init

model_executor/layers/quantization/modelopt.py

@@ -0,0 +1,737 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Callable, Optional, Union
+
+import torch
+from torch.nn import Module
+from torch.nn.parameter import Parameter
+
+from vllm._custom_ops import (cutlass_scaled_fp4_mm,
+                              cutlass_scaled_mm_supports_fp4, scaled_fp4_quant)
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
+    apply_fp4_marlin_linear, is_fp4_marlin_supported,
+    prepare_fp4_layer_for_marlin, prepare_moe_fp4_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    is_layer_skipped)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    Fp8LinearOp, requantize_with_max_scale)
+from vllm.model_executor.parameter import (ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+QUANT_ALGOS = ["FP8", "NVFP4"]
+KV_CACHE_QUANT_ALGOS = ["FP8"]
+
+
+class ModelOptFp8Config(QuantizationConfig):
+    """Config class for ModelOpt FP8."""
+
+    def __init__(
+        self,
+        is_checkpoint_fp8_serialized: bool = False,
+    ) -> None:
+        super().__init__()
+        self.is_checkpoint_fp8_serialized = is_checkpoint_fp8_serialized
+        if is_checkpoint_fp8_serialized:
+            logger.warning("Detected ModelOpt fp8 checkpoint. Please note that"
+                           " the format is experimental and could change.")
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "modelopt"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16, torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 89
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return ["hf_quant_config.json"]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "ModelOptFp8Config":
+        quant_config = cls.get_from_keys(config, ["quantization"])
+        quant_method = quant_config["quant_algo"]
+        if quant_method not in QUANT_ALGOS:
+            raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
+                             " quantizations in vLLM. Please check the "
+                             "`hf_quant_config.json` file for your model's "
+                             "quant configuration.")
+        is_checkpoint_fp8_serialized = ("FP8" in quant_method)
+
+        return cls(is_checkpoint_fp8_serialized)
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+        if isinstance(layer, LinearBase):
+            return ModelOptFp8LinearMethod(self)
+        elif isinstance(layer, Attention):
+            return ModelOptFp8KVCacheMethod(self)
+        return None
+
+
+class ModelOptFp8LinearMethod(LinearMethodBase):
+    """Linear method for Model Optimizer static quantization.
+    Supports loading FP8 checkpoints with static weight scale and
+    activation scale. Future support might be added for dynamic 
+    scales.
+
+    Limitations:
+    1. Only support per-tensor quantization due to torch._scaled_mm support.
+    2. Only support float8_e4m3fn datatype 
+        Args: quant_config: The ModelOpt quantization config.
+    """
+
+    def __init__(self, quant_config: ModelOptFp8Config):
+        self.quant_config = quant_config
+        self.fp8_linear = Fp8LinearOp()
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+        weight_dtype = (torch.float8_e4m3fn
+                        if self.quant_config.is_checkpoint_fp8_serialized else
+                        params_dtype)
+        weight = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=weight_dtype),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        if self.quant_config.is_checkpoint_fp8_serialized:
+            # WEIGHT SCALE
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+            weight_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("weight_scale", weight_scale)
+            # INPUT SCALE
+            scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                            weight_loader=weight_loader)
+
+            scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", scale)
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        weight = layer.weight
+        max_w_scale = layer.weight_scale.max()
+        if not (layer.weight_scale == layer.weight_scale[0]).all():
+            max_w_scale, weight = requantize_with_max_scale(
+                layer.weight, layer.weight_scale, layer.logical_widths)
+        layer.weight = Parameter(weight.t(), requires_grad=False)
+        layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+        layer.input_scale = Parameter(layer.input_scale.max(),
+                                      requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        return self.fp8_linear.apply(input=x,
+                                     weight=layer.weight,
+                                     weight_scale=layer.weight_scale,
+                                     input_scale=layer.input_scale,
+                                     bias=bias)
+
+
+class ModelOptNvFp4Config(QuantizationConfig):
+    """Config class for ModelOpt FP4."""
+
+    def __init__(
+        self,
+        is_checkpoint_nvfp4_serialized: bool,
+        kv_cache_quant_algo: str,
+        exclude_modules: list[str],
+        group_size: int = 16,
+    ) -> None:
+        self.is_checkpoint_nvfp4_serialized = is_checkpoint_nvfp4_serialized
+        if is_checkpoint_nvfp4_serialized:
+            logger.warning(
+                "Detected ModelOpt NVFP4 checkpoint. Please note that"
+                " the format is experimental and could change in future.")
