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[skill] Remove outdated llm-serving-auto-benchmark skill (#29487)

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This commit is contained in:
Xiaoyu Zhang
2026-06-27 14:19:11 +08:00
committed by GitHub
parent 43435a2f8e
commit c36f166364
60 changed files with 563 additions and 6977 deletions
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.claude/skills/llm-torch-profiler-analysis/scripts/triage_kernel_helpers.py
+199 -9
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@@ -530,16 +530,16 @@ FUSION_PATTERN_REGISTRY: Tuple[FusionPatternSpec, ...] = (
likely_share=0.5,
),
FusionPatternSpec(
pattern="DSA fused metadata copy for graph replay",
pattern="NSA fused metadata copy for graph replay",
candidate_path="python/sglang/jit_kernel/fused_metadata_copy.py",
active_keywords=(
"fused_metadata_copy",
"fused_metadata_copy_multi",
"fused_dsa_cache_seqlens",
"fused_nsa_cache_seqlens",
"fused_flashmla_metadata",
),
rationale_hint=(
"DSA replay metadata copies are already fused into one-kernel" " families."
"NSA replay metadata copies are already fused into one-kernel" " families."
),
min_share=0.02,
likely_share=0.2,
@@ -744,23 +744,23 @@ FUSION_PATTERN_REGISTRY: Tuple[FusionPatternSpec, ...] = (
likely_share=1.5,
),
FusionPatternSpec(
pattern="DSA fused top-k transform / page-table build",
candidate_path="python/sglang/srt/layers/attention/dsa_backend.py",
pattern="NSA fused top-k transform / page-table build",
candidate_path="python/sglang/srt/layers/attention/nsa_backend.py",
active_keywords=(
"fast_topk_transform_fused",
"fast_topk_transform_ragged_fused",
),
rationale_hint=(
"DSA top-k metadata preparation already has fused transform kernels."
"NSA top-k metadata preparation already has fused transform kernels."
),
min_share=0.05,
likely_share=0.3,
),
FusionPatternSpec(
pattern="DSA fused quantize + indexed K-cache store",
pattern="NSA fused quantize + indexed K-cache store",
candidate_path=(
"python/sglang/jit_kernel/fused_store_index_cache.py"
"<br>python/sglang/srt/layers/attention/dsa/dsa_indexer.py"
"<br>python/sglang/srt/layers/attention/nsa/nsa_indexer.py"
),
active_keywords=("fused_store_index_k_cache",),
split_groups=(
@@ -768,7 +768,7 @@ FUSION_PATTERN_REGISTRY: Tuple[FusionPatternSpec, ...] = (
("index_k", "cache", "store"),
),
rationale_hint=(
"DSA already has a fused quantize-and-indexed-store kernel family."
"NSA already has a fused quantize-and-indexed-store kernel family."
),
min_share=0.2,
likely_share=1.0,
@@ -855,6 +855,180 @@ FUSION_PATTERN_REGISTRY: Tuple[FusionPatternSpec, ...] = (
likely_share=1.0,
priority=90,
),
FusionPatternSpec(
pattern="SGLang LTX2 fused Ada values",
candidate_path=(
"PR #29390"
"<br>python/sglang/jit_kernel/diffusion/triton/ltx2_ada_values.py"
"<br>python/sglang/multimodal_gen/runtime/models/dits/ltx_2.py"
),
active_keywords=(
"ltx2_ada_values9",
"ltx2_ada_values",
"LTX2TransformerBlock",
),
split_groups=(
("scale_shift_table", "timestep", "reshape"),
("get_ada_values", "ada", "adaln"),
("slice", "split", "unbind"),
),
rationale_hint=(
"SGLang mainline fuses LTX-2.3 Ada value materialization for"
" video/audio streams; split Ada table add/reshape/slice ladders"
" should be checked against this diffusion Triton kernel first."
