Combine fp4.py and mxfp4.py into one file and support dynamic mxfp4 quantization in mxfp4.py (#9049)

Co-authored-by: wunhuang <wunhuang@amd.com>

python/sglang/srt/layers/moe/fused_moe_triton/layer.py

@@ -474,6 +474,7 @@ class FusedMoE(torch.nn.Module):
             not expert_id
             and self.quant_config is not None
             and self.quant_config.get_name() == "mxfp4"
+            and self.quant_config.is_static_cfg()
         ):
             if "bias" in weight_name:
                 dim1 = loaded_weight.shape[1]
@@ -724,7 +725,11 @@ class FusedMoE(torch.nn.Module):
     ) -> None:
         tp_rank = self.moe_tp_rank
 
-        if self.quant_config is not None and self.quant_config.get_name() == "mxfp4":
+        if (
+            self.quant_config is not None
+            and self.quant_config.get_name() == "mxfp4"
+            and self.quant_config.is_static_cfg()
+        ):
             if "bias" in weight_name:
                 dim1 = loaded_weight.shape[1]
                 param.data[:, :dim1].copy_(loaded_weight)

python/sglang/srt/layers/quantization/__init__.py

@@ -48,12 +48,6 @@ from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
 from sglang.srt.layers.quantization.compressed_tensors.compressed_tensors import (
     CompressedTensorsConfig,
 )
-from sglang.srt.utils import is_cuda, is_hip, mxfp_supported
-
-is_mxfp_supported = mxfp_supported()
-if is_mxfp_supported:
-    from sglang.srt.layers.quantization.fp4 import MxFp4Config
-
 from sglang.srt.layers.quantization.fp8 import Fp8Config
 from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQMarlinConfig
 from sglang.srt.layers.quantization.modelopt_quant import (
@@ -67,6 +61,9 @@ from sglang.srt.layers.quantization.qoq import QoQConfig
 from sglang.srt.layers.quantization.w4afp8 import W4AFp8Config
 from sglang.srt.layers.quantization.w8a8_fp8 import W8A8Fp8Config
 from sglang.srt.layers.quantization.w8a8_int8 import W8A8Int8Config
+from sglang.srt.utils import is_cuda, is_hip, mxfp_supported
+
+_is_mxfp_supported = mxfp_supported()
 
 if TYPE_CHECKING:
     from sglang.srt.layers.moe.topk import TopKOutput
@@ -98,11 +95,13 @@ if is_cuda():
             "mxfp4": Mxfp4Config,
         }
     )
-elif is_mxfp_supported and is_hip():
+elif _is_mxfp_supported and is_hip():
+    from sglang.srt.layers.quantization.quark.quark import QuarkConfig
+
     BASE_QUANTIZATION_METHODS.update(
         {
-            "quark": MxFp4Config,
+            "quark": QuarkConfig,
-            "mxfp4": MxFp4Config,
+            "mxfp4": Mxfp4Config,
         }
     )
 # VLLM-dependent quantization methods

