[mypy] Add mypy type annotation part 1 (#4006)

.github/workflows/mypy.yaml

@@ -0,0 +1,50 @@
+name: mypy
+
+on:
+  # Trigger the workflow on push or pull request,
+  # but only for the main branch
+  push:
+    branches:
+      - main
+  pull_request:
+    branches:
+      - main
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ["3.8"]
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v2
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        python -m pip install --upgrade pip
+        pip install mypy==1.9.0
+        pip install types-setuptools
+        pip install types-PyYAML
+        pip install types-requests
+        pip install types-setuptools
+    - name: Mypy
+      run: |
+        mypy vllm/attention/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/core/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/distributed/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/entrypoints/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/executor/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/usage/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/transformers_utils/*.py --follow-imports=skip --config-file pyproject.toml
+
+        # TODO(sang): Follow up
+        # mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/spec_decoding/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/lora/*.py --follow-imports=skip --config-file pyproject.toml
+

format.sh

@@ -93,9 +93,23 @@ fi
 echo 'vLLM yapf: Done'
 
 # Run mypy
-# TODO(zhuohan): Enable mypy
-# echo 'vLLM mypy:'
-# mypy
+echo 'vLLM mypy:'
+mypy vllm/attention/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/core/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/distributed/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/entrypoints/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/executor/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/usage/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/*.py --follow-imports=skip --config-file pyproject.toml
+mypy vllm/transformers_utils/*.py --follow-imports=skip --config-file pyproject.toml
+
+# TODO(sang): Follow up
+# mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
+# mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
+# mypy vllm/spec_decoding/*.py --follow-imports=skip --config-file pyproject.toml
+# mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
+# mypy vllm/lora/*.py --follow-imports=skip --config-file pyproject.toml
+
 
 CODESPELL_EXCLUDES=(
     '--skip' '*docs/source/_build/**'
@@ -228,5 +242,3 @@ if ! git diff --quiet &>/dev/null; then
 
     exit 1
 fi
-
-

pyproject.toml

@@ -46,10 +46,13 @@ ignore = [
 python_version = "3.8"
 
 ignore_missing_imports = true
+ check_untyped_defs = true
 
 files = "vllm"
 # TODO(woosuk): Include the code from Megatron and HuggingFace.
-exclude = "vllm/model_executor/parallel_utils/|vllm/model_executor/models/"
+exclude = [
+    "vllm/model_executor/parallel_utils/|vllm/model_executor/models/",
+]
 
 
 [tool.codespell]

requirements-common.txt

@@ -11,4 +11,5 @@ uvicorn[standard]
 pydantic >= 2.0  # Required for OpenAI server.
 prometheus_client >= 0.18.0
 tiktoken == 0.6.0  # Required for DBRX tokenizer
-outlines == 0.0.34 # Requires torch >= 2.1.0
+outlines == 0.0.34 # Requires torch >= 2.1.0
+typing_extensions

requirements-dev.txt

@@ -7,7 +7,7 @@ codespell==2.2.6
 isort==5.13.2
 
 # type checking
-mypy==0.991
+mypy==1.9.0
 types-PyYAML
 types-requests
 types-setuptools

vllm/config.py

@@ -2,7 +2,7 @@ import enum
 import json
 import os
 from dataclasses import dataclass, fields
-from typing import TYPE_CHECKING, ClassVar, Optional, Union
+from typing import TYPE_CHECKING, ClassVar, List, Optional, Union
 
 import torch
 from packaging.version import Version
@@ -141,7 +141,7 @@ class ModelConfig:
         supported_load_format = [
             "auto", "pt", "safetensors", "npcache", "dummy"
         ]
-        rocm_not_supported_load_format = []
+        rocm_not_supported_load_format: List[str] = []
         if load_format not in supported_load_format:
             raise ValueError(
                 f"Unknown load format: {self.load_format}. Must be one of "
@@ -679,6 +679,9 @@ class SpeculativeConfig:
                 "num_speculative_tokens to be provided, but found "
                 f"{speculative_model=} and {num_speculative_tokens=}.")
 
