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

python/sglang/srt/layers/quantization/w4afp8.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import logging
 from typing import Any, Dict, List, Optional
 
@@ -5,12 +7,13 @@ import torch
 from torch.nn import Module
 from torch.nn.parameter import Parameter
 
-from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
 from sglang.srt.layers.quantization.base_config import (
+    FusedMoEMethodBase,
     QuantizationConfig,
     QuantizeMethodBase,
 )
 from sglang.srt.layers.quantization.fp8 import Fp8LinearMethod
+from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 from sglang.srt.layers.quantization.utils import is_layer_skipped
 from sglang.srt.utils import set_weight_attrs
 
@@ -62,7 +65,7 @@ class W4AFp8Config(QuantizationConfig):
         return []
 
     @classmethod
-    def from_config(cls, config: Dict[str, Any]) -> "W4AFp8Config":
+    def from_config(cls, config: Dict[str, Any]) -> W4AFp8Config:
         quant_method = cls.get_from_keys(config, ["quant_method"])
         is_checkpoint_fp8_serialized = "fp8" in quant_method
         is_checkpoint_w4afp8_serialized = "w4afp8" in quant_method
@@ -79,7 +82,8 @@ class W4AFp8Config(QuantizationConfig):
 
     def get_quant_method(
         self, layer: torch.nn.Module, prefix: str
-    ) -> Optional["QuantizeMethodBase"]:
+    ) -> Optional[QuantizeMethodBase]:
+        from sglang.srt.layers.linear import LinearBase
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
         if isinstance(layer, LinearBase):
@@ -94,7 +98,7 @@ class W4AFp8Config(QuantizationConfig):
         return []
 
 
-class W4AFp8MoEMethod:
+class W4AFp8MoEMethod(FusedMoEMethodBase):
 
     def __init__(self, quant_config: W4AFp8Config):
         self.quant_config = quant_config