Improve configs - the rest! (#17562)

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

tests/compile/test_full_graph.py

@@ -9,7 +9,7 @@ import torch
 
 from tests.quantization.utils import is_quant_method_supported
 from vllm import LLM, SamplingParams
-from vllm.config import CompilationConfig, CompilationLevel
+from vllm.config import CompilationConfig, CompilationLevel, PassConfig
 from vllm.platforms import current_platform
 
 from ..utils import create_new_process_for_each_test
@@ -95,9 +95,6 @@ def test_full_graph(
         run_model(optimization_level, model, model_kwargs)
 
 
-PassConfig = CompilationConfig.PassConfig
-
-
 # TODO(luka) add other supported compilation config scenarios here
 @pytest.mark.parametrize(
     "compilation_config, model_info",

tests/compile/test_functionalization.py

@@ -11,7 +11,7 @@ from vllm.compilation.fusion import (FUSED_OPS, FusionPass, QuantKey,
                                      kFp8DynamicTokenSym, kFp8StaticTensorSym)
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
 from vllm.compilation.noop_elimination import NoOpEliminationPass
-from vllm.config import CompilationConfig, VllmConfig
+from vllm.config import CompilationConfig, PassConfig, VllmConfig
 
 from .backend import TestBackend
 
@@ -53,9 +53,8 @@ def test_fix_functionalization(model: str, quant_key: QuantKey,
     torch.set_default_device("cuda")
 
     vllm_config = VllmConfig()
-    vllm_config.compilation_config = CompilationConfig(pass_config= \
-        CompilationConfig.PassConfig(enable_fusion=do_fusion,
-                                          enable_noop=True))
+    vllm_config.compilation_config = CompilationConfig(
+        pass_config=PassConfig(enable_fusion=do_fusion, enable_noop=True))
     noop_pass = NoOpEliminationPass(vllm_config)
     fusion_pass = FusionPass.instance(vllm_config)
     act_quant_fusion_pass = ActivationQuantFusionPass(vllm_config)

tests/compile/test_fusion.py

@@ -9,7 +9,8 @@ from vllm.compilation.fusion import (FUSED_OPS, QUANT_OPS, FusedRMSQuantKey,
                                      FusionPass, QuantKey)
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe
 from vllm.compilation.noop_elimination import NoOpEliminationPass
-from vllm.config import CompilationConfig, CompilationLevel, VllmConfig
+from vllm.config import (CompilationConfig, CompilationLevel, PassConfig,
+                         VllmConfig)
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     CUTLASS_FP8_SUPPORTED, Fp8LinearOp, maybe_create_device_identity)
@@ -78,8 +79,7 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
     vllm_config = VllmConfig(compilation_config=CompilationConfig(
         level=CompilationLevel.PIECEWISE, custom_ops=["+rms_norm"]))
     vllm_config.compilation_config.pass_config = \
-            CompilationConfig.PassConfig(enable_fusion=True,
-                                              enable_noop=True)
+        PassConfig(enable_fusion=True, enable_noop=True)
     with vllm.config.set_current_vllm_config(vllm_config):
         # Reshape pass is needed for the fusion pass to work
         noop_pass = NoOpEliminationPass(vllm_config)

tests/compile/test_sequence_parallelism.py

@@ -10,7 +10,7 @@ from vllm.compilation.fx_utils import (find_auto_fn, find_auto_fn_maybe,
                                        find_specified_fn_maybe, is_func)
 from vllm.compilation.sequence_parallelism import SequenceParallelismPass
 from vllm.config import (CompilationConfig, DeviceConfig, ModelConfig,
-                         VllmConfig)
+                         PassConfig, VllmConfig)
 from vllm.distributed import tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import (init_distributed_environment,
                                              initialize_model_parallel)
@@ -126,9 +126,8 @@ def sequence_parallelism_pass_on_test_model(local_rank: int, world_size: int,
 
     # configure vllm config for SequenceParallelismPass
     vllm_config = VllmConfig()
-    vllm_config.compilation_config = CompilationConfig(
-        pass_config=CompilationConfig.PassConfig(
-            enable_sequence_parallelism=True, ), )
+    vllm_config.compilation_config = CompilationConfig(pass_config=PassConfig(
+        enable_sequence_parallelism=True))
     vllm_config.device_config = DeviceConfig(device=torch.device("cuda"))
 
     # this is a fake model name to construct the model config

tests/compile/test_silu_mul_quant_fusion.py

@@ -6,7 +6,7 @@ import vllm.envs as envs
 from vllm._custom_ops import scaled_fp8_quant
 from vllm.compilation.activation_quant_fusion import ActivationQuantFusionPass
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe
-from vllm.config import CompilationConfig, VllmConfig
+from vllm.config import CompilationConfig, PassConfig, VllmConfig
 from vllm.model_executor.layers.activation import SiluAndMul
 
 from .backend import TestBackend
@@ -36,8 +36,7 @@ def test_fusion_silu_and_mul_quant(num_tokens, hidden_size):
     # Reshape pass is needed for the fusion pass to work
     config = VllmConfig()
     config.compilation_config = CompilationConfig(
-        pass_config=CompilationConfig.PassConfig(enable_fusion=True,
-                                                 enable_reshape=True))
+        pass_config=PassConfig(enable_fusion=True, enable_reshape=True))
     fusion_pass = ActivationQuantFusionPass(config)
 
     backend = TestBackend(fusion_pass)

tests/distributed/test_sequence_parallel.py

@@ -206,7 +206,7 @@ def _compare_sp(
         'compile_sizes': [4, 8],
         'splitting_ops': [],
         'pass_config': {
-            'enable_sequence_parallism': sp_enabled,
+            'enable_sequence_parallelism': sp_enabled,
             'enable_noop': True,
             'enable_fusion': True,
         },
@@ -223,7 +223,7 @@ def _compare_sp(
         "--distributed-executor-backend",
         distributed_backend,
         "--compilation_config",
-        str(compilation_config),
+        json.dumps(compilation_config),
     ]
 
     tp_env = {

tests/engine/test_arg_utils.py

@@ -8,21 +8,18 @@ from typing import Literal, Optional
 
 import pytest
 
-from vllm.config import config
+from vllm.config import CompilationConfig, config
 from vllm.engine.arg_utils import (EngineArgs, contains_type, get_kwargs,
                                    get_type, is_not_builtin, is_type,
                                    literal_to_kwargs, nullable_kvs,
-                                   optional_type)
+                                   optional_type, parse_type)
 from vllm.utils import FlexibleArgumentParser
 