+
+            self.group_size = group_size
+            self.kv_cache_quant_algo = kv_cache_quant_algo
+            self.exclude_modules = exclude_modules
+
+    @classmethod
+    def get_name(cls) -> QuantizationMethods:
+        return "modelopt_fp4"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.bfloat16, torch.half, torch.float8_e4m3fn]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return ["hf_quant_config.json"]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "ModelOptNvFp4Config":
+        quant_config = cls.get_from_keys(config, ["quantization"])
+        quant_method = quant_config["quant_algo"]
+        if quant_method not in QUANT_ALGOS:
+            raise ValueError(f"ModelOpt currently only supports: {QUANT_ALGOS}"
+                             " quantizations in vLLM. Please check the "
+                             "`hf_quant_config.json` file for your model's "
+                             "quant configuration.")
+        is_checkpoint_nvfp4_serialized = ("NVFP4" in quant_method)
+        if ("group_size" and "kv_cache_quant_algo"
+                and "exclude_modules") not in quant_config:
+            raise ValueError("NVFP4 quantization requires group size and "
+                             "kv_cache_quant_algo specified in "
+                             "hf_quant_config.json")
+        kv_cache_quant_algo = quant_config["kv_cache_quant_algo"]
+        group_size = quant_config["group_size"]
+        exclude_modules = quant_config["exclude_modules"]
+        return cls(is_checkpoint_nvfp4_serialized, kv_cache_quant_algo,
+                   exclude_modules, group_size)
+
+    def is_layer_excluded(self, prefix: str, exclude_modules: list):
+        import regex as re
+        for pattern in exclude_modules:
+            regex_str = pattern.replace('.', r'\.').replace('*', r'.*')
+            if re.fullmatch(regex_str, prefix):
+                return True
+        return False
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+        if isinstance(layer, LinearBase):
+            if (is_layer_skipped(prefix, self.exclude_modules)
+                    or self.is_layer_excluded(prefix, self.exclude_modules)):
+                return UnquantizedLinearMethod()
+            return ModelOptNvFp4LinearMethod(self)
+        elif isinstance(layer, Attention):
+            return ModelOptFp8KVCacheMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return ModelOptNvFp4FusedMoE(self)
+        return None
+
+
+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)
+
+
+class ModelOptFp8KVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from FP8 checkpoints.
+    """
+
+    def __init__(self, quant_config: Union[ModelOptFp8Config,
+                                           ModelOptNvFp4Config]):
+        super().__init__(quant_config)
+
+
+class ModelOptNvFp4LinearMethod(LinearMethodBase):
+    """Linear method for Model Optimizer NVFP4.
+    Supports loading NVFP4 checkpoints with the following structure:
+    
+    input_scale: torch.float32, scalar ,
+    weight: NVFP4(represented as byte) Shape: [1, X, y/2]
+    weight_scale: FP8-E4M3, Shape: [X, Y], aka per block scale,
+    weight_scale_2: torch.float32, scalar,
+    Args: quant_config: The ModelOpt quantization config.
+    """
+
+    def __init__(self, quant_config: ModelOptNvFp4Config):
+        self.quant_config = quant_config
+        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
+        self.use_marlin = False
+
+        if not self.cutlass_nvfp4_supported:
+            if is_fp4_marlin_supported():
+                self.use_marlin = True
+            else:
+                raise ValueError("Current platform does not support NVFP4"
+                                 " quantization. Please use Blackwell and"
+                                 " above.")
+
+    def create_weights(
+        self,
+        layer: torch.nn.Module,
+        input_size_per_partition: int,
+        output_partition_sizes: list[int],
+        input_size: int,
+        output_size: int,
+        params_dtype: torch.dtype,
+        **extra_weight_attrs,
+    ):
+        del input_size, output_size
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError("NVFP4 quantization was selected, "
+                             " dynamic quantization is not supported.")
