[1/N] pass the complete config from engine to executor (#9933)

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

vllm/engine/async_llm_engine.py

@@ -680,7 +680,7 @@ def from_engine_args(
 
         # Create the async LLM engine.
         engine = cls(
-            **engine_config.to_dict(),
+            vllm_config=engine_config,
             executor_class=executor_class,
             log_requests=not engine_args.disable_log_requests,
             log_stats=not engine_args.disable_log_stats,

vllm/engine/llm_engine.py

@@ -13,11 +13,8 @@
 from typing_extensions import TypeIs, TypeVar
 
 import vllm.envs as envs
-from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
-                         EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
-                         ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
-                         SpeculativeConfig)
+from vllm.config import (DecodingConfig, EngineConfig, LoRAConfig, ModelConfig,
+                         ObservabilityConfig, ParallelConfig, SchedulerConfig)
 from vllm.core.scheduler import (ScheduledSequenceGroup, Scheduler,
                                  SchedulerOutputs)
 from vllm.engine.arg_utils import EngineArgs
@@ -222,24 +219,30 @@ def validate_outputs(
 
     def __init__(
         self,
-        model_config: ModelConfig,
-        cache_config: CacheConfig,
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        device_config: DeviceConfig,
-        load_config: LoadConfig,
-        lora_config: Optional[LoRAConfig],
-        speculative_config: Optional[SpeculativeConfig],
-        decoding_config: Optional[DecodingConfig],
-        observability_config: Optional[ObservabilityConfig],
-        prompt_adapter_config: Optional[PromptAdapterConfig],
+        vllm_config: EngineConfig,
         executor_class: Type[ExecutorBase],
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
         input_registry: InputRegistry = INPUT_REGISTRY,
         use_cached_outputs: bool = False,
     ) -> None:
+
+        # TODO: remove the local variables and use self.* throughout the class.
+        model_config = self.model_config = vllm_config.model_config
+        cache_config = self.cache_config = vllm_config.cache_config
+        lora_config = self.lora_config = vllm_config.lora_config
+        parallel_config = self.parallel_config = vllm_config.parallel_config
+        scheduler_config = self.scheduler_config = vllm_config.scheduler_config
+        device_config = self.device_config = vllm_config.device_config
+        speculative_config = self.speculative_config = vllm_config.speculative_config  # noqa
+        load_config = self.load_config = vllm_config.load_config
+        decoding_config = self.decoding_config = vllm_config.decoding_config or DecodingConfig(  # noqa
+        )
+        prompt_adapter_config = self.prompt_adapter_config = vllm_config.prompt_adapter_config  # noqa
+        observability_config = self.observability_config = vllm_config.observability_config or ObservabilityConfig(  # noqa
+        )
+
         logger.info(
             "Initializing an LLM engine (v%s) with config: "
             "model=%r, speculative_config=%r, tokenizer=%r, "
@@ -340,18 +343,7 @@ def get_tokenizer_for_seq(sequence: Sequence) -> AnyTokenizer:
         self.input_processor = input_registry.create_input_processor(
             model_config)
 
-        self.model_executor = executor_class(
-            model_config=model_config,
-            cache_config=cache_config,
-            parallel_config=parallel_config,
-            scheduler_config=scheduler_config,
-            device_config=device_config,
-            lora_config=lora_config,
-            speculative_config=speculative_config,
-            load_config=load_config,
-            prompt_adapter_config=prompt_adapter_config,
-            observability_config=self.observability_config,
-        )
+        self.model_executor = executor_class(vllm_config=vllm_config, )
 
         if self.model_config.task != "embedding":
             self._initialize_kv_caches()
@@ -582,7 +574,7 @@ def from_engine_args(
         executor_class = cls._get_executor_cls(engine_config)
         # Create the LLM engine.
         engine = cls(
-            **engine_config.to_dict(),
+            vllm_config=engine_config,
             executor_class=executor_class,
             log_stats=not engine_args.disable_log_stats,
             usage_context=usage_context,

vllm/engine/multiprocessing/engine.py

@@ -7,8 +7,6 @@
 import zmq
 
 from vllm import AsyncEngineArgs, SamplingParams
-from vllm.config import (DecodingConfig, LoRAConfig, ModelConfig,
-                         ParallelConfig, SchedulerConfig)
 # yapf conflicts with isort for this block
 # yapf: disable
 from vllm.engine.multiprocessing import (ENGINE_DEAD_ERROR, IPC_DATA_EXT,
@@ -30,9 +28,6 @@
 else:
     from vllm.engine.llm_engine import LLMEngine
 
