first commit

vllm/model_executor/layers/quantization/quark/quark.py

@@ -0,0 +1,432 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import fnmatch
+from typing import Any, Optional, cast
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.linear import (LinearBase, LinearMethodBase,
+                                               UnquantizedLinearMethod)
+from vllm.model_executor.layers.quantization import QuantizationMethods
+from vllm.model_executor.layers.quantization.base_config import (  # noqa: E501
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
+from vllm.model_executor.layers.quantization.quark.quark_moe import (  # noqa: E501
+    QuarkMoEMethod)
+from vllm.model_executor.layers.quantization.quark.schemes import (
+    QuarkScheme, QuarkW4A4MXFP4, QuarkW8A8Fp8, QuarkW8A8Int8)
+from vllm.model_executor.layers.quantization.quark.utils import (
+    deep_compare, should_ignore_layer)
+from vllm.platforms import current_platform
+
+__all__ = ["QuarkLinearMethod"]
+
+logger = init_logger(__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
+
+    def get_linear_method(self) -> "QuarkLinearMethod":
+        return QuarkLinearMethod(self)
+
+    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) -> QuantizationMethods:
+        return "quark"
+
+    def get_quant_method(self, layer: torch.nn.Module,
+                         prefix: str) -> Optional["QuantizeMethodBase"]:
+        from vllm.attention.layer import Attention  # Avoid circular import
+
+        # 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, Attention):
+            return QuarkKVCacheMethod(self)
+
+        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 = current_platform.get_device_capability()
+
+        if capability_tuple is not None:
+            capability = capability_tuple.to_int()
+            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_fp8_w8a8(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:
+            return False
+
+        # Confirm weight scheme is supported
+        is_fp8_dtype = (weight_quant.get("dtype") == "fp8_e4m3"
+                        and input_quant.get("dtype") == "fp8_e4m3")
+        is_static_weight = not weight_quant.get("is_dynamic")
+        is_per_tensor_or_channel_weight = (weight_quant.get("qscheme")
+                                           in ["per_tensor", "per_channel"])
+
+        if not (is_fp8_dtype and is_static_weight
+                and is_per_tensor_or_channel_weight):
+            return False
+
+        # Dynamic quantization is always supported if weights supported.
+        if input_quant.get("is_dynamic"):
+            return True
+
+        # Confirm activation scheme is supported.
+        is_per_tensor_activation = (input_quant.get("qscheme") == "per_tensor")
+        return is_per_tensor_activation
+
+    def _is_static_tensor_w8a8(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:
+            return False
+
+        is_int8_dtype = (weight_quant.get("dtype") == "int8"
+                         and input_quant.get("dtype") == "int8")
+
+        is_tensor = (weight_quant.get("qscheme")
+                     in ["per_tensor", "per_channel"]
+                     and input_quant.get("qscheme") == "per_tensor")
+
+        is_static = (not weight_quant.get("is_dynamic")
+                     and not input_quant.get("is_dynamic"))
+
+        is_weight_symmetric = (weight_quant.get("symmetric") is True)
+
+        # Both symmetric and asymmetric input quantization supported.
+        # Only symmetric weight quantization supported.
+        return is_int8_dtype and is_tensor and is_weight_symmetric and is_static
+
+    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
+
+        # 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_fp8_w8a8(weight_config, input_config):
+            is_fp8_w8a8_supported = self._check_scheme_supported(
+                QuarkW8A8Fp8.get_min_capability(), error=False)
+            if is_fp8_w8a8_supported:
+                return QuarkW8A8Fp8(weight_config, input_config)
+        elif self._is_static_tensor_w8a8(weight_config, input_config):
+            weight_qscheme = cast(str, weight_config.get("qscheme"))
+            return QuarkW8A8Int8(qscheme=weight_qscheme,
+                                 is_static_input_scheme=True,
+                                 input_symmetric=input_config.get("symmetric"))
+        elif 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_cache_scale(self, name: str) -> Optional[str]:
+        """
+        Check whether the param name matches the format for k/v cache scales
+        in quark. If this is the case, return its equivalent param name 
+        expected by vLLM
+
+        :param name: param name
+        :return: matching param name for KV cache scale in vLLM
+        """
+        if name.endswith(".output_scale") and ".k_proj" in name:
+            return name.replace(".k_proj.output_scale", ".attn.k_scale")
+        if name.endswith(".output_scale") and ".v_proj" in name:
+            return name.replace(".v_proj.output_scale", ".attn.v_scale")
+        if name.endswith(".output_scale") and ".q_proj" in name:
+            return name.replace(".q_proj.output_scale", ".attn.q_scale")
+        if name.endswith("self_attn.prob_output_scale"):
+            return name.replace(".prob_output_scale", ".attn.prob_scale")
+
+        # If no matches, return None
+        return None
+
+
+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}")

