From a4e59bd1a5d401db37f24777144dc838849ddb20 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 16:37:06 +0000
Subject: [PATCH 01/10] qwen38: fuse gate_up GEMM + silu_mul into
 kern_gemm8_gateup_silu (bs<=8)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

First user of the gemm8 family (tools/kernels-src/gemm8.cu): a streaming
bf16 GEMM for M <= 8 token rows, one persistent CTA per GB300 SM pulling
weight rows through a TMA smem ring, mma.m16n8k16 via ldmatrix.x4 (f32
accumulate), x staged once in smem; the silu(gate)*up epilogue reproduces
vLLM act_and_mul's rounding chain (bf16(gate), bf16(up), bf16(silu),
bf16(product)).  Applied to every tokens<=8 forward: decode (both
manifests), decode_spec / verify / draft (DFlash2); chunked prefill keeps
cuBLAS.  -64 dispatches in decode / decode_spec / verify, -5 in draft.
Cubins are derived (kernels-qwen38*/ gitignored): tools/extract_kernels.sh
rebuilds them with nvcc -cubin -arch=sm_103a.

kern-attest (A = previous commit's manifest, B = this one, GB300):

qwen3.8-27b.json — 64 cuts (gemm+silu_mul -> gemm8_gateup_silu, decode):

| decode tokens=1     | A         | B measured | delta            |
| step, eager         | 14.391 ms | 13.642 ms  | -748 us  -5.2%   |
| step, graph (TPOT)  | 11.478 ms | 11.013 ms  | -464 us  87->91 tok/s |
| sum of 64 cuts      | 4.339 ms  | 3.659 ms   | -679 us  -15.7%  |
| roofline gate_up    | 59.6 us 74.8% of peak | 57.2 us 78.0% | (+silu_mul 8.2 us -> 0) |

qwen3.8-27b-dflash2.json — same 64 cuts in decode (+ decode_spec/verify/
draft changed, driver stages prefill/decode only):

| decode tokens=1     | A         | B measured | delta            |
| step, eager         | 15.761 ms | 15.180 ms  | -581 us  -3.7%   |
| step, graph (TPOT)  | 12.068 ms | 11.631 ms  | -437 us  83->86 tok/s |
| sum of 64 cuts      | 4.405 ms  | 3.663 ms   | -742 us  -16.8%  |

Verdict: INCONCLUSIVE, as expected for a GEMM re-implementation -- the cut
output differs only by f32 accumulation order vs cublasLt.  next_token,
draft_tokens and verify_tokens are bit-identical on the tap prompt; noise
floor clean; fuzz across 6 distributions <= 364 ulp on act, edge/special
bit-identical.  Spec-path e2e sanity (qwen38_compare.py --spec) at the end
of the series.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 7149 +++++------------------------
 examples/qwen3.8-27b.json         | 2361 ++--------
 tools/gen_qwen35.py               |   30 +-
 tools/kernels-src/gemm8.cu        |  484 ++
 4 files changed, 2189 insertions(+), 7835 deletions(-)
 create mode 100644 tools/kernels-src/gemm8.cu

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index 97471fb..9b1cb82 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6644,6 +6644,63 @@
     ]
    }
   },
+  "gemm8_gateup_silu": {
+   "params": [
+    "out buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "i32",
+    "i32",
+    "i32"
+   ],
+   "impl": {
+    "steps": [
+     {
+      "symbol": "kern_gemm8_gateup_silu_bf16",
+      "params": [
+       "out buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "i32",
+       "i32",
+       "i32"
+      ],
+      "block": [
+       288,
+       1,
+       1
+      ],
+      "grid": [
+       152,
+       1,
+       1
+      ],
+      "args": [
+       {
+        "arg": 0
+       },
+       {
+        "arg": 1
+       },
+       {
+        "arg": 2
+       },
+       {
+        "arg": 3
+       },
+       {
+        "arg": 4
+       },
+       {
+        "arg": 5
+       }
+      ],
+      "shared_mem": 224128,
+      "cubin": "gemm8.cubin"
+     }
+    ]
+   }
+  },
   "copy_rows": {
    "params": [
     "out buffer<bf16>",
@@ -47537,50 +47594,26 @@
      ]
     },
     {
-     "label": "l0.gate_up",
-     "kernel": "gemm",
+     "label": "l0.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.0.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.0.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l0.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -48037,50 +48070,26 @@
      ]
     },
     {
-     "label": "l1.gate_up",
-     "kernel": "gemm",
+     "label": "l1.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.1.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.1.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l1.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -48537,50 +48546,26 @@
      ]
     },
     {
-     "label": "l2.gate_up",
-     "kernel": "gemm",
+     "label": "l2.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.2.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.2.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l2.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -49020,50 +49005,26 @@
      ]
     },
     {
-     "label": "l3.gate_up",
-     "kernel": "gemm",
+     "label": "l3.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.3.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.3.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l3.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -49520,50 +49481,26 @@
      ]
     },
     {
-     "label": "l4.gate_up",
-     "kernel": "gemm",
+     "label": "l4.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.4.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.4.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l4.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -50020,50 +49957,26 @@
      ]
     },
     {
-     "label": "l5.gate_up",
-     "kernel": "gemm",
+     "label": "l5.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.5.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.5.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l5.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -50520,50 +50433,26 @@
      ]
     },
     {
-     "label": "l6.gate_up",
-     "kernel": "gemm",
+     "label": "l6.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.6.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.6.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l6.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -51005,50 +50894,26 @@
      ]
     },
     {
-     "label": "l7.gate_up",
-     "kernel": "gemm",
+     "label": "l7.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.7.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.7.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l7.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -51505,50 +51370,26 @@
      ]
     },
     {
-     "label": "l8.gate_up",
-     "kernel": "gemm",
+     "label": "l8.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.8.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.8.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l8.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -52005,50 +51846,26 @@
      ]
     },
     {
-     "label": "l9.gate_up",
-     "kernel": "gemm",
+     "label": "l9.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.9.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.9.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l9.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -52505,50 +52322,26 @@
      ]
     },
     {
-     "label": "l10.gate_up",
-     "kernel": "gemm",
+     "label": "l10.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.10.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.10.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l10.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -52990,50 +52783,26 @@
      ]
     },
     {
-     "label": "l11.gate_up",
-     "kernel": "gemm",
+     "label": "l11.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.11.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.11.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l11.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -53490,50 +53259,26 @@
      ]
     },
     {
-     "label": "l12.gate_up",
-     "kernel": "gemm",
+     "label": "l12.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.12.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.12.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l12.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -53990,50 +53735,26 @@
      ]
     },
     {
-     "label": "l13.gate_up",
-     "kernel": "gemm",
+     "label": "l13.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.13.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.13.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l13.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -54490,50 +54211,26 @@
      ]
     },
     {
-     "label": "l14.gate_up",
-     "kernel": "gemm",
+     "label": "l14.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.14.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.14.mlp.gate_up_proj.weight"
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@@ -145494,50 +140871,26 @@
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-     "kernel": "gemm",
+     "label": "l3.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
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+       "buf": "act"
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@@ -146139,50 +141492,26 @@
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-     "kernel": "gemm",
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diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index eb41957..2635215 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -5883,6 +5883,63 @@
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+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "i32",
+    "i32",
+    "i32"
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+      "params": [
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+       "in buffer<bf16>",
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+      "cubin": "gemm8.cubin"
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@@ -45364,50 +45421,26 @@
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@@ -45700,50 +45733,26 @@
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+     "label": "l1.gate_up_silu",
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@@ -46036,50 +46045,26 @@
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@@ -46519,50 +46504,26 @@
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@@ -46855,50 +46816,26 @@
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@@ -47191,50 +47128,26 @@
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+     "label": "l5.gate_up_silu",
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@@ -47527,50 +47440,26 @@
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+     "label": "l6.gate_up_silu",
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@@ -48012,50 +47901,26 @@
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@@ -48348,50 +48213,26 @@
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@@ -48684,50 +48525,26 @@
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@@ -49020,50 +48837,26 @@
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@@ -49505,50 +49298,26 @@
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+     "kernel": "gemm8_gateup_silu",
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@@ -49841,50 +49610,26 @@
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@@ -50177,50 +49922,26 @@
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-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -63950,50 +62807,26 @@
      ]
     },
     {
-     "label": "l50.gate_up",
-     "kernel": "gemm",
+     "label": "l50.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.50.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.50.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l50.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -64435,50 +63268,26 @@
      ]
     },
     {
-     "label": "l51.gate_up",
-     "kernel": "gemm",
+     "label": "l51.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.51.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.51.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l51.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -64771,50 +63580,26 @@
      ]
     },
     {
-     "label": "l52.gate_up",
-     "kernel": "gemm",
+     "label": "l52.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.52.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.52.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l52.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -65107,50 +63892,26 @@
      ]
     },
     {
-     "label": "l53.gate_up",
-     "kernel": "gemm",
+     "label": "l53.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.53.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.53.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l53.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -65443,50 +64204,26 @@
      ]
     },
     {
-     "label": "l54.gate_up",
-     "kernel": "gemm",
+     "label": "l54.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.54.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.54.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l54.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -65928,50 +64665,26 @@
      ]
     },
     {
-     "label": "l55.gate_up",
-     "kernel": "gemm",
+     "label": "l55.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.55.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.55.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l55.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -66264,50 +64977,26 @@
      ]
     },
     {
-     "label": "l56.gate_up",
-     "kernel": "gemm",
+     "label": "l56.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.56.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.56.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l56.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -66600,50 +65289,26 @@
      ]
     },
     {
-     "label": "l57.gate_up",
-     "kernel": "gemm",
+     "label": "l57.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.57.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.57.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l57.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -66936,50 +65601,26 @@
      ]
     },
     {
-     "label": "l58.gate_up",
-     "kernel": "gemm",
+     "label": "l58.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.58.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.58.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l58.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -67421,50 +66062,26 @@
      ]
     },
     {
-     "label": "l59.gate_up",
-     "kernel": "gemm",
+     "label": "l59.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.59.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.59.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l59.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -67757,50 +66374,26 @@
      ]
     },
     {
-     "label": "l60.gate_up",
-     "kernel": "gemm",
+     "label": "l60.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.60.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.60.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l60.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -68093,50 +66686,26 @@
      ]
     },
     {
-     "label": "l61.gate_up",
-     "kernel": "gemm",
+     "label": "l61.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.61.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.61.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l61.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -68429,50 +66998,26 @@
      ]
     },
     {
-     "label": "l62.gate_up",
-     "kernel": "gemm",
+     "label": "l62.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.62.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.62.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l62.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
@@ -68914,50 +67459,26 @@
      ]
     },
     {
-     "label": "l63.gate_up",
-     "kernel": "gemm",
+     "label": "l63.gate_up_silu",
+     "kernel": "gemm8_gateup_silu",
      "args": [
       {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "model.layers.63.mlp.gate_up_proj.weight"
+       "buf": "x"
       },
       {
-       "buf": "gate_up"
+       "buf": "model.layers.63.mlp.gate_up_proj.weight"
       },
       {
        "sym": "tokens"
       },
-      {
-       "i32": 34816
-      },
-      {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l63.silu_mul",
-     "kernel": "silu_mul",
-     "args": [
-      {
-       "buf": "act"
-      },
-      {
-       "buf": "gate_up"
-      },
       {
        "i32": 17408
       },
       {
-       "i32": 0
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "i32": 0
+       "i32": 5120
       }
      ]
     },
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index c6ee5eb..5a7cbc1 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -103,6 +103,9 @@
 NT_MAX = CHUNK_MAX // FLA_CHUNK          # 32 chunks
 # attention
 BLOCK_SIZE = 784                         # vLLM block_size (constexpr in the kernels)
+GEMM8_GRID = 152                         # one persistent CTA per GB300 SM
+GEMM8_THREADS = 288                      # 1 producer + 8 compute warps
+GEMM8_SMEM = 224128                      # sizeof(Smem) + alignment (gemm8.cu)
 BLOCK_Q = 5                              # unified 2D: query rows per block
 NUM_SEGMENTS = 16                        # unified 3D: grid.z
 BLOCK_TABLE_LEN = 8                      # vLLM block_table_stride
@@ -756,6 +759,14 @@ def layer_norm_kernel(src, grid, rows_shape):
                                    cubin="gemma_rms_norm.cubin"),
         "silu_mul": single(silu_sym, ["out buffer<bf16>", "in buffer<bf16>", "i32", "i32", "f32", "i32"],
                            blk("silu"), [T, 1, 1]),
+        # streaming M<=8 GEMM (tools/kernels-src/gemm8.cu): one persistent CTA
+        # per GB300 SM, weight rows via TMA ring + mma; grid/block/smem are
+        # compile-time constants of that kernel
+        "gemm8_gateup_silu": single("kern_gemm8_gateup_silu_bf16",
+                                    ["out buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>",
+                                     "i32", "i32", "i32"],
+                                    [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
+                                    shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
         "copy_rows": single("kern_copy_rows_bf16",
                             ["out buffer<bf16>", "in buffer<bf16>", "i32", "i32", "i32"],
                             [256, 1, 1], [T, 1, 1], cubin="copy_rows.cubin"),
@@ -1103,6 +1114,9 @@ def forward(decode, taps=False, nacc=None, tail=None):
         next_token), "decode" (row 0 -> next_token), "verify" (all rows ->
         verify_tokens)."""
         tail = tail or ("decode" if decode else "prefill")
+        # bs<=8 forwards (decode / decode_spec / verify) take the streaming
+        # gemm8 kernels; chunked prefill keeps cuBLAS
+        small = decode or tail == "verify"
         ds = [
             d("embed", "embedding",
               [buf("token_ids"), buf("model.embed_tokens.weight"), buf("residual"), T, i32(HIDDEN)]),
@@ -1118,11 +1132,17 @@ def forward(decode, taps=False, nacc=None, tail=None):
             p = f"model.layers.{i}."
             l = f"l{i}."
             ds += attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc)
+            if small:
+                mlp = [d(l + "gate_up_silu", "gemm8_gateup_silu",
+                         [buf("act"), buf("x"), buf(p + "mlp.gate_up_proj.weight"),
+                          T, i32(FFN), i32(HIDDEN)])]
+            else:
+                mlp = [gemm(l + "gate_up", "x", p + "mlp.gate_up_proj.weight", "gate_up", T, 2 * FFN, HIDDEN),
+                       d(l + "silu_mul", "silu_mul",
+                         [buf("act"), buf("gate_up"), i32(FFN), i32(0), f32(1.0), i32(0)])]
             ds += [
                 fused(l + "post_attn_norm", "y", p + "post_attention_layernorm.weight_p1"),
-                gemm(l + "gate_up", "x", p + "mlp.gate_up_proj.weight", "gate_up", T, 2 * FFN, HIDDEN),
-                d(l + "silu_mul", "silu_mul",
-                  [buf("act"), buf("gate_up"), i32(FFN), i32(0), f32(1.0), i32(0)]),
+                *mlp,
                 gemm(l + "down_proj", "act", p + "mlp.down_proj.weight", "y", T, HIDDEN, FFN),
             ]
             last = i + 1 == LAYERS
@@ -1215,8 +1235,8 @@ def draft():
                 gemm(l + "mlp_conv_proj", "y", p + "mlp_conv.kernel_projection.weight", "d_coef",
                      T, D_CONV_PROJ, HIDDEN),
                 d_conv(l + "mlp_conv_pre", "y", p + "mlp_conv", 0),
-                gemm(l + "gate_up", "y", p + "mlp.gate_up_proj.weight", "gate_up", T, 2 * FFN, HIDDEN),
-                d(l + "silu_mul", "silu_mul", [buf("act"), buf("gate_up"), i32(FFN), i32(0), f32(1.0), i32(0)]),
+                d(l + "gate_up_silu", "gemm8_gateup_silu",
+                  [buf("act"), buf("y"), buf(p + "mlp.gate_up_proj.weight"), T, i32(FFN), i32(HIDDEN)]),
                 gemm(l + "down_proj", "act", p + "mlp.down_proj.weight", "x", T, HIDDEN, FFN),
                 d_conv(l + "mlp_conv_post", "x", p + "mlp_conv", 1),
                 d_fused(l + ("final_norm" if last else "next_input_norm"), "x",
diff --git a/tools/kernels-src/gemm8.cu b/tools/kernels-src/gemm8.cu
new file mode 100644
index 0000000..0ce6d33
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+++ b/tools/kernels-src/gemm8.cu
@@ -0,0 +1,484 @@
+// Streaming bf16 GEMM for M <= 8 token rows (bs=1 decode and the 8-row
+// draft / verify blocks of DFlash2), Blackwell sm_103a.  At these shapes the
+// weight matrix is the only real traffic: one persistent CTA per SM pulls its
+// weight rows through a smem ring with cp.async.bulk (TMA 1D, plain pointers,
+// 40 KB stages of 8 rows) and 8 compute warps multiply each stage with
+// mma.m16n8k16 via ldmatrix.x4 (bf16 in, f32 accumulate; the 8 weight rows
+// are the n8 side, the <= 8 tokens fill half of m16).  x is staged once into
+// a dedicated smem area (<= 40960 elements); when M*K exceeds it -- or N is
+// too small to balance the grid -- K is cut into ranges, tasks are ordered
+// range-major so a CTA reloads x only on a range switch, and the f32 range
+// partials go to impl scratch with no atomics: the tail that already reads
+// every output element sums them there.  Smem rows are padded by 16 B so the
+// ldmatrix reads are bank-conflict-free (measured 3.5x slower without).
+//
+// The tail runs after a single end-of-CTA fence + task-count atomic.  With
+// grid <= %nsmid (one ~200 KB-smem CTA per SM, the launch geometry of every
+// call site) all CTAs are co-resident, so CTAs 0..M-1 spin on the counter and
+// each finishes one token row in parallel; on any other geometry the CTA that
+// finished last does all rows.  Counters are back to zero at kernel end.
+//
+// Entry points differ in what is fused around the GEMM (rounding chains
+// reproduce the vLLM / ATen kernels they replace):
+//
+//   kern_gemm8_bf16               y[M,N] = x W^T
+//   kern_gemm8_gateup_silu_bf16   act = silu(gate) * up over the fused [2F,K]
+//                                 gate_up weight (vLLM act_and_mul: bf16(gate),
+//                                 bf16(up), bf16(silu), bf16(product))
+//   kern_gemm8_dual_bf16          y1 = x W1^T, y2 = x W2^T (one weight stream)
+//   kern_gemm8_add_norm_bf16      y = x W^T, then per token row the Gemma
+//                                 fused_add_rms_norm (kern_gemma_rms_norm.cu
+//                                 arithmetic: z = f32(bf16(y)) + f32(res),
+//                                 res = bf16(z), ATen-order mean of z^2,
+//                                 out = bf16((z * rsqrt(var + eps)) * w1))
+//
+//   nvcc -cubin -arch=sm_103a -o kernels-qwen38/gemm8.cubin tools/kernels-src/gemm8.cu
+#include <cuda_bf16.h>
+#include <cstdint>
+
+namespace {
+constexpr int MMAX = 8;                  // token rows per launch
+constexpr int R = 8;                     // weight rows per task (one n8 tile)
+constexpr int SEG_MAX = 2560;            // K elements per weight row per stage
+constexpr int PAD = 8;                   // 16 B row padding: bank shift 4
+constexpr int X_ELEMS = 40960;           // x area capacity (M * kseg elements)
+constexpr int X_SLOT = X_ELEMS + MMAX * PAD;
+constexpr int RING_ELEMS = 69888;        // 3 stages of 8 rows at SEG_MAX; 4-5 for split shapes
+constexpr int STAGES_MAX = 6;
+constexpr int NCW = 8, CTHREADS = NCW * 32, NTHREADS = CTHREADS + 32;
+constexpr int BAR_C = 1;                 // named barrier of the compute warps
+constexpr int NT_ATEN = 512;             // ATen reduce block (emulated)
+
+__device__ __forceinline__ uint32_t smem_u32(const void* p) { return (uint32_t)__cvta_generic_to_shared(p); }
+__device__ __forceinline__ void mbar_init(uint32_t bar, uint32_t count) {
+  asm volatile("mbarrier.init.shared::cta.b64 [%0], %1;" ::"r"(bar), "r"(count));
+}
+__device__ __forceinline__ void mbar_expect_tx(uint32_t bar, uint32_t bytes) {
+  asm volatile("mbarrier.arrive.expect_tx.shared::cta.b64 _, [%0], %1;" ::"r"(bar), "r"(bytes) : "memory");
+}
+__device__ __forceinline__ void mbar_arrive(uint32_t bar) {
+  asm volatile("mbarrier.arrive.shared::cta.b64 _, [%0];" ::"r"(bar) : "memory");
+}
+__device__ __forceinline__ void mbar_wait(uint32_t bar, uint32_t parity) {
+  asm volatile(
+      "{\n.reg .pred p;\nWAIT_%=:\nmbarrier.try_wait.parity.shared::cta.b64 p, [%0], %1;\n@!p bra WAIT_%=;\n}" ::"r"(bar),
+      "r"(parity)
+      : "memory");
+}
+__device__ __forceinline__ void fence_barrier_init() { asm volatile("fence.mbarrier_init.release.cluster;" ::: "memory"); }
+__device__ __forceinline__ void bulk_copy_1d(uint32_t dst, const void* src, uint32_t bytes, uint32_t bar) {
+  asm volatile("cp.async.bulk.shared::cluster.global.mbarrier::complete_tx::bytes [%0], [%1], %2, [%3];" ::"r"(dst), "l"(src),
+               "r"(bytes), "r"(bar)
+               : "memory");
+}
+__device__ __forceinline__ void named_bar_sync(int id, int n) { asm volatile("bar.sync %0, %1;" ::"r"(id), "r"(n) : "memory"); }
+__device__ __forceinline__ void mma_bf16_16816(float& c0, float& c1, float& c2, float& c3, uint32_t a0, uint32_t a1, uint32_t a2,
+                                               uint32_t a3, uint32_t b0, uint32_t b1) {
+  asm volatile("mma.sync.aligned.m16n8k16.row.col.f32.bf16.bf16.f32 {%0,%1,%2,%3}, {%4,%5,%6,%7}, {%8,%9}, {%0,%1,%2,%3};"
+               : "+f"(c0), "+f"(c1), "+f"(c2), "+f"(c3)
+               : "r"(a0), "r"(a1), "r"(a2), "r"(a3), "r"(b0), "r"(b1));
+}
+__device__ __forceinline__ void ldsm_x4(uint32_t& r0, uint32_t& r1, uint32_t& r2, uint32_t& r3, uint32_t addr) {
+  asm volatile("ldmatrix.sync.aligned.m8n8.x4.shared.b16 {%0,%1,%2,%3}, [%4];" : "=r"(r0), "=r"(r1), "=r"(r2), "=r"(r3) : "r"(addr));
+}
+__device__ __forceinline__ int ld_acquire(const int* p) {
+  int v;
+  asm volatile("ld.acquire.gpu.global.s32 %0, [%1];" : "=r"(v) : "l"(p) : "memory");
+  return v;
+}
+__device__ __forceinline__ uint32_t nsmid() {
+  uint32_t n;
+  asm("mov.u32 %0, %%nsmid;" : "=r"(n));
+  return n;
+}
+__device__ __forceinline__ float bf16r(float v) { return __bfloat162float(__float2bfloat16_rn(v)); }
+
+struct Smem {
+  alignas(128) __nv_bfloat16 ring[RING_ELEMS];              // stage s: rows [s*8*(seg+PAD), ...)
+  alignas(128) __nv_bfloat16 x[X_SLOT];                     // current K range of x: M rows, stride kseg+PAD
+  alignas(8) uint64_t full[STAGES_MAX], empty[STAGES_MAX], xfull, xempty;
+  float red[NCW][MMAX][R];
+  int flag;
+};
+constexpr int SMEM_BYTES = sizeof(Smem) + 128;
+
+enum Mode { PLAIN = 0, GATEUP = 1, DUAL = 2, NORM = 3 };
+
+struct Args {
+  const __nv_bfloat16* x;     // [M, ldx]
+  const __nv_bfloat16* W;     // [N, K] (GATEUP: [2F, K])
+  const __nv_bfloat16* W2;    // DUAL: rows >= N1
+  __nv_bfloat16* y;           // PLAIN out [M, N]; DUAL y1; GATEUP act [M, F]; NORM: y scratch [M, N]
+  __nv_bfloat16* y2;          // DUAL y2 [M, N2]
+  float* partial;             // [nranges, MMAX, N] f32 scratch, or nullptr (GATEUP / DUAL: never split)
+  int* gcnt;                  // [2] i32 scratch: finished tasks / finished rows, zero at rest
+  int M, N, N1, K, ldx;
+};
+
+// K ranges: as many as x capacity demands, and at least enough tasks to
+// balance the grid when the mode can split (partial != nullptr).
+__device__ __forceinline__ int pick_nranges(const Args& a, int nblk) {
+  int ks = 1;
+  while ((long long)a.M * ((a.K + ks - 1) / ks + 31 & ~31) > X_ELEMS) ks++;
+  const int kseg = (a.K + ks - 1) / ks + 31 & ~31;   // K % 32 == 0 keeps every stage in whole ldmatrix pairs
+  return (a.K + kseg - 1) / kseg;
+}
+
+template <int MODE>
+__device__ __forceinline__ const __nv_bfloat16* row_ptr(const Args& a, int row) {
+  if (MODE == GATEUP) {  // logical rows are (gate j, up j) pairs
+    const int j = row >> 1;
+    return a.W + (size_t)((row & 1) ? a.N + j : j) * a.K;
+  }
+  if (MODE == DUAL) return row < a.N1 ? a.W + (size_t)row * a.K : a.W2 + (size_t)(row - a.N1) * a.K;
+  return a.W + (size_t)row * a.K;
+}
+
+// Output (m, row) of one range.  Called by 64 threads (m = ct/8, r = ct%8) of
+// two full warps, so the pair shuffle of GATEUP is convergent.
+template <int MODE>
+__device__ __forceinline__ void emit(const Args& a, int split, int nranges, int m, int row, bool valid, float v) {
+  if (nranges > 1) {  // f32 range partial; the tail folds them
+    if (valid) a.partial[((size_t)split * MMAX + m) * a.N + row] = v;
+    return;
+  }
+  if (MODE == GATEUP) {
+    const float other = __shfl_down_sync(0xffffffffu, v, 1);
+    if (valid && (row & 1) == 0) {
+      const float g = bf16r(v), u = bf16r(other);
+      const float sg = bf16r(g / (1.0f + expf(-g)));
+      a.y[(size_t)m * a.N + (row >> 1)] = __float2bfloat16_rn(sg * u);
+    }
+    return;
+  }
+  if (!valid) return;
+  if (MODE == DUAL) {
+    if (row < a.N1) a.y[(size_t)m * a.N1 + row] = __float2bfloat16_rn(v);
+    else a.y2[(size_t)m * (a.N - a.N1) + row - a.N1] = __float2bfloat16_rn(v);
+  } else {
+    a.y[(size_t)m * a.N + row] = __float2bfloat16_rn(v);
+  }
+}
+
+// The streaming GEMM proper.  Returns (in every thread) whether this CTA
+// finished the grid's last task (tail duty when the grid is oversubscribed).
+template <int MODE>
+__device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
+  const int warp = threadIdx.x / 32, lane = threadIdx.x % 32;
+  const int nrows = MODE == GATEUP ? 2 * a.N : a.N;
+  const int nblk = (nrows + R - 1) / R;
+  const int nranges = pick_nranges(a, nblk);
+  const int kseg = (a.K + nranges - 1) / nranges + 31 & ~31;
+  const int nst0 = (kseg + SEG_MAX - 1) / SEG_MAX;
+  const int seg = (kseg + nst0 - 1) / nst0 + 31 & ~31;          // stage slot width, whole ldmatrix pairs
+  const int srow = seg + PAD, xrow = kseg + PAD;                // smem row strides
+  const int nstages = min(STAGES_MAX, RING_ELEMS / (R * srow));
+  const int ntasks = nblk * nranges;
+  if (threadIdx.x == 0) {
+    for (int i = 0; i < nstages; i++) {
+      mbar_init(smem_u32(&s.full[i]), 1);
+      mbar_init(smem_u32(&s.empty[i]), NCW);
+    }
+    mbar_init(smem_u32(&s.xfull), 1);
+    mbar_init(smem_u32(&s.xempty), NCW);
+    fence_barrier_init();
+    s.flag = 0;
+  }
+  __syncthreads();
+  if (warp == 0) {
+    if (lane == 0) {
+      int stage = 0;
+      uint32_t phase = 0, xphase = 0;
+      int cur_split = -1;
+      for (int t = blockIdx.x; t < ntasks; t += gridDim.x) {
+        const int split = t / nblk, blk = t - split * nblk;      // range-major
+        const int r0 = blk * R, rows = min(R, nrows - r0);
+        const int k0 = split * kseg, klen = min(kseg, a.K - k0);
+        if (split != cur_split) {  // stage this range of x (consumers have released the old one)
+          mbar_wait(smem_u32(&s.xempty), xphase);
+          mbar_expect_tx(smem_u32(&s.xfull), a.M * klen * 2);
+          for (int m = 0; m < a.M; m++)
+            bulk_copy_1d(smem_u32(&s.x[m * xrow]), a.x + (size_t)m * a.ldx + k0, klen * 2, smem_u32(&s.xfull));
+          xphase ^= 1;
+          cur_split = split;
+        }
+        for (int st = 0, off = 0; off < klen; st++, off += seg) {
+          const int len = min(seg, klen - off);
+          mbar_wait(smem_u32(&s.empty[stage]), phase ^ 1);
+          mbar_expect_tx(smem_u32(&s.full[stage]), rows * len * 2);
+          __nv_bfloat16* dst = &s.ring[stage * R * srow];
+          for (int r = 0; r < rows; r++)
+            bulk_copy_1d(smem_u32(dst + r * srow), row_ptr<MODE>(a, r0 + r) + k0 + off, len * 2, smem_u32(&s.full[stage]));
+          if (++stage == nstages) { stage = 0; phase ^= 1; }
+        }
+      }
+    }
+    __syncthreads();  // matches the compute warps' final barrier
+    return s.flag != 0;
+  }
+  const int ct = threadIdx.x - 32, cw = warp - 1;
+  int stage = 0;
+  uint32_t phase = 0, xphase = 0;
+  int cur_split = -1;
+  for (int t = blockIdx.x; t < ntasks; t += gridDim.x) {
+    const int split = t / nblk, blk = t - split * nblk;
+    const int r0 = blk * R, rows = min(R, nrows - r0);
+    const int klen = min(kseg, a.K - split * kseg);
+    if (split != cur_split) {  // release the old x range, wait for the new one
+      if (lane == 0) mbar_arrive(smem_u32(&s.xempty));
+      mbar_wait(smem_u32(&s.xfull), xphase);
+      xphase ^= 1;
+      cur_split = split;
+    }
+    // ldmatrix.x4 row addresses: lane l supplies row l%8 of the 8x8 matrix
+    // (l/8) = k tile-half; matrices (k 0-7, 8-15, 16-23, 24-31) of a 32-k
+    // pair of tiles come back as (b0, b1, b0', b1') / (a0, a2, a0', a2').
+    // x rows beyond M are never emitted: clamp them onto row M-1 (broadcast).
+    const uint32_t xbase = smem_u32(&s.x[min(lane & 7, a.M - 1) * xrow + (lane >> 3) * 8]);
+    float c[4][4];
+#pragma unroll
+    for (int j = 0; j < 4; j++) c[j][0] = c[j][1] = c[j][2] = c[j][3] = 0.f;
+    for (int st = 0, off = 0; off < klen; st++, off += seg) {
+      const int npairs = min(seg, klen - off) / 32;              // len % 32 == 0 (every K is)
+      mbar_wait(smem_u32(&s.full[stage]), phase);
+      const uint32_t wbase = smem_u32(&s.ring[stage * R * srow + (lane & 7) * srow + (lane >> 3) * 8]);
+      const uint32_t xoff = xbase + off * 2;
+      int pr = cw;
+      for (; pr + NCW < npairs; pr += 2 * NCW) {
+        uint32_t a0[4], a2[4], b0[4], b1[4];
+#pragma unroll
+        for (int j = 0; j < 2; j++) {
+          const uint32_t o = (pr + j * NCW) * 64;                // 32 elements * 2 B
+          ldsm_x4(a0[2 * j], a2[2 * j], a0[2 * j + 1], a2[2 * j + 1], xoff + o);
+          ldsm_x4(b0[2 * j], b1[2 * j], b0[2 * j + 1], b1[2 * j + 1], wbase + o);
+        }
+#pragma unroll
+        for (int j = 0; j < 4; j++) mma_bf16_16816(c[j][0], c[j][1], c[j][2], c[j][3], a0[j], 0u, a2[j], 0u, b0[j], b1[j]);
+      }
+      if (pr < npairs) {
+        uint32_t a0[2], a2[2], b0[2], b1[2];
+        ldsm_x4(a0[0], a2[0], a0[1], a2[1], xoff + pr * 64);
+        ldsm_x4(b0[0], b1[0], b0[1], b1[1], wbase + pr * 64);
+        mma_bf16_16816(c[0][0], c[0][1], c[0][2], c[0][3], a0[0], 0u, a2[0], 0u, b0[0], b1[0]);
+        mma_bf16_16816(c[1][0], c[1][1], c[1][2], c[1][3], a0[1], 0u, a2[1], 0u, b0[1], b1[1]);
+      }
+      __syncwarp();
+      if (lane == 0) mbar_arrive(smem_u32(&s.empty[stage]));
+      if (++stage == nstages) { stage = 0; phase ^= 1; }
+    }
+    // (c0, c1) = out[token q = lane/4][rows 2p, 2p+1], p = lane%4
+    s.red[cw][lane >> 2][2 * (lane & 3)] = (c[0][0] + c[1][0]) + (c[2][0] + c[3][0]);
+    s.red[cw][lane >> 2][2 * (lane & 3) + 1] = (c[0][1] + c[1][1]) + (c[2][1] + c[3][1]);
+    named_bar_sync(BAR_C, CTHREADS);
+    if (ct < MMAX * R) {
+      const int m = ct >> 3, r = ct & 7;
+      float v = 0.f;
+#pragma unroll
+      for (int w = 0; w < NCW; w++) v += s.red[w][m][r];
+      emit<MODE>(a, split, nranges, m, r0 + r, m < a.M && r < rows, v);
+    }
+    named_bar_sync(BAR_C, CTHREADS);
+  }
+  if (ct == 0) {  // one thread of the compute warps
+    const int my = (ntasks - (int)blockIdx.x + (int)gridDim.x - 1) / (int)gridDim.x;
+    if (my > 0 && a.gcnt) {
+      __threadfence();
+      const int old = atomicAdd(a.gcnt, my);
+      if (old + my == ntasks) { __threadfence(); s.flag = 1; }
+    }
+  }
+  __syncthreads();
+  return s.flag != 0;
+}
+
+// ---- tail: fold range partials, optionally Gemma fused_add_rms_norm ----
+__device__ __forceinline__ int last_pow2(int n) {
+  n |= (n >> 1); n |= (n >> 2); n |= (n >> 4); n |= (n >> 8); n |= (n >> 16);
+  return (n - (n >> 1)) > 0 ? (n - (n >> 1)) : 1;
+}
+// ATen ReduceConfig::set_block_dimension(dim0 = N/4, dim1 = rows), mnt=512.
+__device__ __forceinline__ int aten_block_width(int dim0, int dim1) {
+  const int max_threads = NT_ATEN;
+  int dim0_pow2 = dim0 < max_threads ? last_pow2(dim0) : max_threads;
+  int dim1_pow2 = dim1 < max_threads ? last_pow2(dim1) : max_threads;
+  int block_width = min(dim0_pow2, 32);
+  int block_height = min(dim1_pow2, max_threads / block_width);
+  block_width = min(dim0_pow2, max_threads / block_height);
+  return block_width;
+}
+// Sum of z[j]^2 over j in [0, N) in ATen's order (sq[j] = fmul_rn(z, z), then
+// the vectorized-by-4 reduction of kern_gemma_rms_norm.cu) for W virtual
+// lanes emulated by NTHREADS threads (lane l = t + v*NTHREADS); the result is
+// returned in every thread.
+__device__ float aten_row_sumsq(const float* __restrict__ z, int N, int W, float* __restrict__ s) {
+  const int t = threadIdx.x;
+  constexpr int VL = (NT_ATEN + NTHREADS - 1) / NTHREADS;
+  if (t < 32) s[t] = 0.f;
+  __syncthreads();
+#pragma unroll
+  for (int v = 0; v < VL; v++) {
+    const int l = t + v * NTHREADS;
+    if (l < W) {
+      float acc[4] = {0.f, 0.f, 0.f, 0.f};
+      for (int idx = l; idx * 4 + 3 < N; idx += W) {
+        const float4 x4 = *reinterpret_cast<const float4*>(z + idx * 4);
+        acc[0] += __fmul_rn(x4.x, x4.x); acc[1] += __fmul_rn(x4.y, x4.y);
+        acc[2] += __fmul_rn(x4.z, x4.z); acc[3] += __fmul_rn(x4.w, x4.w);
+      }
+      s[l] = ((acc[0] + acc[1]) + acc[2]) + acc[3];
+    }
+  }
+  __syncthreads();
+  for (int off = W / 2; off >= 32; off >>= 1) {
+#pragma unroll
+    for (int v = 0; v < VL; v++) {
+      const int l = t + v * NTHREADS;
+      if (l < off && l + off < W) s[l] += s[l + off];
+    }
+    __syncthreads();
+  }
+  float value = t < 32 ? s[t] : 0.f;
+  if (t < 32) {
+    for (int off = 16; off > 0; off >>= 1) value += __shfl_down_sync(0xffffffffu, value, off);
+    if (t == 0) s[0] = value;
+  }
+  __syncthreads();
+  return s[0];
+}
+
+// The f32 GEMM value of elements [j8, j8+4) of row m: the folded range
+// partials, or the y scratch when the GEMM ran unsplit.
+__device__ __forceinline__ float4 ysum4(const Args& a, int nranges, int m, int j) {
+  if (nranges == 1) {
+    const __nv_bfloat162 lo = __ldcg(reinterpret_cast<const __nv_bfloat162*>(a.y + (size_t)m * a.N + j));
+    const __nv_bfloat162 hi = __ldcg(reinterpret_cast<const __nv_bfloat162*>(a.y + (size_t)m * a.N + j + 2));
+    const float2 l = __bfloat1622float2(lo), h = __bfloat1622float2(hi);
+    return make_float4(l.x, l.y, h.x, h.y);
+  }
+  // guarded full unroll: all range loads issue before the adds need them
+  float4 p[8];
+#pragma unroll
+  for (int sp = 0; sp < 8; sp++)
+    if (sp < nranges) p[sp] = __ldcg(reinterpret_cast<const float4*>(a.partial + ((size_t)sp * MMAX + m) * a.N + j));
+  float4 v = p[0];
+#pragma unroll
+  for (int sp = 1; sp < 8; sp++)
+    if (sp < nranges) { v.x += p[sp].x; v.y += p[sp].y; v.z += p[sp].z; v.w += p[sp].w; }
+  return v;
+}
+
+// Rows [m0, m1) of the tail.  NORM: vLLM's Gemma fused_add_rms_norm with
+// ATen's reduction width for the launch's M rows; z lives in smem (ring).
+// PLAIN: just fold the partials to bf16 y.
+template <int MODE>
+__device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const float* w1, __nv_bfloat16* res, __nv_bfloat16* out,
+                          float eps, float* __restrict__ z, float* __restrict__ s) {
+  const int N = a.N;
+  for (int m = m0; m < m1; m++) {
+    for (int j = threadIdx.x * 4; j < N; j += NTHREADS * 4) {
+      float4 v = ysum4(a, nranges, m, j);
+      if (MODE == NORM) {
+        __nv_bfloat16* rr = res + (size_t)m * N + j;
+        v.x = bf16r(v.x); v.y = bf16r(v.y); v.z = bf16r(v.z); v.w = bf16r(v.w);   // the bf16 y rounding
+        const float2 r0 = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(rr));
+        const float2 r1 = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(rr + 2));
+        v.x = __fadd_rn(v.x, r0.x); v.y = __fadd_rn(v.y, r0.y);
+        v.z = __fadd_rn(v.z, r1.x); v.w = __fadd_rn(v.w, r1.y);
+        *reinterpret_cast<__nv_bfloat162*>(rr) = __floats2bfloat162_rn(v.x, v.y);
+        *reinterpret_cast<__nv_bfloat162*>(rr + 2) = __floats2bfloat162_rn(v.z, v.w);
+        *reinterpret_cast<float4*>(z + j) = v;
+      } else {
+        __nv_bfloat16* yy = a.y + (size_t)m * N + j;
+        *reinterpret_cast<__nv_bfloat162*>(yy) = __floats2bfloat162_rn(v.x, v.y);
+        *reinterpret_cast<__nv_bfloat162*>(yy + 2) = __floats2bfloat162_rn(v.z, v.w);
+      }
+    }
+    if (MODE != NORM) continue;
+    __syncthreads();
+    const int W = aten_block_width(N / 4, a.M);
+    const float sum = aten_row_sumsq(z, N, W, s);
+    const float factor = (float)a.M / (float)((long long)a.M * (long long)N);
+    const float var = __fmul_rn(sum, factor);
+    const float r = rsqrtf(__fadd_rn(var, eps));
+    for (int j = threadIdx.x * 4; j < N; j += NTHREADS * 4) {
+      const float4 zv = *reinterpret_cast<const float4*>(z + j);
+      const float4 w0 = *reinterpret_cast<const float4*>(w1 + j);
+      __nv_bfloat162 o0 = __floats2bfloat162_rn(__fmul_rn(__fmul_rn(zv.x, r), w0.x), __fmul_rn(__fmul_rn(zv.y, r), w0.y));
+      __nv_bfloat162 o1 = __floats2bfloat162_rn(__fmul_rn(__fmul_rn(zv.z, r), w0.z), __fmul_rn(__fmul_rn(zv.w, r), w0.w));
+      *reinterpret_cast<__nv_bfloat162*>(out + (size_t)m * N + j) = o0;
+      *reinterpret_cast<__nv_bfloat162*>(out + (size_t)m * N + j + 2) = o1;
+    }
+    __syncthreads();
+  }
+}
+
+// Tail driver: cooperative one-row-per-CTA when the whole grid is resident,
+// else all rows in the CTA that finished last.
+template <int MODE>
+__device__ __forceinline__ void run_tail(const Args& a, Smem& s, bool last, __nv_bfloat16* res, const float* w1,
+                                         __nv_bfloat16* out, float eps) {
+  const int nrows = MODE == GATEUP ? 2 * a.N : a.N;
+  const int nblk = (nrows + R - 1) / R;
+  const int nranges = pick_nranges(a, nblk);
+  if (MODE != NORM && nranges == 1) return;   // nothing left to do
+  static_assert(sizeof(s.ring) >= 6144 * 4, "z row: N <= 6144");
+  float* z = reinterpret_cast<float*>(&s.ring[0]);
+  if (gridDim.x <= nsmid()) {
+    if ((int)blockIdx.x >= a.M) return;
+    if (threadIdx.x == 0)
+      while (ld_acquire(a.gcnt) < nblk * nranges) __nanosleep(64);
+    __syncthreads();
+    tail_rows<MODE>(a, nranges, blockIdx.x, blockIdx.x + 1, w1, res, out, eps, z, &s.red[0][0][0]);
+    __syncthreads();
+    if (threadIdx.x == 0 && atomicAdd(a.gcnt + 1, 1) == a.M - 1) {
+      a.gcnt[1] = 0;
+      a.gcnt[0] = 0;
+      __threadfence();
+    }
+  } else if (last) {
+    __threadfence();
+    tail_rows<MODE>(a, nranges, 0, a.M, w1, res, out, eps, z, &s.red[0][0][0]);
+    __syncthreads();
+    if (threadIdx.x == 0) { *a.gcnt = 0; __threadfence(); }
+  }
+}
+}  // namespace
+
+#define GEMM8_SMEM                                                                                     \
+  extern __shared__ char smem_raw[];                                                                  \
+  Smem& s = *reinterpret_cast<Smem*>((reinterpret_cast<uintptr_t>(smem_raw) + 127) & ~uintptr_t(127))
+
+extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
+kern_gemm8_bf16(__nv_bfloat16* __restrict__ y, const __nv_bfloat16* __restrict__ x, const __nv_bfloat16* __restrict__ W,
+                float* __restrict__ partial, int* __restrict__ gcnt, int M, int N, int K) {
+  GEMM8_SMEM;
+  Args a{x, W, nullptr, y, nullptr, partial, gcnt, M, N, 0, K, K};
+  const bool last = gemm8_core<PLAIN>(a, s);
+  run_tail<PLAIN>(a, s, last, nullptr, nullptr, nullptr, 0.f);
+}
+
+extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
+kern_gemm8_gateup_silu_bf16(__nv_bfloat16* __restrict__ act, const __nv_bfloat16* __restrict__ x,
+                            const __nv_bfloat16* __restrict__ W, int M, int F, int K) {
+  GEMM8_SMEM;
+  Args a{x, W, nullptr, act, nullptr, nullptr, nullptr, M, F, 0, K, K};   // M*K <= X_ELEMS: never splits
+  gemm8_core<GATEUP>(a, s);
+}
+
+extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
+kern_gemm8_dual_bf16(__nv_bfloat16* __restrict__ y1, __nv_bfloat16* __restrict__ y2, const __nv_bfloat16* __restrict__ x,
+                     const __nv_bfloat16* __restrict__ W1, const __nv_bfloat16* __restrict__ W2, int M, int N1, int N2, int K) {
+  GEMM8_SMEM;
+  Args a{x, W1, W2, y1, y2, nullptr, nullptr, M, N1 + N2, N1, K, K};      // M*K <= X_ELEMS: never splits
+  gemm8_core<DUAL>(a, s);
+}
+
+extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
+kern_gemm8_add_norm_bf16(__nv_bfloat16* __restrict__ out, const __nv_bfloat16* __restrict__ x, const __nv_bfloat16* __restrict__ W,
+                         __nv_bfloat16* __restrict__ res, const float* __restrict__ w1, __nv_bfloat16* __restrict__ y,
+                         float* __restrict__ partial, int* __restrict__ gcnt, int M, int N, int K, float eps) {
+  GEMM8_SMEM;
+  Args a{x, W, nullptr, y, nullptr, partial, gcnt, M, N, 0, K, K};
+  const bool last = gemm8_core<NORM>(a, s);
+  run_tail<NORM>(a, s, last, res, w1, out, eps);
+}

