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pytorch - 💡(How to fix) Fix Adding a UserWarning when pickling / deepcopying tensor views [1 comments, 1 participants]

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pytorch/pytorch#182105•Fetched 2026-05-02 05:27:13
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Root Cause

Serializing (via pickle, copy.deepcopy, or torch.save) a small tensor view can result in unexpectedly large memory usage because the entire underlying storage is serialized rather than just the visible elements. While this is the expected behavior and it is documented (here and there) it can be surprising and when users are unaware of it. Moreover this is not numpy default behavior which might be confusing for people transitioning from numpy.

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🚀 The feature, motivation and pitch

Serializing (via pickle, copy.deepcopy, or torch.save) a small tensor view can result in unexpectedly large memory usage because the entire underlying storage is serialized rather than just the visible elements. While this is the expected behavior and it is documented (here and there) it can be surprising and when users are unaware of it. Moreover this is not numpy default behavior which might be confusing for people transitioning from numpy.

This behavior can:

  • Lead to unintentionally large serialized objects
  • Cause memory/performance issues in data pipelines
  • Be difficult to diagnose without knowledge of PyTorch’s storage model

It personnaly took a couple hour to understand why my RAM was blowing up when moving tensor views between ray processes. Other pytorch users have also reported similar confusion #1995.

I think the simplest and most user friendly fix would be to simply emit a UserWarning when serializing a view.

Alternatives

Expose a flag to the user to mimic numpy's behavior.

Additional context

No response

cc @mruberry @mikaylagawarecki

extent analysis

TL;DR

Emitting a UserWarning when serializing a tensor view could help prevent unexpectedly large memory usage due to the serialization of the entire underlying storage.

Guidance

  • Consider adding a warning when serializing tensor views to alert users of potential memory issues.
  • Review the PyTorch documentation on serialization and storage sharing to understand the expected behavior.
  • When working with tensor views, verify the memory usage after serialization to catch any unexpected increases.
  • If possible, test the serialization with a small tensor view to observe the memory usage before applying it to larger tensors.

Example

import torch
import warnings

# Create a tensor view
tensor = torch.randn(10, 10)
view = tensor[:, :5]

# Serialize the view with a warning
with warnings.catch_warnings(record=True) as w:
    torch.save(view, 'view.pt')
    if w:
        print("Warning: Serializing a tensor view may result in large memory usage.")

Notes

This solution assumes that the issue is primarily related to user awareness of PyTorch's serialization behavior. The proposed fix focuses on notifying users of potential memory issues when serializing tensor views.

Recommendation

Apply workaround: Emit a UserWarning when serializing a tensor view, as it provides a simple and user-friendly way to alert users of potential memory issues without changing the underlying behavior.

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