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -0,0 +1,561 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Callable, Optional, Union
+
+import torch
+
+import vllm.envs as envs
+from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEConfig,
+                                                  FusedMoEMethodBase,
+                                                  FusedMoeWeightScaleSupported)
+from vllm.model_executor.layers.fused_moe.config import (
+    FusedMoEQuantConfig, fp8_w8a8_moe_quant_config,
+    mxfp4_w4a4_moe_quant_config)
+from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (
+    is_rocm_aiter_moe_enabled)
+from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
+    prepare_moe_fp8_layer_for_marlin)
+from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
+    OCP_MX_BLOCK_SIZE)
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    all_close_1d, normalize_e4m3fn_to_e4m3fnuz, per_tensor_dequantize)
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.scalar_type import scalar_types
+
+logger = init_logger(__name__)
+
+__all__ = [
+    "QuarkMoEMethod", "QuarkW8A8Fp8MoEMethod", "QuarkW4A4MXFp4MoEMethod"
+]
+
+
+class QuarkMoEMethod(FusedMoEMethodBase):
+
+    def __init__(self, moe: FusedMoEConfig):
+        super().__init__(moe)
+
+    @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_fp8_w8a8(weight_config, input_config):
+            return QuarkW8A8Fp8MoEMethod(weight_config, input_config,
+                                         module.moe_config)
+        elif quant_config._is_mx_fp4(weight_config, input_config):
+            return QuarkW4A4MXFp4MoEMethod(weight_config, input_config,
+                                           module.moe_config)
+        else:
+            raise RuntimeError("Unsupported FusedMoe scheme")
+
+
+class QuarkW8A8Fp8MoEMethod(QuarkMoEMethod):
+
+    def __init__(
+        self,
+        weight_config: dict[str, Any],
+        input_config: dict[str, Any],
+        moe: FusedMoEConfig,
+    ):
+        super().__init__(moe)
+        self.weight_quant = weight_config
+        self.input_quant = input_config
+
+        self.weight_qscheme = self.weight_quant.get("qscheme")
+        self.input_qscheme = self.input_quant.get("qscheme")
+        per_tensor = (self.weight_qscheme == "per_tensor"
+                      and self.input_qscheme == "per_tensor")
+        per_channel = (self.weight_qscheme == "per_channel"
+                       and self.input_qscheme == "per_channel")
+        self.act_quant_group_shape = GroupShape.PER_TOKEN \
+            if per_channel else GroupShape.PER_TENSOR
+        if not (per_tensor or per_channel):
+            raise ValueError(
+                "For FP8 Fused MoE layers, only per-tensor and per-channel "
+                "scales for weights and activations are supported. Found "
+                f"{self.weight_qscheme}, {self.input_qscheme}")  # noqa E501
+
+        self.static_input_scales = not self.input_quant.get("is_dynamic")
+        if self.static_input_scales and per_channel:
+            raise ValueError(
+                "For FP8 Fused MoE layer, we require either per tensor or "
+                "channelwise, dynamic per token quantization.")
+
+        # For GPUs that lack FP8 hardware support, we can leverage the Marlin
+        # kernel for fast weight-only FP8 quantization
+        self.use_marlin = (not current_platform.has_device_capability(89)
+                           or envs.VLLM_TEST_FORCE_FP8_MARLIN)
+        # Disable marlin for rocm
+        if current_platform.is_rocm():
+            self.use_marlin = False
+
+        self.rocm_aiter_moe_enabled = is_rocm_aiter_moe_enabled()
+
+    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):
+
+        layer.intermediate_size_per_partition = intermediate_size_per_partition
+        layer.hidden_size = hidden_size
+        layer.num_experts = num_experts
+        layer.orig_dtype = params_dtype
+        layer.weight_block_size = None
+        params_dtype = torch.float8_e4m3fn
+
+        # WEIGHTS
+        w13_weight = torch.nn.Parameter(torch.empty(
+            num_experts,
+            2 * intermediate_size_per_partition,
+            hidden_size,
+            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,
+            dtype=params_dtype),
+                                       requires_grad=False)
+        layer.register_parameter("w2_weight", w2_weight)
+        set_weight_attrs(w2_weight, extra_weight_attrs)
+
+        # WEIGHT_SCALES
+        if self.weight_qscheme == "per_tensor":
+            # Allocate 2 scales for w1 and w3 respectively.
+            # They are 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)
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-TENSOR quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.TENSOR.value})
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+        elif self.weight_qscheme == "per_channel":
+            # quark's scale is 1 dim.
+            w13_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts,
+                2 * intermediate_size_per_partition,
+                dtype=torch.float32),
+                                                  requires_grad=False)
+            layer.register_parameter("w13_weight_scale", w13_weight_scale)
+            w2_weight_scale = torch.nn.Parameter(torch.ones(
+                num_experts, hidden_size, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w2_weight_scale", w2_weight_scale)
+            # Add PER-CHANNEL quantization for FusedMoE.weight_loader.
+            extra_weight_attrs.update(
+                {"quant_method": FusedMoeWeightScaleSupported.CHANNEL.value})
+            set_weight_attrs(w13_weight_scale, extra_weight_attrs)
+            set_weight_attrs(w2_weight_scale, extra_weight_attrs)
+
+        # INPUT_SCALES
+        if self.static_input_scales:
+            w13_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                 requires_grad=False)
