extract-kernel-definitions

name	extract-kernel-definitions
description	Generate Definition JSON files for the flashinfer-trace HuggingFace dataset by harvesting them from a short SGLang inference pass (FlashInfer's @flashinfer_api(trace=...) dumper) — or, as a fallback, by manually transcribing the schema from SGLang sources when FlashInfer doesn't yet have a trace template. Use when adding a new model, extracting GPU kernels (MLA, MoE, GQA, RMSNorm, GEMM, GDN, RoPE, sampling), or filling gaps in the dataset. Use when this capability is needed.
metadata	{"author":"flashinfer-ai"}

Extract Kernel Definitions

Produce per-(op, shape) Definition JSONs and stage them in the HuggingFace dataset clone at tmp/flashinfer-trace/definitions/{op_type}/. PR submission is out of scope here — see submit-onboarding-prs (Phase 4 of /onboard-model).

Two paths

Path	When to use	What you do
A. Trace-dump (primary)	Kernel is fi_supported per /discover-models — i.e. the FlashInfer API used by SGLang carries a @flashinfer_api(trace=...) template (see coverage list).	Run a short SGLang inference pass with FLASHINFER_TRACE_DUMP=1. The dumper writes one JSON per unique (op, shape) before the kernel runs (crash-safe, deduplicated).
B. Manual extraction (fallback)	Kernel is fi_missing, or the relevant FlashInfer API is not yet trace-instrumented.	Read the SGLang model source + sgl-cookbook serving config + HF model config; write the Definition JSON by hand using the schema reference.

The trace-dump path is the default — it eliminates manual axis derivation and produces JSONs that already carry axes, inputs, outputs, tags (fi_api:*, status:verified), and a reference implementation.

Background: the trace dumper was added in flashinfer-ai/flashinfer#2931. Schema and full env-var docs live at docs/fi_trace.rst in the FlashInfer repo. SGLang harness reference: tests/trace/example_sglang.py.

Usage

# Path A — auto-dump every fi_supported definition for a model in one inference pass
/extract-kernel-definitions --model-name llama-3.2-3b --hf-repo-id meta-llama/Llama-3.2-3B-Instruct

# Path A — multi-config: one short run per (TP, EP) listed in sgl-cookbook
/extract-kernel-definitions --model-name qwen3-next --tp-list 2,4

# Path B — manual fallback for fi_missing kernels (or names that didn't appear in the dump)
/extract-kernel-definitions --model-name kimi-k2 --manual --op-types new_op_type

Parameters

• --model-name (required): Model slug (e.g. llama, deepseek-v3, qwen3-next). Used to look up the SGLang model file and the sgl-cookbook YAML.
• --hf-repo-id (optional): HuggingFace repo override; inferred from --model-name if omitted.
• --tp-list (optional): Comma-separated TP values to run for; default reads sgl-cookbook YAML.
• --ep-list (optional): Comma-separated EP values for MoE models.
• --manual (optional): Force Path B (manual extraction) even for fi_supported ops.
• --op-types (optional): Comma-separated op_type filter when using --manual or for --dry-run reporting.
• --dry-run (optional): Report what would be dumped/written without running anything.
• --skip-existing (optional, default true): Skip any definition whose name already exists under tmp/flashinfer-trace/definitions/.

Prerequisites

• /clone-repos has been run, so tmp/sglang/, tmp/flashinfer/, tmp/sgl-cookbook/, and tmp/flashinfer-trace/ are present and current. The HF dataset clone at tmp/flashinfer-trace/ is the only home for definitions — the in-repo flashinfer_trace/ directory was removed in the trace-dataset refactor.
• For Path A: a working CUDA-enabled environment, GPU memory sufficient for the chosen model + TP, and attention_backend="flashinfer" available in the installed SGLang.
• For Path B: HuggingFace config.json access for the target model.