 
 @pytest.mark.parametrize(("type", "value", "expected"), [
     (int, "42", 42),
-    (int, "None", None),
     (float, "3.14", 3.14),
-    (float, "None", None),
     (str, "Hello World!", "Hello World!"),
-    (str, "None", None),
     (json.loads, '{"foo":1,"bar":2}', {
         "foo": 1,
         "bar": 2
@@ -31,15 +28,20 @@ from vllm.utils import FlexibleArgumentParser
         "foo": 1,
         "bar": 2
     }),
-    (json.loads, "None", None),
 ])
-def test_optional_type(type, value, expected):
-    optional_type_func = optional_type(type)
+def test_parse_type(type, value, expected):
+    parse_type_func = parse_type(type)
     context = nullcontext()
     if value == "foo=1,bar=2":
         context = pytest.warns(DeprecationWarning)
     with context:
-        assert optional_type_func(value) == expected
+        assert parse_type_func(value) == expected
+
+
+def test_optional_type():
+    optional_type_func = optional_type(int)
+    assert optional_type_func("None") is None
+    assert optional_type_func("42") == 42
 
 
 @pytest.mark.parametrize(("type_hint", "type", "expected"), [
@@ -89,7 +91,40 @@ def test_literal_to_kwargs(type_hints, expected):
 
 @config
 @dataclass
-class DummyConfigClass:
+class NestedConfig:
+    field: int = 1
+    """field"""
+
+
+@config
+@dataclass
+class FromCliConfig1:
+    field: int = 1
+    """field"""
+
+    @classmethod
+    def from_cli(cls, cli_value: str):
+        inst = cls(**json.loads(cli_value))
+        inst.field += 1
+        return inst
+
+
+@config
+@dataclass
+class FromCliConfig2:
+    field: int = 1
+    """field"""
+
+    @classmethod
+    def from_cli(cls, cli_value: str):
+        inst = cls(**json.loads(cli_value))
+        inst.field += 2
+        return inst
+
+
+@config
+@dataclass
+class DummyConfig:
     regular_bool: bool = True
     """Regular bool with default True"""
     optional_bool: Optional[bool] = None
@@ -108,18 +143,24 @@ class DummyConfigClass:
     """Literal of literals with default 1"""
     json_tip: dict = field(default_factory=dict)
     """Dict which will be JSON in CLI"""
+    nested_config: NestedConfig = field(default_factory=NestedConfig)
+    """Nested config"""
+    from_cli_config1: FromCliConfig1 = field(default_factory=FromCliConfig1)
+    """Config with from_cli method"""
+    from_cli_config2: FromCliConfig2 = field(default_factory=FromCliConfig2)
+    """Different config with from_cli method"""
 
 
 @pytest.mark.parametrize(("type_hint", "expected"), [
     (int, False),
-    (DummyConfigClass, True),
+    (DummyConfig, True),
 ])
 def test_is_not_builtin(type_hint, expected):
     assert is_not_builtin(type_hint) == expected
 
 
 def test_get_kwargs():
-    kwargs = get_kwargs(DummyConfigClass)
+    kwargs = get_kwargs(DummyConfig)
     print(kwargs)
 
     # bools should not have their type set
@@ -142,6 +183,11 @@ def test_get_kwargs():
     # dict should have json tip in help
     json_tip = "\n\nShould be a valid JSON string."
     assert kwargs["json_tip"]["help"].endswith(json_tip)
+    # nested config should should construct the nested config
+    assert kwargs["nested_config"]["type"]('{"field": 2}') == NestedConfig(2)
+    # from_cli configs should be constructed with the correct method
+    assert kwargs["from_cli_config1"]["type"]('{"field": 2}').field == 3
+    assert kwargs["from_cli_config2"]["type"]('{"field": 2}').field == 4
 
 
 @pytest.mark.parametrize(("arg", "expected"), [
@@ -177,7 +223,7 @@ def test_compilation_config():
 
     # default value
     args = parser.parse_args([])
-    assert args.compilation_config is None
+    assert args.compilation_config == CompilationConfig()
 
     # set to O3
     args = parser.parse_args(["-O3"])
@@ -194,7 +240,7 @@ def test_compilation_config():
     # set to string form of a dict
     args = parser.parse_args([
         "--compilation-config",
-        "{'level': 3, 'cudagraph_capture_sizes': [1, 2, 4, 8]}",
+        '{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}',
     ])
     assert (args.compilation_config.level == 3 and
             args.compilation_config.cudagraph_capture_sizes == [1, 2, 4, 8])
@@ -202,7 +248,7 @@ def test_compilation_config():
     # set to string form of a dict
     args = parser.parse_args([
         "--compilation-config="
-        "{'level': 3, 'cudagraph_capture_sizes': [1, 2, 4, 8]}",
+        '{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}',
     ])
     assert (args.compilation_config.level == 3 and
             args.compilation_config.cudagraph_capture_sizes == [1, 2, 4, 8])

vllm/compilation/vllm_inductor_pass.py

@@ -4,7 +4,7 @@ import time
 
 import torch
 
-from vllm.config import CompilationConfig, VllmConfig
+from vllm.config import PassConfig, VllmConfig
 # yapf: disable
 from vllm.distributed import get_tensor_model_parallel_rank as get_tp_rank
 from vllm.distributed import (
@@ -56,10 +56,7 @@ class VllmInductorPass(InductorPass):
 
 class PrinterInductorPass(VllmInductorPass):
 
-    def __init__(self,
-                 name: str,
-                 config: CompilationConfig.PassConfig,
-                 always=False):
+    def __init__(self, name: str, config: PassConfig, always=False):
         super().__init__(config)
         self.name = name
         self.always = always

vllm/config.py

@@ -11,8 +11,8 @@ import textwrap
 import warnings
 from collections import Counter
 from contextlib import contextmanager
-from dataclasses import (MISSING, dataclass, field, fields, is_dataclass,
-                         replace)
+from dataclasses import (MISSING, Field, asdict, dataclass, field, fields,
+                         is_dataclass, replace)
 from functools import cached_property
 from importlib.util import find_spec
 from pathlib import Path
@@ -20,7 +20,6 @@ from typing import (TYPE_CHECKING, Any, Callable, ClassVar, Literal, Optional,
                     Protocol, TypeVar, Union, cast, get_args, get_origin)
 
 import torch
-from pydantic import BaseModel, Field, PrivateAttr
 from torch.distributed import ProcessGroup, ReduceOp
 from transformers import PretrainedConfig
 from typing_extensions import deprecated
@@ -57,7 +56,7 @@ if TYPE_CHECKING:
 
     ConfigType = type[DataclassInstance]
 else:
-    QuantizationConfig = None
+    QuantizationConfig = Any
     ConfigType = type
 
 logger = init_logger(__name__)
@@ -169,6 +168,12 @@ def config(cls: ConfigT) -> ConfigT:
     """
     A decorator that ensures all fields in a dataclass have default values
     and that each field has a docstring.
+
+    If a `ConfigT` is used as a CLI argument itself, the default value provided
+    by `get_kwargs` will be the result parsing a JSON string as the kwargs
+    (i.e. `ConfigT(**json.loads(cli_arg))`). However, if a particular `ConfigT`
+    requires custom construction from CLI (i.e. `CompilationConfig`), it can
+    have a `from_cli` method, which will be called instead.
     """
     if not is_dataclass(cls):
         raise TypeError("The decorated class must be a dataclass.")
@@ -202,7 +207,7 @@ def get_field(cls: ConfigType, name: str) -> Field:
     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)
+    named_field: Field = cls_fields[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:
@@ -211,6 +216,10 @@ def get_field(cls: ConfigType, name: str) -> Field:
         f"{cls.__name__}.{name} must have a default value or default factory.")
 
