Inference Graph Diff

inference-graph-diff

Purpose

Diff the compiled torch FX / Inductor graphs between two vLLM configs to surface exactly which compilation choices changed. The most common need: "I added pass_config.fuse_allreduce_rms=true, which graph nodes were affected?" - torch.compile decisions are not visible in the vllm startup logs alone.

Three diff layers cover the stack:

• inference-kernel-profile (nsys .nsys-rep timelines - runtime)
• analyze-zymtrace-workload (per-category kernel time)
• This skill (the COMPILATION graph - build-time)

When to use

• Confirming a compilation_config change actually took effect (the vllm logs print the requested config, not the post-fusion graph)
• Investigating an unexpected perf delta after a 1-line config tweak ("did the fused op activate?")
• Pre-deploy gate when migrating between vllm versions (catch silent inductor pass behavior changes)
• Documenting WHY a variant landed for the eventual upstream PR or release notes

Do not use this skill for:

• Runtime timeline analysis - use nsys or zymtrace instead
• Per-request latency breakdown - those are different layers
• SGLang arms - this skill is vLLM-specific (it diffs vLLM's torch.compile FX / Inductor graph dumps via TORCH_LOGS=+dynamo,+inductor). SGLang has a different compilation/fusion model (no vLLM compilation_config / cudagraph_mode), so this skill fails closed on an SGLang arm. To compare two SGLang configs' execution structure, capture a per-kernel nsys timeline for each (launch-wrap sglang.launch_server under nsys) and diff the cuda_gpu_kern_sum kernel sets instead.

Recipe

1. Dump the graph from each side of the diff

For each helm config A and B:

NS=inference
TARGET_POD=$(kubectl -n $NS get pods -l app=basic-inference -o jsonpath='{.items[0].metadata.name}')

# Need vllm to log the compile graph. Set the env var BEFORE engine init:
kubectl -n $NS set env deployment/basic-inference \
  TORCH_LOGS="+dynamo,+inductor,+graph_breaks" \
  TORCHDYNAMO_VERBOSE=1 \
  PT_LOGGING_LEVEL=DEBUG

# Wait for rolling restart...
# Then grab the logs which contain the FX dump:
kubectl -n $NS logs $TARGET_POD --tail=10000 > side-A.log

Repeat with the alternate config to get side-B.log.

2. Extract the FX graphs

import re, pathlib


def extract_fx_graphs(log_path):
    text = pathlib.Path(log_path).read_text(errors="replace")
    graphs = re.findall(r"=== FX GRAPH ===\n(.*?)\n=== END FX GRAPH ===", text, re.DOTALL)
    return graphs


a_graphs = extract_fx_graphs("side-A.log")
b_graphs = extract_fx_graphs("side-B.log")

(The exact log marker strings depend on vLLM version + torch version, the recipe above is the v0.21.x default. For other versions, dump via TORCH_COMPILE_DEBUG=1 and parse the compile_debug_*.txt files torch.compile writes under ~/.cache/torch/.)

3. Diff the graphs

diff -u side-A-graph0.fx side-B-graph0.fx > graph0.diff
# For the inductor lowered graph:
diff -u side-A-inductor.py side-B-inductor.py > inductor.diff

4. Optional: render summary with torch._dynamo.explain

kubectl -n $NS exec $TARGET_POD -- python3 -c "
import torch._dynamo as dynamo
# torch._dynamo.explain takes a callable + sample inputs and prints
# the compile decisions (recompiles, graph breaks, ops list)
# For vllm the callable is the model.forward; sample inputs are mocked.
# See the dynamo.explain doc for the full surface.
"

5. Emit a structured graph_diff.json into the bundle

{
  "schema": "inference_graph_diff_v1",
  "side_a": {"helm_rev": 28, "config_summary": "...", "compile_passes": [...]},
  "side_b": {"helm_rev": 29, "config_summary": "...", "compile_passes": [...]},
  "added_passes": ["fuse_allreduce_rms"],
  "removed_passes": [],
  "graph_size_delta": {"nodes_before": 1234, "nodes_after": 1187, "delta_pct": -3.8},
  "notes": "1-line helm bump landed; -47 nodes from the allreduce-rms fusion."
}

Output

Bundle artifact layout:

experiments/artifacts/inference-perf-bench/<bundle>/
  graph-diff/
    side-A.log              # raw torch.compile log from config A
    side-B.log              # raw torch.compile log from config B
    side-A-graph0.fx        # extracted FX graph
    side-B-graph0.fx
    graph0.diff             # unified diff
    inductor.diff           # diff of the lowered inductor code
    graph_diff.json         # structured summary

Skill maturity

This skill is research-grade, with the same status as the nsys-sidecar approach. The recipe is documented but not yet wrapped in a dedicated MCP verb because the dump-format depends on torch version. A future release could add a perf_tune_report_graph_diff verb that automates the recipe end-to-end.

