القائمة
Investigate and fix model conversion issues in OpenVINO Frontends (ONNX, PyTorch) — triage, debugging, accuracy comparison, and pre-submission verification.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Write unit tests for OpenVINO ov::Model graph transformations (passes). Use when the user asks to write, add, or refactor tests for a transformation pass (MatcherPass, ModelPass), or to modernize legacy transformation tests.
Troubleshooting all sorts of failures, crashes, exceptions and errors using debug capabilities. Analyze accuracy, performance, model compilation, or memory issues. Dump tensors and intermediate blobs. Serialize and visualize IRs, execution graphs. Enable verbose, logging. Profile execution. Compare layer outputs. Inspect, trace or dump transformations. Identify executed operations, nodes, primitives, kernels.
Upgrade the PyTorch version used by OpenVINO tests (torch / torchvision / torchaudio) and resolve fallout — missing operator translators, new functionalized `*_copy` aten ops, decomposition changes, FX-only tests failing in TorchScript mode, and accuracy regressions caused by stricter typing. Use when the user asks to "bump torch", "update pytorch to X.Y", "upgrade torch tests", or when pytorch_tests / model_hub pytorch tests fail after a torch version change. Do not use for: enabling a single new PyTorch operator unrelated to a version bump, GenAI / Optimum upgrades, or plugin-level numerical bugs unrelated to the frontend.
Adds a core operator to the OpenVINO toolkit. Use when asked to implement a new operation into OpenVINO.
Add a new operation to the OpenVINO CPU plugin — node registration, JIT/oneDNN executors (AVX2/AVX-512/AMX), and functional tests.
Adds a new operation to OpenVINO Frontend pipelines with translator updates, registration, and tests.
| name | conversion-issues |
| description | Investigate and fix model conversion issues in OpenVINO Frontends (ONNX, PyTorch) — triage, debugging, accuracy comparison, and pre-submission verification. |
Diagnose and fix issues where models fail to convert to OpenVINO IR or produce incorrect inference results through an OpenVINO frontend.
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 |
| 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 |