 
+def is_init_field(cls: ConfigType, name: str) -> bool:
+    return next(f for f in fields(cls) if f.name == name).init
+
+
 TokenizerMode = Literal["auto", "slow", "mistral", "custom"]
 ModelDType = Literal["auto", "half", "float16", "bfloat16", "float", "float32"]
 
@@ -2007,13 +2016,13 @@ class SchedulerConfig:
     def __post_init__(self) -> None:
         if self.max_model_len is None:
             self.max_model_len = 8192
-            logger.warning(
+            logger.warning_once(
                 "max_model_len was is not set. Defaulting to arbitrary value "
                 "of %d.", self.max_model_len)
 
         if self.max_num_seqs is None:
             self.max_num_seqs = 128
-            logger.warning(
+            logger.warning_once(
                 "max_num_seqs was is not set. Defaulting to arbitrary value "
                 "of %d.", self.max_num_seqs)
 
@@ -2840,8 +2849,8 @@ class PromptAdapterConfig:
 class MultiModalConfig:
     """Controls the behavior of multimodal models."""
 
-    limit_per_prompt: dict[str, int] = get_field(ModelConfig,
-                                                 "limit_mm_per_prompt")
+    limit_per_prompt: dict[str, int] = \
+        cast(dict[str, int], get_field(ModelConfig, "limit_mm_per_prompt"))
     """
     The maximum number of input items allowed per prompt for each modality.
     Defaults to 1 (V0) or 999 (V1) for each modality.
@@ -3415,41 +3424,49 @@ class ObservabilityConfig:
             self.collect_detailed_traces[0].split(","))
 
 
-class KVTransferConfig(BaseModel):
+KVProducer = Literal["kv_producer", "kv_both"]
+KVConsumer = Literal["kv_consumer", "kv_both"]
+KVRole = Literal[KVProducer, KVConsumer]
+
+
+@config
+@dataclass
+class KVTransferConfig:
     """Configuration for distributed KV cache transfer."""
 
-    # The KV connector for vLLM to transmit KV caches between vLLM instances.
     kv_connector: Optional[str] = None
+    """The KV connector for vLLM to transmit KV caches between vLLM instances.
+    """
 
-    # The device used by kv connector to buffer the KV cache.
-    # Currently only support 'cuda'.
     kv_buffer_device: Optional[str] = "cuda"
+    """The device used by kv connector to buffer the KV cache.
+    Currently only support 'cuda'."""
 
-    # The buffer size for TorchDistributedConnector. Measured in number of
-    # bytes. Recommended value: 1e9 (about 1GB).
     kv_buffer_size: float = 1e9
+    """The buffer size for TorchDistributedConnector. Measured in number of
+    bytes. Recommended value: 1e9 (about 1GB)."""
 
-    # Whether this vLLM instance produces, consumes KV cache, or both. Choices
-    # are 'kv_producer', 'kv_consumer', and 'both'.
-    kv_role: Optional[str] = None
+    kv_role: Optional[KVRole] = None
+    """Whether this vLLM instance produces, consumes KV cache, or both. Choices
+    are 'kv_producer', 'kv_consumer', and 'both'."""
 
-    # The rank of this vLLM instance in the KV cache transfer. Typical value:
-    # 0 for prefill instance, 1 for decode instance.
-    # Currently only 1P1D is supported.
     kv_rank: Optional[int] = None
+    """The rank of this vLLM instance in the KV cache transfer. Typical value:
+    0 for prefill instance, 1 for decode instance.
+    Currently only 1P1D is supported."""
 
-    # The number of parallel instances for KV cache transfer. For
-    # PyNcclConnector, this should be 2.
     kv_parallel_size: int = 1
+    """The number of parallel instances for KV cache transfer. For
+    PyNcclConnector, this should be 2."""
 
-    # The KV connector ip, used to build distributed connection
     kv_ip: str = "127.0.0.1"
+    """The KV connector ip, used to build distributed connection."""
 
-    # The KV connector port, used to build distributed connection
     kv_port: int = 14579
+    """The KV connector port, used to build distributed connection."""
 
-    # any extra config that the connector may need
-    kv_connector_extra_config: dict[str, Any] = {}
+    kv_connector_extra_config: dict[str, Any] = field(default_factory=dict)
+    """any extra config that the connector may need."""
 
     def compute_hash(self) -> str:
         """
@@ -3470,46 +3487,37 @@ class KVTransferConfig(BaseModel):
                                usedforsecurity=False).hexdigest()
         return hash_str
 
-    @classmethod
-    def from_cli(cls, cli_value: str) -> "KVTransferConfig":
-        """Parse the CLI value for the kv cache transfer config."""
-        return KVTransferConfig.model_validate_json(cli_value)
-
-    def model_post_init(self, __context: Any) -> None:
-
-        if self.kv_role is not None and self.kv_role not in [
-                "kv_producer", "kv_consumer", "kv_both"
-        ]:
-            raise ValueError(
-                f"Unsupported kv_role: {self.kv_role}. "
-                f"Supported roles are `kv_producer`, `kv_consumer`, "
-                f"and `kv_both`")
+    def __post_init__(self) -> None:
+        if self.kv_role is not None and self.kv_role not in get_args(KVRole):
+            raise ValueError(f"Unsupported kv_role: {self.kv_role}. "
+                             f"Supported roles are {get_args(KVRole)}")
 
         if self.kv_connector is not None and self.kv_role is None:
             raise ValueError("Please specify kv_disagg_role when kv_connector "
-                             "is set, supported roles are `kv_producer`, "
-                             "`kv_consumer`, and `kv_both`")
+                             f"is set, supported roles are {get_args(KVRole)}")
 
     @property
     def is_kv_transfer_instance(self) -> bool:
         return self.kv_connector is not None and \
-            self.kv_role in ["kv_producer", "kv_consumer", "kv_both"]
+            self.kv_role in get_args(KVRole)
 
     @property
     def is_kv_producer(self) -> bool:
         return self.kv_connector is not None and \
-            self.kv_role in ["kv_producer", "kv_both"]
+            self.kv_role in get_args(KVProducer)
 
     @property
     def is_kv_consumer(self) -> bool:
         return self.kv_connector is not None and \
-            self.kv_role in ["kv_consumer", "kv_both"]
+            self.kv_role in get_args(KVConsumer)
 
     def get_from_extra_config(self, key, default) -> Any:
         return self.kv_connector_extra_config.get(key, default)
 
 
-class KVEventsConfig(BaseModel):
+@config
+@dataclass
+class KVEventsConfig:
     """Configuration for KV event publishing."""
 
