# Copyright 2023-2024 SGLang Team # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A controller that dispatches requests to multiple data parallel workers.""" import faulthandler import logging import multiprocessing as mp import signal import threading import time from enum import Enum, auto from typing import Callable, List, Optional import psutil import setproctitle import zmq from sglang.srt.environ import envs from sglang.srt.layers.dp_attention import compute_dp_attention_world_info from sglang.srt.managers.io_struct import ( ActiveRanksOutput, BlockReqInput, TokenizedEmbeddingReqInput, TokenizedGenerateReqInput, WatchLoadUpdateReq, ) from sglang.srt.managers.schedule_batch import Req, RequestStage from sglang.srt.managers.scheduler import run_scheduler_process from sglang.srt.metrics.cpu_monitor import start_cpu_monitor_thread from sglang.srt.server_args import ( DP_ATTENTION_HANDSHAKE_PORT_DELTA, PortArgs, ServerArgs, ) from sglang.srt.tracing.trace import ( process_tracing_init, trace_get_proc_propagate_context, trace_set_proc_propagate_context, trace_set_thread_info, trace_slice_end, trace_slice_start, ) from sglang.srt.utils import numa_utils from sglang.srt.utils.common import ( bind_port, configure_ipv6, configure_logger, get_zmq_socket, kill_itself_when_parent_died, maybe_reindex_device_id, ) from sglang.srt.utils.torch_memory_saver_adapter import TorchMemorySaverAdapter from sglang.srt.utils.watchdog import Watchdog from sglang.utils import TypeBasedDispatcher, get_exception_traceback logger = logging.getLogger(__name__) class LoadBalanceMethod(Enum): """Load balance method.""" ROUND_ROBIN = auto() FOLLOW_BOOTSTRAP_ROOM = auto() TOTAL_REQUESTS = auto() TOTAL_TOKENS = auto() @classmethod def from_str(cls, method: str): method = method.upper() try: return cls[method] except KeyError as exc: raise ValueError(f"Invalid load balance method: {method}") from exc class DPBudget: def __init__(self, dp_size: int): self.dp_size = dp_size self.total_requests = [0] * dp_size self.total_tokens = [0] * dp_size def update_budget(self, load_update: WatchLoadUpdateReq): """Update the budget.""" for load in load_update.loads: self.total_requests[load.dp_rank] = load.num_reqs self.total_tokens[load.dp_rank] = load.num_tokens def dispatch(self, method: LoadBalanceMethod): if method == LoadBalanceMethod.TOTAL_REQUESTS: target_rank = self.total_requests.index(min(self.total_requests)) elif method == LoadBalanceMethod.TOTAL_TOKENS: # Use total_requests as a tie-breaker when total_tokens are equal target_rank = min( range(self.dp_size), key=lambda i: (self.total_tokens[i], self.total_requests[i]), ) else: return None # Increment the load of that worker by one as a heuristic self.total_requests[target_rank] += 1 return target_rank class DataParallelController: """A controller that dispatches requests to multiple data parallel workers.""" def __init__( self, server_args: ServerArgs, port_args: PortArgs, run_scheduler_process_func: Callable, ) -> None: # Parse args self.server_args = server_args self.port_args = port_args self.load_balance_method = LoadBalanceMethod.from_str( server_args.load_balance_method ) self.run_scheduler_process_func = run_scheduler_process_func # For DP balance self.global_balance_id = 0 # Init inter-process communication self.context = zmq.Context(1 + server_args.dp_size) if server_args.node_rank == 0: self.recv_from_tokenizer = get_zmq_socket( self.context, zmq.PULL, port_args.scheduler_input_ipc_name, False ) # Dispatch method self.round_robin_counter = 0 dispatch_lookup = { LoadBalanceMethod.ROUND_ROBIN: self.round_robin_scheduler, LoadBalanceMethod.FOLLOW_BOOTSTRAP_ROOM: self.follow_bootstrap_room_scheduler, LoadBalanceMethod.TOTAL_REQUESTS: self.total_requests_scheduler, LoadBalanceMethod.TOTAL_TOKENS: self.total_tokens_scheduler, } self.dispatching = dispatch_lookup[self.load_balance_method] # Load balance budget self.dp_budget = DPBudget(server_args.dp_size) # To protect changing env vars to set CUDA_VISIBLE_DEVICES. self.env_lock = threading.Lock() # Launch data parallel workers self.scheduler_procs = [] self.workers: List[zmq.Socket] = [None] * server_args.dp_size self.status: List[bool] = [True] * server_args.dp_size if server_args.enable_dp_attention: self.launch_dp_attention_schedulers(server_args, port_args) self.control_message_step = server_args.tp_size else: self.launch_dp_schedulers(server_args, port_args) self.control_message_step = 1 self.init_dispatcher() self.soft_watchdog = Watchdog.create( debug_name="DataParallelController", watchdog_timeout=server_args.soft_watchdog_timeout, soft=True, test_stuck_time=envs.SGLANG_TEST_STUCK_DP_CONTROLLER.get(), ) if server_args.enable_metrics: start_cpu_monitor_thread("data_parallel_controller") def send_to_all_workers(self, obj): for i, worker in enumerate(self.workers): if self.status[i]: worker.send_pyobj(obj) def send_control_message(self, obj): # Send control messages to first worker of tp group for worker in self.workers[:: self.control_message_step]: worker.send_pyobj(obj) def handle_load_update_req(self, obj): self.dp_budget.update_budget(obj) def update_active_ranks(self, ranks: ActiveRanksOutput): self.status = ranks.status def dispatching_with_trace(self, req: Req): if self.server_args.enable_trace: