[mypy] Enable type checking for test directory (#5017)

tests/spec_decode/utils.py

@@ -1,5 +1,7 @@
 from itertools import count
-from typing import Dict, Iterable, List, Optional, Union
+from typing import Callable, Dict, List, Optional
+from typing import Sequence as GenericSequence
+from typing import TypeVar, Union
 from unittest.mock import MagicMock
 
 import torch
@@ -14,6 +16,8 @@ from vllm.utils import get_distributed_init_method, get_ip, get_open_port
 from vllm.worker.cache_engine import CacheEngine
 from vllm.worker.worker import Worker
 
+T = TypeVar("T", bound=Worker)
+
 
 def round_up_to_next_block(seq_len: int, block_size: int) -> int:
     return (seq_len + block_size - 1) // block_size
@@ -56,13 +60,13 @@ def zero_kv_cache(cache_engine: CacheEngine):
         value_blocks.zero_()
 
 
-def create_worker(cls: type,
+def create_worker(cls: Callable[..., T],
                   model_name: str,
                   block_size: int,
                   num_gpu_blocks: int,
                   seed: int,
                   is_driver_worker: bool = True,
-                  enforce_eager: bool = True):
+                  enforce_eager: bool = True) -> T:
     engine_args = EngineArgs(
         model=model_name,
         seed=seed,
@@ -159,8 +163,8 @@ def assert_logprobs_dict_allclose(
 
 def create_sampler_output_list(
         token_ids: torch.Tensor,
-        probs: Iterable[Optional[torch.Tensor]],
-        logprobs: Iterable[Optional[torch.Tensor]],
+        probs: GenericSequence[Optional[torch.Tensor]],
+        logprobs: GenericSequence[Optional[torch.Tensor]],
         seq_ids: Optional[List[int]] = None) -> List[SamplerOutput]:
     num_steps, batch_size = token_ids.shape
     token_ids_by_step = token_ids.tolist()