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PyTorch Tensor Corruption Bug: Resize Failure Leads To Crashes

Alex Johnson
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PyTorch Tensor Corruption Bug: Resize Failure Leads To Crashes

The Problem with Xhzhpj and Corrupted Tensors

We've stumbled upon a rather thorny issue within PyTorch, specifically concerning how the library handles tensor shape metadata when a storage resize operation fails. This bug, which we'll refer to as the "Xhzhpj update bug," can lead to what we're calling corrupted "Xxlylm" tensors. It's a critical problem because it can manifest as segmentation faults or internal runtime errors, completely derailing your deep learning workflows. The core of the issue lies in the fact that even when PyTorch correctly identifies that a tensor's storage cannot be resized (e.g., when it's backed by a non-resizable NumPy array), it unfortunately proceeds to update the tensor's shape and stride information before the error is fully processed. This leaves the tensor in a precarious, inconsistent state – a sort of digital "zombie" – where the reported shape suggests a sizable tensor, but its actual storage is effectively empty or zero bytes. This mismatch is a recipe for disaster, as subsequent operations attempting to access this malformed tensor will inevitably lead to crashes. We'll dive deeper into the technical details and provide a minimal reproduction case to illustrate just how this happens.

This problem typically surfaces when you're working with tensors that share their underlying storage with buffers that are not meant to be resized. A common scenario for this is when a NumPy array is injected into PyTorch using set_(). PyTorch is designed to catch this and will raise a RuntimeError with a message like: "Trying to resize storage that is not resizable." This is the correct behavior, indicating that the operation cannot be performed as requested. However, the exception handling, or rather the lack thereof in this specific sequence of operations, is where the bug resides. The tensor's internal metadata, which includes its shape (dimensions) and strides (how elements are laid out in memory), gets updated to reflect the intended new size before the system checks if the storage itself can accommodate this change. Imagine telling your computer to arrange books on a shelf that's too small for them – it first starts moving the books according to the new arrangement, but then realizes it can't actually fit them all, leaving the books in a messy, half-arranged state. In PyTorch's case, the "messy state" is the corrupted tensor. The shape might now say it's a 5x5x5 tensor, but the underlying storage still reports zero bytes, meaning there's no actual data to hold those dimensions. This inconsistency is the root cause of the subsequent crashes. Debugging such issues can be incredibly frustrating, especially in complex training loops where the corrupted tensor might be passed around through many functions before the error becomes apparent. The provided minimal reproduction case aims to isolate this specific failure mode, making it easier to understand and hopefully leading to a robust fix.

Understanding the "Zombie Tensor" State

Let's break down what happens when PyTorch encounters this resize failure. The bug essentially creates what we're calling a "Zombie Tensor". This isn't a formal PyTorch term, but it aptly describes the state of the tensor after the failed resize_() operation. Normally, when you call resize_() on a tensor, PyTorch first checks if the underlying storage can actually be resized to the new dimensions. If the storage is fixed or shared in a way that prevents resizing (like when it's linked to a NumPy array's memory), PyTorch is supposed to raise an error and leave the tensor's metadata untouched. This ensures that the tensor remains in a consistent state, even if the operation failed. However, in the case of this bug, the sequence of events is different. PyTorch attempts to update the tensor's shape and stride information to match the requested new size before it fully validates the storage's resizability. So, even though the RuntimeError is eventually thrown – correctly informing you that the storage isn't resizable – the damage to the tensor's internal structure has already been done. The tensor now thinks it has a new shape (e.g., torch.Size([5, 5, 5])), but its storage() remains empty, reporting 0 bytes. This is the "zombie" state: it has the appearance of a valid tensor with a certain shape, but it lacks the actual data to support it. Accessing this zombie tensor, whether through printing it, trying to perform calculations, or passing it to other functions, becomes problematic. Since the tensor's metadata claims it should contain data at specific offsets (determined by its shape and strides), but the storage has no data, operations that try to read or write to these non-existent locations will fail. This commonly results in a Segmentation Fault, which is a low-level memory access error, or an internal RuntimeError within PyTorch's C++ backend, as it tries to operate on corrupted internal structures. The minimal reproduction code provided demonstrates this precisely: we create a tensor with zero-byte storage, attempt to resize it to a significant size, catch the expected RuntimeError, but then find that t.shape reports the new, incorrect size while t.untyped_storage().nbytes() is still zero. Printing t at this point is what triggers the crash, showcasing the severe inconsistency.