+            layer.register_parameter("w13_input_scale", w13_input_scale)
+            set_weight_attrs(w13_input_scale, extra_weight_attrs)
+
+            w2_input_scale = torch.nn.Parameter(torch.ones(
+                num_experts, dtype=torch.float32),
+                                                requires_grad=False)
+            layer.register_parameter("w2_input_scale", w2_input_scale)
+            set_weight_attrs(w2_input_scale, extra_weight_attrs)
+        else:
+            layer.w13_input_scale = None
+            layer.w2_input_scale = None
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        # Fp8 moe kernels require a single activation scale.
+        # We take the max of all the scales in case they differ.
+        if self.static_input_scales:
+            if (layer.w13_input_scale is None or layer.w2_input_scale is None):
+                raise ValueError(
+                    "QuantConfig has static quantization, but found "
+                    "activation scales are None.")
+            if (not all_close_1d(layer.w13_input_scale)
+                    or not all_close_1d(layer.w2_input_scale)):
+                logger.warning_once(
+                    "Found input_scales that are not equal for "
+                    "fp8 MoE layer. Using the maximum across experts "
+                    "for each layer. ")
+            layer.w13_input_scale = torch.nn.Parameter(
+                layer.w13_input_scale.max(), requires_grad=False)
+            layer.w2_input_scale = torch.nn.Parameter(
+                layer.w2_input_scale.max(), requires_grad=False)
+
+        if current_platform.is_fp8_fnuz():
+            # Normalize the weights and scales
+            w13_weight, w13_weight_scale, w13_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w13_weight, layer.w13_weight_scale,
+                    layer.w13_input_scale)
+            w2_weight, w2_weight_scale, w2_input_scale = \
+                normalize_e4m3fn_to_e4m3fnuz(
+                    layer.w2_weight, layer.w2_weight_scale,
+                    layer.w2_input_scale)
+            # Reset the parameter
+            layer.w13_weight = torch.nn.Parameter(w13_weight,
+                                                  requires_grad=False)
+            layer.w13_weight_scale = torch.nn.Parameter(w13_weight_scale,
+                                                        requires_grad=False)
+            if w13_input_scale is not None:
+                layer.w13_input_scale = torch.nn.Parameter(w13_input_scale,
+                                                           requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(w2_weight,
+                                                 requires_grad=False)
+            layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                       requires_grad=False)
+            if w2_input_scale is not None:
+                layer.w2_input_scale = torch.nn.Parameter(w2_input_scale,
+                                                          requires_grad=False)
+
+        # For per-tensor case, Fp8 moe kernel needs single weight scale
+        # for w13 per expert. Use max then dequant and requant each expert.
+        if self.weight_qscheme == "per_tensor":
+            assert layer.w13_weight_scale is not None
+            shard_size = layer.intermediate_size_per_partition
+            max_w13_scales = layer.w13_weight_scale.max(dim=1).values
+            for expert_id in range(layer.local_num_experts):
+                start = 0
+                for shard_id in range(2):
+                    dq_weight = per_tensor_dequantize(
+                        layer.w13_weight[expert_id][start:start +
+                                                    shard_size, :],
+                        layer.w13_weight_scale[expert_id][shard_id])
+                    layer.w13_weight[expert_id][
+                        start:start + shard_size, :], _ = ops.scaled_fp8_quant(
+                            dq_weight, max_w13_scales[expert_id])
+                    start += shard_size
+
+            layer.w13_weight_scale = torch.nn.Parameter(max_w13_scales,
+                                                        requires_grad=False)
+        # quark's scale is 1 dim.
+        elif self.weight_qscheme == "per_channel":
+            if self.act_quant_group_shape == GroupShape.PER_TOKEN:
+                w13_weight_scale = layer.w13_weight_scale.unsqueeze(-1)
+                layer.w13_weight_scale = torch.nn.Parameter(
+                    w13_weight_scale, requires_grad=False)
+                w2_weight_scale = layer.w2_weight_scale.unsqueeze(-1)
+                layer.w2_weight_scale = torch.nn.Parameter(w2_weight_scale,
+                                                           requires_grad=False)
+        # Property to determine if AITER is used
+        if self.rocm_aiter_moe_enabled:
+            from vllm.model_executor.layers.fused_moe.rocm_aiter_fused_moe import (  # noqa E501
+                rocm_aiter_fused_experts, shuffle_weights)
+
+            # reshaping weights is required for aiter moe kernel.
+            shuffled_w13, shuffled_w2 = shuffle_weights(
+                layer.w13_weight.data, layer.w2_weight.data)
+
+            layer.w13_weight = torch.nn.Parameter(shuffled_w13,
+                                                  requires_grad=False)
+            layer.w2_weight = torch.nn.Parameter(shuffled_w2,
+                                                 requires_grad=False)
+
+            self.rocm_aiter_fused_experts_func = rocm_aiter_fused_experts
+        elif self.use_marlin:
+
+            prepare_moe_fp8_layer_for_marlin(layer, False)
+            # Activations not quantized for marlin.
+            del layer.w13_input_scale
+            del layer.w2_input_scale
+            self.fused_experts_func = None
+        else:
+            from vllm.model_executor.layers.fused_moe import fused_experts
+            self.fused_experts_func = fused_experts