+        assert (speculative_model is not None
+                and num_speculative_tokens is not None)
+
         # TODO: The user should be able to specify revision/quantization/max
         # model len for the draft model. It is not currently supported.
         draft_revision = None
@@ -993,7 +996,7 @@ def _get_and_verify_max_len(
         derived_max_model_len *= scaling_factor
 
     if max_model_len is None:
-        max_model_len = derived_max_model_len
+        max_model_len = int(derived_max_model_len)
     elif max_model_len > derived_max_model_len:
         # Some models might have a separate key for specifying model_max_length
         # that will be bigger than derived_max_model_len. We compare user input

vllm/core/block_manager_v1.py

@@ -1,5 +1,6 @@
 """A block manager that manages token blocks."""
 from abc import ABC, abstractmethod
+from collections.abc import Sequence as GenericSequence
 from itertools import count, takewhile
 from os.path import commonprefix
 from typing import Dict, List, Optional, Set
@@ -231,10 +232,10 @@ class BlockSpaceManagerV1(BlockSpaceManager):
 
         if self.enable_caching:
             logger.info("Automatic prefix caching is enabled.")
-            self.gpu_allocator = CachedBlockAllocator(Device.GPU, block_size,
-                                                      num_gpu_blocks)
-            self.cpu_allocator = CachedBlockAllocator(Device.CPU, block_size,
-                                                      num_cpu_blocks)
+            self.gpu_allocator: BlockAllocatorBase = CachedBlockAllocator(
+                Device.GPU, block_size, num_gpu_blocks)
+            self.cpu_allocator: BlockAllocatorBase = CachedBlockAllocator(
+                Device.CPU, block_size, num_cpu_blocks)
         else:
             self.gpu_allocator = UncachedBlockAllocator(
                 Device.GPU, block_size, num_gpu_blocks)
@@ -588,7 +589,8 @@ class BlockSpaceManagerV1(BlockSpaceManager):
             for b in takewhile(lambda b: b.computed, block_table[:-1])
         ]
 
-    def get_common_computed_block_ids(self, seqs: List[Sequence]) -> List[int]:
+    def get_common_computed_block_ids(
+            self, seqs: List[Sequence]) -> GenericSequence[int]:
         """Return the block ids that are common for a given sequence group.
 
         Used in prefill (can skip prefill of some blocks).

vllm/core/block_manager_v2.py

@@ -1,4 +1,5 @@
 """A block manager that manages token blocks."""
+from collections.abc import Sequence as GenericSequence
 from typing import Dict, List, Optional
 
 from vllm.core.block.block_table import BlockTable
@@ -205,7 +206,8 @@ class BlockSpaceManagerV2(BlockSpaceManager):
         # as computed.
         self.block_allocator.mark_blocks_as_computed()
 
-    def get_common_computed_block_ids(self, seqs: List[Sequence]) -> List[int]:
+    def get_common_computed_block_ids(
+            self, seqs: List[Sequence]) -> GenericSequence[int]:
         """Determine which blocks for which we skip prefill.
 
         With prefix caching we can skip prefill for previously-generated blocks.

vllm/core/interfaces.py

@@ -1,5 +1,6 @@
 import enum
 from abc import ABC, abstractmethod
+from collections.abc import Sequence as GenericSequence
 from typing import Dict, List
 
 from vllm.sequence import Sequence, SequenceGroup
@@ -103,7 +104,8 @@ class BlockSpaceManager(ABC):
         pass
 
     @abstractmethod
-    def get_common_computed_block_ids(self, seqs: List[Sequence]) -> List[int]:
+    def get_common_computed_block_ids(
+            self, seqs: List[Sequence]) -> GenericSequence[int]:
         pass
 
     @abstractmethod

vllm/core/scheduler.py

@@ -42,8 +42,8 @@ class SchedulingBudget:
     """
     token_budget: int
     max_num_seqs: int
-    _requeset_ids_num_batched_tokens: Set[int] = field(default_factory=set)
-    _requeset_ids_num_curr_seqs: Set[int] = field(default_factory=set)
+    _requeset_ids_num_batched_tokens: Set[str] = field(default_factory=set)
+    _requeset_ids_num_curr_seqs: Set[str] = field(default_factory=set)
     _num_batched_tokens: int = 0
     _num_curr_seqs: int = 0
 
@@ -133,7 +133,7 @@ class SchedulerOutputs:
         return (not self.scheduled_seq_groups and not self.blocks_to_swap_in
                 and not self.blocks_to_swap_out and not self.blocks_to_copy)
 
