update self._q_scale

vllm/attention/layer.py

@@ -101,12 +101,13 @@ class Attention(nn.Module):
         if torch.cuda.get_device_properties("cuda").gcnArchName.split(':')[0] == "gfx938" and kv_cache_dtype == "fp8_e4m3" and envs.VLLM_USE_FLASH_MLA_FP8:
             self._k_scale = torch.ones((1), dtype=torch.float32)
             self._v_scale = torch.ones((1), dtype=torch.float32)
+            self._q_scale = torch.ones((1), dtype=torch.float32)
         else:
             self._k_scale = torch.tensor(1.0, dtype=torch.float32)
             self._v_scale = torch.tensor(1.0, dtype=torch.float32)
+            self._q_scale = torch.tensor(1.0, dtype=torch.float32)
         # FlashAttn doesn't support quantizing the kv-cache only
         # but requires q to be quantized as well.
-        self._q_scale = torch.tensor(1.0, dtype=torch.float32)
         self._prob_scale = torch.tensor(1.0, dtype=torch.float32)
 
         # We also keep the float32 versions of k/v_scale for attention