---

Path A: trace-dump from a short SGLang pass

The dumper fires inside FlashInfer when both env vars are set before the FlashInfer import. SGLang routes through @flashinfer_api(trace=...)-decorated APIs whenever attention_backend="flashinfer" is selected, so a single short prefill+decode pass exercises most ops at once.

A1. Pick the serving config(s)

Open the sgl-cookbook YAML for the target model and list the unique TP/EP values — one trace-dump pass per unique combination is enough to cover every shape variant.

ls tmp/sgl-cookbook/data/models/generated/v0.5.6/ | grep -i {model_name}
cat tmp/sgl-cookbook/data/models/generated/v0.5.6/{model_yaml}

If the model has no cookbook entry, default to TP=1 (single-GPU baseline) and skip EP.

A2. Run the trace-dump pass

Use tools/gpu-lock so CUDA_VISIBLE_DEVICES is set correctly. The script below mirrors tests/trace/example_sglang.py in the FlashInfer repo — adapt the model_path, tp_size, and attention_backend:

DUMP_DIR=tmp/dumps/fi_trace_{model_slug}_tp{TP}_ep{EP}

tools/gpu-lock --gpus {TP} --exec-timeout 1800 -- python - <<EOF
import os, shutil
from pathlib import Path

# Must be set BEFORE flashinfer / sglang import.
os.environ["FLASHINFER_TRACE_DUMP"] = "1"
os.environ["FLASHINFER_TRACE_DUMP_DIR"] = "$DUMP_DIR"
os.environ.setdefault("SGLANG_SKIP_CUBIN_DOWNLOAD", "1")

dump = Path("$DUMP_DIR")
if dump.exists():
    shutil.rmtree(dump)

from sglang.srt.entrypoints.engine import Engine
engine = Engine(
    model_path="{hf_repo_id}",
    attention_backend="flashinfer",
    disable_cuda_graph=True,         # keep first call on the Python path
    mem_fraction_static=0.5,
    tp_size={TP},
    disable_radix_cache=True,
    log_level="warning",
)
engine.generate(
    ["The capital of France is"],
    {"temperature": 0.0, "max_new_tokens": 4, "top_k": 50, "top_p": 0.9},
)
engine.shutdown()
EOF

A few non-obvious requirements:

• Set the env vars before import. FLASHINFER_TRACE_DUMP and FLASHINFER_TRACE_DUMP_DIR are read at call time, but the @flashinfer_api decorator binding happens at import — set them in the shell or at the top of the entry script before any import flashinfer / import sglang runs.
• Use attention_backend="flashinfer". Other SGLang backends bypass the FlashInfer APIs and produce no dumps.
• Disable CUDA graphs (disable_cuda_graph=True) for the trace pass. Cached graphs skip the Python path and therefore the dumper.
• Page-size variants need separate runs. SGLang's page size is fixed per server, so to capture both _ps16 and _ps64 shapes (for example) you must run twice with different --page-size. Enumerate the page sizes used by the target model.
• MoE routing methods. Each routing_method_type (Default, Renormalize, DeepSeekV3, Llama4, RenormalizeNaive, TopK) emits its own template; only the routing actually exercised by the model in your prompts will dump. For DeepSeek-V3 use a real DSv3 model to capture the ds_routing variant.
• Quantized variants (fp8/mxfp8/fp4 GEMM, fp8/fp4 block-scale MoE) require the model to actually use that quant config — load with the matching --quantization flag.

