ad-add-fusion-transformation

Add TensorRT-LLM AutoDeploy fusion transformation passes for graph pattern rewriting.

Updated May 23, 2026
One-click install
npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill ad-add-fusion-transformation
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ad-add-fusion-transformation
Source: https://github.com/yo-steven/skills-exploration-20260522/tree/main/skills/TensorRT-LLM/ad-add-fusion-transformation
Command: npx skills add https://github.com/yo-steven/skills-exploration-20260522 --skill ad-add-fusion-transformation

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you implement or extend TensorRT-LLM AutoDeploy fusion transformations so targeted graph patterns are rewritten correctly and reliably during deployment.

Core Features & Use Cases

  • Graph-pattern-driven fusion work: Uses graph dumps and evidence to design a minimal fusion transform that actually matches the current SSA graph structure.
  • Existing-kernel-first strategy: Recommends and validates reuse of existing kernels/custom ops before falling back to Triton (only when needed).
  • Registration and rollout readiness: Guides you through transform implementation, default.yaml registration, and model-registry enablement with guardrails to avoid unproven fusions.
  • Validation and test alignment: Emphasizes dump-based validation, match-count interpretation, and the unit/integration tests needed to prevent regressions.

Quick Start

Use the skill to implement a new AutoDeploy fusion transform under transform/library/ for a pattern confirmed by graph dumps, then register it in default.yaml and add the corresponding unit tests.

Frequently Asked Questions about ad-add-fusion-transformation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I add a TensorRT-LLM fusion pass to rewrite specific graph patterns?▼

To add a TensorRT-LLM fusion pass, implement the transform under transform/library/ based on observed SSA graph dumps, register it in default.yaml, and add unit tests to reliably rewrite targeted graph patterns during deployment.

How do I validate match counts before and after applying an AutoDeploy fusion transform?▼

You validate AutoDeploy fusion transforms by using graph dump evidence to interpret match counts before and after execution. This dump-based validation confirms the transform correctly rewrites the targeted SSA graph structure without regressions.

When should I use existing kernels versus Triton fallbacks for TensorRT-LLM custom ops?▼

You should use existing kernel paths for TensorRT-LLM custom ops before falling back to Triton. This existing-kernel-first strategy validates reuse decisions to ensure reliable kernel integration during fusion transformations.

How do I register a new fusion transformation in the TensorRT-LLM model registry?▼

You register a fusion transformation in the TensorRT-LLM model registry by updating default.yaml and enabling model-registry rollout with guardrails. This prevents unproven fusions from deploying while ensuring metadata is preserved.

What tests do I need to prevent regressions when adding TensorRT-LLM fusion passes?▼

To prevent regressions when adding TensorRT-LLM fusion passes, you need transform unit tests and integration tests. These tests align with dump-based validation and match-count interpretation to ensure graph pattern stability.