[Quantization][w8a8_int8] Fix weight loading issue for w8a8_int8 path with "ignore" layer list in quantization config (#7820)

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

@@ -347,8 +347,8 @@ class UnquantizedFusedMoEMethod(FusedMoEMethodBase, CustomOp):
         if use_intel_amx_backend(layer):
 
             from sglang.srt.layers.moe.topk import (
-                select_experts,
                 apply_topk_weights_cpu,
+                select_experts,
             )
 
             topk_weights, topk_ids = select_experts(

python/sglang/srt/layers/quantization/w8a8_int8.py

@@ -3,7 +3,7 @@ from __future__ import annotations
 import importlib
 import sys
 from types import MappingProxyType
-from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union
+from typing import Any, Callable, Dict, List, Mapping, Optional, Tuple, Union, cast
 
 import torch
 from torch.nn.parameter import Parameter
@@ -24,6 +24,7 @@ from sglang.srt.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
+from sglang.srt.layers.quantization.compressed_tensors.utils import should_ignore_layer
 from sglang.srt.layers.quantization.int8_kernel import per_token_quant_int8
 from sglang.srt.layers.quantization.unquant import UnquantizedLinearMethod
 from sglang.srt.utils import (
@@ -178,17 +179,18 @@ class W8A8Int8Config(QuantizationConfig):
     - Activation: dynamic, per-token, symmetric
     """
 
-    def __init__(self, quant_config: Dict[str, Any]):
+    def __init__(self, quant_config: Dict[str, Any] = {}):
         super().__init__()
         self.quant_description = quant_config
         self.is_dynamic = quant_config.get("is_dynamic", False)
-        if _is_npu:
-            if (
-                "packed_modules_mapping" in quant_config
-                and quant_config["packed_modules_mapping"] is not None
-            ):
-                self.packed_modules_mapping = quant_config["packed_modules_mapping"]
+        ignore = cast(List[str], quant_config.get("ignore", []))
+        self.ignore = ignore if ignore is not None else []
+        packed_modules_mapping = quant_config.get("packed_modules_mapping", {})
+        self.packed_modules_mapping = (
+            packed_modules_mapping if packed_modules_mapping is not None else {}
+        )
 
+        if _is_npu:
             # Ascend w8a8_int8 quantization with bias, use wrappers to isolate the effects between models
             for name in self.quant_description.keys():
                 if "norm.bias" in name:
@@ -237,7 +239,7 @@ class W8A8Int8Config(QuantizationConfig):
         layer: torch.nn.Module,
         prefix: str,
     ) -> Optional[QuantizeMethodBase]:
-        from sglang.srt.layers.linear import LinearBase
+        from sglang.srt.layers.linear import LinearBase, UnquantizedLinearMethod
         from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
 
         if _is_npu:
@@ -262,12 +264,16 @@ class W8A8Int8Config(QuantizationConfig):
             elif isinstance(layer, FusedMoE):
                 return NPU_W8A8MoEMethod(self)
             return None
-        else:
-            if isinstance(layer, LinearBase):
-                return W8A8Int8LinearMethod(self)
-            elif isinstance(layer, FusedMoE):
-                return W8A8Int8MoEMethod(self)
-            return None
+
+        if should_ignore_layer(
+            prefix, ignore=self.ignore, fused_mapping=self.packed_modules_mapping
+        ):
+            return UnquantizedLinearMethod()
+        if isinstance(layer, LinearBase):
+            return W8A8Int8LinearMethod(self)
+        elif isinstance(layer, FusedMoE):
+            return W8A8Int8MoEMethod(self)
+        return None
 
     def is_layer_skipped(
         self, prefix: str, fused_mapping: Mapping[str, List[str]] = MappingProxyType({})