+
+    def get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        return fp8_w8a8_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=layer.w13_input_scale,
+            a2_scale=layer.w2_input_scale,
+            per_act_token_quant=self.weight_qscheme == "per_channel",
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        assert self.fused_experts is None
+
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for `QuarkW8A8Fp8MoEMethod` yet.")
+
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=self.topk_indices_dtype)
+
+        if self.rocm_aiter_moe_enabled:
+            return self.rocm_aiter_fused_experts_func(
+                hidden_states=x,
+                w1=layer.w13_weight,
+                w2=layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                activation=activation,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                quant_config=self.moe_quant_config,
+                expert_map=expert_map)
+        if self.use_marlin:
+            assert activation == "silu", (
+                f"{activation} not supported for Marlin MoE.")
+            return torch.ops.vllm.fused_marlin_moe(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                None,
+                None,
+                layer.w13_weight_scale,
+                layer.w2_weight_scale,
+                router_logits,
+                topk_weights,
+                topk_ids,
+                quant_type_id=scalar_types.float8_e4m3fn.id,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                expert_map=expert_map)
+
+        assert self.fused_experts_func is not None
+
+        return self.fused_experts_func(
+            hidden_states=x,
+            w1=layer.w13_weight,
+            w2=layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            global_num_experts=global_num_experts,
+            expert_map=expert_map,
+            quant_config=self.moe_quant_config)
+
+
+class QuarkW4A4MXFp4MoEMethod(QuarkMoEMethod):
+
+    def __init__(
+        self,
+        weight_config: dict[str, Any],
+        input_config: dict[str, Any],
+        moe: FusedMoEConfig,
+    ):
+        super().__init__(moe)
+        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")
+
+        if self.static_input_scales:
+            raise NotImplementedError(
+                "QuarkW4A4MXFp4MoEMethod with static input scales is currently "
+                "not implemented. Please open an issue.")
+
+        if not current_platform.supports_mx():
+            self.emulate = True
+            logger.warning_once(
+                "The current platform does not support native MXFP4 "
+                "computation. Simulated weight dequantization and activation "
+                "QDQ (quantize and dequantize) will be used, with the linear "
+                "layers computed in high precision.")
+        else:
+            self.emulate = True
+            logger.warning_once(
+                "The current platform supports native MXFP4 "
+                "computation, but kernels are not yet integrated in vLLM. "
+                "Simulated weight dequantization and activation "
+                "QDQ (quantize and dequantize) will be used, with the linear "
+                "layers computed in high precision.")
+
+    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):
+
+        # 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 get_fused_moe_quant_config(
+            self, layer: torch.nn.Module) -> Optional[FusedMoEQuantConfig]:
+        return mxfp4_w4a4_moe_quant_config(
+            w1_scale=layer.w13_weight_scale,
+            w2_scale=layer.w2_weight_scale,
+            a1_scale=None,
+            a2_scale=None,
+            block_shape=None,
+        )
+
+    def apply(
+        self,
+        layer: torch.nn.Module,
+        x: torch.Tensor,
+        router_logits: torch.Tensor,
+        top_k: int,
+        renormalize: bool,
+        use_grouped_topk: bool = False,
+        topk_group: Optional[int] = None,
+        num_expert_group: Optional[int] = None,
+        global_num_experts: int = -1,
+        expert_map: Optional[torch.Tensor] = None,
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        routed_scaling_factor: float = 1.0,
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        enable_eplb: bool = False,
+        expert_load_view: Optional[torch.Tensor] = None,
+        logical_to_physical_map: Optional[torch.Tensor] = None,
+        logical_replica_count: Optional[torch.Tensor] = None,
+    ) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
+        assert self.fused_experts is None
+
+        if enable_eplb:
+            raise NotImplementedError(
+                "EPLB not supported for `QuarkW4A4MXFp4MoEMethod` yet.")
+
+        from vllm.model_executor.layers.fused_moe import fused_experts
+
+        topk_weights, topk_ids, _ = FusedMoE.select_experts(
+            hidden_states=x,
+            router_logits=router_logits,
+            use_grouped_topk=use_grouped_topk,
+            top_k=top_k,
+            renormalize=renormalize,
+            topk_group=topk_group,
+            num_expert_group=num_expert_group,
+            custom_routing_function=custom_routing_function,
+            scoring_func=scoring_func,
+            routed_scaling_factor=routed_scaling_factor,
+            e_score_correction_bias=e_score_correction_bias,
+            indices_type=self.topk_indices_dtype)
+
+        out = fused_experts(
+            x,
+            layer.w13_weight,
+            layer.w2_weight,
+            topk_weights=topk_weights,
+            topk_ids=topk_ids,
+            inplace=True,
+            activation=activation,
+            global_num_experts=global_num_experts,
+            apply_router_weight_on_input=apply_router_weight_on_input,
+            expert_map=expert_map,
+            quant_config=self.moe_quant_config,
+        )
+        return out