-    def _sort_by_lora_ids(self) -> bool:
+    def _sort_by_lora_ids(self):
         self.scheduled_seq_groups = sorted(
             self.scheduled_seq_groups,
             key=lambda g: (g.seq_group.lora_int_id, g.seq_group.request_id))
@@ -337,7 +337,8 @@ class Scheduler:
                     self.free_seq(seq)
 
     def has_unfinished_seqs(self) -> bool:
-        return self.waiting or self.running or self.swapped
+        return len(self.waiting) != 0 or len(self.running) != 0 or len(
+            self.swapped) != 0
 
     def get_num_unfinished_seq_groups(self) -> int:
         return len(self.waiting) + len(self.running) + len(self.swapped)
@@ -404,7 +405,7 @@ class Scheduler:
                 budget.subtract_num_seqs(seq_group.request_id,
                                          num_running_seqs)
                 if curr_loras is not None and seq_group.lora_int_id > 0:
-                    curr_loras.pop(seq_group.lora_int_id)
+                    curr_loras.remove(seq_group.lora_int_id)
 
                 if running_queue:
                     # Preempt the lowest-priority sequence groups.
@@ -496,7 +497,7 @@ class Scheduler:
         now = time.time()
         swapped_queue = policy.sort_by_priority(now, swapped_queue)
 
-        leftover_swapped = deque()
+        leftover_swapped: Deque[SequenceGroup] = deque()
         while swapped_queue:
             seq_group = swapped_queue[0]
 
@@ -507,7 +508,9 @@ class Scheduler:
             lora_int_id = 0
             if self.lora_enabled:
                 lora_int_id = seq_group.lora_int_id
-                if (lora_int_id > 0 and lora_int_id not in curr_loras
+                assert curr_loras is not None
+                assert self.lora_config is not None
+                if (lora_int_id > 0 and (lora_int_id not in curr_loras)
                         and len(curr_loras) >= self.lora_config.max_loras):
                     # We don't have a space for another LoRA, so
                     # we ignore this request for now.
@@ -593,7 +596,7 @@ class Scheduler:
         # Copy the queue so that the input queue is not modified.
         waiting_queue = deque([s for s in waiting_queue])
 
-        leftover_waiting_sequences = deque()
+        leftover_waiting_sequences: Deque[SequenceGroup] = deque()
         while self._passed_delay(time.time()) and waiting_queue:
             seq_group = waiting_queue[0]
 
@@ -635,6 +638,8 @@ class Scheduler:
             lora_int_id = 0
             if self.lora_enabled:
                 lora_int_id = seq_group.lora_int_id
+                assert curr_loras is not None
+                assert self.lora_config is not None
                 if (self.lora_enabled and lora_int_id > 0
                         and lora_int_id not in curr_loras
                         and len(curr_loras) >= self.lora_config.max_loras):
@@ -780,7 +785,7 @@ class Scheduler:
             token_budget=self.scheduler_config.max_num_batched_tokens,
             max_num_seqs=self.scheduler_config.max_num_seqs,
         )
-        curr_loras = set()
+        curr_loras: Set[int] = set()
 
         remaining_waiting, prefills = (self.waiting,
                                        SchedulerPrefillOutputs.create_empty())
@@ -1087,7 +1092,7 @@ class Scheduler:
 
     def _get_num_new_tokens(self, seq_group: SequenceGroup,
                             status: SequenceStatus, enable_chunking: bool,
-                            budget: SchedulingBudget) -> Tuple[int, bool]:
+                            budget: SchedulingBudget) -> int:
         """Get the next new tokens to compute for a given sequence group
             that's in a given `status`.
 

vllm/distributed/communication_op.py

@@ -1,5 +1,5 @@
 from collections import namedtuple
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
 from torch.distributed import ProcessGroup
@@ -144,7 +144,7 @@ def broadcast_tensor_dict(
     tensor_dict: Optional[Dict[Any, Union[torch.Tensor, Any]]] = None,
     src: int = 0,
     group: Optional[ProcessGroup] = None,
-) -> Dict[Any, Union[torch.Tensor, Any]]:
+) -> Optional[Dict[Any, Union[torch.Tensor, Any]]]:
     """Broadcast the input tensor dictionary."""
     group = group or torch.distributed.group.WORLD
     ranks = torch.distributed.get_process_group_ranks(group)
@@ -157,10 +157,10 @@ def broadcast_tensor_dict(
 
     rank = torch.distributed.get_rank()
     if rank == src:
+        metadata_list: List[Tuple[Any, Any]] = []
         assert isinstance(
             tensor_dict,
             dict), (f"Expecting a dictionary, got {type(tensor_dict)}")
-        metadata_list = []
         for key, value in tensor_dict.items():
             if isinstance(value, torch.Tensor):
                 assert value.is_cuda, (
@@ -190,10 +190,10 @@ def broadcast_tensor_dict(
         torch.distributed.broadcast_object_list(recv_metadata_list,
                                                 src=src,
                                                 group=group)
-        metadata_list = recv_metadata_list[0]
+        assert recv_metadata_list[0] is not None
         tensor_dict = {}
         async_handles = []
-        for key, value in metadata_list:
+        for key, value in recv_metadata_list[0]:
             if isinstance(value, TensorMetadata):
                 tensor = torch.empty(value.size,
                                      dtype=value.dtype,

