feat: NVFP4 mega MoE kernel (scale_vec::4X, UE4M3 block scales)

- New CUDA kernel: sm100_fp8_nvfp4_mega_moe_impl
  - kGranK=16 (NVFP4 group_size=16, vs MXFP4's 32)
  - kind::mxf4nvf4.block_scale.scale_vec::4X PTX instruction
  - float_ue4m3_t scale factor type in instruction descriptor
  - SF layout: scale_vec::4X (4 TMEM sub-columns per UMMA atom)
  - UTCCP column stride: i*8 (vs MXFP4's i*4) for 4X layout
  - L1 epilogue: UE4M3 activation scales (float→cutlass::float_e4m3_t)
  - SF loading: kNumSFUint32 = kHidden/64 (4 UE4M3 per int32)

- New PTX wrappers: SM100_MMA_MXF4NVF4_2x1SM_SS, SM100_MMA_MXF4NVF4_SS

- Python API:
  - fp8_nvfp4_mega_moe() with recipe=(1,1,16)
  - transform_nvfp4_weights_for_mega_moe() for UE4M3→int32 UTCCP packing
  - _pack_nvfp4_sf_for_utccp() helper

- C++ bindings:
  - mega_nvfp4.hpp with NVFP4-specific SymmBuffer (SF stride K/16)
  - JIT kernel header with kGranK=16 TMA descriptors
  - Registered in python_api.cpp

NOTE: Both SFA and SFB must use UE4M3 (scale_format_ is 1-bit, shared).
The L1 epilogue converts float→UE4M3 for activation scales.

deep_gemm/mega/__init__.py

@@ -105,6 +105,57 @@ def transform_weights_for_mega_moe(
     return l1_weights, l2_weights
 
 
+def _pack_nvfp4_sf_for_utccp(sf: torch.Tensor) -> torch.Tensor:
+    """Pack NVFP4 UE4M3 block scales (float8_e4m3fn) into int32 UTCCP layout.
+    
+    NVFP4 uses UE4M3 scales with group_size=16 (scale_vec::4X).
+    The UTCCP layout packs 4 consecutive scale bytes into each int32,
+    then applies the 4x32 transpose for TMA consumption.
+    
+    Input: (num_experts, mn, K//16) float8_e4m3fn scales
+    Output: (num_experts, mn, K//64) int32 packed UTCCP-transposed scales
+    """
+    num_groups, mn, sf_k = sf.shape
+    assert sf_k % 4 == 0, f"NVFP4 SF K dim must be divisible by 4, got {sf_k}"
+    assert mn % 128 == 0, f"MN dim must be divisible by 128, got {mn}"
+    
+    # View as uint8 and pack 4 consecutive bytes into int32
+    sf_uint8 = sf.view(torch.uint8)  # (num_groups, mn, sf_k)
+    # Pack: every 4 uint8 → 1 int32
+    packed = (sf_uint8[..., 0::4].to(torch.int32) |
+              (sf_uint8[..., 1::4].to(torch.int32) << 8) |
+              (sf_uint8[..., 2::4].to(torch.int32) << 16) |
+              (sf_uint8[..., 3::4].to(torch.int32) << 24))  # (num_groups, mn, sf_k//4)
+    
+    # Apply UTCCP 4x32 transpose (same as MXFP4 — the transpose is determined
+    # by the 128-element alignment, not the scale vector size)
+    packed_sf_k = sf_k // 4
+    result = (packed.reshape(num_groups, -1, 4, 32, packed_sf_k)
+                .transpose(2, 3)
+                .reshape(num_groups, mn, packed_sf_k))
+    return torch.empty_like(packed).copy_(result)
+
+
+def transform_nvfp4_weights_for_mega_moe(
+    l1_weights: Tuple[torch.Tensor, torch.Tensor],
+    l2_weights: Tuple[torch.Tensor, torch.Tensor]
+) -> Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor]]:
+    """Transform NVFP4 expert weights for the mega_moe kernel.
+    
+    NVFP4 weights come as (weight, scale) where:
+    - weight: uint8 E2M1 packed, shape (num_experts, N, K//2)
+    - 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.
+    """
+    # 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]))
+    # 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
+
+
 def fp8_fp4_mega_moe(y: torch.Tensor,
                      l1_weights: Tuple[torch.Tensor, torch.Tensor],
                      l2_weights: Tuple[torch.Tensor, torch.Tensor],
@@ -126,3 +177,32 @@ def fp8_fp4_mega_moe(y: torch.Tensor,
         activation, activation_clamp,
         fast_math
     )
+
+
+def fp8_nvfp4_mega_moe(y: torch.Tensor,
+                       l1_weights: Tuple[torch.Tensor, torch.Tensor],
+                       l2_weights: Tuple[torch.Tensor, torch.Tensor],
+                       sym_buffer: SymmBuffer,
+                       cumulative_local_expert_recv_stats: Optional[torch.Tensor] = None,
+                       recipe: Tuple[int, int, int] = (1, 1, 16),
+                       activation: str = 'swiglu',
+                       activation_clamp: Optional[float] = None,
+                       fast_math: bool = True):
+    """NVFP4 mega MoE: uses kind::mxf4nvf4.block_scale.scale_vec::4X
+    with UE4M3 block scales (group_size=16).
+    
+    Weight format: (uint8 E2M1 packed, int32 packed UTCCP UE4M3 scales)
+    Recipe: (1, 1, 16) — kGranK=16 for NVFP4 group_size=16.
+    """
+    _C.fp8_nvfp4_mega_moe(
+        y,
+        l1_weights, l2_weights,
+        cumulative_local_expert_recv_stats,
+        sym_buffer.buffer,
+        sym_buffer.handle.buffer_ptrs, sym_buffer.group.rank(),
+        sym_buffer.num_max_tokens_per_rank,
+        sym_buffer.num_experts, sym_buffer.num_topk,
+        recipe,
+        activation, activation_clamp,
+        fast_math
+    )