feat: fold weight_scale_2 into block scales in NVFP4 transform

- transform_nvfp4_weights_for_mega_moe now accepts weight_scale_2
- Folds global scale into block scales: UE4M3 * FP32 -> UE4M3
- Dequantize to f32, multiply by global scale, clamp [0,448], re-quantize
- This is needed because the kernel only applies one level of block scaling

deep_gemm/mega/__init__.py

@@ -138,7 +138,9 @@ def _pack_nvfp4_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
 
 def transform_nvfp4_weights_for_mega_moe(
     l1_weights: Tuple[torch.Tensor, torch.Tensor],
-    l2_weights: Tuple[torch.Tensor, torch.Tensor]
+    l2_weights: Tuple[torch.Tensor, torch.Tensor],
+    l1_weight_scale_2: Optional[torch.Tensor] = None,
+    l2_weight_scale_2: Optional[torch.Tensor] = None
 ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
     """Transform NVFP4 expert weights for the mega_moe kernel.
     
@@ -147,13 +149,36 @@ def transform_nvfp4_weights_for_mega_moe(
     - scale: float8_e4m3fn UE4M3 block scales, shape (num_experts, N, K//16)
     
     The kernel expects (weight, packed_sf) where packed_sf is int32 UTCCP layout.
+    
+    If weight_scale_2 (float32 global scale) is provided, it is folded into the
+    block scales: effective_scale = block_scale * global_scale → re-quantized to UE4M3.
+    This is needed because the kernel only applies one level of scaling.
     """
+    def fold_global_scale(sf: torch.Tensor, scale_2: Optional[torch.Tensor]) -> torch.Tensor:
+        """Fold weight_scale_2 into block scales: UE4M3 * FP32 → UE4M3"""
+        if scale_2 is None:
+            return sf
+        # Dequantize UE4M3 → float32
+        sf_f32 = sf.to(torch.float32)
+        # Multiply by global scale
+        # scale_2 shape: (num_experts, 1, 1) or (num_experts,) — broadcast over mn and K
+        if scale_2.dim() == 1:
+            scale_2 = scale_2.view(-1, 1, 1)
+        sf_f32 = sf_f32 * scale_2
+        # Re-quantize to UE4M3 (clamp to [0, 448])
+        sf_f32 = sf_f32.clamp(0.0, 448.0)
+        return sf_f32.to(torch.float8_e4m3fn)
+    
+    # Fold global scales into block scales
+    l1_sf = fold_global_scale(l1_weights[1], l1_weight_scale_2)
+    l2_sf = fold_global_scale(l2_weights[1], l2_weight_scale_2)
+    
     # L1: interleave gate/up, then pack + transpose SF for UTCCP
-    l1_interleaved = _interleave_l1_weights(l1_weights)
-    l1_weights = (l1_interleaved[0], _pack_nvfp4_sf_for_utccp(l1_interleaved[1]))
+    l1_interleaved = _interleave_l1_weights((l1_weights[0], l1_sf))
+    l1_out = (l1_interleaved[0], _pack_nvfp4_sf_for_utccp(l1_interleaved[1]))
     # L2: only pack + transpose SF for UTCCP
-    l2_weights = (l2_weights[0], _pack_nvfp4_sf_for_utccp(l2_weights[1]))
-    return l1_weights, l2_weights
+    l2_out = (l2_weights[0], _pack_nvfp4_sf_for_utccp(l2_sf))
+    return l1_out, l2_out
 
 
 def fp8_fp4_mega_moe(y: torch.Tensor,