vllm/engine/ray_utils.py

@@ -1,9 +1,10 @@
 import pickle
-from typing import List, Optional, Tuple
+from typing import Callable, List, Optional, Tuple
 
 from vllm.config import ParallelConfig
 from vllm.logger import init_logger
 from vllm.utils import get_ip, is_hip, set_cuda_visible_devices
+from vllm.worker.worker import Worker
 
 logger = init_logger(__name__)
 
@@ -18,15 +19,20 @@ try:
             if init_cached_hf_modules:
                 from transformers.dynamic_module_utils import init_hf_modules
                 init_hf_modules()
-            self.worker = None
+            self._worker: Optional[Worker] = None
             # Since the compiled DAG runs a main execution
             # in a different thread that calls cuda.set_device.
             # The flag indicates is set_device is called on
             # that thread.
             self.compiled_dag_cuda_device_set = False
 
-        def init_worker(self, worker_init_fn):
-            self.worker = worker_init_fn()
+        def init_worker(self, worker_init_fn: Callable[[], Worker]):
+            self._worker = worker_init_fn()
+
+        @property
+        def worker(self) -> Worker:
+            assert self._worker is not None
+            return self._worker
 
         def __getattr__(self, name):
             return getattr(self.worker, name)
@@ -70,8 +76,8 @@ except ImportError as e:
     logger.warning(f"Failed to import Ray with {e!r}. "
                    "For distributed inference, please install Ray with "
                    "`pip install ray`.")
-    ray = None
-    RayWorkerVllm = None
+    ray = None  # type: ignore
+    RayWorkerVllm = None  # type: ignore
 
 
 def initialize_ray_cluster(

vllm/entrypoints/api_server.py

@@ -47,6 +47,7 @@ async def generate(request: Request) -> Response:
     sampling_params = SamplingParams(**request_dict)
     request_id = random_uuid()
 
+    assert engine is not None
     results_generator = engine.generate(prompt, sampling_params, request_id)
 
     # Streaming case

vllm/entrypoints/llm.py

@@ -170,8 +170,12 @@ class LLM:
             multi_modal_data.data = multi_modal_data.data.to(torch.float16)
 
         # Add requests to the engine.
-        num_requests = len(prompts) if prompts is not None else len(
-            prompt_token_ids)
+        if prompts is not None:
+            num_requests = len(prompts)
+        else:
+            assert prompt_token_ids is not None
+            num_requests = len(prompt_token_ids)
+
         for i in range(num_requests):
             prompt = prompts[i] if prompts is not None else None
             token_ids = None if prompt_token_ids is None else prompt_token_ids[

vllm/executor/cpu_executor.py

@@ -1,5 +1,5 @@
 import os
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 import torch
 
@@ -61,7 +61,7 @@ class CPUExecutor(ExecutorBase):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """

vllm/executor/gpu_executor.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -66,7 +66,7 @@ class GPUExecutor(ExecutorBase):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """

vllm/executor/neuron_executor.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Optional
+from typing import Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -47,7 +47,7 @@ class NeuronExecutor(ExecutorBase):
         self.driver_worker.init_device()
         self.driver_worker.load_model()
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks by invoking the
         underlying worker.
         """

vllm/executor/ray_gpu_executor.py

@@ -3,7 +3,7 @@ import copy
 import os
 import pickle
 from collections import defaultdict
-from typing import TYPE_CHECKING, Any, Dict, List, Optional
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
 
 from vllm.config import (CacheConfig, DeviceConfig, LoRAConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
@@ -197,7 +197,7 @@ class RayGPUExecutor(ExecutorBase):
             max_parallel_loading_workers,
         )
 
-    def determine_num_available_blocks(self) -> tuple[int, int]:
+    def determine_num_available_blocks(self) -> Tuple[int, int]:
         """Determine the number of available KV blocks.
 
         This invokes `determine_num_available_blocks` on each worker and takes
@@ -205,7 +205,7 @@ class RayGPUExecutor(ExecutorBase):
         compatible with all workers.
 