vllm/model_executor/layers/quantization/quark/schemes/__init__.py

@@ -0,0 +1,9 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from .quark_scheme import QuarkScheme
+from .quark_w4a4_mxfp4 import QuarkW4A4MXFP4
+from .quark_w8a8_fp8 import QuarkW8A8Fp8
+from .quark_w8a8_int8 import QuarkW8A8Int8
+
+__all__ = ["QuarkScheme", "QuarkW8A8Fp8", "QuarkW8A8Int8", "QuarkW4A4MXFP4"]

vllm/model_executor/layers/quantization/quark/schemes/quark_scheme.py

@@ -0,0 +1,55 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+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

vllm/model_executor/layers/quantization/quark/schemes/quark_w4a4_mxfp4.py

@@ -0,0 +1,239 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from functools import cache
+from typing import Any, Callable, Optional
+
+import torch
+import torch.nn.functional as F
+
+from vllm import envs
+from vllm.model_executor.layers.quantization.quark.schemes import QuarkScheme
+from vllm.model_executor.layers.quantization.utils.mxfp4_utils import (
+    OCP_MX_BLOCK_SIZE, dequant_mxfp4, quant_dequant_mxfp4)
+from vllm.model_executor.parameter import (GroupQuantScaleParameter,
+                                           PackedvLLMParameter)
+from vllm.platforms import current_platform
+
+
+@cache
+def is_rocm_aiter_fp4_asm_gemm_enabled() -> bool:
+    return current_platform.is_rocm() \
+        and envs.VLLM_ROCM_USE_AITER_FP4_ASM_GEMM \
+        and envs.VLLM_ROCM_USE_AITER
+
+
+try:
+    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 vllm.utils import direct_register_custom_op
+    if is_rocm_aiter_fp4_asm_gemm_enabled():
+        from aiter import gemm_a4w4, per_1x32_f4_quant_hip
+
+    def gemm_with_dynamic_quant(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        rocm_use_aiter_fp4_asm_gemm: bool = False,
+        out_dtype: Optional[torch.dtype] = torch.bfloat16,
+        x_scales: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        M = x.shape[0]
+        if rocm_use_aiter_fp4_asm_gemm:
+            if x_scales is None:
+                # use hip quant kernel for performance
+                x_q, x_s = per_1x32_f4_quant_hip(x, shuffle=True)
+            else:
+                x_q = x
+                x_s = x_scales
+
+            # 32 alignment is enough for dim0 padding of output for
+            # gemm_a4w4 kernel
+            y = torch.empty((M + 31) // 32 * 32,
+                            weight.shape[0],
+                            device=x_q.device,
+                            dtype=out_dtype)
+
+            gemm_a4w4(x_q,
+                      weight,
+                      x_s,
+                      weight_scale.view(x_s.dtype),
+                      y,
+                      bpreshuffle=True)
+            return y[:M]
+        else:
+            if x_scales is None:
+                x_q, x_s = dynamic_mxfp4_quant(x)
+            else:
+                x_q = x
+                x_s = x_scales
+            y = torch.empty(x_q.shape[0],
+                            weight.shape[0],
+                            device=x_q.device,
+                            dtype=out_dtype)
+
+            gemm_afp4wfp4(x_q, weight, x_s, weight_scale.T, out_dtype, y)
+            return y
+
+    def gemm_with_dynamic_quant_fake(
+        x: torch.Tensor,
+        weight: torch.Tensor,
+        weight_scale: torch.Tensor,
+        x_scales: torch.Tensor = None,
+        rocm_use_aiter_fp4_asm_gemm: bool = False,
+        out_dtype: Optional[torch.dtype] = torch.bfloat16,
+    ) -> torch.Tensor:
+        return torch.empty((*x.shape[:-1], weight.shape[0]),
+                           dtype=out_dtype,
+                           device=x.device)
+
+    direct_register_custom_op(
+        op_name="gemm_with_dynamic_quant",
+        op_func=gemm_with_dynamic_quant,
+        mutates_args=[],
+        fake_impl=gemm_with_dynamic_quant_fake,
+        dispatch_key=current_platform.dispatch_key,
+    )
+
+except ImportError:
+    dynamic_mxfp4_quant = gemm_afp4wfp4 = None
+
+__all__ = ["QuarkW4A4MXFP4"]
