discover-models

name	discover-models
description	Discover candidate LLMs and produce a kernel inventory — required definitions, classified as existing/new and fi_supported/fi_missing — for onboarding. Use as Phase 1 of /onboard-model, or standalone to plan onboarding work.

Discover Models

Identify target models and produce a per-kernel inventory:

• which definitions are needed,
• which already live in the HuggingFace dataset (tmp/flashinfer-trace/definitions/),
• which are new and supported by FlashInfer,
• which are new and missing from FlashInfer (so a kernel-request issue is needed).

Produces the kernels block of the onboard-model run manifest.

Usage

# Auto-discover candidate models added to SGLang in the last 30 days
/discover-models --discover

# Plan a specific model
/discover-models --model-name qwen3-235b-a22b --hf-repo-id Qwen/Qwen3-235B-A22B

# Write the inventory to a manifest file (consumed by /onboard-model)
/discover-models --model-name kimi-k2 --manifest tmp/onboard_kimi-k2_20260427.json

Parameters

• --discover (optional): Auto-discover candidates from SGLang day-0 additions and sgl-cookbook YAMLs.
• --model-name (optional): Specific model slug to plan (e.g. qwen3-235b-a22b).
• --hf-repo-id (optional): HuggingFace repo override (e.g. Qwen/Qwen3-235B-A22B). Inferred from --model-name if omitted.
• --manifest (optional): Path to an onboard-model run manifest. The skill writes the model_slug, hf_repo_id, repo_shas, and kernels array. If the file already exists, fields are merged; existing per-kernel statuses are preserved unless --refresh is set.
• --refresh (optional): Re-classify all kernels even if entries already exist in the manifest.

Prerequisites

• /clone-repos has been run, so tmp/sglang/, tmp/flashinfer/, tmp/sgl-cookbook/, and tmp/flashinfer-trace/ are present and current.
• huggingface_hub is installed and (for gated models) authenticated.

---

Phase 1a: Discover candidate models

Run only when --discover is set.

Day-0 SGLang additions (highest priority — production-ready):

git -C tmp/sglang log --since="30 days ago" --name-status --diff-filter=A \
    -- "python/sglang/srt/models/*.py" | grep "^A" | awk '{print $2}'

Models with a brand-new .py under python/sglang/srt/models/ in the last 30 days are day-0 candidates. Parse the model class to derive a slug.

sgl-cookbook new entries:

git -C tmp/sgl-cookbook log --since="30 days ago" --name-status --diff-filter=A \
    -- "data/models/generated/v0.5.6/*.yaml" | grep "^A" | awk '{print $2}'

A new YAML signals a model with a recommended serving config.

Filter already-tracked models: read docs/model_coverage.mdx Summary table and skip any candidate already listed.

Phase 1b: Fetch model config from HuggingFace

For each candidate (or the specified --model-name):

from huggingface_hub import hf_hub_download
import json

config_path = hf_hub_download(repo_id=hf_repo_id, filename="config.json")
with open(config_path) as f:
    config = json.load(f)

Key fields to extract: see track-models SKILL.md for the full config.json → kernel param mapping table.

Phase 1c: Determine required kernel definitions

Use the per-op-type formulas in track-models Phase 3a to compute the expected definition names from the model config and the sgl-cookbook TP/EP values. Each formula yields a fully qualified definition name like gqa_paged_decode_h40_kv8_d128_ps1.

Phase 1d: Classify existing vs new

For each expected definition name, search the HuggingFace dataset clone (definitions live only there after the trace-dataset refactor):

find tmp/flashinfer-trace/definitions/ -name "{definition_name}.json"

Result	Classification
File found	existing — no new definition needed
Not found	new — proceed to FlashInfer-availability classification

Phase 1e: Check FlashInfer kernel availability for new definitions

For each new definition, determine whether FlashInfer already implements the underlying kernel.

op_type	Check path in tmp/flashinfer/
rmsnorm	flashinfer/norm.py — grep for rmsnorm
gqa_paged	flashinfer/decode.py, flashinfer/prefill.py
gqa_ragged	flashinfer/prefill.py
mla_paged	flashinfer/mla.py
dsa_paged	flashinfer/sparse.py
gdn	flashinfer/gdn.py or flashinfer/gdn/
moe	flashinfer/fused_moe/ — check the specific variant
gemm	always available via PyTorch
sampling	flashinfer/sampling.py
mamba_ssu	flashinfer/mamba.py — grep for selective_state_update
rope	flashinfer/rope.py — grep for apply_rope_with_cos_sin_cache

Also check tmp/flashinfer/tests/ for a corresponding test file — its presence is a strong signal the kernel is implemented and tested.