-CONFIG_TYPE = Union[ModelConfig, DecodingConfig, ParallelConfig,
-                    SchedulerConfig, LoRAConfig]
-
 logger = init_logger(__name__)
 
 POLLING_TIMEOUT_MS = 10000
@@ -130,7 +125,7 @@ def from_engine_args(cls, engine_args: AsyncEngineArgs,
 
         return cls(ipc_path=ipc_path,
                    use_async_sockets=use_async_sockets,
-                   **engine_config.to_dict(),
+                   vllm_config=engine_config,
                    executor_class=executor_class,
                    log_requests=not engine_args.disable_log_requests,
                    log_stats=not engine_args.disable_log_stats,

vllm/executor/executor_base.py

@@ -1,10 +1,7 @@
 from abc import ABC, abstractmethod
 from typing import List, Optional, Set, Tuple
 
-from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
-                         ModelConfig, ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
-                         SpeculativeConfig)
+from vllm.config import EngineConfig
 from vllm.lora.request import LoRARequest
 from vllm.model_executor.layers.sampler import SamplerOutput
 from vllm.prompt_adapter.request import PromptAdapterRequest
@@ -23,27 +20,19 @@ class ExecutorBase(ABC):
 
     def __init__(
         self,
-        model_config: ModelConfig,
-        cache_config: CacheConfig,
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        device_config: DeviceConfig,
-        load_config: LoadConfig,
-        lora_config: Optional[LoRAConfig],
-        speculative_config: Optional[SpeculativeConfig],
-        prompt_adapter_config: Optional[PromptAdapterConfig],
-        observability_config: Optional[ObservabilityConfig],
+        vllm_config: EngineConfig,
     ) -> None:
-        self.model_config = model_config
-        self.cache_config = cache_config
-        self.lora_config = lora_config
-        self.load_config = load_config
-        self.parallel_config = parallel_config
-        self.scheduler_config = scheduler_config
-        self.device_config = device_config
-        self.speculative_config = speculative_config
-        self.prompt_adapter_config = prompt_adapter_config
-        self.observability_config = observability_config
+        self.vllm_config = vllm_config
+        self.model_config = vllm_config.model_config
+        self.cache_config = vllm_config.cache_config
+        self.lora_config = vllm_config.lora_config
+        self.load_config = vllm_config.load_config
+        self.parallel_config = vllm_config.parallel_config
+        self.scheduler_config = vllm_config.scheduler_config
+        self.device_config = vllm_config.device_config
+        self.speculative_config = vllm_config.speculative_config
+        self.prompt_adapter_config = vllm_config.prompt_adapter_config
+        self.observability_config = vllm_config.observability_config
         self._init_executor()
 
     @abstractmethod

vllm/executor/xpu_executor.py

@@ -2,10 +2,7 @@
 
 import torch
 
-from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, LoRAConfig,
-                         ModelConfig, ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
-                         SpeculativeConfig)
+from vllm.config import ModelConfig, ParallelConfig
 from vllm.executor.executor_base import ExecutorAsyncBase
 from vllm.executor.gpu_executor import GPUExecutor
 from vllm.logger import init_logger
@@ -21,38 +18,13 @@ class XPUExecutor(GPUExecutor):
 
     uses_ray: bool = False
 
-    def __init__(
-        self,
-        model_config: ModelConfig,
-        cache_config: CacheConfig,
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        device_config: DeviceConfig,
-        load_config: LoadConfig,
-        lora_config: Optional[LoRAConfig],
-        prompt_adapter_config: Optional[PromptAdapterConfig],
-        speculative_config: Optional[SpeculativeConfig],
-        observability_config: Optional[ObservabilityConfig],
-    ) -> None:
-        assert device_config.device_type == "xpu"
-        assert (not speculative_config
-                ), "Speculative decoding not yet supported for XPU backend"
-
-        model_config = _verify_and_get_model_config(model_config)
-
-        self.model_config = model_config
-        self.cache_config = cache_config
-        self.load_config = load_config
-        self.lora_config = lora_config
-        self.parallel_config = _verify_and_get_parallel_config(parallel_config)
-        self.scheduler_config = scheduler_config
-        self.device_config = device_config
-        self.prompt_adapter_config = prompt_adapter_config
-        self.speculative_config = None
-        self.observability_config = observability_config
-
-        # Instantiate the worker and load the model to GPU.
-        self._init_executor()
+    def _init_executor(self) -> None:
+        assert self.device_config.device_type == "xpu"
+        assert self.speculative_config is None, (
+            "Speculative decoding not yet supported for XPU backend")
+
+        self.model_config = _verify_and_get_model_config(self.model_config)
+        GPUExecutor._init_executor(self)
 
     def _get_worker_module_and_class(
             self) -> Tuple[str, str, Optional[Callable[[], Type[WorkerBase]]]]:

vllm/v1/engine/llm_engine.py

@@ -2,11 +2,8 @@
 from typing import (Any, Dict, Iterable, List, Mapping, Optional, Tuple, Type,
                     Union)
 