+
+
+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
+        self.emulate = not current_platform.supports_mx()
+        self.rocm_use_aiter_fp4_asm_gemm = is_rocm_aiter_fp4_asm_gemm_enabled()
+        if not self.emulate and (dynamic_mxfp4_quant is None
+                                 or gemm_afp4wfp4 is None):
+            # Currently need these kernels if not emulating
+            raise NotImplementedError(
+                f"{self.__class__.__name__} requires AITER to be installed "
+                "for non-emulation mode! Please refer to "
+                "https://github.com/ROCm/aiter for installation details.")
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 70
+
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.weight = torch.nn.Parameter(layer.weight.data,
+                                          requires_grad=False)
+
+        if self.emulate:
+            layer.weight_scale = torch.nn.Parameter(layer.weight_scale.data,
+                                                    requires_grad=False)
+            try:
+                from quark.torch.export.nn.modules import realquantizer
+                from quark.torch.quantization.config.config import (
+                    QuantizationSpec)
+            except ImportError as err:
+                raise ImportError(
+                    "The package `amd-quark` is required to use AMD Quark "
+                    "MX-FP4 models. Please install it with `pip install "
+                    "amd-quark`.") from err
+
+            weight_quant_spec = QuantizationSpec.from_dict(
+                self.weight_quant_spec)
+
+            weight_quantizer = realquantizer.get_real_quantizer(
+                qspec=weight_quant_spec,
+                quantizer=None,
+                real_quantized=True,
+                reorder=False,
+                float_dtype=self.out_dtype,
+                scale_shape=layer.weight_scale.shape,
+                zero_point_shape=None,
+            )
+            weight_quantizer.scale.data = layer.weight_scale.data
+
+            layer.weight = torch.nn.Parameter(
+                weight_quantizer(layer.weight.data).to(self.out_dtype),
+                requires_grad=False,
+            )
+            layer.weight_scale = None
+
+            # This call is necessary to release the scales memory.
+            torch.cuda.empty_cache()
+        else:
+            if self.rocm_use_aiter_fp4_asm_gemm:
+                # shuffle weight scale
+                weight_scale_shuffle = layer.weight_scale.data
+                sm, sn = weight_scale_shuffle.shape
+                weight_scale_shuffle = weight_scale_shuffle.view(
+                    sm // 32, 2, 16, sn // 8, 2, 4, 1)
+                weight_scale_shuffle = weight_scale_shuffle.permute(
+                    0, 3, 5, 2, 4, 1, 6).contiguous()
+                weight_scale_shuffle = weight_scale_shuffle.view(sm, sn)
+                layer.weight_scale = torch.nn.Parameter(weight_scale_shuffle,
+                                                        requires_grad=False)
+
+                # shuffle weight
+                weight_shuffle = layer.weight.data
+                weight_shuffle = shuffle_weight(weight_shuffle,
+                                                layout=(16, 16))
+                layer.weight = torch.nn.Parameter(weight_shuffle,
+                                                  requires_grad=False)
+            else:
+                layer.weight_scale = torch.nn.Parameter(
+                    layer.weight_scale.data.T.contiguous(),
+                    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:
+
+        if self.emulate:
+            dq_w = dequant_mxfp4(layer.weight, layer.weight_scale, x.dtype)
+            x = quant_dequant_mxfp4(x)
+            return F.linear(x, dq_w, bias)
+        else:
+            return torch.ops.vllm.gemm_with_dynamic_quant(
+                x, layer.weight, layer.weight_scale,
+                self.rocm_use_aiter_fp4_asm_gemm, self.out_dtype)

vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_fp8.py

@@ -0,0 +1,163 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Any, Callable, Optional, cast
+
+import torch
+from torch.nn import Parameter
+
+from vllm.model_executor.layers.quantization.quark.schemes import QuarkScheme
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    GroupShape)
+from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
+    Fp8LinearOp, normalize_e4m3fn_to_e4m3fnuz, requantize_with_max_scale)
+from vllm.model_executor.parameter import (ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+from vllm.platforms import current_platform
+
+__all__ = ["QuarkW8A8Fp8"]
+
+
+class QuarkW8A8Fp8(QuarkScheme):
+
+    def __init__(self, weight_config: dict[str, Any],
+                 input_config: Optional[dict[str, Any]]):
+        self.weight_qscheme = cast(str, weight_config.get("qscheme"))
+        self.is_static_input_scheme: bool = False
+        self.input_qscheme: Optional[str] = None
+        if input_config is not None:
+            self.is_static_input_scheme = not cast(
+                bool, input_config.get("is_dynamic"))
+            self.input_qscheme = cast(str, input_config.get("qscheme"))
+
+        per_token = (not self.is_static_input_scheme
+                     and self.input_qscheme == "per_channel")
+        self.act_quant_group_shape = GroupShape.PER_TOKEN \
+            if per_token else GroupShape.PER_TENSOR
+        self.fp8_linear = Fp8LinearOp(
+            act_quant_static=self.is_static_input_scheme,
+            act_quant_group_shape=self.act_quant_group_shape)
+        self.out_dtype = torch.get_default_dtype()
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # lovelace and up
+        return 89
+
+    def process_weights_after_loading(self, layer) -> None:
+        # If per tensor, when we have a fused module (e.g. QKV) with per
+        # tensor scales (thus N scales being passed to the kernel),
+        # requantize so we can always run per tensor
+        if self.weight_qscheme == "per_tensor":
+            if current_platform.is_fp8_fnuz():
+                input_scale = getattr(layer, 'input_scale', None)
+                weight, max_w_scale, input_scale = normalize_e4m3fn_to_e4m3fnuz(
+                    weight=layer.weight,
+                    weight_scale=layer.weight_scale,
+                    input_scale=input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+            else:
+                max_w_scale = layer.weight_scale
+                weight = layer.weight
+
+            max_w_scale, weight = requantize_with_max_scale(
+                weight=weight,
+                weight_scale=max_w_scale,
+                logical_widths=layer.logical_widths,
+            )
+
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            layer.weight_scale = Parameter(max_w_scale, requires_grad=False)
+
+        # If channelwise, scales are already lined up, so just transpose.
+        elif self.weight_qscheme == "per_channel":
+            weight = layer.weight
+
+            if current_platform.is_fp8_fnuz():
+                input_scale = getattr(layer, 'input_scale', None)
+                weight, weight_scale, input_scale = \
+                    normalize_e4m3fn_to_e4m3fnuz(
+                        weight=weight,
+                        weight_scale=layer.weight_scale,
+                        input_scale=input_scale)
+                if input_scale is not None:
+                    layer.input_scale = Parameter(input_scale,
+                                                  requires_grad=False)
+            else:
+                weight_scale = layer.weight_scale.data
+            if self.act_quant_group_shape == GroupShape.PER_TOKEN:
+                weight_scale = weight_scale.view(-1, 1)
+            layer.weight = Parameter(weight.t(), requires_grad=False)
+            # required by torch.compile to be torch.nn.Parameter
+            layer.weight_scale = Parameter(weight_scale, requires_grad=False)
+
+        else:
+            raise ValueError(
+                f"Unknown quantization scheme {self.weight_qscheme}")
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            layer.input_scale = Parameter(layer.input_scale.max(),
+                                          requires_grad=False)
+        else:
+            layer.input_scale = None
+
+    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 = ModelWeightParameter(data=torch.empty(
+            output_size_per_partition,
+            input_size_per_partition,
+            dtype=torch.float8_e4m3fn),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        # TODO: update create_xxx_parameter functions to return
+        # the newly added parameters
+        if self.weight_qscheme == "per_channel":
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes)),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.weight_qscheme == "per_tensor"
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+
+        # min requirement for fp8 kernels
+        weight_scale[:] = torch.finfo(torch.float32).min
+        layer.register_parameter("weight_scale", weight_scale)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                  weight_loader=weight_loader)
+            input_scale[:] = torch.finfo(torch.float32).min
+            layer.register_parameter("input_scale", input_scale)
+
+    def apply_weights(self,
+                      layer: torch.nn.Module,
+                      x: torch.Tensor,
+                      bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+
+        return self.fp8_linear.apply(input=x,
+                                     weight=layer.weight,
+                                     weight_scale=layer.weight_scale,
+                                     out_dtype=self.out_dtype,
+                                     input_scale=layer.input_scale,
+                                     bias=bias)