A3. Dedupe and stage into the dataset

# 1. List what was dumped
ls "$DUMP_DIR"

# 2. For each {name}.json: sort it under the right op_type subdirectory.
#    The op_type field inside the JSON is the source of truth for the subfolder.
python - <<'EOF'
import json, shutil
from pathlib import Path
src = Path("$DUMP_DIR")
dst_root = Path("tmp/flashinfer-trace/definitions")
for p in src.glob("*.json"):
    op_type = json.loads(p.read_text())["op_type"]
    dst = dst_root / op_type / p.name
    if dst.exists():
        print(f"skip (exists): {dst.relative_to(dst_root)}")
        continue
    dst.parent.mkdir(parents=True, exist_ok=True)
    shutil.copy2(p, dst)
    print(f"added: {dst.relative_to(dst_root)}")
EOF

A3b. Normalize the staged JSONs for flashinfer-bench validate

The trace dumper's output has three known mismatches with flashinfer-bench's Definition schema (all originate in the trace templates shipped with flashinfer-ai/flashinfer#2931):

1. reference declares the function as def _<name>_reference(...), but flashinfer-bench requires a top-level def run(...).
2. Plan-time index tensors (kv_indptr, kv_indices, qo_indptr) come back with dtype: "unknown" because the dumper inspects only run()'s kwargs, not the wrapper state set during plan(). The validator only accepts concrete dtypes from its enum.
3. In-place ops (e.g. fused_add_rmsnorm's residual) declare the same name in both inputs and outputs. flashinfer-bench rejects overlapping I/O names; the dumper's reference function only returns the non-overlap outputs anyway, so it's safe to drop the duplicates from outputs.

Run this once per staging pass to make the JSONs validate:

python - <<'EOF'
import json, re
from pathlib import Path
INDEX_TENSOR_DTYPE = "int32"
KNOWN_INDEX_TENSORS = {
    "kv_indptr", "kv_indices", "qo_indptr",
    "paged_kv_indptr", "paged_kv_indices", "kv_last_page_len",
}
REF_RE = re.compile(r"^def\s+_[A-Za-z0-9_]+_reference\b", re.MULTILINE)
for p in Path("tmp/flashinfer-trace/definitions").rglob("*.json"):
    d = json.loads(p.read_text()); changed = False
    ref = d.get("reference", "")
    if ref and "def run(" not in ref:
        new_ref, n = REF_RE.subn("def run", ref, count=1)
        if n == 1: d["reference"], changed = new_ref, True
    for name, spec in d.get("inputs", {}).items():
        if isinstance(spec, dict) and spec.get("dtype") == "unknown" and name in KNOWN_INDEX_TENSORS:
            spec["dtype"], changed = INDEX_TENSOR_DTYPE, True
    overlap = set(d.get("inputs", {})) & set(d.get("outputs", {}))
    for name in overlap:
        d["outputs"].pop(name, None)
        changed = True
    if changed:
        p.write_text(json.dumps(d, indent=2) + "\n")
        print(f"normalized: {p}")
EOF

These three patches are mechanical — file a follow-up issue against flashinfer-ai/flashinfer to emit def run(...), resolve plan-time dtypes, and drop in-place outputs (or rename them) inside the dumper itself, after which A3b becomes a no-op.

A3c. Validate

flashinfer-bench validate --dataset tmp/flashinfer-trace --disable-gpu

Newly staged definitions should report [WARNING] (missing descriptions on axes/inputs/outputs are advisory) and not [ERROR]. Any [ERROR] on a definition you just staged means A3b didn't normalize a new failure mode — inspect the report (tmp/flashinfer-trace/reports/report-*.txt) and extend the snippet.

That's it for Path A — once normalized, the staged JSONs carry axes, inputs, outputs, tags (fi_api:*, status:verified), and a reference implementation, so they're ready for the rest of the onboarding pipeline (workloads → baseline → eval → Phase 4 PRs).