     enable_kv_cache_events: bool = False
@@ -3548,11 +3556,6 @@ class KVEventsConfig(BaseModel):
     this topic to receive events.
     """
 
-    @classmethod
-    def from_cli(cls, cli_value: str) -> "KVEventsConfig":
-        """Parse the CLI value for the event publisher config."""
-        return KVEventsConfig.model_validate_json(cli_value)
-
 
 class CompilationLevel:
     # constants for the levels of the compilation process
@@ -3562,80 +3565,72 @@ class CompilationLevel:
     PIECEWISE = 3
 
 
-class CompilationConfig(BaseModel):
-    """
-    Configuration for compilation.
-    It has three parts:
+@config
+@dataclass
+class PassConfig:
+    """Configuration for custom Inductor passes.
+
+    This is separate from general `CompilationConfig` so that inductor passes
+    don't all have access to full configuration - that would create a cycle as
+    the `PassManager` is set as a property of config."""
+
+    dump_graph_stages: list[str] = field(default_factory=list)
+    """List of stages for which we want to dump the graph. Each pass defines
+    its own stages (before, after, maybe in-between)."""
+    dump_graph_dir: Path = Path(".")
+    """Directory to dump the graphs."""
+    # TODO(luka) better pass enabling system.
+    enable_fusion: bool = True
+    """Whether to enable the custom fusion pass."""
+    enable_noop: bool = True
+    """Whether to enable the custom no-op elimination pass."""
+    enable_sequence_parallelism: bool = False
+    """Whether to enable sequence parallelism."""
+
+    def uuid(self):
+        """
+        Produces a hash unique to the pass configuration.
+        Any new fields that affect compilation should be added to the hash.
+        Do not include dump_graph_* in the hash - they don't affect
+        compilation.
+        """
+        include = {
+            "enable_fusion", "enable_noop", "enable_sequence_parallelism"
+        }
+        dict_ = {k: v for k, v in asdict(self).items() if k in include}
+        return InductorPass.hash_dict(dict_)
+
+    def __post_init__(self) -> None:
+        if not self.enable_noop and self.enable_fusion:
+            logger.warning_once(
+                "Fusion enabled but reshape elimination disabled. "
+                "RMSNorm + quant (fp8) fusion might not work")
+
+
+@config
+@dataclass
+class CompilationConfig:
+    """Configuration for compilation. It has three parts:
+
     - Top-level Compilation control:
-        - level: the level of compilation.
-            - 0: no compilation.
-            - 1: dynamo as is.
-            - 2: dynamo once.
-            - 3: piecewise compilation.
-        - debug_dump_path: the path to dump the debug information.
-        - cache_dir: the directory to store the compiled graph, to
-            accelerate Inductor compilation. By default, it will use
-            model-related information to generate a cache directory.
-        - backend: the backend for compilation. It needs to be a string.
-            - "" (empty string): use the default backend.
-            - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
-            - "full.module.name": a qualified name which can be used to import the backend function.
-            We use string to avoid serialization issues when using compilation in a distributed setting.
-            When the compilation level is 1 or 2, the backend is used for the compilation directly (it sees the whole graph).
-            When the compilation level is 3, the backend is used for the piecewise compilation (it sees a part of the graph).
-        - custom_ops: fine-grained control over which custom ops to enable/disable.
-            Use 'all' to enable all, 'none' to disable all.
-            Also specify a list of custom op names to enable (prefixed with a '+'),
-            or disable (prefixed with a '-').
-            Examples:
-                - 'all,-op1' to enable all except op1
-                - 'none,+op1,+op2' to enable only op1 and op2
-            By default, all custom ops are enabled when running without Inductor
-                and disabled when running with Inductor (compile_level >= Inductor).
-        - splitting_ops: a list of ops to split the full graph into subgraphs, used in piecewise compilation.
+        - {attr}`level`
+        - {attr}`debug_dump_path`
+        - {attr}`cache_dir`
+        - {attr}`backend`
+        - {attr}`custom_ops`
+        - {attr}`splitting_ops`
     - CudaGraph capture:
-        - use_cudagraph: whether to use cudagraph inside compilation.
-            - False: cudagraph inside compilation is not used.
-            - True: cudagraph inside compilation is used. It requires
-                that all input buffers have fixed addresses, and all
-                splitting ops write their outputs to input buffers.
-            Note that this is orthogonal to the cudagraph capture logic
-            outside of compilation.
-            TODO: move outside cudagraph logic into compilation.
-            torch.compile will handle cudagraph capture logic in the future.
-        - cudagraph_capture_sizes: sizes to capture cudagraph.
-            - None (default): capture sizes are inferred from vllm config.
-            - list[int]: capture sizes are specified as given.
-        - cudagraph_num_of_warmups: number of warmup runs for cudagraph.
-            It means the first several runs will be treated as warmup runs.
-            Only after that, the execution will be recorded, and the recorded
-            cudagraph will be used for subsequent runs.
-        - cudagraph_copy_inputs: whether to copy input tensors for
-            cudagraph. If the caller can guarantee that the same input buffers
-            are always used, it can set this to False. Otherwise, it should
-            set this to True, and the compiler will copy the input to an
-            internally managed buffer. Default is False.
-        - full_cuda_graph: whether to use a full cuda graph for the entire forward 
-            pass rather than splitting certain operations such as attention into subgraphs. 
-            Thus this flag cannot be used together with splitting_ops. This may provide 
-            performance benefits for smaller models.
+        - {attr}`use_cudagraph`
+        - {attr}`cudagraph_capture_sizes`
+        - {attr}`cudagraph_num_of_warmups`
+        - {attr}`cudagraph_copy_inputs`
+        - {attr}`full_cuda_graph`
     - Inductor compilation:
-        - use_inductor: whether to use inductor compilation.
-            - False: inductor compilation is not used. graph runs in eager.
-            - True: inductor compilation is used. one graph for symbolic shape
-                is compiled. In addition, compile for compile_sizes,
-                using configurations in inductor_compile_config.
-        - compile_sizes: sizes to compile for inductor. In addition
-            to integers, it also supports "cudagraph_capture_sizes" to
-            specify the sizes for cudagraph capture.
-        - inductor_compile_config: additional configurations for inductor.
-            - None: use default configurations.
-        - inductor_passes: additional passes for inductor. It is a dictionary
-            from pass name to pass function qualified name. We use function
-            name because the config uses json format. If we pass the config
-            from Python, functions can also be passed directly via Python object
-            constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`
-        - custom inductor passes: see PassConfig for more details
+        - {attr}`use_inductor`
+        - {attr}`compile_sizes`
+        - {attr}`inductor_compile_config`
+        - {attr}`inductor_passes`
+        - custom inductor passes
 
