Improve configs - TokenizerPoolConfig + DeviceConfig (#16603)

Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/test_config.py

@@ -1,14 +1,36 @@
 # SPDX-License-Identifier: Apache-2.0
 
-from dataclasses import asdict
+from dataclasses import MISSING, Field, asdict, dataclass, field
 
 import pytest
 
-from vllm.config import ModelConfig, PoolerConfig
+from vllm.config import ModelConfig, PoolerConfig, get_field
 from vllm.model_executor.layers.pooler import PoolingType
 from vllm.platforms import current_platform
 
 
+def test_get_field():
+
+    @dataclass
+    class TestConfig:
+        a: int
+        b: dict = field(default_factory=dict)
+        c: str = "default"
+
+    with pytest.raises(ValueError):
+        get_field(TestConfig, "a")
+
+    b = get_field(TestConfig, "b")
+    assert isinstance(b, Field)
+    assert b.default is MISSING
+    assert b.default_factory is dict
+
+    c = get_field(TestConfig, "c")
+    assert isinstance(c, Field)
+    assert c.default == "default"
+    assert c.default_factory is MISSING
+
+
 @pytest.mark.parametrize(
     ("model_id", "expected_runner_type", "expected_task"),
     [

vllm/config.py

@@ -182,6 +182,23 @@ def config(cls: type[Config]) -> type[Config]:
     return cls
 
 
+def get_field(cls: type[Config], name: str) -> Field:
+    """Get the default factory field of a dataclass by name. Used for getting
+    default factory fields in `EngineArgs`."""
+    if not is_dataclass(cls):
+        raise TypeError("The given class is not a dataclass.")
+    cls_fields = {f.name: f for f in fields(cls)}
+    if name not in cls_fields:
+        raise ValueError(f"Field '{name}' not found in {cls.__name__}.")
+    named_field: Field = cls_fields.get(name)
+    if (default_factory := named_field.default_factory) is not MISSING:
+        return field(default_factory=default_factory)
+    if (default := named_field.default) is not MISSING:
+        return field(default=default)
+    raise ValueError(
+        f"{cls.__name__}.{name} must have a default value or default factory.")
+
+
 class ModelConfig:
     """Configuration for the model.
 
@@ -1364,20 +1381,26 @@ class CacheConfig:
             logger.warning("Possibly too large swap space. %s", msg)
 
 
+PoolType = Literal["ray"]
+
+
+@config
 @dataclass
 class TokenizerPoolConfig:
-    """Configuration for the tokenizer pool.
+    """Configuration for the tokenizer pool."""
 
-    Args:
+    pool_size: int = 0
-        pool_size: Number of tokenizer workers in the pool.
+    """Number of tokenizer workers in the pool to use for asynchronous
-        pool_type: Type of the pool.
+    tokenization. If 0, will use synchronous tokenization."""
-        extra_config: Additional config for the pool.
+
-            The way the config will be used depends on the
+    pool_type: Union[PoolType, type["BaseTokenizerGroup"]] = "ray"
-            pool type.
+    """Type of tokenizer pool to use for asynchronous tokenization. Ignored if
-    """
+    tokenizer_pool_size is 0."""
-    pool_size: int
+
-    pool_type: Union[str, type["BaseTokenizerGroup"]]
+    extra_config: dict = field(default_factory=dict)
-    extra_config: dict
+    """Additional config for the pool. The way the config will be used depends
+    on the pool type. This should be a JSON string that will be parsed into a
+    dictionary. Ignored if tokenizer_pool_size is 0."""
 
