o پii @sdZddlZddlZddlZddlmZmZmZm Z ddl m Z ddl m Z ddlmZmZddlmZmZddlmZeeZd d Z dd ed eed e eeeejffddZdS)z Scheduler process management for gRPC server. This module handles launching and managing scheduler processes for the gRPC server, including tensor parallelism, pipeline parallelism, and data parallelism configurations. N)DictListOptionalTuple)$run_data_parallel_controller_process)run_scheduler_process)PortArgs ServerArgs)configure_logger numa_utils)TorchMemorySaverAdaptercOs"ttjtjt|i|dS)a` Wrapper for run_scheduler_process that ignores SIGINT. The scheduler process should not handle Ctrl+C - it should only terminate when the parent gRPC server exits (via kill_itself_when_parent_died). Args: *args: Positional arguments for run_scheduler_process **kwargs: Keyword arguments for run_scheduler_process N)signalSIGINTSIG_IGNr)argskwargsrV/home/ubuntu/.local/lib/python3.10/site-packages/sglang/srt/grpc/scheduler_launcher.py"run_scheduler_with_signal_handlings r server_args port_argsreturncCst||tjddd|dur t|}td|g}|jdkrt j |j d}g}t |j |jd}|j|}t||j|||j|d}t |j|j d}t||j|||j|d} | D]} |D]} tjdd \} } |j| ||| ||j}|jr|jnd}|j||j}| ||j}| |j|j}| |j|j|j|j|j}tjt|||| |||| d| f d }|"t|| |Wdn1swYWdn1swY|||| qpqlntjdd \} } | g}tjt||| fd }|||g}t |D]P\}} z| !}Wn)t"y]t#d |d ||$t#d ||j%t&d|w|'ddkrtt&d|d|'dd||q*tdt(|d|d||fS)a Launch only the scheduler process(es) without tokenizer/detokenizer. This function handles all scheduler startup logic including: - Tensor parallelism (tp_size) - Pipeline parallelism (pp_size) - Data parallelism (dp_size) - Multi-node distributed setup Args: server_args: Server configuration port_args: Port configuration (created if None) Returns: Tuple of (scheduler_info, port_args, scheduler_processes): - scheduler_info: Dict with model metadata and configuration - port_args: Port configuration used for IPC - scheduler_processes: List of launched scheduler Process objects Raises: RuntimeError: If any scheduler process fails to initialize spawnT)forceNz server_args=)enableF)duplex)targetrzRank z< scheduler is dead. Please check if there are relevant logs.z Exit code: z$Failed to initialize scheduler rank statusreadyzScheduler rank z initialization failed: errorz Unknown errorzAll z/ scheduler process(es) initialized successfullyr))r check_server_argsmpset_start_methodrinit_newloggerinfodp_sizer createenable_memory_savermaxnnodespp_sizetp_sizerange node_rankPipe base_gpu_id gpu_id_stepenable_dp_attention attn_cp_size moe_dp_sizeep_sizeProcessrconfigure_subprocessr startappendr enumeraterecvEOFErrorr joinexitcode RuntimeErrorgetlen)rrscheduler_procsmemory_saver_adapterscheduler_pipe_readersnnodes_per_tp_grouptp_size_per_node tp_rank_rangepp_size_per_node pp_rank_rangepp_ranktp_rankreaderwritergpu_id attn_dp_size attn_tp_size attn_cp_rank moe_dp_rank moe_ep_rankprocscheduler_infosidatarrrlaunch_scheduler_process_only*s             :    rY)N)__doc__loggingmultiprocessingr"r typingrrrr,sglang.srt.managers.data_parallel_controllerrsglang.srt.managers.schedulerrsglang.srt.server_argsrr sglang.srt.utilsr r +sglang.srt.utils.torch_memory_saver_adapterr getLogger__name__r%rr7rYrrrrs(