     Why we have different sizes for cudagraph and inductor:
     - cudagraph: a cudagraph captured for a specific size can only be used
@@ -3646,83 +3641,135 @@ class CompilationConfig(BaseModel):
         static shapes. However, we find the general shape compilation is
         sufficient for most cases. It might be beneficial to compile for
         certain small batchsizes, where inductor is good at optimizing.
-    """ # noqa
+    """
+    # Top-level Compilation control
     level: int = 0
+    """The level of compilation:
+
+    - 0: no compilation.
+    - 1: dynamo as is.
+    - 2: dynamo once.
+    - 3: piecewise compilation."""
     debug_dump_path: str = ""
+    """The path to dump the debug information."""
     cache_dir: str = ""
+    """The directory to store the compiled graph, to accelerate Inductor
+    compilation. By default, it will use model-related information to generate
+    a cache directory."""
     backend: str = ""
-    custom_ops: list[str] = Field(default_factory=list)
-    splitting_ops: list[str] = Field(default=None)  # type: ignore
-
+    """The backend for compilation. It needs to be a string:
+
+    - "" (empty string): use the default backend.
+    - "eager"/"openxla"/...: use the specified backend registered in PyTorch.
+    - "full.module.name": a qualified name which can be used to import the
+
+    backend function.
+    We use string to avoid serialization issues when using compilation in a
+    distributed setting. When the compilation level is 1 or 2, the backend is
+    used for the compilation directly (it sees the whole graph). When the
+    compilation level is 3, the backend is used for the piecewise compilation
+    (it sees a part of the graph)."""
+    custom_ops: list[str] = field(default_factory=list)
+    """Fine-grained control over which custom ops to enable/disable. Use 'all'
+    to enable all, 'none' to disable all. Also specify a list of custom op
+    names to enable (prefixed with a '+'), or disable (prefixed with a '-').
+    Examples:
+
+    - 'all,-op1' to enable all except op1
+    - 'none,+op1,+op2' to enable only op1 and op2
+
+    By default, all custom ops are enabled when running without Inductor and
+    disabled when running with Inductor (compile_level >= Inductor)."""
+    splitting_ops: list[str] = field(default_factory=list)
+    """A list of ops to split the full graph into subgraphs, used in piecewise
+    compilation."""
+
+    # Inductor capture
     use_inductor: bool = True
-    compile_sizes: Optional[list[Union[int, str]]] = Field(default=None)
-    inductor_compile_config: dict = Field(default_factory=dict)
-    inductor_passes: dict[str, str] = Field(default_factory=dict)
-
+    """Whether to use inductor compilation:
+
+    - False: inductor compilation is not used. graph runs in eager.
+    - True: inductor compilation is used. one graph for symbolic shape
+        is compiled. In addition, compile for compile_sizes,
+        using configurations in inductor_compile_config."""
+    compile_sizes: Optional[list[Union[int, str]]] = None
+    """Sizes to compile for inductor. In addition
+    to integers, it also supports "cudagraph_capture_sizes" to
+    specify the sizes for cudagraph capture."""
+    inductor_compile_config: dict = field(default_factory=dict)
+    """Additional configurations for inductor.
+    - None: use default configurations."""
+    inductor_passes: dict[str, str] = field(default_factory=dict)
+    """Additional passes for inductor. It is a dictionary
+    from pass name to pass function qualified name. We use function
+    name because the config uses JSON format. If we pass the config
+    from Python, functions can also be passed directly via Python object
+    constructor, e.g. `CompilationConfig(inductor_passes={"a": func})`."""
+
+    # CudaGraph compilation
     use_cudagraph: bool = False
+    """Whether to use cudagraph inside compilation.
+    - False: cudagraph inside compilation is not used.
+    - True: cudagraph inside compilation is used. It requires
+        that all input buffers have fixed addresses, and all
+        splitting ops write their outputs to input buffers.
+    Note that this is orthogonal to the cudagraph capture logic
+    outside of compilation.
+    TODO: move outside cudagraph logic into compilation.
+    torch.compile will handle cudagraph capture logic in the future."""
     cudagraph_num_of_warmups: int = 0
+    """Number of warmup runs for cudagraph.
+    It means the first several runs will be treated as warmup runs.
+    Only after that, the execution will be recorded, and the recorded
+    cudagraph will be used for subsequent runs."""
     cudagraph_capture_sizes: Optional[list[int]] = None
+    """Sizes to capture cudagraph.
+    - None (default): capture sizes are inferred from vllm config.
+    - list[int]: capture sizes are specified as given."""
     cudagraph_copy_inputs: bool = False
+    """Whether to copy input tensors for
+    cudagraph. If the caller can guarantee that the same input buffers
+    are always used, it can set this to False. Otherwise, it should
+    set this to True, and the compiler will copy the input to an
+    internally managed buffer. Default is False."""
     full_cuda_graph: bool = False
-
-    class PassConfig(BaseModel):
-        """
-        Configuration for custom Inductor passes.
-        This is separate from general CompilationConfig so that inductor passes
-        don't all have access to full configuration - that would create a cycle
-        as the PassManager is set as a property of config.
-        - dump_graph_stages: list of stages for which we want to dump the graph.
-            Each pass defines its own stages (before, after, maybe in-between).
-        - dump_graph_dir: directory to dump the graphs. Default is .
-        - enable_fusion: whether to enable the custom fusion pass.
-        - enable_noop: whether to enable the custom no-op elimination pass.
-            TODO(luka) better pass enabling system.
-        - enable_sequence_parallelism: whether to enable sequence parallelism.
-        """
-        dump_graph_stages: list[str] = Field(default_factory=list)
-        dump_graph_dir: Path = Field(default=Path("."))
-        enable_fusion: bool = True
-        enable_noop: bool = True
-        enable_sequence_parallelism: bool = False
-
-        def uuid(self):
-            """
-            Produces a hash unique to the pass configuration.
-            Any new fields that affect compilation should be added to the hash.
-            Do not include dump_graph_* in the hash - they don't affect
-            compilation.
-            """
-            dict_ = self.model_dump(include={"enable_fusion", "enable_noop", \
-                "enable_sequence_parallelism"})
-            return InductorPass.hash_dict(dict_)
-
-        def model_post_init(self, __context: Any) -> None:
-            if not self.enable_noop and self.enable_fusion:
-                logger.warning_once(
-                    "Fusion enabled but reshape elimination disabled. "
-                    "RMSNorm + quant (fp8) fusion might not work")
-
-    pass_config: PassConfig = Field(default_factory=PassConfig)
-
-    # not configurable, computed after init
-    max_capture_size: int = PrivateAttr
-    local_cache_dir: str = PrivateAttr  # local cache dir for each rank
-    # optimization:
-    # Intuitively, bs_to_padded_graph_size should be dict[int, int].
-    # since we know all keys are in a range [0, max_capture_size],
-    # we can optimize it to list[int] for better lookup performance.
-    bs_to_padded_graph_size: list[int] = PrivateAttr
+    """whether to use a full cuda graph for the entire forward pass rather than
+    splitting certain operations such as attention into subgraphs. Thus this
+    flag cannot be used together with splitting_ops. This may provide
+    performance benefits for smaller models."""
+
+    pass_config: PassConfig = field(default_factory=PassConfig)
+    """Custom inductor passes, see PassConfig for more details"""
+
+    max_capture_size: int = field(default=None, init=False)  # type: ignore
+    """not configurable, computed after init"""
+    local_cache_dir: str = field(default=None, init=False)  # type: ignore
+    """local cache dir for each rank"""
+    bs_to_padded_graph_size: list[int] = field(
+        default=None,  # type: ignore
+        init=False)
+    """optimization:
+    Intuitively, bs_to_padded_graph_size should be dict[int, int].
+    since we know all keys are in a range [0, max_capture_size],
+    we can optimize it to list[int] for better lookup performance."""
 