From 773eff539267a833b9c1714fc109eb9bc4ae899a Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 16:40:05 +0000
Subject: [PATCH 02/10] qwen38: fuse down GEMM + residual add + next input norm
 (bs<=8)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

kern_gemm8_add_norm_bf16: the down_proj GEMM with the following
fused_add_rms_norm folded into its tail.  K=17408 splits into ranges that
fit the x smem area (f32 range partials in impl scratch, no atomics on the
hot path); after one end-of-CTA fence + task-count atomic, CTAs 0..M-1 each
normalize one token row (all 152 CTAs are co-resident at ~220 KB smem /
CTA; on any other geometry the last CTA does all rows).  The tail
reproduces the ATen chain of kern_gemma_rms_norm.cu, including the bf16
rounding of the GEMM output that cublasLt used to store.  Replaces
gemm(down_proj) + gemma_fused_norm in decode / decode_spec / verify (target
layers; the draft's down_proj feeds a grouped conv instead of a norm and
keeps cuBLAS).  -64 dispatches per program; the y buffer leaves the decode
dataflow.  gcnt scratch is declared `out` although the kernel reads it: its
contents are zero at rest (alloc_zeros + self-reset in the tail).

kern-attest (A = previous commit, B = this one, GB300):

qwen3.8-27b.json — 64 cuts (gemm+gemma_fused_norm -> gemm8_add_norm):

| decode tokens=1     | A         | B measured | delta            |
| step, eager         | 13.654 ms | 13.521 ms  | -133 us  -1.0%   |
| step, graph (TPOT)  | 11.031 ms | 10.937 ms  | -94 us   91 tok/s |
| sum of 64 cuts      | 3.148 ms  | 2.875 ms   | -272 us  -8.6%   |

qwen3.8-27b-dflash2.json — same 64 cuts in decode:

| step, eager         | 14.999 ms | 14.663 ms  | −336.0 µs −2.2% |
| step, graph (TPOT)  | 11.559 ms | 11.381 ms  | −177.9 µs −1.5% |
| Σ 64 cuts (the swap) | 3.162 ms  | 2.868 ms   | −293.9 µs −9.3% |

Verdict: INCONCLUSIVE as in the previous commit (f32 accumulation order vs
cublasLt inside the cut); next_token / draft_tokens / verify_tokens
bit-identical on the tap prompt, noise floor clean, fuzz <= 7 ulp on
uniform/normal/laplace/outliers, <= 194 ulp on edge, special bit-identical.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 7019 ++++++-----------------------
 examples/qwen3.8-27b.json         | 2411 ++--------
 tools/gen_qwen35.py               |   41 +-
 3 files changed, 1784 insertions(+), 7687 deletions(-)

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index 9b1cb82..b0b12bb 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6701,6 +6701,113 @@
     ]
    }
   },
+  "gemm8_add_norm": {
+   "params": [
+    "out buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "inout buffer<bf16>",
+    "in buffer<f32>",
+    "i32",
+    "i32",
+    "i32",
+    "f32"
+   ],
+   "impl": {
+    "scratch": {
+     "y": {
+      "dtype": "bf16",
+      "shape": [
+       8,
+       5120
+      ]
+     },
+     "partial": {
+      "dtype": "f32",
+      "shape": [
+       8,
+       8,
+       5120
+      ]
+     },
+     "gcnt": {
+      "dtype": "i32",
+      "shape": [
+       2
+      ]
+     }
+    },
+    "steps": [
+     {
+      "symbol": "kern_gemm8_add_norm_bf16",
+      "params": [
+       "out buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "inout buffer<bf16>",
+       "in buffer<f32>",
+       "out buffer<bf16>",
+       "out buffer<f32>",
+       "out buffer<i32>",
+       "i32",
+       "i32",
+       "i32",
+       "f32"
+      ],
+      "block": [
+       288,
+       1,
+       1
+      ],
+      "grid": [
+       152,
+       1,
+       1
+      ],
+      "args": [
+       {
+        "arg": 0
+       },
+       {
+        "arg": 1
+       },
+       {
+        "arg": 2
+       },
+       {
+        "arg": 3
+       },
+       {
+        "arg": 4
+       },
+       {
+        "scratch": "y"
+       },
+       {
+        "scratch": "partial"
+       },
+       {
+        "scratch": "gcnt"
+       },
+       {
+        "arg": 5
+       },
+       {
+        "arg": 6
+       },
+       {
+        "arg": 7
+       },
+       {
+        "arg": 8
+       }
+      ],
+      "shared_mem": 224128,
+      "cubin": "gemm8.cubin"
+     }
+    ]
+   }
+  },
   "copy_rows": {
    "params": [
     "out buffer<bf16>",
@@ -47618,38 +47725,17 @@
      ]
     },
     {
-     "label": "l0.down_proj",
-     "kernel": "gemm",
+     "label": "l0.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.0.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l0.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.0.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -47657,9 +47743,6 @@
       {
        "buf": "model.layers.1.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -47667,7 +47750,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -48094,38 +48177,17 @@
      ]
     },
     {
-     "label": "l1.down_proj",
-     "kernel": "gemm",
+     "label": "l1.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.1.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l1.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.1.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -48133,9 +48195,6 @@
       {
        "buf": "model.layers.2.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -48143,7 +48202,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -48570,38 +48629,17 @@
      ]
     },
     {
-     "label": "l2.down_proj",
-     "kernel": "gemm",
+     "label": "l2.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.2.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l2.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.2.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -48609,9 +48647,6 @@
       {
        "buf": "model.layers.3.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -48619,7 +48654,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -49029,38 +49064,17 @@
      ]
     },
     {
-     "label": "l3.down_proj",
-     "kernel": "gemm",
+     "label": "l3.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.3.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l3.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.3.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -49068,9 +49082,6 @@
       {
        "buf": "model.layers.4.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -49078,7 +49089,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -49505,38 +49516,17 @@
      ]
     },
     {
-     "label": "l4.down_proj",
-     "kernel": "gemm",
+     "label": "l4.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.4.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l4.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.4.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -49544,9 +49534,6 @@
       {
        "buf": "model.layers.5.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -49554,7 +49541,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -49981,38 +49968,17 @@
      ]
     },
     {
-     "label": "l5.down_proj",
-     "kernel": "gemm",
+     "label": "l5.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.5.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l5.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.5.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -50020,9 +49986,6 @@
       {
        "buf": "model.layers.6.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -50030,7 +49993,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -50457,38 +50420,17 @@
      ]
     },
     {
-     "label": "l6.down_proj",
-     "kernel": "gemm",
+     "label": "l6.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.6.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l6.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.6.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -50496,9 +50438,6 @@
       {
        "buf": "model.layers.7.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -50506,7 +50445,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -50918,38 +50857,17 @@
      ]
     },
     {
-     "label": "l7.down_proj",
-     "kernel": "gemm",
+     "label": "l7.down_norm",
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@@ -51394,38 +51309,17 @@
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@@ -51870,38 +51761,17 @@
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@@ -52346,38 +52213,17 @@
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@@ -52807,38 +52650,17 @@
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@@ -53283,38 +53102,17 @@
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@@ -53759,38 +53554,17 @@
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@@ -54235,38 +54006,17 @@
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@@ -54696,38 +54443,17 @@
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@@ -55172,38 +54895,17 @@
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@@ -55648,38 +55347,17 @@
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@@ -55687,9 +55365,6 @@
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@@ -55697,7 +55372,7 @@
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@@ -56124,38 +55799,17 @@
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@@ -56163,9 +55817,6 @@
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@@ -56173,7 +55824,7 @@
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@@ -56585,38 +56236,17 @@
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@@ -56624,9 +56254,6 @@
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@@ -56634,7 +56261,7 @@
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@@ -57061,38 +56688,17 @@
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@@ -57100,9 +56706,6 @@
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@@ -57110,7 +56713,7 @@
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@@ -57537,38 +57140,17 @@
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@@ -57576,9 +57158,6 @@
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@@ -57586,7 +57165,7 @@
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@@ -58013,38 +57592,17 @@
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@@ -58474,38 +58029,17 @@
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@@ -58950,38 +58481,17 @@
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@@ -59426,38 +58933,17 @@
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@@ -59902,38 +59385,17 @@
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@@ -60363,38 +59822,17 @@
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@@ -60839,38 +60274,17 @@
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@@ -61315,38 +60726,17 @@
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@@ -61791,38 +61178,17 @@
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@@ -62252,38 +61615,17 @@
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@@ -62728,38 +62067,17 @@
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@@ -63204,38 +62519,17 @@
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@@ -63680,38 +62971,17 @@
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@@ -64141,38 +63408,17 @@
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@@ -64617,38 +63860,17 @@
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@@ -64656,9 +63878,6 @@
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@@ -65093,38 +64312,17 @@
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@@ -65132,9 +64330,6 @@
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@@ -65142,7 +64337,7 @@
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@@ -70284,38 +69239,17 @@
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@@ -70760,38 +69691,17 @@
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@@ -71236,38 +70143,17 @@
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@@ -71697,38 +70580,17 @@
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@@ -72173,38 +71032,17 @@
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@@ -72649,38 +71484,17 @@
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@@ -77364,38 +75959,17 @@
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@@ -77984,38 +76555,17 @@
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@@ -78460,38 +77007,17 @@
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@@ -78936,38 +77459,17 @@
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@@ -79395,38 +77894,17 @@
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@@ -79871,38 +78346,17 @@
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@@ -80347,38 +78798,17 @@
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@@ -80847,38 +79274,17 @@
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@@ -81308,38 +79711,17 @@
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@@ -81784,38 +80163,17 @@
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@@ -82260,38 +80615,17 @@
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@@ -82736,38 +81067,17 @@
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@@ -83197,38 +81504,17 @@
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@@ -83673,38 +81956,17 @@
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@@ -84149,38 +82408,17 @@
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@@ -84625,38 +82860,17 @@
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@@ -85086,38 +83297,17 @@
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@@ -85125,9 +83315,6 @@
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@@ -85135,7 +83322,7 @@
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@@ -85562,38 +83749,17 @@
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@@ -86038,38 +84201,17 @@
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@@ -86077,9 +84219,6 @@
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@@ -86087,7 +84226,7 @@
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@@ -86514,38 +84653,17 @@
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@@ -86553,9 +84671,6 @@
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@@ -86563,7 +84678,7 @@
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@@ -86975,38 +85090,17 @@
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@@ -87014,9 +85108,6 @@
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@@ -87024,7 +85115,7 @@
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@@ -87475,38 +85566,17 @@
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@@ -87951,38 +86018,17 @@
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@@ -88427,38 +86470,17 @@
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@@ -88888,38 +86907,17 @@
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@@ -89364,38 +87359,17 @@
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@@ -89840,38 +87811,17 @@
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@@ -90316,38 +88263,17 @@
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@@ -90777,38 +88700,17 @@
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@@ -91253,38 +89152,17 @@
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@@ -91729,38 +89604,17 @@
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@@ -92205,38 +90056,17 @@
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@@ -92244,9 +90074,6 @@
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@@ -92666,38 +90493,17 @@
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@@ -93142,38 +90945,17 @@
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@@ -93181,9 +90963,6 @@
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@@ -93618,38 +91397,17 @@
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@@ -93657,9 +91415,6 @@
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@@ -93667,7 +91422,7 @@
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@@ -94118,38 +91873,17 @@
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@@ -94157,9 +91891,6 @@
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@@ -94167,7 +91898,7 @@
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@@ -94579,38 +92310,17 @@
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@@ -95055,38 +92762,17 @@
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@@ -95531,38 +93214,17 @@
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@@ -96007,38 +93666,17 @@
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@@ -96468,38 +94103,17 @@
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@@ -96944,38 +94555,17 @@
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@@ -97896,38 +95459,17 @@
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@@ -98833,38 +96348,17 @@
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@@ -99309,38 +96800,17 @@
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@@ -99785,38 +97252,17 @@
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@@ -100246,38 +97689,17 @@
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@@ -100746,38 +98165,17 @@
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@@ -101222,38 +98617,17 @@
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@@ -101698,38 +99069,17 @@
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@@ -101737,9 +99087,6 @@
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@@ -108470,38 +105505,17 @@
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@@ -108946,38 +105957,17 @@
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@@ -109422,38 +106409,17 @@
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@@ -109881,38 +106844,17 @@
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@@ -110357,38 +107296,17 @@
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@@ -110833,38 +107748,17 @@
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@@ -111333,38 +108224,17 @@
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@@ -111372,9 +108242,6 @@
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@@ -111794,38 +108661,17 @@
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@@ -111833,9 +108679,6 @@
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@@ -112270,38 +109113,17 @@
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@@ -112309,9 +109131,6 @@
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@@ -112746,38 +109565,17 @@
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@@ -113222,38 +110017,17 @@
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@@ -113683,38 +110454,17 @@
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@@ -113722,9 +110472,6 @@
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@@ -114159,38 +110906,17 @@
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@@ -114198,9 +110924,6 @@
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@@ -114635,38 +111358,17 @@
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@@ -114674,9 +111376,6 @@
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@@ -114684,7 +111383,7 @@
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@@ -115111,38 +111810,17 @@
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@@ -115150,9 +111828,6 @@
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@@ -115160,7 +111835,7 @@
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@@ -115572,38 +112247,17 @@
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@@ -115611,9 +112265,6 @@
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@@ -115621,7 +112272,7 @@
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@@ -116048,38 +112699,17 @@
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@@ -116087,9 +112717,6 @@
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@@ -116097,7 +112724,7 @@
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@@ -116524,38 +113151,17 @@
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@@ -116563,9 +113169,6 @@
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@@ -117000,38 +113603,17 @@
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@@ -117461,38 +114040,17 @@
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@@ -117961,38 +114516,17 @@
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@@ -118000,9 +114534,6 @@
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@@ -118437,38 +114968,17 @@
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@@ -118476,9 +114986,6 @@
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@@ -118913,38 +115420,17 @@
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@@ -118952,9 +115438,6 @@
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@@ -118962,7 +115445,7 @@
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@@ -119374,38 +115857,17 @@
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@@ -119413,9 +115875,6 @@
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@@ -119423,7 +115882,7 @@
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@@ -119850,38 +116309,17 @@
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@@ -119889,9 +116327,6 @@
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@@ -119899,7 +116334,7 @@
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@@ -120326,38 +116761,17 @@
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@@ -120365,9 +116779,6 @@
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@@ -120375,7 +116786,7 @@
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@@ -120802,38 +117213,17 @@
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@@ -120841,9 +117231,6 @@
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@@ -120851,7 +117238,7 @@
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@@ -121263,38 +117650,17 @@
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@@ -121302,9 +117668,6 @@
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@@ -121312,7 +117675,7 @@
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@@ -121739,38 +118102,17 @@
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+     "label": "l28.down_norm",
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@@ -121778,9 +118120,6 @@
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@@ -121788,7 +118127,7 @@
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@@ -122215,38 +118554,17 @@
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-     "kernel": "gemm",
+     "label": "l29.down_norm",
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@@ -122254,9 +118572,6 @@
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@@ -122264,7 +118579,7 @@
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@@ -122691,38 +119006,17 @@
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-     "kernel": "gemm",
+     "label": "l30.down_norm",
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@@ -122730,9 +119024,6 @@
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@@ -122740,7 +119031,7 @@
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@@ -123152,38 +119443,17 @@
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-     "kernel": "gemm",
+     "label": "l31.down_norm",
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@@ -123191,9 +119461,6 @@
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@@ -123201,7 +119468,7 @@
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@@ -123628,38 +119895,17 @@
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-     "kernel": "gemm",
+     "label": "l32.down_norm",
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@@ -123667,9 +119913,6 @@
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@@ -124604,38 +120823,17 @@
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@@ -125065,38 +121260,17 @@
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@@ -125541,38 +121712,17 @@
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@@ -126017,38 +122164,17 @@
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@@ -126493,38 +122616,17 @@
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@@ -126954,38 +123053,17 @@
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@@ -127430,38 +123505,17 @@
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@@ -127906,38 +123957,17 @@
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@@ -128382,38 +124409,17 @@
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@@ -128843,38 +124846,17 @@
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@@ -129319,38 +125298,17 @@
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@@ -129795,38 +125750,17 @@
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@@ -130271,38 +126202,17 @@
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@@ -130732,38 +126639,17 @@
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@@ -130771,9 +126657,6 @@
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@@ -131232,38 +127115,17 @@
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@@ -136423,38 +132042,17 @@
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@@ -137375,38 +132946,17 @@
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@@ -137875,38 +133422,17 @@
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@@ -138336,38 +133859,17 @@
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-     "kernel": "gemma_fused_norm",
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+       "buf": "act"
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-       "buf": "y"
+       "buf": "model.layers.63.mlp.down_proj.weight"
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@@ -138375,9 +133877,6 @@
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@@ -138385,7 +133884,7 @@
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diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 2635215..8bce876 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -5940,6 +5940,113 @@
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+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "inout buffer<bf16>",
+    "in buffer<f32>",
+    "i32",
+    "i32",
+    "i32",
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+      "params": [
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+       "in buffer<bf16>",
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+      "cubin": "gemm8.cubin"
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@@ -45445,38 +45552,17 @@
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-     "kernel": "gemm",
+     "label": "l0.down_norm",
+     "kernel": "gemm8_add_norm",
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+       "buf": "act"
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@@ -45484,9 +45570,6 @@
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@@ -45494,7 +45577,7 @@
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@@ -45757,38 +45840,17 @@
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-     "kernel": "gemm",
+     "label": "l1.down_norm",
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@@ -45796,9 +45858,6 @@
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@@ -45806,7 +45865,7 @@
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@@ -46069,38 +46128,17 @@
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-     "kernel": "gemm",
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+     "kernel": "gemm8_add_norm",
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+       "buf": "act"
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@@ -46108,9 +46146,6 @@
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@@ -46118,7 +46153,7 @@
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@@ -46528,38 +46563,17 @@
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-     "kernel": "gemm",
+     "label": "l3.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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@@ -46567,9 +46581,6 @@
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@@ -46577,7 +46588,7 @@
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@@ -46840,38 +46851,17 @@
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+     "label": "l4.down_norm",
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-     "args": [
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+       "buf": "act"
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@@ -46879,9 +46869,6 @@
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@@ -46889,7 +46876,7 @@
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@@ -47152,38 +47139,17 @@
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-     "kernel": "gemm",
+     "label": "l5.down_norm",
+     "kernel": "gemm8_add_norm",
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+       "buf": "act"
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+       "buf": "model.layers.5.mlp.down_proj.weight"
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@@ -47191,9 +47157,6 @@
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@@ -47201,7 +47164,7 @@
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@@ -47464,38 +47427,17 @@
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-     "kernel": "gemm",
+     "label": "l6.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.6.mlp.down_proj.weight"
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@@ -47503,9 +47445,6 @@
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@@ -47513,7 +47452,7 @@
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@@ -47925,38 +47864,17 @@
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-     "kernel": "gemm",
+     "label": "l7.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.7.mlp.down_proj.weight"
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@@ -47964,9 +47882,6 @@
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@@ -47974,7 +47889,7 @@
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@@ -48237,38 +48152,17 @@
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-     "kernel": "gemm",
+     "label": "l8.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.8.mlp.down_proj.weight"
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@@ -48276,9 +48170,6 @@
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@@ -48286,7 +48177,7 @@
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@@ -48549,38 +48440,17 @@
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-     "label": "l9.down_proj",
-     "kernel": "gemm",
+     "label": "l9.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.9.mlp.down_proj.weight"
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@@ -48588,9 +48458,6 @@
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@@ -48598,7 +48465,7 @@
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@@ -48861,38 +48728,17 @@
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-     "label": "l10.down_proj",
-     "kernel": "gemm",
+     "label": "l10.down_norm",
+     "kernel": "gemm8_add_norm",
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+       "buf": "act"
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+       "buf": "model.layers.10.mlp.down_proj.weight"
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@@ -48900,9 +48746,6 @@
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@@ -48910,7 +48753,7 @@
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@@ -49322,38 +49165,17 @@
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-     "kernel": "gemm",
+     "label": "l11.down_norm",
+     "kernel": "gemm8_add_norm",
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.11.mlp.down_proj.weight"
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@@ -49361,9 +49183,6 @@
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@@ -49371,7 +49190,7 @@
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@@ -49634,38 +49453,17 @@
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-     "label": "l12.down_proj",
-     "kernel": "gemm",
+     "label": "l12.down_norm",
+     "kernel": "gemm8_add_norm",
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+       "buf": "x"
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-     "args": [
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+       "buf": "act"
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+       "buf": "model.layers.12.mlp.down_proj.weight"
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@@ -49673,9 +49471,6 @@
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@@ -49683,7 +49478,7 @@
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@@ -49946,38 +49741,17 @@
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@@ -50258,38 +50029,17 @@
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@@ -50719,38 +50466,17 @@
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@@ -51031,38 +50754,17 @@
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@@ -51343,38 +51042,17 @@
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@@ -51655,38 +51330,17 @@
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@@ -52116,38 +51767,17 @@
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@@ -52155,9 +51785,6 @@
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@@ -52428,38 +52055,17 @@
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@@ -52740,38 +52343,17 @@
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@@ -52779,9 +52361,6 @@
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@@ -53052,38 +52631,17 @@
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@@ -53091,9 +52649,6 @@
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@@ -53513,38 +53068,17 @@
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@@ -53552,9 +53086,6 @@
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@@ -53562,7 +53093,7 @@
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@@ -53825,38 +53356,17 @@
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@@ -53864,9 +53374,6 @@
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@@ -53874,7 +53381,7 @@
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@@ -54137,38 +53644,17 @@
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@@ -54176,9 +53662,6 @@
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@@ -54186,7 +53669,7 @@
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@@ -54449,38 +53932,17 @@
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@@ -54488,9 +53950,6 @@
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@@ -54498,7 +53957,7 @@
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@@ -54910,38 +54369,17 @@
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@@ -54949,9 +54387,6 @@
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@@ -54959,7 +54394,7 @@
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@@ -55222,38 +54657,17 @@
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@@ -55534,38 +54945,17 @@
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@@ -55846,38 +55233,17 @@
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@@ -56307,38 +55670,17 @@
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@@ -56619,38 +55958,17 @@
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@@ -56931,38 +56246,17 @@
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@@ -57243,38 +56534,17 @@
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@@ -57704,38 +56971,17 @@
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@@ -58016,38 +57259,17 @@
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@@ -58328,38 +57547,17 @@
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@@ -58640,38 +57835,17 @@
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@@ -59101,38 +58272,17 @@
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@@ -59140,9 +58290,6 @@
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@@ -59413,38 +58560,17 @@
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@@ -59452,9 +58578,6 @@
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@@ -59725,38 +58848,17 @@
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@@ -59764,9 +58866,6 @@
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@@ -59774,7 +58873,7 @@
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@@ -60037,38 +59136,17 @@
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@@ -60076,9 +59154,6 @@
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@@ -60086,7 +59161,7 @@
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@@ -60498,38 +59573,17 @@
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@@ -60537,9 +59591,6 @@
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@@ -64228,38 +63039,17 @@
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@@ -64689,38 +63476,17 @@
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@@ -65001,38 +63764,17 @@
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@@ -65313,38 +64052,17 @@
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@@ -65625,38 +64340,17 @@
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@@ -66086,38 +64777,17 @@
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-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l59.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.59.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -66125,9 +64795,6 @@
       {
        "buf": "model.layers.60.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -66135,7 +64802,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -66398,38 +65065,17 @@
      ]
     },
     {
-     "label": "l60.down_proj",
-     "kernel": "gemm",
+     "label": "l60.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.60.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l60.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.60.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -66437,9 +65083,6 @@
       {
        "buf": "model.layers.61.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -66447,7 +65090,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -66710,38 +65353,17 @@
      ]
     },
     {
-     "label": "l61.down_proj",
-     "kernel": "gemm",
+     "label": "l61.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.61.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l61.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.61.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -66749,9 +65371,6 @@
       {
        "buf": "model.layers.62.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -66759,7 +65378,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -67022,38 +65641,17 @@
      ]
     },
     {
-     "label": "l62.down_proj",
-     "kernel": "gemm",
+     "label": "l62.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.62.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l62.next_input_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.62.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -67061,9 +65659,6 @@
       {
        "buf": "model.layers.63.input_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -67071,7 +65666,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
@@ -67483,38 +66078,17 @@
      ]
     },
     {
-     "label": "l63.down_proj",
-     "kernel": "gemm",
+     "label": "l63.down_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "act"
-      },
-      {
-       "buf": "model.layers.63.mlp.down_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 17408
-      }
-     ]
-    },
-    {
-     "label": "l63.final_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "act"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.63.mlp.down_proj.weight"
       },
       {
        "buf": "residual"
@@ -67522,9 +66096,6 @@
       {
        "buf": "model.norm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -67532,7 +66103,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 17408
       },
       {
        "f32": 9.999999974752427e-07
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 5a7cbc1..f3205ee 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -703,6 +703,12 @@ def tri(tag, params, grid, src=pre, **kw):
         return single(TRITON[tag], params, blk(tag, src), grid,
                       shared_mem=smem(tag, src) or None, cubin=cubin, sha256=sha, **kw)
 
+    # gcnt is declared `out`: the kernel does read it (atomic task counter),
+    # but its contents are zero at rest -- alloc_zeros at load, and the tail
+    # resets it before the dispatch ends
+    GEMM8_AN_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>", "inout buffer<bf16>",
+                       "in buffer<f32>", "out buffer<bf16>", "out buffer<f32>", "out buffer<i32>",
+                       "i32", "i32", "i32", "f32"]
     GEMMA_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "in buffer<f32>",
                     "i32", "i32", "i32", "i32", "i32", "i32", "i32", "f32"]
     GEMMA_FUSED_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "inout buffer<bf16>",
@@ -767,6 +773,22 @@ def layer_norm_kernel(src, grid, rows_shape):
                                      "i32", "i32", "i32"],
                                     [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
                                     shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
+        # down GEMM + residual add + Gemma fused_add_rms_norm of the next
+        # layer input (gemm8's split-K partials, y and the task counters are
+        # impl scratch; the norm tail reproduces kern_gemma_rms_norm.cu)
+        "gemm8_add_norm": {
+            "params": GEMM8_AN_PARAMS[:5] + GEMM8_AN_PARAMS[8:],
+            "impl": {
+                "scratch": {"y": {"dtype": "bf16", "shape": [8, HIDDEN]},
+                            "partial": {"dtype": "f32", "shape": [8, 8, HIDDEN]},
+                            "gcnt": {"dtype": "i32", "shape": [2]}},
+                "steps": [step("kern_gemm8_add_norm_bf16", GEMM8_AN_PARAMS,
+                               [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
+                               [a(0), a(1), a(2), a(3), a(4), scr("y"), scr("partial"), scr("gcnt"),
+                                a(5), a(6), a(7), a(8)],
+                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin")],
+            },
+        },
         "copy_rows": single("kern_copy_rows_bf16",
                             ["out buffer<bf16>", "in buffer<bf16>", "i32", "i32", "i32"],
                             [256, 1, 1], [T, 1, 1], cubin="copy_rows.cubin"),
@@ -1140,14 +1162,19 @@ def forward(decode, taps=False, nacc=None, tail=None):
                 mlp = [gemm(l + "gate_up", "x", p + "mlp.gate_up_proj.weight", "gate_up", T, 2 * FFN, HIDDEN),
                        d(l + "silu_mul", "silu_mul",
                          [buf("act"), buf("gate_up"), i32(FFN), i32(0), f32(1.0), i32(0)])]
-            ds += [
-                fused(l + "post_attn_norm", "y", p + "post_attention_layernorm.weight_p1"),
-                *mlp,
-                gemm(l + "down_proj", "act", p + "mlp.down_proj.weight", "y", T, HIDDEN, FFN),
-            ]
             last = i + 1 == LAYERS
-            ds.append(fused(l + ("final_norm" if last else "next_input_norm"), "y",
-                            "model.norm.weight_p1" if last else f"model.layers.{i + 1}.input_layernorm.weight_p1"))
+            wnorm = "model.norm.weight_p1" if last else f"model.layers.{i + 1}.input_layernorm.weight_p1"
+            nlabel = l + ("final_norm" if last else "next_input_norm")
+            ds.append(fused(l + "post_attn_norm", "y", p + "post_attention_layernorm.weight_p1"))
+            ds += mlp
+            if small:
+                # down GEMM + residual add + next input norm in one dispatch
+                ds.append(d(l + "down_norm", "gemm8_add_norm",
+                            [buf("x"), buf("act"), buf(p + "mlp.down_proj.weight"), buf("residual"),
+                             buf(wnorm), T, i32(HIDDEN), i32(FFN), f32(eps)]))
+            else:
+                ds.append(gemm(l + "down_proj", "act", p + "mlp.down_proj.weight", "y", T, HIDDEN, FFN))
+                ds.append(fused(nlabel, "y", wnorm))
             if taps and i in TAPS:
                 # residual now holds hidden + residual = the input of layer
                 # i+1, vLLM's aux hidden state; fc's column block j, β=1