),
origin="upstream",
model_include=("ltx", "ltx-2", "ltx2"),
min_share=0.2,
likely_share=1.0,
),
FusionPatternSpec(
pattern="SGLang LTX2 residual-gate add CUDA fast path",
candidate_path=(
"PR #29361"
"<br>python/sglang/jit_kernel/diffusion/residual_gate_add.py"
"<br>python/sglang/jit_kernel/csrc/diffusion/residual_gate_add.cuh"
"<br>python/sglang/multimodal_gen/runtime/models/dits/ltx_2.py"
),
active_keywords=(
"diffusion_residual_gate_add",
"residual_gate_add",
"_ltx2_residual_gate_add",
),
split_groups=(
("add", "mul", "gate"),
("residual", "update", "gate"),
("hidden_states", "attn_hidden_states", "gate"),
),
rationale_hint=(
"SGLang mainline fuses LTX2 residual + update * gate sites into"
" a CUDA custom op; split add/mul gate ladders should be checked"
" against this path before proposing a new diffusion elementwise"
" fusion."
),
origin="upstream",
model_include=("ltx", "ltx-2", "ltx2"),
min_share=0.2,
likely_share=1.0,
),
FusionPatternSpec(
pattern="TokenSpeed CuTe DSL MLA prefill / decode",
candidate_path=(
"python/tokenspeed/runtime/layers/attention/backends/tokenspeed_mla.py"
"<br>tokenspeed-mla/python/tokenspeed_mla/mla_decode.py"
"<br>tokenspeed-mla/python/tokenspeed_mla/mla_prefill.py"
"<br>tokenspeed-kernel/python/tokenspeed_kernel/ops/attention/"
"tokenspeed_mla/__init__.py"
),
active_keywords=(
"tokenspeed_mla_decode",
"tokenspeed_mla_prefill",
"BlackwellMultiHeadLatentAttentionForward",
),
split_groups=(
("mla", "flashmla", "attention", "fmha"),
("prefill", "decode", "verify"),
("fp8", "kv_cache", "page_table"),
),
rationale_hint=(
"TokenSpeed ships Blackwell CuTe DSL MLA prefill/decode kernels;"
" split MLA support kernels should be checked against backend"
" selection before being called novel."
),
origin="upstream",
model_include=("deepseek", "kimi", "qwen3.5", "qwen3_5"),
min_share=0.4,
likely_share=2.0,
),
FusionPatternSpec(
pattern="TokenSpeed MLA KV pack + FP8 quantize",
candidate_path=(
"tokenspeed-mla/python/tokenspeed_mla/mla_kv_pack_quantize_fp8.py"
"<br>tokenspeed-kernel/python/tokenspeed_kernel/ops/attention/"
"tokenspeed_mla/__init__.py"
),
active_keywords=(
"_mla_kv_pack_quantize_fp8_kernel",
"mla_kv_pack_quantize_fp8",
),
split_groups=(
("k_nope", "k_pe", "cat", "concat", "pack"),
("quant", "fp8", "float8"),
("v", "kv", "cache"),
),
rationale_hint=(
"TokenSpeed fuses MLA K/V pack, concat, and FP8 quantization into"
" one Triton kernel for chunked prefill."
),
origin="upstream",
model_include=("deepseek", "kimi", "qwen3.5", "qwen3_5"),
min_share=0.2,
likely_share=1.0,
),
FusionPatternSpec(
pattern="TokenSpeed fused top-k + top-p sampling",
candidate_path=(
"tokenspeed-kernel/python/tokenspeed_kernel/thirdparty/cuda/" # codespell:ignore thirdparty
"fused_topk_topp.py"
"<br>tokenspeed-kernel/python/tokenspeed_kernel/thirdparty/cuda/" # codespell:ignore thirdparty
"csrc/fused_topk_topp/fused_topk_topp.cu"
),
active_keywords=("fused_topk_topp", "fused_topk_topp_renorm"),
split_groups=(
("topk", "top_k"),
("topp", "top_p"),
("sampling", "renorm", "softmax"),
),
rationale_hint=(
"TokenSpeed has a fused top-k/top-p renormalization path for"
" decode sampling."
