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>

tests/e2e/singlecard/test_ascend_scheduler.py

@@ -18,9 +18,8 @@ def test_concurrent_partial_prefill(enforce_eager):
                         },
                     },
                     max_num_seqs=3,
-                    max_num_batched_tokens=2048,
+                    max_num_batched_tokens=8192,
                     enforce_eager=enforce_eager,
-                    max_model_len=2048,
                     gpu_memory_utilization=0.7) as vllm_model:
         outputs = vllm_model.model.generate(["Hello my name is Robert and I"] *
                                             3)
@@ -38,9 +37,8 @@ def test_prefix_cache_stats_is_recorded(enforce_eager):
                         },
                     },
                     max_num_seqs=3,
-                    max_num_batched_tokens=2048,
+                    max_num_batched_tokens=8192,
                     enforce_eager=enforce_eager,
-                    max_model_len=2048,
                     gpu_memory_utilization=0.7) as vllm_model:
         # 17 tokens will make sure first 16 tokens are cached in a block
         input_tokens = {"prompt_token_ids": [101] * 129}
@@ -51,7 +49,7 @@ def test_prefix_cache_stats_is_recorded(enforce_eager):
 
 @pytest.mark.parametrize("max_tokens",
                          [4])  # cannot align results when max_tokens > 4
-@pytest.mark.parametrize("chunked_prefill_token_size", [16])
+@pytest.mark.parametrize("chunked_prefill_token_size", [2048])
 def test_chunked_prefill_with_ascend_scheduler(
         max_tokens: int, chunked_prefill_token_size: int) -> None:
     example_prompts = [
@@ -93,7 +91,7 @@ def test_chunked_prefill_with_ascend_scheduler(
 
 @pytest.mark.parametrize("max_tokens",
                          [4])  # cannot align results when max_tokens > 4
-@pytest.mark.parametrize("chunked_prefill_token_size", [16])
+@pytest.mark.parametrize("chunked_prefill_token_size", [2048])
 def test_chunked_prefill_with_scheduler_dynamic_batch(
         max_tokens: int, chunked_prefill_token_size: int) -> None:
     example_prompts = [