[BugFix]Fix mtp torchair bug caused by #2719 (#3566)

### What this PR does / why we need it?
Fix mtp tochair bug cuased by #2719
Since FIA need extra space for padding, we need to enforce
`self.max_num_seqs > self.scheduler_config.max_num_seqs` in KV consumer
+ MTP
This means that, `self.max_num_seqs` **>** the actual maximum requests
(`self.scheduler_config.max_num_seqs`)

### Does this PR introduce _any_ user-facing change?

### How was this patch tested?


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: xuyexiong <xuyexiong@huawei.com>

vllm_ascend/torchair/torchair_model_runner.py

@@ -467,7 +467,7 @@ class NPUTorchairModelRunner(NPUModelRunner):
             # pd disaggregation scenario may incorrectly calculate the batch in mtp scenario, so we force set it to max_num_reqs
             self.torchair_graph_batch_sizes = [self.max_num_reqs]
             logger.warning(
-                "is kv_consumer, torch_graph_batch_sizes sets to [max_num_seqs]"
+                f"is kv_consumer, torch_graph_batch_sizes sets to [max_num_seqs] {[self.max_num_reqs]}"
             )
         elif len(self.torchair_graph_batch_sizes) == 0:
             self.torchair_graph_batch_sizes = [1, self.max_num_reqs]
@@ -517,11 +517,16 @@ class NPUTorchairModelRunner(NPUModelRunner):
                     f"torchair_graph_batch_sizes {cur_graph_batch_size} is bigger than max_num_batched_tokens",
                     f"{self.scheduler_config.max_num_batched_tokens} will skip this batch size."
                 )
-        new_max_num_reqs = max(new_graph_batch_sizes)
+        new_max_num_reqs = math.ceil(
+            max(new_graph_batch_sizes) / self.decode_token_per_req)
         if self.max_num_reqs != new_max_num_reqs:
             logger.warning(f"max_num_reqs is updated to {new_max_num_reqs}")
             self.max_num_reqs = new_max_num_reqs
-            self.scheduler_config.max_num_seqs = new_max_num_reqs
+            if not (self.decode_token_per_req > 1 and self.is_kv_consumer):
+                # Do not update scheduler_config.max_num_seqs in KV consumer + MTP
+                # Since FIA need extra space for padding
+                # Enforce self.max_num_seqs > self.scheduler_config.max_num_seqs in KV consumer + MTP
+                self.scheduler_config.max_num_seqs = new_max_num_reqs
 
         if new_graph_batch_sizes != self.torchair_graph_batch_sizes:
             logger.warning(

vllm_ascend/worker/model_runner_v1.py

@@ -2390,7 +2390,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
         # for dummy run with LoRA so that the num_reqs collectively
         # has num_tokens in total.
         assert num_tokens <= self.scheduler_config.max_num_batched_tokens
-        max_num_reqs = self.scheduler_config.max_num_seqs
+        max_num_reqs = self.max_num_reqs
         if uniform_decode:
             num_reqs = cdiv(num_tokens, max_query_len)
             num_scheduled_tokens_list = [max_query_len] * num_reqs