python/sglang/srt/layers/quantization/fp4.py

@@ -1,540 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-import fnmatch
-import logging
-from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Union, cast
-
-import aiter
-import torch
-import torch.nn.functional as F
-from aiter import ActivationType, QuantType, dtypes
-from aiter.fused_moe import fused_moe
-from aiter.fused_moe_bf16_asm import asm_moe, ck_moe_2stages
-from aiter.ops.gemm_op_a4w4 import gemm_a4w4
-from aiter.ops.quant import get_torch_quant
-from aiter.ops.shuffle import shuffle_weight
-from aiter.ops.triton.gemm_afp4wfp4 import gemm_afp4wfp4
-from aiter.ops.triton.quant import dynamic_mxfp4_quant
-from aiter.utility.fp4_utils import e8m0_shuffle
-from torch.nn import Module
-
-from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
-from sglang.srt.layers.parameter import ModelWeightParameter
-from sglang.srt.layers.quantization.base_config import (
-    FusedMoEMethodBase,
-    LinearMethodBase,
-    QuantizationConfig,
-    QuantizeMethodBase,
-)
-from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
-from sglang.srt.layers.quantization.quark.schemes import QuarkScheme, QuarkW4A4MXFP4
-from sglang.srt.layers.quantization.quark.utils import deep_compare, should_ignore_layer
-from sglang.srt.layers.radix_attention import RadixAttention
-from sglang.srt.utils import (
-    get_bool_env_var,
-    get_device_capability,
-    log_info_on_rank0,
-    mxfp_supported,
-    set_weight_attrs,
-)
-
-if TYPE_CHECKING:
-    from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
-    from sglang.srt.layers.moe.topk import TopKOutput
-
-logger = logging.getLogger(__name__)
-
-use_dynamic_mxfp4_linear = get_bool_env_var("SGLANG_USE_DYNAMIC_MXFP4_linear")
-
-OCP_MX_BLOCK_SIZE = 32
-
-
-class Mxfp4Config(QuantizationConfig):
-
-    def __init__(self, ignored_layers: Optional[list[str]] = None):
-        super().__init__()
-        self.ignored_layers = ignored_layers
-
-    @classmethod
-    def from_config(cls, config):
-        return cls()
-
-    @classmethod
-    def get_min_capability(cls) -> int:
-        return 80
-
-    @classmethod
-    def get_name(cls) -> QuantizationMethods:
-        return "mxfp4"
-
-    @classmethod
-    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
-        return [torch.bfloat16]
-
-    @classmethod
-    def get_config_filenames(cls) -> list[str]:
-        return []
-
-    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 self.ignored_layers and is_layer_skipped(
-                prefix=prefix,
-                ignored_layers=self.ignored_layers,
-                fused_mapping=self.packed_modules_mapping,
-            ):
-                return UnquantizedLinearMethod()
-            raise NotImplementedError("Mxfp4 linear layer is not implemented")
-        elif isinstance(layer, FusedMoE):
-            return Mxfp4MoEMethod(layer.moe_config)
-        elif isinstance(layer, Attention):
-            raise NotImplementedError("Mxfp4 attention layer is not implemented")
-        return None
-
-
-class MxFp4LinearMethod(LinearMethodBase):
-
-    def __init__(self, quantization_config: MxFp4Config):
-        self.quantization_config = quantization_config
-
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        return
-        # if self.quantization_config.is_checkpoint_fp4_serialized:
-        #    layer.scheme.process_weights_after_loading(layer)
-        # else:
-        #    #w, w_scales = dynamic_mxfp4_quant(layer.weight.data)
-        #    ##log_info_on_rank0(logger, f"w.shape: {w.shape}")
-
-        #    #wshuffle = w#shuffle_weight(w, layout=(16, 16))
-        #    #w_scales_shuffle = w_scales#e8m0_shuffle(w_scales).view(dtypes.fp8_e8m0)
-
-        #    quant_func = aiter.get_triton_quant(aiter.QuantType.per_1x32)
-
-        #    w, w_scales_shuffle = quant_func(layer.weight.data, shuffle=True)
-
-        #    wshuffle = shuffle_weight(w, layout=(16, 16))
-
-        #    layer.weight = torch.nn.Parameter(wshuffle,
-        #                                      requires_grad=False)
-        #    layer.weight_scale = torch.nn.Parameter(w_scales_shuffle,
-        #                                            requires_grad=False)
-
-    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,
-    ):
-        """
-        Use the CompressedTensorsScheme associated with each layer to create
-        the necessary parameters for the layer. See LinearMethodBase for param
-        details
-        """
-        weight_loader = extra_weight_attrs.get("weight_loader")
-
-        if self.quantization_config.is_checkpoint_fp4_serialized:
-            layer.scheme.create_weights(
-                layer=layer,
-                input_size=input_size,
-                input_size_per_partition=input_size_per_partition,
-                output_partition_sizes=output_partition_sizes,
-                output_size=output_size,
-                params_dtype=params_dtype,
-                weight_loader=weight_loader,
-            )
-        else:
-            output_size_per_partition = sum(output_partition_sizes)
-            layer.logical_widths = output_partition_sizes
-            layer.input_size_per_partition = input_size_per_partition
-            layer.output_size_per_partition = output_size_per_partition
-            layer.orig_dtype = params_dtype
-
-            weight_dtype = 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)
-            layer.register_parameter("weight_scale", None)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        bias: Optional[torch.Tensor] = None,
-    ):
-        """
-        Use the output of create_weights and the CompressedTensorsScheme
-        associated with the layer to apply the forward pass with the
-        layer input.  See LinearMethodBase for param details
-
-        """
-        if self.quantization_config.is_checkpoint_fp4_serialized:
-            scheme = layer.scheme
-            if scheme is None:
-                raise ValueError("A scheme must be defined for each layer")
-            return scheme.apply_weights(layer, x, bias=bias)
-        else:
-            out_dtype = x.dtype
-
-            # ck or asm implement
-            # M = x.shape[0]
-            # N = layer.weight.shape[0]
-
-            # quant_func = aiter.get_triton_quant(aiter.QuantType.per_1x32)
-
-            # x, x_scales_shuffle = quant_func(x, shuffle=True)
-
-            # y = torch.zeros((M + 255) // 256 * 256, N, device=x.device, dtype=out_dtype)
-
-            # out = gemm_a4w4(x, layer.weight.data, x_scales_shuffle, layer.weight_scale.data, y, bias=bias)
-
-            # return out[:M]
-
-            # triton implement
-            x_q, x_s = dynamic_mxfp4_quant(x)
-            y = torch.empty(
-                x_q.shape[0], layer.weight.shape[0], device=x_q.device, dtype=out_dtype
-            )
-
-            out = gemm_afp4wfp4(
-                x_q, layer.weight, x_s, layer.weight_scale, out_dtype, y
-            )
-
-            return out
-
-
-class MxFp4MoEMethod(FusedMoEMethodBase):
-
-    def __init__(self, quant_config: Mxfp4Config):
-        self.quant_config = quant_config
-
-    @staticmethod
-    def get_moe_method(
-        quant_config: "MxFp4Config",  # type: ignore # noqa E501 # noqa F821
-        module: torch.nn.Module,
-        layer_name: str,
-    ) -> "MxFp4MoEMethod":
-
-        if quant_config.is_checkpoint_fp4_serialized:
-            layer_quant_config = quant_config._find_matched_config(layer_name, module)
-
-            if layer_quant_config.get("output_tensors") or layer_quant_config.get(
-                "bias"
-            ):
-                raise NotImplementedError(
-                    "Currently, Quark models with "
-                    "output_tensors and bias "
-                    "quantized are not supported"
-                )
-            weight_config = layer_quant_config.get("weight")
-            input_config = layer_quant_config.get("input_tensors")
-
-            if quant_config._is_mx_fp4(weight_config, input_config):
-                return W4A4MXFp4MoEStaticMethod(weight_config, input_config)
-            else:
-                raise RuntimeError("Unsupported FusedMoe scheme")
-        else:
-            return W4A4MXFp4MoEDynamicMethod(quant_config)
-
-
-class W4A4MXFp4MoEDynamicMethod(MxFp4MoEMethod):
-    def __init__(self, quant_config):
-        self.quant_config = quant_config
-
-    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,
-    ):
-
-        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
-
-        w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                2 * intermediate_size_per_partition,
