Support OCP MXFP4 quantization on AMD GPUs (#8255)

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

python/sglang/srt/configs/model_config.py

@@ -401,6 +401,8 @@ class ModelConfig:
             "fbgemm_fp8",
             "w8a8_fp8",
             "petit_nvfp4",
+            "quark",
+            "mxfp4",
         ]
         optimized_quantization_methods = [
             "fp8",

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

@@ -47,6 +47,12 @@ 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 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,
@@ -84,7 +90,13 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "w4afp8": W4AFp8Config,
     "petit_nvfp4": PetitNvFp4Config,
 }
-
+if is_mxfp_supported:
+    BASE_QUANTIZATION_METHODS.update(
+        {
+            "quark": MxFp4Config,
+            "mxfp4": MxFp4Config,
+        }
+    )
 # VLLM-dependent quantization methods
 VLLM_QUANTIZATION_METHODS = {
     "aqlm": AQLMConfig,

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

+# 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.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,
+        is_checkpoint_fp4_serialized: bool = False,
+        quant_config: dict[str, Any] = None,
+        kv_cache_group: Optional[list[str]] = None,
+        kv_cache_config: Optional[dict[str, Any]] = None,
+        pack_method: str = "reorder",
+        ignored_layers: Optional[List[str]] = None,
+    ):
+        super().__init__()
+        if kv_cache_group is None:
+            kv_cache_group = []
+
+        self.is_checkpoint_fp4_serialized = is_checkpoint_fp4_serialized
+        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"]
+            if is_checkpoint_fp4_serialized
+            else None
+        )
+
+        self.ignored_layers = ignored_layers or []
+
+    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 "fp4"
+
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional["QuantizeMethodBase"]:
+
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+
+        # Check if the layer is skipped for quantization.
+        if len(self.ignored_layers) > 0 and should_ignore_layer(
+            prefix,
+            ignore=self.ignored_layers,
+            fused_mapping=self.packed_modules_mapping,
+        ):
+            return UnquantizedLinearMethod()
+
+        if isinstance(layer, LinearBase):
+            if self.is_checkpoint_fp4_serialized:
+                scheme = self.get_scheme(layer=layer, layer_name=prefix)
+                layer.scheme = scheme
+                return MxFp4LinearMethod(self)
+
+            elif use_dynamic_mxfp4_linear:
+                return MxFp4LinearMethod(self)
+            else:
+                return UnquantizedLinearMethod()
+
+        if isinstance(layer, RadixAttention):
+            return MxFp4KVCacheMethod(self)
+
+        if isinstance(layer, FusedMoE):
+            return MxFp4MoEMethod.get_moe_method(self, module=layer, layer_name=prefix)
+
+        return None
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "MxFp4Config":
+        if not mxfp_supported():
+            platform = torch.cuda.get_device_properties(0).gcnArchName
+            raise ValueError(
+                f"Current platform {platform} not support mxfp4 computation"
+            )
+        quant_method = cls.get_from_keys(config, ["quant_method"])
+        is_checkpoint_fp4_serialized = (
+            True if quant_method else False
+        )  # "quark" in quant_method
+
+        kv_cache_group = []
+        pack_method = None
+
+        if is_checkpoint_fp4_serialized:
+            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
+
+        ignored_layers = cls.get_from_keys_or(config, ["exclude"], None)
+
+        return cls(
+            is_checkpoint_fp4_serialized=is_checkpoint_fp4_serialized,
+            quant_config=config,
+            kv_cache_group=kv_cache_group,
+            kv_cache_config=kv_cache_config,
+            pack_method=pack_method,
+            ignored_layers=ignored_layers,
+        )
+
+    @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 = cast(str, type(module))
+            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=}, "
+            f"{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 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:
+    def __new__(cls, *args, **kwargs):
+        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: "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,
+        *,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        inplace: bool = True,
+        no_combine: bool = False,
+        routed_scaling_factor: Optional[float] = None,
+    ) -> 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 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,
+        *,
+        activation: str = "silu",
+        apply_router_weight_on_input: bool = False,
+        inplace: bool = True,
+        no_combine: bool = False,
+        routed_scaling_factor: Optional[float] = None,
+    ) -> 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 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/quark/schemes/__init__.py

+# SPDX-License-Identifier: Apache-2.0
+
+from .quark_scheme import QuarkScheme
+from .quark_w4a4_mxfp4 import QuarkW4A4MXFP4
+
+__all__ = ["QuarkScheme", "QuarkW4A4MXFP4"]

