enable auto-round quantization model (#6226)

Signed-off-by: Zhang, Weiwei1 <weiwei1.zhang@intel.com>

python/sglang/srt/configs/model_config.py

@@ -450,6 +450,7 @@ class ModelConfig:
             "petit_nvfp4",
             "quark",
             "mxfp4",
+            "auto-round",
         ]
         optimized_quantization_methods = [
             "fp8",

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

@@ -192,6 +192,14 @@ class FusedMoE(torch.nn.Module):
 
         self.use_triton_kernels = get_moe_runner_backend().is_triton_kernel()
 
+        moe_quant_params = {}
+        if self.quant_method.__class__.__name__ in (
+            "GPTQMarlinMoEMethod",
+            "CompressedTensorsWNA16MarlinMoEMethod",
+            "CompressedTensorsWNA16MoEMethod",
+        ):
+            moe_quant_params["intermediate_size_full"] = intermediate_size
+
         self.quant_config = quant_config
         self.use_flashinfer_mxfp4_moe = get_moe_runner_backend().is_flashinfer_mxfp4()
         # TODO maybe we should remove this `if`, since `Mxfp4MoEMethod` does another round-up logic
@@ -243,6 +251,7 @@ class FusedMoE(torch.nn.Module):
                 else self.weight_loader_fused
             ),
             with_bias=with_bias,
+            **moe_quant_params,
         )
 
         self.quant_method.create_moe_runner(self, self.moe_runner_config)

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

@@ -41,6 +41,7 @@ except ImportError as e:
     )
 
 
+from sglang.srt.layers.quantization.auto_round import AutoRoundConfig
 from sglang.srt.layers.quantization.awq import AWQConfig, AWQMarlinConfig
 from sglang.srt.layers.quantization.base_config import QuantizationConfig
 from sglang.srt.layers.quantization.blockwise_int8 import BlockInt8Config
@@ -86,6 +87,7 @@ BASE_QUANTIZATION_METHODS: Dict[str, Type[QuantizationConfig]] = {
     "w4afp8": W4AFp8Config,
     "petit_nvfp4": PetitNvFp4Config,
     "fbgemm_fp8": FBGEMMFp8Config,
+    "auto-round": AutoRoundConfig,
 }
 
 