A stronger signal that the kernel is fully ready for the trace-dump path (Path A in extract-kernel-definitions) is whether the FlashInfer API carries an @flashinfer_api(trace=...) decorator (added by flashinfer-ai/flashinfer#2931). Check with:

grep -rn "@flashinfer_api(trace=" tmp/flashinfer/flashinfer/ | grep -i "{module_or_api}"

If the API is decorated, Phase 2 can produce its Definition JSON automatically by running a short SGLang inference pass with FLASHINFER_TRACE_DUMP=1. If FlashInfer has the kernel but not the decorator, classification is still fi_supported but Phase 2 falls back to manual extraction. Record the decorator-presence flag on the manifest entry as fi_trace_template (true/false) so reviewers know which path to expect.

Classify each new definition:

• fi_supported: FlashInfer has the kernel → onboard-model Phase 2 (trace-dump if fi_trace_template=true, else manual extraction; see extract-kernel-definitions).
• fi_missing: FlashInfer does not have the kernel → onboard-model Phase 2 (manual extraction from SGLang + file kernel-request issue).

Phase 1f: Check SGLang integration for fi_supported definitions

For each fi_supported definition, determine whether SGLang already routes through the FlashInfer kernel. The result drives Phase 3 (workload collection).

# Use the fi_api tag from the definition (or the expected wrapper name) to grep:
grep -r "{flashinfer_api_name}" tmp/sglang/python/sglang/srt/ 2>/dev/null | grep -v __pycache__

Common mapping:

fi_api	SGLang integration file	Search term
flashinfer.mla.BatchMLAPagedAttentionWrapper	layers/attention/flashinfer_backend.py	BatchMLAPagedAttentionWrapper
flashinfer.decode.BatchDecodeWithPagedKVCacheWrapper	layers/attention/flashinfer_backend.py	BatchDecodeWithPagedKVCacheWrapper
flashinfer.prefill.BatchPrefillWithPagedKVCacheWrapper	layers/attention/flashinfer_backend.py	BatchPrefillWithPagedKVCacheWrapper
flashinfer.norm.rmsnorm	layers/layernorm.py	flashinfer.norm
flashinfer.fused_moe.trtllm_fp8_block_scale_moe	layers/moe/fused_moe.py	trtllm_fp8_block_scale_moe
flashinfer.gdn.gated_delta_rule_decode	layers/attention/gdn_backend.py	gated_delta_rule_decode
flashinfer.mamba.selective_state_update	layers/mamba/mamba_mixer.py	selective_state_update

Classify:

• sgl_integrated: SGLang already calls this FlashInfer API → Phase 3 collects workloads directly.
• sgl_missing: SGLang does not yet wire this API → Phase 3 must submit an SGLang PR first.

For fi_missing definitions, SGLang integration is moot (no FlashInfer kernel to call) — set sgl_status to n/a.

Phase 1g: Report

Print a classification table:

Model: Qwen3-235B-A22B
HF repo: Qwen/Qwen3-235B-A22B
Architecture: 94 layers, GQA + MoE

Kernel inventory:
  EXISTING (skip):
    ✅ rmsnorm_h7168
    ✅ moe_fp8_block_scale_ds_routing_topk8_ng8_kg4_e32_h7168_i2048
  NEW — FlashInfer supported, SGLang integrated → ready for workload collection:
    🆕 gqa_paged_decode_h40_kv8_d128_ps1
    🆕 gqa_paged_decode_h40_kv8_d128_ps64
  NEW — FlashInfer supported, SGLang missing → submit SGLang PR first:
    🆕 dsa_topk_indexer_fp8_h64_d128_topk2048_ps64
  NEW — FlashInfer MISSING → file kernel-request issue, skip workload collection:
    ❓ <new_op_type>_<params>

Output: run-manifest contract

When --manifest <path> is set, write/update a JSON file with this shape (the same manifest consumed by /onboard-model and /submit-onboarding-prs):

{
  "model_slug": "qwen3-235b-a22b",
  "hf_repo_id": "Qwen/Qwen3-235B-A22B",
  "date": "2026-04-27",
  "repo_shas": {
    "sglang": "abc1234",
    "flashinfer": "def5678",
    "sgl_cookbook": "ghi9012",
    "flashinfer_trace": "jkl3456"
  },
  "kernels": [
    {
      "definition_name": "gqa_paged_decode_h40_kv8_d128_ps1",
      "op_type": "gqa_paged",
      "phase1_status": "new",
      "fi_status": "fi_supported",
      "fi_trace_template": true,
      "sgl_status": "sgl_integrated"
    },
    {
      "definition_name": "rmsnorm_h7168",
      "op_type": "rmsnorm",
      "phase1_status": "existing"
    },
    {
      "definition_name": "new_op_h512",
      "op_type": "new_op",
      "phase1_status": "new",
      "fi_status": "fi_missing",
      "sgl_status": "n/a"
    }
  ]
}

Existing entries written by later phases (phase2_status, phase3_status, workload_entries, fi_issue_url, phase4) are preserved on update.

See Also

• onboard-model — full pipeline that consumes this skill's output
• track-models — config-field and per-op-type formula reference
• clone-repos — must run first
• submit-onboarding-prs — Phase 4 counterpart