From 30294a381836146c8de2e4dfa47a84a254fab574 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 16:41:44 +0000
Subject: [PATCH 03/10] qwen38: fuse o_proj / out_proj GEMM + residual add +
 post-attention norm (bs<=8)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Same kern_gemm8_add_norm_bf16 as the down_proj commit, now also closing
every layer's attention side: the attention layers' o_proj (gated input,
K=6144) and the GDN layers' out_proj (core_attn_out, K=6144) fold the
residual add and gemma_fused_norm(post_attention_layernorm) into the GEMM
tail.  Replaces gemm + gemma_fused_norm in decode / decode_spec / verify;
-64 dispatches per program (decode 614 -> 550); together with the previous
commit the bf16 `y` buffer is gone from the small-batch dataflow entirely
(o_proj/out_proj wrote y, the norm read it back).

kern-attest (A = previous commit, B = this one, GB300):

qwen3.8-27b.json — 64 cuts (gemm+gemma_fused_norm -> gemm8_add_norm):

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 13.527 ms | 12.973 ms  | -554 us  -4.1%    |
| step, graph (TPOT)  | 10.960 ms | 10.829 ms  | -132 us  91->92 tok/s |
| sum of 64 cuts      | 2.121 ms  | 1.693 ms   | -428 us  -20.2%   |

qwen3.8-27b-dflash2.json — same 64 cuts in decode:

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 14.776 ms | 14.284 ms  | -492 us  -3.3%    |
| step, graph (TPOT)  | 11.499 ms | 11.272 ms  | -228 us  87->89 tok/s |
| sum of 64 cuts      | 2.140 ms  | 1.685 ms   | -454 us  -21.2%   |

Verdict: INCONCLUSIVE (accumulation order inside the cut, as before);
next_token / draft_tokens / verify_tokens bit-identical on the tap prompt,
noise floor clean.  Fuzz: <= 61 ulp on uniform/normal/laplace/outliers,
special bit-identical; the `edge` round (+-inf frontier inputs) yields NaN
on both sides with differing bit patterns (max |delta| 0.0, 5113 nan) --
inf through a GEMM + rsqrt norm is NaN either way, the two sides just
canonicalize differently; finite activations never reach it.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 6912 +++++------------------------
 examples/qwen3.8-27b.json         | 2368 ++--------
 tools/gen_qwen35.py               |   14 +-
 3 files changed, 1580 insertions(+), 7714 deletions(-)

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index b0b12bb..f5eb0ac 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -47644,38 +47644,17 @@
      ]
     },
     {
-     "label": "l0.out_proj",
-     "kernel": "gemm",
+     "label": "l0.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.0.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l0.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.0.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -47683,9 +47662,6 @@
       {
        "buf": "model.layers.0.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -47693,7 +47669,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -48096,38 +48072,17 @@
      ]
     },
     {
-     "label": "l1.out_proj",
-     "kernel": "gemm",
+     "label": "l1.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.1.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l1.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.1.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -48135,9 +48090,6 @@
       {
        "buf": "model.layers.1.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -48145,7 +48097,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -48548,38 +48500,17 @@
      ]
     },
     {
-     "label": "l2.out_proj",
-     "kernel": "gemm",
+     "label": "l2.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.2.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l2.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.2.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -48587,9 +48518,6 @@
       {
        "buf": "model.layers.2.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -48597,7 +48525,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -48983,38 +48911,17 @@
      ]
     },
     {
-     "label": "l3.o_proj",
-     "kernel": "gemm",
+     "label": "l3.o_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "gated"
-      },
-      {
-       "buf": "model.layers.3.self_attn.o_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l3.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "gated"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.3.self_attn.o_proj.weight"
       },
       {
        "buf": "residual"
@@ -49022,9 +48929,6 @@
       {
        "buf": "model.layers.3.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -49032,7 +48936,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -49435,38 +49339,17 @@
      ]
     },
     {
-     "label": "l4.out_proj",
-     "kernel": "gemm",
+     "label": "l4.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.4.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l4.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.4.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -49474,9 +49357,6 @@
       {
        "buf": "model.layers.4.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -49484,7 +49364,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -49887,38 +49767,17 @@
      ]
     },
     {
-     "label": "l5.out_proj",
-     "kernel": "gemm",
+     "label": "l5.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.5.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l5.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
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@@ -54814,38 +54430,17 @@
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@@ -55266,38 +54858,17 @@
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@@ -55718,38 +55286,17 @@
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@@ -56155,38 +55699,17 @@
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@@ -56194,9 +55717,6 @@
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@@ -56607,38 +56127,17 @@
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@@ -62438,38 +61646,17 @@
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@@ -62890,38 +62074,17 @@
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@@ -63327,38 +62487,17 @@
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@@ -69610,38 +68434,17 @@
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@@ -70062,38 +68862,17 @@
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@@ -76474,38 +74938,17 @@
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@@ -80986,38 +79210,17 @@
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@@ -81423,38 +79623,17 @@
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@@ -82327,38 +80479,17 @@
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@@ -82779,38 +80907,17 @@
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@@ -83216,38 +81320,17 @@
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@@ -83255,9 +81338,6 @@
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@@ -83668,38 +81748,17 @@
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@@ -88182,38 +86022,17 @@
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@@ -88619,38 +86435,17 @@
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@@ -89071,38 +86863,17 @@
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@@ -89523,38 +87291,17 @@
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@@ -89975,38 +87719,17 @@
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@@ -90412,38 +88132,17 @@
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@@ -96267,38 +93675,17 @@
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@@ -96719,38 +94103,17 @@
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@@ -97171,38 +94531,17 @@
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@@ -105876,38 +102780,17 @@
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@@ -106328,38 +103208,17 @@
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@@ -110373,38 +107037,17 @@
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@@ -110825,38 +107465,17 @@
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@@ -115776,38 +112152,17 @@
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@@ -116228,38 +112580,17 @@
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@@ -116267,9 +112598,6 @@
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@@ -116680,38 +113008,17 @@
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@@ -117132,38 +113436,17 @@
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@@ -117569,38 +113849,17 @@
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@@ -122083,38 +118123,17 @@
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@@ -122535,38 +118551,17 @@
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@@ -122972,38 +118964,17 @@
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@@ -123424,38 +119392,17 @@
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@@ -123876,38 +119820,17 @@
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@@ -124328,38 +120248,17 @@
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@@ -129731,38 +125363,17 @@
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@@ -130620,38 +126204,17 @@
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@@ -131072,38 +126632,17 @@
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diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 8bce876..45c1383 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -45471,38 +45471,17 @@
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@@ -45759,38 +45735,17 @@
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@@ -45798,9 +45753,6 @@
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@@ -46047,38 +45999,17 @@
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@@ -46086,9 +46017,6 @@
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@@ -46482,38 +46410,17 @@
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@@ -46521,9 +46428,6 @@
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@@ -46770,38 +46674,17 @@
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@@ -46809,9 +46692,6 @@
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@@ -46819,7 +46699,7 @@
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@@ -47058,38 +46938,17 @@
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@@ -47097,9 +46956,6 @@
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@@ -47107,7 +46963,7 @@
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@@ -47346,38 +47202,17 @@
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+       "buf": "core_attn_out"
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@@ -47385,9 +47220,6 @@
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@@ -47395,7 +47227,7 @@
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@@ -47783,9 +47615,12 @@
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-     "kernel": "gemm",
+     "label": "l7.o_norm",
+     "kernel": "gemm8_add_norm",
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@@ -47793,7 +47628,10 @@
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+      {
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@@ -47803,36 +47641,6 @@
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@@ -48359,38 +48143,17 @@
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@@ -48647,38 +48407,17 @@
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@@ -51249,38 +50817,17 @@
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@@ -51686,38 +51230,17 @@
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@@ -51974,38 +51494,17 @@
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@@ -52262,9 +51758,12 @@
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@@ -52550,38 +52022,17 @@
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     {
-     "label": "l52.out_proj",
-     "kernel": "gemm",
+     "label": "l52.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.52.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l52.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.52.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -62421,9 +61152,6 @@
       {
        "buf": "model.layers.52.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -62431,7 +61159,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -62670,38 +61398,17 @@
      ]
     },
     {
-     "label": "l53.out_proj",
-     "kernel": "gemm",
+     "label": "l53.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.53.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l53.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.53.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -62709,9 +61416,6 @@
       {
        "buf": "model.layers.53.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -62719,7 +61423,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -62958,38 +61662,17 @@
      ]
     },
     {
-     "label": "l54.out_proj",
-     "kernel": "gemm",
+     "label": "l54.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.54.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l54.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.54.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -62997,9 +61680,6 @@
       {
        "buf": "model.layers.54.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -63007,7 +61687,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -63395,38 +62075,17 @@
      ]
     },
     {
-     "label": "l55.o_proj",
-     "kernel": "gemm",
+     "label": "l55.o_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "gated"
-      },
-      {
-       "buf": "model.layers.55.self_attn.o_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l55.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "gated"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.55.self_attn.o_proj.weight"
       },
       {
        "buf": "residual"
@@ -63434,9 +62093,6 @@
       {
        "buf": "model.layers.55.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -63444,7 +62100,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -63683,47 +62339,23 @@
      ]
     },
     {
-     "label": "l56.out_proj",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.56.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
-      },
-      {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l56.post_attn_norm",
-     "kernel": "gemma_fused_norm",
+     "label": "l56.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
        "buf": "x"
       },
       {
-       "buf": "y"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "residual"
+       "buf": "model.layers.56.linear_attn.out_proj.weight"
       },
       {
-       "buf": "model.layers.56.post_attention_layernorm.weight_p1"
+       "buf": "residual"
       },
       {
-       "i32": 5120
+       "buf": "model.layers.56.post_attention_layernorm.weight_p1"
       },
       {
        "sym": "tokens"
@@ -63732,7 +62364,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -63971,38 +62603,17 @@
      ]
     },
     {
-     "label": "l57.out_proj",
-     "kernel": "gemm",
+     "label": "l57.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.57.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l57.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.57.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -64010,9 +62621,6 @@
       {
        "buf": "model.layers.57.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -64020,7 +62628,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -64259,38 +62867,17 @@
      ]
     },
     {
-     "label": "l58.out_proj",
-     "kernel": "gemm",
+     "label": "l58.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.58.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l58.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.58.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -64298,9 +62885,6 @@
       {
        "buf": "model.layers.58.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -64308,7 +62892,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -64696,38 +63280,17 @@
      ]
     },
     {
-     "label": "l59.o_proj",
-     "kernel": "gemm",
+     "label": "l59.o_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "gated"
-      },
-      {
-       "buf": "model.layers.59.self_attn.o_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l59.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "gated"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.59.self_attn.o_proj.weight"
       },
       {
        "buf": "residual"
@@ -64735,9 +63298,6 @@
       {
        "buf": "model.layers.59.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -64745,7 +63305,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -64984,38 +63544,17 @@
      ]
     },
     {
-     "label": "l60.out_proj",
-     "kernel": "gemm",
+     "label": "l60.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.60.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l60.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.60.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -65023,9 +63562,6 @@
       {
        "buf": "model.layers.60.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -65033,7 +63569,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -65272,38 +63808,17 @@
      ]
     },
     {
-     "label": "l61.out_proj",
-     "kernel": "gemm",
+     "label": "l61.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.61.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l61.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.61.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -65311,9 +63826,6 @@
       {
        "buf": "model.layers.61.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -65321,7 +63833,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -65560,38 +64072,17 @@
      ]
     },
     {
-     "label": "l62.out_proj",
-     "kernel": "gemm",
+     "label": "l62.out_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.62.linear_attn.out_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l62.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.62.linear_attn.out_proj.weight"
       },
       {
        "buf": "residual"
@@ -65599,9 +64090,6 @@
       {
        "buf": "model.layers.62.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -65609,7 +64097,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
@@ -65997,38 +64485,17 @@
      ]
     },
     {
-     "label": "l63.o_proj",
-     "kernel": "gemm",
+     "label": "l63.o_norm",
+     "kernel": "gemm8_add_norm",
      "args": [
       {
-       "buf": "gated"
-      },
-      {
-       "buf": "model.layers.63.self_attn.o_proj.weight"
-      },
-      {
-       "buf": "y"
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i32": 5120
+       "buf": "x"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l63.post_attn_norm",
-     "kernel": "gemma_fused_norm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "gated"
       },
       {
-       "buf": "y"
+       "buf": "model.layers.63.self_attn.o_proj.weight"
       },
       {
        "buf": "residual"
@@ -66036,9 +64503,6 @@
       {
        "buf": "model.layers.63.post_attention_layernorm.weight_p1"
       },
-      {
-       "i32": 5120
-      },
       {
        "sym": "tokens"
       },
@@ -66046,7 +64510,7 @@
        "i32": 5120
       },
       {
-       "i32": 5120
+       "i32": 6144
       },
       {
        "f32": 9.999999974752427e-07
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index f3205ee..cfc611a 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -1153,7 +1153,16 @@ def forward(decode, taps=False, nacc=None, tail=None):
         for i in range(LAYERS):
             p = f"model.layers.{i}."
             l = f"l{i}."
-            ds += attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc)
+            lay = attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc)
+            if small:
+                # o_proj / out_proj GEMM + residual add + post-attention norm
+                attn = i in ATTN_LAYERS
+                lay[-1] = d(l + ("o_norm" if attn else "out_norm"), "gemm8_add_norm",
+                            [buf("x"), buf("gated" if attn else "core_attn_out"),
+                             buf(p + ("self_attn.o_proj.weight" if attn else "linear_attn.out_proj.weight")),
+                             buf("residual"), buf(p + "post_attention_layernorm.weight_p1"),
+                             T, i32(HIDDEN), i32(Q_DIM if attn else GDN_V), f32(eps)])
+            ds += lay
             if small:
                 mlp = [d(l + "gate_up_silu", "gemm8_gateup_silu",
                          [buf("act"), buf("x"), buf(p + "mlp.gate_up_proj.weight"),
@@ -1165,7 +1174,8 @@ def forward(decode, taps=False, nacc=None, tail=None):
             last = i + 1 == LAYERS
             wnorm = "model.norm.weight_p1" if last else f"model.layers.{i + 1}.input_layernorm.weight_p1"
             nlabel = l + ("final_norm" if last else "next_input_norm")
-            ds.append(fused(l + "post_attn_norm", "y", p + "post_attention_layernorm.weight_p1"))
+            if not small:
+                ds.append(fused(l + "post_attn_norm", "y", p + "post_attention_layernorm.weight_p1"))
             ds += mlp
             if small:
                 # down GEMM + residual add + next input norm in one dispatch

From 2a167a91806096e0396e14cb4dfd1f66e441b6ce Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 16:46:44 +0000
Subject: [PATCH 04/10] qwen38: fuse GDN in_proj_qkvz + in_proj_ba into one
 weight stream (bs<=8)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

New kern_gemm8_dual_bf16 in gemm8.cu: the 48 GDN layers each project x
into qkvz (N=16384) and ba (N=96) from the same input; the two nvjet
GEMMs become one persistent-CTA pass that streams W_qkvz and W_ba
back-to-back over one x staging, writing both outputs.  ba alone is far
too small to reach peak (96 rows), qkvz+ba together run at the same
streaming rate as the big GEMMs.  Replaces 2 gemms with 1 dispatch in
decode / decode_spec / verify; decode 550 -> 502 dispatches.

kern-attest (A = previous commit, B = this one, GB300):

qwen3.8-27b.json — 48 cuts (gemm+gemm -> gemm8_dual):

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 12.969 ms | 12.566 ms  | -403 us  -3.1%    |
| step, graph (TPOT)  | 10.749 ms | 10.624 ms  | -125 us  93->94 tok/s |
| sum of 48 cuts      | 1.929 ms  | 1.496 ms   | -433 us  -22.5%   |

qwen3.8-27b-dflash2.json — same 48 cuts in decode:

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 14.351 ms | 13.880 ms  | -471 us  -3.3%    |
| step, graph (TPOT)  | 11.296 ms | 11.118 ms  | -178 us  89->90 tok/s |
| sum of 48 cuts      | 1.963 ms  | 1.484 ms   | -479 us  -24.4%   |

Roofline: 2x gemm 20.1 us each (52.5% of peak) -> 1x gemm8_dual 31.2 us
(67.7% of peak).

Verdict: INCONCLUSIVE (f32 accumulation order vs nvjet split-K inside the
cut; downstream lockstep taps amplify through the GDN recurrence exactly
as in the previous commits); next_token / draft_tokens / verify_tokens
bit-identical, noise floor clean, fuzz <= 54 ulp on 0.4% of elements,
special bit-identical.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 4248 ++++++++---------------------
 examples/qwen3.8-27b.json         | 1464 +++-------
 tools/gen_qwen35.py               |   24 +-
 3 files changed, 1506 insertions(+), 4230 deletions(-)

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index f5eb0ac..ad56e1c 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6701,6 +6701,78 @@
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   },
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+    "out buffer<bf16>",
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+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "i32",
+    "i32",
+    "i32",
+    "i32"
+   ],
+   "impl": {
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+      "params": [
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+       "out buffer<bf16>",
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+       "i32",
+       "i32",
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+      "cubin": "gemm8.cubin"
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@@ -47307,44 +47379,29 @@
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@@ -47734,44 +47791,29 @@
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-     "kernel": "gemm",
+     "label": "l1.in_proj",
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@@ -48162,44 +48204,29 @@
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-     "kernel": "gemm",
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@@ -49001,44 +49028,29 @@
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-     "kernel": "gemm",
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@@ -49429,44 +49441,29 @@
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-     "kernel": "gemm",
+     "label": "l5.in_proj",
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@@ -49857,44 +49854,29 @@
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-     "kernel": "gemm",
+     "label": "l6.in_proj",
+     "kernel": "gemm8_dual",
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@@ -50698,44 +50680,29 @@
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@@ -51126,44 +51093,29 @@
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-     "kernel": "gemm",
+     "label": "l9.in_proj",
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@@ -51554,44 +51506,29 @@
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-     "kernel": "gemm",
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@@ -52395,44 +52332,29 @@
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-     "kernel": "gemm",
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@@ -52823,44 +52745,29 @@
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-     "kernel": "gemm",
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@@ -53251,44 +53158,29 @@
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-     "kernel": "gemm",
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@@ -54092,44 +53984,29 @@
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-     "kernel": "gemm",
+     "label": "l16.in_proj",
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@@ -54520,44 +54397,29 @@
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-     "kernel": "gemm",
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@@ -54948,44 +54810,29 @@
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-     "kernel": "gemm",
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@@ -55789,44 +55636,29 @@
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        "i32": 96
@@ -56217,44 +56049,29 @@
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@@ -57486,44 +57288,29 @@
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@@ -57914,44 +57701,29 @@
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@@ -58342,44 +58114,29 @@
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@@ -59183,44 +58940,29 @@
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@@ -59611,44 +59353,29 @@
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@@ -60039,44 +59766,29 @@
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@@ -60880,44 +60592,29 @@
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@@ -61308,44 +61005,29 @@
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@@ -61736,44 +61418,29 @@
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@@ -62577,44 +62244,29 @@
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@@ -63005,44 +62657,29 @@
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@@ -63433,44 +63070,29 @@
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@@ -64274,44 +63896,29 @@
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@@ -64702,44 +64309,29 @@
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@@ -65130,44 +64722,29 @@
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@@ -65971,44 +65548,29 @@
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@@ -66399,44 +65961,29 @@
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@@ -66827,44 +66374,29 @@
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@@ -68096,44 +67613,29 @@
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@@ -68524,44 +68026,29 @@
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@@ -69365,44 +68852,29 @@
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@@ -69793,44 +69265,29 @@
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@@ -70221,44 +69678,29 @@
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@@ -71062,44 +70504,29 @@
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@@ -71490,44 +70917,29 @@
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@@ -71918,44 +71330,29 @@
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@@ -72759,44 +72156,29 @@
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@@ -73187,44 +72569,29 @@
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@@ -73615,44 +72982,29 @@
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@@ -74601,44 +73953,29 @@
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@@ -75028,44 +74365,29 @@
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@@ -75456,44 +74778,29 @@
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@@ -76295,44 +75602,29 @@
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@@ -76723,44 +76015,29 @@
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@@ -77175,44 +76452,29 @@
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@@ -78016,44 +77278,29 @@
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@@ -78444,44 +77691,29 @@
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@@ -78872,44 +78104,29 @@
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@@ -79713,44 +78930,29 @@
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@@ -80141,44 +79343,29 @@
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@@ -80569,44 +79756,29 @@
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@@ -81410,44 +80582,29 @@
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@@ -81838,44 +80995,29 @@
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@@ -82266,44 +81408,29 @@
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@@ -83131,44 +82258,29 @@
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@@ -83559,44 +82671,29 @@
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@@ -83987,44 +83084,29 @@
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@@ -84828,44 +83910,29 @@
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@@ -85256,44 +84323,29 @@
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@@ -85684,44 +84736,29 @@
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@@ -86525,44 +85562,29 @@
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@@ -86953,44 +85975,29 @@
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        "i32": 96
@@ -87381,44 +86388,29 @@
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@@ -88222,44 +87214,29 @@
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@@ -88650,44 +87627,29 @@
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@@ -89102,44 +88064,29 @@
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@@ -89943,44 +88890,29 @@
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@@ -90371,44 +89303,29 @@
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@@ -90799,44 +89716,29 @@
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@@ -91640,44 +90542,29 @@
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@@ -92068,44 +90955,29 @@
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@@ -92496,44 +91368,29 @@
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@@ -93337,44 +92194,29 @@
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@@ -93765,44 +92607,29 @@
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@@ -94193,44 +93020,29 @@
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@@ -95058,44 +93870,29 @@
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@@ -95486,44 +94283,29 @@
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@@ -95914,44 +94696,29 @@
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@@ -96755,44 +95522,29 @@
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@@ -97183,44 +95935,29 @@
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@@ -97611,44 +96348,29 @@
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        "i32": 96
@@ -98452,44 +97174,29 @@
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@@ -98880,44 +97587,29 @@
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@@ -99308,44 +98000,29 @@
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@@ -100149,44 +98826,29 @@
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@@ -100577,44 +99239,29 @@
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@@ -101029,44 +99676,29 @@
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@@ -102015,44 +100647,29 @@
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@@ -102442,44 +101059,29 @@
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@@ -102870,44 +101472,29 @@
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@@ -103709,44 +102296,29 @@
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@@ -104137,44 +102709,29 @@
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@@ -104589,44 +103146,29 @@
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@@ -105430,44 +103972,29 @@
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@@ -105858,44 +104385,29 @@
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@@ -106286,44 +104798,29 @@
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@@ -107127,44 +105624,29 @@
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@@ -107555,44 +106037,29 @@
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@@ -107983,44 +106450,29 @@
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@@ -108824,44 +107276,29 @@
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@@ -109252,44 +107689,29 @@
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@@ -109680,44 +108102,29 @@
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@@ -110545,44 +108952,29 @@
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@@ -110973,44 +109365,29 @@
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@@ -111401,44 +109778,29 @@
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@@ -112242,44 +110604,29 @@
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@@ -112670,44 +111017,29 @@
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@@ -113098,44 +111430,29 @@
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@@ -113939,44 +112256,29 @@
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@@ -114367,44 +112669,29 @@
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@@ -114795,44 +113082,29 @@
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@@ -115636,44 +113908,29 @@
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@@ -116064,44 +114321,29 @@
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@@ -116516,44 +114758,29 @@
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@@ -117357,44 +115584,29 @@
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@@ -117785,44 +115997,29 @@
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@@ -118213,44 +116410,29 @@
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@@ -119054,44 +117236,29 @@
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@@ -119482,44 +117649,29 @@
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-     "kernel": "gemm",
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@@ -119910,44 +118062,29 @@
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-     "kernel": "gemm",
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@@ -120751,44 +118888,29 @@
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-     "kernel": "gemm",
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@@ -121179,44 +119301,29 @@
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-     "kernel": "gemm",
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@@ -121607,44 +119714,29 @@
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     {
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-     "kernel": "gemm",
+     "label": "l46.in_proj",
+     "kernel": "gemm8_dual",
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@@ -122472,44 +120564,29 @@
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-     "label": "l48.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l48.in_proj",
+     "kernel": "gemm8_dual",
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        "i32": 96
@@ -122900,44 +120977,29 @@
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-     "label": "l49.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l49.in_proj",
+     "kernel": "gemm8_dual",
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        "i32": 96
@@ -123328,44 +121390,29 @@
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     {
-     "label": "l50.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l50.in_proj",
+     "kernel": "gemm8_dual",
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        "buf": "qkvz"
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-     "label": "l50.in_proj_ba",
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        "i32": 96
@@ -124169,44 +122216,29 @@
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-     "label": "l52.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l52.in_proj",
+     "kernel": "gemm8_dual",
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        "buf": "model.layers.52.linear_attn.in_proj_ba.weight"
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        "i32": 96
@@ -124597,44 +122629,29 @@
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-     "label": "l53.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l53.in_proj",
+     "kernel": "gemm8_dual",
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        "i32": 96
@@ -125025,44 +123042,29 @@
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-     "label": "l54.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l54.in_proj",
+     "kernel": "gemm8_dual",
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        "i32": 96
@@ -125866,44 +123868,29 @@
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-     "label": "l56.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l56.in_proj",
+     "kernel": "gemm8_dual",
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       {
        "i32": 96
@@ -126294,44 +124281,29 @@
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     {
-     "label": "l57.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l57.in_proj",
+     "kernel": "gemm8_dual",
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       {
        "buf": "qkvz"
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-     "label": "l57.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
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+       "buf": "model.layers.57.linear_attn.in_proj_qkvz.weight"
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       {
        "i32": 96
@@ -126722,44 +124694,29 @@
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     {
-     "label": "l58.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l58.in_proj",
+     "kernel": "gemm8_dual",
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        "buf": "qkvz"
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-     "label": "l58.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
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+       "buf": "model.layers.58.linear_attn.in_proj_qkvz.weight"
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        "i32": 96
@@ -127563,44 +125520,29 @@
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     {
-     "label": "l60.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l60.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
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-      {
-       "buf": "model.layers.60.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
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       {
-       "sym": "tokens"
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-     "label": "l60.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
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+       "buf": "model.layers.60.linear_attn.in_proj_qkvz.weight"
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        "buf": "model.layers.60.linear_attn.in_proj_ba.weight"
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-       "buf": "ba"
+       "sym": "tokens"
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-       "sym": "tokens"
+       "i32": 16384
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       {
        "i32": 96
@@ -127991,44 +125933,29 @@
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     },
     {
-     "label": "l61.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l61.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
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-      {
-       "buf": "model.layers.61.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
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-       "i32": 16384
+       "buf": "x"
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-    },
-    {
-     "label": "l61.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.61.linear_attn.in_proj_qkvz.weight"
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        "buf": "model.layers.61.linear_attn.in_proj_ba.weight"
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-       "buf": "ba"
+       "sym": "tokens"
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-       "sym": "tokens"
+       "i32": 16384
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       {
        "i32": 96
@@ -128443,44 +126370,29 @@
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     {
-     "label": "l62.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l62.in_proj",
+     "kernel": "gemm8_dual",
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        "buf": "qkvz"
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-    {
-     "label": "l62.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.62.linear_attn.in_proj_qkvz.weight"
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       {
        "buf": "model.layers.62.linear_attn.in_proj_ba.weight"
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-       "buf": "ba"
+       "sym": "tokens"
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        "i32": 96
diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 45c1383..6092d9a 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -5940,6 +5940,78 @@
     ]
    }
   },
+  "gemm8_dual": {
+   "params": [
+    "out buffer<bf16>",
+    "out buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "i32",
+    "i32",
+    "i32",
+    "i32"
+   ],
+   "impl": {
+    "steps": [
+     {
+      "symbol": "kern_gemm8_dual_bf16",
+      "params": [
+       "out buffer<bf16>",
+       "out buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "i32",
+       "i32",
+       "i32",
+       "i32"
+      ],
+      "block": [
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+       1,
+       1
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@@ -45297,44 +45369,29 @@
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@@ -45561,44 +45618,29 @@
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@@ -45825,44 +45867,29 @@
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@@ -46500,44 +46527,29 @@
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@@ -46764,44 +46776,29 @@
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@@ -47028,44 +47025,29 @@
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@@ -47705,44 +47687,29 @@
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@@ -47969,44 +47936,29 @@
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@@ -48233,44 +48185,29 @@
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@@ -48910,44 +48847,29 @@
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@@ -49174,44 +49096,29 @@
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@@ -49438,44 +49345,29 @@
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@@ -50115,44 +50007,29 @@
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@@ -50379,44 +50256,29 @@
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@@ -50643,44 +50505,29 @@
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@@ -51320,44 +51167,29 @@
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@@ -51584,44 +51416,29 @@
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        "i32": 96
@@ -51848,44 +51665,29 @@
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@@ -52525,44 +52327,29 @@
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@@ -52789,44 +52576,29 @@
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@@ -53053,44 +52825,29 @@
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@@ -53730,44 +53487,29 @@
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@@ -53994,44 +53736,29 @@
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@@ -54258,44 +53985,29 @@
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@@ -54935,44 +54647,29 @@
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@@ -55199,44 +54896,29 @@
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@@ -55463,44 +55145,29 @@
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@@ -56140,44 +55807,29 @@
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@@ -56404,44 +56056,29 @@
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@@ -56668,44 +56305,29 @@
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@@ -57345,44 +56967,29 @@
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@@ -57609,44 +57216,29 @@
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@@ -57873,44 +57465,29 @@
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@@ -58550,44 +58127,29 @@
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@@ -58814,44 +58376,29 @@
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@@ -59078,44 +58625,29 @@
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       {
        "i32": 96
@@ -59755,44 +59287,29 @@
      ]
     },
     {
-     "label": "l48.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l48.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.48.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l48.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.48.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.48.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -60019,44 +59536,29 @@
      ]
     },
     {
-     "label": "l49.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l49.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.49.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l49.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.49.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.49.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -60283,44 +59785,29 @@
      ]
     },
     {
-     "label": "l50.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l50.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.50.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l50.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.50.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.50.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -60960,44 +60447,29 @@
      ]
     },
     {
-     "label": "l52.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l52.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.52.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l52.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.52.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.52.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -61224,44 +60696,29 @@
      ]
     },
     {
-     "label": "l53.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l53.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.53.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l53.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.53.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.53.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -61488,44 +60945,29 @@
      ]
     },
     {
-     "label": "l54.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l54.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.54.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l54.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.54.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.54.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -62165,44 +61607,29 @@
      ]
     },
     {
-     "label": "l56.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l56.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.56.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l56.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.56.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.56.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -62429,44 +61856,29 @@
      ]
     },
     {
-     "label": "l57.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l57.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.57.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l57.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.57.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.57.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -62693,44 +62105,29 @@
      ]
     },
     {
-     "label": "l58.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l58.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.58.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l58.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.58.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.58.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -63370,44 +62767,29 @@
      ]
     },
     {
-     "label": "l60.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l60.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.60.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l60.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.60.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.60.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -63634,44 +63016,29 @@
      ]
     },
     {
-     "label": "l61.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l61.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.61.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l61.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.61.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.61.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
@@ -63898,44 +63265,29 @@
      ]
     },
     {
-     "label": "l62.in_proj_qkvz",
-     "kernel": "gemm",
+     "label": "l62.in_proj",
+     "kernel": "gemm8_dual",
      "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "model.layers.62.linear_attn.in_proj_qkvz.weight"
-      },
       {
        "buf": "qkvz"
       },
       {
-       "sym": "tokens"
+       "buf": "ba"
       },
       {
-       "i32": 16384
+       "buf": "x"
       },
       {
-       "i32": 5120
-      }
-     ]
-    },
-    {
-     "label": "l62.in_proj_ba",
-     "kernel": "gemm",
-     "args": [
-      {
-       "buf": "x"
+       "buf": "model.layers.62.linear_attn.in_proj_qkvz.weight"
       },
       {
        "buf": "model.layers.62.linear_attn.in_proj_ba.weight"
       },
       {
-       "buf": "ba"
+       "sym": "tokens"
       },
       {
-       "sym": "tokens"
+       "i32": 16384
       },
       {
        "i32": 96
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index cfc611a..4eb6555 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -776,6 +776,12 @@ def layer_norm_kernel(src, grid, rows_shape):
         # down GEMM + residual add + Gemma fused_add_rms_norm of the next
         # layer input (gemm8's split-K partials, y and the task counters are
         # impl scratch; the norm tail reproduces kern_gemma_rms_norm.cu)
+        # two weights sharing one x: in_proj_qkvz + in_proj_ba per GDN layer
+        "gemm8_dual": single("kern_gemm8_dual_bf16",
+                             ["out buffer<bf16>", "out buffer<bf16>", "in buffer<bf16>",
+                              "in buffer<bf16>", "in buffer<bf16>", "i32", "i32", "i32", "i32"],
+                             [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
+                             shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
         "gemm8_add_norm": {
             "params": GEMM8_AN_PARAMS[:5] + GEMM8_AN_PARAMS[8:],
             "impl": {
@@ -995,7 +1001,7 @@ def fused(label, x_in, w):
                  [buf("x"), buf(x_in), buf("residual"), buf(w), i32(HIDDEN), T,
                   i32(HIDDEN), i32(HIDDEN), f32(eps)])
 
-    def gdn_layer(i, decode, nacc=None):
+    def gdn_layer(i, decode, nacc=None, small=False):
         """decode=False: the chunked FLA prefill chain.  decode=True: the
         recurrent chain — Stage 1's packed kernels, or, when `nacc` names
         the num_accepted_tokens buffer, vLLM's speculative kernels over the
@@ -1006,10 +1012,16 @@ def gdn_layer(i, decode, nacc=None):
         line = g + 1
         idx = buf(line_table, 4 * line)
         page = gdn_page(i)
-        ds = [
-            gemm(l + "in_proj_qkvz", "x", p + "in_proj_qkvz.weight", "qkvz", T, QKVZ_DIM, HIDDEN),
-            gemm(l + "in_proj_ba", "x", p + "in_proj_ba.weight", "ba", T, BA_DIM, HIDDEN),
-        ]
+        if small:
+            # one weight stream for both input projections
+            ds = [d(l + "in_proj", "gemm8_dual",
+                    [buf("qkvz"), buf("ba"), buf("x"), buf(p + "in_proj_qkvz.weight"),
+                     buf(p + "in_proj_ba.weight"), T, i32(QKVZ_DIM), i32(BA_DIM), i32(HIDDEN)])]
+        else:
+            ds = [
+                gemm(l + "in_proj_qkvz", "x", p + "in_proj_qkvz.weight", "qkvz", T, QKVZ_DIM, HIDDEN),
+                gemm(l + "in_proj_ba", "x", p + "in_proj_ba.weight", "ba", T, BA_DIM, HIDDEN),
+            ]
         a_ = buf("ba", GDN_V_HEADS * BF16)   # ba row = [b | a]
         b_ = buf("ba")
         if not decode and nacc is None:
@@ -1153,7 +1165,7 @@ def forward(decode, taps=False, nacc=None, tail=None):
         for i in range(LAYERS):
             p = f"model.layers.{i}."
             l = f"l{i}."
-            lay = attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc)
+            lay = attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc, small)
             if small:
                 # o_proj / out_proj GEMM + residual add + post-attention norm
                 attn = i in ATTN_LAYERS

From 10b9ded1993c961d665705563adb22ddcd0d9e77 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 16:56:39 +0000
Subject: [PATCH 05/10] qwen38: fuse the sigmoid attention gate into o_proj +
 add + norm (bs<=8)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

New kern_gemm8_sgate_add_norm_bf16 in gemm8.cu: the 16 gated-attention
layers compute gated = attn_out * sigmoid(gate) (gate is the strided
per-head [q | gate] view of the qkv projection) and feed it to o_proj.
kern_sigmoid_mul_bf16 launches [tokens, 1, 1] blocks -- at decode that is
ONE CTA on the whole GPU, ~19 us of pure latency.  The fused kernel's
compute warps build x = bf16(f32(attn) * bf16(sigmoid(f32(gate)))) (the
exact sigmoid_mul.cu chain) straight into the x staging smem at each K
range switch; 8-element aligned batches make the gate/attn loads and the
smem store one 16 B vector each, so the fill hides under the first weight
stages (o_proj+gate+add+norm 22.6 us vs 22.8 us for the unfused GEMM
alone).  Replaces sigmoid_mul + gemm8_add_norm in decode / decode_spec /
verify; decode 502 -> 486 dispatches, and the `gated` buffer leaves the
small-batch dataflow.

kern-attest (A = previous commit, B = this one, GB300):

qwen3.8-27b.json — 16 cuts (sigmoid_mul+gemm8_add_norm -> gemm8_sgate_add_norm):

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 12.551 ms | 12.298 ms  | -253 us  -2.0%    |
| step, graph (TPOT)  | 10.561 ms | 10.366 ms  | -195 us  95->96 tok/s |
| sum of 16 cuts      | 733.8 us  | 448.8 us   | -285 us  -38.8%   |

qwen3.8-27b-dflash2.json — same 16 cuts in decode:

| decode tokens=1     | A         | B measured | delta             |
| step, eager         | 13.869 ms | 13.599 ms  | -270 us  -1.9%    |
| step, graph (TPOT)  | 11.154 ms | 10.927 ms  | -227 us  90->92 tok/s |
| sum of 16 cuts      | 694.7 us  | 454.7 us   | -240 us  -34.5%   |

Verdict: plain PASS -- bit-identical at every cut on real and synthesized
inputs (uniform/normal/laplace/outliers/edge/special all bit-identical),
next_token / draft_tokens / verify_tokens bit-identical, noise floor
clean.  Spec manifest INCONCLUSIVE only because the workload driver
cannot stage decode_spec / verify (same cuts, fuzz bit-identical there
too).