Kernel rubric (K/R/H/P/A)

A compilation-graph change is often a representation (R) or hardware specialization (H) move in the kernel rubric (docs/METHODOLOGY.md "Kernel-work classification"). A fusion pass that replaces a generic Triton op with a library/sm100f tensor-core kernel raises both R (R2→R1) and H (toward H4), fuse_allreduce_rms / fuse_attn_quant shift which hardware path the graph dispatches. When a graph diff backs a custom-kernel comparison, note the R/H delta in graph_diff.json notes and carry the candidate + baseline (K,R,H,P,A) coordinates into the bundle's SOURCE.md/summary.md. The graph diff shows R/H changed. It does NOT prove the new path engages tensor cores or hits its roofline - defer that H + P proof to inference-kernel-ncu-profile, the gate's enforcement point. A win over a strictly-lower-H/R baseline stays a DRAFT, never a VERDICT. If the comparison campaign reaches L4 (an ncu roofline renders), the candidate + baseline (K,R,H,P,A) must also be emitted as a structured krhpa: block in config.yaml (see inference-kernel-ncu-profile for the YAML) - publish_to_lake fails closed without it.

Cross-references

• perf-baseline-diff for run-over-run baseline diffs
• analyze-zymtrace-workload for per-category zymtrace diffs
• inference-kernel-profile for sidecar nsys captures

Full-context reporting (no bare numbers)

Per the canon "Every performance number carries its full context (no bare numbers)" (docs/METHODOLOGY.md "Full-context reporting"): every number this skill emits MUST carry its full measurement-context descriptor, and every comparison MUST be matched on it. A bare tok/s / TPOT / BW / %SoL / speedup is a defect - it cannot set a default, ship a config, or appear in a report.

• Identity: model (+HF path), hardware (exact ceiling token GB300/B200), quant, kv-cache dtype.
• Parallelism: TP, DP (replicas), PP, EP, parallel_strategy.
• Serving cfg: max-num-seqs, max-num-batched-tokens, gpu-memory-utilization, max-model-len, cudagraph_mode/enforce_eager, async_scheduling, prefix-caching.
• Workload: dataset, ISL/OSL (or mean in/out tokens), concurrency, num-prompts.
• Regime: warm vs cold. Latency vs throughput tier.
• Stack: image/vllm commit, bench backend, serving engine.
• Grounding: %SoL (+ ceiling key from configs/sol-ceilings.yaml - never inline a peak), sol_rigor (L1-L4), trials n (mean±std), same-node, baseline named.
• Per-number exact shape (no smoothing): when reporting more than one number, keep EACH with its own exact shape (ISL/OSL, concurrency, dataset, regime) - never normalize a set to one uniform descriptor that hides per-point variation (e.g. c=1 @ ISL1024/OSL256 + c=64 @ ISL4096/OSL512, NOT one shared "random").

A graph diff is structural, not a measurement, so it is exempt from the %SoL requirement. If you pair it with a perf measurement (tok/s, latency, speedup), that measurement follows the rigor discipline: capture L1+L3 (L4 where feasible) Speed-of-Light + publish --strict. Canonical map: docs/METHODOLOGY.md.

Next lever / BREAKTHROUGH (Grind Mandate)

If this skill emits a measured result, its output MUST end by naming the next perf lever, its expected unlock (direction + rough magnitude), and the gate that proves/refutes it, per docs/METHODOLOGY.md "Always be grinding (next-lever framing)". A measured win is the new floor, not the finish -- so do everything we can to find the next BREAKTHROUGH: the highest-EV unlock toward Speed-of-Light (a new champion / kernel / router / quant / parallelism / spec-decode win, or an unblocked stack), not just the next micro-lever. Rank the candidate breakthrough levers by value x cost (the GRIND FRONTIER, perftunereport value_view), pursue the top, bank the rest with evidence. Record WHY a refuted lever loses, update the standing frontier in the active bundle's HANDOFF.md. Never conclude "exhausted/optimal/done" without an explicit next-lever frontier (an empty frontier AND a documented SoL wall only). Delete this section ONLY if the skill produces no measurements.