[Perf][CLI] Improve overall startup time (#19941)

vllm/config.py

@@ -28,7 +28,7 @@ from pydantic.dataclasses import dataclass
 from safetensors.torch import _TYPES as _SAFETENSORS_TO_TORCH_DTYPE
 from torch.distributed import ProcessGroup, ReduceOp
 from transformers import PretrainedConfig
-from typing_extensions import deprecated, runtime_checkable
+from typing_extensions import Self, deprecated, runtime_checkable
 
 import vllm.envs as envs
 from vllm import version
@@ -1537,7 +1537,6 @@ class CacheConfig:
     def __post_init__(self) -> None:
         self.swap_space_bytes = self.swap_space * GiB_bytes
 
-        self._verify_args()
         self._verify_cache_dtype()
         self._verify_prefix_caching()
 
@@ -1546,7 +1545,8 @@ class CacheConfig:
         # metrics info
         return {key: str(value) for key, value in self.__dict__.items()}
 
-    def _verify_args(self) -> None:
+    @model_validator(mode='after')
+    def _verify_args(self) -> Self:
         if self.cpu_offload_gb < 0:
             raise ValueError("CPU offload space must be non-negative"
                              f", but got {self.cpu_offload_gb}")
@@ -1556,6 +1556,8 @@ class CacheConfig:
                 "GPU memory utilization must be less than 1.0. Got "
                 f"{self.gpu_memory_utilization}.")
 
+        return self
+
     def _verify_cache_dtype(self) -> None:
         if self.cache_dtype == "auto":
             pass
@@ -1942,15 +1944,14 @@ class ParallelConfig:
         if self.distributed_executor_backend is None and self.world_size == 1:
             self.distributed_executor_backend = "uni"
 
-        self._verify_args()
-
     @property
     def use_ray(self) -> bool:
         return self.distributed_executor_backend == "ray" or (
             isinstance(self.distributed_executor_backend, type)
             and self.distributed_executor_backend.uses_ray)
 
-    def _verify_args(self) -> None:
+    @model_validator(mode='after')
+    def _verify_args(self) -> Self:
         # Lazy import to avoid circular import
         from vllm.executor.executor_base import ExecutorBase
         from vllm.platforms import current_platform
@@ -1977,8 +1978,7 @@ class ParallelConfig:
             raise ValueError("Unable to use nsight profiling unless workers "
                              "run with Ray.")
 
-        assert isinstance(self.worker_extension_cls, str), (
-            "worker_extension_cls must be a string (qualified class name).")
+        return self
 
 
 PreemptionMode = Literal["swap", "recompute"]
@@ -2202,9 +2202,8 @@ class SchedulerConfig:
                 self.max_num_partial_prefills, self.max_long_partial_prefills,
                 self.long_prefill_token_threshold)
 
-        self._verify_args()
-
-    def _verify_args(self) -> None:
+    @model_validator(mode='after')
+    def _verify_args(self) -> Self:
         if (self.max_num_batched_tokens < self.max_model_len
                 and not self.chunked_prefill_enabled):
             raise ValueError(
@@ -2263,6 +2262,8 @@ class SchedulerConfig:
                 "must be greater than or equal to 1 and less than or equal to "
                 f"max_num_partial_prefills ({self.max_num_partial_prefills}).")
 
+        return self
+
     @property
     def is_multi_step(self) -> bool:
         return self.num_scheduler_steps > 1
@@ -2669,8 +2670,6 @@ class SpeculativeConfig:
             if self.posterior_alpha is None:
                 self.posterior_alpha = 0.3
 
-        self._verify_args()
-
     @staticmethod
     def _maybe_override_draft_max_model_len(
         speculative_max_model_len: Optional[int],
@@ -2761,7 +2760,8 @@ class SpeculativeConfig:
 
         return draft_parallel_config
 
-    def _verify_args(self) -> None:
+    @model_validator(mode='after')
+    def _verify_args(self) -> Self:
         if self.num_speculative_tokens is None:
             raise ValueError(
                 "num_speculative_tokens must be provided with "
@@ -2812,6 +2812,8 @@ class SpeculativeConfig:
                 "Eagle3 is only supported for Llama models. "
                 f"Got {self.target_model_config.hf_text_config.model_type=}")
 
+        return self
+
     @property
     def num_lookahead_slots(self) -> int:
         """The number of additional slots the scheduler should allocate per