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 1999 ++++++++++-------------------
 examples/qwen3.8-27b.json         |  751 +++++------
 tools/gen_qwen35.py               |   45 +-
 tools/kernels-src/gemm8.cu        |   61 +-
 4 files changed, 1110 insertions(+), 1746 deletions(-)

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index ad56e1c..555e3a5 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6773,6 +6773,133 @@
     ]
    }
   },
+  "gemm8_sgate_add_norm": {
+   "params": [
+    "out buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "inout buffer<bf16>",
+    "in buffer<f32>",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "f32"
+   ],
+   "impl": {
+    "scratch": {
+     "y": {
+      "dtype": "bf16",
+      "shape": [
+       8,
+       5120
+      ]
+     },
+     "partial": {
+      "dtype": "f32",
+      "shape": [
+       8,
+       8,
+       5120
+      ]
+     },
+     "gcnt": {
+      "dtype": "i32",
+      "shape": [
+       2
+      ]
+     }
+    },
+    "steps": [
+     {
+      "symbol": "kern_gemm8_sgate_add_norm_bf16",
+      "params": [
+       "out buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "inout buffer<bf16>",
+       "in buffer<f32>",
+       "out buffer<bf16>",
+       "out buffer<f32>",
+       "out buffer<i32>",
+       "i32",
+       "i32",
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@@ -48910,11 +49037,11 @@
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@@ -48923,30 +49050,6 @@
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@@ -48965,6 +49068,15 @@
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@@ -50562,11 +50674,11 @@
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@@ -50575,30 +50687,6 @@
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@@ -50617,6 +50705,15 @@
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@@ -52214,11 +52311,11 @@
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@@ -52227,30 +52324,6 @@
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@@ -52269,6 +52342,15 @@
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@@ -53866,11 +53948,11 @@
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@@ -53921,6 +53979,15 @@
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@@ -55518,11 +55585,11 @@
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@@ -55531,30 +55598,6 @@
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@@ -55573,6 +55616,15 @@
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@@ -57170,11 +57222,11 @@
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@@ -57183,30 +57235,6 @@
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@@ -57225,6 +57253,15 @@
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@@ -58822,11 +58859,11 @@
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@@ -58835,30 +58872,6 @@
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@@ -58877,6 +58890,15 @@
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@@ -60474,11 +60496,11 @@
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@@ -60487,30 +60509,6 @@
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@@ -60529,6 +60527,15 @@
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@@ -62126,11 +62133,11 @@
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@@ -62139,30 +62146,6 @@
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@@ -62181,6 +62164,15 @@
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@@ -63778,11 +63770,11 @@
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@@ -63791,30 +63783,6 @@
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@@ -63833,6 +63801,15 @@
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@@ -65430,11 +65407,11 @@
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@@ -65443,30 +65420,6 @@
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@@ -65485,6 +65438,15 @@
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@@ -67082,11 +67044,11 @@
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@@ -67095,30 +67057,6 @@
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@@ -67137,6 +67075,15 @@
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@@ -68734,11 +68681,11 @@
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@@ -68747,30 +68694,6 @@
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@@ -68789,6 +68712,15 @@
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@@ -70386,11 +70318,11 @@
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@@ -70399,30 +70331,6 @@
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@@ -70441,6 +70349,15 @@
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@@ -72038,11 +71955,11 @@
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@@ -72051,30 +71968,6 @@
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@@ -72093,6 +71986,15 @@
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@@ -73690,11 +73592,11 @@
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@@ -75484,11 +75371,11 @@
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@@ -77160,11 +77032,11 @@
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@@ -77215,6 +77063,15 @@
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@@ -78812,11 +78669,11 @@
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@@ -80464,11 +80306,11 @@
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@@ -80519,6 +80337,15 @@
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@@ -82116,11 +81943,11 @@
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@@ -82171,6 +81974,15 @@
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@@ -83792,11 +83604,11 @@
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@@ -83847,6 +83635,15 @@
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@@ -85444,11 +85241,11 @@
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@@ -85457,30 +85254,6 @@
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@@ -85499,6 +85272,15 @@
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@@ -87096,11 +86878,11 @@
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@@ -87151,6 +86909,15 @@
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@@ -88772,11 +88539,11 @@
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@@ -88827,6 +88570,15 @@
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@@ -90424,11 +90176,11 @@
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@@ -90479,6 +90207,15 @@
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@@ -92076,11 +91813,11 @@
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@@ -92131,6 +91844,15 @@
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@@ -93728,11 +93450,11 @@
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@@ -93783,6 +93481,15 @@
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@@ -95404,11 +95111,11 @@
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@@ -95417,30 +95124,6 @@
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@@ -95459,6 +95142,15 @@
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@@ -97056,11 +96748,11 @@
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@@ -97069,30 +96761,6 @@
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@@ -97111,6 +96779,15 @@
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@@ -98708,11 +98385,11 @@
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@@ -98721,30 +98398,6 @@
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@@ -98763,6 +98416,15 @@
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@@ -100384,11 +100046,11 @@
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@@ -100397,30 +100059,6 @@
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@@ -100439,6 +100077,15 @@
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@@ -102178,11 +101825,11 @@
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@@ -102191,30 +101838,6 @@
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@@ -102233,6 +101856,15 @@
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@@ -103854,11 +103486,11 @@
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@@ -103867,30 +103499,6 @@
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@@ -103909,6 +103517,15 @@
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@@ -105506,11 +105123,11 @@
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+     "label": "l11.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
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-       "buf": "gated"
+       "buf": "x"
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@@ -105519,30 +105136,6 @@
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@@ -105561,6 +105154,15 @@
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@@ -107158,11 +106760,11 @@
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+     "label": "l15.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
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+       "buf": "x"
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@@ -107171,30 +106773,6 @@
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@@ -107213,6 +106791,15 @@
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@@ -108810,11 +108397,11 @@
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-     "kernel": "sigmoid_mul",
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@@ -108823,30 +108410,6 @@
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        "buf": "model.layers.19.self_attn.o_proj.weight"
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@@ -108865,6 +108428,15 @@
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@@ -110486,11 +110058,11 @@
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     {
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+     "label": "l23.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
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+       "buf": "x"
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@@ -110499,30 +110071,6 @@
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@@ -110541,6 +110089,15 @@
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@@ -112138,11 +111695,11 @@
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     {
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-     "kernel": "sigmoid_mul",
+     "label": "l27.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
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@@ -112151,30 +111708,6 @@
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        "buf": "model.layers.27.self_attn.o_proj.weight"
       },
@@ -112193,6 +111726,15 @@
       {
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+      {
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@@ -113790,11 +113332,11 @@
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     {
-     "label": "l31.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l31.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
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+       "buf": "x"
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       {
        "buf": "attn_out"
@@ -113803,30 +113345,6 @@
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-     "args": [
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-      {
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       {
        "buf": "model.layers.31.self_attn.o_proj.weight"
       },
@@ -113845,6 +113363,15 @@
       {
        "i32": 6144
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+      {
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@@ -115466,11 +114993,11 @@
      ]
     },
     {
-     "label": "l35.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l35.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
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       {
        "buf": "attn_out"
@@ -115479,30 +115006,6 @@
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-     "kernel": "gemm8_add_norm",
-     "args": [
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-      {
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       {
        "buf": "model.layers.35.self_attn.o_proj.weight"
       },
@@ -115521,6 +115024,15 @@
       {
        "i32": 6144
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+      {
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+      {
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       {
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@@ -117118,11 +116630,11 @@
      ]
     },
     {
-     "label": "l39.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l39.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
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       {
        "buf": "attn_out"
@@ -117131,30 +116643,6 @@
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-     "kernel": "gemm8_add_norm",
-     "args": [
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-      {
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       {
        "buf": "model.layers.39.self_attn.o_proj.weight"
       },
@@ -117173,6 +116661,15 @@
       {
        "i32": 6144
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+      {
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+      {
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@@ -118770,11 +118267,11 @@
      ]
     },
     {
-     "label": "l43.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l43.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
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       {
        "buf": "attn_out"
@@ -118783,30 +118280,6 @@
        "buf": "qkv",
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-     "kernel": "gemm8_add_norm",
-     "args": [
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-      {
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       {
        "buf": "model.layers.43.self_attn.o_proj.weight"
       },
@@ -118825,6 +118298,15 @@
       {
        "i32": 6144
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+      {
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+      {
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+      {
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       {
        "f32": 9.999999974752427e-07
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@@ -120422,11 +119904,11 @@
      ]
     },
     {
-     "label": "l47.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l47.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
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       {
        "buf": "attn_out"
@@ -120435,30 +119917,6 @@
        "buf": "qkv",
        "offset": 512
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-      {
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-    {
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-     "kernel": "gemm8_add_norm",
-     "args": [
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-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.47.self_attn.o_proj.weight"
       },
@@ -120477,6 +119935,15 @@
       {
        "i32": 6144
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+      {
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+      {
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+      },
+      {
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       {
        "f32": 9.999999974752427e-07
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@@ -122098,11 +121565,11 @@
      ]
     },
     {
-     "label": "l51.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l51.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
       },
       {
        "buf": "attn_out"
@@ -122111,30 +121578,6 @@
        "buf": "qkv",
        "offset": 512
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-      {
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-      {
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-      {
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-    {
-     "label": "l51.o_norm",
-     "kernel": "gemm8_add_norm",
-     "args": [
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-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.51.self_attn.o_proj.weight"
       },
@@ -122153,6 +121596,15 @@
       {
        "i32": 6144
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+      {
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+      {
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+      },
+      {
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       {
        "f32": 9.999999974752427e-07
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@@ -123750,11 +123202,11 @@
      ]
     },
     {
-     "label": "l55.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l55.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
       },
       {
        "buf": "attn_out"
@@ -123763,30 +123215,6 @@
        "buf": "qkv",
        "offset": 512
       },
-      {
-       "i32": 24
-      },
-      {
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-      {
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-      {
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-     ]
-    },
-    {
-     "label": "l55.o_norm",
-     "kernel": "gemm8_add_norm",
-     "args": [
-      {
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-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.55.self_attn.o_proj.weight"
       },
@@ -123805,6 +123233,15 @@
       {
        "i32": 6144
       },
+      {
+       "i32": 256
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+      {
+       "i32": 14336
+      },
+      {
+       "i32": 512
+      },
       {
        "f32": 9.999999974752427e-07
       }
@@ -125402,11 +124839,11 @@
      ]
     },
     {
-     "label": "l59.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l59.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
       },
       {
        "buf": "attn_out"
@@ -125415,30 +124852,6 @@
        "buf": "qkv",
        "offset": 512
       },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 256
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-      {
-       "i32": 14336
-      },
-      {
-       "i32": 512
-      }
-     ]
-    },
-    {
-     "label": "l59.o_norm",
-     "kernel": "gemm8_add_norm",
-     "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.59.self_attn.o_proj.weight"
       },
@@ -125457,6 +124870,15 @@
       {
        "i32": 6144
       },
+      {
+       "i32": 256
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+      {
+       "i32": 14336
+      },
+      {
+       "i32": 512
+      },
       {
        "f32": 9.999999974752427e-07
       }
@@ -127078,11 +126500,11 @@
      ]
     },
     {
-     "label": "l63.gate",
-     "kernel": "sigmoid_mul",
+     "label": "l63.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
       },
       {
        "buf": "attn_out"
@@ -127091,30 +126513,6 @@
        "buf": "qkv",
        "offset": 512
       },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 256
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-      {
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-      },
-      {
-       "i32": 512
-      }
-     ]
-    },
-    {
-     "label": "l63.o_norm",
-     "kernel": "gemm8_add_norm",
-     "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.63.self_attn.o_proj.weight"
       },
@@ -127133,6 +126531,15 @@
       {
        "i32": 6144
       },
+      {
+       "i32": 256
+      },
+      {
+       "i32": 14336
+      },
+      {
+       "i32": 512
+      },
       {
        "f32": 9.999999974752427e-07
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diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 6092d9a..5399ce4 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -6012,6 +6012,133 @@
     ]
    }
   },
+  "gemm8_sgate_add_norm": {
+   "params": [
+    "out buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "inout buffer<bf16>",
+    "in buffer<f32>",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "i32",
+    "f32"
+   ],
+   "impl": {
+    "scratch": {
+     "y": {
+      "dtype": "bf16",
+      "shape": [
+       8,
+       5120
+      ]
+     },
+     "partial": {
+      "dtype": "f32",
+      "shape": [
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@@ -46409,11 +46536,11 @@
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@@ -46464,6 +46567,15 @@
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@@ -47569,11 +47681,11 @@
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@@ -47582,30 +47694,6 @@
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@@ -47624,6 +47712,15 @@
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@@ -48729,11 +48826,11 @@
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@@ -48784,6 +48857,15 @@
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@@ -49889,11 +49971,11 @@
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@@ -51049,11 +51116,11 @@
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@@ -51104,6 +51147,15 @@
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@@ -52209,11 +52261,11 @@
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@@ -52222,30 +52274,6 @@
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@@ -52264,6 +52292,15 @@
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@@ -53369,11 +53406,11 @@
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@@ -53382,30 +53419,6 @@
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@@ -53424,6 +53437,15 @@
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@@ -54529,11 +54551,11 @@
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@@ -54542,30 +54564,6 @@
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@@ -54584,6 +54582,15 @@
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@@ -55689,11 +55696,11 @@
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@@ -55702,30 +55709,6 @@
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@@ -55744,6 +55727,15 @@
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@@ -56849,11 +56841,11 @@
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@@ -56862,30 +56854,6 @@
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@@ -56904,6 +56872,15 @@
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@@ -58009,11 +57986,11 @@
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@@ -58022,30 +57999,6 @@
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@@ -58064,6 +58017,15 @@
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@@ -59169,11 +59131,11 @@
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@@ -59182,30 +59144,6 @@
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@@ -59224,6 +59162,15 @@
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@@ -60329,11 +60276,11 @@
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@@ -60342,30 +60289,6 @@
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@@ -60384,6 +60307,15 @@
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@@ -61489,11 +61421,11 @@
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@@ -61502,30 +61434,6 @@
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@@ -61544,6 +61452,15 @@
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@@ -62649,11 +62566,11 @@
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@@ -62662,30 +62579,6 @@
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@@ -62704,6 +62597,15 @@
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@@ -63809,11 +63711,11 @@
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+     "label": "l63.o_norm",
+     "kernel": "gemm8_sgate_add_norm",
      "args": [
       {
-       "buf": "gated"
+       "buf": "x"
       },
       {
        "buf": "attn_out"
@@ -63822,30 +63724,6 @@
        "buf": "qkv",
        "offset": 512
       },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 256
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 512
-      }
-     ]
-    },
-    {
-     "label": "l63.o_norm",
-     "kernel": "gemm8_add_norm",
-     "args": [
-      {
-       "buf": "x"
-      },
-      {
-       "buf": "gated"
-      },
       {
        "buf": "model.layers.63.self_attn.o_proj.weight"
       },
@@ -63864,6 +63742,15 @@
       {
        "i32": 6144
       },
+      {
+       "i32": 256
+      },
+      {
+       "i32": 14336
+      },
+      {
+       "i32": 512
+      },
       {
        "f32": 9.999999974752427e-07
       }
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 4eb6555..6e14574 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -709,6 +709,9 @@ def tri(tag, params, grid, src=pre, **kw):
     GEMM8_AN_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>", "inout buffer<bf16>",
                        "in buffer<f32>", "out buffer<bf16>", "out buffer<f32>", "out buffer<i32>",
                        "i32", "i32", "i32", "f32"]
+    GEMM8_SG_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>",
+                       "inout buffer<bf16>", "in buffer<f32>", "out buffer<bf16>", "out buffer<f32>",
+                       "out buffer<i32>", "i32", "i32", "i32", "i32", "i32", "i32", "f32"]
     GEMMA_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "in buffer<f32>",
                     "i32", "i32", "i32", "i32", "i32", "i32", "i32", "f32"]
     GEMMA_FUSED_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "inout buffer<bf16>",
@@ -782,6 +785,21 @@ def layer_norm_kernel(src, grid, rows_shape):
                               "in buffer<bf16>", "in buffer<bf16>", "i32", "i32", "i32", "i32"],
                              [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
                              shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
+        # o_proj + the sigmoid gate on its input + residual add + post-attention
+        # norm: x = attn * sigmoid(gate) is built inside the GEMM
+        "gemm8_sgate_add_norm": {
+            "params": GEMM8_SG_PARAMS[:6] + GEMM8_SG_PARAMS[9:],
+            "impl": {
+                "scratch": {"y": {"dtype": "bf16", "shape": [8, HIDDEN]},
+                            "partial": {"dtype": "f32", "shape": [8, 8, HIDDEN]},
+                            "gcnt": {"dtype": "i32", "shape": [2]}},
+                "steps": [step("kern_gemm8_sgate_add_norm_bf16", GEMM8_SG_PARAMS,
+                               [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
+                               [a(0), a(1), a(2), a(3), a(4), a(5), scr("y"), scr("partial"), scr("gcnt"),
+                                a(6), a(7), a(8), a(9), a(10), a(11), a(12)],
+                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin")],
+            },
+        },
         "gemm8_add_norm": {
             "params": GEMM8_AN_PARAMS[:5] + GEMM8_AN_PARAMS[8:],
             "impl": {
@@ -1104,13 +1122,13 @@ def gdn_layer(i, decode, nacc=None, small=False):
         ds.append(gemm(l + "out_proj", "core_attn_out", p + "out_proj.weight", "y", T, HIDDEN, GDN_V))
         return ds
 
-    def attn_layer(i, decode):
+    def attn_layer(i, decode, small=False):
         p = f"model.layers.{i}.self_attn."
         l = f"l{i}."
         koff = ATTN_LAYERS.index(i) * LAYER_KV_BYTES
         ks, vs = buf("kv_scales"), buf("kv_scales", 4)
         kv_k, kv_v = state("kv", koff), state("kv", koff + V_BYTE_OFF)
-        return [
+        ds = [
             gemm(l + "qkv_proj", "x", p + "qkv_proj.weight", "qkv", T, QKV_DIM, HIDDEN),
             d(l + "q_norm", "gemma_norm_qhead",
               [buf("q_n"), buf("qkv"), buf(p + "q_norm.weight_p1"), i32(HEAD_DIM),
@@ -1139,6 +1157,14 @@ def attn_layer(i, decode):
                i32(QKV_DIM), i32(2 * HEAD_DIM)]),
             gemm(l + "o_proj", "gated", p + "o_proj.weight", "y", T, HIDDEN, Q_DIM),
         ]
+        if small:
+            # sigmoid gate + o_proj + residual add + post-attention norm
+            ds[-2:] = [d(l + "o_norm", "gemm8_sgate_add_norm",
+                         [buf("x"), buf("attn_out"), buf("qkv", GATE_OFF * BF16), buf(p + "o_proj.weight"),
+                          buf("residual"), buf(f"model.layers.{i}.post_attention_layernorm.weight_p1"),
+                          T, i32(HIDDEN), i32(Q_DIM), i32(HEAD_DIM), i32(QKV_DIM), i32(2 * HEAD_DIM),
+                          f32(eps)])]
+        return ds
 
     def forward(decode, taps=False, nacc=None, tail=None):
         """Target forward.  decode: recurrent GDN + split-KV attention (else
@@ -1165,15 +1191,14 @@ def forward(decode, taps=False, nacc=None, tail=None):
         for i in range(LAYERS):
             p = f"model.layers.{i}."
             l = f"l{i}."
-            lay = attn_layer(i, decode) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc, small)
-            if small:
-                # o_proj / out_proj GEMM + residual add + post-attention norm
-                attn = i in ATTN_LAYERS
-                lay[-1] = d(l + ("o_norm" if attn else "out_norm"), "gemm8_add_norm",
-                            [buf("x"), buf("gated" if attn else "core_attn_out"),
-                             buf(p + ("self_attn.o_proj.weight" if attn else "linear_attn.out_proj.weight")),
+            lay = attn_layer(i, decode, small) if i in ATTN_LAYERS else gdn_layer(i, decode, nacc, small)
+            if small and i not in ATTN_LAYERS:
+                # out_proj GEMM + residual add + post-attention norm (the
+                # attention layers fold their sigmoid gate in too: attn_layer)
+                lay[-1] = d(l + "out_norm", "gemm8_add_norm",
+                            [buf("x"), buf("core_attn_out"), buf(p + "linear_attn.out_proj.weight"),
                              buf("residual"), buf(p + "post_attention_layernorm.weight_p1"),
-                             T, i32(HIDDEN), i32(Q_DIM if attn else GDN_V), f32(eps)])
+                             T, i32(HIDDEN), i32(GDN_V), f32(eps)])
             ds += lay
             if small:
                 mlp = [d(l + "gate_up_silu", "gemm8_gateup_silu",
diff --git a/tools/kernels-src/gemm8.cu b/tools/kernels-src/gemm8.cu
index 0ce6d33..3fd18c8 100644
--- a/tools/kernels-src/gemm8.cu
+++ b/tools/kernels-src/gemm8.cu
@@ -31,6 +31,11 @@
 //                                 arithmetic: z = f32(bf16(y)) + f32(res),
 //                                 res = bf16(z), ATen-order mean of z^2,
 //                                 out = bf16((z * rsqrt(var + eps)) * w1))
+//   kern_gemm8_sgate_add_norm_bf16  the add_norm chain with x computed on
+//                                 the fly as attn * sigmoid(gate) (the
+//                                 sigmoid_mul.cu chain: g = bf16(sigmoid
+//                                 f32), x = bf16(f32(attn) * g)) -- o_proj
+//                                 of the gated-attention layers
 //
 //   nvcc -cubin -arch=sm_103a -o kernels-qwen38/gemm8.cubin tools/kernels-src/gemm8.cu
 #include <cuda_bf16.h>
@@ -102,7 +107,7 @@ struct Smem {
 };
 constexpr int SMEM_BYTES = sizeof(Smem) + 128;
 
-enum Mode { PLAIN = 0, GATEUP = 1, DUAL = 2, NORM = 3 };
+enum Mode { PLAIN = 0, GATEUP = 1, DUAL = 2, NORM = 3, SGATE = 4 };
 
 struct Args {
   const __nv_bfloat16* x;     // [M, ldx]
@@ -113,6 +118,8 @@ struct Args {
   float* partial;             // [nranges, MMAX, N] f32 scratch, or nullptr (GATEUP / DUAL: never split)
   int* gcnt;                  // [2] i32 scratch: finished tasks / finished rows, zero at rest
   int M, N, N1, K, ldx;
+  const __nv_bfloat16* gate;  // SGATE: strided gate view; x is then attn [M, ldx]
+  int head_dim, gate_ts, gate_hs;
 };
 
 // K ranges: as many as x capacity demands, and at least enough tasks to
@@ -194,7 +201,7 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
         const int split = t / nblk, blk = t - split * nblk;      // range-major
         const int r0 = blk * R, rows = min(R, nrows - r0);
         const int k0 = split * kseg, klen = min(kseg, a.K - k0);
-        if (split != cur_split) {  // stage this range of x (consumers have released the old one)
+        if (MODE != SGATE && split != cur_split) {  // stage this range of x (consumers have released the old one)
           mbar_wait(smem_u32(&s.xempty), xphase);
           mbar_expect_tx(smem_u32(&s.xfull), a.M * klen * 2);
           for (int m = 0; m < a.M; m++)
@@ -225,9 +232,35 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
     const int r0 = blk * R, rows = min(R, nrows - r0);
     const int klen = min(kseg, a.K - split * kseg);
     if (split != cur_split) {  // release the old x range, wait for the new one
-      if (lane == 0) mbar_arrive(smem_u32(&s.xempty));
-      mbar_wait(smem_u32(&s.xfull), xphase);
-      xphase ^= 1;
+      if (MODE == SGATE) {  // the compute warps make this range of x themselves
+        const int k0 = split * kseg, total = a.M * klen;
+        named_bar_sync(BAR_C, CTHREADS);  // everyone is done reading the old range
+        // 8 consecutive elements per thread: klen, head_dim and the batch
+        // stride are all multiples of 8, so a batch never crosses a token row
+        // or a head boundary and both loads and the smem store are one 16 B
+        // vector each (elementwise scalar code here is latency-bound)
+        for (int i0 = ct * 8; i0 < total; i0 += CTHREADS * 8) {
+          const int m = i0 / klen, kk = i0 - m * klen, k = k0 + kk;
+          const int h = k / a.head_dim, hd = k - h * a.head_dim;
+          const uint4 gq = *reinterpret_cast<const uint4*>(a.gate + (size_t)m * a.gate_ts + (size_t)h * a.gate_hs + hd);
+          const uint4 aq = *reinterpret_cast<const uint4*>(a.x + (size_t)m * a.ldx + k);
+          const __nv_bfloat16* gv = reinterpret_cast<const __nv_bfloat16*>(&gq);
+          const __nv_bfloat16* av = reinterpret_cast<const __nv_bfloat16*>(&aq);
+          uint4 oq;
+          __nv_bfloat16* ov = reinterpret_cast<__nv_bfloat16*>(&oq);
+#pragma unroll
+          for (int j = 0; j < 8; j++) {
+            const float g = bf16r(1.0f / (1.0f + expf(-__bfloat162float(gv[j]))));
+            ov[j] = __float2bfloat16_rn(__bfloat162float(av[j]) * g);
+          }
+          *reinterpret_cast<uint4*>(&s.x[m * xrow + kk]) = oq;
+        }
+        named_bar_sync(BAR_C, CTHREADS);
+      } else {
+        if (lane == 0) mbar_arrive(smem_u32(&s.xempty));
+        mbar_wait(smem_u32(&s.xfull), xphase);
+        xphase ^= 1;
+      }
       cur_split = split;
     }
     // ldmatrix.x4 row addresses: lane l supplies row l%8 of the 8x8 matrix
@@ -377,7 +410,7 @@ __device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const floa
   for (int m = m0; m < m1; m++) {
     for (int j = threadIdx.x * 4; j < N; j += NTHREADS * 4) {
       float4 v = ysum4(a, nranges, m, j);
-      if (MODE == NORM) {
+      if (MODE >= NORM) {
         __nv_bfloat16* rr = res + (size_t)m * N + j;
         v.x = bf16r(v.x); v.y = bf16r(v.y); v.z = bf16r(v.z); v.w = bf16r(v.w);   // the bf16 y rounding
         const float2 r0 = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(rr));
@@ -393,7 +426,7 @@ __device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const floa
         *reinterpret_cast<__nv_bfloat162*>(yy + 2) = __floats2bfloat162_rn(v.z, v.w);
       }
     }
-    if (MODE != NORM) continue;
+    if (MODE < NORM) continue;
     __syncthreads();
     const int W = aten_block_width(N / 4, a.M);
     const float sum = aten_row_sumsq(z, N, W, s);
@@ -420,7 +453,7 @@ __device__ __forceinline__ void run_tail(const Args& a, Smem& s, bool last, __nv
   const int nrows = MODE == GATEUP ? 2 * a.N : a.N;
   const int nblk = (nrows + R - 1) / R;
   const int nranges = pick_nranges(a, nblk);
-  if (MODE != NORM && nranges == 1) return;   // nothing left to do
+  if (MODE == PLAIN && nranges == 1) return;  // nothing left to do
   static_assert(sizeof(s.ring) >= 6144 * 4, "z row: N <= 6144");
   float* z = reinterpret_cast<float*>(&s.ring[0]);
   if (gridDim.x <= nsmid()) {
@@ -473,6 +506,18 @@ kern_gemm8_dual_bf16(__nv_bfloat16* __restrict__ y1, __nv_bfloat16* __restrict__
   gemm8_core<DUAL>(a, s);
 }
 
+extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
+kern_gemm8_sgate_add_norm_bf16(__nv_bfloat16* __restrict__ out, const __nv_bfloat16* __restrict__ attn,
+                               const __nv_bfloat16* __restrict__ gate, const __nv_bfloat16* __restrict__ W,
+                               __nv_bfloat16* __restrict__ res, const float* __restrict__ w1, __nv_bfloat16* __restrict__ y,
+                               float* __restrict__ partial, int* __restrict__ gcnt, int M, int N, int K, int head_dim,
+                               int gate_tstride, int gate_hstride, float eps) {
+  GEMM8_SMEM;
+  Args a{attn, W, nullptr, y, nullptr, partial, gcnt, M, N, 0, K, K, gate, head_dim, gate_tstride, gate_hstride};
+  const bool last = gemm8_core<SGATE>(a, s);
+  run_tail<SGATE>(a, s, last, res, w1, out, eps);
+}
+
 extern "C" __global__ void __launch_bounds__(NTHREADS, 1)
 kern_gemm8_add_norm_bf16(__nv_bfloat16* __restrict__ out, const __nv_bfloat16* __restrict__ x, const __nv_bfloat16* __restrict__ W,
                          __nv_bfloat16* __restrict__ res, const float* __restrict__ w1, __nv_bfloat16* __restrict__ y,

From bfd82af1f667013a0992fcf23be529ef49850ba6 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 18:20:13 +0000
Subject: [PATCH 06/10] qwen38 dflash2: fuse the spec-decode GDN chain into one
 kernel

The speculative-path GDN chain of every linear-attention layer was seven
dispatches (conv_update_spec + post_conv + a/b copies + recurrent_spec +
z copy + gated_norm); kern_gdn_spec_bf16 (tools/kernels-src/gdn_spec.cu)
does the whole chain in one launch across the decode, decode_spec and
verify programs of the DFlash2 manifest (the plain manifest is
untouched), reproducing every buffer write of the chain: the in-place
qkvz conv writeback, the token-major conv state roll from the
accepted-token offset, the per-token SSM checkpoints to spec_slots, the
double q/k l2norm the pinned pair actually computes (post_conv
sqrt.approx+rcp.approx form to bf16, then the recurrent rsqrt form on
the reloaded values), both softplus flavors of the gating, and the
gated RMSNorm.  Division inside the Triton kernels is rcp.approx + mul,
not IEEE division; the fused kernel replicates those instructions.

kern-attest (A = previous commit, B = this), dflash2 decode program:

    step, eager        13.423 ms -> 11.998 ms   (-1424 us, -10.6%)
    step, graph (TPOT) 10.814 ms -> 10.516 ms   (-299 us, -2.8%, 92 -> 95 tok/s)
    sum of 48 cuts      2.240 ms -> 710.4 us    (-68.3%)

next_token, draft_tokens and verify_tokens are bit-identical; the
layer-0 cut is bit-identical on every buffer, later cuts sit inside A's
own replay band (the cuts mutate the GDN state and cannot be replayed
idempotently; the noise floor shows the same lockstep pattern on A vs
A).  The verdict is INCONCLUSIVE only because the driver cannot stage
decode_spec/verify; those run the same fused cut, and
tools/qwen38_compare.py --spec covers them end to end: against vLLM the
divergence points move from the baseline's [66,93,62,255,178]-class
near-ties to [66,139,147,91,91], and spec-vs-plain (kern as its own
oracle) matches [89,93,62,91,91] with two prompts token-for-token
identical to the previous commit's spec path.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 59822 ++++++++--------------------
 tools/gen_qwen35.py               |    86 +-
 tools/kernels-src/gdn_spec.cu     |   369 +
 3 files changed, 16139 insertions(+), 44138 deletions(-)
 create mode 100644 tools/kernels-src/gdn_spec.cu

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index 555e3a5..d45411d 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -8784,49 +8784,111 @@
     ]
    }
   },
-  "conv_update_spec": {
+  "gdn_spec": {
    "params": [
-    "in buffer<bf16>",
+    "inout buffer<bf16>",
     "in buffer<bf16>",
     "inout ptr",
+    "inout ptr",
+    "in buffer<i32>",
     "in buffer<i32>",
     "in buffer<i32>",
     "in buffer<i32>",
+    "in buffer<bf16>",
+    "in buffer<f32>",
+    "in buffer<bf16>",
     "out buffer<bf16>",
+    "in buffer<bf16>",
+    "out buffer<bf16>",
+    "out buffer<bf16>",
+    "out buffer<bf16>",
+    "out buffer<f32>",
+    "out buffer<f32>",
+    "out buffer<bf16>",
+    "out buffer<bf16>",
+    "out buffer<bf16>",
+    "f32",
+    "f32",
     "i32",
     "i32",
-    "i64",
-    "i64",
-    "i64",
-    "i64"
+    "i32",
+    "i32",
+    "i32"
    ],
    "impl": {
+    "scratch": {
+     "xcnt": {
+      "dtype": "i32",
+      "shape": [
+       16
+      ]
+     },
+     "hpart": {
+      "dtype": "f32",
+      "shape": [
+       8,
+       48,
+       4
+      ]
+     },
+     "hcnt": {
+      "dtype": "i32",
+      "shape": [
+       48
+      ]
+     },
+     "gcnt": {
+      "dtype": "i32",
+      "shape": [
+       1
+      ]
+     }
+    },
     "steps": [
      {
-      "symbol": "_causal_conv1d_update_kernel",
+      "symbol": "kern_gdn_spec_bf16",
       "params": [
+       "inout buffer<bf16>",
        "in buffer<bf16>",
-       "in buffer<bf16>",
+       "inout ptr",
        "inout ptr",
        "in buffer<i32>",
        "in buffer<i32>",
        "in buffer<i32>",
+       "in buffer<i32>",
+       "in buffer<bf16>",
+       "in buffer<f32>",
+       "in buffer<bf16>",
+       "out buffer<bf16>",
+       "in buffer<bf16>",
+       "out buffer<bf16>",
+       "out buffer<bf16>",
+       "out buffer<bf16>",
+       "out buffer<f32>",
+       "out buffer<f32>",
        "out buffer<bf16>",
+       "out buffer<bf16>",
+       "out buffer<bf16>",
+       "out buffer<i32>",
+       "out buffer<f32>",
+       "out buffer<i32>",
+       "out buffer<i32>",
+       "f32",
+       "f32",
        "i32",
        "i32",
-       "i64",
-       "i64",
-       "i64",
-       "i64"
+       "i32",
+       "i32",
+       "i32"
       ],
       "block": [
-       128,
+       256,
        1,
        1
       ],
       "grid": [
+       192,
        1,
-       40,
        1
       ],
       "args": [
@@ -8868,138 +8930,66 @@
        },
        {
         "arg": 12
-       }
-      ],
-      "cubin": "159465ea16be.cubin",
-      "sha256": "159465ea16be78a5440e457e32159ace2a074114488122dc35be1b30962006b1"
-     }
-    ]
-   }
-  },
-  "recurrent_spec": {
-   "params": [
-    "in buffer<f32>",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "f32",
-    "f32",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "out buffer<bf16>",
-    "inout ptr",
-    "inout ptr",
-    "in buffer<i32>",
-    "in buffer<i32>",
-    "in buffer<i32>",
-    "f32",
-    "i64",
-    "i64",
-    "i64",
-    "i64"
-   ],
-   "impl": {
-    "steps": [
-     {
-      "symbol": "fused_sigmoid_gating_delta_rule_update_kernel",
-      "params": [
-       "in buffer<f32>",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "f32",
-       "f32",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "out buffer<bf16>",
-       "inout ptr",
-       "inout ptr",
-       "in buffer<i32>",
-       "in buffer<i32>",
-       "in buffer<i32>",
-       "f32",
-       "i64",
-       "i64",
-       "i64",
-       "i64"
-      ],
-      "block": [
-       128,
-       1,
-       1
-      ],
-      "grid": [
-       1,
-       4,
-       48
-      ],
-      "args": [
-       {
-        "arg": 0
        },
        {
-        "arg": 1
+        "arg": 13
        },
        {
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@@ -54051,32 +53371,23 @@
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@@ -54431,7 +53736,7 @@
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@@ -54464,7 +53769,7 @@
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@@ -54497,21 +53802,25 @@
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       },
       {
-       "f32": 0.0883883461356163
+       "buf": "model.layers.61.linear_attn.A_log"
       },
       {
-       "i64": 1
+       "buf": "model.layers.61.linear_attn.dt_bias"
       },
       {
-       "sym": "tokens"
+       "buf": "core_attn_out"
       },
       {
-       "i64": 0
+       "buf": "model.layers.61.linear_attn.norm.weight"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l61.z_copy",
-     "kernel": "copy_rows",
-     "args": [
-      {
-       "buf": "z_c"
+       "buf": "gdn_q"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
+       "buf": "gdn_k"
       },
       {
-       "i32": 6144
+       "buf": "gdn_v"
       },
       {
-       "i32": 16384
+       "buf": "g"
       },
       {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l61.gated_norm",
-     "kernel": "gated_norm",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "beta"
       },
       {
-       "buf": "core_attn_out"
+       "buf": "a_c"
       },
       {
-       "buf": "model.layers.61.linear_attn.norm.weight"
+       "buf": "b_c"
       },
       {
        "buf": "z_c"
       },
       {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 128
+       "sym": "tokens"
       },
       {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         48
-        ]
-       }
+       "i32": 385
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1703936
       },
       {
-       "i64": 0
+       "i32": 10
       },
       {
-       "i64": 0
+       "i32": 851968
       }
      ]
     },
@@ -125825,8 +97644,8 @@
      ]
     },
     {
-     "label": "l62.conv_update",
-     "kernel": "conv_update_spec",
+     "label": "l62.gdn",
+     "kernel": "gdn_spec",
      "args": [
       {
        "buf": "qkvz"
@@ -125837,6 +97656,10 @@
       {
        "state": "gdn"
       },
+      {
+       "state": "gdn",
+       "offset": 204800
+      },
       {
        "buf": "gdn.spec_line_index",
        "offset": 192
@@ -125848,47 +97671,23 @@
        "buf": "cu_seqlens_q"
       },
       {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 385
-      },
-      {
-       "i64": 16384
-      },
-      {
-       "i64": 16384
-      },
-      {
-       "i64": 0
+       "buf": "gdn.spec_slots",
+       "offset": 1504
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l62.post_conv",
-     "kernel": "post_conv",
-     "args": [
-      {
-       "buf": "qkvz"
+       "buf": "ba"
       },
       {
-       "buf": "ba",
-       "offset": 96
+       "buf": "model.layers.62.linear_attn.A_log"
       },
       {
-       "buf": "ba"
+       "buf": "model.layers.62.linear_attn.dt_bias"
       },
       {
-       "buf": "model.layers.62.linear_attn.A_log"
+       "buf": "core_attn_out"
       },
       {
-       "buf": "model.layers.62.linear_attn.dt_bias"
+       "buf": "model.layers.62.linear_attn.norm.weight"
       },
       {
        "buf": "gdn_q"
@@ -125905,85 +97704,6 @@
       {
        "buf": "beta"
       },
-      {
-       "i32": 16384
-      },
-      {
-       "i32": 96
-      },
-      {
-       "i32": 96
-      },
-      {
-       "i32": 2048
-      },
-      {
-       "i32": 2048
-      },
-      {
-       "i32": 6144
-      },
-      {
-       "sym": "tokens"
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l62.a_copy",
-     "kernel": "copy_rows",
-     "args": [
-      {
-       "buf": "a_c"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "i32": 48
-      },
-      {
-       "i32": 96
-      },
-      {
-       "i32": 48
-      }
-     ]
-    },
-    {
-     "label": "l62.b_copy",
-     "kernel": "copy_rows",
-     "args": [
-      {
-       "buf": "b_c"
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "i32": 48
-      },
-      {
-       "i32": 96
-      },
-      {
-       "i32": 48
-      }
-     ]
-    },
-    {
-     "label": "l62.recurrent",
-     "kernel": "recurrent_spec",
-     "args": [
-      {
-       "buf": "model.layers.62.linear_attn.A_log"
-      },
       {
        "buf": "a_c"
       },
@@ -125991,126 +97711,28 @@
        "buf": "b_c"
       },
       {
-       "buf": "model.layers.62.linear_attn.dt_bias"
-      },
-      {
-       "f32": 1.0
-      },
-      {
-       "f32": 20.0
-      },
-      {
-       "buf": "gdn_q"
-      },
-      {
-       "buf": "gdn_k"
-      },
-      {
-       "buf": "gdn_v"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 204800
-      },
-      {
-       "state": "gdn",
-       "offset": 204800
-      },
-      {
-       "buf": "cu_seqlens_q"
-      },
-      {
-       "buf": "gdn.spec_slots",
-       "offset": 1504
-      },
-      {
-       "buf": "num_accepted_tokens"
+       "buf": "z_c"
       },
       {
        "f32": 0.0883883461356163
       },
       {
-       "i64": 1
+       "f32": 9.999999974752427e-07
       },
       {
        "sym": "tokens"
       },
       {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l62.z_copy",
-     "kernel": "copy_rows",
-     "args": [
-      {
-       "buf": "z_c"
-      },
-      {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 6144
-      },
-      {
-       "i32": 16384
-      },
-      {
-       "i32": 6144
-      }
-     ]
-    },
-    {
-     "label": "l62.gated_norm",
-     "kernel": "gated_norm",
-     "args": [
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "buf": "model.layers.62.linear_attn.norm.weight"
-      },
-      {
-       "buf": "z_c"
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         48
-        ]
-       }
+       "i32": 385
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1703936
       },
       {
-       "i64": 0
+       "i32": 10
       },
       {
-       "i64": 0
+       "i32": 851968
       }
      ]
     },
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 6e14574..1c870ef 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -932,6 +932,23 @@ def layer_norm_kernel(src, grid, rows_shape):
     Z2 = [i64(0), i64(0)]
 
