conversion-issues
Investigate and fix model conversion issues in OpenVINO Frontends (ONNX, PyTorch) — triage, debugging, accuracy comparison, and pre-submission verification.
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Agent Skill: Investigate and Fix Frontend Conversion Issues
Goal
Diagnose and fix issues where models fail to convert to OpenVINO IR or produce incorrect inference results through an OpenVINO frontend.
Framework-Specific Workflows
Each frontend has its own detailed investigation workflow. Read the one matching the target framework:
| Frontend | Skill file | What it covers |
|---|---|---|
| ONNX | onnx.md | Triage (unsupported op / conversion bug / shape-type / opset gap), ORT baseline comparison, translator debugging, .prototxt test models, C++ GTest, pre-submission checklist |
| PyTorch | pytorch.md | Triage (unsupported op / tracing mode / inplace / normalize-step), TorchScript vs torch.export identification, layer test debugging, pre-submission checklist |
Related Skills (adding new ops)
| Frontend | Skill file | When to use |
|---|---|---|
| ONNX | add-fe-op/onnx.md | Implementing a new ONNX op translator from scratch |
| PyTorch | add-fe-op/pytorch.md | Implementing a new PyTorch op translator from scratch |
Notes
- Always verify the model works with the framework's reference runtime (ONNX Runtime / PyTorch) before investigating OpenVINO code.
- Prefer minimal, root-cause fixes over broad refactors.
- Every fix needs a test and must pass the full frontend test suite.
- Repository
- openvinotoolkit/openvino
- Commit
4c20980
- Last updated
- First committed
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