PyTorch Bug: Corrupted Tensors From Failed Resizes

by Alex Johnson 51 views

If you're working with PyTorch, especially in scenarios involving NumPy arrays or shared storage, you might encounter a rather nasty bug that can lead to corrupted tensors and even crashes. This issue arises when a tensor's storage cannot be resized, but PyTorch incorrectly updates the tensor's shape and stride metadata before realizing the storage is immutable. The result? A "zombie tensor" – a tensor that claims to have a shape and size, but its underlying storage is empty and inaccessible, leading to segmentation faults or runtime errors down the line. Let's dive deep into this problem, understand how it happens, and what the implications are.

The Anatomy of the Bug: A Tale of Two States

The core of the problem lies in the way PyTorch handles tensor resizing, particularly when the underlying storage is not flexible. When you call resize_() on a tensor, PyTorch first attempts to adjust the tensor's shape and stride information to reflect the new dimensions you've requested. It then proceeds to check if the tensor's storage can accommodate this change. For tensors backed by standard PyTorch Tensors, this is usually fine. However, when a tensor shares its storage with a non-resizable buffer – such as a NumPy array that you've integrated using set_() – this resize operation will inevitably fail. PyTorch correctly identifies this failure and raises a RuntimeError with a message like: "Trying to resize storage that is not resizable."

This is where the bug sneaks in. The RuntimeError is raised after the tensor's shape and stride metadata have already been updated. So, even though the storage resize failed, the tensor thinks it has been resized. It now points to a shape indicating a much larger size (e.g., torch.Size([5, 5, 5])), but its actual storage() remains at 0 bytes, as it was initially defined. This creates a critical inconsistency: the tensor's metadata is completely out of sync with its data, or lack thereof. This state is colloquially termed a "zombie tensor" because it appears to exist and have dimensions, but it contains no actual data and is effectively dead. Any subsequent attempt to access or even print this tensor will likely result in a segmentation fault or an internal RuntimeError, as the program tries to access memory that doesn't exist or is incorrectly interpreted.

Minimal Reproduction: Witnessing the Corruption

To truly grasp the severity of this bug, let's look at a minimal reproduction case. This code snippet clearly demonstrates how to trigger the issue.

import torch
import numpy as np

# Create non-resizable storage (0 bytes)
# This simulates storage that cannot be modified after creation.
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Inject this locked storage into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# Attempt to resize the tensor
# We expect this to fail because the storage is not resizable.
# The bug occurs because the shape is updated *before* the failure is caught.
try:
    t.resize_((5, 5, 5)) # Requesting a resize to a 5x5x5 tensor
except RuntimeError:
    # The exception is caught here, but the damage is already done.
    pass

# Verify the corruption
# The shape is now [5, 5, 5], but the storage size is still 0 bytes.
print(f"Shape: {t.shape}")
print(f"Storage: {t.untyped_storage().nbytes()}")
# print(t) # This line would typically cause a crash (Segmentation Fault or RuntimeError)

When you run this code, you'll observe:

  • Shape: torch.Size([5, 5, 5]): The tensor reports it has the dimensions 5x5x5.
  • Storage: 0: However, its underlying storage is still 0 bytes. There is no data.

The comment # print(t) # This line would typically cause a crash highlights the critical point. If you uncomment this line, your program will likely crash. The original report mentioned a segmentation fault, while the minimal reproduction might throw a RuntimeError when trying to print the tensor's contents, depending on the exact PyTorch version and environment. The core issue remains the same: a state of extreme inconsistency.

Expected vs. Actual Behavior: The Guarantee Gap

In robust software design, especially in libraries dealing with memory management like PyTorch, exception safety is paramount. There are different levels of exception guarantees, but for operations that modify state, a strong exception guarantee is highly desirable. This means that if an exception is thrown during an operation, the program state remains unchanged as if the operation never happened. In the context of resize_(), the expected behavior would be:

  • If resize_() throws a RuntimeError because the storage is not resizable, the tensor's metadata (shape and stride) should remain exactly as they were before the resize_() call. In our minimal reproduction, this would mean the shape should stay torch.Size([0]).

However, as the reproduction demonstrates, the actual behavior deviates significantly:

  • The exception is thrown, indicating the operation failed as expected at the storage level.
  • But, the tensor's shape metadata is updated to the new target size (torch.Size([5, 5, 5]) in our example) before the exception is handled.

This creates a "zombie" or "corrupted" tensor state where the shape suggests data exists, but the storage is empty. This inconsistency is what leads to downstream errors, ranging from unexpected RuntimeErrors to hard crashes like segmentation faults when the corrupted tensor is accessed or printed.

Implications for Your Workflow

This bug, while specific in its trigger conditions, can have broad implications for developers using PyTorch, especially in research or production environments where tensors might be manipulated in complex ways.

  1. Data Corruption: The most immediate concern is data corruption. If this bug occurs within a larger program, especially in loops or complex data pipelines, the corrupted tensors can propagate, leading to incorrect computations, unexpected model behavior, or silent data integrity issues.
  2. Crashes and Instability: Segmentation faults and internal runtime errors are highly disruptive. They can cause your entire application to crash, leading to lost work and significant debugging effort. Debugging these low-level memory errors can be particularly challenging.
  3. Integration Challenges: The bug is most likely to manifest when integrating PyTorch with other libraries that manage their own memory, such as NumPy. If you frequently use torch.from_numpy() or tensor.set_() to manage tensors with external data buffers, you are at a higher risk.
  4. Difficulty in Debugging: As the original report hinted, reproducing this bug in a minimal fashion can be difficult if it occurs within a complex execution flow. The