To elaborate further on the "zombie tensor" concept, imagine you have a beautifully crafted blueprint for a skyscraper, detailing every floor, room, and window. Now, imagine you try to build this skyscraper on a tiny plot of land that can only accommodate a small shed. The blueprint (tensor metadata) might still say "skyscraper," but the actual construction site (tensor storage) can't possibly match it. If you tried to use the skyscraper blueprint to direct construction on the shed site, you'd run into immediate problems. You might try to place a massive pillar where only a tiny support beam should go, leading to a structural collapse (segmentation fault or runtime error). This is analogous to what happens with the "Xxlylm" tensors. The resize_() operation, when applied to a tensor with non-resizable storage, attempts to write the new shape and stride information into the tensor's metadata structure. This metadata is typically stored in a separate control block from the actual data buffer. The crucial error occurs because this metadata update happens before the check that verifies if the data buffer (the storage) can actually hold data for the new shape. So, the tensor's internal pointers and size information are modified to point to a hypothetical, larger data structure, but the actual data buffer remains unchanged and empty. When the RuntimeError is raised, it signals the failure of the storage resize, but the metadata has already been corrupted. This leaves the tensor in an invalid state. Trying to access elements of this tensor involves calculating memory addresses based on its shape and strides. Since the shape and strides now indicate a larger tensor, these calculations will point to memory locations that don't exist within the actual (zero-byte) storage. This is precisely why printing the tensor or performing any operation that accesses its elements leads to memory access violations or internal errors. The minimal reproduction example highlights this vividly:

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

The output Shape: torch.Size([5, 5, 5]) and Storage: 0 clearly shows the discrepancy. The subsequent print(t) would then attempt to render this inconsistent tensor, leading to a crash. This behavior violates the strong exception guarantee, which dictates that if an operation fails, the system should be left in the state it was in before the operation began.

The Root Cause: Exception Safety Failure

At its heart, this issue is a failure in exception safety. In robust software design, especially in libraries like PyTorch that handle complex memory operations, operations are expected to adhere to certain guarantees when errors occur. One of the most desirable is the Strong Exception Guarantee. This guarantee states that if an operation throws an exception, the program's state should remain exactly as it was before the operation was invoked. In simpler terms, if something goes wrong, it shouldn't leave your data in a half-corrupted or inconsistent mess.

PyTorch's resize_() operation, when applied to a tensor whose underlying storage cannot be resized (such as a tensor derived from a NumPy array via set_()), is supposed to fail gracefully. It correctly identifies the problem and attempts to raise a RuntimeError. However, the bug lies in the timing of the metadata updates. Before the RuntimeError is fully raised and the operation aborted, PyTorch modifies the tensor's internal shape and stride metadata to reflect the new, intended dimensions. This means that even though the RuntimeError is caught, the tensor's metadata has already been altered. It now thinks it has a new shape (e.g., torch.Size([5, 5, 5])) but its storage size remains unchanged (e.g., 0 bytes). This creates a fundamental inconsistency: the tensor's description of itself (its shape) no longer matches its actual capacity (its storage). This orphaned metadata, pointing to a non-existent data buffer, is what causes subsequent operations to fail. When you try to access elements of this "zombie tensor," PyTorch uses the corrupted shape and stride information to calculate memory addresses. Since the actual storage is empty, these calculations lead to attempts to read from or write to invalid memory locations, resulting in segmentation faults or other low-level errors. The minimal reproduction example starkly illustrates this. We create a tensor with zero storage, attempt to resize it, catch the RuntimeError, but the tensor's shape attribute is already updated, while its untyped_storage().nbytes() remains zero. Printing this tensor then triggers the crash because the printing mechanism tries to interpret the malformed shape information against the empty storage.

This specific bug highlights a vulnerability in the sequence of operations within resize_(). Ideally, the check for storage resizability should happen before any metadata is altered. If the storage is found to be non-resizable, the RuntimeError should be raised immediately, and the tensor's metadata should remain untouched. This would uphold the strong exception guarantee. The current implementation, however, performs a partial update of the metadata first, and only then checks the storage, leading to the corrupted state when the check fails. This is a critical flaw because it can lead to silent corruption that only manifests much later in the execution, making debugging extremely difficult. The widespread nature of this issue, as indicated by the numerous linked GitHub issues and discussions, underscores the importance of addressing it promptly to ensure the reliability of PyTorch applications.