     if spec:
+        # the whole spec-path GDN chain of a layer in one launch
+        # (tools/kernels-src/gdn_spec.cu): conv update + post_conv split/
+        # l2norm/gating + a/b/z copies + T-row delta rule with per-token
+        # SSM checkpoints + gated RMSNorm.  Counter scratch is zero at
+        # rest (the last CTA resets); hpart holds data, write-before-read.
+        GDN_SPEC_PARAMS = [
+            "inout buffer<bf16>",                                    # qkvz (conv writeback)
+            "in buffer<bf16>", "inout ptr", "inout ptr",             # conv weight, conv state, ssm state
+            "in buffer<i32>", "in buffer<i32>", "in buffer<i32>",    # line, num_accepted, cu_seqlens_q
+            "in buffer<i32>",                                        # spec_slots row
+            "in buffer<bf16>", "in buffer<f32>", "in buffer<bf16>",  # ba, A_log, dt_bias
+            "out buffer<bf16>", "in buffer<bf16>",                   # core_attn_out, norm.weight
+            "out buffer<bf16>", "out buffer<bf16>", "out buffer<bf16>",  # gdn_q/k/v
+            "out buffer<f32>", "out buffer<f32>",                    # g, beta
+            "out buffer<bf16>", "out buffer<bf16>", "out buffer<bf16>",  # a_c, b_c, z_c
+            "out buffer<i32>", "out buffer<f32>", "out buffer<i32>", "out buffer<i32>",  # scratch
+            "f32", "f32", "i32", "i32", "i32", "i32", "i32"]         # scale, eps, tokens, n_pages, cls, cds, sls
         RMS_PARAMS = ["out buffer<bf16>", "in buffer<bf16>", "i64", "i64", "i64", "i64", "i64",
                       "in buffer<bf16>", "i64", "f32", "i32", "i32"]
         ad = spec["attn_draft"]
@@ -939,24 +956,25 @@ def layer_norm_kernel(src, grid, rows_shape):
             "gemm_acc": single("extern:cublaslt_bf16_tn_acc",
                                ["in buffer<bf16>", "in buffer<bf16>", "inout buffer<bf16>", "i32", "i32", "i32"],
                                [1, 1, 1], [1, 1, 1]),
-            # --- target GDN under speculation (vLLM's spec-decode kernels)
-            # conv update: seqlen 8 / state_len 10 are constexprs; reads the
-            # history taps at conv-line offset num_accepted-1, writes the
-            # new 10-wide window (in place on the qkvz rows)
-            "conv_update_spec": spec_kernel("conv_update_spec",
-                                            ["in buffer<bf16>", "in buffer<bf16>", "inout ptr", "in buffer<i32>",
-                                             "in buffer<i32>", "in buffer<i32>", "out buffer<bf16>",
-                                             "i32", "i32", "i64", "i64"] + I2,
-                                            [1, CONV_DIM // 256, 1]),
+            # --- target GDN under speculation: the whole chain is fused
+            # into gdn_spec below (kern_gdn_spec_bf16)
+            "gdn_spec": {
+                "params": GDN_SPEC_PARAMS[:21] + GDN_SPEC_PARAMS[25:],
+                "impl": {
+                    "scratch": {"xcnt": {"dtype": "i32", "shape": [GDN_Q // GDN_D]},
+                                "hpart": {"dtype": "f32", "shape": [SPEC_BLOCK, GDN_V_HEADS, 4]},
+                                "hcnt": {"dtype": "i32", "shape": [GDN_V_HEADS]},
+                                "gcnt": {"dtype": "i32", "shape": [1]}},
+                    "steps": [step("kern_gdn_spec_bf16", GDN_SPEC_PARAMS, [256, 1, 1], [192, 1, 1],
+                                   [a(i) for i in range(21)]
+                                   + [scr("xcnt"), scr("hpart"), scr("hcnt"), scr("gcnt")]
+                                   + [a(i) for i in range(21, 28)],
+                                   cubin="gdn_spec.cubin")],
+                },
+            },
             # recurrent delta rule over T rows with the sigmoid gating fused:
             # initial state from SSM slot num_accepted-1, every row's state
             # checkpointed to its own slot (ssm_state_indices)
-            "recurrent_spec": spec_kernel("recurrent_spec",
-                                          ["in buffer<f32>", "in buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>",
-                                           "f32", "f32", "in buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>",
-                                           "out buffer<bf16>", "inout ptr", "inout ptr", "in buffer<i32>",
-                                           "in buffer<i32>", "in buffer<i32>", "f32", "i64", "i64"] + I2,
-                                          [1, GDN_D // 32, GDN_V_HEADS]),
             "argmax": {
                 "params": ["in buffer<bf16>", "out buffer<i64>", "i32"],
                 "impl": {
@@ -1094,30 +1112,22 @@ def gdn_layer(i, decode, nacc=None, small=False):
                    f32(eps)] + Z2),
             ]
         else:
-            # vLLM's spec-decode GDN path: conv update with the accepted-token
-            # offset (seqlen 8 baked: it reads/writes 8 qkvz rows, rows past
-            # `tokens` are scratch), post_conv split, contiguous a/b, the
-            # recurrent kernel over T rows checkpointing every row's state.
+            # vLLM's spec-decode GDN path, fused into one launch (the chain
+            # was: conv update with the accepted-token offset, post_conv
+            # split + l2norm + gating, contiguous a/b copies, the recurrent
+            # kernel over T rows checkpointing every row's state, z copy,
+            # gated RMSNorm).  The fused kernel reproduces every buffer
+            # write of that chain, including the qkvz conv writeback and
+            # the per-token SSM checkpoints.
             ds += [
-                d(l + "conv_update", "conv_update_spec",
-                  [buf("qkvz"), buf(p + "conv1d.weight"), state("gdn"), idx, nacc, buf("cu_seqlens_q"),
-                   buf("qkvz"), i32(1), i32(n_pages), i64(QKVZ_DIM), i64(QKVZ_DIM)] + Z2),
-                d(l + "post_conv", "post_conv",
-                  [buf("qkvz"), a_, b_, buf(p + "A_log"), buf(p + "dt_bias"),
-                   buf("gdn_q"), buf("gdn_k"), buf("gdn_v"), buf("g"), buf("beta"),
-                   i32(QKVZ_DIM), i32(BA_DIM), i32(BA_DIM), i32(GDN_Q), i32(GDN_Q), i32(GDN_V), T] + Z2),
-                d(l + "a_copy", "copy_rows", [buf("a_c"), a_, i32(GDN_V_HEADS), i32(BA_DIM), i32(GDN_V_HEADS)]),
-                d(l + "b_copy", "copy_rows", [buf("b_c"), b_, i32(GDN_V_HEADS), i32(BA_DIM), i32(GDN_V_HEADS)]),
-                d(l + "recurrent", "recurrent_spec",
-                  [buf(p + "A_log"), buf("a_c"), buf("b_c"), buf(p + "dt_bias"), f32(1.0), f32(20.0),
-                   buf("gdn_q"), buf("gdn_k"), buf("gdn_v"), buf("core_attn_out"),
-                   state("gdn", ssm_off), state("gdn", ssm_off), buf("cu_seqlens_q"),
-                   buf("gdn.spec_slots", 4 * SPEC_BLOCK * g), nacc, f32(gdn_scale), i64(1), T] + Z2),
-                d(l + "z_copy", "copy_rows",
-                  [buf("z_c"), buf("qkvz", Z_OFF * BF16), i32(GDN_V), i32(QKVZ_DIM), i32(GDN_V)]),
-                d(l + "gated_norm", "gated_norm",
-                  [buf("core_attn_out"), buf("core_attn_out"), buf(p + "norm.weight"), buf("z_c"),
-                   i32(GDN_D), i32(GDN_D), i32(GDN_D), expr(mul(T, GDN_V_HEADS)), f32(eps)] + Z2),
+                d(l + "gdn", "gdn_spec",
+                  [buf("qkvz"), buf(p + "conv1d.weight"), state("gdn"), state("gdn", ssm_off),
+                   idx, nacc, buf("cu_seqlens_q"), buf("gdn.spec_slots", 4 * SPEC_BLOCK * g),
+                   buf("ba"), buf(p + "A_log"), buf(p + "dt_bias"), buf("core_attn_out"),
+                   buf(p + "norm.weight"), buf("gdn_q"), buf("gdn_k"), buf("gdn_v"),
+                   buf("g"), buf("beta"), buf("a_c"), buf("b_c"), buf("z_c"),
+                   f32(gdn_scale), f32(eps), T, i32(n_pages),
+                   i32(page_bytes // BF16), i32(ssm_off // (CONV_DIM * BF16)), i32(page_bytes // 4)]),
             ]
         ds.append(gemm(l + "out_proj", "core_attn_out", p + "out_proj.weight", "y", T, HIDDEN, GDN_V))
         return ds
diff --git a/tools/kernels-src/gdn_spec.cu b/tools/kernels-src/gdn_spec.cu
new file mode 100644
index 0000000..9857390
--- /dev/null
+++ b/tools/kernels-src/gdn_spec.cu
@@ -0,0 +1,369 @@
+// The speculative-path GDN chain of one layer in one launch (tokens <= 8):
+//
+//   causal_conv1d_update      (IS_SPEC_DECODING,    _causal_conv1d_update_kernel
+//                              width 4, silu, state
+//                              roll from slot nacc-1)
+//   fused post-conv prep      (split + l2norm +     _fused_post_conv_kernel
+//                              gating)
+//   a/b contiguous copies                           kern_copy_rows_bf16 x2
+//   gated delta rule, T rows  (l2norm again, per-   fused_sigmoid_gating_delta_rule_update_kernel
+//                              token SSM checkpoint
+//                              to spec_slots[t])
+//   z copy                                          kern_copy_rows_bf16
+//   gated RMSNorm             (norm, then *silu(z)) layer_norm_fwd_kernel
+//
+// The pipeline the manifest pins normalizes q/k twice: the post_conv
+// instance is the prefill one (APPLY_L2NORM, 1/sqrt form) and the
+// recurrent kernel was captured with USE_QK_L2NORM_IN_KERNEL (rsqrt
+// form); the fused kernel reproduces both, feeding the bf16-rounded
+// first-pass values into the second, like the buffers did.
+//
+// Four CTAs per v head (192 total, co-resident) stream 32 state rows
+// each and redundantly convolve + normalize the 256 q/k channels of
+// their k head from registers/smem -- no producer/consumer chain.  The
+// only synchronization: a per-k-head staging counter before the conv
+// writeback may overwrite qkvz (twelve sibling CTAs read those raw
+// columns), and the fixed-order variance merge of the gated norm.
+//
+// Rounding chains reproduce the Triton kernels bit-for-bit where the
+// order is defined (bf16 x*w conv products rounded before the f32
+// accumulate; silu -> bf16 store; the post_conv l2norm divides by
+// tl.sqrt and rounds to bf16; the recurrent reloads that value,
+// multiplies by tl.rsqrt of its own sum, then by scale; its softplus is
+// the one-sided log(1+exp) form while the g/beta buffers keep the
+// two-sided post_conv form; state updates go through fmaf like the
+// Triton fma; the norm input is the bf16-rounded recurrent output).
+// Only the sum orders of the wide reductions differ.
+//
+// Buffers past the cut carry every write of the chain: qkvz conv
+// writeback, the conv state roll, the per-token SSM checkpoints,
+// gdn_q/k/v, g/beta, a_c/b_c/z_c and the normed core_attn_out.
+//
+// All counter scratch is zero at rest: alloc_zeros at load, the last
+// CTA resets (hpart holds data, write-before-read behind the counters).
+//
+//   nvcc -cubin -arch=sm_103a -o kernels-qwen38-dflash2/gdn_spec.cubin tools/kernels-src/gdn_spec.cu
+#include <cuda_bf16.h>
+#include <cstdint>
+
+namespace {
+constexpr int HV = 48, K = 128, V = 128;
+constexpr int CONV_DIM = 10240;             // q | k | v channels
+constexpr int Z_OFF = CONV_DIM;             // z columns after q|k|v
+constexpr int QKVZ = 16384, BA = 96;
+constexpr int KOFF = 2048, VOFF = 4096;
+constexpr int NT = 256;
+constexpr int SPLIT = 4, ROWS = V / SPLIT;  // CTAs per v head, state rows per CTA
+constexpr int NB = SPLIT * HV;
+constexpr int TMAX = 8;                     // SPEC_BLOCK
+constexpr float L2EPS = 1e-6f;
+
+__device__ __forceinline__ int ld_acquire(const int* p) {
+  int v;
+  asm volatile("ld.acquire.gpu.global.s32 %0, [%1];" : "=r"(v) : "l"(p) : "memory");
+  return v;
+}
+__device__ __forceinline__ float bf16r(float v) { return __bfloat162float(__float2bfloat16_rn(v)); }
+// Triton lowers float division to rcp.approx + mul and tl.sqrt to
+// sqrt.approx (see the pinned cubins' SASS); replicate the instructions.
+__device__ __forceinline__ float rcpa(float x) {
+  float y;
+  asm("rcp.approx.f32 %0, %1;" : "=f"(y) : "f"(x));
+  return y;
+}
+__device__ __forceinline__ float sqrta(float x) {
+  float y;
+  asm("sqrt.approx.f32 %0, %1;" : "=f"(y) : "f"(x));
+  return y;
+}
+__device__ __forceinline__ float sigmoidf_(float x) { return rcpa(1.0f + expf(-x)); }
+__device__ __forceinline__ float ldcg(const float* p) { return __ldcg(p); }
+
+struct SmemT {
+  __nv_bfloat16 cq[TMAX][K], ck[TMAX][K];   // conv outputs (raw x rows past T)
+  __nv_bfloat16 nq[TMAX][K], nk[TMAX][K];   // post_conv l2norm'd values
+  __nv_bfloat16 cv[TMAX][ROWS];             // v conv outputs (this CTA's rows)
+  __nv_bfloat16 oarr[TMAX][ROWS];           // recurrent output rows
+  float s1q[TMAX], s1k[TMAX];               // post_conv sums of squares
+  float s2q[TMAX], s2k[TMAX];               // recurrent sums of the rounded values
+  float red[TMAX][8];                       // warp partials for the sums
+  float gg[TMAX], bb[TMAX];                 // exp(g) and beta, recurrent form
+};
+}  // namespace
+
+// scratch: xcnt i32 [16] per-k-head staging counters, hpart f32
+// [TMAX*HV*SPLIT] variance partials, hcnt i32 [HV] arrive counters,
+// gcnt i32 [1] exit counter for the reset.
+extern "C" __global__ void __launch_bounds__(NT) kern_gdn_spec_bf16(
+    __nv_bfloat16* __restrict__ qkvz,          // [tokens, 16384] inout
+    const __nv_bfloat16* __restrict__ w,       // [10240, 4]
+    __nv_bfloat16* __restrict__ conv_state,    // state pool, conv area
+    float* __restrict__ ssm_state,             // state pool, ssm area
+    const int* __restrict__ line_p,            // conv line page id
+    const int* __restrict__ nacc_p,            // num accepted (resume slot + 1)
+    const int* __restrict__ cu,                // cu_seqlens_q [2]
+    const int* __restrict__ slots,             // 8 checkpoint page ids
+    const __nv_bfloat16* __restrict__ ba,      // [tokens, 96] b | a
+    const float* __restrict__ A_log,           // [48]
+    const __nv_bfloat16* __restrict__ dt_bias, // [48]
+    __nv_bfloat16* __restrict__ out,           // core_attn_out [tokens, 6144]
+    const __nv_bfloat16* __restrict__ nw,      // norm.weight [128]
+    __nv_bfloat16* __restrict__ gdn_q,         // [tokens, 2048]
+    __nv_bfloat16* __restrict__ gdn_k,         // [tokens, 2048]
+    __nv_bfloat16* __restrict__ gdn_v,         // [tokens, 6144]
+    float* __restrict__ g_buf,                 // [tokens, 48]
+    float* __restrict__ beta_buf,              // [tokens, 48]
+    __nv_bfloat16* __restrict__ a_c,           // [tokens, 48]
+    __nv_bfloat16* __restrict__ b_c,           // [tokens, 48]
+    __nv_bfloat16* __restrict__ z_c,           // [tokens, 6144]
+    int* __restrict__ xcnt, float* __restrict__ hpart,
+    int* __restrict__ hcnt, int* __restrict__ gcnt,
+    float scale, float eps, int tokens,
+    int nlines, int cls, int cds, int sls) {
+  __shared__ SmemT s;
+  const int tid = threadIdx.x;
+  const int hv = blockIdx.x >> 2, sp = blockIdx.x & 3;
+  const int ih = hv / 3;
+  const bool qkwriter = (hv % 3) == 0;
+  const int line = line_p[0], nacc = nacc_p[0];
+  const int cu0 = cu[0];
+  const int T = cu[1] - cu0;              // conv/recurrent extent
+  const int tt = tokens;                  // post_conv/copies/norm extent
+  const bool live = line > 0;
+
+  // ---- stage: conv history + raw x for this thread's channel(s) ----
+  // threads 0..127: q channel c of head ih; 128..255: k channel c.
+  const int c = tid & 127;
+  const int qk_ch = (tid < 128 ? ih * K : KOFF + ih * K) + c;
+  const int v_ch = VOFF + hv * V + sp * ROWS + (tid < ROWS ? tid : 0);
+  float wq[4], wv[4], hq[3], hvv[3];
+  __nv_bfloat16 xq[TMAX], xv[TMAX];
+  for (int j = 0; j < 4; j++) {
+    wq[j] = __bfloat162float(w[qk_ch * 4 + j]);
+    wv[j] = __bfloat162float(w[v_ch * 4 + j]);
+  }
+  // spec-pool conv state layout is token-major: [conv_len rows][CONV_DIM]
+  const int64_t cbase_qk = (int64_t)line * cls + qk_ch;
+  const int64_t cbase_v = (int64_t)line * cls + v_ch;
+  for (int j = 0; j < 3; j++) {
+    hq[j] = live ? __bfloat162float(conv_state[cbase_qk + (int64_t)(nacc - 1 + j) * CONV_DIM]) : 0.f;
+    hvv[j] = live ? __bfloat162float(conv_state[cbase_v + (int64_t)(nacc - 1 + j) * CONV_DIM]) : 0.f;
+  }
+#pragma unroll
+  for (int t = 0; t < TMAX; t++) {
+    if (t >= tt) break;
+    xq[t] = qkvz[(int64_t)(cu0 + t) * QKVZ + qk_ch];
+    xv[t] = qkvz[(int64_t)(cu0 + t) * QKVZ + v_ch];
+  }
+  __threadfence();
+  __syncthreads();
+  if (tid == 0) atomicAdd(&xcnt[ih], 1);
+
+  // initial SSM state, prefetched early so the cold load overlaps the conv
+  const int row = tid >> 3, lane8 = tid & 7, c0 = lane8 * 16;
+  const int page0 = slots[nacc - 1];
+  const bool rec = page0 > 0;
+  const int64_t hb = (int64_t)hv * V * K + (int64_t)(sp * ROWS + row) * K + c0;
+  float bh[16];
+  if (rec) {
+    const float4* h0 = (const float4*)(ssm_state + (int64_t)page0 * sls + hb);
+#pragma unroll
+    for (int j = 0; j < 4; j++) *(float4*)&bh[j * 4] = h0[j];
+  }
+
+  // ---- conv (Triton: f32 acc of bf16-rounded products, then silu) ----
+  auto conv1 = [&](const float* h, const float* wt, const __nv_bfloat16* x, int t) {
+    float acc = 0.f;
+#pragma unroll
+    for (int j = 0; j < 4; j++) {
+      const int i = t + j;  // window over [h0 h1 h2 x0 x1 ...]
+      const float xf = i < 3 ? h[i] : __bfloat162float(x[i - 3]);
+      acc = __fadd_rn(acc, bf16r(__fmul_rn(xf, wt[j])));
+    }
+    return __float2bfloat16_rn(__fmul_rn(acc, rcpa(1.0f + expf(-acc))));
+  };
+  {
+    __nv_bfloat16* dst = tid < 128 ? &s.cq[0][0] : &s.ck[0][0];
+#pragma unroll
+    for (int t = 0; t < TMAX; t++) {
+      if (t >= tt) break;
+      dst[t * K + c] = (live && t < T) ? conv1(hq, wq, xq, t) : xq[t];
+      if (tid < ROWS) s.cv[t][tid] = (live && t < T) ? conv1(hvv, wv, xv, t) : xv[t];
+    }
+  }
+  // gating scalars for this v head (both softplus forms)
+  if (tid < tt) {
+    const int t = tid;
+    const float bv = __bfloat162float(ba[(int64_t)t * BA + hv]);
+    const float av = __bfloat162float(ba[(int64_t)t * BA + 48 + hv]);
+    const float x = av + __bfloat162float(dt_bias[hv]);
+    const float ea = expf(A_log[hv]);
+    // recurrent form: (1/beta) * log(1 + exp(beta*x)), beta = 1
+    const float spr = x <= 20.f ? 1.0f * logf(1.0f + expf(x)) : x;
+    s.gg[t] = expf(-ea * spr);
+    s.bb[t] = sigmoidf_(bv);
+    if (sp == 0) {
+      // post_conv form: two-sided stable softplus
+      float spp = x > 0.f ? x + logf(1.0f + expf(-x)) : logf(1.0f + expf(x));
+      if (x > 20.f) spp = x;
+      g_buf[(int64_t)t * HV + hv] = -ea * spp;
+      beta_buf[(int64_t)t * HV + hv] = sigmoidf_(bv);
+      a_c[(int64_t)t * HV + hv] = ba[(int64_t)t * BA + 48 + hv];
+      b_c[(int64_t)t * HV + hv] = ba[(int64_t)t * BA + hv];
+    }
+  }
+  __syncthreads();
+
+  // ---- sums of squares of the conv outputs (post_conv l2norm) ----
+  auto sumsq = [&](const __nv_bfloat16* v128, int t, int part) {
+    // 128 threads (4 warps): warp-tree then fixed-order partial merge
+    const float x = __bfloat162float(v128[c]);
+    float p = __fmul_rn(x, x);
+    for (int d = 16; d > 0; d >>= 1) p += __shfl_xor_sync(0xffffffffu, p, d);
+    if ((tid & 31) == 0) s.red[t][part * 4 + ((tid >> 5) & 3)] = p;
+  };
+  for (int t = 0; t < tt; t++) sumsq(tid < 128 ? s.cq[t] : s.ck[t], t, tid < 128 ? 0 : 1);
+  __syncthreads();
+  if (tid < 2 * tt) {
+    const int t = tid >> 1;
+    const float* r = &s.red[t][(tid & 1) * 4];
+    (tid & 1 ? s.s1k : s.s1q)[t] = (r[0] + r[1]) + (r[2] + r[3]);
+  }
+  __syncthreads();
+  {
+    __nv_bfloat16* dst = tid < 128 ? &s.nq[0][0] : &s.nk[0][0];
+    const __nv_bfloat16* src = tid < 128 ? &s.cq[0][0] : &s.ck[0][0];
+    const float* ss = tid < 128 ? s.s1q : s.s1k;
+    for (int t = 0; t < tt; t++) {
+      const float inv = rcpa(sqrta(ss[t] + L2EPS));
+      dst[t * K + c] = __float2bfloat16_rn(__bfloat162float(src[t * K + c]) * inv);
+    }
+  }
+  __syncthreads();
+  // sums of squares of the rounded values (recurrent l2norm)
+  for (int t = 0; t < tt; t++) sumsq(tid < 128 ? s.nq[t] : s.nk[t], t, tid < 128 ? 0 : 1);
+  __syncthreads();
+  if (tid < 2 * tt) {
+    const int t = tid >> 1;
+    const float* r = &s.red[t][(tid & 1) * 4];
+    (tid & 1 ? s.s2k : s.s2q)[t] = (r[0] + r[1]) + (r[2] + r[3]);
+  }
+  __syncthreads();
+
+  // ---- writebacks that only this CTA touches ----
+  for (int t = 0; t < tt; t++) {
+    if (tid < ROWS) {
+      const int col = hv * V + sp * ROWS + tid;
+      gdn_v[(int64_t)t * 6144 + col] = s.cv[t][tid];
+      z_c[(int64_t)t * 6144 + col] = qkvz[(int64_t)t * QKVZ + Z_OFF + col];
+    }
+    if (tid < 128) {
+      gdn_q[(int64_t)t * KOFF + ih * K + c] = s.nq[t][c];
+    } else {
+      gdn_k[(int64_t)t * KOFF + ih * K + c] = s.nk[t][c];
+    }
+  }
+  // conv state roll + qkvz writeback (v channels: this CTA is the only
+  // reader; q/k channels: wait for the twelve sibling CTAs to stage)
+  auto roll = [&](int64_t cbase, const float* h, const __nv_bfloat16* x) {
+    const bool ok = line < nlines;  // A masks the shifted loads by this
+    for (int i = 0; i < T + 2 && i < cds; i++) {
+      __nv_bfloat16 val;
+      if (i < 2)
+        val = ok ? __float2bfloat16_rn(h[1 + i]) : __float2bfloat16_rn(0.f);
+      else
+        val = x[i - 2];
+      conv_state[cbase + (int64_t)i * CONV_DIM] = val;
+    }
+  };
+  if (live && tid < ROWS) {
+    roll(cbase_v, hvv, xv);
+    for (int t = 0; t < T; t++) qkvz[(int64_t)(cu0 + t) * QKVZ + v_ch] = s.cv[t][tid];
+  }
+  if (live && qkwriter && (c >> 5) == sp) {
+    if (tid == (sp << 5)) while (ld_acquire(&xcnt[ih]) < 3 * SPLIT) {}
+    __syncwarp();
+    roll(cbase_qk, hq, xq);
+    for (int t = 0; t < T; t++)
+      qkvz[(int64_t)(cu0 + t) * QKVZ + qk_ch] = (tid < 128 ? s.cq : s.ck)[t][c];
+  }
+
+  // ---- gated delta rule over T rows, checkpoint each row's state ----
+  if (rec) {
+    for (int t = 0; t < T; t++) {
+      const float i2q = rsqrtf(s.s2q[t] + L2EPS), i2k = rsqrtf(s.s2k[t] + L2EPS);
+      float qv[16], kv[16];
+      uint4 qraw[2], kraw[2];
+      qraw[0] = ((const uint4*)&s.nq[t][c0])[0]; qraw[1] = ((const uint4*)&s.nq[t][c0])[1];
+      kraw[0] = ((const uint4*)&s.nk[t][c0])[0]; kraw[1] = ((const uint4*)&s.nk[t][c0])[1];
+#pragma unroll
+      for (int j = 0; j < 16; j++) {
+        qv[j] = __bfloat162float(((const __nv_bfloat16*)qraw)[j]) * i2q * scale;
+        kv[j] = __bfloat162float(((const __nv_bfloat16*)kraw)[j]) * i2k;
+      }
+      float dot = 0.f;
+      for (int j = 0; j < 16; j++) {
+        bh[j] *= s.gg[t];
+        dot += __fmul_rn(bh[j], kv[j]);
+      }
+      for (int d = 1; d < 8; d <<= 1) dot += __shfl_xor_sync(0xffffffffu, dot, d);
+      float bv = __bfloat162float(s.cv[t][row]) - dot;
+      bv *= s.bb[t];
+      float o = 0.f;
+      for (int j = 0; j < 16; j++) {
+        bh[j] = fmaf(bv, kv[j], bh[j]);
+        o += __fmul_rn(bh[j], qv[j]);
+      }
+      for (int d = 1; d < 8; d <<= 1) o += __shfl_xor_sync(0xffffffffu, o, d);
+      if (lane8 == 0) s.oarr[t][row] = __float2bfloat16_rn(o);
+      const int st = slots[t];
+      if (st > 0) {
+        float4* ht = (float4*)(ssm_state + (int64_t)st * sls + hb);
+        for (int j = 0; j < 4; j++) ht[j] = *(float4*)&bh[j * 4];
+      }
+    }
+  }
+  __syncthreads();
+
+  // ---- gated RMSNorm, variance merged across the four sibling CTAs ----
+  if (tid < ROWS) {
+    for (int t = 0; t < tt; t++) {
+      float x;
+      if (rec && t < T)
+        x = __bfloat162float(s.oarr[t][tid]);
+      else
+        x = __bfloat162float(out[(int64_t)t * 6144 + hv * V + sp * ROWS + tid]);
+      s.oarr[t][tid] = __float2bfloat16_rn(x);  // keep the norm input
+      float p = __fmul_rn(x, x);
+      for (int d = 16; d > 0; d >>= 1) p += __shfl_xor_sync(0xffffffffu, p, d);
+      if (tid == 0) hpart[((int64_t)t * HV + hv) * SPLIT + sp] = p;
+    }
+  }
+  __threadfence();
+  __syncthreads();
+  if (tid == 0) atomicAdd(&hcnt[hv], 1);
+  if (tid == 0) while (ld_acquire(&hcnt[hv]) < SPLIT) {}
+  __syncthreads();
+  if (tid < ROWS) {
+    for (int t = 0; t < tt; t++) {
+      const float* p = hpart + ((int64_t)t * HV + hv) * SPLIT;
+      const float var = ((ldcg(p) + ldcg(p + 1)) + (ldcg(p + 2) + ldcg(p + 3))) / (float)V;
+      const float rstd = rsqrtf(var + eps);
+      const int col = hv * V + sp * ROWS + tid;
+      float y = __bfloat162float(s.oarr[t][tid]) * rstd;
+      y *= __bfloat162float(nw[sp * ROWS + tid]);
+      const float z = __bfloat162float(qkvz[(int64_t)t * QKVZ + Z_OFF + col]);
+      y *= z * sigmoidf_(z);
+      out[(int64_t)t * 6144 + col] = __float2bfloat16_rn(y);
+    }
+  }
+
+  // ---- last CTA resets the counter scratch ----
+  __syncthreads();
+  if (tid == 0 && atomicAdd(gcnt, 1) == NB - 1) {
+    for (int i = 0; i < 16; i++) xcnt[i] = 0;
+    for (int i = 0; i < HV; i++) hcnt[i] = 0;
+    *gcnt = 0;
+    __threadfence();
+  }
+}

From 1d9e7fc1f8f75036ad4ecc64958aa338e174bfad Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 18:51:09 +0000
Subject: [PATCH 07/10] qwen38: fuse per-head q/k norm + rope + KV cache write
 into attn_prep

The attention prep chain of every attention layer was four dispatches
(gemma_norm_qhead + gemma_norm_khead + mrope + reshape_and_cache);
kern_attn_prep_bf16 (tools/kernels-src/attn_prep.cu) does the whole
chain in one launch with a CTA per (token, head) and no cross-CTA
traffic, across decode and prefill of both manifests (and decode_spec /
verify of the DFlash2 one).  It removes three graph nodes per attention
layer and the q_n/k_n round trips between norm, rope and the cache
write.

Bit-exactness against the chain (driver-API harness launching the
pinned cubins with manifest-exact args, T = 1/8/96: every buffer
bit-identical):

  - the norm reproduces ATen's reduction order (same code as
    gemma_rms_norm.cu, block width from the original per-instance
    `rows`, which stays an arg);
  - the rope replicates the Triton kernel's instruction shapes read
    from its SASS: y1 = bf16fma(x1, cos, -bf16(x2*sin)),
    y2 = bf16fma(x1, sin, bf16(x2*cos)) -- the x1 product is
    HFMA2-fused in both outputs, the x2 product pre-rounded;
  - the cache write is a plain bf16 copy (FP8 off).

kern-attest (A = previous commit, B = this), --capacity 3920:

  plain manifest, decode program:
    step, eager        12.256 ms -> 11.999 ms   (-257 us, -2.1%)
    step, graph (TPOT) 10.495 ms -> 10.409 ms   (-86 us, -0.8%, 95 -> 96 tok/s)
    sum of 16 cuts     379.9 us  -> 127.9 us    (-66.3%)
  plain manifest, prefill (16 tokens):
    step, eager        17.909 ms -> 17.572 ms   (-337 us, -1.9%)
  dflash2 manifest, decode program:
    step, eager        11.900 ms -> 11.740 ms   (-160 us, -1.3%)
    step, graph (TPOT) 10.434 ms -> 10.415 ms   (-19 us, -0.2%)
    sum of 16 cuts     380.7 us  -> 130.0 us    (-65.9%)

The plain manifest's verdict is PASS (value-identical at every cut and
under every fuzz distribution; only signed zeros differ under `edge`).
The DFlash2 verdict is INCONCLUSIVE only because the driver cannot
stage decode_spec/verify; those run the same fused kernel, and
tools/qwen38_compare.py --spec covers them end to end.

next_token (and draft_tokens/verify_tokens) bit-identical; every cut's
q_n, k_n and kv state bit-identical on both manifests' decode and
prefill.  End to end (tools/qwen38_compare.py): the plain manifest
generates token-for-token identical output to the previous commit on
all five reference prompts, and the DFlash2 spec path reproduces the
previous commit's vs-vLLM divergence points [66,139,147,91,91] and
acceptance rates exactly.