Trade-off vs. tag enrichment

The dumper does not auto-emit tp:N, ep:N, model:*, or quantization:* tags — those are workflow-level metadata, not kernel-shape metadata. After staging, append the appropriate tags to the JSONs you just produced:

python - <<'EOF'
import json
from pathlib import Path
extra_tags = ["model:{model_slug}", "tp:{TP}"]   # add ep:{EP} for MoE
for p in Path("tmp/flashinfer-trace/definitions").rglob("*.json"):
    if p.stat().st_mtime < {dump_run_start_epoch}:
        continue
    j = json.loads(p.read_text())
    j["tags"] = sorted(set(j.get("tags", []) + extra_tags))
    p.write_text(json.dumps(j, indent=2) + "\n")
EOF

FlashInfer trace coverage

Per docs/fi_trace.rst, the trace registry currently covers:

FlashInfer module	API(s)	op_type
flashinfer.norm	rmsnorm, fused_add_rmsnorm (and gemma / quant variants)	rmsnorm
flashinfer.sampling	top_k_sampling_from_probs, top_p_sampling_from_probs, top_k_top_p_sampling_from_probs, min_p_sampling_from_probs, chain_speculative_sampling	sampling
flashinfer.gemm	mm_bf16, mm_fp8, mm_mxfp8, mm_fp4	gemm_bf16 / gemm_fp8 / gemm_mxfp8 / gemm_fp4
flashinfer.decode	BatchDecodeWithPagedKVCacheWrapper.run	gqa_paged
flashinfer.prefill	BatchPrefillWithPagedKVCacheWrapper.run, BatchPrefillWithRaggedKVCacheWrapper.run	gqa_paged / gqa_ragged
flashinfer.mla	BatchMLAPagedAttentionWrapper.run	mla_paged
flashinfer.gdn_decode	gated_delta_rule_decode, gated_delta_rule_mtp	gdn
flashinfer.gdn_prefill	chunk_gated_delta_rule	gdn
flashinfer.fused_moe	trtllm_fp8_block_scale_moe × 6 routings, trtllm_fp4_block_scale_moe × 6 routings	moe
flashinfer.rope	apply_rope_* family

Anything outside this list falls through to Path B. To check up-to-date coverage:

grep -rn "@flashinfer_api(trace=" tmp/flashinfer/flashinfer/

---

Path B: manual extraction (fallback)

Use this when:

• The kernel is fi_missing (no FlashInfer kernel exists yet — definition JSON will carry status:unverified plus a link to the kernel-request issue), or
• The kernel exists in FlashInfer but the API does not yet have a @flashinfer_api(trace=...) template (rare; check coverage list above).

B1. Read the model + serving config

1. Locate the SGLang model file:

   ls tmp/sglang/python/sglang/srt/models/ | grep -i {model_name}
   

2. Find sgl-cookbook YAML and parse unique tp / ep values.
3. Pull config.json from HuggingFace (hidden_size, num_attention_heads, num_key_value_heads, head_dim, intermediate_size, num_experts, num_experts_per_tok, vocab_size, etc.). See track-models SKILL.md for the full field-to-axis mapping.

B2. Compute kernel parameters per (TP, EP)

Apply the parallelism rules (TP/EP-affected kernels split head/expert counts; norm / GEMM / RoPE / sampling are parallelism-agnostic):

op_type	TP affects	EP affects	Naming pattern
gqa_paged	q_heads/=TP, kv_heads/=TP	—	gqa_paged_{decode,prefill}_h{q}_kv{kv}_d{d}_ps{P}
gqa_ragged	same as gqa_paged	—	gqa_ragged_{prefill}_h{q}_kv{kv}_d{d}
mla_paged	q_heads/=TP	—	mla_paged_{decode,prefill}_h{q}_ckv{ckv}_kpe{kpe}_ps{P}
gdn	q_heads/=TP, v_heads/=TP	—	gdn_{decode,mtp,prefill}_qk{q}_v{v}_d{d}_k_last
mamba_ssu	nheads/=TP, ngroups/=TP	—	mamba_ssu_decode_h{n}_d{d}_s{s}_ng{g}
moe	—	num_experts/=EP	moe_{quant}_{routing}_topk{k}_e{local_e}_h{H}_i{I}
rmsnorm	—	—	rmsnorm_h{H} / fused_add_rmsnorm_h{H}
gemm	—	—	gemm_n{N}_k{K} (or gemm_{quant}_N{N}_K{K})
rope	—	—	rope_with_cos_sin_cache_{neox,gptj}_style_d{d}_rd{rd}
sampling	—	—	{topk,topp,topk_topp}_sampling_from_probs_v{vocab}

Where ckv = kv_lora_rank + qk_rope_head_dim and kpe = qk_rope_head_dim for MLA.

