upgrade to vllm 0.11.2 (#4400)

Bump vLLM version to v0.11.2

What's broken and changed by vLLM:
1. structured_output is broken by
https://github.com/vllm-project/vllm/pull/26866
2. get_mrope_input_positions is broken by
https://github.com/vllm-project/vllm/pull/28399
3. graph mode is broken by
https://github.com/vllm-project/vllm/pull/25110 we'll upgrade torch to
2.8 to fix the problem later
4. embedding is broken by
https://github.com/vllm-project/vllm/pull/27583
5. `get_attn_backend_cls` and attention backend is broken are broken by
https://github.com/vllm-project/vllm/pull/28534
6. spec decode is broken by
https://github.com/vllm-project/vllm/pull/28771
7. sp feature is broken by
https://github.com/vllm-project/vllm/pull/27126
8. mtp is broken by https://github.com/vllm-project/vllm/pull/27922
9. lora is broken by https://github.com/vllm-project/vllm/pull/21068
10. execute_model is broken by
https://github.com/vllm-project/vllm/pull/26866
11. `VLLM_DISABLE_SHARED_EXPERTS_STREAM` env is broken by
https://github.com/vllm-project/vllm/pull/28159
12. kv cahe is broken by https://github.com/vllm-project/vllm/pull/27753
13. dp is broken by https://github.com/vllm-project/vllm/pull/25110

 
What's broken and changed by ourself:
1. qwen vl is broken by https://github.com/vllm-project/vllm/pull/28455
We'll remove model files in the future to avoid this kind of error
2. Engine core is broken by
https://github.com/vllm-project/vllm/pull/23691 We'll remove the patch
file in the future.
3. Ascend scheduler is broken by
https://github.com/vllm-project/vllm/pull/28733 We'll remove ascend
scheudler later.
4. qwen3-next is broken by
https://github.com/vllm-project/vllm/pull/28083 We'll remove model files
in the future to avoid this kind of error
5. qwen vl is broken by https://github.com/vllm-project/vllm/pull/27764.
We'll remove model files in the future

Known issue:
1. ray doesn't work 
2. the accuracy of qwen3-next is not correct
3. qwen3-vl is broken
4. prefix cache+ ascend scheduler + deepseek v2 lite is broken.

Co-authored-by: MengqingCao <cmq0113@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: 22dimensions <waitingwind@foxmail.com>
Co-authored-by: shen-shanshan <467638484@qq.com>


- vLLM version: v0.11.2

---------

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Signed-off-by: MengqingCao <cmq0113@163.com>
Signed-off-by: hfadzxy <starmoon_zhang@163.com>
Signed-off-by: leo-pony <nengjunma@outlook.com>
Co-authored-by: MengqingCao <cmq0113@163.com>
Co-authored-by: hfadzxy <starmoon_zhang@163.com>
Co-authored-by: leo-pony <nengjunma@outlook.com>

vllm_ascend/spec_decode/eagle_proposer.py

@@ -136,7 +136,7 @@ class EagleProposer(Proposer):
             )
 
     def generate_token_ids(self,
-                           valid_sampled_token_ids: list[list[int]],
+                           valid_sampled_token_ids: list[np.ndarray],
                            sampling_metadata: SamplingMetadata = None,
                            scheduler_output: SchedulerOutput = None,
                            spec_decode_metadata: SpecDecodeMetadata = None,
@@ -149,7 +149,7 @@ class EagleProposer(Proposer):
         attn_metadata = self._get_eagle_atten_dict(scheduler_output)
         next_token_ids: list[int] = []
         for i, token_ids in enumerate(valid_sampled_token_ids):
-            if token_ids:
+            if token_ids.shape[0] > 0:
                 # Common case.
                 next_token_id = token_ids[-1]
             else:
@@ -161,7 +161,7 @@ class EagleProposer(Proposer):
                            scheduler_output.num_scheduled_tokens[req_id])
 
                 next_token_id = req_state.get_token_id(seq_len)
-            next_token_ids.append(next_token_id)
+            next_token_ids.append(next_token_id.item())
         next_token_ids = torch.tensor(next_token_ids,
                                       dtype=torch.int32,
                                       device=self.device)
@@ -181,7 +181,7 @@ class EagleProposer(Proposer):
         else:
             num_draft_tokens = spec_decode_metadata.num_draft_tokens
             num_rejected_tokens = [
-                n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
+                n + 1 - valid_sampled_token_ids[i].shape[0] if n > 0 else 0
                 for i, n in enumerate(num_draft_tokens)
             ]
             num_rejected_tokens = torch.tensor(