     # keep track of enabled and disabled custom ops
-    enabled_custom_ops: Counter[str] = PrivateAttr
-    disabled_custom_ops: Counter[str] = PrivateAttr
-    traced_files: set[str] = PrivateAttr
-    compilation_time: float = PrivateAttr
-
-    # Per-model forward context
-    # Map from layer name to layer objects that need to be accessed outside
-    # model code, e.g., Attention, FusedMOE when dp_size>1.
-    static_forward_context: dict[str, Any] = PrivateAttr
+    enabled_custom_ops: Counter[str] = field(default_factory=Counter,
+                                             init=False)
+    """custom ops that are enabled"""
+    disabled_custom_ops: Counter[str] = field(default_factory=Counter,
+                                              init=False)
+    """custom ops that are disabled"""
+    traced_files: set[str] = field(default_factory=set, init=False)
+    """files that are traced for compilation"""
+    compilation_time: float = field(default=0.0, init=False)
+    """time taken for compilation"""
+
+    static_forward_context: dict[str, Any] = field(default_factory=dict,
+                                                   init=False)
+    """Per-model forward context
+    Map from layer name to layer objects that need to be accessed outside
+    model code, e.g., Attention, FusedMOE when dp_size>1."""
 
     def compute_hash(self) -> str:
         """
@@ -3757,7 +3804,17 @@ class CompilationConfig(BaseModel):
             "pass_config",
             "traced_files",
         }
-        return self.model_dump_json(exclude=exclude, exclude_unset=True)
+        include = dict()
+        for k, v in asdict(self).items():
+            if k in exclude:
+                continue
+            f = get_field(CompilationConfig, k)
+            if (d := f.default) is not MISSING and d == v:
+                continue
+            if (df := f.default_factory) is not MISSING and df() == v:
+                continue
+            include[k] = v
+        return json.dumps(include)
 
     __str__ = __repr__
 
@@ -3766,12 +3823,9 @@ class CompilationConfig(BaseModel):
         """Parse the CLI value for the compilation config."""
         if cli_value in ["0", "1", "2", "3"]:
             return cls(level=int(cli_value))
-        # do not use `eval`, it is dangerous and can execute arbitrary code
-        dict_value = ast.literal_eval(cli_value)
-        return CompilationConfig.model_validate(dict_value)
-
-    def model_post_init(self, __context: Any) -> None:
+        return cls(**json.loads(cli_value))
 
+    def __post_init__(self) -> None:
         count_none = self.custom_ops.count("none")
         count_all = self.custom_ops.count("all")
         assert count_none + count_all <= 1, "Can only specify 'none' or 'all'"
@@ -3789,9 +3843,6 @@ class CompilationConfig(BaseModel):
             if KEY not in self.inductor_compile_config:
                 self.inductor_compile_config[KEY] = False
 
-        if self.splitting_ops is None:
-            self.splitting_ops = []
-
         for k, v in self.inductor_passes.items():
             if not isinstance(v, str):
                 assert callable(v), (
@@ -3808,11 +3859,8 @@ class CompilationConfig(BaseModel):
             self.inductor_compile_config[k] = func if isinstance(
                 func, InductorPass) else CallableInductorPass(func)
 
-        self.enabled_custom_ops = Counter()
-        self.disabled_custom_ops = Counter()
-        self.traced_files = set()
-        self.static_forward_context = {}
-        self.compilation_time = 0.0
+        if isinstance(self.pass_config, dict):
+            self.pass_config = PassConfig(**self.pass_config)
 
     def init_backend(self, vllm_config: "VllmConfig") -> Union[str, Callable]:
         if self.level == CompilationLevel.NO_COMPILATION:
@@ -3899,39 +3947,67 @@ class CompilationConfig(BaseModel):
             ]
 
 
+@config
 @dataclass
 class VllmConfig:
     """Dataclass which contains all vllm-related configuration. This
     simplifies passing around the distinct configurations in the codebase.
     """
 
-    model_config: ModelConfig = field(default=None, init=True)  # type: ignore
-    cache_config: CacheConfig = field(default=None, init=True)  # type: ignore
-    parallel_config: ParallelConfig = field(default_factory=ParallelConfig,
-                                            init=True)
-    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig,
-                                              init=True)
-    device_config: DeviceConfig = field(default=None,
-                                        init=True)  # type: ignore
-    load_config: LoadConfig = field(default=None, init=True)  # type: ignore
+    model_config: ModelConfig = field(default_factory=ModelConfig)
+    """Model configuration."""
+    cache_config: CacheConfig = field(default_factory=CacheConfig)
+    """Cache configuration."""
+    parallel_config: ParallelConfig = field(default_factory=ParallelConfig)
+    """Parallel configuration."""
+    scheduler_config: SchedulerConfig = field(default_factory=SchedulerConfig)
+    """Scheduler configuration."""
+    device_config: DeviceConfig = field(default_factory=DeviceConfig)
+    """Device configuration."""
+    load_config: LoadConfig = field(default_factory=LoadConfig)
+    """Load configuration."""
     lora_config: Optional[LoRAConfig] = None
-    speculative_config: SpeculativeConfig = field(default=None,
-                                                  init=True)  # type: ignore
+    """LoRA configuration."""
+    speculative_config: Optional[SpeculativeConfig] = None
+    """Speculative decoding configuration."""
     decoding_config: Optional[DecodingConfig] = None
+    """Decoding configuration."""
     observability_config: Optional[ObservabilityConfig] = None
+    """Observability configuration."""
     prompt_adapter_config: Optional[PromptAdapterConfig] = None
+    """Prompt adapter configuration."""
     quant_config: Optional[QuantizationConfig] = None
-    compilation_config: CompilationConfig = field(default=None,
-                                                  init=True)  # type: ignore
-    kv_transfer_config: KVTransferConfig = field(default=None,
-                                                 init=True)  # type: ignore
+    """Quantization configuration."""
+    compilation_config: CompilationConfig = field(
+        default_factory=CompilationConfig)
+    """`torch.compile` configuration for the model.
+
+    When it is a number (0, 1, 2, 3), it will be interpreted as the
+    optimization level.
+
+    NOTE: level 0 is the default level without any optimization. level 1 and 2
+    are for internal testing only. level 3 is the recommended level for
+    production.
+
+    Following the convention of traditional compilers, using `-O` without space
+    is also supported. `-O3` is equivalent to `-O 3`.
+
+    You can specify the full compilation config like so:
+    `{"level": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
+    """
+    kv_transfer_config: Optional[KVTransferConfig] = None
+    """The configurations for distributed KV cache transfer."""
     kv_events_config: Optional[KVEventsConfig] = None
+    """The configurations for event publishing."""
     # some opaque config, only used to provide additional information
     # for the hash computation, mainly used for testing, debugging or out of
     # tree config registration.
-    additional_config: SupportsHash = field(default=None,
-                                            init=True)  # type: ignore
+    additional_config: Union[dict, SupportsHash] = field(default_factory=dict)
+    """Additional config for specified platform. Different platforms may
+    support different configs. Make sure the configs are valid for the platform
+    you are using. Contents must be hashable."""
     instance_id: str = ""
+    """The ID of the vLLM instance."""
 
