[mypy] Add mypy type annotation part 1 (vllm-project#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

@@ -45,10 +45,13 @@ 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 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
@@ -147,7 +147,7 @@ def _verify_load_format(self) -> None:
         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 "
@@ -719,6 +719,9 @@ def maybe_create_spec_config(
                 "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
@@ -1033,7 +1036,7 @@ 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 @@ def __init__(
 
         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 @@ def get_all_computed_blocks(self, seq: Sequence) -> List[int]:
             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 @@ def mark_blocks_as_computed(self, seq_group: SequenceGroup):
         # 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 @@ def access_all_blocks_in_seq(
         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 @@ def is_empty(self) -> bool:
         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 @@ def abort_seq_group(self, request_id: Union[str, Iterable[str]]) -> None:
                     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 @@ def _schedule_running(
                 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 @@ def _schedule_swapped(
         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 @@ def _schedule_swapped(
             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 @@ def _schedule_prefills(
         # 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 @@ def _schedule_prefills(
             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 @@ def _schedule_chunked_prefill(self):
             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())
@@ -1108,7 +1113,7 @@ def _get_num_lookahead_slots(self, is_prefill: bool) -> int:
 
     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,