perf: P2 landed (gsa fill elimination). P0/P1 fused SwiGLU disabled — CuTeDSL kernel arg-binding bug.

P0/P1: The fused SwiGLU kernel's warmup_fused_swiglu_compilation() triggers
'TypeError: too many positional arguments' during cute.compile(). The kernel
signature doesn't match the positional args being passed. This is a kernel-side
fix, not a single_shot fix. Disabled until the fused kernel is debugged.

P2: Landed — Nvfp4Linear skips redundant _gsa_buf.fill_() after warmup.

SE fused SwiGLU infrastructure (set_fused_swiglu, _run_l1_fused, interleaved
weight path) is wired but disabled. Will activate once kernel fix lands.

dsv4/layers/shared_expert.py

@@ -254,14 +254,13 @@ class Nvfp4SharedExpert:
         # Run fused grouped GEMM with 1 group
         l1_out = run_fused_swiglu_grouped_gemm(
             mat_a=x_fp4,
-            scale_a=x_sf,
-            global_scale_a=gsa,
             mat_b=self._l1_mat_b,
+            scale_a=x_sf,
             scale_b=self._l1_sf_view,
-            global_scale_b=self._l1_gs_view,
             expert_offsets=torch.tensor([num_tokens], dtype=torch.int64, device=x_fp4.device),
+            global_scale_a=gsa,
+            global_scale_b=self._l1_gs_view,
             swiglu_limit=self.swiglu_limit if self.swiglu_limit is not None else 0.0,
-            num_tokens=num_tokens,
         )
         return l1_out  # (num_tokens, intermediate_size) BF16, SwiGLU already applied
 

single_shot_inference.py

@@ -1021,7 +1021,7 @@ def main():
                        intermediate_size=cfg.get("moe_intermediate_size", 3072),
                        top_k=cfg.get("num_experts_per_tok", 6), device=dev)
         moe.set_swiglu_limit(cfg.get("swiglu_limit", 10.0))
-        moe._fused_swiglu = True  # P0: Enable fused SwiGLU kernel — eliminates 8 BF16 launches per MoE per token
+        moe._fused_swiglu = False  # P0: Fused SwiGLU kernel has CuTeDSL arg-binding issue — disabled until kernel fix
         _load_moe_weights_stacked(all_w, li, pfx, dev, moe, cfg)
         # EAGERLY process stacked weights → K-major + swizzle, free raw tensors
         moe._ensure_stacked()
@@ -1037,7 +1037,8 @@ def main():
                                device=dev, swiglu_limit=cfg.get("swiglu_limit", 10.0))
         _load_shared_expert_weights(all_w, li, pfx, dev, se, cfg)
         # P1: Enable fused SwiGLU for shared expert (1-group variant of MoE fused kernel)
-        se.set_fused_swiglu(True)
+        # DISABLED: Same CuTeDSL arg-binding issue as MoE fused kernel
+        se.set_fused_swiglu(False)
         # EAGERLY process shared expert weights
         se._ensure_initialized()
         # Fix activation global scales — _ensure_initialized sets gsa from l1_gs (which is 1.0)