[platforms] enable platform plugins (#11602)

Signed-off-by: youkaichao <youkaichao@gmail.com>

vllm/config.py

@@ -22,7 +22,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.layers.quantization import (QUANTIZATION_METHODS,
                                                      get_quantization_config)
 from vllm.model_executor.models import ModelRegistry
-from vllm.platforms import current_platform, interface
+from vllm.platforms import CpuArchEnum
 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,
@@ -349,6 +349,7 @@ class ModelConfig:
         self.is_hybrid = self._init_is_hybrid()
         self.has_inner_state = self._init_has_inner_state()
 
+        from vllm.platforms import current_platform
         if current_platform.is_neuron():
             self.override_neuron_config = override_neuron_config
         else:
@@ -589,6 +590,7 @@ class ModelConfig:
                 raise ValueError(
                     f"Unknown quantization method: {self.quantization}. Must "
                     f"be one of {supported_quantization}.")
+            from vllm.platforms import current_platform
             current_platform.verify_quantization(self.quantization)
             if self.quantization not in optimized_quantization_methods:
                 logger.warning(
@@ -644,6 +646,7 @@ class ModelConfig:
 
         # Reminder: Please update docs/source/usage/compatibility_matrix.md
         # If the feature combo become valid
+        from vllm.platforms import current_platform
         if not current_platform.is_async_output_supported(self.enforce_eager):
             logger.warning(
                 "Async output processing is not supported on the "
@@ -1012,6 +1015,7 @@ class CacheConfig:
             raise ValueError(
                 "GPU memory utilization must be less than 1.0. Got "
                 f"{self.gpu_memory_utilization}.")
+        from vllm.platforms import current_platform
         if (current_platform.is_cuda() and self.block_size is not None
                 and self.block_size > 32):
             raise ValueError("CUDA Paged Attention kernel only supports "
@@ -1279,6 +1283,7 @@ class ParallelConfig:
                                  f"distributed executor backend "
                                  f"'{self.distributed_executor_backend}'.")
         ray_only_devices = ["tpu", "hpu"]
+        from vllm.platforms import current_platform
         if (current_platform.device_type in ray_only_devices
                 and self.world_size > 1):
             if self.distributed_executor_backend is None:
@@ -1327,7 +1332,7 @@ class ParallelConfig:
     def _verify_args(self) -> None:
         # Lazy import to avoid circular import
         from vllm.executor.executor_base import ExecutorBase
-
+        from vllm.platforms import current_platform
         if self.distributed_executor_backend not in (
                 "ray", "mp", None) and not (isinstance(
                     self.distributed_executor_backend, type) and issubclass(
@@ -1528,6 +1533,7 @@ class DeviceConfig:
     def __init__(self, device: str = "auto") -> None:
         if device == "auto":
             # Automated device type detection
+            from vllm.platforms import current_platform
             self.device_type = current_platform.device_type
             if not self.device_type:
                 raise RuntimeError("Failed to infer device type")
@@ -2241,9 +2247,10 @@ def _get_and_verify_dtype(
             else:
                 torch_dtype = config_dtype
 
+            from vllm.platforms import current_platform
             if (current_platform.is_cpu()
                     and current_platform.get_cpu_architecture()
-                    == interface.CpuArchEnum.POWERPC
+                    == CpuArchEnum.POWERPC
                     and (config_dtype == torch.float16
                          or config_dtype == torch.float32)):
                 logger.info(
@@ -3083,6 +3090,7 @@ class VllmConfig:
             model_config: ModelConfig,
             load_config: LoadConfig) -> Optional[QuantizationConfig]:
         """Get the quantization config."""
+        from vllm.platforms import current_platform
         if model_config.quantization is not None:
             from vllm.model_executor.model_loader.weight_utils import (
                 get_quant_config)
@@ -3145,6 +3153,7 @@ class VllmConfig:
             self.quant_config = VllmConfig._get_quantization_config(
                 self.model_config, self.load_config)
 
+        from vllm.platforms import current_platform
         if self.scheduler_config is not None and \
             self.model_config is not None and \
             self.scheduler_config.chunked_prefill_enabled and \