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

+# SPDX-License-Identifier: Apache-2.0
+
+from abc import ABC, abstractmethod
+from typing import Optional
+
+import torch
+
+__all__ = ["QuarkScheme"]
+
+
+class QuarkScheme(ABC):
+    """
+    Abstract class used to describe the weight creation and forward pass
+    of different quantization schemes supported by Quark.
+    """
+
+    @classmethod
+    @abstractmethod
+    def get_min_capability(cls) -> int:
+        """
+        Get minimum device capability.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def create_weights(self, *args, **kwargs):
+        """
+        Weight creation for the particular scheme. Inputs to this function
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_weights(
+        self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor]
+    ):
+        """
+        Run the forward pass for the particular scheme. This is where
+        scheme-specific dequant/quant steps/kernels should be applied.
+
+        :param layer: torch.nn.Module with the registered weights and
+            other parameters relevant to the particular scheme.
+        :param x: input to the layer
+        :param bias: bias parameter
+
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def process_weights_after_loading(self, layer: torch.nn.Module):
+        """
+        Called after weight loading is complete for any cleanup that
+        needs to occur.
+        """
+        raise NotImplementedError

python/sglang/srt/layers/quantization/quark/schemes/quark_w4a4_mxfp4.py

+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Any, Callable, Optional
+
+import aiter
+import torch
+import torch.nn.functional as F
+from aiter.ops.gemm_op_a4w4 import gemm_a4w4
+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 import dtypes
+from aiter.utility.fp4_utils import e8m0_shuffle
+
+from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
+from sglang.srt.layers.quantization.quark.schemes import QuarkScheme
+from sglang.srt.utils import get_bool_env_var
+
+__all__ = ["QuarkW4A4MXFP4"]
+
+OCP_MX_BLOCK_SIZE = 32
+
+
+class QuarkW4A4MXFP4(QuarkScheme):
+
+    def __init__(
+        self, weight_quant_spec: dict[str, Any], input_quant_spec: dict[str, Any]
+    ):
+        self.out_dtype = torch.get_default_dtype()
+        self.qscheme = "per_group"
+        self.weight_quant_spec = weight_quant_spec
+        self.input_quant_spec = input_quant_spec
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        return
+
+        # for aiter implement
+        # wshuffle = shuffle_weight(layer.weight.data, layout=(16, 16))
+        # w_scales_shuffle = e8m0_shuffle(layer.weight_scale.data).view(dtypes.fp8_e8m0)
+
+        # 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,
+        output_partition_sizes: list[int],
+        input_size_per_partition: int,
+        params_dtype: torch.dtype,
+        weight_loader: Callable,
+        **kwargs
+    ):
+        output_size_per_partition = sum(output_partition_sizes)
+        layer.logical_widths = output_partition_sizes
+
+        # WEIGHT
+        weight = PackedvLLMParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // 2,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            packed_dim=1,
+            packed_factor=2,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        weight_scale = GroupQuantScaleParameter(
+            data=torch.empty(
+                output_size_per_partition,
+                input_size_per_partition // OCP_MX_BLOCK_SIZE,
+                dtype=torch.uint8,
+            ),
+            input_dim=1,
+            output_dim=0,
+            weight_loader=weight_loader,
+        )
+        layer.register_parameter("weight_scale", weight_scale)
+
+    def apply_weights(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        out_dtype = x.dtype
+        # 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=self.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