B3. Write Definition JSON

Hand-write the JSON under tmp/flashinfer-trace/definitions/{op_type}/{name}.json. Use the canonical schema below. For fi_missing definitions add the status tag and the issue back-pointer in the description.

For the reference field: write a plain-PyTorch run(...) implementation. Source it from SGLang's vanilla forward (tmp/sglang/python/sglang/srt/layers/...) when FlashInfer doesn't have it, otherwise mirror FlashInfer's own test harness. See add-reference-tests for validation flow.

B4. File a "missing trace template" issue against FlashInfer

Path B is friction we want to remove. Whenever you fall through to manual extraction because FlashInfer has the kernel but the API isn't decorated yet (the "decorator-gap" case — fi_status=fi_supported and fi_trace_template=false in the manifest), file a follow-up issue in flashinfer-ai/flashinfer so the next onboarding of the same op_type can use Path A. Skip this step for fi_missing kernels — those already have a kernel-request issue from /onboard-model Phase 2a, and the trace template will be added together with the kernel implementation.

gh issue create \
  --repo flashinfer-ai/flashinfer \
  --title "trace: add @flashinfer_api(trace=...) for {fi_api}" \
  --label "enhancement,flashinfer-trace" \
  --body "$(cat <<'EOF'
## Missing trace template

`{fi_api}` already has a working FlashInfer kernel but no `@flashinfer_api(trace=...)`
template, so flashinfer-bench cannot auto-dump its Definition JSON during a
`FLASHINFER_TRACE_DUMP=1` run. We had to fall back to manual extraction for
`{model_display_name}`.

### What we need

A `TraceTemplate` (see `flashinfer/trace/template.py` and the existing per-family
modules under `flashinfer/trace/templates/`) attached to the existing `@flashinfer_api`
decorator on `{fi_api}`. The template should declare the full set of axes / inputs /
outputs so `tests/trace/test_fi_trace_template_consistency.py` passes.

### Reference (manual extraction)

The Definition JSON we hand-wrote for this op while waiting for the trace template:
- `tmp/flashinfer-trace/definitions/{op_type}/{definition_name}.json` (will land in the
  next flashinfer-trace dataset PR — link from this comment once open).
- Used by: {model_display_name} ({hf_repo_id}).

### Acceptance

- [ ] `@flashinfer_api(trace=...)` attached to `{fi_api}`.
- [ ] Running `FLASHINFER_TRACE_DUMP=1 python tests/trace/example_sglang.py` (or the
      relevant model harness) emits a JSON for this op with the same `axes` /
      `inputs` / `outputs` / `name` we wrote by hand.
- [ ] `pytest tests/trace/ -v` passes.

### Related

- flashinfer-bench onboarding skill: `.claude/skills/extract-kernel-definitions/SKILL.md`
  Path B → step B4 (this issue is filed by that step).
EOF
)"

Record the issue URL on the manifest entry as fi_trace_template_request_url so reviewers of the dataset PR can see the follow-up is in flight.

---

Schema reference

This applies to both paths — it's the format the trace dumper produces (Path A) and the format your hand-written JSON must match (Path B).

