[Refactor] Refactor MOE NVFP4 Code Base: ModelOpt + Compressed Tensor (#21631)

Signed-off-by: yewentao256 <zhyanwentao@126.com>

vllm/model_executor/layers/quantization/utils/quant_utils.py

@@ -8,8 +8,10 @@ from typing import ClassVar, NamedTuple, Optional
 import numpy
 import torch
 
+from vllm._custom_ops import cutlass_scaled_mm_supports_fp4
 from vllm.model_executor.layers.quantization.qqq import (
     MARLIN_QQQ_SUPPORTED_NUM_BITS)
+from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
 
 
@@ -592,3 +594,56 @@ def awq_pack(
     q_w = q_w.reshape((-1, size_n)).contiguous()
 
     return pack_cols(q_w, num_bits, size_k, size_n)
+
+
+def swizzle_blockscale(scale: torch.Tensor) -> torch.Tensor:
+    """
+    Pad and block-interleave the FP4 block-scales so that they match the data
+    layout expected by the CUTLASS / FlashInfer kernels.
+
+    Parameters
+    ----------
+    scale: torch.Tensor
+
+    Returns
+    -------
+    torch.Tensor
+        The swizzled tensor with the same logical shape as *scale*.
+    """
+    assert scale.dtype == torch.float8_e4m3fn, (
+        "swizzle_blockscale expects the input tensor to be in "
+        "torch.float8_e4m3fn format.")
+
+    scale_ndim = scale.ndim
+    if scale_ndim == 2:
+        scale = scale.unsqueeze(0)  # (1, M, K)
+    assert scale.ndim == 3, "Expected a 2-D or 3-D tensor for block scales."
+
+    B, M, K = scale.shape
+
+    def _round_up(x: int, m: int) -> int:
+        return (x + m - 1) // m * m
+
+    M_padded = _round_up(M, 128)
+    K_padded = _round_up(K, 4)
+
+    padded = torch.zeros((B, M_padded, K_padded),
+                         dtype=scale.dtype,
+                         device=scale.device)
+    padded[:B, :M, :K] = scale
+
+    # Reshape / permute to the layout required by the kernel.
+    padded = padded.reshape(B, M_padded // 128, 4, 32, K_padded // 4, 4)
+    swizzled = padded.permute(0, 1, 4, 3, 2, 5).contiguous().cuda()
+
+    if scale_ndim == 2:
+        return swizzled.reshape(M, K)
+    return swizzled.reshape(B, M, K)
+
+
+def cutlass_fp4_supported() -> bool:
+    if not current_platform.is_cuda():
+        return False
+    capability_tuple = current_platform.get_device_capability()
+    capability = -1 if capability_tuple is None else capability_tuple.to_int()
+    return cutlass_scaled_mm_supports_fp4(capability)