ad-graph-dump

Original：🇺🇸 English

Translated

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.

2installs

Sourcenvidia/skills

Added on2026-05-22

NPX Install

npx skill4agent add nvidia/skills ad-graph-dump

SKILL.md Content

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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}_{<stage.value>}_{<transform_key>}.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: <transform_key>
# Stage: <stage.value>
# GraphModules found: <count>

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 <hf-model-id> --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=<stage.value>, transform=<transform_key>]

(see

with_transform_logging

in

transform/interface.py

). Transform summaries log

[SUMMARY]

with

matches=<n>

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.

ad-graph-dump

NPX Install

Tags

SKILL.md Content

AutoDeploy: Graph dumps (
`AD_DUMP_GRAPHS_DIR`
)

Where this skill applies

When to use this skill

Related skills in this plugin

Environment variable

When dumps are produced

Rank / process behavior

Directory lifecycle

File naming and ordering

File contents

Example: capture dumps from a registry build

Logs vs dump files

Pitfalls

Source references

ad-graph-dump

NPX Install

Tags

SKILL.md Content

AutoDeploy: Graph dumps (AD_DUMP_GRAPHS_DIR)

Where this skill applies

When to use this skill

Related skills in this plugin

Environment variable

When dumps are produced

Rank / process behavior

Directory lifecycle

File naming and ordering

File contents

Example: capture dumps from a registry build

Logs vs dump files

Pitfalls

Source references

AutoDeploy: Graph dumps (
`AD_DUMP_GRAPHS_DIR`
)