{
  "name": "rmsnorm_h7168",
  "description": "Root Mean Square Normalization. Epsilon is fixed at 1e-6.",
  "op_type": "rmsnorm",
  "tags": [
    "fi_api:flashinfer.norm.rmsnorm",
    "status:verified",
    "model:{model_slug}",
    "tp:{N}"
  ],
  "axes": {
    "batch_size":  {"type": "var"},
    "hidden_size": {"type": "const", "value": 7168}
  },
  "constraints": ["..."],
  "inputs": 
         
                             
  
   
         
  
   

Field rules:

• name — Path A: auto-generated by the trace dumper from op_type / name_prefix + const-axis values. Path B: assemble per the naming patterns.
• op_type — selects the subdirectory under definitions/ (rmsnorm, gqa_paged, mla_paged, gdn, moe, gemm, gemm_fp8, sampling, rope, …).
• tags — always include fi_api:<qualified.name> (e.g. fi_api:flashinfer.norm.rmsnorm) and status:verified (use status:unverified for fi_missing). Add model:*, tp:N, ep:N, quantization:* as applicable. Path A emits the first two automatically; the rest are workflow metadata you append after staging.
• axes — var axes vary at runtime (batch, sequence length, num_pages); const axes are model constants and carry a "value". Const-axis values plus name_prefix produce the file name.
• constraints (optional) — string expressions like "len_indptr == batch_size + 1", evaluated against axis values when validating workloads.
• inputs / outputs — each entry has shape (list of axis names) and dtype. Optional inputs: "optional": true. Output dtype may be inherited from an input via "dtype_from": "{input_name}" in trace templates (the dumper resolves it before writing).
• reference — pure-PyTorch run() for correctness checking. Required for status:verified. Path A emits this when the trace template includes one; Path B writes it by hand.

Examples of fully populated definitions live in tests/trace/fi_trace_out/ in the FlashInfer repo — read these as canonical templates rather than re-deriving the schema by hand.

---

After staging

1. Validate: flashinfer-bench validate --dataset tmp/flashinfer-trace --disable-gpu.
2. Add reference tests for any newly staged definitions: /add-reference-tests --definition-name {name} (or --op-type {op_type}).
3. Move on to workload collection: /collect-workloads --definition-names {names}. Tip: /collect-workloads can also dump definitions in the same SGLang run by setting the trace env vars — useful for picking up shapes you missed in step A2.
4. PR submission is handled separately by /submit-onboarding-prs (Phase 4 of /onboard-model). Do not add definition JSONs to a flashinfer_trace/... path inside flashinfer-bench — that directory was removed in the refactor.

---

Error handling

• No JSONs appeared in the dump dir. Either the env vars were set after the FlashInfer import, the SGLang attention backend isn't flashinfer, CUDA graphs were enabled, or the inference path didn't reach a decorated API. Re-check the env-var ordering, ensure attention_backend="flashinfer" and disable_cuda_graph=True, and add print(flashinfer.norm.rmsnorm.fi_trace.__doc__) to confirm the decorator is bound.
• Names collide with existing definitions. Path A is content-deterministic — if a staged file with the same name already exists and differs, the dump captured a different shape under the same const-axis values. Compare the JSONs; the existing one usually wins unless the new shape is the intended target (then update tags / file an issue rather than overwriting silently).
• MoE routing variants didn't all dump. Each routing_method_type is its own template; only the routings the model actually invokes will fire. Run a model with the required routing (e.g. real DeepSeek-V3 for ds_routing).
• GPU OOM. Reduce mem_fraction_static, increase tp_size, or use a smaller variant of the model — the trace pass needs only a couple of generated tokens.

See also

• discover-models — Phase 1 classifier; tells you which kernels are fi_supported (Path A) vs fi_missing (Path B).
• add-reference-tests — pytest validation against FlashInfer / SGLang ground truth.
• collect-workloads — runs another SGLang pass and can dump definitions in the same run.
• submit-onboarding-prs — Phase 4 PR flow.
• FlashInfer trace docs: docs/fi_trace.rst.
• Reference SGLang harness: tests/trace/example_sglang.py.

---

Source: flashinfer-ai/flashinfer-bench — distributed by TomeVault.