update List[int] and update num_rejected_tokens

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -659,7 +659,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
                             use_int8_w8a16: bool,
                             use_int4_w4a16: bool,
                             per_channel_quant: bool,
-                            block_shape: Optional[list[int]] = None,
+                            block_shape: Optional[List[int]] = None,
                             use_nn_moe: Optional[bool]=False) -> None:
     assert topk_weights is not None or not mul_routed_weight
     assert topk_weights is None or topk_weights.stride(1) == 1
@@ -1328,7 +1328,7 @@ def flashinfer_fused_moe_blockscale_fp8(
         intermediate_size: int,
         expert_offset: int,
         local_num_experts: int,
-        block_shape: list[int],
+        block_shape: List[int],
         routed_scaling: float = 1.0) -> torch.Tensor:
     from vllm.utils.flashinfer import flashinfer_trtllm_fp8_block_scale_moe
     assert top_k <= global_num_experts
@@ -1381,7 +1381,7 @@ def flashinfer_fused_moe_blockscale_fp8_fake(
         intermediate_size: int,
         expert_offset: int,
         local_num_experts: int,
-        block_shape: list[int],
+        block_shape: List[int],
         routed_scaling: float = 1.0) -> torch.Tensor:
     return torch.empty_like(x)
 

vllm/v1/attention/backends/utils.py

@@ -55,7 +55,7 @@ class CommonAttentionMetadata:
     """Total number of tokens in batch"""
     max_query_len: int
     """Longest query in batch"""
-    num_rejected_tokens: list[int] = None
+    num_rejected_tokens: list[int]
     """(batch_size,), record the rejected tokens number in cpu and gpu"""
 
     block_table_tensor: torch.Tensor