+        output_size_per_partition = sum(output_partition_sizes)
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.logical_widths = output_partition_sizes
+        layer.input_size_per_partition = input_size_per_partition
+        layer.output_size_per_partition = output_size_per_partition
+
+        if (input_size_per_partition % 16 != 0):
+            raise ValueError("Unsupported model when in features size is "
+                             "not multiple of 16")
+        # The nvfp4 weight is still represented as
+        weight_dtype = (torch.float8_e4m3fn
+                        if self.quant_config.is_checkpoint_nvfp4_serialized
+                        else params_dtype)
+        # Weight
+        weight = ModelWeightParameter(
+            data=torch.empty(
+                # 2 fp4 items are packed in the input dimension
+                layer.output_size_per_partition,
+                layer.input_size_per_partition // 2,
+                dtype=torch.uint8),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # Input Weight Scale
+        input_scale = PerTensorScaleParameter(data=torch.empty(
+            len(output_partition_sizes), dtype=torch.float32),
+                                              weight_loader=weight_loader)
+        layer.register_parameter("input_scale", input_scale)
+
+        # Global Weight Scale
+        weight_scale_2 = PerTensorScaleParameter(data=torch.empty(
+            len(output_partition_sizes), dtype=torch.float32),
+                                                 weight_loader=weight_loader)
+        layer.register_parameter("weight_scale_2", weight_scale_2)
+
+        # Per Block Weight Scale
+        weight_scale = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition // self.quant_config.group_size,
+            dtype=weight_dtype,
+        ),
+                                            input_dim=1,
+                                            output_dim=0,
+                                            weight_loader=weight_loader)
+
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def swizzle_blockscale(self, scale: torch.tensor):
+        assert (scale.dtype == torch.float8_e4m3fn)
+        # Pad and blockwise interleave weight_scale
+        scale_ndim = scale.ndim
+        if scale.ndim == 2:
+            scale = scale.unsqueeze(0)
+        assert scale.ndim == 3
+        B, M, K = scale.shape
+        round_up_multiple = lambda x, m: (x + m - 1) // m * m
+        M_padded = round_up_multiple(M, 128)
+        K_padded = round_up_multiple(K, 4)
+        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
+        padded_scale[:B, :M, :K] = scale
+        batches, rows, cols = padded_scale.shape
+        assert rows % 128 == 0
+        assert cols % 4 == 0
+        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
+                                            cols // 4, 4)
+        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
+        swizzled_scale = swizzled_scale.contiguous().cuda()
+        return (swizzled_scale.reshape(M, K)
+                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+
+        # global scales:
+        input_scale_2 = layer.input_scale.max().to(torch.float32)
+        layer.input_scale = Parameter(input_scale_2, requires_grad=False)
+
+        weight_scale_2 = layer.weight_scale_2.max().to(torch.float32)
+        layer.weight_scale_2 = Parameter(weight_scale_2, requires_grad=False)
+
+        layer.alpha = Parameter(layer.input_scale * layer.weight_scale_2,
+                                requires_grad=False)
+
+        # Swizzle the weight blockscale.
+        # contracting dimension is input dimension
+        # block_size = 16;
+        assert (layer.weight_scale.shape[1] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Block scale must be represented as FP8-E4M3")
+        swizzled_weight_scale = self.swizzle_blockscale(layer.weight_scale)
+
+        layer.weight_scale_swizzled = Parameter(swizzled_weight_scale,
+                                                requires_grad=False)
+        layer.weight = Parameter(layer.weight.data, requires_grad=False)
+
+        if self.use_marlin:
+            prepare_fp4_layer_for_marlin(layer)
+            del layer.alpha
+            del layer.input_scale
+            del layer.weight_scale_swizzled
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if self.use_marlin:
+            return apply_fp4_marlin_linear(
+                input=x,
+                weight=layer.weight,
+                weight_scale=layer.weight_scale,
+                weight_scale_2=layer.weight_scale_2,
+                workspace=layer.workspace,
+                size_n=layer.output_size_per_partition,
+                size_k=layer.input_size_per_partition,
+                bias=bias)
+
+        output_dtype = x.dtype
+        output_shape = [x.shape[0], layer.weight.shape[0]]
+
+        # quantize BF16 or FP16 to (FP4 and interleaved block scale)
+        s_quant = 1 / layer.input_scale
+        x_fp4, x_blockscale = scaled_fp4_quant(x, s_quant)
+
+        # validate dtypes of quantized input, input block scale,
+        # weight and weight_blockscale
+        assert (x_fp4.dtype == torch.uint8)
+        assert (layer.weight.dtype == torch.uint8)
+        assert (x_blockscale.dtype == torch.float8_e4m3fn)
+        assert (layer.weight_scale_swizzled.dtype == torch.float8_e4m3fn)
+        assert (layer.alpha.dtype == torch.float32)
+
+        out = cutlass_scaled_fp4_mm(x_fp4, layer.weight, x_blockscale,
+                                    layer.weight_scale_swizzled, layer.alpha,
+                                    output_dtype)
+        if bias is not None:
+            out = out + bias
+        return out.view(*output_shape)
+
+
+class ModelOptNvFp4FusedMoE(FusedMoEMethodBase):
+    """
+    MoE Method for FP4 Quantization.
