[Frontend] speed up import time of vllm.config (vllm-project#18036)

Signed-off-by: David Xia <david@davidxia.com>

Author: davidxia

vllm/config.py

@@ -27,19 +27,13 @@
 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 Self, deprecated, runtime_checkable
 
 import vllm.envs as envs
 from vllm import version
 from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
 from vllm.logger import init_logger
-from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
-                                                     QuantizationMethods,
-                                                     get_quantization_config)
-from vllm.model_executor.models import ModelRegistry
 from vllm.platforms import current_platform
-from vllm.tracing import is_otel_available, otel_import_error_traceback
 from vllm.transformers_utils.config import (
     ConfigFormat, get_config, get_hf_image_processor_config,
     get_hf_text_config, get_pooling_config,
@@ -48,32 +42,49 @@
     try_get_tokenizer_config, uses_mrope)
 from vllm.transformers_utils.s3_utils import S3Model
 from vllm.transformers_utils.utils import is_s3, maybe_model_redirect
+# yapf conflicts with isort for this block
+# yapf: disable
 from vllm.utils import (DEFAULT_MAX_NUM_BATCHED_TOKENS,
                         MULTIMODAL_MODEL_MAX_NUM_BATCHED_TOKENS,
                         POOLING_MODEL_MAX_NUM_BATCHED_TOKENS, GiB_bytes,
-                        LayerBlockType, common_broadcastable_dtype,
+                        LayerBlockType, LazyLoader, common_broadcastable_dtype,
                         cuda_device_count_stateless, get_cpu_memory,
                         get_open_port, is_torch_equal_or_newer, random_uuid,
                         resolve_obj_by_qualname)
 
+# yapf: enable
+
 if TYPE_CHECKING:
     from _typeshed import DataclassInstance
     from ray.util.placement_group import PlacementGroup
+    from transformers.configuration_utils import PretrainedConfig
 
+    import vllm.model_executor.layers.quantization as me_quant
+    import vllm.model_executor.models as me_models
     from vllm.executor.executor_base import ExecutorBase
+    from vllm.model_executor.layers.quantization import QuantizationMethods
     from vllm.model_executor.layers.quantization.base_config import (
         QuantizationConfig)
     from vllm.model_executor.model_loader import BaseModelLoader
     from vllm.model_executor.model_loader.tensorizer import TensorizerConfig
 
     ConfigType = type[DataclassInstance]
+    HfOverrides = Union[dict, Callable[[type], type]]
 else:
     PlacementGroup = Any
+    PretrainedConfig = Any
     ExecutorBase = Any
     QuantizationConfig = Any
+    QuantizationMethods = Any
     BaseModelLoader = Any
     TensorizerConfig = Any
     ConfigType = type
+    HfOverrides = Union[dict[str, Any], Callable[[type], type]]
+
+    me_quant = LazyLoader("model_executor", globals(),
+                          "vllm.model_executor.layers.quantization")
+    me_models = LazyLoader("model_executor", globals(),
+                           "vllm.model_executor.models")
 
 logger = init_logger(__name__)
 
@@ -100,9 +111,6 @@
     for task in tasks
 }
 
-HfOverrides = Union[dict[str, Any], Callable[[PretrainedConfig],
-                                             PretrainedConfig]]
-
 
 @runtime_checkable
 class SupportsHash(Protocol):
@@ -648,7 +656,7 @@ def validate_model_config_after(self: "ModelConfig") -> "ModelConfig":
 
     @property
     def registry(self):
-        return ModelRegistry
+        return me_models.ModelRegistry
 
     @property
     def architectures(self) -> list[str]:
@@ -861,14 +869,15 @@ def _parse_quant_hf_config(self):
         return quant_cfg
 
     def _verify_quantization(self) -> None:
-        supported_quantization = QUANTIZATION_METHODS
+        supported_quantization = me_quant.QUANTIZATION_METHODS
         optimized_quantization_methods = [
             "fp8", "marlin", "modelopt", "gptq_marlin_24", "gptq_marlin",
             "awq_marlin", "fbgemm_fp8", "compressed-tensors", "experts_int8",
             "quark", "modelopt_fp4", "bitblas", "gptq_bitblas"
         ]
         if self.quantization is not None:
-            self.quantization = cast(QuantizationMethods, self.quantization)
+            self.quantization = cast(me_quant.QuantizationMethods,
+                                     self.quantization)
 
         # Parse quantization method from the HF model config, if available.
         quant_cfg = self._parse_quant_hf_config()
@@ -902,14 +911,14 @@ def _verify_quantization(self) -> None:
 
             # Detect which checkpoint is it
             for name in quantization_methods:
-                method = get_quantization_config(name)
+                method = me_quant.get_quantization_config(name)
                 quantization_override = method.override_quantization_method(
                     quant_cfg, self.quantization)
                 if quantization_override is not None:
                     # Raise error if the override is not custom (custom would
                     # be in QUANTIZATION_METHODS but not QuantizationMethods)
                     # and hasn't been added to the overrides list.
-                    if (name in get_args(QuantizationMethods)
+                    if (name in get_args(me_quant.QuantizationMethods)
                             and name not in overrides):
                         raise ValueError(
                             f"Quantization method {name} is an override but "
@@ -1419,7 +1428,7 @@ def runner_type(self) -> RunnerType:
     @property
     def is_v1_compatible(self) -> bool:
         architectures = getattr(self.hf_config, "architectures", [])
-        return ModelRegistry.is_v1_compatible(architectures)
+        return me_models.ModelRegistry.is_v1_compatible(architectures)
 
     @property
     def is_matryoshka(self) -> bool:
@@ -2378,7 +2387,7 @@ class SpeculativeConfig:
     according to the log probability settings in SamplingParams."""
 
     # Draft model configuration
-    quantization: Optional[QuantizationMethods] = None
+    quantization: Optional[me_quant.QuantizationMethods] = None
     """Quantization method that was used to quantize the draft model weights.
     If `None`, we assume the model weights are not quantized. Note that it only
     takes effect when using the draft model-based speculative method."""
@@ -3631,6 +3640,7 @@ def __post_init__(self):
                 and "," in self.collect_detailed_traces[0]):
             self._parse_collect_detailed_traces()
 
+        from vllm.tracing import is_otel_available, otel_import_error_traceback
         if not is_otel_available() and self.otlp_traces_endpoint is not None:
             raise ValueError(
                 "OpenTelemetry is not available. Unable to configure "