--- a/vllm/model_executor/layers/attention/mla_attention.py
+++ b/vllm/model_executor/layers/attention/mla_attention.py
@@ -403,7 +403,7 @@
         # If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
         self.is_aiter_triton_fp4_bmm_enabled = (
             rocm_aiter_ops.is_fp4bmm_enabled()
-            and self.kv_b_proj.weight.dtype == torch.bfloat16
+            and (self.kv_b_proj.weight.dtype if hasattr(self.kv_b_proj, "weight") else torch.bfloat16) == torch.bfloat16
         )

         # Attributes for forward_impl method
@@ -2358,9 +2358,9 @@
             # model dtype input and will quantize internally.
             if (
                 use_fp8_prefill
-                or self.kv_b_proj.weight.dtype != current_platform.fp8_dtype()
+                or (self.kv_b_proj.weight.dtype if hasattr(self.kv_b_proj, "weight") else torch.bfloat16) != current_platform.fp8_dtype()
             ):
-                kv_c_normed = kv_c_normed.to(self.kv_b_proj.weight.dtype)
+                kv_c_normed = kv_c_normed.to((self.kv_b_proj.weight.dtype if hasattr(self.kv_b_proj, "weight") else torch.bfloat16))

             k_pe = workspace[:toks][..., self.kv_lora_rank :].unsqueeze(1)
             kv_nope = self.kv_b_proj(kv_c_normed)[0].view(