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

+# SPDX-License-Identifier: Apache-2.0
+
+import re
+from collections.abc import Iterable, Mapping
+from types import MappingProxyType
+from typing import Any, Optional
+
+
+def deep_compare(dict1: Any, dict2: Any) -> bool:
+    if type(dict1) is not type(dict2):
+        return False
+    if isinstance(dict1, dict):
+        if dict1.keys() != dict2.keys():
+            return False
+        return all(deep_compare(dict1[k], dict2[k]) for k in dict1)
+    elif isinstance(dict1, list):
+        return set(dict1) == set(dict2)
+    else:
+        return dict1 == dict2
+
+
+def should_ignore_layer(
+    layer_name: Optional[str],
+    ignore: Iterable[str],
+    fused_mapping: Mapping[str, list[str]] = MappingProxyType({}),
+) -> bool:
+    if layer_name is None:
+        return False
+
+    # layer_name = model.layers.0.self_attn.qkv_proj
+    # proj_name = qkv_proj
+    proj_name = layer_name.split(".")[-1]
+
+    # Fused layers like gate_up_proj or qkv_proj will not be fused
+    # in the safetensors checkpoint. So, we convert the name
+    # from the fused version to unfused + check to make sure that
+    # each shard of the fused layer has the same scheme.
+    if proj_name in fused_mapping:
+        shard_proj_names = fused_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
+        ]
+
+        # Layer should be ignored if shards are ignored.
+        should_ignore_layer = None
+        for shard_name in shard_names:
+            should_ignore_shard = check_equal_or_regex_match(
+                layer_name=shard_name, targets=ignore
+            )
+
+            # If shard_idx=0, set layer ignore to match shard.
+            if should_ignore_layer is None:
+                should_ignore_layer = should_ignore_shard
+
+            # If shard_idx=1+ confirm scheme matches prior shards.
+            elif should_ignore_shard != should_ignore_layer:
+                raise ValueError(
+                    f"Found a different quantization schemes for "
+                    f"{shard_proj_names} in {layer_name}. vLLM "
+                    "requires all to use the same scheme."
+                )
+
+    # Unfused layers like down_proj and o_proj will match
+    # the safetensors checkpoint already.
+    else:
+        should_ignore_layer = check_equal_or_regex_match(
+            layer_name=layer_name, targets=ignore
+        )
+
+    assert should_ignore_layer is not None
+
+    return should_ignore_layer
+
+
+def check_equal_or_regex_match(layer_name: str, targets: Iterable[str]) -> bool:
+    """
+    Checks whether a layer_name is exactly equal or a regex match for
+    if target starts with 're:' to any target in list.
+    """
+    for target in targets:
+        if _is_equal_or_regex_match(layer_name, target):
+            return True
+    return False
+
+
+def _is_equal_or_regex_match(
+    value: str, target: str, check_contains: bool = False
+) -> bool:
+    """
+    Checks whether a value is exactly equal or a regex match for target
+    if target starts with 're:'. If check_contains is set to True,
+    additionally checks if the target string is contained within the value.
+    """
+
+    if target.startswith("re:"):
+        pattern = target[3:]
+        if re.match(pattern, value):
+            return True
+    elif check_contains:
+        if target.lower() in value.lower():
+            return True
+    elif target == value:
+        return True
+    return False

python/sglang/srt/model_loader/weight_utils.py

@@ -843,6 +843,16 @@ def maybe_remap_kv_scale_name(name: str, params_dict: dict) -> Optional[str]:
                 return None
             return remapped_name
 
+    quark_scale_names = {
+        ".q_proj.output_scale": ".attn.q_scale",
+        ".k_proj.output_scale": ".attn.k_scale",
+        ".v_proj.output_scale": ".attn.v_scale",
+        "self_attn.prob_output_scale": ".attn.prob_scale",
+    }
+    for quark_scale_name, sglang_scale_name in quark_scale_names.items():
+        if name.endswith(quark_scale_name):
+            return name.replace(quark_scale_name, sglang_scale_name)
+
     # If there were no matches, return the untouched param name
     return name
 

python/sglang/srt/models/deepseek_v2.py

@@ -2061,6 +2061,8 @@ class DeepseekV2Model(nn.Module):
 
 
 class DeepseekV2ForCausalLM(nn.Module):
+    # for quark model load
+    packed_modules_mapping = {}
 
     def __init__(
         self,
@@ -2069,6 +2071,18 @@ class DeepseekV2ForCausalLM(nn.Module):
         prefix: str = "",
     ) -> None:
         super().__init__()
+
+        # for quark model load
+        # Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
+        self.fuse_qkv_a_proj = (
+            hasattr(config, "q_lora_rank") and config.q_lora_rank is not None
+        )
+        if self.fuse_qkv_a_proj:
+            self.packed_modules_mapping["fused_qkv_a_proj_with_mqa"] = [
+                "q_a_proj",
+                "kv_a_proj_with_mqa",
+            ]
+
         self.config = config
         self.tp_size = get_tensor_model_parallel_world_size()
         self.quant_config = quant_config

python/sglang/srt/server_args.py

@@ -813,6 +813,7 @@ class ServerArgs:
                 "moe_wna16",
                 "qoq",
                 "w4afp8",
+                "mxfp4",
             ],
             help="The quantization method.",
         )

python/sglang/srt/utils.py

@@ -2832,6 +2832,17 @@ def parse_module_path(module_path, function_name, create_dummy):
     return final_module, None
 
 
+def mxfp_supported():
+    """
+    Returns whether the current platform supports MX types.
+    """
+    if torch.version.hip:
+        gcn_arch = torch.cuda.get_device_properties(0).gcnArchName
+        return any(gfx in gcn_arch for gfx in ["gfx95"])
+    else:
+        return False
+
+
 # LoRA-related constants and utilities
 SUPPORTED_LORA_TARGET_MODULES = [
     "q_proj",