[Quantization] Improve AWQ logic (vllm-project#19431)

Signed-off-by: Jee Jee Li <pandaleefree@gmail.com>
Signed-off-by: minpeter <kali2005611@gmail.com>

Author: jeejeelee

vllm/model_executor/layers/quantization/awq.py

@@ -1,19 +1,23 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-from typing import Any, Optional
+from typing import Any, Optional, Union
 
 import torch
 
 from vllm import _custom_ops as ops
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.layer 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 (
-    QuantizationConfig)
+    QuantizationConfig, QuantizeMethodBase)
 from vllm.model_executor.parameter import (GroupQuantScaleParameter,
                                            PackedvLLMParameter)
 
+logger = init_logger(__name__)
+
 
 class AWQConfig(QuantizationConfig):
     """Config class for AWQ.
@@ -74,12 +78,42 @@ def from_config(cls, config: dict[str, Any]) -> "AWQConfig":
             config, ["modules_to_not_convert"], None)
         return cls(weight_bits, group_size, zero_point, modules_to_not_convert)
 
-    def get_quant_method(self, layer: torch.nn.Module,
-                         prefix: str) -> Optional["LinearMethodBase"]:
+    def get_quant_method(
+        self, layer: torch.nn.Module, prefix: str
+    ) -> Optional[Union["LinearMethodBase", "QuantizeMethodBase"]]:
         if isinstance(layer, LinearBase):
             if is_layer_skipped_awq(prefix, self.modules_to_not_convert):
                 return UnquantizedLinearMethod()
             return AWQLinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            # Lazy import to avoid circular import.
+            from .awq_marlin import AWQMarlinConfig, AWQMoEMethod
+            from .moe_wna16 import MoeWNA16Config
+            from .utils.marlin_utils import check_moe_marlin_supports_layer
+            if not check_moe_marlin_supports_layer(layer, self.group_size):
+                logger.warning_once(
+                    f"Layer '{prefix}' is not supported by AWQMoeMarlin. "
+                    "Falling back to Moe WNA16 kernels.")
+                config = {
+                    "quant_method": "awq",
+                    "bits": self.weight_bits,
+                    "group_size": self.group_size,
+                    "zero_point": self.zero_point,
+                    "lm_head": False,
+                }
+                return MoeWNA16Config.from_config(config).get_quant_method(
+                    layer, prefix)
+            marlin_compatible_config_dict = {
+                "quant_method": "awq",
+                "bits": self.weight_bits,
+                "group_size": self.group_size,
+                "zero_point": self.zero_point,
+                "lm_head": False,
+                "modules_to_not_convert": self.modules_to_not_convert,
+            }
+            awq_marlin_config = AWQMarlinConfig.from_config(
+                marlin_compatible_config_dict)
+            return AWQMoEMethod(awq_marlin_config)
         return None