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

@@ -0,0 +1,360 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import logging
+from collections.abc import Iterable, Mapping
+from fractions import Fraction
+from typing import Any, Optional, Union
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+from sglang.srt.layers.quantization.utils import get_scalar_types, replace_parameter
+
+ScalarType, scalar_types = get_scalar_types()
+
+
+from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
+from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.vocab_parallel_embedding import ParallelLMHead
+
+
+class AutoRoundConfig(QuantizationConfig):
+    """Config class for AutoRound.
+    Reference: https://arxiv.org/pdf/2309.05516
+    """
+
+    SUPPORTED_BITS = {2, 3, 4, 8}
+    SUPPORTED_DTYPES = {"int"}
+    SUPPORTED_FORMATS = {"auto_round:auto_gptq", "auto_round:auto_awq"}
+    SUPPORTED_BACKENDS = {"auto", "gptq", "gptq:marlin", "awq", "awq:marlin", "marlin"}
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        sym: bool = True,
+        packing_format: str = "auto_round:auto_gptq",
+        block_name_to_quantize: Optional[Union[str, list[str]]] = None,
+        extra_config: Optional[dict[str, Any]] = None,
+        data_type: str = "int",
+        backend: str = "auto",
+    ) -> None:
+        super().__init__()
+        if weight_bits not in self.SUPPORTED_BITS:
+            raise ValueError(
+                f"Unsupported weight_bits: {weight_bits}, "
+                f"currently only support  {self.SUPPORTED_BITS}"
+            )
+        if data_type not in self.SUPPORTED_DTYPES:
+            raise ValueError(
+                f"Unsupported data_type: {data_type},"
+                f" currently only support  {self.SUPPORTED_DTYPES}"
+            )
+        if packing_format not in self.SUPPORTED_FORMATS:
+            raise ValueError(
+                f"Unsupported packing_format: {packing_format}, "
+                f"currently only support  {self.SUPPORTED_FORMATS}"
+            )
+        if backend not in self.SUPPORTED_BACKENDS:
+            raise ValueError(
+                f"Unsupported backend: {backend},  "
+                f"currently only support  {self.SUPPORTED_BACKENDS}"
+            )
+
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.sym = sym
+        self.packing_format = packing_format
+        self.block_name_to_quantize = (
+            block_name_to_quantize.split(",")
+            if isinstance(block_name_to_quantize, str)
+            else block_name_to_quantize
+        )
+        self.extra_config = extra_config
+        self.data_type = data_type
+        self.backend = backend
+        self.pack_factor = Fraction(32, weight_bits)
+
+    def __repr__(self) -> str:
+        return (
+            f"AutoRoundConfig(weight_bits={self.weight_bits}, "
+            f"group_size={self.group_size}, sym={self.sym})"
+        )
+
+    @classmethod
+    def get_name(cls):  ## use str will trigger preci issue
+        return "auto-round"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> list[torch.dtype]:
+        return [torch.half, torch.bfloat16]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        return 60
+
+    @classmethod
+    def get_config_filenames(cls) -> list[str]:
+        return ["quantization_config.json"]
+
+    @classmethod
+    def from_config(cls, config: dict[str, Any]) -> "AutoRoundConfig":
+        return cls(
+            weight_bits=cls.get_from_keys(config, ["bits"]),
+            group_size=cls.get_from_keys(config, ["group_size"]),
+            sym=cls.get_from_keys(config, ["sym"]),
+            packing_format=cls.get_from_keys_or(
+                config,
+                ["packing_format"],
+                "auto_round:auto_gptq",
+            ),
+            block_name_to_quantize=cls.get_from_keys_or(
+                config, ["block_name_to_quantize", "to_quant_block_names"], None
+            ),
+            extra_config=cls.get_from_keys_or(config, ["extra_config"], None),
+            data_type=cls.get_from_keys_or(config, ["data_type"], "int"),
+            backend=cls.get_from_keys_or(
+                config, ["backend", "vllm_backend", "sglang_backend"], "auto"
+            ),
+        )
+
+    def get_scaled_act_names(self) -> list[str]:
+        """Returns the activation function names that should be post-scaled.
+
+        For now, this is only used by AWQ.
+        """
+        raise NotImplementedError
+
+    def get_layer_config(self, layer, layer_name: str):
+
+        def get_config(name: str, quantized: bool = True):
+            cfg = self.extra_config.get(name, {}) if self.extra_config else {}
+            return (
+                cfg.get("bits", self.weight_bits if quantized else 16),
+                cfg.get("group_size", self.group_size if quantized else -1),
+                cfg.get("sym", self.sym if quantized else True),
+            )
+
+        # 1. Exact match from config
+        if self.extra_config and layer_name in self.extra_config:
+            return get_config(layer_name)
+
+        # 2. Determine whether layer should be quantized
+        quantized = not isinstance(layer, ParallelLMHead)
+        if self.block_name_to_quantize:
+            quantized = any(
+                layer_name.startswith(name) for name in self.block_name_to_quantize
+            )
+
+        # 3. Handle fused MoE
+        if self.extra_config and "fusedmoe" in layer.__class__.__name__.lower():
+            moe_configs = [
+                get_config(name, quantized)
+                for name in self.extra_config
+                if name.startswith(layer_name)
+            ]
+            if moe_configs:
+                if len(set(moe_configs)) == 1:
+                    return moe_configs[0]
+                raise ValueError(
+                    f"Fused MoE layer '{layer_name}' requires "
+                    f"consistent quant config for all sub-layers"
+                )
+
+        # 4. Handle fused QKV or other patterns
+        if self.extra_config:
+            for fusion_key, sub_keys in self.packed_modules_mapping.items():
+                if fusion_key in layer_name and layer_name.count(fusion_key) == 1:
+                    sub_names = [
+                        layer_name.replace(fusion_key, sub_key) for sub_key in sub_keys
+                    ]
+                    sub_configs = [get_config(name, quantized) for name in sub_names]
+                    if len(set(sub_configs)) == 1:
+                        return sub_configs[0]
+                    raise ValueError(
