[Doc] Add typing hints / mypy types cleanup (#3816)

Co-authored-by: Roger Wang <136131678+ywang96@users.noreply.github.com>

benchmarks/backend_request_func.py

@@ -27,8 +27,8 @@ class RequestFuncInput:
 class RequestFuncOutput:
     generated_text: str = ""
     success: bool = False
-    latency: float = 0
-    ttft: float = 0  # Time to first token
+    latency: float = 0.0
+    ttft: float = 0.0  # Time to first token
     itl: List[float] = field(
         default_factory=list)  # List of inter-token latencies
     prompt_len: int = 0
@@ -58,23 +58,24 @@ async def async_request_tgi(
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
-        ttft = 0
+        ttft = 0.0
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload) as response:
                 if response.status == 200:
-                    async for chunk in response.content:
-                        chunk = chunk.strip()
-                        if not chunk:
+                    async for chunk_bytes in response.content:
+                        chunk_bytes = chunk_bytes.strip()
+                        if not chunk_bytes:
                             continue
 
-                        chunk = remove_prefix(chunk.decode("utf-8"), "data:")
+                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                                              "data:")
 
                         data = json.loads(chunk)
                         timestamp = time.perf_counter()
                         # First token
-                        if ttft == 0:
+                        if ttft == 0.0:
                             ttft = time.perf_counter() - st
                             output.ttft = ttft
 
@@ -119,23 +120,24 @@ async def async_request_trt_llm(
         output = RequestFuncOutput()
         output.prompt_len = request_func_input.prompt_len
 
-        ttft = 0
+        ttft = 0.0
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload) as response:
                 if response.status == 200:
-                    async for chunk in response.content:
-                        chunk = chunk.strip()
-                        if not chunk:
+                    async for chunk_bytes in response.content:
+                        chunk_bytes = chunk_bytes.strip()
+                        if not chunk_bytes:
                             continue
 
-                        chunk = remove_prefix(chunk.decode("utf-8"), "data:")
+                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                                              "data:")
 
                         data = json.loads(chunk)
                         timestamp = time.perf_counter()
                         # First token
-                        if ttft == 0:
+                        if ttft == 0.0:
                             ttft = time.perf_counter() - st
                             output.ttft = ttft
 
@@ -151,7 +153,7 @@ async def async_request_trt_llm(
                     output.success = True
 
                 else:
-                    output.error = response.reason
+                    output.error = response.reason or ""
                     output.success = False
         except Exception:
             output.success = False
@@ -195,7 +197,7 @@ async def async_request_deepspeed_mii(
                     output.generated_text = parsed_resp["text"][0]
                     output.success = True
                 else:
-                    output.error = response.reason
+                    output.error = response.reason or ""
                     output.success = False
         except Exception:
             output.success = False
@@ -234,19 +236,20 @@ async def async_request_openai_completions(
         output.prompt_len = request_func_input.prompt_len
 
         generated_text = ""
-        ttft = 0
+        ttft = 0.0
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload,
                                     headers=headers) as response:
                 if response.status == 200:
-                    async for chunk in response.content:
-                        chunk = chunk.strip()
-                        if not chunk:
+                    async for chunk_bytes in response.content:
+                        chunk_bytes = chunk_bytes.strip()
+                        if not chunk_bytes:
                             continue
 
-                        chunk = remove_prefix(chunk.decode("utf-8"), "data: ")
+                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                                              "data: ")
                         if chunk == "[DONE]":
                             latency = time.perf_counter() - st
                         else:
@@ -255,7 +258,7 @@ async def async_request_openai_completions(
                             if data["choices"][0]["text"]:
                                 timestamp = time.perf_counter()
                                 # First token
-                                if ttft == 0:
+                                if ttft == 0.0:
                                     ttft = time.perf_counter() - st
                                     output.ttft = ttft
 
@@ -315,19 +318,20 @@ async def async_request_openai_chat_completions(
         output.prompt_len = request_func_input.prompt_len
 
         generated_text = ""
-        ttft = 0
+        ttft = 0.0
         st = time.perf_counter()
         most_recent_timestamp = st
         try:
             async with session.post(url=api_url, json=payload,
                                     headers=headers) as response:
                 if response.status == 200:
-                    async for chunk in response.content:
-                        chunk = chunk.strip()
-                        if not chunk:
+                    async for chunk_bytes in response.content:
+                        chunk_bytes = chunk_bytes.strip()
+                        if not chunk_bytes:
                             continue
 
