fix torchair execute issue on padding data, and mtp padding logic -- by#1160

Signed-off-by: 刘哲续 <liuzhexu1@huawei.com>

Author: 刘哲续

vllm_ascend/attention/mla_v1.py

@@ -376,7 +376,10 @@ def build(
             seq_lens = seq_lens[:self._num_decode_tokens]
             input_positions = input_positions[:self._num_decode_tokens]
             block_table = block_table[:self._num_decode_tokens, ...]
-            if use_torchair_graph and self.runner.attn_state == AscendAttentionState.DecodeOnly:
+            if use_torchair_graph and self.runner.attn_state in [
+                    AscendAttentionState.DecodeOnly,
+                    AscendAttentionState.SpecDecoding
+            ]:
                 num_seqs = len(seq_lens)
                 if graph_pad_size != 0:
                     pad_value = 1

vllm_ascend/worker/model_runner_v1.py

@@ -943,11 +943,6 @@ def _process_reqs(
             self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
         input_ids = self.input_ids[:num_input_tokens]
 
-        if (envs_ascend.VLLM_ENABLE_MC2
-                or self.torchair_graph_enabled) and not with_prefill:
-            input_ids = self.input_ids[:padded_batch_size]
-            positions = self.positions[:padded_batch_size]
-
         # prepare the MRoPE for mllm if using multimodal
         num_input_tokens = total_num_scheduled_tokens
         # _prepare_inputs may reorder the batch, so we must gather multi
@@ -985,6 +980,11 @@ def _process_reqs(
         else:
             positions = self.positions[:num_input_tokens]
 
+        if (envs_ascend.VLLM_ENABLE_MC2
+                or self.torchair_graph_enabled) and not with_prefill:
+            input_ids = self.input_ids[:padded_batch_size]
+            positions = self.positions[:padded_batch_size]
+
         # Run forward pass
         with set_forward_context(attn_metadata,
                                  self.vllm_config,