PyTorch Tensor Corruption Bug: Updates Shape On Resize Failure
Ever run into a mysterious crash in your PyTorch code, perhaps a Segmentation Fault or an internal RuntimeError that seems to come out of nowhere? It can be incredibly frustrating, especially when you're in the middle of a complex training loop or data processing pipeline. Sometimes, the culprit isn't a logic error in your algorithm but a subtle bug within the framework itself. This article dives into one such insidious issue: a bug where PyTorch incorrectly updates tensor shape metadata even when a storage resize operation fails, leading to what we'll call a "corrupted tensor" state. This can leave your tensors in a precarious, unusable condition, leading to those dreaded runtime crashes. We'll explore what causes this, demonstrate it with a minimal reproduction, and discuss the implications for your deep learning workflows.
Understanding the "Zombie Tensor" Problem
The core of this bug lies in how PyTorch handles tensor resizing, particularly when a tensor shares its underlying storage with a non-resizable buffer. Think of a tensor's storage as the actual block of memory holding your data, and its shape and stride as the metadata that tells PyTorch how to interpret that memory as a multi-dimensional array. Normally, when you try to resize a tensor using resize_(), PyTorch checks if the underlying storage can accommodate the new size. If the storage is fixed (e.g., when it originates from a NumPy array using set_()), PyTorch correctly raises a RuntimeError, stating: Trying to resize storage that is not resizable. This is the expected behavior, and it's crucial for maintaining data integrity.
However, the problem arises because this check happens after the tensor's shape and stride metadata have already been updated. So, even though the RuntimeError is caught, the tensor is left in an inconsistent state. Its shape attribute might now reflect a much larger size than intended, but its storage remains unchanged and effectively empty (0 bytes). We can liken this to a "zombie tensor" – it looks like it has a shape and dimensions, but it has no underlying data to back it up. Subsequent attempts to access this tensor, such as printing it or performing operations on it, will fail spectacularly. Because PyTorch expects data to be present based on the reported shape, it will likely lead to a Segmentation Fault or another internal RuntimeError as it tries to access memory that doesn't exist or is incorrectly interpreted. This bug is particularly concerning because it can manifest as hard-to-debug crashes deep within the library, making it challenging to pinpoint the root cause.
Reproducing the Bug: A Minimal Example
To truly grasp the issue, let's walk through a minimal reproduction. The goal here is to create a scenario where a tensor's storage is deliberately made non-resizable and then attempt to resize it. We'll use PyTorch and NumPy to set this up.
First, we need to create a non-resizable storage. A common way to achieve this is by using a NumPy array and then converting its storage. We initialize an empty NumPy array with a specific data type (e.g., np.int32) and then obtain its untyped storage using torch.from_numpy(...).untyped_storage(). This locked_storage is effectively immutable in terms of its size and capacity.
import torch
import numpy as np
# Create non-resizable storage (0 bytes)
locked_storage = torch.from_numpy(np.array([], dtype=np.int32)).untyped_storage()
Next, we create a fresh, empty PyTorch tensor. Crucially, we then use the .set_() method to assign our locked_storage to this new tensor. This effectively links the tensor's metadata to the non-resizable storage.
# Inject into a fresh tensor
t = torch.tensor([], dtype=torch.int32)
t.set_(locked_storage)
Now comes the critical step: attempting to resize this tensor. We'll call t.resize_((5, 5, 5)). According to the PyTorch documentation and expected behavior, this operation should fail because locked_storage cannot be resized. The RuntimeError should be raised, and ideally, the tensor's metadata (shape and stride) should remain unchanged, reflecting its original state (an empty tensor with torch.Size([0])).
However, this is where the bug manifests. PyTorch does raise the RuntimeError, but after it has already updated the tensor's shape metadata. So, while the exception is caught, the tensor's shape attribute is now incorrectly set to torch.Size([5, 5, 5]).
# Attempt to resize (Expected: Fail, maintain original shape)
# (Actual: Fails, but updates shape to 5x5x5)
try:
t.resize_((5, 5, 5))
except RuntimeError:
pass # The error is caught here
To verify the corruption, we can inspect the tensor's properties. The print(f"Shape: {t.shape}") statement will output torch.Size([5, 5, 5]), indicating the erroneous shape. Simultaneously, print(f"Storage: {t.untyped_storage().nbytes()}") will show 0, confirming that the underlying storage is still empty. The true disaster strikes when you try to actually use this tensor, for instance, by printing its contents:
print(t) # THIS LINE WILL LIKELY CRASH
This print(t) call, or any other operation that attempts to access the tensor's data based on its reported shape, will trigger a crash. The program might terminate with a Segmentation Fault or an internal RuntimeError because it's trying to read data from a 0-byte storage as if it contained a 5x5x5 array. The mismatch is fatal.
Expected vs. Actual Behavior
Let's clearly outline the expected and actual behaviors to highlight the bug's impact. The expected behavior when resize_() encounters a RuntimeError due to non-resizable storage is that the tensor's metadata—its shape and strides—should remain completely untouched. The operation should fail cleanly, and the tensor should retain its original dimensions, which in our minimal example is torch.Size([0]). This adheres to the principle of a strong exception guarantee, meaning that if an operation fails, the system remains in a state as if the operation never occurred.
On the actual behavior, as demonstrated by the minimal reproduction, the exception is raised and caught, but the tensor's shape metadata is partially updated. It incorrectly reflects the target size (torch.Size([5, 5, 5])) rather than its original size. This creates a critical inconsistency: the tensor reports having a shape that requires a significant amount of memory, but its actual storage() is empty, holding 0 bytes. This inconsistency between the metadata (shape) and the reality of the storage (size) is what leads to runtime failures like segmentation faults or unexpected internal errors when the tensor is accessed subsequently. The original report mentioned a RuntimeError upon printing in the gist, but the user's original program encountered a segmentation fault, underscoring the unpredictable and severe nature of this bug.
Implications for Your PyTorch Projects
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