Minimal Reproduction Case

To make this bug as clear as possible, we've created a minimal reproduction case using Python and PyTorch. This snippet of code isolates the exact scenario that triggers the tensor corruption. It allows developers and users to easily replicate the problem and understand the underlying mechanics without needing to navigate complex codebases.

Here's the code:

import torch
import numpy as np

# Step 1: Create non-resizable storage (0 bytes)
# We start by creating an empty NumPy array and converting its storage.
# This storage is inherently not resizable in the way PyTorch expects for its tensors.
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()

# Step 2: Inject into a fresh tensor
# A new PyTorch tensor is created, initially empty.
# Then, we use set_() to make this tensor share the non-resizable storage we created.
# At this point, the tensor has shape torch.Size([]) and 0 storage bytes.
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)

# Step 3: Attempt to resize (The problematic operation)
# We try to resize the tensor to a 5x5x5 shape. 
# This is where the core issue occurs. PyTorch's resize_() function will:
# a) Attempt to update the tensor's shape and stride metadata to (5, 5, 5).
# b) THEN check if the underlying storage can be resized.
# Since 'locked_storage' is not resizable, it raises a RuntimeError.
# However, the shape metadata has already been updated!
try:
    t.resize_((5, 5, 5))
except RuntimeError:
    # We catch the expected RuntimeError, but the tensor is now in a corrupted state.
    print("Caught expected RuntimeError: Cannot resize non-resizable storage.")
    pass

# Step 4: Verify the corruption
# Now we inspect the tensor's state after the failed resize operation.
print(f"Shape: {t.shape}")       # Expected: torch.Size([]), Actual: torch.Size([5, 5, 5])
print(f"Storage: {t.untyped_storage().nbytes()}") # Expected: 0, Actual: 0 (This confirms the storage wasn't resized)

# Step 5: Trigger the crash
# Attempting to print or access elements of this inconsistent tensor will cause a crash.
# This is because the shape metadata claims there are elements, but the storage has none.
# print(t) # Uncommenting this line will likely result in a Segmentation Fault or internal error.
print("Tensor state after failed resize (metadata updated, storage empty): ")
print(f"  Shape: {t.shape}")
print(f"  Storage bytes: {t.untyped_storage().nbytes()}")
print("Attempting to print the tensor would typically cause a crash.")

When you run this code, you'll observe the following:

  1. RuntimeError Caught: The try...except block successfully catches the RuntimeError because locked_storage is indeed not resizable.
  2. Corrupted Shape: Despite the error, the print(f"Shape: {t.shape}") statement will output Shape: torch.Size([5, 5, 5]). This is the key indicator of corruption – the shape has been updated.
  3. Empty Storage: The print(f"Storage: {t.untyped_storage().nbytes()}") statement correctly shows Storage: 0. This confirms that the actual data buffer was not resized, highlighting the mismatch between the shape and the storage.
  4. Imminent Crash: If you were to uncomment the print(t) line, your program would likely terminate with a segmentation fault or a similar low-level error. This happens because PyTorch tries to render the tensor based on its reported torch.Size([5, 5, 5]) shape, but finds no data in the underlying 0-byte storage.

This minimal example effectively demonstrates how PyTorch can enter an inconsistent state where the tensor's metadata is out of sync with its actual data storage, leading to potential crashes and data corruption. This behavior violates the strong exception guarantee, which is crucial for reliable software.

Versions Information

To help diagnose and fix this issue, it's important to provide the environment details where the bug was observed. These details can be crucial for pinpointing the exact version of PyTorch, the operating system, and other dependencies that might be involved.

Here's the collected environment information:

  • PyTorch version: 2.9.0+cu126

  • Debug build: False

  • CUDA build: 12.6

  • ROCM build: N/A

  • Operating System: Ubuntu 22.04.4 LTS (x86_64)

  • GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0

  • Clang version: Could not collect

  • CMake version: version 3.31.10

  • Libc version: glibc-2.35

  • Python version: 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)

  • Python platform: Linux-6.6.105+-x86_64-with-glibc2.35

  • CUDA available: False

  • CUDA runtime version: 12.5.82

  • CUDA_MODULE_LOADING: N/A

  • GPU models and configuration: Could not collect

  • Nvidia driver version: Could not collect

  • cuDNN version: Probably one of the following: /usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1 ... (list of cuDNN files)

  • XPU available: False

  • HIP runtime version: N/A

  • MIOpen runtime version: N/A

  • XNNPACK available: True

  • CPU: Architecture: x86_64, CPU op-mode(s): 32-bit, 64-bit

This detailed information is vital for the PyTorch development team to reproduce the bug accurately and implement a fix. It helps ensure that the solution addresses the specific conditions under which this corruption occurs.