     def compute_hash(self) -> str:
         """
@@ -1408,7 +1431,7 @@ class TokenizerPoolConfig:
     @classmethod
     def create_config(
         cls, tokenizer_pool_size: int,
-        tokenizer_pool_type: Union[str, type["BaseTokenizerGroup"]],
+        tokenizer_pool_type: Union[PoolType, type["BaseTokenizerGroup"]],
         tokenizer_pool_extra_config: Optional[Union[str, dict]]
     ) -> Optional["TokenizerPoolConfig"]:
         """Create a TokenizerPoolConfig from the given parameters.
@@ -1483,7 +1506,7 @@ class LoadConfig:
     download_dir: Optional[str] = None
     """Directory to download and load the weights, default to the default
     cache directory of Hugging Face."""
-    model_loader_extra_config: Optional[Union[str, dict]] = None
+    model_loader_extra_config: dict = field(default_factory=dict)
     """Extra config for model loader. This will be passed to the model loader
     corresponding to the chosen load_format. This should be a JSON string that
     will be parsed into a dictionary."""
@@ -1514,10 +1537,6 @@ class LoadConfig:
         return hash_str
 
     def __post_init__(self):
-        model_loader_extra_config = self.model_loader_extra_config or {}
-        if isinstance(model_loader_extra_config, str):
-            self.model_loader_extra_config = json.loads(
-                model_loader_extra_config)
         if isinstance(self.load_format, str):
             load_format = self.load_format.lower()
             self.load_format = LoadFormat(load_format)
@@ -2029,9 +2048,19 @@ class SchedulerConfig:
         return self.num_scheduler_steps > 1
 
 
+Device = Literal["auto", "cuda", "neuron", "cpu", "tpu", "xpu", "hpu"]
+
+
+@config
+@dataclass
 class DeviceConfig:
-    device: Optional[torch.device]
+    """Configuration for the device to use for vLLM execution."""
-    device_type: str
+
+    device: Union[Device, torch.device] = "auto"
+    """Device type for vLLM execution."""
+    device_type: str = field(init=False)
+    """Device type from the current platform. This is set in
+    `__post_init__`."""
 
     def compute_hash(self) -> str:
         """
@@ -2053,8 +2082,8 @@ class DeviceConfig:
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    def __init__(self, device: str = "auto") -> None:
+    def __post_init__(self):
-        if device == "auto":
+        if self.device == "auto":
             # Automated device type detection
             from vllm.platforms import current_platform
             self.device_type = current_platform.device_type
@@ -2065,7 +2094,7 @@ class DeviceConfig:
                     "to turn on verbose logging to help debug the issue.")
         else:
             # Device type is assigned explicitly
-            self.device_type = device
+            self.device_type = self.device
 
         # Some device types require processing inputs on CPU
         if self.device_type in ["neuron"]:

vllm/engine/arg_utils.py

@@ -16,15 +16,15 @@ from typing_extensions import TypeIs
 
 import vllm.envs as envs
 from vllm import version
-from vllm.config import (CacheConfig, CompilationConfig, ConfigFormat,
+from vllm.config import (CacheConfig, CompilationConfig, Config, ConfigFormat,
-                         DecodingConfig, DeviceConfig,
+                         DecodingConfig, Device, DeviceConfig,
                          DistributedExecutorBackend, HfOverrides,
                          KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
                          ModelConfig, ModelImpl, ObservabilityConfig,
-                         ParallelConfig, PoolerConfig, PromptAdapterConfig,
+                         ParallelConfig, PoolerConfig, PoolType,
-                         SchedulerConfig, SchedulerPolicy, SpeculativeConfig,
+                         PromptAdapterConfig, SchedulerConfig, SchedulerPolicy,
-                         TaskOption, TokenizerPoolConfig, VllmConfig,
+                         SpeculativeConfig, TaskOption, TokenizerPoolConfig,
-                         get_attr_docs)
+                         VllmConfig, get_attr_docs, get_field)
 from vllm.executor.executor_base import ExecutorBase
 from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
@@ -44,27 +44,17 @@ logger = init_logger(__name__)
 
 ALLOWED_DETAILED_TRACE_MODULES = ["model", "worker", "all"]
 