The attest capacity is a whole number of KV blocks (5 x 784) because
the fuzz stage generates slot_mapping over the full provisioned token
capacity: with the default 4096 the pool ends mid-block, and a fuzzed
slot in the torn block at a late attention layer writes past the pool
end.  That overflow predates this change -- the pinned
reshape_and_cache_kernel_flash writes the identical out-of-bounds
addresses under the same inputs (compute-sanitizer shows the same
invalid writes on both) -- vLLM can never produce such a slot because
real pools are allocated in whole blocks.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b-dflash2.json | 10361 +++++-----------------------
 examples/qwen3.8-27b.json         |  5405 +++------------
 tools/gen_qwen35.py               |    45 +-
 tools/kernels-src/attn_prep.cu    |   149 +
 4 files changed, 2776 insertions(+), 13184 deletions(-)
 create mode 100644 tools/kernels-src/attn_prep.cu

diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index d45411d..984bfe8 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6335,34 +6335,46 @@
     ]
    }
   },
-  "gemma_norm_qhead": {
+  "attn_prep": {
    "params": [
+    "out buffer<bf16>",
     "out buffer<bf16>",
     "in buffer<bf16>",
     "in buffer<f32>",
+    "in buffer<f32>",
+    "in buffer<bf16>",
+    "in buffer<bf16>",
+    "inout ptr",
+    "inout ptr",
+    "in buffer<i64>",
     "i32",
     "i32",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
+    "i64",
+    "i64",
+    "i64",
     "i32",
     "f32"
    ],
    "impl": {
     "steps": [
      {
-      "symbol": "kern_gemma_rms_norm_bf16",
+      "symbol": "kern_attn_prep_bf16",
       "params": [
+       "out buffer<bf16>",
        "out buffer<bf16>",
        "in buffer<bf16>",
        "in buffer<f32>",
+       "in buffer<f32>",
+       "in buffer<bf16>",
+       "in buffer<bf16>",
+       "inout ptr",
+       "inout ptr",
+       "in buffer<i64>",
        "i32",
        "i32",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
+       "i64",
+       "i64",
+       "i64",
        "i32",
        "f32"
       ],
@@ -6377,7 +6389,7 @@
          {
           "sym": "tokens"
          },
-         24
+         28
         ]
        },
        1,
@@ -6416,99 +6428,27 @@
        },
        {
         "arg": 10
-       }
-      ],
-      "shared_mem": 4096,
-      "cubin": "gemma_rms_norm.cubin"
-     }
-    ]
-   }
-  },
-  "gemma_norm_khead": {
-   "params": [
-    "out buffer<bf16>",
-    "in buffer<bf16>",
-    "in buffer<f32>",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
-    "f32"
-   ],
-   "impl": {
-    "steps": [
-     {
-      "symbol": "kern_gemma_rms_norm_bf16",
-      "params": [
-       "out buffer<bf16>",
-       "in buffer<bf16>",
-       "in buffer<f32>",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
-       "f32"
-      ],
-      "block": [
-       512,
-       1,
-       1
-      ],
-      "grid": [
-       {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         4
-        ]
-       },
-       1,
-       1
-      ],
-      "args": [
-       {
-        "arg": 0
-       },
-       {
-        "arg": 1
-       },
-       {
-        "arg": 2
-       },
-       {
-        "arg": 3
-       },
-       {
-        "arg": 4
        },
        {
-        "arg": 5
+        "arg": 11
        },
        {
-        "arg": 6
+        "arg": 12
        },
        {
-        "arg": 7
+        "arg": 13
        },
        {
-        "arg": 8
+        "arg": 14
        },
        {
-        "arg": 9
+        "arg": 15
        },
        {
-        "arg": 10
+        "arg": 16
        }
       ],
-      "shared_mem": 4096,
-      "cubin": "gemma_rms_norm.cubin"
+      "cubin": "attn_prep.cubin"
      }
     ]
    }
@@ -8106,121 +8046,86 @@
     ]
    }
   },
-  "mrope": {
-   "params": [
-    "inout buffer<bf16>",
-    "inout buffer<bf16>",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "i32",
-    "i64",
-    "i64"
-   ],
-   "impl": {
-    "steps": [
-     {
-      "symbol": "_triton_mrope_forward",
-      "params": [
-       "inout buffer<bf16>",
-       "inout buffer<bf16>",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
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+       "buf": "model.layers.63.self_attn.k_norm.weight_p1"
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@@ -97959,28 +91027,6 @@
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@@ -97993,38 +91039,39 @@
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diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 5399ce4..537c631 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -5574,34 +5574,46 @@
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     "out buffer<bf16>",
     "in buffer<bf16>",
     "in buffer<f32>",
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      {
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+      "symbol": "kern_attn_prep_bf16",
       "params": [
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        "in buffer<bf16>",
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@@ -5616,7 +5628,7 @@
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@@ -5655,99 +5667,27 @@
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-      "cubin": "gemma_rms_norm.cubin"
+      "cubin": "attn_prep.cubin"
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@@ -7610,121 +7550,86 @@
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@@ -7775,190 +7680,52 @@
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@@ -10191,67 +9958,49 @@
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     {
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-     "kernel": "gemma_norm_qhead",
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+       "state": "kv"
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@@ -10263,107 +10012,20 @@
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@@ -12501,107 +12163,23 @@
      ]
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     {
-     "label": "l7.q_norm",
-     "kernel": "gemma_norm_qhead",
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+       "buf": "model.layers.7.self_attn.q_norm.weight_p1"
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@@ -12609,28 +12187,6 @@
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        "state": "kv",
        "offset": 38535168
@@ -60147,38 +56868,39 @@
        "buf": "slot_mapping"
       },
       {
-       "buf": "kv_scales"
-      },
-      {
-       "buf": "kv_scales",
-       "offset": 4
-      },
-      {
-       "i64": 1024
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         24
+        ]
+       }
       },
       {
-       "i64": 14336
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         4
+        ]
+       }
       },
       {
        "i64": 25690112
       },
       {
-       "i64": 512
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
+       "i64": 2048
       },
       {
-       "i64": 2048
+       "i64": 512
       },
       {
-       "i64": 0
+       "i32": 784
       },
       {
-       "i64": 0
+       "f32": 9.999999974752427e-07
       }
      ]
     },
@@ -61150,107 +57872,23 @@
      ]
     },
     {
-     "label": "l55.q_norm",
-     "kernel": "gemma_norm_qhead",
+     "label": "l55.prep",
+     "kernel": "attn_prep",
      "args": [
       {
        "buf": "q_n"
       },
-      {
-       "buf": "qkv"
-      },
-      {
-       "buf": "model.layers.55.self_attn.q_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         24
-        ]
-       }
-      },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 512
-      },
-      {
-       "i32": 6144
-      },
-      {
-       "i32": 256
-      },
-      {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l55.k_norm",
-     "kernel": "gemma_norm_khead",
-     "args": [
       {
        "buf": "k_n"
       },
       {
-       "buf": "qkv",
-       "offset": 24576
-      },
-      {
-       "buf": "model.layers.55.self_attn.k_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         4
-        ]
-       }
-      },
-      {
-       "i32": 4
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 256
-      },
-      {
-       "i32": 1024
-      },
-      {
-       "i32": 256
+       "buf": "qkv"
       },
       {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l55.rope",
-     "kernel": "mrope",
-     "args": [
-      {
-       "buf": "q_n"
+       "buf": "model.layers.55.self_attn.q_norm.weight_p1"
       },
       {
-       "buf": "k_n"
+       "buf": "model.layers.55.self_attn.k_norm.weight_p1"
       },
       {
        "buf": "cos_g"
@@ -61258,28 +57896,6 @@
       {
        "buf": "sin_g"
       },
-      {
-       "i32": 0
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l55.kv_write",
-     "kernel": "reshape_and_cache",
-     "args": [
-      {
-       "buf": "k_n"
-      },
-      {
-       "buf": "qkv",
-       "offset": 26624
-      },
       {
        "state": "kv",
        "offset": 41746432
@@ -61292,38 +57908,39 @@
        "buf": "slot_mapping"
       },
       {
-       "buf": "kv_scales"
-      },
-      {
-       "buf": "kv_scales",
-       "offset": 4
-      },
-      {
-       "i64": 1024
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         24
+        ]
+       }
       },
       {
-       "i64": 14336
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         4
+        ]
+       }
       },
       {
        "i64": 25690112
       },
       {
-       "i64": 512
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
+       "i64": 2048
       },
       {
-       "i64": 2048
+       "i64": 512
       },
       {
-       "i64": 0
+       "i32": 784
       },
       {
-       "i64": 0
+       "f32": 9.999999974752427e-07
       }
      ]
     },
@@ -62295,107 +58912,23 @@
      ]
     },
     {
-     "label": "l59.q_norm",
-     "kernel": "gemma_norm_qhead",
+     "label": "l59.prep",
+     "kernel": "attn_prep",
      "args": [
       {
        "buf": "q_n"
       },
-      {
-       "buf": "qkv"
-      },
-      {
-       "buf": "model.layers.59.self_attn.q_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         24
-        ]
-       }
-      },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 512
-      },
-      {
-       "i32": 6144
-      },
-      {
-       "i32": 256
-      },
-      {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l59.k_norm",
-     "kernel": "gemma_norm_khead",
-     "args": [
       {
        "buf": "k_n"
       },
       {
-       "buf": "qkv",
-       "offset": 24576
-      },
-      {
-       "buf": "model.layers.59.self_attn.k_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         4
-        ]
-       }
-      },
-      {
-       "i32": 4
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 256
-      },
-      {
-       "i32": 1024
-      },
-      {
-       "i32": 256
+       "buf": "qkv"
       },
       {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l59.rope",
-     "kernel": "mrope",
-     "args": [
-      {
-       "buf": "q_n"
+       "buf": "model.layers.59.self_attn.q_norm.weight_p1"
       },
       {
-       "buf": "k_n"
+       "buf": "model.layers.59.self_attn.k_norm.weight_p1"
       },
       {
        "buf": "cos_g"
@@ -62403,28 +58936,6 @@
       {
        "buf": "sin_g"
       },
-      {
-       "i32": 0
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l59.kv_write",
-     "kernel": "reshape_and_cache",
-     "args": [
-      {
-       "buf": "k_n"
-      },
-      {
-       "buf": "qkv",
-       "offset": 26624
-      },
       {
        "state": "kv",
        "offset": 44957696
@@ -62437,38 +58948,39 @@
        "buf": "slot_mapping"
       },
       {
-       "buf": "kv_scales"
-      },
-      {
-       "buf": "kv_scales",
-       "offset": 4
-      },
-      {
-       "i64": 1024
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         24
+        ]
+       }
       },
       {
-       "i64": 14336
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         4
+        ]
+       }
       },
       {
        "i64": 25690112
       },
       {
-       "i64": 512
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
+       "i64": 2048
       },
       {
-       "i64": 2048
+       "i64": 512
       },
       {
-       "i64": 0
+       "i32": 784
       },
       {
-       "i64": 0
+       "f32": 9.999999974752427e-07
       }
      ]
     },
@@ -63440,107 +59952,23 @@
      ]
     },
     {
-     "label": "l63.q_norm",
-     "kernel": "gemma_norm_qhead",
+     "label": "l63.prep",
+     "kernel": "attn_prep",
      "args": [
       {
        "buf": "q_n"
       },
-      {
-       "buf": "qkv"
-      },
-      {
-       "buf": "model.layers.63.self_attn.q_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         24
-        ]
-       }
-      },
-      {
-       "i32": 24
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 512
-      },
-      {
-       "i32": 6144
-      },
-      {
-       "i32": 256
-      },
-      {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l63.k_norm",
-     "kernel": "gemma_norm_khead",
-     "args": [
       {
        "buf": "k_n"
       },
       {
-       "buf": "qkv",
-       "offset": 24576
-      },
-      {
-       "buf": "model.layers.63.self_attn.k_norm.weight_p1"
-      },
-      {
-       "i32": 256
-      },
-      {
-       "expr": {
-        "mul": [
-         {
-          "sym": "tokens"
-         },
-         4
-        ]
-       }
-      },
-      {
-       "i32": 4
-      },
-      {
-       "i32": 14336
-      },
-      {
-       "i32": 256
-      },
-      {
-       "i32": 1024
-      },
-      {
-       "i32": 256
+       "buf": "qkv"
       },
       {
-       "f32": 9.999999974752427e-07
-      }
-     ]
-    },
-    {
-     "label": "l63.rope",
-     "kernel": "mrope",
-     "args": [
-      {
-       "buf": "q_n"
+       "buf": "model.layers.63.self_attn.q_norm.weight_p1"
       },
       {
-       "buf": "k_n"
+       "buf": "model.layers.63.self_attn.k_norm.weight_p1"
       },
       {
        "buf": "cos_g"
@@ -63548,28 +59976,6 @@
       {
        "buf": "sin_g"
       },
-      {
-       "i32": 0
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l63.kv_write",
-     "kernel": "reshape_and_cache",
-     "args": [
-      {
-       "buf": "k_n"
-      },
-      {
-       "buf": "qkv",
-       "offset": 26624
-      },
       {
        "state": "kv",
        "offset": 48168960
@@ -63582,38 +59988,39 @@
        "buf": "slot_mapping"
       },
       {
-       "buf": "kv_scales"
-      },
-      {
-       "buf": "kv_scales",
-       "offset": 4
-      },
-      {
-       "i64": 1024
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         24
+        ]
+       }
       },
       {
-       "i64": 14336
+       "expr": {
+        "mul": [
+         {
+          "sym": "tokens"
+         },
+         4
+        ]
+       }
       },
       {
        "i64": 25690112
       },
       {
-       "i64": 512
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
+       "i64": 2048
       },
       {
-       "i64": 2048
+       "i64": 512
       },
       {
-       "i64": 0
+       "i32": 784
       },
       {
-       "i64": 0
+       "f32": 9.999999974752427e-07
       }
      ]
     },
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 1c870ef..4dcdbed 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -757,12 +757,15 @@ def layer_norm_kernel(src, grid, rows_shape):
         # reduction width depends on the row count, so `rows` is an arg).
         "gemma_norm": single("kern_gemma_rms_norm_bf16", GEMMA_PARAMS, [512, 1, 1], [T, 1, 1],
                              shared_mem=GEMMA_SMEM, cubin="gemma_rms_norm.cubin"),
-        "gemma_norm_qhead": single("kern_gemma_rms_norm_bf16", GEMMA_PARAMS, [512, 1, 1],
-                                   [mul(T, HEADS), 1, 1], shared_mem=GEMMA_HEAD_SMEM,
-                                   cubin="gemma_rms_norm.cubin"),
-        "gemma_norm_khead": single("kern_gemma_rms_norm_bf16", GEMMA_PARAMS, [512, 1, 1],
-                                   [mul(T, KV_HEADS), 1, 1], shared_mem=GEMMA_HEAD_SMEM,
-                                   cubin="gemma_rms_norm.cubin"),
+        # per-head q/k gemma norm + partial rope + kv cache write of one
+        # attention layer (tools/kernels-src/attn_prep.cu): CTA = (token, head)
+        "attn_prep": single("kern_attn_prep_bf16",
+                            ["out buffer<bf16>", "out buffer<bf16>", "in buffer<bf16>",
+                             "in buffer<f32>", "in buffer<f32>", "in buffer<bf16>",
+                             "in buffer<bf16>", "inout ptr", "inout ptr", "in buffer<i64>",
+                             "i32", "i32", "i64", "i64", "i64", "i32", "f32"],
+                            [512, 1, 1], [mul(T, HEADS + KV_HEADS), 1, 1],
+                            cubin="attn_prep.cubin"),
         "gemma_fused_norm": single("kern_gemma_fused_add_rms_norm_bf16", GEMMA_FUSED_PARAMS,
                                    [512, 1, 1], [T, 1, 1], shared_mem=GEMMA_SMEM,
                                    cubin="gemma_rms_norm.cubin"),
@@ -894,14 +897,6 @@ def layer_norm_kernel(src, grid, rows_shape):
                          [GDN_D // 32, GDN_V_HEADS, 1], src=dec),
         "gated_norm_decode": layer_norm_kernel(dec, [GDN_V_HEADS, 1, 1], [1, GDN_V_HEADS]),
         # --- attention
-        # one generic instance for both programs (decode's launch in vLLM is
-        # the num_tokens==1 specialization: same arithmetic, one arg fewer)
-        "mrope": tri("mrope", ["inout buffer<bf16>", "inout buffer<bf16>", "in buffer<bf16>",
-                               "in buffer<bf16>", "i32"] + I2, [T, 1, 1]),
-        "reshape_and_cache": tri("cache",
-                                 ["in buffer<bf16>", "in buffer<bf16>", "inout ptr", "inout ptr",
-                                  "in buffer<i64>", "in buffer<f32>", "in buffer<f32>"] + ["i64"] * 9,
-                                 [T, 1, 1]),
         "attn_prefill": tri("unified", ATTN_IFACE, [cdiv(T, BLOCK_Q), KV_HEADS, 1]),
         "attn": {
             "params": ATTN_IFACE,
@@ -1140,20 +1135,14 @@ def attn_layer(i, decode, small=False):
         kv_k, kv_v = state("kv", koff), state("kv", koff + V_BYTE_OFF)
         ds = [
             gemm(l + "qkv_proj", "x", p + "qkv_proj.weight", "qkv", T, QKV_DIM, HIDDEN),
-            d(l + "q_norm", "gemma_norm_qhead",
-              [buf("q_n"), buf("qkv"), buf(p + "q_norm.weight_p1"), i32(HEAD_DIM),
-               expr(mul(T, HEADS)), i32(HEADS), i32(QKV_DIM), i32(2 * HEAD_DIM), i32(Q_DIM),
-               i32(HEAD_DIM), f32(eps)]),
-            d(l + "k_norm", "gemma_norm_khead",
-              [buf("k_n"), buf("qkv", HEADS * 2 * HEAD_DIM * BF16), buf(p + "k_norm.weight_p1"),
-               i32(HEAD_DIM), expr(mul(T, KV_HEADS)), i32(KV_HEADS), i32(QKV_DIM), i32(HEAD_DIM),
-               i32(KV_DIM), i32(HEAD_DIM), f32(eps)]),
-            d(l + "rope", "mrope", [buf("q_n"), buf("k_n"), buf("cos_g"), buf("sin_g"), i32(0)] + Z2),
-            d(l + "kv_write", "reshape_and_cache",
-              [buf("k_n"), buf("qkv", (HEADS * 2 * HEAD_DIM + KV_DIM) * BF16), kv_k, kv_v,
-               buf("slot_mapping"), ks, vs,
-               i64(KV_DIM), i64(QKV_DIM), i64(BLOCK_STRIDE), i64(2 * HEAD_DIM), i64(0), i64(0),
-               i64(KV_HEADS * 2 * HEAD_DIM), i64(0), i64(0)]),
+            # q/k gemma norm + partial rope + kv cache write, one CTA per
+            # (token, head) -- see tools/kernels-src/attn_prep.cu
+            d(l + "prep", "attn_prep",
+              [buf("q_n"), buf("k_n"), buf("qkv"), buf(p + "q_norm.weight_p1"),
+               buf(p + "k_norm.weight_p1"), buf("cos_g"), buf("sin_g"), kv_k, kv_v,
+               buf("slot_mapping"), expr(mul(T, HEADS)), expr(mul(T, KV_HEADS)),
+               i64(BLOCK_STRIDE), i64(KV_HEADS * 2 * HEAD_DIM), i64(2 * HEAD_DIM),
+               i32(BLOCK_SIZE), f32(eps)]),
             d(l + "attn", "attn" if decode else "attn_prefill",
               [buf("attn_out"), buf("q_n"), kv_k, kv_v, buf("block_table"), buf("seq_lens"),
                f32(attn_scale), ks, vs, f32(1.0), f32(0.0),
diff --git a/tools/kernels-src/attn_prep.cu b/tools/kernels-src/attn_prep.cu
new file mode 100644
index 0000000..eba81a2
--- /dev/null
+++ b/tools/kernels-src/attn_prep.cu
@@ -0,0 +1,149 @@
+// The attention prep chain of one layer in one launch:
+//
+//   per-head Gemma RMSNorm of q and k   kern_gemma_rms_norm_bf16 x2
+//   partial rotary (dim 64 of 256)      _triton_mrope_forward
+//   KV cache write                      reshape_and_cache_kernel_flash
+//
+// One CTA per (token, head): 24 q heads norm + rope into q_n, 4 k heads
+// norm + rope into k_n and copy the k/v pair into the paged KV cache.
+// No cross-CTA traffic at all -- the fusion removes three graph nodes
+// per layer and the q_n/k_n round trips between norm, rope and the
+// cache write.
+//
+// Rounding is bit-exact against the chain: the norm reproduces ATen's
+// reduction order (same code as gemma_rms_norm.cu, W from the original
+// per-kernel `rows`), the rope operates on the bf16-rounded norm output
+// with the Triton kernel's instruction shapes (product of the far
+// element rounded to bf16, then a single-rounding bf16 fma with the
+// near element -- HMUL2/HFMA2 in the pinned cubin), and the cache write
+// is a plain bf16 copy (FP8 off, scales unused).
+//
+//   nvcc -cubin -arch=sm_103a -o kernels-qwen38/attn_prep.cubin tools/kernels-src/attn_prep.cu
+#include <cuda_bf16.h>
+#include <cstdint>
+
+namespace {
+constexpr int NT = 512;
+constexpr int HEADS = 24, KV_HEADS = 4, HD = 256, ROT = 32;  // rotary_dim 64
+constexpr int QKV_ROW = 14336;               // q|gate 24*512 | k 4*256 | v 4*256
+constexpr int K_OFF = HEADS * 2 * HD;        // 12288
+constexpr int V_OFF = K_OFF + KV_HEADS * HD; // 13312
+constexpr int GROUPS = HEADS + KV_HEADS;     // CTAs per token
+
+__device__ inline int last_pow2(int n) {
+  n |= (n >> 1); n |= (n >> 2); n |= (n >> 4); n |= (n >> 8); n |= (n >> 16);
+  return (n - (n >> 1)) > 0 ? (n - (n >> 1)) : 1;
+}
+// ATen ReduceConfig::set_block_dimension(dim0 = N/vec, dim1 = rows), mnt=512.
+__device__ inline int aten_block_width(int dim0, int dim1) {
+  int dim0_pow2 = dim0 < NT ? last_pow2(dim0) : NT;
+  int dim1_pow2 = dim1 < NT ? last_pow2(dim1) : NT;
+  int block_width = min(dim0_pow2, 32);
+  int block_height = min(dim1_pow2, NT / block_width);
+  block_width = min(dim0_pow2, NT / block_height);
+  return block_width;
+}
+// Sum of sq[0..N) in ATen's order for W lanes (see gemma_rms_norm.cu).
+__device__ float aten_row_sum(const float* __restrict__ sq, int N, int W,
+                              float* __restrict__ s) {
+  const int t = threadIdx.x;
+  float value = 0.f;
+  if (t < W) {
+    float acc[4] = {0.f, 0.f, 0.f, 0.f};
+    for (int idx = t; idx * 4 + 3 < N; idx += W) {
+      const float4 v = *reinterpret_cast<const float4*>(sq + idx * 4);
+      acc[0] += v.x; acc[1] += v.y; acc[2] += v.z; acc[3] += v.w;
+    }
+    value = ((acc[0] + acc[1]) + acc[2]) + acc[3];
+  }
+  if (W > 32) {
+    s[t] = value;
+    for (int off = W / 2; off >= 32; off >>= 1) {
+      __syncthreads();
+      if (t < off && t + off < W) { value += s[t + off]; s[t] = value; }
+    }
+  }
+  __syncthreads();
+  if (t < 32) {
+    for (int off = 16; off > 0; off >>= 1)
+      value += __shfl_down_sync(0xffffffffu, value, off);
+    if (t == 0) s[0] = value;
+  }
+  __syncthreads();
+  return s[0];
+}
+}  // namespace
+
+extern "C" __global__ void __launch_bounds__(NT) kern_attn_prep_bf16(
+    __nv_bfloat16* __restrict__ q_n,          // [tokens, 6144] out (post rope)
+    __nv_bfloat16* __restrict__ k_n,          // [tokens, 1024] out (post rope)
+    const __nv_bfloat16* __restrict__ qkv,    // [tokens, 14336]
+    const float* __restrict__ qw1,            // q_norm weight + 1
+    const float* __restrict__ kw1,            // k_norm weight + 1
+    const __nv_bfloat16* __restrict__ cos_g,  // [tokens, 32]
+    const __nv_bfloat16* __restrict__ sin_g,  // [tokens, 32]
+    __nv_bfloat16* __restrict__ kv_k,         // paged cache, k plane of this layer
+    __nv_bfloat16* __restrict__ kv_v,         // paged cache, v plane (k + 256)
+    const int64_t* __restrict__ slot_mapping, // [tokens]
+    int rows_q, int rows_k,                   // T*24 / T*4 (ATen width inputs)
+    int64_t block_stride, int64_t page_stride, int64_t head_stride,
+    int block_size, float eps) {
+  __shared__ float z[HD], s[NT], sq[HD];
+  __shared__ __nv_bfloat16 nb[HD];
+  const int tok = blockIdx.x / GROUPS, h = blockIdx.x % GROUPS;
+  const bool isq = h < HEADS;
+  const int kh = h - HEADS;
+  const __nv_bfloat16* in =
+      qkv + (int64_t)tok * QKV_ROW + (isq ? h * 2 * HD : K_OFF + kh * HD);
+  const float* w1 = isq ? qw1 : kw1;
+
+  // ---- Gemma RMSNorm, ATen reduction order, rounded to bf16 ----
+  for (int j = threadIdx.x; j < HD; j += NT) {
+    const float v = __bfloat162float(in[j]);
+    z[j] = v;
+    sq[j] = __fmul_rn(v, v);
+  }
+  __syncthreads();
+  const int rows = isq ? rows_q : rows_k;
+  const int W = aten_block_width(HD / 4, rows);
+  const float sum = aten_row_sum(sq, HD, W, s);
+  const float factor = (float)rows / (float)((long long)rows * (long long)HD);
+  const float var = __fmul_rn(sum, factor);
+  const float r = rsqrtf(__fadd_rn(var, eps));
+  for (int j = threadIdx.x; j < HD; j += NT)
+    nb[j] = __float2bfloat16_rn(__fmul_rn(__fmul_rn(z[j], r), w1[j]));
+  __syncthreads();
+
+  // ---- partial neox rotary on dims [0, 64): pair (d, d+32) ----
+  // Triton emits (per SASS): y1 = bf16fma(x1, cos, -bf16(x2 * sin))
+  //                           y2 = bf16fma(x1, sin,  bf16(x2 * cos))
+  // -- the x1 product is HFMA2-fused in both outputs, the x2 product
+  // pre-rounded.
+  if (threadIdx.x < ROT) {
+    const int j = threadIdx.x;
+    const __nv_bfloat16 c = cos_g[(int64_t)tok * ROT + j];
+    const __nv_bfloat16 sn = sin_g[(int64_t)tok * ROT + j];
+    const __nv_bfloat16 x1 = nb[j], x2 = nb[j + ROT];
+    nb[j] = __hfma(x1, c, __hneg(__hmul(x2, sn)));
+    nb[j + ROT] = __hfma(x1, sn, __hmul(x2, c));
+  }
+  __syncthreads();
+
+  // ---- stores: q_n / k_n, and the paged KV cache for k heads ----
+  if (isq) {
+    for (int j = threadIdx.x; j < HD; j += NT)
+      q_n[(int64_t)tok * (HEADS * HD) + h * HD + j] = nb[j];
+    return;
+  }
+  for (int j = threadIdx.x; j < HD; j += NT)
+    k_n[(int64_t)tok * (KV_HEADS * HD) + kh * HD + j] = nb[j];
+  const int64_t slot = slot_mapping[tok];
+  if (slot < 0) return;  // padding token
+  const int64_t base = (slot / block_size) * block_stride +
+                       (slot % block_size) * page_stride + kh * head_stride;
+  const __nv_bfloat16* v_src = qkv + (int64_t)tok * QKV_ROW + V_OFF + kh * HD;
+  for (int j = threadIdx.x; j < HD; j += NT) {
+    kv_k[base + j] = nb[j];
+    kv_v[base + j] = v_src[j];
+  }
+}

From 8262fdae8960a7cccb64ab51939258e80d5ca007 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Mon, 31 Aug 2026 19:10:47 +0000
Subject: [PATCH 08/10] qwen38: fuse the GDN decode chain into one cluster
 launch

The decode-path GDN chain of every linear-attention layer was three
dispatches (conv_update + recurrent + gated_norm_decode);
kern_gdn_decode_bf16 (tools/kernels-src/gdn_decode.cu) does the whole
chain in one launch of the plain manifest's decode program (the DFlash2
manifest's spec path was already fused as gdn_spec).

An earlier attempt at this fusion was abandoned: the chain's kernels
overlap under programmatic dependent launch in graph mode, so a fused
kernel that merely removes launches -- and serializes the sibling CTAs
behind a global-scratch q/k handoff -- replays slower than the chain it
replaces.  This kernel instead uses one thread-block cluster per q/k
head (2 CTAs per v head x 3 v heads): rank 0 convolves and l2-normalizes
the head's q/k row once and the siblings pick it up over distributed
shared memory after a single cluster.sync(), with their state-tile loads
issued before the sync so the handoff hides under them.  No global
scratch, no flags, nothing to reset.  Standalone graph replay of one
layer: 8.19 us for the pinned three-kernel chain, 6.3 us fused.

The conv state is token-major (tap j of channel ch at j*10240 + ch),
proved by probing the pinned cubin with marker values -- the earlier
attempt had it channel-major, which survives no bit comparison.
Rounding replicates the Triton kernels instruction-for-instruction
(bf16-rounded x*w conv products into an f32 accumulate, division as
rcp.approx + mul, l2norm via sqrt.approx, beta through bf16, rstd as
rsqrtf); in the driver-API A/B harness qkvz, the conv state and
core_attn_out are bit-identical against the pinned chain, and only the
f32 SSM state carries the wide-reduction sum-order band.

kern-attest (A = previous commit, B = this), --capacity 3920:

  step, eager        12.007 ms -> 11.522 ms   (-486 us, -4.0%)
  step, graph (TPOT) 10.322 ms -> 10.152 ms   (-170 us, -1.6%, 97 -> 99 tok/s)
  sum of 48 cuts      1.033 ms -> 579.0 us    (-43.9%)

next_token is bit-identical over the tapped workload.  The verdict is
INCONCLUSIVE because the cuts mutate the conv/SSM state and cannot be
replayed idempotently: attest's own A-vs-A replay differs by ~129 MB of
state at every cut, and B's divergence from the reference (the f32 SSM
sum-order band, ~280 KB at the first cut; qkvz, the conv state and
core_attn_out are bit-identical in the driver-API harness) sits far
inside that band.  tools/qwen38_compare.py covers the path end to end:
the divergence points against vLLM move from the previous commit's
[130,93,98,91,178] to [124,139,341,185,178] -- the same class, later on
four of five prompts.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 examples/qwen3.8-27b.json       | 4901 +++++--------------------------
 tools/gen_qwen35.py             |   37 +-
 tools/kernels-src/gdn_decode.cu |  238 ++
 3 files changed, 944 insertions(+), 4232 deletions(-)
 create mode 100644 tools/kernels-src/gdn_decode.cu

diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 537c631..56bece8 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -7285,129 +7285,55 @@
     ]
    }
   },
-  "conv_update": {
+  "gdn_decode": {
    "params": [
+    "inout buffer<bf16>",
     "in buffer<bf16>",
-    "in buffer<bf16>",
+    "inout ptr",
     "inout ptr",
     "in buffer<i32>",
-    "out buffer<bf16>",
-    "i32",
-    "i32",
-    "i64",
-    "i64",
-    "i64",
-    "i64"
-   ],
-   "impl": {
-    "steps": [
-     {
-      "symbol": "_causal_conv1d_update_kernel",
-      "params": [
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "inout ptr",
-       "in buffer<i32>",
-       "out buffer<bf16>",
-       "i32",
-       "i32",
-       "i64",
-       "i64",
-       "i64",
-       "i64"
-      ],
-      "block": [
-       128,
-       1,
-       1
-      ],
-      "grid": [
-       1,
-       40,
-       1
-      ],
-      "args": [
-       {
-        "arg": 0
-       },
-       {
-        "arg": 1
-       },
-       {
-        "arg": 2
-       },
-       {
-        "arg": 3
-       },
-       {
-        "arg": 4
-       },
-       {
-        "arg": 5
-       },
-       {
-        "arg": 6
-       },
-       {
-        "arg": 7
-       },
-       {
-        "arg": 8
-       },
-       {
-        "arg": 9
-       },
-       {
-        "arg": 10
-       }
-      ],
-      "cubin": "module_139.cubin",
-      "sha256": "c2504833f4aa2997dc27f621fa8168695ba8773c41e0ced1c8ffb7d189cf8ae4"
-     }
-    ]
-   }
-  },
-  "recurrent": {
-   "params": [
-    "in buffer<bf16>",
-    "in buffer<bf16>",
     "in buffer<bf16>",
     "in buffer<f32>",
     "in buffer<bf16>",
     "out buffer<bf16>",
-    "inout ptr",
-    "inout ptr",
-    "in buffer<i32>",
+    "in buffer<bf16>",
     "f32",
-    "i64",
-    "i64"
+    "f32",
+    "i32",
+    "i32",
+    "i32",
+    "i32"
    ],
    "impl": {
     "steps": [
      {
-      "symbol": "fused_recurrent_gated_delta_rule_packed_decode_kernel",
+      "symbol": "kern_gdn_decode_bf16",
       "params": [
+       "inout buffer<bf16>",
        "in buffer<bf16>",
-       "in buffer<bf16>",
+       "inout ptr",
+       "inout ptr",
+       "in buffer<i32>",
        "in buffer<bf16>",
        "in buffer<f32>",
        "in buffer<bf16>",
        "out buffer<bf16>",
-       "inout ptr",
-       "inout ptr",
-       "in buffer<i32>",
+       "in buffer<bf16>",
        "f32",
-       "i64",
-       "i64"
+       "f32",
+       "i32",
+       "i32",
+       "i32",
+       "i32"
       ],
       "block": [
-       32,
+       256,
        1,
        1
       ],
       "grid": [
-       4,
-       48,
+       96,
+       1,
        1
       ],
       "args": [
@@ -7446,106 +7372,21 @@
        },
        {
         "arg": 11
-       }
-      ],
-      "shared_mem": 64,
-      "cubin": "module_141.cubin",
-      "sha256": "ecc0dc457582a5d0ca3348d366168eb51a68deaaf59de409fb800b2665c903de"
-     }
-    ]
-   }
-  },
-  "gated_norm_decode": {
-   "params": [
-    "inout buffer<bf16>",
-    "out buffer<bf16>",
-    "in buffer<bf16>",
-    "in buffer<bf16>",
-    "i32",
-    "i32",
-    "i32",
-    "i32",
-    "f32",
-    "i64",
-    "i64"
-   ],
-   "impl": {
-    "scratch": {
-     "rstd": {
-      "dtype": "f32",
-      "shape": [
-       1,
-       48
-      ]
-     }
-    },
-    "steps": [
-     {
-      "symbol": "layer_norm_fwd_kernel",
-      "params": [
-       "inout buffer<bf16>",
-       "out buffer<bf16>",
-       "in buffer<bf16>",
-       "in buffer<bf16>",
-       "out buffer<f32>",
-       "i32",
-       "i32",
-       "i32",
-       "i32",
-       "f32",
-       "i64",
-       "i64"
-      ],
-      "block": [
-       32,
-       1,
-       1
-      ],
-      "grid": [
-       48,
-       1,
-       1
-      ],
-      "args": [
-       {
-        "arg": 0
-       },
-       {
-        "arg": 1
-       },
-       {
-        "arg": 2
-       },
-       {
-        "arg": 3
-       },
-       {
-        "scratch": "rstd"
-       },
-       {
-        "arg": 4
        },
        {
-        "arg": 5
-       },
-       {
-        "arg": 6
-       },
-       {
-        "arg": 7
+        "arg": 12
        },
        {
-        "arg": 8
+        "arg": 13
        },
        {
-        "arg": 9
+        "arg": 14
        },
        {
-        "arg": 10
+        "arg": 15
        }
       ],
-      "cubin": "module_143.cubin",
-      "sha256": "ab8921a843b50459d97332df3d691f9b442ef9c4f56e4bdca6fb3689746582e1"
+      "cubin": "gdn_decode.cubin"
      }
     ]
    }
@@ -43616,8 +43457,8 @@
      ]
     },
     {
-     "label": "l0.conv_update",
-     "kernel": "conv_update",
+     "label": "l0.gdn",
+     "kernel": "gdn_decode",
      "args": [
       {
        "buf": "qkvz"
@@ -43628,60 +43469,6 @@
       {
        "state": "gdn"
       },
-      {
-       "buf": "gdn.line_index",
-       "offset": 4
-      },
-      {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 49
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l0.recurrent",
-     "kernel": "recurrent",
-     "args": [
-      {
-       "buf": "qkvz"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "buf": "model.layers.0.linear_attn.A_log"
-      },
-      {
-       "buf": "model.layers.0.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -43691,22 +43478,13 @@
        "offset": 4
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.0.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l0.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.0.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -43715,29 +43493,22 @@
        "buf": "model.layers.0.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
@@ -43865,8 +43636,8 @@
      ]
     },
     {
-     "label": "l1.conv_update",
-     "kernel": "conv_update",
+     "label": "l1.gdn",
+     "kernel": "gdn_decode",
      "args": [
       {
        "buf": "qkvz"
@@ -43877,60 +43648,6 @@
       {
        "state": "gdn"
       },
-      {
-       "buf": "gdn.line_index",
-       "offset": 8
-      },
-      {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 49
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l1.recurrent",
-     "kernel": "recurrent",
-     "args": [
-      {
-       "buf": "qkvz"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "buf": "model.layers.1.linear_attn.A_log"
-      },
-      {
-       "buf": "model.layers.1.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -43940,22 +43657,13 @@
        "offset": 8
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.1.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l1.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.1.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -43964,29 +43672,22 @@
        "buf": "model.layers.1.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
@@ -44114,8 +43815,8 @@
      ]
     },
     {
-     "label": "l2.conv_update",
-     "kernel": "conv_update",
+     "label": "l2.gdn",
+     "kernel": "gdn_decode",
      "args": [
       {
        "buf": "qkvz"
@@ -44126,60 +43827,6 @@
       {
        "state": "gdn"
       },
-      {
-       "buf": "gdn.line_index",
-       "offset": 12
-      },
-      {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 49
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l2.recurrent",
-     "kernel": "recurrent",
-     "args": [
-      {
-       "buf": "qkvz"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "buf": "model.layers.2.linear_attn.A_log"
-      },
-      {
-       "buf": "model.layers.2.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -44189,22 +43836,13 @@
        "offset": 12
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.2.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l2.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.2.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -44213,29 +43851,22 @@
        "buf": "model.layers.2.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
@@ -44654,8 +44285,8 @@
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-      },
-      {
-       "buf": "model.layers.60.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -59289,22 +55926,13 @@
        "offset": 184
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.60.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l60.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.60.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -59313,29 +55941,22 @@
        "buf": "model.layers.60.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
@@ -59463,8 +56084,8 @@
      ]
     },
     {
-     "label": "l61.conv_update",
-     "kernel": "conv_update",
+     "label": "l61.gdn",
+     "kernel": "gdn_decode",
      "args": [
       {
        "buf": "qkvz"
@@ -59475,60 +56096,6 @@
       {
        "state": "gdn"
       },
-      {
-       "buf": "gdn.line_index",
-       "offset": 188
-      },
-      {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 49
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l61.recurrent",
-     "kernel": "recurrent",
-     "args": [
-      {
-       "buf": "qkvz"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "buf": "model.layers.61.linear_attn.A_log"
-      },
-      {
-       "buf": "model.layers.61.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -59538,22 +56105,13 @@
        "offset": 188
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.61.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l61.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.61.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -59562,29 +56120,22 @@
        "buf": "model.layers.61.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
@@ -59712,8 +56263,8 @@
      ]
     },
     {
-     "label": "l62.conv_update",
-     "kernel": "conv_update",
+     "label": "l62.gdn",
+     "kernel": "gdn_decode",
      "args": [
       {
        "buf": "qkvz"
@@ -59724,60 +56275,6 @@
       {
        "state": "gdn"
       },
-      {
-       "buf": "gdn.line_index",
-       "offset": 192
-      },
-      {
-       "buf": "qkvz"
-      },
-      {
-       "i32": 1
-      },
-      {
-       "i32": 49
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 1
-      },
-      {
-       "i64": 0
-      },
-      {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l62.recurrent",
-     "kernel": "recurrent",
-     "args": [
-      {
-       "buf": "qkvz"
-      },
-      {
-       "buf": "ba",
-       "offset": 96
-      },
-      {
-       "buf": "ba"
-      },
-      {
-       "buf": "model.layers.62.linear_attn.A_log"
-      },
-      {
-       "buf": "model.layers.62.linear_attn.dt_bias"
-      },
-      {
-       "buf": "core_attn_out"
-      },
-      {
-       "state": "gdn",
-       "offset": 61440
-      },
       {
        "state": "gdn",
        "offset": 61440
@@ -59787,22 +56284,13 @@
        "offset": 192
       },
       {
-       "f32": 0.0883883461356163
+       "buf": "ba"
       },
       {
-       "i64": 0
+       "buf": "model.layers.62.linear_attn.A_log"
       },
       {
-       "i64": 0
-      }
-     ]
-    },
-    {
-     "label": "l62.gated_norm",
-     "kernel": "gated_norm_decode",
-     "args": [
-      {
-       "buf": "core_attn_out"
+       "buf": "model.layers.62.linear_attn.dt_bias"
       },
       {
        "buf": "core_attn_out"
@@ -59811,29 +56299,22 @@
        "buf": "model.layers.62.linear_attn.norm.weight"
       },
       {
-       "buf": "qkvz",
-       "offset": 20480
-      },
-      {
-       "i32": 128
-      },
-      {
-       "i32": 128
+       "f32": 0.0883883461356163
       },
       {
-       "i32": 128
+       "f32": 9.999999974752427e-07
       },
       {
-       "i32": 48
+       "i32": 49
       },
       {
-       "f32": 9.999999974752427e-07
+       "i32": 1605632
       },
       {
-       "i64": 0
+       "i32": 10240
       },
       {
-       "i64": 0
+       "i32": 802816
       }
      ]
     },
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 4dcdbed..986f548 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -885,17 +885,14 @@ def layer_norm_kernel(src, grid, rows_shape):
                        [GDN_D // 64, cdiv(T, FLA_CHUNK), GDN_V_HEADS]),
         "gated_norm": layer_norm_kernel(pre, [mul(T, GDN_V_HEADS // LN_ROWS_PER_BLOCK), 1, 1],
                                         ["tokens", GDN_V_HEADS]),
-        # --- GDN decode
-        "conv_update": tri("conv_update",
-                           ["in buffer<bf16>", "in buffer<bf16>", "inout ptr", "in buffer<i32>",
-                            "out buffer<bf16>", "i32", "i32", "i64", "i64"] + I2,
-                           [1, CONV_DIM // 256, 1], src=dec),
-        "recurrent": tri("recurrent",
-                         ["in buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>", "in buffer<f32>",
-                          "in buffer<bf16>", "out buffer<bf16>", "inout ptr", "inout ptr",
-                          "in buffer<i32>", "f32"] + I2,
-                         [GDN_D // 32, GDN_V_HEADS, 1], src=dec),
-        "gated_norm_decode": layer_norm_kernel(dec, [GDN_V_HEADS, 1, 1], [1, GDN_V_HEADS]),
+        # --- GDN decode: the whole chain in one launch, one thread-block
+        # cluster per q/k head (tools/kernels-src/gdn_decode.cu)
+        "gdn_decode": single("kern_gdn_decode_bf16",
+                             ["inout buffer<bf16>", "in buffer<bf16>", "inout ptr", "inout ptr",
+                              "in buffer<i32>", "in buffer<bf16>", "in buffer<f32>",
+                              "in buffer<bf16>", "out buffer<bf16>", "in buffer<bf16>",
+                              "f32", "f32", "i32", "i32", "i32", "i32"],
+                             [256, 1, 1], [2 * GDN_V_HEADS, 1, 1], cubin="gdn_decode.cubin"),
         # --- attention
         "attn_prefill": tri("unified", ATTN_IFACE, [cdiv(T, BLOCK_Q), KV_HEADS, 1]),
         "attn": {
@@ -1093,18 +1090,14 @@ def gdn_layer(i, decode, nacc=None, small=False):
                    i32(GDN_D), i32(GDN_D), i32(GDN_D), expr(mul(T, GDN_V_HEADS)), f32(eps)] + Z2),
             ]
         elif nacc is None:
+            # the decode chain (conv update + delta rule step + gated norm),
+            # fused into one launch -- see tools/kernels-src/gdn_decode.cu
             ds += [
-                d(l + "conv_update", "conv_update",
-                  [buf("qkvz"), buf(p + "conv1d.weight"), state("gdn"), idx, buf("qkvz"),
-                   i32(1), i32(n_pages), i64(1), i64(1)] + Z2),
-                d(l + "recurrent", "recurrent",
-                  [buf("qkvz"), a_, b_, buf(p + "A_log"), buf(p + "dt_bias"), buf("core_attn_out"),
-                   state("gdn", ssm_off), state("gdn", ssm_off), idx,
-                   f32(gdn_scale)] + Z2),
-                d(l + "gated_norm", "gated_norm_decode",
-                  [buf("core_attn_out"), buf("core_attn_out"), buf(p + "norm.weight"),
-                   buf("qkvz", Z_OFF * BF16), i32(GDN_D), i32(GDN_D), i32(GDN_D), i32(GDN_V_HEADS),
-                   f32(eps)] + Z2),
+                d(l + "gdn", "gdn_decode",
+                  [buf("qkvz"), buf(p + "conv1d.weight"), state("gdn"), state("gdn", ssm_off),
+                   idx, buf("ba"), buf(p + "A_log"), buf(p + "dt_bias"), buf("core_attn_out"),
+                   buf(p + "norm.weight"), f32(gdn_scale), f32(eps), i32(n_pages),
+                   i32(page_bytes // BF16), i32(CONV_DIM), i32(page_bytes // 4)]),
             ]
         else:
             # vLLM's spec-decode GDN path, fused into one launch (the chain
diff --git a/tools/kernels-src/gdn_decode.cu b/tools/kernels-src/gdn_decode.cu
new file mode 100644
index 0000000..ad80034
--- /dev/null
+++ b/tools/kernels-src/gdn_decode.cu
@@ -0,0 +1,238 @@
+// The whole GDN decode chain of one layer in one launch (tokens == 1):
+//
+//   causal_conv1d_update  (width 4, silu)       _causal_conv1d_update_kernel
+//   gated delta rule step (l2norm q/k)          fused_recurrent_gated_delta_rule_packed_decode_kernel
+//   gated RMSNorm         (norm, then *silu(z)) layer_norm_fwd_kernel
+//
+// The real work is one 48-head recurrent state update (48 x 128 x 128 f32,
+// 3 MB each way); everything else is bandwidth-free.  One thread-block
+// cluster per q/k head: GDN_SPLIT CTAs per v head, 3 v heads, so the q/k
+// row is convolved and l2-normalized once (cluster rank 0) and picked up
+// by the siblings over distributed shared memory after one cluster.sync()
+// -- no global scratch, no flags, no spinning, nothing to reset.  Every
+// CTA owns its v channels and state rows exclusively: it convolves them,
+// shifts their conv state, runs its rows of the delta rule, and applies
+// the gated norm (GDN_SPLIT == 2 exchanges the two variance partials over
+// the same cluster).  The sibling CTAs' state loads are issued before the
+// sync, so rank 0's extra conv work hides under them.
+//
+// Rounding chains reproduce the Triton kernels bit-for-bit where the
+// order is defined: bf16 x*w products rounded before the f32 conv
+// accumulate; division is rcp.approx + mul and sqrt is sqrt.approx, the
+// instructions the pinned cubins actually contain (MUFU.RCP / MUFU.SQRT
+// -- Triton never emits IEEE division); silu -> bf16 store; the recurrent
+// re-loads that bf16 value; beta goes through bf16; the norm input is the
+// bf16-rounded recurrent output; rstd is rsqrtf (MUFU.RSQ); libdevice
+// expf/logf match tl.exp / tl.log.  Only the sum orders of the wide
+// reductions (l2norm, variance) differ.
+//
+// State layout: a cache line holds the conv state [3, CONV_DIM] bf16
+// (token-major: tap j of channel ch at j*cds + ch, cds = CONV_DIM) at
+// offset 0 and the SSM state [HV, V, K] f32 at the ssm offset; line
+// strides are passed in elements of the respective dtype.
+//
+//   nvcc -cubin -arch=sm_103a -o kernels-qwen38/gdn_decode.cubin tools/kernels-src/gdn_decode.cu
+#include <cooperative_groups.h>
+#include <cuda_bf16.h>
+#include <cstdint>
+
+namespace cg = cooperative_groups;
+
+#ifndef GDN_SPLIT
+#define GDN_SPLIT 2
+#endif
+
+namespace {
+constexpr int HV = 48, H = 16, K = 128, V = 128;
+constexpr int CONV_DIM = 10240;          // q | k | v channels
+constexpr int Z_OFF = CONV_DIM;          // z columns after q|k|v in the qkvz row
+constexpr int QOFF = 0, KOFF = 2048, VOFF = 4096;
+constexpr int WIDTH = 4;                 // conv kernel width; state_len = 3
+constexpr int NT = 256;
+constexpr int SPLIT = GDN_SPLIT, ROWS = V / SPLIT;  // CTAs per v head, state rows per CTA
+constexpr int CL = 3 * SPLIT;            // CTAs per cluster (= per q/k head)
+constexpr float SOFTPLUS_THRESHOLD = 20.0f;
+
+__device__ __forceinline__ float bf16r(float v) { return __bfloat162float(__float2bfloat16_rn(v)); }
+__device__ __forceinline__ float rcpa(float x) {
+  float r;
+  asm("rcp.approx.f32 %0, %1;" : "=f"(r) : "f"(x));
+  return r;
+}
+__device__ __forceinline__ float sqrta(float x) {
+  float r;
+  asm("sqrt.approx.f32 %0, %1;" : "=f"(r) : "f"(x));
+  return r;
+}
+__device__ __forceinline__ float sigmoidf_(float x) { return rcpa(1.0f + expf(-x)); }
+
+struct SmemT {
+  float qk[2 * K];      // rank 0: normalized q|k of the head; others: local copy
+  float v[ROWS], o[ROWS];
+  float red[8];
+};
+}  // namespace
+
+extern "C" __global__ void __launch_bounds__(NT) __cluster_dims__(CL, 1, 1)
+kern_gdn_decode_bf16(__nv_bfloat16* __restrict__ qkvz, const __nv_bfloat16* __restrict__ w,
+                     __nv_bfloat16* __restrict__ conv_state, float* __restrict__ ssm_state,
+                     const int* __restrict__ idx, const __nv_bfloat16* __restrict__ ba,
+                     const float* __restrict__ A_log, const __nv_bfloat16* __restrict__ dt_bias,
+                     __nv_bfloat16* __restrict__ out, const __nv_bfloat16* __restrict__ nw,
+                     float scale, float eps, int nlines, int cls, int cds, int sls) {
+  __shared__ SmemT s;
+  const int hv = blockIdx.x / SPLIT, sp = blockIdx.x % SPLIT;
+  const int ih = hv / (HV / H), t = threadIdx.x;
+  const int r0 = sp * ROWS;                       // this CTA's first state row
+  const int coord = idx[0];
+  cg::cluster_group cluster = cg::this_cluster();
+
+  if (coord <= 0) {  // null cache line: conv skips, the recurrent zeroes o,
+                     // and the norm output is (0 * w) * (z * sigmoid(z)) --
+                     // signed zeros (or NaN from an inf z) exactly as A emits
+    if (t < ROWS) {
+      const int j = r0 + t;
+      const float z = __bfloat162float(qkvz[Z_OFF + hv * V + j]);
+      const float y = __fmul_rn(__fmul_rn(0.0f, __bfloat162float(nw[j])), __fmul_rn(z, sigmoidf_(z)));
+      out[hv * V + j] = __float2bfloat16_rn(y);
+    }
+    return;  // uniform across the cluster: no CTA reaches a cluster.sync
+
+  }
+
+  // ---- issue the SSM tile loads of the first row group now: thread t owns
+  // state row r0 + t/8 (+32 per pass), k columns [(t&7)*16, +16)
+  const int rl = t >> 3, c0 = (t & 7) * 16;
+  float* hb0 = ssm_state + (size_t)coord * sls + ((size_t)hv * V + r0 + rl) * K + c0;
+  float hh[ROWS / 32][16], h[16];
+#pragma unroll
+  for (int pass = 0; pass < ROWS / 32; pass++)
+#pragma unroll
+    for (int j = 0; j < 16; j += 4)
+      *reinterpret_cast<float4*>(&hh[pass][j]) =
+          *reinterpret_cast<const float4*>(hb0 + (size_t)pass * 32 * K + j);
+
+  // ---- conv: silu(w0 s0 + w1 s1 + w2 s2 + w3 x) per channel, every value
+  // rounded to bf16 exactly as the in-place Triton store does
+  __nv_bfloat16* cbase = conv_state + (size_t)coord * cls;
+  const bool valid_line = coord < nlines;   // Triton load mask (reads 0 beyond the pool)
+  auto conv1 = [&](int ch) {
+    const __nv_bfloat16* cs = cbase + ch;
+    const float s0 = valid_line ? __bfloat162float(cs[0]) : 0.f;
+    const float s1 = valid_line ? __bfloat162float(cs[cds]) : 0.f;
+    const float s2 = valid_line ? __bfloat162float(cs[2 * cds]) : 0.f;
+    const float x = __bfloat162float(qkvz[ch]);
+    // Triton's `acc += x * w` multiplies two bf16 tiles: the product is
+    // rounded to bf16 before the f32 accumulate
+    float acc = 0.f;
+    acc = __fadd_rn(acc, bf16r(__fmul_rn(s0, __bfloat162float(w[ch * WIDTH + 0]))));
+    acc = __fadd_rn(acc, bf16r(__fmul_rn(s1, __bfloat162float(w[ch * WIDTH + 1]))));
+    acc = __fadd_rn(acc, bf16r(__fmul_rn(s2, __bfloat162float(w[ch * WIDTH + 2]))));
+    acc = __fadd_rn(acc, bf16r(__fmul_rn(x, __bfloat162float(w[ch * WIDTH + 3]))));
+    const float y = bf16r(__fmul_rn(acc, sigmoidf_(acc)));  // silu, division as rcp.approx
+    // the channel is exclusive to its convolving CTA: shift the conv state
+    // and store the conv output over the qkvz column right away
+    __nv_bfloat16* csw = cbase + ch;
+    csw[0] = __float2bfloat16_rn(s1); csw[cds] = __float2bfloat16_rn(s2); csw[2 * cds] = __float2bfloat16_rn(x);
+    qkvz[ch] = __float2bfloat16_rn(y);
+    return y;
+  };
+  // own v channels
+  for (int j = t; j < ROWS; j += NT) s.v[j] = conv1(VOFF + hv * V + r0 + j);
+
+  if (cluster.block_rank() == 0) {
+    // q/k conv + l2norm.  q in threads 0..127, k in 128..255.
+    const float y = conv1(t < K ? QOFF + ih * K + t : KOFF + ih * K + (t - K));
+    s.qk[t] = y;
+    __syncthreads();
+    if (t < 64) {  // warps 0/1 reduce q, k
+      const float* src = s.qk + (t < 32 ? 0 : K);
+      const int lane = t & 31;
+      float p = 0.f;
+      for (int j = lane; j < K; j += 32) p += __fmul_rn(src[j], src[j]);
+      for (int off = 16; off > 0; off >>= 1) p += __shfl_down_sync(0xffffffffu, p, off);
+      if (lane == 0) s.red[t >> 5] = p;
+    }
+    __syncthreads();
+    // Triton: b_q / tl.sqrt(sum + 1e-6) lowers to sqrt.approx + rcp.approx + mul
+    const float iq = rcpa(sqrta(s.red[0] + 1e-6f)), ik = rcpa(sqrta(s.red[1] + 1e-6f));
+    s.qk[t] = t < K ? __fmul_rn(__fmul_rn(s.qk[t], iq), scale) : __fmul_rn(s.qk[t], ik);
+  }
+  cluster.sync();
+  if (cluster.block_rank() != 0) {  // copy the head's q/k rows to local smem
+    const float* remote = cluster.map_shared_rank(s.qk, 0);
+    s.qk[t] = remote[t];
+    __syncthreads();
+  }
+
+  // ---- delta rule step over this CTA's ROWS state rows, 32 per pass
+  const float a_val = __bfloat162float(ba[HV + hv]);
+  const float b_val = __bfloat162float(ba[hv]);
+  const float xg = a_val + __bfloat162float(dt_bias[hv]);
+  const float sp_ = xg <= SOFTPLUS_THRESHOLD ? logf(1.0f + expf(xg)) : xg;
+  const float g_e = expf(-expf(A_log[hv]) * sp_);
+  const float beta = bf16r(sigmoidf_(b_val));  // Triton rounds beta through bf16
+
+  const float* kk = s.qk + K;
+  // all row groups' state tiles are already prefetched into hh (issued
+  // before the conv/cluster.sync so the loads run under them)
+#pragma unroll
+  for (int pass = 0; pass < ROWS / 32; pass++) {
+    const int row = pass * 32 + rl;
+    float* hb = hb0 + (size_t)pass * 32 * K;
+#pragma unroll
+    for (int j = 0; j < 16; j++) h[j] = hh[pass][j];
+    float dot = 0.f;
+#pragma unroll
+    for (int j = 0; j < 16; j++) {
+      h[j] *= g_e;
+      dot = __fadd_rn(dot, __fmul_rn(h[j], kk[c0 + j]));
+    }
+    dot += __shfl_xor_sync(0xffffffffu, dot, 1);
+    dot += __shfl_xor_sync(0xffffffffu, dot, 2);
+    dot += __shfl_xor_sync(0xffffffffu, dot, 4);
+    const float vn = (s.v[pass * 32 + rl] - dot) * beta;
+    float op = 0.f;
+#pragma unroll
+    for (int j = 0; j < 16; j++) {
+      h[j] = __fmaf_rn(vn, kk[c0 + j], h[j]);
+      op = __fadd_rn(op, __fmul_rn(h[j], s.qk[c0 + j]));
+    }
+    op += __shfl_xor_sync(0xffffffffu, op, 1);
+    op += __shfl_xor_sync(0xffffffffu, op, 2);
+    op += __shfl_xor_sync(0xffffffffu, op, 4);
+#pragma unroll
+    for (int j = 0; j < 16; j += 4) *reinterpret_cast<float4*>(hb + j) = *reinterpret_cast<const float4*>(&h[j]);
+    if ((t & 7) == 0) s.o[row] = bf16r(op);  // Triton stores o as bf16; the norm re-loads it
+  }
+  __syncthreads();
+
+  // ---- gated RMSNorm: y = (x * rstd) * w, then y *= z * sigmoid(z)
+  if (t < 32) {
+    float p = 0.f;
+    for (int j = t; j < ROWS; j += 32) p += __fmul_rn(s.o[j], s.o[j]);
+    for (int off = 16; off > 0; off >>= 1) p += __shfl_down_sync(0xffffffffu, p, off);
+    if (t == 0) s.red[0] = p;
+  }
+#if GDN_SPLIT == 2
+  cluster.sync();  // exchange the two variance partials of this v head
+  if (t == 0) {
+    const float* other = cluster.map_shared_rank(s.red, cluster.block_rank() ^ 1);
+    const float p0 = (sp == 0) ? s.red[0] : other[0];
+    const float p1 = (sp == 0) ? other[0] : s.red[0];
+    s.red[1] = rsqrtf(__fadd_rn(p0, p1) / (float)V + eps);
+  }
+  __syncthreads();
+  const float rstd = s.red[1];
+#else
+  __syncthreads();
+  const float rstd = rsqrtf(s.red[0] / (float)V + eps);
+#endif
+  for (int j = t; j < ROWS; j += NT) {
+    const int col = r0 + j;
+    const float z = __bfloat162float(qkvz[Z_OFF + hv * V + col]);
+    float y = __fmul_rn(__fmul_rn(s.o[j], rstd), __bfloat162float(nw[col]));
+    y = __fmul_rn(y, __fmul_rn(z, sigmoidf_(z)));
+    out[hv * V + col] = __float2bfloat16_rn(y);
+  }
+}

From 37292794979142b5ae7c29e13f0c5e8edc467a36 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Tue, 1 Sep 2026 02:21:36 +0000
Subject: [PATCH 09/10] qwen38: programmatic dependent launch for the decode
 GEMM chain

Where the decode step goes: nsys on the c9 graph shows 43 us of launch gaps
in a 10.19 ms step -- the graph is already tight, the cost is inside the
kernels.  gateup (356 MB) streams at 6.65 TB/s against lm_head's 7.05, but the
small-N GEMMs do not: out_proj+norm 21.6 us for 63 MB (2.9 TB/s), down+norm
40.1 us for 178 MB (4.4 TB/s).  A single-shape microbench decomposes the
~12 us fixed cost per launch into the ramp before the first weight bytes
land, the single-CTA norm tail, and the drain after the last full round of
tasks.

Programmatic dependent launch removes the ramp: a `pdl` step flag in the
manifest (kern-manifest `Step::pdl`, schema regenerated) makes the runtime
launch with CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION via
cuLaunchKernelEx; under stream capture that is a programmatic graph edge.
The kernel contract: only the launch's own inputs (weights) may be read
before griddepcontrol.wait, every read of an upstream product and every
global write comes after it.  gemm8 fills its ring with the first task's
weights ahead of the wait and signals launch_dependents once its stream is
issued, so the next launch's CTAs take the SMs it vacates during its drain
and tail.  (Triggering at kernel entry instead is measurably worse: the
dependent's ring is full long before the primary's tail, and it only stole
bandwidth in the meantime.  L2-prefetching the dependent's weights beyond
the ring was slower still.)

The four gemm8 steps of a layer are flagged; gdn_decode is not.  A
gdn_decode that triggers at entry with the gemm8 behind it launched
programmatically hits CUDA_ERROR_LAUNCH_FAILED after a few hundred graph
steps (a cluster kernel as the primary of an early dependent -- reproduced
with the harness and the decode loop; triggering at the end, or either
kernel alone, is fine), and it bought nothing measurable: gdn's 76 x 256
registers cannot co-reside with gemm8's 167 x 288 anyway.

gemm8's other fixed costs, arithmetic untouched:
- tail: the norm weight is fetched into smem under the counter spin, and
  the y and residual loads of a row are issued as one batch (the runtime
  branch on `nranges` inside the per-group loop had kept ptxas from
  batching them).  Folding the ATen-order sum of squares in one warp's
  registers instead of the smem tree was tried and dropped: 40 more
  registers and a local-memory tree in every add_norm variant, +3 us per
  launch on the M = 8 verify pass of the DFlash2 loop, nothing at M = 1;
- tasks: whole 8-row blocks in a grid multiple plus, at M = 1, the leftover
  rows spread over the grid in two widths one granule apart, scheduled
  first so their latency-bound stages hide under the blocks streaming
  behind them.  At M > 1 the leftover stays whole blocks: a narrow task
  runs the full MMA rounds of a wide one, and the verify pass is not
  bandwidth-bound.  Every task walks the same K stages, so which warp
  accumulates which K pair does not depend on the geometry (a wider stage
  for narrow tasks changed the f32 summation order of the leftover rows
  for K=17408 and moved the decode/vLLM divergence points -- dropped);
- the ring/x split of the shared area is per launch (bs=1: 5-6 stages).

Harness (kern-out/pdl): an 8-layer in_proj -> gdn -> out_proj+norm -> gateup
-> down+norm chain replayed as a graph, A (c9 cubins) vs B, all activations
and the GDN state pool bit-identical; B is -5.4 us/layer on tray14
(144.7 -> 139.2) and -13 us/layer on tray06's GPU0, whose lower SM clock
made the latency-bound tails longer to begin with.

kern-attest, tray14 (GB300), --capacity 3920, A = c9, B = this commit:

| manifest | program | cuts | identity | eager step | graph TPOT |
| qwen3.8-27b | decode | 65 | act/ba/qkvz/residual/x/next_token bit-identical, real and every synthesized distribution | 11.324 -> 11.229 ms (-0.8%) | 10.120 -> 9.733 ms (-387 us, -3.8%, 99 -> 103 tok/s) |
| qwen3.8-27b-dflash2 | decode | 65 | all buffers + draft/next/verify tokens bit-identical, every fuzz distribution | 11.217 -> 11.229 ms | 9.975 -> 9.693 ms (-282 us, -2.8%, 100 -> 103 tok/s) |

Verdict PASS on the plain manifest; INCONCLUSIVE on dflash2 for the known
driver reason (decode_spec/verify cannot be staged), the replays are
bit-identical throughout.  The decode/vLLM comparison (tools/qwen38_compare)
diverges at the same tokens as c9 on all five prompts, plain and spec, with
the same per-position acceptance; the DFlash2 round (draft + M = 8 verify),
which attest's tokens=1 timing does not cover, goes 14.38 -> 14.12 ms
(-1.8%) on the same GPU back to back.  Prefill is untouched.

Signed-off-by: JinYan Su <751080330@qq.com>
---
 .gitignore                         |   4 +
 crates/kern-manifest/src/types.rs  |   8 +
 crates/kern-runtime/src/compile.rs |   3 +
 crates/kern-runtime/src/lib.rs     |  30 +++-
 examples/qwen3.8-27b-dflash2.json  |  12 +-
 examples/qwen3.8-27b.json          |  12 +-
 schema/manifest-v2.schema.json     |   4 +
 tools/gen_qwen35.py                |  16 +-
 tools/kernels-src/gemm8.cu         | 233 +++++++++++++++++++++++------
 9 files changed, 263 insertions(+), 59 deletions(-)

diff --git a/.gitignore b/.gitignore
index c6769a2..d588b7d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -10,3 +10,7 @@ tools/kernel-capture/libkernelcapture.so
 /.wrangler/
 /attestation.json
 /kernels-qwen38-dflash2
+kernels-c9/
+kernels-gdnT/
+kernels-varA/
+kernels-varB/
diff --git a/crates/kern-manifest/src/types.rs b/crates/kern-manifest/src/types.rs
index e8d5040..e6f9247 100644
--- a/crates/kern-manifest/src/types.rs
+++ b/crates/kern-manifest/src/types.rs
@@ -586,6 +586,14 @@ pub struct Step {
     /// Dynamic shared memory in bytes, if the step needs any.
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub shared_mem: Option<Expr>,
+    /// Programmatic dependent launch: the runtime launches this step with
+    /// `CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION`, so it may
+    /// start while the preceding launch is still draining. Only for kernels
+    /// that put every read of an upstream product and every global write
+    /// behind `griddepcontrol.wait` (their own inputs — weights, state —
+    /// may stream ahead of it).
+    #[serde(default, skip_serializing_if = "is_false")]
+    pub pdl: bool,
     /// Wiring: where each step param comes from — a forwarded interface
     /// arg, a scratch buffer, or an implementation-private literal.
     pub args: Vec<StepArg>,
diff --git a/crates/kern-runtime/src/compile.rs b/crates/kern-runtime/src/compile.rs
index eae524d..32d2826 100644
--- a/crates/kern-runtime/src/compile.rs
+++ b/crates/kern-runtime/src/compile.rs
@@ -68,6 +68,8 @@ pub(crate) enum LaunchKind {
         block: [u32; 3],
         grid: [CExpr; 3],
         shared_mem: Option<CExpr>,
+        /// Launch with programmatic stream serialization (see `Step::pdl`).
+        pdl: bool,
     },
     /// `extern:cublaslt_bf16_tn` / `..._acc` (beta 0.0 / 1.0).
     Gemm { beta: f32 },
@@ -350,6 +352,7 @@ fn compile_dispatch(
                     compile_expr(&st.grid[2], syms)?,
                 ],
                 shared_mem: st.shared_mem.as_ref().map(|e| compile_expr(e, syms)).transpose()?,
+                pdl: st.pdl,
             },
         };
         launches.push(Launch {
diff --git a/crates/kern-runtime/src/lib.rs b/crates/kern-runtime/src/lib.rs
index c81f144..da95d4b 100644
--- a/crates/kern-runtime/src/lib.rs
+++ b/crates/kern-runtime/src/lib.rs
@@ -626,7 +626,7 @@ impl Runtime {
         }
         match &l.kind {
             LaunchKind::Gemm { beta } => gemm_bf16_tn(&self.blt, &self.stream, &vals, *beta),
-            LaunchKind::Cubin { func, block, grid, shared_mem } => {
+            LaunchKind::Cubin { func, block, grid, shared_mem, pdl } => {
                 let grid =
                     (grid[0].eval(env)? as u32, grid[1].eval(env)? as u32, grid[2].eval(env)? as u32);
                 let smem = match shared_mem {
@@ -638,6 +638,34 @@ impl Runtime {
                 let raw: Vec<u64> = vals.iter().map(|r| r.val).collect();
                 let mut params: Vec<*mut c_void> =
                     raw.iter().map(|s| s as *const u64 as *mut c_void).collect();
+                if *pdl {
+                    // Programmatic dependent launch: inside stream capture
+                    // this becomes a programmatic graph edge, so the kernel
+                    // may begin (and stream its own inputs) while the
+                    // previous launch drains; its griddepcontrol.wait keeps
+                    // the data dependency.
+                    let mut attr = sys::CUlaunchAttribute {
+                        id: sys::CUlaunchAttributeID::CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION,
+                        pad: [0; 4],
+                        value: sys::CUlaunchAttributeValue { programmaticStreamSerializationAllowed: 1 },
+                    };
+                    let cfg = sys::CUlaunchConfig {
+                        gridDimX: grid.0,
+                        gridDimY: grid.1,
+                        gridDimZ: grid.2,
+                        blockDimX: block[0],
+                        blockDimY: block[1],
+                        blockDimZ: block[2],
+                        sharedMemBytes: smem,
+                        hStream: self.stream.cu_stream(),
+                        attrs: &mut attr,
+                        numAttrs: 1,
+                    };
+                    let r = unsafe {
+                        sys::cuLaunchKernelEx(&cfg, *func, params.as_mut_ptr(), std::ptr::null_mut())
+                    };
+                    return cuda_check(r, "cuLaunchKernelEx");
+                }
                 unsafe {
                     cu::launch_kernel(
                         *func,
diff --git a/examples/qwen3.8-27b-dflash2.json b/examples/qwen3.8-27b-dflash2.json
index 984bfe8..75eebd3 100644
--- a/examples/qwen3.8-27b-dflash2.json
+++ b/examples/qwen3.8-27b-dflash2.json
@@ -6636,7 +6636,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -6708,7 +6709,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -6835,7 +6837,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -6942,7 +6945,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
diff --git a/examples/qwen3.8-27b.json b/examples/qwen3.8-27b.json
index 56bece8..09c3d0c 100644
--- a/examples/qwen3.8-27b.json
+++ b/examples/qwen3.8-27b.json
@@ -5875,7 +5875,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -5947,7 +5948,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -6074,7 +6076,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
@@ -6181,7 +6184,8 @@
        }
       ],
       "shared_mem": 224128,
-      "cubin": "gemm8.cubin"
+      "cubin": "gemm8.cubin",
+      "pdl": true
      }
     ]
    }
diff --git a/schema/manifest-v2.schema.json b/schema/manifest-v2.schema.json
index 4a3b70f..4d14d0e 100644
--- a/schema/manifest-v2.schema.json
+++ b/schema/manifest-v2.schema.json
@@ -564,6 +564,10 @@
           },
           "type": "array"
         },
+        "pdl": {
+          "description": "Programmatic dependent launch: the runtime launches this step with\n`CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION`, so it may\nstart while the preceding launch is still draining. Only for kernels\nthat put every read of an upstream product and every global write\nbehind `griddepcontrol.wait` (their own inputs — weights, state —\nmay stream ahead of it).",
+          "type": "boolean"
+        },
         "sha256": {
           "description": "sha256 of that cubin file, checked by the runtime when both are set.",
           "type": [
diff --git a/tools/gen_qwen35.py b/tools/gen_qwen35.py
index 986f548..dc3d0a8 100644
--- a/tools/gen_qwen35.py
+++ b/tools/gen_qwen35.py
@@ -514,7 +514,7 @@ def scr(name, off=0):
     return {"scratch": name, "offset": off} if off else {"scratch": name}
 
 
-def step(symbol, params, block, grid, args, shared_mem=None, cubin=None, sha256=None):
+def step(symbol, params, block, grid, args, shared_mem=None, cubin=None, sha256=None, pdl=False):
     s = {"symbol": symbol, "params": params, "block": block, "grid": grid, "args": args}
     if shared_mem is not None:
         s["shared_mem"] = shared_mem
@@ -522,13 +522,15 @@ def step(symbol, params, block, grid, args, shared_mem=None, cubin=None, sha256=
         s["cubin"] = cubin
     if sha256 is not None:
         s["sha256"] = sha256
+    if pdl:  # the kernel puts its dependent reads / all writes behind griddepcontrol.wait
+        s["pdl"] = True
     return s
 
 
-def single(symbol, params, block, grid, shared_mem=None, cubin=None, sha256=None):
+def single(symbol, params, block, grid, shared_mem=None, cubin=None, sha256=None, pdl=False):
     return {"params": params,
             "impl": {"steps": [step(symbol, params, block, grid, [a(i) for i in range(len(params))],
-                                    shared_mem, cubin, sha256)]}}
+                                    shared_mem, cubin, sha256, pdl)]}}
 
 
 TOKEN_DOMAIN = {"index_into": "model.embed_tokens.weight"}
@@ -778,7 +780,7 @@ def layer_norm_kernel(src, grid, rows_shape):
                                     ["out buffer<bf16>", "in buffer<bf16>", "in buffer<bf16>",
                                      "i32", "i32", "i32"],
                                     [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
-                                    shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
+                                    shared_mem=GEMM8_SMEM, cubin="gemm8.cubin", pdl=True),
         # down GEMM + residual add + Gemma fused_add_rms_norm of the next
         # layer input (gemm8's split-K partials, y and the task counters are
         # impl scratch; the norm tail reproduces kern_gemma_rms_norm.cu)
@@ -787,7 +789,7 @@ def layer_norm_kernel(src, grid, rows_shape):
                              ["out buffer<bf16>", "out buffer<bf16>", "in buffer<bf16>",
                               "in buffer<bf16>", "in buffer<bf16>", "i32", "i32", "i32", "i32"],
                              [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
-                             shared_mem=GEMM8_SMEM, cubin="gemm8.cubin"),
+                             shared_mem=GEMM8_SMEM, cubin="gemm8.cubin", pdl=True),
         # o_proj + the sigmoid gate on its input + residual add + post-attention
         # norm: x = attn * sigmoid(gate) is built inside the GEMM
         "gemm8_sgate_add_norm": {
@@ -800,7 +802,7 @@ def layer_norm_kernel(src, grid, rows_shape):
                                [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
                                [a(0), a(1), a(2), a(3), a(4), a(5), scr("y"), scr("partial"), scr("gcnt"),
                                 a(6), a(7), a(8), a(9), a(10), a(11), a(12)],
-                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin")],
+                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin", pdl=True)],
             },
         },
         "gemm8_add_norm": {
@@ -813,7 +815,7 @@ def layer_norm_kernel(src, grid, rows_shape):
                                [GEMM8_THREADS, 1, 1], [GEMM8_GRID, 1, 1],
                                [a(0), a(1), a(2), a(3), a(4), scr("y"), scr("partial"), scr("gcnt"),
                                 a(5), a(6), a(7), a(8)],
-                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin")],
+                               shared_mem=GEMM8_SMEM, cubin="gemm8.cubin", pdl=True)],
             },
         },
         "copy_rows": single("kern_copy_rows_bf16",
diff --git a/tools/kernels-src/gemm8.cu b/tools/kernels-src/gemm8.cu
index 3fd18c8..cb7a8d1 100644
--- a/tools/kernels-src/gemm8.cu
+++ b/tools/kernels-src/gemm8.cu
@@ -12,11 +12,26 @@
 // every output element sums them there.  Smem rows are padded by 16 B so the
 // ldmatrix reads are bank-conflict-free (measured 3.5x slower without).
 //
+// Tasks: whole 8-row blocks, a multiple of the grid so every CTA streams the
+// same number, plus the leftover rows spread over the grid in tasks one
+// granule apart -- scheduled first, where their latency-bound stages hide
+// under the blocks behind them.  Every task walks the same K stages, so the
+// per-row accumulation order does not depend on the geometry.  The ring/x
+// split of the shared area is per launch, so bs=1 gets 5-6 stages in flight.
+//
 // The tail runs after a single end-of-CTA fence + task-count atomic.  With
 // grid <= %nsmid (one ~200 KB-smem CTA per SM, the launch geometry of every
 // call site) all CTAs are co-resident, so CTAs 0..M-1 spin on the counter and
 // each finishes one token row in parallel; on any other geometry the CTA that
 // finished last does all rows.  Counters are back to zero at kernel end.
+// The tail is pure latency: the norm weight is fetched under the spin and
+// each global round trip of a row is one batch of loads.
+//
+// Programmatic dependent launch (manifest step `pdl`): the ring is filled
+// with the first task's weights before griddepcontrol.wait, everything that
+// reads an upstream product or writes comes after it, and the CTA signals
+// launch_dependents once its stream is issued, so the next launch's ramp
+// overlaps this launch's drain and tail.
 //
 // Entry points differ in what is fused around the GEMM (rounding chains
 // reproduce the vLLM / ATen kernels they replace):
@@ -48,7 +63,8 @@ constexpr int SEG_MAX = 2560;            // K elements per weight row per stage
 constexpr int PAD = 8;                   // 16 B row padding: bank shift 4
 constexpr int X_ELEMS = 40960;           // x area capacity (M * kseg elements)
 constexpr int X_SLOT = X_ELEMS + MMAX * PAD;
-constexpr int RING_ELEMS = 69888;        // 3 stages of 8 rows at SEG_MAX; 4-5 for split shapes
+constexpr int RING_ELEMS = 69888;        // ring minimum: 3 stages of 8 rows at SEG_MAX (x at capacity)
+constexpr int BUF_ELEMS = RING_ELEMS + X_SLOT;  // ring + x share one area, split per launch by M * kseg
 constexpr int STAGES_MAX = 6;
 constexpr int NCW = 8, CTHREADS = NCW * 32, NTHREADS = CTHREADS + 32;
 constexpr int BAR_C = 1;                 // named barrier of the compute warps
@@ -97,15 +113,21 @@ __device__ __forceinline__ uint32_t nsmid() {
   return n;
 }
 __device__ __forceinline__ float bf16r(float v) { return __bfloat162float(__float2bfloat16_rn(v)); }
+// Programmatic dependent launch: the caller may launch this kernel with
+// CU_LAUNCH_ATTRIBUTE_PROGRAMMATIC_STREAM_SERIALIZATION.  Every global read of
+// an upstream product and every global write sit behind pdl_wait(); only the
+// weight stream (this launch's own inputs) runs ahead of it.  Without the
+// attribute both instructions are no-ops.
+__device__ __forceinline__ void pdl_wait() { asm volatile("griddepcontrol.wait;" ::: "memory"); }
+__device__ __forceinline__ void pdl_trigger() { asm volatile("griddepcontrol.launch_dependents;" ::: "memory"); }
 
 struct Smem {
-  alignas(128) __nv_bfloat16 ring[RING_ELEMS];              // stage s: rows [s*8*(seg+PAD), ...)
-  alignas(128) __nv_bfloat16 x[X_SLOT];                     // current K range of x: M rows, stride kseg+PAD
+  alignas(128) __nv_bfloat16 buf[BUF_ELEMS];  // [ring: stage s at s*8*(seg+PAD)] ... [x: M rows, stride kseg+PAD]
   alignas(8) uint64_t full[STAGES_MAX], empty[STAGES_MAX], xfull, xempty;
   float red[NCW][MMAX][R];
   int flag;
 };
-constexpr int SMEM_BYTES = sizeof(Smem) + 128;
+static_assert(sizeof(Smem) + 128 <= 224128, "manifest shared_mem (GEMM8_SMEM) covers Smem");
 
 enum Mode { PLAIN = 0, GATEUP = 1, DUAL = 2, NORM = 3, SGATE = 4 };
 
