[CI] Upgrade vllm to 0.9.1 (#1165)

1. upgrade vllm to 0.9.1. 0.9.0 is not supported for main branch now.
keep doc to 0.9.0 until we release the first 0.9.1 release.
2. disable V0 test for PR
3. move actionlint check to lint job

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>

tests/singlecard/test_scheduler.py

@@ -31,7 +31,6 @@ from vllm.v1.request import Request, RequestStatus
 from vllm.v1.structured_output import StructuredOutputManager
 
 from vllm_ascend.core.scheduler import AscendScheduler
-from vllm_ascend.utils import vllm_version_is
 
 EOS_TOKEN_ID = 50256
 
@@ -87,27 +86,15 @@ def create_scheduler(
     vllm_config = VllmConfig(scheduler_config=scheduler_config,
                              model_config=model_config,
                              cache_config=cache_config)
-
-    if vllm_version_is("0.9.0"):
-        kv_cache_config = KVCacheConfig(
-            num_blocks=10000,  # A large number of blocks to hold all requests
-            tensors={},
-            kv_cache_groups=[
-                KVCacheGroupSpec(['layer'],
-                                 FullAttentionSpec(16, 1, 1, torch.float32,
-                                                   False))
-            ],
-        )
-    else:
-        kv_cache_config = KVCacheConfig(
-            num_blocks=10000,  # A large number of blocks to hold all requests
-            kv_cache_tensors=[KVCacheTensor(size=1024, shared_by=[1])],
-            kv_cache_groups=[
-                KVCacheGroupSpec(['layer'],
-                                 FullAttentionSpec(16, 1, 1, torch.float32,
-                                                   False, None))
-            ],
-        )
+    kv_cache_config = KVCacheConfig(
+        num_blocks=10000,  # A large number of blocks to hold all requests
+        kv_cache_tensors=[KVCacheTensor(size=1024, shared_by=[1])],
+        kv_cache_groups=[
+            KVCacheGroupSpec(['layer'],
+                             FullAttentionSpec(16, 1, 1, torch.float32, False,
+                                               None))
+        ],
+    )
     cache_config.num_gpu_blocks = 10000
     return AscendScheduler(
         vllm_config,
@@ -135,27 +122,15 @@ def create_requests(num_requests: int,
         else:
             mm_position = None
             mm_inputs = None
-        if vllm_version_is("0.9.0"):
-            request = Request(
-                request_id=f"{i}",
-                prompt_token_ids=[i] * num_tokens,
-                sampling_params=sampling_params,
-                multi_modal_inputs=mm_inputs,
-                multi_modal_placeholders=mm_position,
-                multi_modal_hashes=None,
-                arrival_time=0,
-                eos_token_id=EOS_TOKEN_ID,
-            )
-        else:
-            request = Request(
-                request_id=f"{i}",
-                prompt_token_ids=[i] * num_tokens,
-                sampling_params=sampling_params,
-                multi_modal_inputs=mm_inputs,
-                multi_modal_placeholders=mm_position,
-                multi_modal_hashes=None,
-                eos_token_id=EOS_TOKEN_ID,
-            )
+        request = Request(
+            request_id=f"{i}",
+            prompt_token_ids=[i] * num_tokens,
+            sampling_params=sampling_params,
+            multi_modal_inputs=mm_inputs,
+            multi_modal_placeholders=mm_position,
+            multi_modal_hashes=None,
+            eos_token_id=EOS_TOKEN_ID,
+        )
         requests.append(request)
     return requests