),
origin="upstream",
min_share=0.1,
likely_share=0.8,
),
FusionPatternSpec(
pattern="TokenSpeed persistent lm_head GEMM",
candidate_path=(
"tokenspeed-kernel/python/tokenspeed_kernel/thirdparty/cuda/" # codespell:ignore thirdparty
"lm_head_gemm.py"
"<br>tokenspeed-kernel/python/tokenspeed_kernel/thirdparty/cuda/" # codespell:ignore thirdparty
"csrc/lm_head_gemm.cu"
),
active_keywords=("lm_head_gemm",),
split_groups=(
("lm_head", "logits", "vocab"),
("gemm", "matmul", "linear"),
),
rationale_hint=(
"TokenSpeed has a shape-gated persistent lm_head GEMM path; visible"
" lm_head matmul ladders should be compared against it."
),
origin="upstream",
model_include=("kimi", "qwen"),
min_share=0.2,
likely_share=1.0,
),
FusionPatternSpec(
pattern="TokenSpeed NVFP4 GEMM + SwiGLU + quant",
candidate_path=(
"tokenspeed-kernel/python/tokenspeed_kernel/thirdparty/cute_dsl/" # codespell:ignore thirdparty
"nvfp4_gemm_swiglu_nvfp4_quant.py"
),
active_keywords=("nvfp4_gemm_swiglu_nvfp4_quant",),
split_groups=(
("gemm", "nvfp4", "fp4"),
("swiglu", "silu", "activation", "mul"),
("quant", "scale", "sfc"),
),
rationale_hint=(
"TokenSpeed's CuTe DSL kernel fuses NVFP4 GEMM, SwiGLU, and"
" optional output quantization in one expert-style path."
),
origin="upstream",
min_share=0.3,
likely_share=1.5,
),
FusionPatternSpec(
pattern="vLLM-origin Attention + Quantization",
candidate_path=(
@@ -1235,6 +1409,8 @@ def source_location_priority(location: str) -> int:
return 290 - penalty
if text.startswith("vllm/"):
return 285 - penalty
if text.startswith("python/tokenspeed/") or text.startswith("tokenspeed/"):
return 283 - penalty
if text.startswith("tensorrt_llm/"):
return 280 - penalty
if text.startswith("sgl_kernel/"):
@@ -1254,6 +1430,8 @@ def is_preferred_source_location(location: str) -> bool:
text.startswith("python/sglang/")
or text.startswith("sglang/")
or text.startswith("vllm/")
or text.startswith("python/tokenspeed/")
or text.startswith("tokenspeed/")
or text.startswith("tensorrt_llm/")
or text.startswith("sgl_kernel/")
)
@@ -1316,6 +1494,10 @@ def frame_priority(frame_name: str) -> int:
return 290 - penalty
if normalized_text.startswith("vllm/"):
return 285 - penalty
if normalized_text.startswith("python/tokenspeed/") or normalized_text.startswith(
"tokenspeed/"
):
return 283 - penalty
if normalized_text.startswith("tensorrt_llm/"):
return 280 - penalty
if normalized_text.startswith("sgl_kernel/"):
@@ -1329,6 +1511,8 @@ def frame_priority(frame_name: str) -> int:
return 120
if "/vllm/" in raw_text:
return 118
if "/tokenspeed/" in raw_text or "/TokenSpeed/" in raw_text:
return 117
if "/TensorRT-LLM/" in raw_text or "/tensorrt_llm/" in raw_text:
return 116
return 100
@@ -1336,6 +1520,10 @@ def frame_priority(frame_name: str) -> int:
return 110
if ".py(" in raw_text and "/vllm/" in raw_text:
return 108
if ".py(" in raw_text and (
"/tokenspeed/" in raw_text or "/TokenSpeed/" in raw_text
):
return 107
if ".py(" in raw_text and (
"/TensorRT-LLM/" in raw_text or "/tensorrt_llm/" in raw_text
):
@@ -2438,6 +2626,8 @@ def fusion_framework_hints(spec: FusionPatternSpec) -> set[str]:
hints: set[str] = set()
if "vllm/" in text:
hints.add("vllm")
if any(token in text for token in ("tokenspeed/", "tokenspeed-", "tokenspeed_")):
hints.add("tokenspeed")
if "tensorrt_llm/" in text:
hints.add("trtllm")
if any(token in text for token in ("python/sglang/", "sgl-kernel/", "sgl_kernel/")):