@@ -122,6 +144,42 @@ struct Args {
   int head_dim, gate_ts, gate_hs;
 };
 
+// Tasks of one K range: nfull whole 8-row blocks, a multiple of the grid so
+// every CTA streams the same number, plus the leftover rows spread over the
+// grid in tasks of `rem_g` rows (GATEUP: whole gate/up pairs).  The small
+// tasks come first: their stages are latency-bound, and at the head of a
+// CTA's queue that latency hides under the full blocks streaming behind
+// them instead of being the launch's last few microseconds.  The per-row
+// accumulation order is the same either way.
+struct Geom {
+  int nrows, nfull, nsmall, na, hi, lo, nps;  // rows; full blocks; small tasks: na of hi rows then hi-gran rows; tasks per range
+};
+template <int MODE>
+__device__ __forceinline__ Geom task_geom(const Args& a) {
+  Geom g;
+  g.nrows = MODE == GATEUP ? 2 * a.N : a.N;
+  const int nblk = (g.nrows + R - 1) / R, grid = gridDim.x;
+  g.nfull = nblk / grid * grid;
+  // leftover rows over the whole grid in two task widths differing by one
+  // granule, so no CTA streams more than a granule beyond another
+  const int rem = g.nrows - g.nfull * R, gran = MODE == GATEUP ? 2 : 1;
+  g.hi = min(R, ((rem + grid - 1) / grid + gran - 1) / gran * gran);
+  g.lo = g.hi - gran;
+  if (rem == 0) { g.na = g.nsmall = 0; }
+  else if (a.M > 1) { g.hi = R; g.lo = 0; g.na = g.nsmall = (rem + R - 1) / R; }  // whole blocks: a narrow task costs full MMA rounds
+  else if (g.lo == 0) { g.na = g.nsmall = (rem + g.hi - 1) / g.hi; }
+  else { g.na = (rem - g.lo * grid + gran - 1) / gran; g.nsmall = grid; }
+  g.nps = g.nfull + g.nsmall;
+  return g;
+}
+__device__ __forceinline__ void task_rows(const Geom& g, int u, int& r0, int& rows) {
+  if (u < g.nsmall) {
+    r0 = g.nfull * R + (u < g.na ? u * g.hi : g.na * g.hi + (u - g.na) * g.lo);
+    rows = min(u < g.na ? g.hi : g.lo, g.nrows - r0);
+  } else { r0 = (u - g.nsmall) * R; rows = R; }
+}
+
+
 // K ranges: as many as x capacity demands, and at least enough tasks to
 // balance the grid when the mode can split (partial != nullptr).
 __device__ __forceinline__ int pick_nranges(const Args& a, int nblk) {
@@ -172,15 +230,21 @@ __device__ __forceinline__ void emit(const Args& a, int split, int nranges, int
 template <int MODE>
 __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
   const int warp = threadIdx.x / 32, lane = threadIdx.x % 32;
-  const int nrows = MODE == GATEUP ? 2 * a.N : a.N;
+  const Geom g = task_geom<MODE>(a);
+  const int nrows = g.nrows, nps = g.nps;
   const int nblk = (nrows + R - 1) / R;
   const int nranges = pick_nranges(a, nblk);
   const int kseg = (a.K + nranges - 1) / nranges + 31 & ~31;
   const int nst0 = (kseg + SEG_MAX - 1) / SEG_MAX;
   const int seg = (kseg + nst0 - 1) / nst0 + 31 & ~31;          // stage slot width, whole ldmatrix pairs
   const int srow = seg + PAD, xrow = kseg + PAD;                // smem row strides
-  const int nstages = min(STAGES_MAX, RING_ELEMS / (R * srow));
-  const int ntasks = nblk * nranges;
+  // x sits at the top of the shared area; whatever it leaves (never less
+  // than RING_ELEMS) is ring depth -- at M = 1 that is 5-6 stages
+  const int xbeg = (BUF_ELEMS - a.M * xrow) & ~63;
+  __nv_bfloat16* const ring = s.buf;
+  __nv_bfloat16* const xs = s.buf + xbeg;
+  const int nstages = min(STAGES_MAX, xbeg / (R * srow));
+  const int ntasks = nps * nranges;
   if (threadIdx.x == 0) {
     for (int i = 0; i < nstages; i++) {
       mbar_init(smem_u32(&s.full[i]), 1);
@@ -197,39 +261,64 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
       int stage = 0;
       uint32_t phase = 0, xphase = 0;
       int cur_split = -1;
+      // Weight stages of this CTA's first task fill the ring before the
+      // upstream dependency resolves; the main loop skips those `pre` stages.
+      int pre = 0;
+      if ((int)blockIdx.x < ntasks) {
+        const int t = blockIdx.x;
+        const int split = t / nps;
+        int r0, rows;
+        task_rows(g, t - split * nps, r0, rows);
+        const int k0 = split * kseg, klen = min(kseg, a.K - k0);
+        for (int off = 0; off < klen && pre < nstages; off += seg, pre++) {
+          const int len = min(seg, klen - off);
+          mbar_wait(smem_u32(&s.empty[stage]), phase ^ 1);
+          mbar_expect_tx(smem_u32(&s.full[stage]), rows * len * 2);
+          __nv_bfloat16* dst = &ring[stage * R * srow];
+          for (int r = 0; r < rows; r++)
+            bulk_copy_1d(smem_u32(dst + r * srow), row_ptr<MODE>(a, r0 + r) + k0 + off, len * 2, smem_u32(&s.full[stage]));
+          if (++stage == nstages) { stage = 0; phase ^= 1; }
+        }
+      }
+      pdl_wait();
       for (int t = blockIdx.x; t < ntasks; t += gridDim.x) {
-        const int split = t / nblk, blk = t - split * nblk;      // range-major
-        const int r0 = blk * R, rows = min(R, nrows - r0);
+        const int split = t / nps;                                // range-major
+        int r0, rows;
+        task_rows(g, t - split * nps, r0, rows);
         const int k0 = split * kseg, klen = min(kseg, a.K - k0);
         if (MODE != SGATE && split != cur_split) {  // stage this range of x (consumers have released the old one)
           mbar_wait(smem_u32(&s.xempty), xphase);
           mbar_expect_tx(smem_u32(&s.xfull), a.M * klen * 2);
           for (int m = 0; m < a.M; m++)
-            bulk_copy_1d(smem_u32(&s.x[m * xrow]), a.x + (size_t)m * a.ldx + k0, klen * 2, smem_u32(&s.xfull));
+            bulk_copy_1d(smem_u32(&xs[m * xrow]), a.x + (size_t)m * a.ldx + k0, klen * 2, smem_u32(&s.xfull));
           xphase ^= 1;
           cur_split = split;
         }
-        for (int st = 0, off = 0; off < klen; st++, off += seg) {
+        for (int off = 0; off < klen; off += seg) {
+          if (pre > 0) { pre--; continue; }  // already in flight (first task only)
           const int len = min(seg, klen - off);
           mbar_wait(smem_u32(&s.empty[stage]), phase ^ 1);
           mbar_expect_tx(smem_u32(&s.full[stage]), rows * len * 2);
-          __nv_bfloat16* dst = &s.ring[stage * R * srow];
+          __nv_bfloat16* dst = &ring[stage * R * srow];
           for (int r = 0; r < rows; r++)
             bulk_copy_1d(smem_u32(dst + r * srow), row_ptr<MODE>(a, r0 + r) + k0 + off, len * 2, smem_u32(&s.full[stage]));
           if (++stage == nstages) { stage = 0; phase ^= 1; }
         }
       }
+      pdl_trigger();  // our weight stream is issued: the dependent launch may take the SMs we vacate
     }
     __syncthreads();  // matches the compute warps' final barrier
     return s.flag != 0;
   }
   const int ct = threadIdx.x - 32, cw = warp - 1;
+  pdl_wait();  // everything from here on touches upstream products or writes
   int stage = 0;
   uint32_t phase = 0, xphase = 0;
   int cur_split = -1;
   for (int t = blockIdx.x; t < ntasks; t += gridDim.x) {
-    const int split = t / nblk, blk = t - split * nblk;
-    const int r0 = blk * R, rows = min(R, nrows - r0);
+    const int split = t / nps;
+    int r0, rows;
+    task_rows(g, t - split * nps, r0, rows);
     const int klen = min(kseg, a.K - split * kseg);
     if (split != cur_split) {  // release the old x range, wait for the new one
       if (MODE == SGATE) {  // the compute warps make this range of x themselves
@@ -253,7 +342,7 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
             const float g = bf16r(1.0f / (1.0f + expf(-__bfloat162float(gv[j]))));
             ov[j] = __float2bfloat16_rn(__bfloat162float(av[j]) * g);
           }
-          *reinterpret_cast<uint4*>(&s.x[m * xrow + kk]) = oq;
+          *reinterpret_cast<uint4*>(&xs[m * xrow + kk]) = oq;
         }
         named_bar_sync(BAR_C, CTHREADS);
       } else {
@@ -267,14 +356,15 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
     // (l/8) = k tile-half; matrices (k 0-7, 8-15, 16-23, 24-31) of a 32-k
     // pair of tiles come back as (b0, b1, b0', b1') / (a0, a2, a0', a2').
     // x rows beyond M are never emitted: clamp them onto row M-1 (broadcast).
-    const uint32_t xbase = smem_u32(&s.x[min(lane & 7, a.M - 1) * xrow + (lane >> 3) * 8]);
+    const uint32_t xbase = smem_u32(&xs[min(lane & 7, a.M - 1) * xrow + (lane >> 3) * 8]);
     float c[4][4];
 #pragma unroll
     for (int j = 0; j < 4; j++) c[j][0] = c[j][1] = c[j][2] = c[j][3] = 0.f;
-    for (int st = 0, off = 0; off < klen; st++, off += seg) {
+    const int wrow = min(lane & 7, rows - 1);  // rows beyond the task: clamp (never emitted)
+    for (int off = 0; off < klen; off += seg) {
       const int npairs = min(seg, klen - off) / 32;              // len % 32 == 0 (every K is)
       mbar_wait(smem_u32(&s.full[stage]), phase);
-      const uint32_t wbase = smem_u32(&s.ring[stage * R * srow + (lane & 7) * srow + (lane >> 3) * 8]);
+      const uint32_t wbase = smem_u32(&ring[stage * R * srow + wrow * srow + (lane >> 3) * 8]);
       const uint32_t xoff = xbase + off * 2;
       int pr = cw;
       for (; pr + NCW < npairs; pr += 2 * NCW) {
@@ -312,6 +402,7 @@ __device__ __forceinline__ bool gemm8_core(const Args& a, Smem& s) {
     }
     named_bar_sync(BAR_C, CTHREADS);
   }
+  pdl_trigger();
   if (ct == 0) {  // one thread of the compute warps
     const int my = (ntasks - (int)blockIdx.x + (int)gridDim.x - 1) / (int)gridDim.x;
     if (my > 0 && a.gcnt) {
@@ -341,8 +432,10 @@ __device__ __forceinline__ int aten_block_width(int dim0, int dim1) {
 }
 // Sum of z[j]^2 over j in [0, N) in ATen's order (sq[j] = fmul_rn(z, z), then
 // the vectorized-by-4 reduction of kern_gemma_rms_norm.cu) for W virtual
-// lanes emulated by NTHREADS threads (lane l = t + v*NTHREADS); the result is
-// returned in every thread.
+// lanes; the result is returned in every thread.  (Folding the W lanes in
+// the registers of one warp instead costs 40 registers and a local-memory
+// tree in every add_norm variant, and the M = 8 verify pass pays 3 us per
+// launch for it; the smem tree is the same additions in the same order.)
 __device__ float aten_row_sumsq(const float* __restrict__ z, int N, int W, float* __restrict__ s) {
   const int t = threadIdx.x;
   constexpr int VL = (NT_ATEN + NTHREADS - 1) / NTHREADS;
@@ -403,27 +496,71 @@ __device__ __forceinline__ float4 ysum4(const Args& a, int nranges, int m, int j
 // Rows [m0, m1) of the tail.  NORM: vLLM's Gemma fused_add_rms_norm with
 // ATen's reduction width for the launch's M rows; z lives in smem (ring).
 // PLAIN: just fold the partials to bf16 y.
+// The tail is latency, not bandwidth: every global round trip of a row is
+// issued for all of the thread's groups at once (TG = 6 float4 per thread
+// covers N <= 6144), and w1 is already in smem (loaded during the wait).
+constexpr int TG = (6144 / 4 + NTHREADS - 1) / NTHREADS;
 template <int MODE>
-__device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const float* w1, __nv_bfloat16* res, __nv_bfloat16* out,
-                          float eps, float* __restrict__ z, float* __restrict__ s) {
+__device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const float* __restrict__ w1s, __nv_bfloat16* res,
+                          __nv_bfloat16* out, float eps, float* __restrict__ z, float* __restrict__ s) {
   const int N = a.N;
   for (int m = m0; m < m1; m++) {
-    for (int j = threadIdx.x * 4; j < N; j += NTHREADS * 4) {
-      float4 v = ysum4(a, nranges, m, j);
+    float4 v[TG];
+    float2 r0[TG], r1[TG];
+    if (MODE >= NORM) {  // the residual pair of every group, issued as one batch
+      uint32_t rw[TG][2];
+#pragma unroll
+      for (int gi = 0; gi < TG; gi++) {
+        const int j = (threadIdx.x + gi * NTHREADS) * 4;
+        const uint32_t* rr = reinterpret_cast<const uint32_t*>(res + (size_t)m * N + j);
+        rw[gi][0] = j < N ? rr[0] : 0u;
+        rw[gi][1] = j < N ? rr[1] : 0u;
+      }
+#pragma unroll
+      for (int gi = 0; gi < TG; gi++) {
+        r0[gi] = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(&rw[gi][0]));
+        r1[gi] = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(&rw[gi][1]));
+      }
+    }
+    if (nranges == 1) {  // y scratch, bf16: one batch of plain loads
+      uint32_t yw[TG][2];
+#pragma unroll
+      for (int gi = 0; gi < TG; gi++) {
+        const int j = (threadIdx.x + gi * NTHREADS) * 4;
+        const uint32_t* yy = reinterpret_cast<const uint32_t*>(a.y + (size_t)m * N + j);
+        yw[gi][0] = j < N ? __ldcg(yy) : 0u;
+        yw[gi][1] = j < N ? __ldcg(yy + 1) : 0u;
+      }
+#pragma unroll
+      for (int gi = 0; gi < TG; gi++) {
+        const float2 l = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(&yw[gi][0]));
+        const float2 h = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(&yw[gi][1]));
+        v[gi] = make_float4(l.x, l.y, h.x, h.y);
+      }
+    } else {
+#pragma unroll
+      for (int gi = 0; gi < TG; gi++) {
+        const int j = (threadIdx.x + gi * NTHREADS) * 4;
+        if (j < N) v[gi] = ysum4(a, nranges, m, j);
+      }
+    }
+#pragma unroll
+    for (int gi = 0; gi < TG; gi++) {
+      const int j = (threadIdx.x + gi * NTHREADS) * 4;
+      if (j >= N) continue;
+      float4 y = v[gi];
       if (MODE >= NORM) {
         __nv_bfloat16* rr = res + (size_t)m * N + j;
-        v.x = bf16r(v.x); v.y = bf16r(v.y); v.z = bf16r(v.z); v.w = bf16r(v.w);   // the bf16 y rounding
-        const float2 r0 = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(rr));
-        const float2 r1 = __bfloat1622float2(*reinterpret_cast<const __nv_bfloat162*>(rr + 2));
-        v.x = __fadd_rn(v.x, r0.x); v.y = __fadd_rn(v.y, r0.y);
-        v.z = __fadd_rn(v.z, r1.x); v.w = __fadd_rn(v.w, r1.y);
-        *reinterpret_cast<__nv_bfloat162*>(rr) = __floats2bfloat162_rn(v.x, v.y);
-        *reinterpret_cast<__nv_bfloat162*>(rr + 2) = __floats2bfloat162_rn(v.z, v.w);
-        *reinterpret_cast<float4*>(z + j) = v;
+        y.x = bf16r(y.x); y.y = bf16r(y.y); y.z = bf16r(y.z); y.w = bf16r(y.w);   // the bf16 y rounding
+        y.x = __fadd_rn(y.x, r0[gi].x); y.y = __fadd_rn(y.y, r0[gi].y);
+        y.z = __fadd_rn(y.z, r1[gi].x); y.w = __fadd_rn(y.w, r1[gi].y);
+        *reinterpret_cast<__nv_bfloat162*>(rr) = __floats2bfloat162_rn(y.x, y.y);
+        *reinterpret_cast<__nv_bfloat162*>(rr + 2) = __floats2bfloat162_rn(y.z, y.w);
+        *reinterpret_cast<float4*>(z + j) = y;
       } else {
         __nv_bfloat16* yy = a.y + (size_t)m * N + j;
-        *reinterpret_cast<__nv_bfloat162*>(yy) = __floats2bfloat162_rn(v.x, v.y);
-        *reinterpret_cast<__nv_bfloat162*>(yy + 2) = __floats2bfloat162_rn(v.z, v.w);
+        *reinterpret_cast<__nv_bfloat162*>(yy) = __floats2bfloat162_rn(y.x, y.y);
+        *reinterpret_cast<__nv_bfloat162*>(yy + 2) = __floats2bfloat162_rn(y.z, y.w);
       }
     }
     if (MODE < NORM) continue;
@@ -433,9 +570,12 @@ __device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const floa
     const float factor = (float)a.M / (float)((long long)a.M * (long long)N);
     const float var = __fmul_rn(sum, factor);
     const float r = rsqrtf(__fadd_rn(var, eps));
-    for (int j = threadIdx.x * 4; j < N; j += NTHREADS * 4) {
+#pragma unroll
+    for (int gi = 0; gi < TG; gi++) {
+      const int j = (threadIdx.x + gi * NTHREADS) * 4;
+      if (j >= N) continue;
       const float4 zv = *reinterpret_cast<const float4*>(z + j);
-      const float4 w0 = *reinterpret_cast<const float4*>(w1 + j);
+      const float4 w0 = *reinterpret_cast<const float4*>(w1s + j);
       __nv_bfloat162 o0 = __floats2bfloat162_rn(__fmul_rn(__fmul_rn(zv.x, r), w0.x), __fmul_rn(__fmul_rn(zv.y, r), w0.y));
       __nv_bfloat162 o1 = __floats2bfloat162_rn(__fmul_rn(__fmul_rn(zv.z, r), w0.z), __fmul_rn(__fmul_rn(zv.w, r), w0.w));
       *reinterpret_cast<__nv_bfloat162*>(out + (size_t)m * N + j) = o0;
@@ -450,18 +590,22 @@ __device__ void tail_rows(const Args& a, int nranges, int m0, int m1, const floa
 template <int MODE>
 __device__ __forceinline__ void run_tail(const Args& a, Smem& s, bool last, __nv_bfloat16* res, const float* w1,
                                          __nv_bfloat16* out, float eps) {
-  const int nrows = MODE == GATEUP ? 2 * a.N : a.N;
-  const int nblk = (nrows + R - 1) / R;
+  const Geom g = task_geom<MODE>(a);
+  const int nblk = (g.nrows + R - 1) / R;
   const int nranges = pick_nranges(a, nblk);
+  const int ntasks = g.nps * nranges;
   if (MODE == PLAIN && nranges == 1) return;  // nothing left to do
-  static_assert(sizeof(s.ring) >= 6144 * 4, "z row: N <= 6144");
-  float* z = reinterpret_cast<float*>(&s.ring[0]);
+  static_assert(RING_ELEMS * 2 >= 2 * 6144 * 4, "z row + w1 copy: N <= 6144");
+  float* z = reinterpret_cast<float*>(&s.buf[0]);
+  float* w1s = z + 6144;
   if (gridDim.x <= nsmid()) {
     if ((int)blockIdx.x >= a.M) return;
+    if (MODE >= NORM)  // the norm weight is not waiting on anyone: fetch it under the spin
+      for (int j = threadIdx.x * 4; j < a.N; j += NTHREADS * 4) *reinterpret_cast<float4*>(w1s + j) = *reinterpret_cast<const float4*>(w1 + j);
     if (threadIdx.x == 0)
-      while (ld_acquire(a.gcnt) < nblk * nranges) __nanosleep(64);
+      while (ld_acquire(a.gcnt) < ntasks) __nanosleep(64);
     __syncthreads();
-    tail_rows<MODE>(a, nranges, blockIdx.x, blockIdx.x + 1, w1, res, out, eps, z, &s.red[0][0][0]);
+    tail_rows<MODE>(a, nranges, blockIdx.x, blockIdx.x + 1, w1s, res, out, eps, z, &s.red[0][0][0]);
     __syncthreads();
     if (threadIdx.x == 0 && atomicAdd(a.gcnt + 1, 1) == a.M - 1) {
       a.gcnt[1] = 0;
@@ -470,7 +614,10 @@ __device__ __forceinline__ void run_tail(const Args& a, Smem& s, bool last, __nv
     }
   } else if (last) {
     __threadfence();
-    tail_rows<MODE>(a, nranges, 0, a.M, w1, res, out, eps, z, &s.red[0][0][0]);
+    if (MODE >= NORM)
+      for (int j = threadIdx.x * 4; j < a.N; j += NTHREADS * 4) *reinterpret_cast<float4*>(w1s + j) = *reinterpret_cast<const float4*>(w1 + j);
+    __syncthreads();
+    tail_rows<MODE>(a, nranges, 0, a.M, w1s, res, out, eps, z, &s.red[0][0][0]);
     __syncthreads();
     if (threadIdx.x == 0) { *a.gcnt = 0; __threadfence(); }
   }

From 958efa0151de38b56ceeff6053569bb503bae3a8 Mon Sep 17 00:00:00 2001
From: JinYan Su <751080330@qq.com>
Date: Tue, 1 Sep 2026 03:35:04 +0000
Subject: [PATCH 10/10] docs: qwen3.8 kern vs vLLM 0.28 on one GB300 (tray14),
 same five prompts

bs=1, greedy, 5 prompts x 400 tokens, measured back to back on the same
tray (tools/qwen38_vllm_perf.py, tools/qwen38_compare.py):

| | vLLM default (FlashInfer, trtllm-gen decode, CUDA graph) | vLLM pinned (TRITON_ATTN + triton GDN) | kern c10 |
| plain decode | 95.1 tok/s (94.8-95.3) | 95.9 (95.8-96.1) | 105.1 (102.8-105.8), 9.5 ms/step |
| DFlash2 (7 draft tokens) | 171.4 tok/s (148.5-206.4), 2.45 tok/round | - | 181.4 (156.9-210.4), 2.57 tok/round, 14.1 ms/round |

vLLM's DFlash2 acceptance per position [521, 307, 164, 93, 45, 35, 25] over
818 rounds; kern's draft runs its attention on the Stage 1 Triton unified
attention instead of vLLM's cute FA, so the accepted counts differ a little
(greedy verification keeps the output exact either way).

Signed-off-by: JinYan Su <751080330@qq.com>
---
 docs/qwen38-compare-c10-plain-tray14.json     | 2078 +++++++++++++++++
 docs/qwen38-compare-c10-spec-tray14.json      | 2078 +++++++++++++++++
 .../qwen38-vllm-perf-plain-pinned-tray14.json | 2048 ++++++++++++++++
 docs/qwen38-vllm-perf-plain-tray14.json       | 2048 ++++++++++++++++
 docs/qwen38-vllm-perf-spec-tray14.json        | 2061 ++++++++++++++++
 5 files changed, 10313 insertions(+)
 create mode 100644 docs/qwen38-compare-c10-plain-tray14.json
 create mode 100644 docs/qwen38-compare-c10-spec-tray14.json
 create mode 100644 docs/qwen38-vllm-perf-plain-pinned-tray14.json
 create mode 100644 docs/qwen38-vllm-perf-plain-tray14.json
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index 0000000..de1e92b
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+   "text": "Explain, in plain language and with one concrete example each, why a bridge expands in summer, why a kettle sings before it boils, and why the far side of the moon is never seen from Earth.\n\nHere is an explanation of these three phenomena in plain language, along with a concrete example for each.\n\n### 1. Why a Bridge Expands in Summer\n\n**The Explanation:**\nEverything in the world is made of tiny particles (atoms and molecules) that are constantly moving. When the temperature rises, these particles gain energy and move faster, causing them to push slightly further apart from one another. This is called **thermal expansion**.\n\nWhile the expansion of a single atom is invisible, when you multiply that tiny movement by the billions of atoms in a massive structure like a steel bridge, the total change in length becomes significant. If a bridge were built as one solid, unbroken piece, the heat in summer would cause it to stretch. Without room to stretch, the internal pressure would build up, potentially causing the bridge to buckle, crack, or even collapse.\n\n**Concrete Example:**\nImagine a **1,000-foot-long steel bridge**. Steel expands at a rate of about 0.000006 inches per inch for every degree Fahrenheit increase in temperature.\n*   If the temperature rises by **50\u00b0F** from winter to summer, that 1,000-foot bridge will expand by roughly **3.6 inches**.\n*   To accommodate this, engineers install **expansion joints** (the grooves you see in the road surface). These joints act like shock absorbers, allowing the two ends of the bridge to slide slightly past each other as the metal stretches in the heat and contracts in the cold.\n\n***\n\n### 2. Why a Kettle Sings Before It Boils\n\n**The Explanation:**\nThe \"singing\" or whistling sound is actually a result of **vibration caused by steam being trapped and then released**.\n\nWhen you first heat water, the bottom of the kettle gets hot before the water at the top. This creates a layer of hot water at the bottom that\n"
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+   "text": "The lighthouse keeper's daughter learned to read from shipping manifests and weather logs, so her first stories were inventories: forty barrels of salt cod, a crate of oranges gone soft, one bishop travelling incognito. By twelve she had filled three notebooks with lists that were secretly poems. By fifteen she had written a novel about a woman who could hear the sea's moods through the floorboards, and she'd given it to her father, who read it in one sitting and then said, \"You've made the sea sound like a drunk uncle.\" She took this as the highest praise.\n\nThe novel was never published. It sat in a drawer for eleven years, gathering the particular silence of things that are loved but not yet ready. When she finally sent it to a publisher, they called it \"charming\" and \"a little too long.\" She rewrote it twice. The third version was better, but it was no longer the novel she'd written at fifteen, and she kept the original in a box labelled DO NOT EDIT, which was a lie, because she edited it constantly, in her head, while walking the cliff path, while mending nets, while listening to the gulls argue over a dead mackerel.\n\nShe published her first book at thirty-four. It was a collection of short stories about the people who lived in the village below the lighthouse: the fisherman who kept a diary of every storm, the widow who sold her late husband's boat and used the money to buy a piano, the boy who collected bottle caps and believed each one contained a different ocean. The book was well received. A critic wrote that it had \"the patience of tide tables\" and \"the sudden violence of a wave breaking on a flat shore.\" She framed the review and hung it above her desk, where it stayed for six years until a storm blew the window open and the glass shattered on the floor. She swept up the pieces and kept the review, now with a jagged edge, in a drawer with the original novel.\n\nThe second book took four years. It was a novel about a woman who inherited a lighthouse and discovered that the light had been turned off for thirty years,\n"
+  }
+ ]
+}
\ No newline at end of file
diff --git a/docs/qwen38-compare-c10-spec-tray14.json b/docs/qwen38-compare-c10-spec-tray14.json
new file mode 100644
index 0000000..9257c98
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+   "text": "The harbor master kept a ledger of every ship that wintered in the bay, noting cargo, crew, and the state of each hull in a cramped, looping hand. In the spring of the third year he found a single entry that did not match any of the others: a vessel called the *Aurora* had been recorded as \u201clost at sea\u201d on the night of the equinox, yet the same night a fisherman\u2019s log described a bright, silver\u2011scaled fish leaping from the water and a strange, humming light over the harbor. The master\u2019s pen trembled; the two records could not both be true, and the contradiction forced him to ask a question that had haunted sailors for centuries: **what, if anything, lies beneath the surface of the world we think we know?**\n\nThat question is the engine of every mystery that follows. It begins with a simple observation\u2014a fish that glitters like a mirror, a light that hums like a distant choir\u2014and it expands into a chain of evidence, each link a fact that can be checked, a story that can be retold, a law that can be tested. By tracing that chain we move from the concrete (the ledger, the fisherman\u2019s log) to the abstract (the nature of knowledge itself), and we discover how the same method that solves a harbor mystery also underpins the great scientific revolutions of the last two centuries.\n\n### 1. From a Single Anomaly to a General Method\n\nThe harbor master\u2019s dilemma illustrates a universal problem: **how do we decide which of two conflicting accounts is true?** The answer is not a single rule but a process:\n\n1. **Identify the anomaly.** A \u201clost\u201d ship and a \u201cseen\u201d light cannot coexist without explanation.\n2. **Gather independent evidence.** The ledger, the fisherman\u2019s log, tide tables, and the condition of the hull all become data points.\n3. **Form a hypothesis.** Perhaps the *Aurora* was not lost but hidden in a fog bank, and the \u201clight\u201d was a bioluminescent bloom.\n4. **Test the hypothesis.** Check whether fog was\n"
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+   "text": "When the observatory finally reopened after the renovation, the docents discovered that the old refractor had been quietly recollimated by a retired machinist who lived nearby. He left no note, only a faint smell of machine oil and a set of brass shims tucked under the focuser. The first night under the new sky, the star field looked sharper than anyone remembered, yet the faintest nebulae still seemed to wobble. The team realized that the telescope\u2019s optical train was no longer a simple \u201clens\u2011and\u2011mirror\u201d story; it was a layered puzzle of surfaces, each with its own curvature, spacing, and material.\n\nTo untangle that puzzle, we must first ask: **how do we describe a system of many refracting surfaces in a way that lets us predict where an image will form, how large it will be, and how the light will bend?** The answer lies in a compact set of equations that treat the whole assembly as a single \u201cequivalent\u201d lens, while still keeping track of the individual elements that give the system its character.\n\n### 1. From a single surface to a whole system\n\nFor a single refracting surface the paraxial relation is\n\n\\[\n\\frac{n'}{s'}-\\frac{n}{s}=\\frac{n'-n}{r},\n\\]\n\nwhere \\(s\\) and \\(s'\\) are the object and image distances measured from the surface, \\(n\\) and \\(n'\\) are the refractive indices on either side, and \\(r\\) is the surface radius.  \n\nWhen several surfaces are placed in sequence, the same form survives if we replace the individual quantities by **system\u2011wide** ones:\n\n\\[\n\\frac{n'}{s'}-\\frac{n}{s}=\\frac{n'-n}{r_{\\text{eq}}}.\n\\]\n\nHere \\(r_{\\text{eq}}\\) is the radius of curvature of an *equivalent* single surface that would produce exactly the same paraxial imaging as the whole stack. The power of the system is\n\n\\[\n\\Phi = n' - n = \\frac{n'-\n"
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+   "text": "The floodplain census took three summers to complete. In the first summer the crews mapped oxbow lakes and counted heron rookeries from canoes, losing two clipboards and one outboard motor to the river. In the second summer they walked the levees with transect tapes, logging every stand of cypress, every patch of sedge, every abandoned crawfish trap. In the third summer they returned to the same transects and found the river had moved.\n\nThe census was never finished. The river kept adding data.\n\n---\n\nThe first thing you notice about the floodplain is that it is not a place. It is a process wearing the costume of a place. The land you stand on was riverbed a decade ago and will be riverbed again in a decade. The trees are not rooted in soil so much as in a negotiation with water. The cypress knees are not roots. They are the tree's way of breathing when the ground is a lake.\n\nThe second thing you notice is the silence. Not the absence of sound. The presence of a different kind of sound. The air is thick with the noise of water moving through vegetation, of insects working the surface tension, of birds calling in frequencies that seem designed to travel through wet air. The silence is the sound of a system in equilibrium. Or the sound of a system pretending to be in equilibrium.\n\nThe third thing you notice is the smell. Sulfur. Decomposition. The particular sweetness of water that has been through a thousand years of leaf litter and root exudate. The smell of a place that has been digesting itself for a very long time.\n\n---\n\nThe levees were built in the 1930s. The Army Corps of Engineers came with their surveying equipment and their concrete and their certainty that a river could be made to behave. They built the levees to protect the cotton fields and the railroads and the small towns that had grown up along the banks. They did not build them to protect the floodplain. They built them to make the floodplain go away.\n\nThe levees worked. For a while. The river stayed in its channel. The cotton grew\n"
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+   "text": "Explain, in plain language and with one concrete example each, why a bridge expands in summer, why a kettle sings before it boils, and why the far side of the moon is never seen from Earth.\n\nHere is an explanation of these three phenomena in plain language, along with a concrete example for each.\n\n### 1. Why a Bridge Expands in Summer\n\n**The Explanation:**\nEverything in the world is made of tiny particles (atoms and molecules) that are constantly moving. When the temperature rises, these particles gain energy and move faster, causing them to push slightly further apart from one another. This is called **thermal expansion**.\n\nWhile a bridge might look like a solid, unchanging block of steel or concrete, it is actually a collection of billions of these particles. In the summer heat, the material absorbs energy, the particles vibrate more intensely, and the entire structure physically gets longer. If engineers didn't account for this, the bridge could buckle or crack under the pressure of its own expansion.\n\n**Concrete Example:**\nImagine a **long metal ruler** left on a hot car dashboard in July. If you measure it with a caliper, it will be a tiny fraction of a millimeter longer than it was in January. A bridge is just a giant version of this ruler; because it is hundreds of meters long, that tiny expansion adds up to several inches of movement, which is why you see gaps (expansion joints) between sections of the bridge.\n\n***\n\n### 2. Why a Kettle Sings Before It Boils\n\n**The Explanation:**\nThe \"singing\" or whistling sound is actually a result of **vibration caused by steam being trapped and then released**.\n\nWhen you first heat water, bubbles form at the bottom of the kettle where it is hottest. These bubbles are made of steam. However, the water higher up in the kettle is still cool. As the steam bubbles rise, they hit the cooler water and instantly collapse (condense back into liquid). This rapid collapsing creates a chaotic, rattling noise.\n\nAs the water gets closer to boiling, the entire volume of water becomes hot. The\n"
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+  }
+ ]
+}
\ No newline at end of file
diff --git a/docs/qwen38-vllm-perf-plain-pinned-tray14.json b/docs/qwen38-vllm-perf-plain-pinned-tray14.json
new file mode 100644
index 0000000..13d3daf
--- /dev/null
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+  }
+ ],
+ "metrics": {
+  "vllm:spec_decode_num_drafts": 818,
+  "vllm:spec_decode_num_draft_tokens": 5726,
+  "vllm:spec_decode_num_accepted_tokens": 1190,
+  "vllm:spec_decode_num_accepted_tokens_per_pos": [
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+   45,
+   35,
+   25
+  ]
+ },
+ "mean_tok_s": 171.4
+}
\ No newline at end of file