-DEVICE_OPTIONS = [
-    "auto",
-    "cuda",
-    "neuron",
-    "cpu",
-    "tpu",
-    "xpu",
-    "hpu",
-]
-
 # object is used to allow for special typing forms
 T = TypeVar("T")
 TypeHint = Union[type[Any], object]
 TypeHintT = Union[type[T], object]
 
 
-def optional_arg(val: str, return_type: type[T]) -> Optional[T]:
+def optional_arg(val: str, return_type: Callable[[str], T]) -> Optional[T]:
     if val == "" or val == "None":
         return None
     try:
-        return cast(Callable, return_type)(val)
+        return return_type(val)
     except ValueError as e:
         raise argparse.ArgumentTypeError(
             f"Value {val} cannot be converted to {return_type}.") from e
@@ -82,8 +72,11 @@ def optional_float(val: str) -> Optional[float]:
     return optional_arg(val, float)
 
 
-def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
+def nullable_kvs(val: str) -> Optional[dict[str, int]]:
-    """Parses a string containing comma separate key [str] to value [int]
+    """NOTE: This function is deprecated, args should be passed as JSON
+    strings instead.
+    
+    Parses a string containing comma separate key [str] to value [int]
     pairs into a dictionary.
 
     Args:
@@ -117,6 +110,17 @@ def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
     return out_dict
 
 
+def optional_dict(val: str) -> Optional[dict[str, int]]:
+    try:
+        return optional_arg(val, json.loads)
+    except ValueError:
+        logger.warning(
+            "Failed to parse JSON string. Attempting to parse as "
+            "comma-separated key=value pairs. This will be deprecated in a "
+            "future release.")
+        return nullable_kvs(val)
+
+
 @dataclass
 class EngineArgs:
     """Arguments for vLLM engine."""
@@ -178,12 +182,14 @@ class EngineArgs:
     enforce_eager: Optional[bool] = None
     max_seq_len_to_capture: int = 8192
     disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
-    tokenizer_pool_size: int = 0
+    tokenizer_pool_size: int = TokenizerPoolConfig.pool_size
     # Note: Specifying a tokenizer pool by passing a class
     # is intended for expert use only. The API may change without
     # notice.
-    tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
+    tokenizer_pool_type: Union[PoolType, Type["BaseTokenizerGroup"]] = \
-    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
+        TokenizerPoolConfig.pool_type
+    tokenizer_pool_extra_config: dict[str, Any] = \
+        get_field(TokenizerPoolConfig, "extra_config")
     limit_mm_per_prompt: Optional[Mapping[str, int]] = None
     mm_processor_kwargs: Optional[Dict[str, Any]] = None
     disable_mm_preprocessor_cache: bool = False
@@ -199,14 +205,14 @@ class EngineArgs:
     long_lora_scaling_factors: Optional[Tuple[float]] = None
     lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
     max_cpu_loras: Optional[int] = None
-    device: str = 'auto'
+    device: Device = DeviceConfig.device
     num_scheduler_steps: int = SchedulerConfig.num_scheduler_steps
     multi_step_stream_outputs: bool = SchedulerConfig.multi_step_stream_outputs
     ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
     num_gpu_blocks_override: Optional[int] = None
     num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
-    model_loader_extra_config: Optional[
+    model_loader_extra_config: dict = \
-        dict] = LoadConfig.model_loader_extra_config
+        get_field(LoadConfig, "model_loader_extra_config")
     ignore_patterns: Optional[Union[str,
                                     List[str]]] = LoadConfig.ignore_patterns
     preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
@@ -294,14 +300,15 @@ class EngineArgs:
             """Check if the class is a custom type."""
             return cls.__module__ != "builtins"
 
