--- name: ad-graph-dump-5dcd458d4779 description: > Enable and interpret TensorRT-LLM AutoDeploy FX graph text dumps via AD_DUMP_GRAPHS_DIR. Use when you need before/after graphs per transform, to locate subgraphs, or to confirm a rewrite ran. Paths and behavior are grounded in tensorrt_llm/_torch/auto_deploy (GraphWriter, BaseTransform). Complements ad-add-fusion-transformation. license: Apache-2.0 tags: - tensorrt-llm - autodeploy - graph-dump - debugging metadata: author: NVIDIA Corporation --- # AutoDeploy: Graph dumps (`AD_DUMP_GRAPHS_DIR`) ## Where this skill applies This file is part of **trtllm-agent-toolkit**. Commands and paths such as `examples/auto_deploy/` and `tensorrt_llm/` are relative to a **TensorRT-LLM source checkout**, not the plugin repository. ## When to use this skill - You need to see how the FX graph changes **after each registered transform** runs. - You are verifying that a subgraph exists, that a fusion matched, or that metadata / wrappers (`getitem`, `view`, `reshape`) appeared or disappeared between dumps. - You are pairing log output with on-disk graph files while debugging AutoDeploy. ## Related skills in this plugin | Skill | Use it for | |-------|------------| | **ad-layer-visualizer** | Extracting and visualizing a single decoder layer from a dump as a DOT/PNG diagram. | | **ad-add-fusion-transformation** | Implementing or reviewing fusion passes once you know what the graphs show. | | **trtllm-codebase-exploration** | Searching the TRT-LLM tree for transforms, custom ops, and patterns. | | **trtllm-code-contribution** | Tests and contribution hygiene after you change TRT-LLM. | ## Environment variable Set: ```bash export AD_DUMP_GRAPHS_DIR=/path/to/output/dir ``` Implementation: `GraphWriter.DUMP_GRAPHS_ENV == "AD_DUMP_GRAPHS_DIR"` in `tensorrt_llm/_torch/auto_deploy/utils/graph_writer.py`. If unset, no graph files are written. ## When dumps are produced After **each** transform application, `BaseTransform` calls `graph_writer.dump_graph(mod, t_name, self.config.stage.value)` from `tensorrt_llm/_torch/auto_deploy/transform/interface.py` (immediately after `_visualize_graph`). So the dump reflects the module **after** that transform has run. ## Rank / process behavior From `GraphWriter.dump_graph`: - Dumps run only when `AD_DUMP_GRAPHS_DIR` is set. - If `ADLogger.rank` is set and is not `0`, dumping is skipped (non–rank-0 processes do not write files). ## Directory lifecycle On the **first** dump on rank 0, `GraphWriter` **removes** the target directory if it already exists, then recreates it. Do not point `AD_DUMP_GRAPHS_DIR` at a directory that must be preserved without copying it first. ## File naming and ordering Files are named: ```text {NNN}_{}_{}.txt ``` - `NNN` is a monotonically increasing three-digit counter (001, 002, …) in run order across all dumps in that process. - The middle segment is each transform’s `config.stage` value (same enum/string used in `default.yaml` under each transform’s `stage:` field). - The last segment is the transform’s registry key (`transform_name` passed into `dump_graph`). So lexicographic sort by filename matches **pipeline order** for that run. ## File contents Each file is text and starts with headers similar to: ```text # Transform: # Stage: # GraphModules found: ``` Then, for every `torch.fx.GraphModule` found under `mod.named_modules()` (including the root), the writer emits a section title and an SSA-style listing with shape/dtype metadata via `dump_ssa_with_meta()` in the same module. Use this to compare operator chains, consumers, and `node.meta` shape/dtype hints across consecutive files. ## Example: capture dumps from a registry build From the **root of the TensorRT-LLM clone** (adjust the script and flags to your workflow): ```bash AD_DUMP_GRAPHS_DIR=/tmp/ad-graphs \ python examples/auto_deploy/build_and_run_ad.py --model --use-registry ``` Pick any AutoDeploy entrypoint you already use; the requirement is only that the code path runs the transform pipeline with `AD_DUMP_GRAPHS_DIR` set in the environment. ## Logs vs dump files While a transform runs, logging is patched so messages can be prefixed with `[stage=, transform=]` (see `with_transform_logging` in `transform/interface.py`). Transform summaries log `[SUMMARY]` with `matches=` or `skipped` / `disabled` (`_log_transform_summary`). Use those lines together with the numbered dump files to tie **match counts** to **graph shape** before and after a specific transform. ## Pitfalls - **Stale directory**: Because the dump dir is deleted on first use, a second run in the same shell without changing `AD_DUMP_GRAPHS_DIR` overwrites prior output. - **No GraphModules**: If the module has no `GraphModule` children, `dump_graph` returns without creating a new file for that step (see early return in `graph_writer.py`). - **Distributed**: Only rank 0 writes; other ranks skip silently. ## Source references - `tensorrt_llm/_torch/auto_deploy/utils/graph_writer.py` — env var, filenames, SSA dump. - `tensorrt_llm/_torch/auto_deploy/transform/interface.py` — call site after each transform; log prefix decorator. --- Source attribution: yo-steven Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/TensorRT-LLM/ad-graph-dump/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice SPDX-License-Identifier: Apache-2.0 AND CC-BY-4.0 Copyright (c) 2026 NVIDIA Corporation This project is licensed under a dual license: - Apache License 2.0 (for code and configuration) - Creative Commons Attribution 4.0 International (CC BY 4.0) (for documentation) You may use the contents of this repository under the terms of either license, or both, as applicable to the type of content. ================================================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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For the full legal text, see: https://creativecommons.org/licenses/by/4.0/legalcode # Mega AI Hub resource metadata Creator / publisher: yo-steven Original project: https://github.com/yo-steven/skills-exploration-20260522/blob/6e1127ffb7690aa8038b172d0ae7cc720754870d/skills/TensorRT-LLM/ad-graph-dump/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering Roles: Backend Developer, Data Scientist, ML / AI Engineer Specialties: Backend Tools: Tensorrt Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: bcbdbdbe0d987f6fb8a987e40e2a47bde3ed7eff63a710be172753c7fb6f7d20