diff --git a/vllm_ascend/spec_decode/mtp_proposer.py b/vllm_ascend/spec_decode/mtp_proposer.py
index b20b73d7..462fa33f 100644
--- a/vllm_ascend/spec_decode/mtp_proposer.py
+++ b/vllm_ascend/spec_decode/mtp_proposer.py
@@ -779,9 +779,9 @@ class MtpProposer(Proposer):
                         hidden_states = torch.ops.vllm.maybe_pad_and_reduce(
                             hidden_states)
 
-                    if self.use_async_scheduling and attn_metadata[
-                            layer_name].decode is not None:
-                        for layer_name in self.attn_layer_name:
+                    for layer_name in self.attn_layer_name:
+                        if self.use_async_scheduling and attn_metadata[
+                                layer_name].decode is not None:
                             actual_size = len(attn_metadata[layer_name].decode.
                                               actual_seq_lengths_q)
 
diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index 4c3c42d9..830270ea 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -1014,17 +1014,14 @@ class NPUModelRunner(GPUModelRunner):
                 num_reqs_padded = num_input_tokens // self.uniform_decode_query_len
                 pad_size = num_reqs_padded - num_reqs
                 if pad_size > 0:
-                    last_query_loc = self.query_start_loc.gpu[num_reqs]
+                    last_query_loc = self.query_start_loc.np[num_reqs]
 
-                    steps = torch.arange(1,
-                                         pad_size + 1,
-                                         device=self.device,
-                                         dtype=self.query_start_loc.gpu.dtype)
-                    fill_values = last_query_loc + (
-                        steps * self.uniform_decode_query_len)
+                    self.query_start_loc.np[
+                        num_reqs + 1:num_reqs_padded + 1] = self.arange_np[
+                            1:pad_size +
+                            1] * self.uniform_decode_query_len + last_query_loc
+                    self.query_start_loc.copy_to_gpu(num_reqs_padded + 1)
 
-                    self.query_start_loc.gpu[num_reqs + 1:num_reqs_padded +
-                                             1] = fill_values
                 # So we are trying to simulate the behavior of GPUModelRunner's
                 # prepare_inputs for uniform decode mode by padding query_start_loc
                 num_reqs = num_reqs_padded