Expected vs. Actual Behavior

Understanding the difference between what should happen and what is happening is key to identifying and fixing bugs. In the case of the Xhzhpj update bug, the discrepancy is quite stark and directly leads to the observed instability.

Expected Behavior:

When the resize_() operation is called on a PyTorch tensor whose underlying storage is not resizable (e.g., when sharing storage with a NumPy array via set_()), the following should occur:

  1. Error Detection: PyTorch should detect that the storage cannot be resized.
  2. Immediate Exception: A RuntimeError should be raised immediately, indicating the problem (e.g., "Trying to resize storage that is not resizable.").
  3. No Metadata Change: Critically, the tensor's shape and stride metadata must remain unchanged. The tensor should continue to reflect its original dimensions.
  4. Strong Exception Guarantee: The tensor should be left in a consistent state, identical to its state before the resize_() call.

In essence, if the resize operation cannot be safely completed, it should be treated as if it never happened, from the perspective of the tensor's metadata. For our minimal reproduction case, the tensor t starts with shape torch.Size([]) and storage().nbytes() == 0. After a failed resize_((5, 5, 5)) call, the expected state would be the same: shape torch.Size([]) and storage().nbytes() == 0, with a RuntimeError having been raised.

Actual Behavior:

As demonstrated by the minimal reproduction case and the description of the bug, the actual behavior deviates significantly:

  1. Error Detection: PyTorch still detects that the storage cannot be resized and raises a RuntimeError.
  2. Metadata Update (Prematurely): The problem is that before the RuntimeError is fully processed and the operation aborted, PyTorch updates the tensor's shape and stride metadata to match the requested new size (e.g., (5, 5, 5)).
  3. Inconsistent State: After the RuntimeError is caught, the tensor is left in a corrupted state. Its shape attribute reflects the new, incorrect dimensions (e.g., torch.Size([5, 5, 5])), but its underlying storage().nbytes() remains unchanged (e.g., 0).
  4. Violation of Guarantee: This state violates the strong exception guarantee. The tensor is left in an inconsistent, "zombie" state where its metadata does not accurately represent its data storage.
  5. Crashes: Subsequent attempts to access or operate on this corrupted tensor (e.g., by printing it) lead to segmentation faults or internal runtime errors because the program tries to interpret the tensor's shape information against a non-existent data buffer.

The core of the issue is that the metadata update occurs in a code path that is not exception-safe with respect to the storage resizability check. The fix needs to ensure that metadata is only updated after the storage is confirmed to be resizable, or that all metadata changes are rolled back if the storage check fails.

Conclusion and Next Steps

The "Xhzhpj updates tensor shape metadata even when storage resize fails" bug is a critical issue within PyTorch that can lead to significant instability and data corruption. The core problem lies in the failure to uphold the strong exception guarantee during resize_() operations on tensors with non-resizable storage. By updating the tensor's shape and stride metadata before confirming the storage's resizability, PyTorch can leave tensors in an inconsistent "zombie" state, which subsequently causes crashes when these tensors are accessed.

We've detailed the bug, provided a minimal reproduction case, and outlined the expected versus actual behavior, including the environment details. The minimal reproduction code clearly shows how a tensor can be left with a seemingly valid shape but zero storage, setting the stage for memory access errors.

The recommended next step is to address the exception safety of the resize_() operation. Specifically, the code path responsible for updating the tensor's shape and stride metadata needs to be modified. The safest approach would be to perform all checks (including storage resizability) before making any modifications to the tensor's metadata. If any check fails, the RuntimeError should be raised, and the tensor's state should remain unchanged. This ensures that PyTorch consistently adheres to the strong exception guarantee.

We hope this detailed explanation and the provided code will aid the PyTorch development team in quickly identifying and resolving this bug. Reliable tensor operations are fundamental to the success of deep learning research and applications, and fixing this issue will contribute to the overall robustness of the PyTorch ecosystem.

For more information on PyTorch's internal workings and best practices for handling tensors, you can refer to the official PyTorch documentation.

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