Refactor: move all quantization-related code to srt/layer/quantization (#7989)

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

@@ -1,16 +1,17 @@
 # SPDX-License-Identifier: Apache-2.0
+from __future__ import annotations
+
 import logging
 from typing import Any, Dict, List, Optional
 
 import torch
 
-from sglang.srt.layers.linear import (
-    LinearBase,
-    LinearMethodBase,
-    UnquantizedLinearMethod,
-)
 from sglang.srt.layers.parameter import GroupQuantScaleParameter, PackedvLLMParameter
-from sglang.srt.layers.quantization.base_config import QuantizationConfig
+from sglang.srt.layers.quantization.base_config import (
+    LinearMethodBase,
+    QuantizationConfig,
+)
+from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 from sglang.srt.utils import is_cuda
 
 _is_cuda = is_cuda()
@@ -81,7 +82,7 @@ class AWQConfig(QuantizationConfig):
         ]
 
     @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
+    def from_config(cls, config: Dict[str, Any]) -> AWQConfig:
         weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
         group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
         zero_point = cls.get_from_keys(config, ["zero_point"])
@@ -92,7 +93,8 @@ class AWQConfig(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["LinearMethodBase"]:
+    ) -> Optional[LinearMethodBase]:
+        from sglang.srt.layers.linear import LinearBase
 
         if isinstance(layer, LinearBase):
             if is_layer_skipped_awq(prefix, self.modules_to_not_convert):