-                        chunk = remove_prefix(chunk.decode("utf-8"), "data: ")
+                        chunk = remove_prefix(chunk_bytes.decode("utf-8"),
+                                              "data: ")
                         if chunk == "[DONE]":
                             latency = time.perf_counter() - st
                         else:
@@ -337,7 +341,7 @@ async def async_request_openai_chat_completions(
                             delta = data["choices"][0]["delta"]
                             if delta.get("content", None):
                                 # First token
-                                if ttft == 0:
+                                if ttft == 0.0:
                                     ttft = time.perf_counter() - st
                                     output.ttft = ttft
 
@@ -354,7 +358,7 @@ async def async_request_openai_chat_completions(
                     output.success = True
                     output.latency = latency
                 else:
-                    output.error = response.reason
+                    output.error = response.reason or ""
                     output.success = False
         except Exception:
             output.success = False

docs/source/conf.py

@@ -12,6 +12,7 @@
 
 import logging
 import sys
+from typing import List
 
 from sphinx.ext import autodoc
 
@@ -45,7 +46,7 @@ templates_path = ['_templates']
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = []
+exclude_patterns: List[str] = []
 
 # Exclude the prompt "$" when copying code
 copybutton_prompt_text = r"\$ "

setup.py

@@ -5,7 +5,7 @@ import re
 import subprocess
 import sys
 from shutil import which
-from typing import List
+from typing import Dict, List
 
 import torch
 from packaging.version import Version, parse
@@ -52,7 +52,7 @@ class CMakeExtension(Extension):
 
 class cmake_build_ext(build_ext):
     # A dict of extension directories that have been configured.
-    did_config = {}
+    did_config: Dict[str, bool] = {}
 
     #
     # Determine number of compilation jobs and optionally nvcc compile threads.
@@ -261,6 +261,7 @@ def get_nvcc_cuda_version() -> Version:
 
     Adapted from https://github.com/NVIDIA/apex/blob/8b7a1ff183741dd8f9b87e7bafd04cfde99cea28/setup.py
     """
+    assert CUDA_HOME is not None, "CUDA_HOME is not set"
     nvcc_output = subprocess.check_output([CUDA_HOME + "/bin/nvcc", "-V"],
                                           universal_newlines=True)
     output = nvcc_output.split()

vllm/core/block/interfaces.py

-from abc import ABC, abstractmethod, abstractproperty
-from typing import Dict, List, Optional, Protocol
+from abc import ABC, abstractmethod
+from typing import Dict, FrozenSet, List, Optional, Protocol
 
 from vllm.utils import Device
 
@@ -10,23 +10,28 @@ class Block(ABC):
     def append_token_ids(self, token_ids: List[int]) -> None:
         pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def block_id(self) -> Optional[int]:
         pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def token_ids(self) -> List[int]:
         pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def num_empty_slots(self) -> int:
         pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def is_full(self) -> bool:
         pass
 
-    @abstractproperty
+    @property
+    @abstractmethod
     def prev_block(self) -> Optional["Block"]:
         pass
 
@@ -47,12 +52,13 @@ class Block(ABC):
 class BlockAllocator(ABC):
 
     @abstractmethod
-    def allocate_mutable(self, prev_block: Optional[Block]) -> Block:
+    def allocate_mutable(self, prev_block: Optional[Block],
+                         device: Device) -> Block:
         pass
 
     @abstractmethod
     def allocate_immutable(self, prev_block: Optional[Block],
-                           token_ids: List[int]) -> Block:
+                           token_ids: List[int], device: Device) -> Block:
         pass
 
     @abstractmethod
@@ -64,11 +70,12 @@ class BlockAllocator(ABC):
         pass
 
     @abstractmethod
-    def get_num_free_blocks(self) -> int:
+    def get_num_free_blocks(self, device: Device) -> int:
         pass
 
-    @abstractproperty
-    def all_block_ids(self) -> frozenset[int]:
+    @property
+    @abstractmethod
+    def all_block_ids(self) -> FrozenSet[int]:
         pass
 
     @abstractmethod

vllm/engine/metrics.py

 import time
 from dataclasses import dataclass
-from typing import Dict, List
+from typing import Dict, List, Protocol
 
 import numpy as np
 from prometheus_client import (REGISTRY, Counter, Gauge, Histogram, Info,
@@ -119,6 +119,12 @@ class Stats:
     time_e2e_requests: List[float]
 
 
+class SupportsMetricsInfo(Protocol):
+
+    def metrics_info(self) -> Dict[str, str]:
+        ...
+
+
 class StatLogger:
     """StatLogger is used LLMEngine to log to Promethus and Stdout."""
 