+    Args: 
+        quant_config: NVFP4 Quant Config
+    """
+
+    def __init__(self, quant_config: ModelOptNvFp4Config):
+        self.quant_config = quant_config
+        self.cutlass_nvfp4_supported = cutlass_fp4_supported()
+        self.use_marlin = False
+
+        if not self.cutlass_nvfp4_supported:
+            if is_fp4_marlin_supported():
+                self.use_marlin = True
+            else:
+                raise ValueError("Current platform does not support NVFP4"
+                                 " quantization. Please use Blackwell and"
+                                 " above.")
+
+    def create_weights(self, layer: torch.nn.Module, num_experts: int,
+                       hidden_size: int, intermediate_size_per_partition: int,
+                       params_dtype: torch.dtype, **extra_weight_attrs):
+        if not self.quant_config.is_checkpoint_nvfp4_serialized:
+            raise ValueError("NVFP4 quantization was selected, "
+                             " dynamic quantization is not supported.")
+
+        layer.num_experts = num_experts
+        layer.params_dtype = params_dtype
+        layer.quant_config = self.quant_config
+        weight_dtype = torch.uint8
+        weight_scale_dtype = torch.float8_e4m3fn
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        # GEMM 1
+        w13_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // 2,
+                dtype=weight_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight", w13_weight)
+
+        # GEMM 2
+        w2_weight = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition // 2,
+                dtype=weight_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight", w2_weight)
+
+        w13_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                # 2 fp4 items are packed in the input dimension
+                hidden_size // self.quant_config.group_size,
+                dtype=weight_scale_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+
+        w2_weight_scale = ModelWeightParameter(
+            data=torch.empty(
+                num_experts,
+                hidden_size,
+                # 2 fp4 items are packed in the input dimension
+                intermediate_size_per_partition //
+                self.quant_config.group_size,
+                dtype=weight_scale_dtype),
+            input_dim=1,
+            output_dim=2,
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value})
+
+        w13_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, 2, dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("w13_weight_scale_2", w13_weight_scale_2)
+
+        w2_weight_scale_2 = PerTensorScaleParameter(
+            data=torch.empty(num_experts, dtype=torch.float32),
+            weight_loader=weight_loader)
+        layer.register_parameter("w2_weight_scale_2", w2_weight_scale_2)
+
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+
+        w13_input_scale = PerTensorScaleParameter(data=torch.empty(
+            num_experts, 2, dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+        layer.register_parameter("w13_input_scale", w13_input_scale)
+
+        w2_input_scale = PerTensorScaleParameter(data=torch.empty(
+            num_experts, dtype=torch.float32),
+                                                 weight_loader=weight_loader)
+        layer.register_parameter("w2_input_scale", w2_input_scale)
+
+    def swizzle_blockscale(self, scale: torch.tensor):
+        assert (scale.dtype == torch.float8_e4m3fn)
+        # Pad and blockwise interleave weight_scale
+        scale_ndim = scale.ndim
+        if scale.ndim == 2:
+            scale = scale.unsqueeze(0)
+        assert scale.ndim == 3
+        B, M, K = scale.shape
+        round_up_multiple = lambda x, m: (x + m - 1) // m * m
+        M_padded = round_up_multiple(M, 128)
+        K_padded = round_up_multiple(K, 4)
+        padded_scale = torch.zeros((B, M_padded, K_padded), dtype=scale.dtype)
+        padded_scale[:B, :M, :K] = scale
+        batches, rows, cols = padded_scale.shape
+        assert rows % 128 == 0
+        assert cols % 4 == 0
+        padded_scale = padded_scale.reshape(batches, rows // 128, 4, 32,
+                                            cols // 4, 4)
+        swizzled_scale = padded_scale.permute((0, 1, 4, 3, 2, 5))
+        swizzled_scale = swizzled_scale.contiguous().cuda()
+        return (swizzled_scale.reshape(M, K)
+                if scale_ndim == 2 else swizzled_scale.reshape(B, M, K))
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+
+        # GEMM 1
+        if not torch.allclose(layer.w13_weight_scale_2[:, 0],
+                              layer.w13_weight_scale_2[:, 1]):
+            logger.warning_once(
+                "w1_weight_scale_2 must match w3_weight_scale_2. "
+                "Accuracy may be affected.")