         Returns:
-            - tuple[num_gpu_blocks, num_cpu_blocks]
+            - Tuple[num_gpu_blocks, num_cpu_blocks]
         """
         # Get the maximum number of blocks that can be allocated on GPU and CPU.
         num_blocks = self._run_workers("determine_num_available_blocks", )
@@ -276,7 +276,7 @@ class RayGPUExecutor(ExecutorBase):
         self,
         method: str,
         *args,
-        driver_args: Optional[List[Any]] = None,
+        driver_args: Optional[Tuple[Any, ...]] = None,
         driver_kwargs: Optional[Dict[str, Any]] = None,
         max_concurrent_workers: Optional[int] = None,
         use_ray_compiled_dag: bool = False,
@@ -291,6 +291,7 @@ class RayGPUExecutor(ExecutorBase):
         if use_ray_compiled_dag:
             # Right now, compiled DAG can only accept a single
             # input. TODO(sang): Fix it.
+            assert self.forward_dag is not None
             output_channels = self.forward_dag.execute(1)
         else:
             # Start the ray workers first.
@@ -369,7 +370,7 @@ class RayGPUExecutorAsync(RayGPUExecutor, ExecutorAsyncBase):
         self,
         method: str,
         *args,
-        driver_args: Optional[List[Any]] = None,
+        driver_args: Optional[Tuple[Any, ...]] = None,
         driver_kwargs: Optional[Dict[str, Any]] = None,
         **kwargs,
     ) -> Any:

vllm/sampling_params.py

@@ -5,7 +5,8 @@ from functools import cached_property
 from typing import Callable, List, Optional, Union
 
 import torch
-from pydantic import conint
+from pydantic import Field
+from typing_extensions import Annotated
 
 _SAMPLING_EPS = 1e-5
 
@@ -127,7 +128,7 @@ class SamplingParams:
         skip_special_tokens: bool = True,
         spaces_between_special_tokens: bool = True,
         logits_processors: Optional[List[LogitsProcessor]] = None,
-        truncate_prompt_tokens: Optional[conint(ge=1)] = None,
+        truncate_prompt_tokens: Optional[Annotated[int, Field(ge=1)]] = None,
     ) -> None:
         self.n = n
         self.best_of = best_of if best_of is not None else n

vllm/sequence.py

@@ -171,10 +171,10 @@ class SequenceData:
             return self.prompt_token_ids[-1]
         return self.output_token_ids[-1]
 
-    def get_prompt_token_ids(self) -> int:
+    def get_prompt_token_ids(self) -> List[int]:
         return self.prompt_token_ids
 
-    def get_output_token_ids(self) -> int:
+    def get_output_token_ids(self) -> List[int]:
         return self.output_token_ids
 
     @property
@@ -370,7 +370,7 @@ class SequenceGroupState:
     """Mutable state tied to a specific sequence group"""
 
     # torch.Generator used in seeded sampling
-    generator: Optional = None
+    generator: Optional = None  # type: ignore
 
 
 class MultiModalData:
@@ -599,7 +599,7 @@ class SequenceGroupMetadata:
         return self.lora_request.lora_int_id if self.lora_request else 0
 
     @property
-    def token_chunk_size(self) -> int:
+    def token_chunk_size(self) -> Optional[int]:
         """Return the number of tokens to be processed (chunk size)."""
         return self._token_chunk_size
 

vllm/transformers_utils/config.py

@@ -2,7 +2,8 @@ from typing import Dict, Optional
 
 from transformers import AutoConfig, PretrainedConfig
 
-from vllm.transformers_utils.configs import *
+from vllm.transformers_utils.configs import (ChatGLMConfig, DbrxConfig,
+                                             JAISConfig, MPTConfig, RWConfig)
 
 _CONFIG_REGISTRY: Dict[str, PretrainedConfig] = {
     "chatglm": ChatGLMConfig,

vllm/transformers_utils/detokenizer.py

@@ -168,8 +168,8 @@ def _convert_tokens_to_string_with_added_encoders(
     # NOTE(woosuk): The following code is slow because it runs a for loop over
     # the output_tokens. In Python, running a for loop over a list can be slow
     # even when the loop body is very simple.
-    sub_texts = []
-    current_sub_text = []
+    sub_texts: List[str] = []
+    current_sub_text: List[str] = []
     all_special_tokens = set(tokenizer.all_special_tokens)
     for token in output_tokens:
         if skip_special_tokens and token in all_special_tokens:
@@ -263,6 +263,7 @@ def detokenize_incrementally(
              tokenizer,
              all_input_ids[:-1],
              skip_special_tokens=skip_special_tokens)
+    assert prev_tokens is not None
 