-                hidden_size,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        w2_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                hidden_size,
-                intermediate_size_per_partition,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-
-        layer.register_parameter("w13_weight", w13_weight)
-        set_weight_attrs(w13_weight, extra_weight_attrs)
-
-        layer.register_parameter("w2_weight", w2_weight)
-        set_weight_attrs(w2_weight, extra_weight_attrs)
-
-        # Allocate 2 scales for w1 and w3 respectively.
-        # They will be combined to a single scale after weight loading.
-        w13_weight_scale = torch.nn.Parameter(
-            torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
-        )
-        w2_weight_scale = torch.nn.Parameter(
-            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
-        )
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
-        layer.register_parameter("w2_weight_scale", w2_weight_scale)
-
-        # Add the quantization method used (per tensor/grouped/channel)
-        # to ensure the weight scales are loaded in properly
-        extra_weight_attrs.update(
-            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
-        )
-
-        layer.w13_input_scale = None
-        layer.w2_input_scale = None
-
-    def mxfp4_quantize(self, w):
-        w_shape = w.shape
-        w_need_reshape = True if w.dim() != 2 else False
-
-        if w_need_reshape:
-            w_last_dim_size = w_shape[-1]
-            w = w.view(-1, w_last_dim_size)
-
-        # log_info_on_rank0(logger, f"[Pre-quant] w.shape: {w.shape}")
-        w, mx_scales = dynamic_mxfp4_quant(w)
-        # log_info_on_rank0(logger, f"[Post-quant] w.shape: {w.shape} mx_scales.shape: {mx_scales.shape}")
-
-        if w_need_reshape:
-            w_new_shape = w_shape[:-1] + (w.shape[-1],)
-            w = w.view(w_new_shape)
-
-        # log_info_on_rank0(logger, f"[re-shape] w.shape: {w.shape} mx_scales.shape: {mx_scales.shape}")
-
-        mx_scales = e8m0_shuffle(mx_scales)
-
-        return w, mx_scales
-
-    def process_weights_after_loading(self, layer: Module) -> None:
-        w13, w13_mx_scales = self.mxfp4_quantize(layer.w13_weight.data)
-        w2, w2_mx_scales = self.mxfp4_quantize(layer.w2_weight.data)
-
-        layer.w13_weight = torch.nn.Parameter(w13, requires_grad=False)
-        layer.w13_weight_scale = torch.nn.Parameter(w13_mx_scales, requires_grad=False)
-
-        layer.w2_weight = torch.nn.Parameter(w2, requires_grad=False)
-        layer.w2_weight_scale = torch.nn.Parameter(w2_mx_scales, requires_grad=False)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        topk_output: TopKOutput,
-        moe_runner_config: MoeRunnerConfig,
-    ) -> torch.Tensor:
-        topk_weights, topk_ids, _ = topk_output
-
-        return fused_moe(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights,
-            topk_ids,
-            quant_type=QuantType.per_1x32,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
-            activation=(
-                ActivationType.Silu
-                if moe_runner_config.activation == "silu"
-                else ActivationType.Gelu
-            ),
-            doweight_stage1=False,
-        )
-
-
-class W4A4MXFp4MoEStaticMethod(MxFp4MoEMethod):
-
-    def __init__(self, weight_config: dict[str, Any], input_config: dict[str, Any]):
-        self.weight_quant = weight_config
-        self.input_quant = input_config
-
-        weight_qscheme = self.weight_quant.get("qscheme")
-        input_qscheme = self.input_quant.get("qscheme")
-        if not (weight_qscheme == "per_group" and input_qscheme == "per_group"):
-            raise ValueError(
-                "For MX(FP4) Fused MoE layers, only per-group scales "
-                "for weights and activations are supported. Found "
-                f"{weight_qscheme=}, {input_qscheme=}"
-            )  # noqa E501
-
-        self.static_input_scales = not self.input_quant.get("is_dynamic")
-
-    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,
-    ):
-
-        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
-
-        # Add the quantization method used (per tensor/grouped/channel)
-        # to ensure the weight scales are loaded in properly
-        extra_weight_attrs.update(
-            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
-        )
-
-        params_dtype = torch.uint8
-
-        # WEIGHTS
-        w13_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                2 * intermediate_size_per_partition,
-                hidden_size // 2,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w13_weight", w13_weight)
-
-        set_weight_attrs(w13_weight, extra_weight_attrs)
-
-        w2_weight = torch.nn.Parameter(
-            torch.empty(
-                num_experts,
-                hidden_size,
-                intermediate_size_per_partition // 2,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        layer.register_parameter("w2_weight", w2_weight)
-
-        set_weight_attrs(w2_weight, extra_weight_attrs)
-
-        # WEIGHT_SCALES
-        w13_weight_scale = torch.nn.Parameter(
-            torch.ones(
-                num_experts,
-                2 * intermediate_size_per_partition,
-                hidden_size // OCP_MX_BLOCK_SIZE,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        w2_weight_scale = torch.nn.Parameter(
-            torch.ones(
-                num_experts,
-                hidden_size,
-                intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
-                dtype=params_dtype,
-            ),
-            requires_grad=False,
-        )
-        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
-        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
-
-        layer.register_parameter("w13_weight_scale", w13_weight_scale)
-        layer.register_parameter("w2_weight_scale", w2_weight_scale)
-
-    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
-        float_dtype = torch.get_default_dtype()
-
-        # Pre-shuffle weight scales
-        s0, s1, _ = layer.w13_weight_scale.shape
-        w13_weight_scale = layer.w13_weight_scale.view(s0 * s1, -1)
-        w13_weight_scale = e8m0_shuffle(w13_weight_scale)
-        layer.w13_weight_scale.data = w13_weight_scale.view(s0, s1, -1)
-
-        s0, s1, _ = layer.w2_weight_scale.shape
-        w2_weight_scale = layer.w2_weight_scale.view(s0 * s1, -1)
-        w2_weight_scale = e8m0_shuffle(w2_weight_scale)
-        layer.w2_weight_scale.data = w2_weight_scale.view(s0, s1, -1)
-
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        topk_output: TopKOutput,
-        moe_runner_config: MoeRunnerConfig,
-    ) -> torch.Tensor:
-        topk_weights, topk_ids, _ = topk_output
-
-        return fused_moe(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights,
-            topk_ids,
-            quant_type=QuantType.per_1x32,
-            w1_scale=layer.w13_weight_scale,
-            w2_scale=layer.w2_weight_scale,
-            activation=(
-                ActivationType.Silu
-                if moe_runner_config.activation == "silu"
-                else ActivationType.Gelu
-            ),
-            doweight_stage1=False,
-        )
-
-
-class MxFp4KVCacheMethod(BaseKVCacheMethod):
-    """
-    Supports loading kv-cache scaling factors from quark checkpoints.
-    """
-
-    def __init__(self, quant_config: MxFp4Config):
-        self.validate_kv_cache_config(quant_config.kv_cache_config)
-        super().__init__(quant_config)
-
-    @staticmethod
-    def validate_kv_cache_config(kv_cache_config: Optional[dict[str, Any]]):
-        """
-        Validator for the kv cache configuration. Useful for controlling the
-        kv cache quantization schemes, that are being supported in vLLM
-        :param kv_cache_config: the quark kv cache scheme
-        """
-        if kv_cache_config is None:
-            return
-
-        dtype = kv_cache_config.get("dtype")
-        if dtype != "fp8_e4m3":
-            raise NotImplementedError(
-                "Currently supported kv cache quantization is "
-                f"dtype=fp8_e4m3, however received {dtype}"
-            )
-
-        qscheme = kv_cache_config.get("qscheme")
-        if qscheme != "per_tensor":
-            raise NotImplementedError(
-                "Only support per-tensor scaling factor "
-                "for quark KV cache. "
-                f"Expected qscheme: per_tensor, found qscheme: {qscheme}"
-            )

