentionally try to cause the error by calling t.resize_((5, 5, 5)). As expected, since the underlying storage is locked and has zero bytes, PyTorch correctly identifies that it cannot fulfill this request and raises a RuntimeError with the message: "Trying to resize storage that is not resizable."

However, the problem lies in what happens after the RuntimeError is raised but before the exception is fully unwound and handled by the program. The resize_() operation, in its flawed execution path, updates the tensor's shape attribute to torch.Size([5, 5, 5]) before it detects the impossibility of resizing the storage. So, when the RuntimeError is caught (or not caught, leading to a crash), the tensor t is left in this inconsistent state: t.shape reports torch.Size([5, 5, 5]), but t.untyped_storage().nbytes() still reports 0. The subsequent attempt to print this tensor (print(t)) or access its elements will inevitably fail. In the provided gist, this leads to a RuntimeError during the print statement. In other, more complex scenarios, this inconsistency has been observed to cause hard crashes with segmentation faults, which are notoriously difficult to debug. This minimal example clearly illustrates how a seemingly routine operation, when encountering an edge case with immutable storage, can corrupt the tensor's internal state, turning it into a ticking time bomb waiting to crash your program.

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:
    print("Caught expected RuntimeError during resize.")

# Verify corruption
print(f"Shape: {t.shape}")       # Prints: torch.Size([5, 5, 5])
print(f"Storage bytes: {t.untyped_storage().nbytes()}") # Prints: 0
# print(t) # This line would likely cause a crash (Segmentation Fault or RuntimeError)

Expected vs. Actual Behavior

To reinforce the understanding of this bug, let's explicitly contrast what should happen with what actually happens when resize_() encounters non-resizable storage. This comparison highlights the violation of a fundamental principle in robust software design: the strong exception guarantee.

A strong exception guarantee means that if an operation fails (i.e., throws an exception), the program's state should be exactly as it was before the operation was attempted. In the context of PyTorch's resize_() method, if the operation fails because the underlying storage cannot be resized, the tensor's metadata – its shape and strides – should remain unchanged. This ensures that the tensor remains in a consistent and valid state, even though the resize operation itself was unsuccessful. Therefore, the expected behavior is that after the RuntimeError is raised and caught, the tensor t should retain its original shape, which in our minimal reproduction case is torch.Size([0]), and its storage should still be empty (0 bytes). The program would continue executing without issues, perhaps handling the failed resize gracefully.

However, the actual behavior, as demonstrated by the reproduction code, is quite different and problematic. When t.resize_((5, 5, 5)) is called, PyTorch updates t.shape to torch.Size([5, 5, 5]) before it verifies that the locked_storage is indeed not resizable. When this verification fails, a RuntimeError is thrown. Crucially, the tensor's metadata (the shape) is not rolled back. This leaves t in a corrupted state: t.shape is torch.Size([5, 5, 5]), but t.untyped_storage().nbytes() is 0. This inconsistency is what causes downstream failures. The subsequent print(t) statement attempts to interpret and display a tensor with a 5x5x5 shape, but finds no actual data in its 0-byte storage. This leads to crashes, either a RuntimeError or a more severe segmentation fault, depending on the specifics of the memory access and the environment. This violation of the strong exception guarantee turns a predictable error into a program-breaking bug.

Versions and Environment

To help diagnose and track this issue, it's essential to know the environment in which it was observed. The following details provide a snapshot of the system and software versions:

  • PyTorch version: 2.9.0+cu126 (Note: This appears to be a hypothetical or future version, as current stable releases are typically lower. For reference, common recent versions might be 2.0.x, 2.1.x, 2.2.x, or 2.3.x).
  • Debug build: False
  • CUDA: Built with CUDA 12.6, but CUDA is not available in the runtime environment (Is CUDA available: False). This might indicate the build was for a CUDA-enabled system, but the execution environment lacks CUDA drivers or hardware.
  • 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
  • XNNPACK: True

The discrepancy between the PyTorch build configuration (CUDA 12.6) and the runtime environment (CUDA not available) is worth noting but likely not the direct cause of this specific tensor corruption bug, which seems to be more related to the internal logic of resize_() and exception handling.

Mitigating the Risk

While this bug highlights a specific flaw in PyTorch's exception handling for tensor resizing, understanding its nature allows for proactive mitigation. The core issue stems from attempting to resize tensors backed by non-resizable storage, often introduced via mechanisms like set_() with NumPy arrays or other C++ backed objects where the storage is fixed. To avoid this "Zombie Tensor" state, the most effective strategy is to avoid calling resize_() on tensors that you know or suspect might have immutable storage.

If you are working with tensors that originate from external sources like NumPy or are created in ways that might tie them to fixed memory buffers, be extra cautious. Instead of in-place resizing with resize_(), consider creating a new tensor with the desired shape and then copying the data over, if appropriate. For example, new_tensor = torch.empty(new_shape, dtype=old_tensor.dtype) followed by new_tensor.copy_(old_tensor) might be a safer, albeit potentially less memory-efficient, alternative.

Furthermore, always ensure your PyTorch version is up-to-date, as such bugs are often fixed in newer releases. Thorough testing, especially of code paths involving tensor manipulation and potential resizing, can also catch these issues early. Pay close attention to any RuntimeError exceptions related to tensor resizing; they might be indicators of underlying storage immutability. If you encounter such errors, investigate the origin of the tensor's storage before proceeding with operations that might fail.

For more in-depth information on PyTorch's tensor operations and memory management, you can refer to the official PyTorch Documentation. Understanding the intricacies of tensor storage and sharing is key to writing robust and error-free deep learning code.