     # If the new token id is out of bounds, return an empty string.
     if new_token_id >= len(tokenizer):
@@ -271,6 +272,8 @@ def detokenize_incrementally(
         # Put new_token_id in a list so skip_special_tokens is respected
         new_tokens = tokenizer.convert_ids_to_tokens(
             [new_token_id], skip_special_tokens=skip_special_tokens)
+        if isinstance(new_tokens, str):
+            new_tokens = [new_tokens]
     output_tokens = prev_tokens + new_tokens
 
     # If this is the first iteration, return all tokens.

vllm/transformers_utils/tokenizer.py

@@ -5,7 +5,7 @@ from transformers import (AutoTokenizer, PreTrainedTokenizer,
 
 from vllm.logger import init_logger
 from vllm.lora.request import LoRARequest
-from vllm.transformers_utils.tokenizers import *
+from vllm.transformers_utils.tokenizers import BaichuanTokenizer
 from vllm.utils import make_async
 
 logger = init_logger(__name__)
@@ -28,7 +28,7 @@ def get_cached_tokenizer(
     tokenizer_all_special_tokens = set(tokenizer.all_special_tokens)
     tokenizer_len = len(tokenizer)
 
-    class CachedTokenizer(tokenizer.__class__):
+    class CachedTokenizer(tokenizer.__class__):  # type: ignore
 
         @property
         def all_special_ids(self):

vllm/usage/usage_lib.py

@@ -7,7 +7,7 @@ import time
 from enum import Enum
 from pathlib import Path
 from threading import Thread
-from typing import Dict, Optional
+from typing import Any, Dict, Optional
 from uuid import uuid4
 
 import cpuinfo
@@ -124,7 +124,7 @@ class UsageMessage:
     def report_usage(self,
                      model_architecture: str,
                      usage_context: UsageContext,
-                     extra_kvs: Dict[str, any] = None) -> None:
+                     extra_kvs: Optional[Dict[str, Any]] = None) -> None:
         t = Thread(target=self._report_usage_worker,
                    args=(model_architecture, usage_context, extra_kvs or {}),
                    daemon=True)
@@ -132,13 +132,13 @@ class UsageMessage:
 
     def _report_usage_worker(self, model_architecture: str,
                              usage_context: UsageContext,
-                             extra_kvs: Dict[str, any]) -> None:
+                             extra_kvs: Dict[str, Any]) -> None:
         self._report_usage_once(model_architecture, usage_context, extra_kvs)
         self._report_continous_usage()
 
     def _report_usage_once(self, model_architecture: str,
                            usage_context: UsageContext,
-                           extra_kvs: Dict[str, any]) -> None:
+                           extra_kvs: Dict[str, Any]) -> None:
         # Platform information
         if torch.cuda.is_available():
             device_property = torch.cuda.get_device_properties(0)

vllm/utils.py

@@ -60,7 +60,7 @@ class LRUCache(Generic[T]):
     def __len__(self) -> int:
         return len(self.cache)
 
-    def __getitem__(self, key: Hashable) -> T:
+    def __getitem__(self, key: Hashable) -> Optional[T]:
         return self.get(key)
 
     def __setitem__(self, key: Hashable, value: T) -> None:
@@ -76,7 +76,7 @@ class LRUCache(Generic[T]):
             key: Hashable,
             default_value: Optional[T] = None) -> Optional[T]:
         if key in self.cache:
-            value = self.cache[key]
+            value: Optional[T] = self.cache[key]
             self.cache.move_to_end(key)
         else:
             value = default_value
@@ -87,7 +87,7 @@ class LRUCache(Generic[T]):
         self.cache.move_to_end(key)
         self._remove_old_if_needed()
 
-    def _on_remove(self, key: Hashable, value: T):
+    def _on_remove(self, key: Hashable, value: Optional[T]):
         pass
 
     def remove_oldest(self):
@@ -100,9 +100,11 @@ class LRUCache(Generic[T]):
         while len(self.cache) > self.capacity:
             self.remove_oldest()
 
-    def pop(self, key: Hashable, default_value: Optional[Any] = None) -> T:
+    def pop(self,
+            key: Hashable,
+            default_value: Optional[T] = None) -> Optional[T]:
         run_on_remove = key in self.cache
-        value = self.cache.pop(key, default_value)
+        value: Optional[T] = self.cache.pop(key, default_value)
         if run_on_remove:
             self._on_remove(key, value)
         return value