     def compute_hash(self) -> str:
         """
@@ -4012,7 +4088,14 @@ class VllmConfig:
         else:
             vllm_factors.append("None")
         if self.additional_config:
-            vllm_factors.append(self.additional_config.compute_hash())
+            if isinstance(additional_config := self.additional_config, dict):
+                additional_config_hash = hashlib.md5(
+                    json.dumps(additional_config, sort_keys=True).encode(),
+                    usedforsecurity=False,
+                ).hexdigest()
+            else:
+                additional_config_hash = additional_config.compute_hash()
+            vllm_factors.append(additional_config_hash)
         else:
             vllm_factors.append("None")
         factors.append(vllm_factors)

vllm/distributed/kv_events.py

@@ -5,6 +5,7 @@ import threading
 import time
 from abc import ABC, abstractmethod
 from collections import deque
+from dataclasses import asdict
 from itertools import count
 from queue import Queue
 from typing import Any, Callable, Optional, Union
@@ -284,7 +285,7 @@ class EventPublisherFactory:
         if not config:
             return NullEventPublisher()
 
-        config_dict = config.model_dump()
+        config_dict = asdict(config)
 
         kind = config_dict.pop("publisher", "null")
         config_dict.pop("enable_kv_cache_events")

vllm/engine/arg_utils.py

@@ -7,10 +7,10 @@ import json
 import re
 import threading
 import warnings
-from dataclasses import MISSING, dataclass, fields
+from dataclasses import MISSING, dataclass, fields, is_dataclass
 from itertools import permutations
-from typing import (Any, Callable, Dict, List, Literal, Optional, Type,
-                    TypeVar, Union, cast, get_args, get_origin)
+from typing import (Annotated, Any, Callable, Dict, List, Literal, Optional,
+                    Type, TypeVar, Union, cast, get_args, get_origin)
 
 import torch
 from typing_extensions import TypeIs, deprecated
@@ -36,7 +36,8 @@ from vllm.reasoning import ReasoningParserManager
 from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
 from vllm.transformers_utils.utils import check_gguf_file
 from vllm.usage.usage_lib import UsageContext
-from vllm.utils import FlexibleArgumentParser, GiB_bytes, is_in_ray_actor
+from vllm.utils import (FlexibleArgumentParser, GiB_bytes, is_in_doc_build,
+                        is_in_ray_actor)
 
 # yapf: enable
 
@@ -48,12 +49,9 @@ TypeHint = Union[type[Any], object]
 TypeHintT = Union[type[T], object]
 
 
-def optional_type(
-        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:
+def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
 
-    def _optional_type(val: str) -> Optional[T]:
-        if val == "" or val == "None":
-            return None
+    def _parse_type(val: str) -> T:
         try:
             if return_type is json.loads and not re.match("^{.*}$", val):
                 return cast(T, nullable_kvs(val))
@@ -62,14 +60,24 @@ def optional_type(
             raise argparse.ArgumentTypeError(
                 f"Value {val} cannot be converted to {return_type}.") from e
 
+    return _parse_type
+
+
+def optional_type(
+        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:
+
+    def _optional_type(val: str) -> Optional[T]:
+        if val == "" or val == "None":
+            return None
+        return parse_type(return_type)(val)
+
     return _optional_type
 
 
 def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
     if not re.match("^{.*}$", val):
         return str(val)
-    else:
-        return optional_type(json.loads)(val)
+    return optional_type(json.loads)(val)
 
 
 @deprecated(
@@ -144,10 +152,25 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
     cls_docs = get_attr_docs(cls)
     kwargs = {}
     for field in fields(cls):
+        # Get the set of possible types for the field
+        type_hints: set[TypeHint] = set()
+        if get_origin(field.type) in {Union, Annotated}:
+            type_hints.update(get_args(field.type))
+        else:
+            type_hints.add(field.type)
+
+        # If the field is a dataclass, we can use the model_validate_json
+        generator = (th for th in type_hints if is_dataclass(th))
+        dataclass_cls = next(generator, None)
+
         # Get the default value of the field
-        default = field.default
-        if field.default_factory is not MISSING:
-            default = field.default_factory()
+        if field.default is not MISSING:
+            default = field.default
+        elif field.default_factory is not MISSING:
+            if is_dataclass(field.default_factory) and is_in_doc_build():
+                default = {}
+            else:
+                default = field.default_factory()
 
         # Get the help text for the field
         name = field.name
@@ -158,16 +181,17 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
         # Initialise the kwargs dictionary for the field
         kwargs[name] = {"default": default, "help": help}
 
-        # Get the set of possible types for the field
-        type_hints: set[TypeHint] = set()
-        if get_origin(field.type) is Union:
-            type_hints.update(get_args(field.type))
-        else:
-            type_hints.add(field.type)
-
         # Set other kwargs based on the type hints
         json_tip = "\n\nShould be a valid JSON string."
-        if contains_type(type_hints, bool):
+        if dataclass_cls is not None:
+            dataclass_init = lambda x, f=dataclass_cls: f(**json.loads(x))
+            # Special case for configs with a from_cli method
+            if hasattr(dataclass_cls, "from_cli"):
+                from_cli = dataclass_cls.from_cli
+                dataclass_init = lambda x, f=from_cli: f(x)
+            kwargs[name]["type"] = dataclass_init
+            kwargs[name]["help"] += json_tip
+        elif contains_type(type_hints, bool):
             # Creates --no-<name> and --<name> flags
             kwargs[name]["action"] = argparse.BooleanOptionalAction
         elif contains_type(type_hints, Literal):
@@ -202,7 +226,7 @@ def get_kwargs(cls: ConfigType) -> dict[str, Any]:
             kwargs[name]["type"] = union_dict_and_str
         elif contains_type(type_hints, dict):
             # Dict arguments will always be optional
-            kwargs[name]["type"] = optional_type(json.loads)
+            kwargs[name]["type"] = parse_type(json.loads)
             kwargs[name]["help"] += json_tip
         elif (contains_type(type_hints, str)
               or any(is_not_builtin(th) for th in type_hints)):
@@ -771,63 +795,20 @@ class EngineArgs:
         scheduler_group.add_argument("--scheduler-cls",
                                      **scheduler_kwargs["scheduler_cls"])
 