vllm/model_executor/layers/quantization/quark/schemes/quark_w8a8_int8.py

@@ -0,0 +1,122 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from typing import Callable, Optional
+
+import torch
+
+from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
+    ScaledMMLinearLayerConfig, choose_scaled_mm_linear_kernel)
+from vllm.model_executor.layers.quantization.quark.schemes import QuarkScheme
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           ChannelQuantScaleParameter,
+                                           ModelWeightParameter,
+                                           PerTensorScaleParameter)
+
+logger = init_logger(__name__)
+
+
+class QuarkW8A8Int8(QuarkScheme):
+    _kernel_backends_being_used: set[str] = set()
+
+    def __init__(self, qscheme: str, is_static_input_scheme: Optional[bool],
+                 input_symmetric: Optional[bool]):
+        self.qscheme = qscheme
+        self.is_static_input_scheme = is_static_input_scheme
+        self.input_symmetric = input_symmetric
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # turing and up
+        return 75
+
+    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):
+        layer.logical_widths = output_partition_sizes
+
+        scaled_mm_linear_kernel_config = ScaledMMLinearLayerConfig(
+            is_channelwise=(self.qscheme == "per_channel"),
+            is_static_input_scheme=(self.is_static_input_scheme is True),
+            input_symmetric=(self.input_symmetric is True))
+
+        kernel_type = choose_scaled_mm_linear_kernel(
+            scaled_mm_linear_kernel_config)
+
+        if kernel_type.__name__ not in self._kernel_backends_being_used:
+            logger.info("Using %s for QuarkW8A8Int8", kernel_type.__name__)
+            self._kernel_backends_being_used.add(kernel_type.__name__)
+
+        # WEIGHT
+        weight = ModelWeightParameter(data=torch.empty(
+            sum(output_partition_sizes),
+            input_size_per_partition,
+            dtype=torch.int8),
+                                      input_dim=1,
+                                      output_dim=0,
+                                      weight_loader=weight_loader)
+
+        layer.register_parameter("weight", weight)
+
+        # WEIGHT SCALE
+        if self.qscheme == "per_channel":
+            weight_scale = ChannelQuantScaleParameter(
+                data=torch.empty((sum(output_partition_sizes)),
+                                 dtype=torch.float32),
+                output_dim=0,
+                weight_loader=weight_loader)
+            ChannelQuantZPParameter = ChannelQuantScaleParameter
+            weight_zero_point = ChannelQuantZPParameter(
+                data=torch.empty((sum(output_partition_sizes)),
+                                 dtype=torch.int8),
+                output_dim=0,
+                weight_loader=weight_loader)
+        else:
+            assert self.qscheme == "per_tensor"
+            weight_scale = PerTensorScaleParameter(data=torch.empty(
+                len(output_partition_sizes), dtype=torch.float32),
+                                                   weight_loader=weight_loader)
+            PerTensorZPParameter = PerTensorScaleParameter
+            weight_zero_point = PerTensorZPParameter(
+                data=torch.empty(len(output_partition_sizes),
+                                 dtype=torch.int8),
+                weight_loader=weight_loader)
+        layer.register_parameter("weight_scale", weight_scale)
+        layer.register_parameter("weight_zero_point", weight_zero_point)
+
+        # INPUT SCALE
+        if self.is_static_input_scheme:
+            input_scale = BasevLLMParameter(data=torch.empty(
+                1, dtype=torch.float32),
+                                            weight_loader=weight_loader)
+            layer.register_parameter("input_scale", input_scale)
+
+            input_zero_point = BasevLLMParameter(data=torch.empty(
+                1, dtype=torch.int8),
+                                                 weight_loader=weight_loader)
+            layer.register_parameter("input_zero_point", input_zero_point)
+
+        self.kernel = kernel_type(c=scaled_mm_linear_kernel_config,
+                                  w_q_param_name="weight",
+                                  w_s_param_name="weight_scale",
+                                  i_s_param_name="input_scale",
+                                  i_zp_param_name="input_zero_point",
+                                  azp_adj_param_name="azp_adj")
+
+    # Checkpoints are serialized in quark format, which is
+    # different from the format the kernel may want. Handle repacking here.
+    def process_weights_after_loading(self, layer: torch.nn.Module) -> None:
+        layer.register_parameter("weight_zero_point", None)
+        delattr(layer, 'weight_zero_point')
+        if self.input_symmetric:
+            layer.register_parameter("input_zero_point", None)
+            delattr(layer, 'input_zero_point')
+
+        self.kernel.process_weights_after_loading(layer)
+
+    def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor,
+                      bias: Optional[torch.Tensor]) -> torch.Tensor:
+        return self.kernel.apply_weights(layer, x, bias)

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

@@ -0,0 +1,105 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from collections.abc import Iterable, Mapping
+from types import MappingProxyType
+from typing import Any, Optional
+
+import regex as re
+
+
+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