python/sglang/srt/layers/quantization/mxfp4.py

@@ -38,6 +38,7 @@ from sglang.srt.utils import (
     is_hip,
     is_triton_kernels_available,
     log_info_on_rank0,
+    mxfp_supported,
     next_power_of_2,
     round_up,
     set_weight_attrs,
@@ -61,7 +62,14 @@ if TYPE_CHECKING:
     from sglang.srt.layers.moe.moe_runner import MoeRunnerConfig
     from sglang.srt.layers.moe.topk import TopKOutput
 
-OCP_MX_BLOCK_SIZE = 32
+_is_hip = is_hip()
+
+if _is_hip:
+    # import aiter
+    from aiter import ActivationType, QuantType, dtypes
+    from aiter.fused_moe import fused_moe
+    from aiter.ops.triton.quant import dynamic_mxfp4_quant
+    from aiter.utility.fp4_utils import e8m0_shuffle
 
 
 def _swizzle_mxfp4(quant_tensor, scale, num_warps):
@@ -162,13 +170,34 @@ except AttributeError as error:
 
 class Mxfp4Config(QuantizationConfig):
 
-    def __init__(self, ignored_layers: Optional[list[str]] = None):
+    def __init__(
+        self,
+        ignored_layers: Optional[list[str]] = None,
+        is_checkpoint_mxfp4_serialized: bool = False,
+    ):
         super().__init__()
+        self.is_checkpoint_mxfp4_serialized = is_checkpoint_mxfp4_serialized
         self.ignored_layers = ignored_layers
 
     @classmethod
     def from_config(cls, config):
-        return cls()
+
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        is_checkpoint_mxfp4_serialized = "mxfp4" in quant_method
+
+        if _is_hip:
+            if mxfp_supported():
+                return cls(
+                    is_checkpoint_mxfp4_serialized=is_checkpoint_mxfp4_serialized
+                )
+            else:
+
+                platform = torch.cuda.get_device_properties(0).gcnArchName
+                raise ValueError(
+                    f"Current platform {platform} not support mxfp4 computation"
+                )
+
+        return cls(is_checkpoint_mxfp4_serialized=is_checkpoint_mxfp4_serialized)
 
     @classmethod
     def get_min_capability(cls) -> int:
@@ -186,6 +215,9 @@ class Mxfp4Config(QuantizationConfig):
     def get_config_filenames(cls) -> list[str]:
         return []
 