+
+        w13_weight_scale_2 = layer.w13_weight_scale_2[:, 0]
+        layer.w13_weight_scale_2 = Parameter(w13_weight_scale_2,
+                                             requires_grad=False)
+
+        w13_input_scale = layer.w13_input_scale.max(dim=1).values.to(
+            torch.float32)
+        layer.g1_alphas = Parameter(
+            (w13_input_scale * w13_weight_scale_2).to(torch.float32),
+            requires_grad=False)
+
+        assert (layer.w13_weight_scale.shape[2] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.w13_weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Blockscale must be represented as FP8-E4M3")
+        w13_blockscale_swizzled = self.swizzle_blockscale(
+            layer.w13_weight_scale)
+
+        layer.w13_blockscale_swizzled = Parameter(w13_blockscale_swizzled,
+                                                  requires_grad=False)
+
+        # This is for quantization, so we need to invert it.
+        layer.w13_input_scale_quant = Parameter(
+            (1 / w13_input_scale).to(torch.float32), requires_grad=False)
+
+        layer.w13_weight = Parameter(layer.w13_weight.data,
+                                     requires_grad=False)
+
+        # GEMM 2
+        layer.g2_alphas = Parameter(
+            (layer.w2_input_scale * layer.w2_weight_scale_2).to(torch.float32),
+            requires_grad=False)
+
+        # This is for quantization, so we need to invert it.
+        layer.w2_input_scale_quant = Parameter(
+            (1 / layer.w2_input_scale).to(torch.float32), requires_grad=False)
+
+        assert (layer.w2_weight_scale.shape[2] % 16 == 0), (
+            "Expected weight_scale.dim(1) to be divisible by 16")
+        assert (layer.w2_weight_scale.dtype == torch.float8_e4m3fn), (
+            "Weight Blockscale must be represented as FP8-E4M3")
+        w2_blockscale_swizzled = self.swizzle_blockscale(layer.w2_weight_scale)
+
+        layer.w2_blockscale_swizzled = Parameter(w2_blockscale_swizzled,
+                                                 requires_grad=False)
+        layer.w2_weight = Parameter(layer.w2_weight.data, requires_grad=False)
+
+        if self.use_marlin:
+            prepare_moe_fp4_layer_for_marlin(layer)
+            del layer.g1_alphas
+            del layer.g2_alphas
+            del layer.w13_input_scale_quant
+            del layer.w2_input_scale_quant
+            del layer.w13_blockscale_swizzled
+            del layer.w2_blockscale_swizzled
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+    ):
+        if self.use_marlin:
+            topk_weights, topk_ids = FusedMoE.select_experts(
+                hidden_states=x,
+                router_logits=router_logits,
+                use_grouped_topk=use_grouped_topk,
+                top_k=top_k,
+                renormalize=renormalize,
+                topk_group=topk_group,
+                num_expert_group=num_expert_group,
+                custom_routing_function=custom_routing_function,
+                scoring_func=scoring_func,
+                e_score_correction_bias=e_score_correction_bias,
+            )
+
+            return torch.ops.vllm.fused_marlin_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                router_logits,
+                topk_weights,
+                topk_ids,
+                global_scale1=layer.w13_weight_scale_2,
+                global_scale2=layer.w2_weight_scale_2,
+                quant_type_id=scalar_types.float4_e2m1f.id,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map)
+
+        assert activation == "silu", "Only SiLU activation is supported."
+        assert not apply_router_weight_on_input, (
+            "Router weight on input is not "
+            "supported for ModelOptNvFp4FusedMoE.")
+        assert expert_map is None, ("Expert Parallelism / expert_map "
+                                    "is currently not supported for "
+                                    "ModelOptNvFp4FusedMoE.")
+
+        topk_weights, topk_ids = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            e_score_correction_bias=e_score_correction_bias)
+
+        from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+            cutlass_moe_fp4)
+
+        # Cutlass moe takes in activations in BF16/Half precision
+        # and fp4 quantized weights loaded from the checkpoint
+        return cutlass_moe_fp4(a=x,
+                               w1_fp4=layer.w13_weight,
+                               w1_blockscale=layer.w13_blockscale_swizzled,
+                               w1_alphas=layer.g1_alphas,
+                               w2_fp4=layer.w2_weight,
+                               w2_blockscale=layer.w2_blockscale_swizzled,
+                               w2_alphas=layer.g2_alphas,
+                               topk_weights=topk_weights,
+                               topk_ids=topk_ids,
+                               m=x.shape[0],
+                               n=layer.w2_weight.shape[2] * 2,
+                               k=x.shape[1],
+                               e=layer.w13_weight.shape[0],
+                               a1_gscale=layer.w13_input_scale_quant,
+                               a2_gscale=layer.w2_input_scale_quant,
+                               device=x.device).to(x.dtype)