+                        f"Fused module '{layer_name}' requires "
+                        f"consistent quant config for {sub_names}"
+                    )
+
+        # 5. Fallback
+        return get_config(layer_name, quantized)
+
+    def check_quantized(self, weight_bits: int) -> bool:
+        return weight_bits < 16
+
+    def apply_awq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.quantization.marlin_utils import (
+            check_marlin_supported,
+            check_marlin_supports_layer,
+            check_moe_marlin_supports_layer,
+        )
+
+        weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+        if not self.check_quantized(weight_bits):
+            if isinstance(layer, (LinearBase, ParallelLMHead)):
+                return UnquantizedLinearMethod()
+            else:
+                return None
+        logger.debug(
+            "[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
+            prefix,
+            layer.__class__.__name__,
+            weight_bits,
+            group_size,
+            sym,
+        )
+        if backend == "auto" or "marlin" in backend:
+            AWQ_TYPE_MAP = {
+                4: scalar_types.uint4,
+                8: scalar_types.uint8,
+            }
+            use_marlin = (weight_bits in AWQ_TYPE_MAP) and check_marlin_supported(
+                AWQ_TYPE_MAP[weight_bits], group_size, not sym
+            )
+            if isinstance(layer, FusedMoE):
+                use_marlin = use_marlin and check_moe_marlin_supports_layer(
+                    layer, group_size
+                )
+
+        else:
+            use_marlin = False
+        if use_marlin:
+            from sglang.srt.layers.quantization.awq import (
+                AWQMarlinConfig,
+                AWQMarlinLinearMethod,
+                AWQMoEMethod,
+            )
+
+            quant_args_marlin = AWQMarlinConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                zero_point=not sym,
+                lm_head_quantized=False,
+                full_config={},
+                modules_to_not_convert=[],
+            )
+        else:
+            from sglang.srt.layers.quantization.awq import AWQConfig, AWQLinearMethod
+
+            quant_args = AWQConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                zero_point=not sym,
+            )
+
+        if isinstance(layer, FusedMoE):
+            if use_marlin:
+                return AWQMoEMethod(quant_args_marlin)
+            from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
+
+            config = {
+                "quant_method": "awq",
+                "bits": weight_bits,
+                "group_size": group_size,
+                "zero_point": not sym,
+                "lm_head": False,
+            }
+            return MoeWNA16Config.from_config(config).get_quant_method(layer, prefix)
+
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
+            if use_marlin:
+                return AWQMarlinLinearMethod(quant_args_marlin)
+            else:
+                return AWQLinearMethod(quant_args)
+        return None
+
+    def apply_gptq_quant_layer(self, layer, prefix: str, backend: str = "auto"):
+        from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
+        from sglang.srt.layers.quantization.marlin_utils import (
+            check_marlin_supported,
+            check_moe_marlin_supports_layer,
+        )
+
+        weight_bits, group_size, sym = self.get_layer_config(layer, prefix)
+        if not self.check_quantized(weight_bits):
+            if isinstance(layer, (LinearBase, ParallelLMHead)):
+                return UnquantizedLinearMethod()
+            else:
+                return None
+
+        logger.debug(
+            "[%s] Type: %s, Bits: %s, Group Size: %s, Sym: %s",
+            prefix,
+            layer.__class__.__name__,
+            weight_bits,
+            group_size,
+            sym,
+        )
+        if backend == "auto" or "marlin" in backend:
+            GPTQ_TYPE_MAP = {
+                (4, True): scalar_types.uint4b8,
+                (8, True): scalar_types.uint8b128,
+            }
+            use_marlin = (weight_bits, sym) in GPTQ_TYPE_MAP and check_marlin_supported(
+                GPTQ_TYPE_MAP[(weight_bits, sym)], group_size, has_zp=not sym
+            )
+            if isinstance(layer, FusedMoE):
+                use_marlin = use_marlin and check_moe_marlin_supports_layer(
+                    layer, group_size
+                )
+        else:
+            use_marlin = False
+        if use_marlin:
+            from sglang.srt.layers.quantization.gptq import (
+                GPTQMarlinConfig,
+                GPTQMarlinLinearMethod,
+                GPTQMarlinMoEMethod,
+            )
+
+            quant_args_marlin = GPTQMarlinConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                is_sym=sym,
+                lm_head_quantized=False,
+                desc_act=False,
+                dynamic={},
+                full_config={},
+            )
+        else:
+            from sglang.srt.layers.quantization.gptq import GPTQConfig, GPTQLinearMethod
+
+            quant_args = GPTQConfig(
+                weight_bits=weight_bits,
+                group_size=group_size,
+                lm_head_quantized=False,
+                desc_act=False,
+                dynamic={},
+            )
+
+        if isinstance(layer, FusedMoE):
+            if use_marlin:
+                from sglang.srt.layers.quantization.moe_wna16 import MoeWNA16Config
+
+                config = {
+                    "quant_method": "gptq",
+                    "bits": weight_bits,
+                    "group_size": group_size,
+                    "sym": sym,
+                    "lm_head": False,
+                }
+                return MoeWNA16Config.from_config(config).get_quant_method(
+                    layer, prefix
+                )
+            return GPTQMarlinMoEMethod(quant_args_marlin)
+
+        if isinstance(layer, (LinearBase, ParallelLMHead)):
+            if use_marlin:
+                return GPTQMarlinLinearMethod(quant_args_marlin)
+            else:
+                return GPTQLinearMethod(quant_args)
+
+        return None
+
+    def get_quant_method(self, layer: torch.nn.Module, prefix: str):
+        # TODO enable CPU quant method later
+        if "gptq" in self.packing_format or "gptq" in self.backend:
+            return self.apply_gptq_quant_layer(layer, prefix)
+        if "awq" in self.packing_format or "awq" in self.backend:
+            return self.apply_awq_quant_layer(layer, prefix)