-        # Compilation arguments
-        # compilation_kwargs = get_kwargs(CompilationConfig)
-        compilation_group = parser.add_argument_group(
-            title="CompilationConfig",
-            description=CompilationConfig.__doc__,
-        )
-        compilation_group.add_argument(
-            "--compilation-config",
-            "-O",
-            type=CompilationConfig.from_cli,
-            default=None,
-            help="torch.compile configuration for the model. "
-            "When it is a number (0, 1, 2, 3), it will be "
-            "interpreted as the optimization level.\n"
-            "NOTE: level 0 is the default level without "
-            "any optimization. level 1 and 2 are for internal "
-            "testing only. level 3 is the recommended level "
-            "for production.\n"
-            "To specify the full compilation config, "
-            "use a JSON string, e.g. ``{\"level\": 3, "
-            "\"cudagraph_capture_sizes\": [1, 2, 4, 8]}``\n"
-            "Following the convention of traditional "
-            "compilers, using ``-O`` without space is also "
-            "supported. ``-O3`` is equivalent to ``-O 3``.")
-
-        # KVTransfer arguments
-        # kv_transfer_kwargs = get_kwargs(KVTransferConfig)
-        kv_transfer_group = parser.add_argument_group(
-            title="KVTransferConfig",
-            description=KVTransferConfig.__doc__,
-        )
-        kv_transfer_group.add_argument(
-            "--kv-transfer-config",
-            type=KVTransferConfig.from_cli,
-            default=None,
-            help="The configurations for distributed KV cache "
-            "transfer. Should be a JSON string.")
-        kv_transfer_group.add_argument(
-            '--kv-events-config',
-            type=KVEventsConfig.from_cli,
-            default=None,
-            help='The configurations for event publishing.')
-
         # vLLM arguments
-        # vllm_kwargs = get_kwargs(VllmConfig)
+        vllm_kwargs = get_kwargs(VllmConfig)
         vllm_group = parser.add_argument_group(
             title="VllmConfig",
             description=VllmConfig.__doc__,
         )
-        vllm_group.add_argument(
-            "--additional-config",
-            type=json.loads,
-            default=None,
-            help="Additional config for specified platform in JSON format. "
-            "Different platforms may support different configs. Make sure the "
-            "configs are valid for the platform you are using. The input format"
-            " is like '{\"config_key\":\"config_value\"}'")
+        vllm_group.add_argument("--kv-transfer-config",
+                                **vllm_kwargs["kv_transfer_config"])
+        vllm_group.add_argument('--kv-events-config',
+                                **vllm_kwargs["kv_events_config"])
+        vllm_group.add_argument("--compilation-config", "-O",
+                                **vllm_kwargs["compilation_config"])
+        vllm_group.add_argument("--additional-config",
+                                **vllm_kwargs["additional_config"])
 
         # Other arguments
         parser.add_argument('--use-v2-block-manager',

vllm/entrypoints/llm.py

@@ -13,7 +13,8 @@ from typing_extensions import TypeVar, deprecated
 
 from vllm.beam_search import (BeamSearchInstance, BeamSearchOutput,
                               BeamSearchSequence, get_beam_search_score)
-from vllm.config import CompilationConfig, ModelDType, TokenizerMode
+from vllm.config import (CompilationConfig, ModelDType, TokenizerMode,
+                         is_init_field)
 from vllm.engine.arg_utils import (EngineArgs, HfOverrides, PoolerConfig,
                                    TaskOption)
 from vllm.engine.llm_engine import LLMEngine
@@ -204,9 +205,13 @@ class LLM:
                 kwargs["worker_cls"] = cloudpickle.dumps(worker_cls)
 
         if compilation_config is not None:
-            if isinstance(compilation_config, (int, dict)):
-                compilation_config_instance = CompilationConfig.from_cli(
-                    str(compilation_config))
+            if isinstance(compilation_config, int):
+                compilation_config_instance = CompilationConfig(
+                    level=compilation_config)
+            elif isinstance(compilation_config, dict):
+                predicate = lambda x: is_init_field(CompilationConfig, x[0])
+                compilation_config_instance = CompilationConfig(
+                    **dict(filter(predicate, compilation_config.items())))
             else:
                 compilation_config_instance = compilation_config
         else:

vllm/platforms/tpu.py

 # SPDX-License-Identifier: Apache-2.0
 
-from typing import TYPE_CHECKING, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Optional, Tuple, Union, cast
 
 import torch
 from tpu_info import device
@@ -13,9 +13,10 @@ from vllm.sampling_params import SamplingParams, SamplingType
 from .interface import Platform, PlatformEnum, _Backend
 
 if TYPE_CHECKING:
-    from vllm.config import ModelConfig, VllmConfig
+    from vllm.config import BlockSize, ModelConfig, VllmConfig
     from vllm.pooling_params import PoolingParams
 else:
+    BlockSize = None
     ModelConfig = None
     VllmConfig = None
     PoolingParams = None
@@ -94,7 +95,7 @@ class TpuPlatform(Platform):
         cache_config = vllm_config.cache_config
         # For v0, the default block size is 16.
         if cache_config and cache_config.block_size is None:
-            cache_config.block_size = 16
+            cache_config.block_size = cast(BlockSize, 16)
         compilation_config = vllm_config.compilation_config
 
         # TPU only supports DYNAMO_ONCE compilation level
@@ -118,7 +119,7 @@ class TpuPlatform(Platform):
             from vllm.v1.attention.backends.pallas import (
                 PallasAttentionBackend)
             cache_config.block_size = PallasAttentionBackend.get_page_size(
-                vllm_config)
+                vllm_config)  # type: ignore[assignment]
             min_page_size = PallasAttentionBackend.get_min_page_size(
                 vllm_config)
             if min_page_size > cache_config.block_size:
@@ -128,7 +129,7 @@ class TpuPlatform(Platform):
                     cache_config.block_size,
                     min_page_size,
                 )
-                cache_config.block_size = min_page_size
+                cache_config.block_size = min_page_size  # type: ignore[assignment]
 
         parallel_config = vllm_config.parallel_config
         scheduler_config = vllm_config.scheduler_config

vllm/utils.py

@@ -1820,6 +1820,14 @@ def get_cuda_view_from_cpu_tensor(cpu_tensor: torch.Tensor) -> torch.Tensor:
     return torch.ops._C.get_cuda_view_from_cpu_tensor(cpu_tensor)
 
 
+def is_in_doc_build() -> bool:
+    try:
+        from sphinx.ext.autodoc.mock import _MockModule
+        return isinstance(zmq, _MockModule)
+    except ModuleNotFoundError:
+        return False
+
+
 def import_from_path(module_name: str, file_path: Union[str, os.PathLike]):
     """
     Import a Python file according to its file path.