---

name: extensibility-and-debugging description: "Use this sub-skill for Torch-TensorRT dryrun analysis, Debugger capture/replay, unsupported-op triage, converter/lowering/plugin authoring, QDP kernels, and ModelOpt or quantization debugging." metadata: disco-role: operating disable-model-invocation: true license: BSD 3-Clause

Torch-TensorRT Extensibility and Debugging

Use this sub-skill when the user needs to understand why a model did not compile cleanly, how to inspect graph partitioning, or how to extend Torch-TensorRT with custom lowering or kernels.

Start with the failure mode

Visible symptom	Likely workstream
no converter, unsupported op, or tiny TRT partition	Unsupported-op triage and converter decisions
Compile coverage question	dryrun, require_full_compilation, torch_executed_ops, min_block_size
Need graph/capture artifacts or layer info	Debugger and capture/replay
Custom op, converter, or lowering implementation	Converter/plugin extension
QDP kernel or custom CUDA/PTX kernel	torch_tensorrt.kernels
Quantization warning or ModelOpt dependency confusion	Quantization support triage

Primary references

• references/debugging.md for dryrun, debugger, and issue-reproduction workflows.
• references/operator-coverage.md for unsupported-op and fallback decisions.
• references/custom-ops-and-plugins.md for converter, lowering, and TensorRT plugin paths.
• references/kernel-api.md for QDP kernel APIs and safe skeletons.
• references/troubleshooting.md for common error interpretations and dependency gaps.
• scripts/debugger_capture_template.py --help for a safe capture template.
• scripts/qdp_kernel_skeleton.py --help for a non-running kernel skeleton generator/checker.

Typical debugging workflow

1. Reproduce on the smallest possible model and input.
2. Run a dryrun or debugger capture to see which operators are partitioned to TensorRT and which stay in PyTorch.
3. Decide whether fallback is acceptable.
4. If not, choose among model rewrite, custom converter, or plugin/QDP implementation.
5. Only after the root cause is identified should you revisit runtime optimization or deployment.

Guardrails

• Do not promise a converter/plugin path without the exact op, schema, input dtype/layout, and target TensorRT version.
• Do not treat ModelOpt or QDP support as universal; version and dependency gates matter.
• Do not use a generic compile smoke as proof that the problematic op is supported.
• Keep debugging artifacts small and self-contained so the user can paste them into an issue or test case.