restore orchestrator_type env

Signed-off-by: Superjomn <[email protected]>

Author: Superjomn

tensorrt_llm/llmapi/llm_args.py

@@ -21,7 +21,6 @@
 from strenum import StrEnum
 from transformers import PreTrainedTokenizerBase
 
-from tensorrt_llm.llmapi.utils import logger_debug, orchestrator_type_env
 from tensorrt_llm.lora_helper import (LoraConfig,
                                       get_default_trtllm_modules_to_hf_modules)
 
@@ -2161,26 +2160,6 @@ def validate_peft_cache_config(self):
                 "while LoRA prefetch is not supported")
         return self
 
-    @model_validator(mode='before')
-    def validate_orchestrator_config(cls, values):
-        # The environment variable will override the orchestrator_type field.
-        # TODO: remove the environment variable after RPC path is stable, then
-        # there will be only two stable options: None(RPC) and 'ray'.
-        if (ev := orchestrator_type_env()) is not None:
-            logger_debug(
-                f"changing orchestrator_type to {ev} from environment variable")
-            if ev not in ['rpc', 'ray']:
-                raise ValueError(
-                    f"Invalid orchestrator type: {ev}. Please set orchestrator_type to 'rpc' or 'ray'."
-                )
-            values['orchestrator_type'] = ev
-
-        if 'orchestrator_type' in values:
-            logger.warning(
-                f"Setting orchestrator_type to: {values['orchestrator_type']}")
-
-        return values
-
     def _update_plugin_config(self, key: str, value: Any):
         setattr(self.build_config.plugin_config, key, value)
 

tensorrt_llm/llmapi/utils.py

@@ -349,10 +349,6 @@ def enable_llmapi_debug() -> bool:
     return _enable_llmapi_debug_
 
 
-def orchestrator_type_env() -> Optional[str]:
-    return os.environ.get("TLLM_ORCHESTRATOR_TYPE", None)
-
-
 @cache
 def enable_worker_single_process_for_tp1() -> bool:
     ''' Tell whether to make worker use single process for TP1.