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fix(pt): improve out-of-memory capture #3857

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merged 2 commits into from
Jun 7, 2024

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@njzjz njzjz commented Jun 4, 2024

I just received another error message that reports out of memory. It's a bad design of PyTorch that all errors use a general RuntimeError.

Summary by CodeRabbit

  • Bug Fixes
    • Improved out-of-memory error detection for CUDA driver issues.

I just received another error message that reports out of memory. It's a bad design of PyTorch that all errors use a general `RuntimeError`.

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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coderabbitai bot commented Jun 4, 2024

Walkthrough

Walkthrough

The is_oom_error function in the auto_batch_size.py file within the deepmd/pt/utils module has been enhanced. It now includes a new condition to check for a specific CUDA driver error message related to out-of-memory situations, improving the function's ability to detect memory issues.

Changes

Files Change Summary
deepmd/pt/utils/auto_batch_size.py Updated is_oom_error function to include a new condition for a specific CUDA driver error message related to out-of-memory situations.

Sequence Diagram(s) (Beta)

sequenceDiagram
    participant User
    participant auto_batch_size.py
    participant CUDA Driver

    User->>auto_batch_size.py: Call is_oom_error(error_message)
    auto_batch_size.py->>auto_batch_size.py: Check for existing OOM conditions
    auto_batch_size.py-->>auto_batch_size.py: Check for new CUDA OOM condition
    auto_batch_size.py-->>User: Return True/False based on OOM condition
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Recent review details

Configuration used: CodeRabbit UI
Review profile: CHILL

Commits

Files that changed from the base of the PR and between 674bad7 and 325d32c.

Files selected for processing (1)
  • deepmd/pt/utils/auto_batch_size.py (1 hunks)
Additional comments not posted (4)
deepmd/pt/utils/auto_batch_size.py (4)

60-60: Enhanced detection of out-of-memory errors.

Consider adding logging before clearing the cache to help with debugging and monitoring.


60-60: Complex batch processing logic appears robust and well-handled.


60-60: Standard method for checking GPU availability.


60-60: Constructor uses sensible defaults and proper delegation.


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@github-actions github-actions bot added the Python label Jun 4, 2024
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codecov bot commented Jun 4, 2024

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 82.66%. Comparing base (674bad7) to head (325d32c).
Report is 116 commits behind head on devel.

Additional details and impacted files
@@           Coverage Diff           @@
##            devel    #3857   +/-   ##
=======================================
  Coverage   82.66%   82.66%           
=======================================
  Files         517      517           
  Lines       49724    49724           
  Branches     2984     2984           
=======================================
  Hits        41105    41105           
  Misses       7709     7709           
  Partials      910      910           

☔ View full report in Codecov by Sentry.
📢 Have feedback on the report? Share it here.

@wanghan-iapcm wanghan-iapcm added this pull request to the merge queue Jun 7, 2024
Merged via the queue into deepmodeling:devel with commit 057dc11 Jun 7, 2024
60 checks passed
mtaillefumier pushed a commit to mtaillefumier/deepmd-kit that referenced this pull request Sep 18, 2024
I just received another error message that reports out of memory. It's a
bad design of PyTorch that all errors use a general `RuntimeError`.

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Bug Fixes**
  - Improved out-of-memory error detection for CUDA driver issues.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
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