@@ -135,7 +141,7 @@ class StatLogger:
         self.labels = labels
         self.metrics = Metrics(labelnames=list(labels.keys()))
 
-    def info(self, type: str, obj: object) -> None:
+    def info(self, type: str, obj: SupportsMetricsInfo) -> None:
         if type == "cache_config":
             self.metrics.info_cache_config.info(obj.metrics_info())
 

vllm/logger.py

@@ -4,6 +4,7 @@
 import logging
 import os
 import sys
+from typing import Optional
 
 VLLM_CONFIGURE_LOGGING = int(os.getenv("VLLM_CONFIGURE_LOGGING", "1"))
 
@@ -26,7 +27,7 @@ class NewLineFormatter(logging.Formatter):
 
 
 _root_logger = logging.getLogger("vllm")
-_default_handler = None
+_default_handler: Optional[logging.Handler] = None
 
 
 def _setup_logger():
@@ -55,7 +56,12 @@ def init_logger(name: str):
     # Use the same settings as above for root logger
     logger = logging.getLogger(name)
     logger.setLevel(os.getenv("LOG_LEVEL", "DEBUG"))
+
     if VLLM_CONFIGURE_LOGGING:
+        if _default_handler is None:
+            raise ValueError(
+                "_default_handler is not set up. This should never happen!"
+                " Please open an issue on Github.")
         logger.addHandler(_default_handler)
         logger.propagate = False
     return logger

vllm/model_executor/layers/rotary_embedding.py

@@ -247,11 +247,12 @@ def _yarn_find_correction_dim(num_rotations: int,
 
 
 # Find dim range bounds based on rotations
-def _yarn_find_correction_range(low_rot: int,
-                                high_rot: int,
-                                dim: int,
-                                base: float = 10000,
-                                max_position_embeddings: int = 2048) -> int:
+def _yarn_find_correction_range(
+        low_rot: int,
+        high_rot: int,
+        dim: int,
+        base: float = 10000,
+        max_position_embeddings: int = 2048) -> Tuple[int, int]:
     low = math.floor(
         _yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings))
     high = math.ceil(
@@ -293,8 +294,8 @@ class YaRNScalingRotaryEmbedding(RotaryEmbedding):
         *,
         extrapolation_factor: float = 1,
         attn_factor: float = 1,
-        beta_fast: float = 32,
-        beta_slow: float = 1,
+        beta_fast: int = 32,
+        beta_slow: int = 1,
     ) -> None:
         self.scaling_factor = scaling_factor
         self.extrapolation_factor = extrapolation_factor

vllm/transformers_utils/config.py

-from typing import Optional
+from typing import Dict, Optional
 
 from transformers import AutoConfig, PretrainedConfig
 
 from vllm.transformers_utils.configs import *
 
-_CONFIG_REGISTRY = {
+_CONFIG_REGISTRY: Dict[str, PretrainedConfig] = {
     "chatglm": ChatGLMConfig,
     "dbrx": DbrxConfig,
     "mpt": MPTConfig,

vllm/transformers_utils/configs/dbrx.py

@@ -12,7 +12,7 @@ from transformers.utils import logging
 
 logger = logging.get_logger(__name__)
 
-DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+DBRX_PRETRAINED_CONFIG_ARCHIVE_MAP = {} # type: ignore
 
 
 class DbrxAttentionConfig(PretrainedConfig):

vllm/transformers_utils/tokenizers/baichuan.py

@@ -16,11 +16,11 @@ logger = logging.get_logger(__name__)
 
 VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
 
-PRETRAINED_VOCAB_FILES_MAP = {
+PRETRAINED_VOCAB_FILES_MAP = { # type: ignore
     "vocab_file": {},
     "tokenizer_file": {},
 }
-PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}  # type: ignore
 
 
 class BaichuanTokenizer(PreTrainedTokenizer):
@@ -148,9 +148,9 @@ class BaichuanTokenizer(PreTrainedTokenizer):
             `Tuple(str)`: Paths to the files saved.
         """
         if not os.path.isdir(save_directory):
-            logger.error(f"Vocabulary path ({save_directory}) "
-                         "should be a directory")
-            return
+            raise ValueError(f"Vocabulary path ({save_directory}) "
+                             "should be a directory")
+
         out_vocab_file = os.path.join(
             save_directory,
             (filename_prefix + "-" if filename_prefix else "") +

vllm/utils.py

@@ -294,7 +294,7 @@ def create_kv_caches_with_random(
     head_size: int,
     cache_dtype: Optional[Union[str, torch.dtype]],
     model_dtype: Optional[Union[str, torch.dtype]] = None,
-    seed: Optional[int] = 0,
+    seed: int = 0,
     device: Optional[str] = "cuda",
 ) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
     torch.random.manual_seed(seed)
@@ -400,7 +400,7 @@ class CudaMemoryProfiler:
         gc.collect()
 
 
-def str_to_int_tuple(s: str) -> Tuple[int]:
+def str_to_int_tuple(s: str) -> Tuple[int, ...]:
     """Convert a string to a tuple of integers."""
     try:
         return tuple(map(int, s.split(",")))