-from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
-                         EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
-                         ObservabilityConfig, ParallelConfig,
-                         PromptAdapterConfig, SchedulerConfig,
-                         SpeculativeConfig)
+from vllm.config import (DecodingConfig, EngineConfig, LoRAConfig, ModelConfig,
+                         ObservabilityConfig, ParallelConfig, SchedulerConfig)
 from vllm.engine.arg_utils import EngineArgs
 from vllm.engine.metrics_types import StatLoggerBase
 from vllm.inputs import (INPUT_REGISTRY, DecoderOnlyInputs,
@@ -35,24 +32,30 @@ class LLMEngine:
 
     def __init__(
         self,
-        model_config: ModelConfig,
-        cache_config: CacheConfig,
-        parallel_config: ParallelConfig,
-        scheduler_config: SchedulerConfig,
-        device_config: DeviceConfig,
-        load_config: LoadConfig,
-        lora_config: Optional[LoRAConfig],
-        speculative_config: Optional[SpeculativeConfig],
-        decoding_config: Optional[DecodingConfig],
-        observability_config: Optional[ObservabilityConfig],
-        prompt_adapter_config: Optional[PromptAdapterConfig],
+        vllm_config: EngineConfig,
         executor_class: Type[GPUExecutor],
         log_stats: bool,
         usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
         stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
         input_registry: InputRegistry = INPUT_REGISTRY,
         use_cached_outputs: bool = False,
     ) -> None:
+
+        # TODO: remove the local variables and use self.* throughout the class.
+        model_config = self.model_config = vllm_config.model_config
+        cache_config = self.cache_config = vllm_config.cache_config
+        lora_config = self.lora_config = vllm_config.lora_config
+        parallel_config = self.parallel_config = vllm_config.parallel_config
+        scheduler_config = self.scheduler_config = vllm_config.scheduler_config
+        device_config = self.device_config = vllm_config.device_config
+        speculative_config = self.speculative_config = vllm_config.speculative_config  # noqa
+        load_config = self.load_config = vllm_config.load_config
+        decoding_config = self.decoding_config = vllm_config.decoding_config or DecodingConfig(  # noqa
+        )
+        prompt_adapter_config = self.prompt_adapter_config = vllm_config.prompt_adapter_config  # noqa
+        observability_config = self.observability_config = vllm_config.observability_config or ObservabilityConfig(  # noqa
+        )
+
         # Override the configs for V1.
         # FIXME
         if usage_context == UsageContext.LLM_CLASS:
@@ -112,18 +115,6 @@ def __init__(
             model_config.mm_processor_kwargs,
         )
 
-        self.model_config = model_config
-        self.cache_config = cache_config
-        self.lora_config = lora_config
-        self.parallel_config = parallel_config
-        self.scheduler_config = scheduler_config
-        self.device_config = device_config
-        self.speculative_config = speculative_config
-        self.load_config = load_config
-        self.decoding_config = decoding_config or DecodingConfig()
-        self.prompt_adapter_config = prompt_adapter_config
-        self.observability_config = observability_config or ObservabilityConfig(
-        )
         self.log_stats = log_stats
 
         assert not self.model_config.skip_tokenizer_init
@@ -154,18 +145,7 @@ def __init__(
         # Request id -> RequestOutput
         self.request_outputs: Dict[str, RequestOutput] = {}
 
-        self.model_executor = executor_class(
-            model_config=model_config,
-            cache_config=cache_config,
-            parallel_config=parallel_config,
-            scheduler_config=scheduler_config,
-            device_config=device_config,
-            lora_config=lora_config,
-            speculative_config=speculative_config,
-            load_config=load_config,
-            prompt_adapter_config=prompt_adapter_config,
-            observability_config=self.observability_config,
-        )
+        self.model_executor = executor_class(vllm_config=vllm_config)
         assert self.model_config.task != "embedding"
         self._initialize_kv_caches()
 
@@ -203,7 +183,7 @@ def from_engine_args(
         executor_class = cls._get_executor_cls(engine_config)
         # Create the LLM engine.
         engine = cls(
-            **engine_config.to_dict(),
+            vllm_config=engine_config,
             executor_class=executor_class,
             log_stats=not engine_args.disable_log_stats,
             usage_context=usage_context,