-        def get_kwargs(cls: type[Any]) -> dict[str, Any]:
+        def get_kwargs(cls: type[Config]) -> dict[str, Any]:
             cls_docs = get_attr_docs(cls)
             kwargs = {}
             for field in fields(cls):
                 name = field.name
-                # One of these will always be present
+                default = field.default
-                default = (field.default_factory
+                # This will only be True if default is MISSING
-                           if field.default is MISSING else field.default)
+                if field.default_factory is not MISSING:
+                    default = field.default_factory()
                 kwargs[name] = {"default": default, "help": cls_docs[name]}
 
                 # Make note of if the field is optional and get the actual
@@ -331,8 +338,9 @@ class EngineArgs:
                 elif can_be_type(field_type, float):
                     kwargs[name][
                         "type"] = optional_float if optional else float
+                elif can_be_type(field_type, dict):
+                    kwargs[name]["type"] = optional_dict
                 elif (can_be_type(field_type, str)
-                      or can_be_type(field_type, dict)
                       or is_custom_type(field_type)):
                     kwargs[name]["type"] = optional_str if optional else str
                 else:
@@ -674,25 +682,19 @@ class EngineArgs:
                             'Additionally for encoder-decoder models, if the '
                             'sequence length of the encoder input is larger '
                             'than this, we fall back to the eager mode.')
-        parser.add_argument('--tokenizer-pool-size',
+
-                            type=int,
+        # Tokenizer arguments
-                            default=EngineArgs.tokenizer_pool_size,
+        tokenizer_kwargs = get_kwargs(TokenizerPoolConfig)
-                            help='Size of tokenizer pool to use for '
+        tokenizer_group = parser.add_argument_group(
-                            'asynchronous tokenization. If 0, will '
+            title="TokenizerPoolConfig",
-                            'use synchronous tokenization.')
+            description=TokenizerPoolConfig.__doc__,
-        parser.add_argument('--tokenizer-pool-type',
+        )
-                            type=str,
+        tokenizer_group.add_argument('--tokenizer-pool-size',
-                            default=EngineArgs.tokenizer_pool_type,
+                                     **tokenizer_kwargs["pool_size"])
-                            help='Type of tokenizer pool to use for '
+        tokenizer_group.add_argument('--tokenizer-pool-type',
-                            'asynchronous tokenization. Ignored '
+                                     **tokenizer_kwargs["pool_type"])
-                            'if tokenizer_pool_size is 0.')
+        tokenizer_group.add_argument('--tokenizer-pool-extra-config',
-        parser.add_argument('--tokenizer-pool-extra-config',
+                                     **tokenizer_kwargs["extra_config"])
-                            type=optional_str,
-                            default=EngineArgs.tokenizer_pool_extra_config,
-                            help='Extra config for tokenizer pool. '
-                            'This should be a JSON string that will be '
-                            'parsed into a dictionary. Ignored if '
-                            'tokenizer_pool_size is 0.')
 
         # Multimodal related configs
         parser.add_argument(
@@ -784,11 +786,15 @@ class EngineArgs:
                             type=int,
                             default=EngineArgs.max_prompt_adapter_token,
                             help='Max number of PromptAdapters tokens')
-        parser.add_argument("--device",
+
-                            type=str,
+        # Device arguments
-                            default=EngineArgs.device,
+        device_kwargs = get_kwargs(DeviceConfig)
-                            choices=DEVICE_OPTIONS,
+        device_group = parser.add_argument_group(
-                            help='Device type for vLLM execution.')
+            title="DeviceConfig",
+            description=DeviceConfig.__doc__,
+        )
+        device_group.add_argument("--device", **device_kwargs["device"])
+
         parser.add_argument('--num-scheduler-steps',
                             type=int,
                             default=1,
@@ -1302,8 +1308,6 @@ class EngineArgs:
 
         if self.qlora_adapter_name_or_path is not None and \
             self.qlora_adapter_name_or_path != "":
-            if self.model_loader_extra_config is None:
-                self.model_loader_extra_config = {}
             self.model_loader_extra_config[
                 "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path