python/sglang/srt/server_args.py

@@ -80,6 +80,7 @@ QUANTIZATION_CHOICES = [
     "qoq",
     "w4afp8",
     "mxfp4",
+    "auto-round",
 ]
 
 ATTENTION_BACKEND_CHOICES = [

python/sglang/test/test_utils.py

@@ -87,6 +87,11 @@ DEFAULT_DEEPPEP_MODEL_NAME_FOR_TEST = "deepseek-ai/DeepSeek-V3-0324"
 DEFAULT_AWQ_MOE_MODEL_NAME_FOR_TEST = (
     "hugging-quants/Mixtral-8x7B-Instruct-v0.1-AWQ-INT4"
 )
+DEFAULT_AUTOROUND_MODEL_NAME_FOR_TEST = (
+    "OPEA/Llama-3.2-11B-Vision-Instruct-qvision-int4-sym-inc",  ## mllm auto_round:auto_gptq
+    "OPEA/Qwen2.5-0.5B-Instruct-int4-sym-inc",  ## auto_round:auto_gptq
+    "Intel/Qwen2-0.5B-Instruct-int4-sym-AutoRound",  ## auto_round:auto_awq
+)
 DEFAULT_ENABLE_THINKING_MODEL_NAME_FOR_TEST = "Qwen/Qwen3-30B-A3B"
 DEFAULT_DEEPSEEK_W4AFP8_MODEL_FOR_TEST = "Barrrrry/DeepSeek-R1-W4AFP8"