+    def is_static_cfg(self):
+        return self.is_checkpoint_mxfp4_serialized
+
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
     ) -> Optional["QuantizeMethodBase"]:
@@ -201,10 +233,16 @@ class Mxfp4Config(QuantizationConfig):
                 fused_mapping=self.packed_modules_mapping,
             ):
                 return UnquantizedLinearMethod()
+            elif _is_hip:
+                return UnquantizedLinearMethod()
         elif isinstance(layer, FusedMoE):
-            return Mxfp4MoEMethod(prefix)
+            if self.is_checkpoint_mxfp4_serialized:
+                return Mxfp4MoEMethod(prefix=prefix)
+            else:
+                return Mxfp4DynamicQuantMoEMethod()
         else:
-            raise NotImplementedError("Mxfp4 attention layer is not implemented")
+            if self.is_checkpoint_mxfp4_serialized:
+                raise NotImplementedError("Mxfp4 attention layer is not implemented")
         return None
 
     def get_scaled_act_names(self) -> List[str]:
@@ -655,3 +693,116 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
                 b1=layer.w13_weight_bias,
                 b2=layer.w2_weight_bias,
             )
+
+
+class Mxfp4DynamicQuantMoEMethod(FusedMoEMethodBase):
+    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,
+    ):
+
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+
+        layer.register_parameter("w13_weight", w13_weight)
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # Allocate 2 scales for w1 and w3 respectively.
+        # They will be combined to a single scale after weight loading.
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, 2, dtype=torch.float32), requires_grad=False
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(num_experts, dtype=torch.float32), requires_grad=False
+        )
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value}
+        )
+
+        layer.w13_input_scale = None
+        layer.w2_input_scale = None
+
+    def mxfp4_quantize(self, w):
+        w_shape = w.shape
+        w_need_reshape = True if w.dim() != 2 else False
+
+        if w_need_reshape:
+            w_last_dim_size = w_shape[-1]
+            w = w.view(-1, w_last_dim_size)
+
+        w, mx_scales = dynamic_mxfp4_quant(w)
+
+        if w_need_reshape:
+            w_new_shape = w_shape[:-1] + (w.shape[-1],)
+            w = w.view(w_new_shape)
+
+        mx_scales = e8m0_shuffle(mx_scales)
+
+        return w, mx_scales
+
+    def process_weights_after_loading(self, layer: Module) -> None:
+        w13, w13_mx_scales = self.mxfp4_quantize(layer.w13_weight.data)
+        w2, w2_mx_scales = self.mxfp4_quantize(layer.w2_weight.data)
+
+        layer.w13_weight = torch.nn.Parameter(w13, requires_grad=False)
+        layer.w13_weight_scale = torch.nn.Parameter(w13_mx_scales, requires_grad=False)
+
+        layer.w2_weight = torch.nn.Parameter(w2, requires_grad=False)
+        layer.w2_weight_scale = torch.nn.Parameter(w2_mx_scales, requires_grad=False)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        topk_output: TopKOutput,
+        moe_runner_config: MoeRunnerConfig,
+    ) -> torch.Tensor:
+        topk_weights, topk_ids, _ = topk_output
+
+        return fused_moe(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights,
+            topk_ids,
+            quant_type=QuantType.per_1x32,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            activation=(
+                ActivationType.Silu
+                if moe_runner_config.activation == "silu"
+                else ActivationType.Gelu
+            ),
+            doweight_stage1=False,
+        )

