update list and type

vllm/_custom_ops.py

@@ -956,7 +956,7 @@ def triton_int8_gemm_helper(m: int,
                              per_token_act_quant: bool,
                              per_out_channel_weight_quant: bool,
                              use_bias: bool,
-                             out_dtype: Type[torch.dtype] = torch.float16,
+                             out_dtype: type[torch.dtype] = torch.float16,
                              device: str = "cuda",
                              best_config:Optional[list] = None):
     return quant_tools.triton_int8_gemm_helper(m,n,k,per_token_act_quant,per_out_channel_weight_quant,use_bias,out_dtype,device,best_config)
@@ -1749,8 +1749,8 @@ def free_shared_buffer(ptr: int) -> None:
 def read_cache(
         keys: torch.Tensor,
         values: torch.Tensor,
-        key_caches: List[torch.Tensor],
-        value_caches: List[torch.Tensor],
+        key_caches: list[torch.Tensor],
+        value_caches: list[torch.Tensor],
         slot_mapping: torch.Tensor,
         kv_cache_dtype: str
 ) -> None:
@@ -1761,8 +1761,8 @@ def read_cache(
 def write_cache_multi_layers(
         keys: torch.Tensor,
         values: torch.Tensor,
-        key_caches: List[torch.Tensor],
-        value_caches: List[torch.Tensor],
+        key_caches: list[torch.Tensor],
+        value_caches: list[torch.Tensor],
         slot_mapping: torch.Tensor,
         kv_cache_dtype: str
 ) -> None:

vllm/sequence.py

@@ -188,7 +188,7 @@ class SequenceData(msgspec.Struct,
 
     @staticmethod
     def from_prompt_token_counts(
-            *token_counts: Tuple[int, int]) -> "SequenceData":
+            *token_counts: tuple[int, int]) -> "SequenceData":
         """
         Construct a :class:`SequenceData` instance by concatenating
         prompt token sequences.
@@ -1334,9 +1334,9 @@ class Logits(msgspec.Struct, array_like=True,
     # all tokens, whereas for decode step, it use used for last accepted tokens.
     logits: torch.Tensor
     # The sequence group metadata list. Only needed for decode step.
-    seq_group_metadata_list: Optional[List[SequenceGroupMetadata]] = None
+    seq_group_metadata_list: Optional[list[SequenceGroupMetadata]] = None
 
-    _seq_ids: List[int] = msgspec.field(default_factory=list)
+    _seq_ids: list[int] = msgspec.field(default_factory=list)
 
     def __post_init__(self):
         if self.seq_group_metadata_list is not None:
@@ -1344,12 +1344,12 @@ class Logits(msgspec.Struct, array_like=True,
             self._seq_ids = get_all_seq_ids(self.seq_group_metadata_list)
 
     @property
-    def seq_ids(self) -> List[int]:
+    def seq_ids(self) -> list[int]:
         return self._seq_ids
     
     def update(self,
                logits: torch.Tensor,
-               seq_group_metadata_list: List[SequenceGroupMetadata]):
+               seq_group_metadata_list: list[SequenceGroupMetadata]):
         """Update hidden states from target model invocation. Only used for
         decode steps"""
         assert len(seq_group_metadata_list) == len(logits)
@@ -1357,7 +1357,7 @@ class Logits(msgspec.Struct, array_like=True,
         self.logits = torch.cat([self.logits, logits])
 
     def prune(self,
-              seq_group_metadata_list: List[SequenceGroupMetadata]) -> None:
+              seq_group_metadata_list: list[SequenceGroupMetadata]) -> None:
         """Prune to provided list of sequence ids. Only used for decode steps.
         """
         # Currently this prunes all seq_ids not present in