add-fe-op

name: add-fe-op description: Adds a new operation to OpenVINO Frontend pipelines with translator updates, registration, and tests.

Agent Skill: Add New Operation to OpenVINO Frontend

Goal

Enable a new operation in an OpenVINO Frontend (FE) pipeline — translator creation, registration, conversion validation, and tests.

Framework-Specific Workflows

Each frontend has its own detailed workflow. Read the one matching the target framework:

Related Skills (investigation & debugging)

Notes

• Keep this skill instruction-only in markdown.

Instruction Workflow (TensorFlow and generic fallback)

The following sections apply to frontends not covered by the framework-specific files above (e.g., TensorFlow), or as a general reference when no framework-specific skill is available.

1) Check current support state

• Verify whether translator file already exists.
• Verify whether op is already registered in frontend mapping table.
• If both exist and conversion is known to pass, skip scaffolding.
• If only partial state exists, repair only missing parts.

2) Add translator logic

• Preferred path: emit real OV conversion logic when the operation maps to an OpenVINO op or a set of operations that will provide reasonable performance.
• If the same operation already exists in another frontend, extract/reuse common translation logic instead of duplicating it.
• For simple 1:1 ops, prefer existing helper translation patterns/utilities already used in FE codebase.
• Fallback path: emit safe placeholder translator only when real mapping is unavailable.
• Never claim full support when fallback translator is used.

3) Register operation in frontend tables

• PyTorch:
  • Add converter declaration in op table.
  • Add TorchScript key registration (for example aten::op).
  • Add FX key registration (for example aten.op.default).
• TensorFlow:
  • For supported unary ops, prefer generic unary registration path.
  • For unsupported cases, register dedicated translator function.
• ONNX:
  • Ensure ONNX_OP macro registration exists in translator file.

4) Add tests

• Add frontend smoke test file under tests/frontend (not available for PyTorch).
• Add framework layer test under tests/layer_tests where supported.
• Ensure test naming follows existing suite conventions.

5) Build prerequisites for validation

• Ensure frontend changes are available to runtime before conversion checks.
• Build frontend targets from source or use an existing OpenVINO build/package that already includes your FE changes.
• If neither is true, do not report conversion status as passed; mark validation as blocked.

6) Validate conversion and finalize

• run conversion check from generated model.
• if automated validation is unavailable, mark as skipped with explicit reason.
• Confirm no duplicate registrations are introduced.
• Confirm rerun is idempotent.
• Report one of three states: fully enabled, partially repaired, or scaffolded with fallback.

Translation Recommendations

• Prefer runtime shape computation over conversion-time shape extraction.

  • Use ShapeOf-based graph logic for shape-dependent behavior.
  • Avoid relying on get_shape() / fully static shape reads during FE conversion.
  • get_partial_shape() is acceptable for compile-time rank-only decisions.

• Handle data types according to framework semantics.

  • If PyTorch op behavior allows mixed input types, preserve this in translation.
  • Do not over-constrain translators to a single dtype when mixed-type execution is valid.

• Avoid constants tied to compile-time element type queries.

  • Do not create Constant values from get_element_type() when type can be dynamic at conversion time.
  • Prefer runtime type-safe construction paths that remain valid for dynamic element types.

Notes

• Keep this skill instruction-only in markdown.
• Prefer minimal, root-cause updates over broad refactors.
• Do not mark operation as supported unless FE conversion produces OV graph nodes (not framework fallback nodes).