python/sglang/srt/layers/quantization/quark/quark.py

@@ -0,0 +1,390 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import fnmatch
+import logging
+from typing import Any, List, Optional, cast
+
+import torch
+
+from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
+from sglang.srt.layers.quantization.base_config import (  # noqa: E501
+    LinearMethodBase,
+    QuantizationConfig,
+    QuantizeMethodBase,
+)
+from sglang.srt.layers.quantization.kv_cache import BaseKVCacheMethod
+from sglang.srt.layers.quantization.quark.quark_moe import QuarkMoEMethod
+from sglang.srt.layers.quantization.quark.schemes import QuarkScheme, QuarkW4A4MXFP4
+from sglang.srt.layers.quantization.quark.utils import deep_compare, should_ignore_layer
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.utils import get_device_capability
+
+__all__ = ["QuarkLinearMethod"]
+
+logger = logging.getLogger(__name__)
+
+
+class QuarkConfig(QuantizationConfig):
+
+    def __init__(
+        self,
+        quant_config: dict[str, Any],
+        kv_cache_group: Optional[list[str]] = None,
+        kv_cache_config: Optional[dict[str, Any]] = None,
+        pack_method: str = "reorder",
+    ):
+        super().__init__()
+        if kv_cache_group is None:
+            kv_cache_group = []
+        self.quant_config = quant_config
+        self.kv_cache_group = kv_cache_group
+        self.kv_cache_config = kv_cache_config
+        self.pack_method = pack_method
+
+        self.packed_modules_mapping = self.quant_config["packed_modules_mapping"]
+
+    def get_linear_method(self) -> "QuarkLinearMethod":
+        return QuarkLinearMethod(self)
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.float16, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def get_name(self) -> str:
+        return "quark"
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+        # Check if the layer is skipped for quantization.
+        exclude_layers = cast(list[str], self.quant_config.get("exclude"))
+        if should_ignore_layer(
+            prefix, ignore=exclude_layers, fused_mapping=self.packed_modules_mapping
+        ):
+            return UnquantizedLinearMethod()
+
+        if isinstance(layer, LinearBase):
+            scheme = self.get_scheme(layer=layer, layer_name=prefix)
+            layer.scheme = scheme
+            return QuarkLinearMethod(self)
+
+        if isinstance(layer, RadixAttention):
+            return QuarkKVCacheMethod(self)
+
+        from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
+
+        if isinstance(layer, FusedMoE):
+            return QuarkMoEMethod.get_moe_method(self, module=layer, layer_name=prefix)
+
+        return None
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "QuarkConfig":
+        export_config = config.get("export")
+        if export_config is None:
+            raise ValueError(
+                "The export key should be included in "
+                "the configurations of Quark quantized model"
+            )
+
+        kv_cache_group = cast(list[str], export_config.get("kv_cache_group"))
+        pack_method = cast(str, export_config.get("pack_method"))
+
+        # In the export model of quark, the quantization configuration
+        # of kv_cache is stored in layer_quant_config. First, it is
+        # judged whether kv_cache_group exists, and then it is judged
+        # whether layer_quant_config has a quantization configuration
+        # that matches kv_cache.
+        if len(kv_cache_group) == 0:
+            kv_cache_config = None
+        else:
+            kv_cache_set = set(kv_cache_group)
+            layer_quant_config = cast(dict[str, Any], config.get("layer_quant_config"))
+            layer_quant_names = list(layer_quant_config.keys())
+            layer_quant_set = set(layer_quant_names)
+
+            if not kv_cache_set.issubset(layer_quant_set):
+                raise ValueError(
+                    "The Quark quantized model has the "
+                    "kv_cache_group parameter setting, "
+                    "but no kv_cache quantization settings "
+                    "were found in the quantization "
+                    "configuration."
+                )
+
+            q_configs = [
+                cast(dict[str, Any], layer_quant_config.get(name))
+                for name in kv_cache_group
+            ]
+            if not all(deep_compare(q_config, q_configs[0]) for q_config in q_configs):
+                raise ValueError(
+                    "The quantization method used for kv_cache should "
+                    "be the same, but the quantization method for the "
+                    "kv_cache layer in the config is different."
+                )
+            kv_cache_config = q_configs[0].get("output_tensors")
+            if kv_cache_config is None:
+                raise ValueError("The kv_cache quantization configuration is empty.")
+
+            # Since we have already set kv_cache quantization configurations,
+            # we will remove the quantization configuration for the
+            # output_tensors corresponding to the kv_cache layer.
+            for q_config in q_configs:
+                q_config["output_tensors"] = None
+
+            # In case q_proj output is also quantized, remove the configuration
+            # to keep qkv consistency.
+            q_proj_q_config = cast(dict[str, Any], layer_quant_config.get("*q_proj"))
+            if q_proj_q_config is not None:
+                q_proj_q_config["output_tensors"] = None
+
+        return cls(
+            quant_config=config,
+            kv_cache_group=kv_cache_group,
+            kv_cache_config=kv_cache_config,
+            pack_method=pack_method,
+        )
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return []
+
+    def _check_scheme_supported(self, min_capability: int, error: bool = True) -> bool:
+        capability_tuple = get_device_capability()
+
+        if capability_tuple is not None:
+            assert 0 <= capability_tuple[1] < 10
+            capability = capability_tuple[0] * 10 + capability_tuple[1]
+
+            supported = capability >= min_capability
+            if error and not supported:
+                raise RuntimeError(
+                    "Quantization scheme is not supported for ",
+                    f"the current GPU. Min capability: {min_capability}. ",
+                    f"Current capability: {capability}.",
+                )
+            return supported
+        else:
+            return False
+
+    def _is_mx_fp4(
+        self,
+        weight_quant: Optional[dict[str, Any]],
+        input_quant: Optional[dict[str, Any]],
+    ) -> bool:
+        # Confirm weights and input quantized.
+        if weight_quant is None or input_quant is None:
+            logger.debug(
+                "Quark model is not in MX-FP4 format: "
+                "weight_quant or input_quant not set"
+            )
+            return False
+
+        # Input and weight dtype needs to be fp4.
+        if weight_quant.get("dtype") != "fp4" or input_quant.get("dtype") != "fp4":
+            logger.debug("Quark model is not in MX-FP4 format: dtype not fp4")
+            return False
+
