tweax n shit

deep_gemm/mega/__init__.py

@@ -187,35 +187,6 @@ 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(