fix dsv3 fused proj check (#7738)

python/sglang/srt/models/deepseek_v2.py

@@ -336,10 +336,12 @@ class DeepseekV2MoE(nn.Module):
                     else {}
                 ),
             )
-            is_packed_weight = (
-                self.shared_experts.gate_up_proj.quant_method.quant_config.get_name()
-                in ["awq", "moe_wna16"]
-            )
+            is_packed_weight = hasattr(
+                self.shared_experts.gate_up_proj.quant_method, "quant_config"
+            ) and self.shared_experts.gate_up_proj.quant_method.quant_config.get_name() in {
+                "awq",
+                "moe_wna16",
+            }
             self.shared_experts_is_int8 = (
                 not is_packed_weight
                 and self.shared_experts.gate_up_proj.weight.dtype == torch.int8
@@ -891,21 +893,20 @@ class DeepseekV2AttentionMLA(nn.Module):
         # If we have self.fused_qkv_a_proj_with_mqa and we're running on CPU, we will choose the torch.ops.sgl_kernel.qkv_proj_with_rope_fused_weight kernel
         # which requires self.w_kc and self.w_vc to be packed.
         # If not, we will use torch.bmm and weight shouldn't be packed in this case
-        if (
-            hasattr(self, "fused_qkv_a_proj_with_mqa")
-            and _is_cpu
-            and _is_cpu_amx_available
-        ):
+        has_fused_proj = hasattr(self, "fused_qkv_a_proj_with_mqa")
+        if has_fused_proj and _is_cpu and _is_cpu_amx_available:
             self.quant_method = PackWeightMethod(
                 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"]
+            has_fused_proj
+            and hasattr(self.fused_qkv_a_proj_with_mqa.quant_method, "quant_config")
+            and 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")
+            has_fused_proj
             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
@@ -915,12 +916,12 @@ class DeepseekV2AttentionMLA(nn.Module):
         )
 
         self.qkv_proj_with_rope_is_int8 = (
-            hasattr(self, "fused_qkv_a_proj_with_mqa")
+            has_fused_proj
             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")
+            has_fused_proj
             and not is_packed_weight
             and self.fused_qkv_a_proj_with_mqa.weight.dtype == torch.float8_e4m3fn
         )