[CI] Update vllm version to 20250922(5aeb925) (#3091)

### What this PR does / why we need it?
This pr bump vllm commit hash to
5aeb925452
fix issues:  
1. https://github.com/vllm-project/vllm/pull/25345 has remove v0
metadata
2. https://github.com/vllm-project/vllm/pull/25332
3. https://github.com/vllm-project/vllm/pull/25334
4. https://github.com/vllm-project/vllm/pull/23558, note that this vllm
commit update the model register logic, which will check all the model
registered have the `vllm.model_executor.models` path , which breaks our
custom registration of the deepseek_v3 model (it doesn't exist in the
vllm model path). so I move deepseek_v3 model registy to deepseek_v2 to
solve temporary

### How was this patch tested?

- vLLM version: v0.10.2
- vLLM main:
9607d5eb44

---------

Signed-off-by: wangli <wangli858794774@gmail.com>

vllm_ascend/worker/model_runner_v1.py

@@ -2022,7 +2022,8 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                         num_scheduled_tokens_np, finished_sending,
                         finished_recving, kv_connector_output)
                 sample_hidden_states = hidden_states[logits_indices]
-                logits = self.model.compute_logits(sample_hidden_states, None)
+                logits = self._compute_logits_wrapper(sample_hidden_states,
+                                                      None)
             if broadcast_pp_output:
                 model_output_broadcast_data = {
                     "logits": logits.contiguous(),
@@ -2469,7 +2470,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                                             dtype=torch.int32)
 
                 def dummy_compute_logits(hidden_states):
-                    return self.model.compute_logits(
+                    return self._compute_logits_wrapper(
                         hidden_states[dummy_indices], None)
 
             with set_ascend_forward_context(
@@ -2539,13 +2540,18 @@ class NPUModelRunner(LoRAModelRunnerMixin):
                 logit_indices = np.cumsum(num_scheduled_tokens) - 1
                 # TODO: need to rum a dummy sampler for generate task
                 hidden_states = hidden_states[logit_indices]
-                output = self.model.compute_logits(hidden_states, None)
+                output = self._compute_logits_wrapper(hidden_states, None)
 
         NPUPlatform.synchronize()
         del hidden_states, output
         self.encoder_cache.clear()
         gc.collect()
 
+    def _compute_logits_wrapper(self, hidden_states, sampling_metadata):
+        if vllm_version_is("0.10.2"):
+            return self.model.compute_logits(hidden_states, sampling_metadata)
+        return self.model.compute_logits(hidden_states)
+
     def _dummy_pooler_run_task(
         self,
         hidden_states: torch.Tensor,
@@ -3516,7 +3522,7 @@ class NPUModelRunner(LoRAModelRunnerMixin):
             req_idx = self.input_batch.req_id_to_index[req_id]
             offset = self.query_start_loc_np[req_idx].item()
             prompt_hidden_states = hidden_states[offset:offset + num_logits]
-            logits = self.model.compute_logits(prompt_hidden_states, None)
+            logits = self._compute_logits_wrapper(prompt_hidden_states, None)
 
             # Get the "target" tokens for each index. For prompt at index i,
             # the token at prompt index i+1 is the "sampled" token we want