Return error when running method with non-planned, unset output tensor data
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 5k
- Forks
- 1.2k
- Avg merge
- 2d 10h
- Merged PRs (30d)
- 581
Description
🚀 The feature, motivation and pitch
When running a model with non-memory planned outputs, if the user doesn't set the output data pointer, the framework will still run the method and crash when trying to write to the null output tensor. It can be tricky for a user to debug this, as it crashes inside a kernel and in a non-recoverable fashion. We should ideally catch this case, log an error message, and return an error code.
Alternatives
No response
Additional context
No response
RFC (Optional)
No response
cc @larryliu0820 @JacobSzwejbka @lucylq @mergennachin @byjlw
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file, test, or entry point is named in the issue. Start by tracing execution for non-memory-planned output tensors, then identify the existing error-reporting and return-code path. Done means an unset output pointer produces a logged error and recoverable error code instead of a kernel crash.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- embedded-iot, machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100