fix awq and dsv3 fused gemm compatible (#7735)

python/sglang/srt/models/deepseek_v2.py

@@ -336,11 +336,17 @@ class DeepseekV2MoE(nn.Module):
                     else {}
                 ),
             )
+            is_packed_weight = (
+                self.shared_experts.gate_up_proj.quant_method.quant_config.get_name()
+                in ["awq", "moe_wna16"]
+            )
             self.shared_experts_is_int8 = (
-                self.shared_experts.gate_up_proj.weight.dtype == torch.int8
+                not is_packed_weight
+                and self.shared_experts.gate_up_proj.weight.dtype == torch.int8
             )
             self.shared_experts_is_fp8 = (
-                self.shared_experts.gate_up_proj.weight.dtype == torch.float8_e4m3fn
+                not is_packed_weight
+                and self.shared_experts.gate_up_proj.weight.dtype == torch.float8_e4m3fn
             )
             if self.shared_experts_is_fp8:
                 assert (
@@ -894,8 +900,13 @@ class DeepseekV2AttentionMLA(nn.Module):
                 weight_names=["w_kc", "w_vc"], transpose_dims=[[1, 2], [1, 2]]
             )
 
+        is_packed_weight = (
+            self.fused_qkv_a_proj_with_mqa.quant_method.quant_config.get_name()
+            in ["awq", "moe_wna16"]
+        )
         self.use_min_latency_fused_a_gemm = (
             hasattr(self, "fused_qkv_a_proj_with_mqa")
+            and not is_packed_weight
             and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.bfloat16
             and self.fused_qkv_a_proj_with_mqa.weight.shape[0] == 2112
             and self.fused_qkv_a_proj_with_mqa.weight.shape[1] == 7168
@@ -905,10 +916,12 @@ class DeepseekV2AttentionMLA(nn.Module):
 
         self.qkv_proj_with_rope_is_int8 = (
             hasattr(self, "fused_qkv_a_proj_with_mqa")
+            and not is_packed_weight
             and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.int8
         )
         self.qkv_proj_with_rope_is_fp8 = (
             hasattr(self, "fused_qkv_a_proj_with_mqa")
+            and not is_packed_weight
             and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.float8_e4m3fn
         )