+        # Input and weight qscheme needs to be per group.
+        if (
+            weight_quant.get("qscheme") != "per_group"
+            or input_quant.get("qscheme") != "per_group"
+        ):
+            logger.debug("Quark model is not in MX-FP4 format: not per_group")
+            return False
+
+        # Input and weight group size needs to be 32.
+        if weight_quant.get("group_size") != 32 or input_quant.get("group_size") != 32:
+            logger.debug("Quark model is not in MX-FP4 format: not group_size=32")
+            return False
+
+        # Weights need to use static quantization.
+        if weight_quant.get("is_dynamic") is True:
+            logger.debug("Quark model is not in MX-FP4 format: not weight static")
+            return False
+
+        # Activations need to use dynamic quantization.
+        if input_quant.get("is_dynamic") is False:
+            logger.debug("Quark model is not in MX-FP4 format: not activation dynamic")
+            return False
+
+        # Activations and weight scales need to be in e8m0 format.
+        if (
+            weight_quant.get("scale_format") != "e8m0"
+            or input_quant.get("scale_format") != "e8m0"
+        ):
+            logger.debug("Quark model is not in MX-FP4 format: not scale_format e8m0")
+            return False
+
+        return True
+
+    def _find_matched_config(
+        self, layer_name: str, module: torch.nn.Module
+    ) -> dict[str, Any]:
+
+        proj_name = layer_name.split(".")[-1]
+        if proj_name in self.packed_modules_mapping:
+            shard_proj_names = self.packed_modules_mapping[proj_name]
+
+            # Convert fused_name --> [shard_names]
+            shard_names = [
+                layer_name.replace(proj_name, shard_proj_name)
+                for shard_proj_name in shard_proj_names
+            ]
+            shard_configs = [
+                self._find_matched_config(shard_name, module)
+                for shard_name in shard_names
+            ]
+            if not all(
+                deep_compare(q_config, shard_configs[0]) for q_config in shard_configs
+            ):
+                raise ValueError(
+                    f"Found a different quantization configuration for "
+                    f"{shard_proj_names} in {layer_name}. vLLM "
+                    "requires all to use the same scheme."
+                )
+            return shard_configs[0]
+        else:
+            layer_quant_config = cast(
+                dict[str, Any], self.quant_config.get("layer_quant_config")
+            )
+            for name_pattern in layer_quant_config:
+                if fnmatch.fnmatch(layer_name, name_pattern):
+                    return layer_quant_config[name_pattern]
+
+            layer_type = type(module).__name__
+            layer_type_quant_config = cast(
+                dict[str, Any], self.quant_config.get("layer_type_quant_config")
+            )
+            if layer_type in layer_type_quant_config:
+                return layer_type_quant_config[layer_type]
+
+            global_quant_config = cast(
+                dict[str, Any], self.quant_config.get("global_quant_config")
+            )
+            return global_quant_config
+
+    def _get_scheme_from_config(self, config: dict[str, Any]) -> "QuarkScheme":
+        if config.get("output_tensors") or config.get("bias"):
+            raise NotImplementedError(
+                "Currently, Quark models with output_tensors "
+                "and bias quantized are not supported"
+            )
+        weight_config = cast(dict[str, Any], config.get("weight"))
+        input_config = cast(dict[str, Any], config.get("input_tensors"))
+
+        if self._is_mx_fp4(weight_config, input_config):
+            return QuarkW4A4MXFP4(weight_config, input_config)
+
+        raise NotImplementedError(
+            "No quark compatible scheme was found. "
+            f"Weight config: {weight_config}, "
+            f"Input config: {input_config}"
+        )
+
+    def get_scheme(self, layer: torch.nn.Module, layer_name: str) -> "QuarkScheme":
+
+        layer_quant_config = self._find_matched_config(layer_name, layer)
+
+        # Find the quant_scheme
+        scheme = self._get_scheme_from_config(layer_quant_config)
+
+        # Raise error if device does not support the scheme
+        # (e.g. fp8 needs ada lovelace)
+        self._check_scheme_supported(scheme.get_min_capability())
+
+        return scheme
+
+    def get_scaled_act_names(self) -> List[str]:
+        return []
+
+
+class QuarkLinearMethod(LinearMethodBase):
+
+    def __init__(self, quantization_config: QuarkConfig):
+        self.quantization_config = quantization_config
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.scheme.process_weights_after_loading(layer)
+
+    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,
+    ):
+        """
+        Use the CompressedTensorsScheme associated with each layer to create
+        the necessary parameters for the layer. See LinearMethodBase for param
+        details
+        """
+        weight_loader = extra_weight_attrs.get("weight_loader")
+        layer.scheme.create_weights(
+            layer=layer,
+            input_size=input_size,
+            input_size_per_partition=input_size_per_partition,
+            output_partition_sizes=output_partition_sizes,
+            output_size=output_size,
+            params_dtype=params_dtype,
+            weight_loader=weight_loader,
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ):
+        """
+        Use the output of create_weights and the CompressedTensorsScheme
+        associated with the layer to apply the forward pass with the
+        layer input.  See LinearMethodBase for param details
+
+        """
+        scheme = layer.scheme
+        if scheme is None:
+            raise ValueError("A scheme must be defined for each layer")
+        return scheme.apply_weights(layer, x, bias=bias)
+
+
+class QuarkKVCacheMethod(BaseKVCacheMethod):
+    """
+    Supports loading kv-cache scaling factors from quark checkpoints.
+    """
+
+    def __init__(self, quant_config: QuarkConfig):
+        self.validate_kv_cache_config(quant_config.kv_cache_config)
+        super().__init__(quant_config)
+
+    @staticmethod
+    def validate_kv_cache_config(kv_cache_config: Optional[dict[str, Any]]):
+        """
+        Validator for the kv cache configuration. Useful for controlling the
+        kv cache quantization schemes, that are being supported in vLLM
+        :param kv_cache_config: the quark kv cache scheme
+        """
+        if kv_cache_config is None:
+            return
+
+        dtype = kv_cache_config.get("dtype")
+        if dtype != "fp8_e4m3":
+            raise NotImplementedError(
+                "Currently supported kv cache quantization is "
+                f"dtype=fp8_e4m3, however received {dtype}"
+            )
+
+        qscheme = kv_cache_config.get("qscheme")
+        if qscheme != "per_tensor":
+            raise NotImplementedError(
+                "Only support per-tensor scaling factor "
+                "for quark KV cache. "
+                f"Expected qscheme: per_tensor, found qscheme: {qscheme}"
+            )

python/sglang/srt/layers/quantization/quark/quark_moe.py

@@ -0,0 +1,197 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from __future__ import annotations
+
+import logging
+from typing import TYPE_CHECKING, Any, Callable, Optional
+
+import torch
+from aiter import ActivationType, QuantType, biased_grouped_topk
+from aiter.fused_moe import fused_moe
+from aiter.utility.fp4_utils import e8m0_shuffle
+
+from sglang.srt.utils import get_bool_env_var, mxfp_supported, set_weight_attrs
+
+logger = logging.getLogger(__name__)
+
+__all__ = ["QuarkMoEMethod", "QuarkW4A4MXFp4MoEMethod"]
+
+OCP_MX_BLOCK_SIZE = 32
+
+if TYPE_CHECKING:
+    from sglang.srt.layers.moe.topk import TopKOutput
+
+
+class QuarkMoEMethod:
+    def __new__(cls, *args, **kwargs):
+        from sglang.srt.layers.quantization.base_config import FusedMoEMethodBase
+
+        if not hasattr(cls, "_initialized"):
+            original_init = cls.__init__
+            new_cls = type(
+                cls.__name__,
+                (FusedMoEMethodBase,),
+                {
+                    "__init__": original_init,
+                    **{k: v for k, v in cls.__dict__.items() if k != "__dict__"},
+                },
+            )
+            obj = super(new_cls, new_cls).__new__(new_cls)
+            obj.__init__(*args, **kwargs)
+            return obj
+        return super().__new__(cls)
+
+    @staticmethod
+    def get_moe_method(
+        quant_config: "QuarkConfig",  # type: ignore # noqa E501 # noqa F821
+        module: torch.nn.Module,
+        layer_name: str,
+    ) -> "QuarkMoEMethod":
+        layer_quant_config = quant_config._find_matched_config(layer_name, module)
+
+        if layer_quant_config.get("output_tensors") or layer_quant_config.get("bias"):
+            raise NotImplementedError(
+                "Currently, Quark models with "
+                "output_tensors and bias "
+                "quantized are not supported"
+            )
+        weight_config = layer_quant_config.get("weight")
+        input_config = layer_quant_config.get("input_tensors")
+
+        if quant_config._is_mx_fp4(weight_config, input_config):
+            return QuarkW4A4MXFp4MoEMethod(weight_config, input_config)
+        else:
+            raise RuntimeError("Unsupported FusedMoe scheme")
+
+
+class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):
+
+    def __init__(self, weight_config: dict[str, Any], input_config: dict[str, Any]):
+        self.weight_quant = weight_config
+        self.input_quant = input_config
+
+        weight_qscheme = self.weight_quant.get("qscheme")
+        input_qscheme = self.input_quant.get("qscheme")
+        if not (weight_qscheme == "per_group" and input_qscheme == "per_group"):
+            raise ValueError(
+                "For MX(FP4) Fused MoE layers, only per-group scales "
+                "for weights and activations are supported. Found "
+                f"{weight_qscheme}, {input_qscheme}"
+            )  # noqa E501
+
+        self.static_input_scales = not self.input_quant.get("is_dynamic")
+        self.with_bias = False
+
+    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,
+    ):
+
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoeWeightScaleSupported
+
+        # Add the quantization method used (per tensor/grouped/channel)
+        # to ensure the weight scales are loaded in properly
+        extra_weight_attrs.update(
+            {"quant_method": FusedMoeWeightScaleSupported.BLOCK.value}
+        )
+
+        params_dtype = torch.uint8
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size // 2,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w13_weight", w13_weight)
+
+        set_weight_attrs(w13_weight, extra_weight_attrs)
+
+        w2_weight = torch.nn.Parameter(
+            torch.empty(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition // 2,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        layer.register_parameter("w2_weight", w2_weight)
+
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        w13_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                hidden_size // OCP_MX_BLOCK_SIZE,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        w2_weight_scale = torch.nn.Parameter(
+            torch.ones(
+                num_experts,
+                hidden_size,
+                intermediate_size_per_partition // OCP_MX_BLOCK_SIZE,
+                dtype=params_dtype,
+            ),
+            requires_grad=False,
+        )
+        set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+
+        layer.register_parameter("w13_weight_scale", w13_weight_scale)
+        layer.register_parameter("w2_weight_scale", w2_weight_scale)
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        float_dtype = torch.get_default_dtype()
+
+        # Pre-shuffle weight scales
+        s0, s1, _ = layer.w13_weight_scale.shape
+        w13_weight_scale = layer.w13_weight_scale.view(s0 * s1, -1)
+        w13_weight_scale = e8m0_shuffle(w13_weight_scale)
+        # layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale, requires_grad=False)
+        layer.w13_weight_scale.data = w13_weight_scale.view(s0, s1, -1)
+
+        s0, s1, _ = layer.w2_weight_scale.shape
+        w2_weight_scale = layer.w2_weight_scale.view(s0 * s1, -1)
+        w2_weight_scale = e8m0_shuffle(w2_weight_scale)
+        # layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale, requires_grad=False)
+        layer.w2_weight_scale.data = w2_weight_scale.view(s0, s1, -1)
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        topk_output: TopKOutput,
+        moe_runner_config: MoeRunnerConfig,
+    ) -> torch.Tensor:
+        topk_weights, topk_ids, _ = topk_output
+
+        return fused_moe(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights,
+            topk_ids,
+            quant_type=QuantType.per_1x32,
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            activation=(
+                ActivationType.Silu
+                if moe_runner_config.activation == "silu"
+                else ActivationType.Gelu
+            ),
+            doweight_stage1=False,
+        )

python/sglang/srt/server_args.py

@@ -33,6 +33,7 @@ from sglang.srt.utils import (
     configure_ipv6,
     get_device,
     get_device_memory_capacity,
+    is_cuda,
     is_flashinfer_available,
     is_hip,
     is_port_available,
@@ -2165,9 +2166,9 @@ class ServerArgs:
         model_arch = hf_config.architectures[0]
         if model_arch in ["GptOssForCausalLM"]:
             if self.attention_backend is None:
-                if is_sm100_supported():
+                if is_cuda() and is_sm100_supported():
                     self.attention_backend = "trtllm_mha"
-                elif is_sm90_supported():
+                elif is_cuda() and is_sm90_supported():
                     self.attention_backend = "fa3"
                 else:
                     self.attention_backend = "triton"