trace_set_proc_propagate_context(req.rid, req.trace_context) trace_slice_start(RequestStage.DC_DISPATCH, req.rid) req.trace_context = trace_get_proc_propagate_context(req.rid) self.dispatching(req) if self.server_args.enable_trace: trace_slice_end(RequestStage.DC_DISPATCH, req.rid, thread_finish_flag=True) def init_dispatcher(self): self._request_dispatcher = TypeBasedDispatcher( [ (TokenizedGenerateReqInput, self.dispatching_with_trace), (TokenizedEmbeddingReqInput, self.dispatching_with_trace), (BlockReqInput, self.send_to_all_workers), (WatchLoadUpdateReq, self.handle_load_update_req), (ActiveRanksOutput, self.update_active_ranks), ] ) self._request_dispatcher.add_fallback_fn(self.send_control_message) def launch_dp_schedulers(self, server_args, port_args): base_gpu_id = 0 threads = [] sockets = [] ready_events = [] for dp_rank in range(server_args.dp_size): tmp_port_args = PortArgs.init_new(server_args) tmp_port_args.tokenizer_ipc_name = port_args.tokenizer_ipc_name tmp_port_args.detokenizer_ipc_name = port_args.detokenizer_ipc_name # This port is checked free in PortArgs.init_new. # We hold it first so that the next dp worker gets a different port sockets.append(bind_port(tmp_port_args.nccl_port)) ready_event = threading.Event() ready_events.append(ready_event) # Create a thread for each worker thread = threading.Thread( target=self.launch_tensor_parallel_group_thread, args=(server_args, tmp_port_args, base_gpu_id, dp_rank, ready_event), ) threads.append(thread) base_gpu_id += ( server_args.tp_size * server_args.pp_size * server_args.gpu_id_step ) if server_args.node_rank == 0: self.workers[dp_rank] = get_zmq_socket( self.context, zmq.PUSH, tmp_port_args.scheduler_input_ipc_name, True, ) # Free all sockets before starting the threads to launch TP workers for sock in sockets: sock.close() # Start all threads for thread in threads: thread.start() for event in ready_events: event.wait() def launch_tensor_parallel_group_thread( self, server_args: ServerArgs, port_args: PortArgs, base_gpu_id: int, dp_rank: int, ready_event: threading.Event, ): self.launch_tensor_parallel_group(server_args, port_args, base_gpu_id, dp_rank) ready_event.set() # This thread cannot be closed because otherwise the `kill_itself_when_parent_died` # function in scheduler.py will kill the scheduler. while True: time.sleep(30 * 24 * 3600) def _broadcast_worker_ports( self, server_args: ServerArgs, worker_ports: Optional[List[int]] = None ) -> List[int]: """Broadcast worker ports from node 0 to all other nodes. Node 0 acts as the server, waiting for all other nodes to connect and sending them the pre-allocated worker ports. Other nodes act as clients, connecting to node 0 to receive their copy of the worker ports. Args: server_args: Server arguments containing node configuration. worker_ports: Pre-allocated worker ports to broadcast. Returns: List of worker ports (same on all nodes after broadcast). """ # Determine the endpoint for inter-node communication if server_args.dist_init_addr is None: endpoint = f"tcp://127.0.0.1:{server_args.port + DP_ATTENTION_HANDSHAKE_PORT_DELTA}" elif server_args.dist_init_addr.startswith("["): # ipv6 address port, host = configure_ipv6(server_args.dist_init_addr) endpoint = f"tcp://{host}:{int(port) + DP_ATTENTION_HANDSHAKE_PORT_DELTA}" else: host, port = server_args.dist_init_addr.split(":") endpoint = f"tcp://{host}:{int(port) + DP_ATTENTION_HANDSHAKE_PORT_DELTA}" if server_args.node_rank == 0: # Node 0: Broadcast worker ports to all other nodes return self._broadcast_ports_as_server( endpoint, server_args.nnodes - 1, worker_ports ) else: # Other nodes: Receive worker ports from node 0 return self._receive_ports_as_client(endpoint, server_args.node_rank) def _broadcast_ports_as_server( self, endpoint: str, expected_clients: int, worker_ports: List[int] ) -> List[int]: """Broadcast worker ports to all client nodes.""" logger.debug(f"Broadcasting worker ports to {expected_clients} client nodes") logger.debug(f"Worker ports: {worker_ports}") rep_socket = get_zmq_socket(self.context, zmq.REP, endpoint, True) try: connected_clients = 0 while connected_clients < expected_clients: # Wait for client handshake client_rank = rep_socket.recv().decode() logger.debug(f"Received handshake from node {client_rank}") # Send worker ports to client rep_socket.send_pyobj(worker_ports) connected_clients += 1 logger.debug( f"Sent worker ports to {connected_clients}/{expected_clients} nodes" ) logger.debug("Worker port broadcast completed") return worker_ports finally: rep_socket.close() def _receive_ports_as_client(self, endpoint: str, node_rank: int) -> List[int]: """Receive worker ports from the server node.""" logger.debug(f"Connecting to node 0 to receive worker ports") req_socket = get_zmq_socket(self.context, zmq.REQ, endpoint, False) req_socket.setsockopt(zmq.RCVTIMEO, 600 * 1000) # 10 minute timeout req_socket.setsockopt(zmq.SNDTIMEO, 600 * 1000) try: # Send handshake with our node rank req_socket.send(str(node_rank).encode()) # Receive worker ports worker_ports = req_socket.recv_pyobj() logger.debug(f"Received {len(worker_ports)} worker ports from node 0") return worker_ports except zmq.Again: logger.error("Timeout waiting for worker ports from node 0") raise RuntimeError( "Failed to receive worker ports from node 0 within timeout" ) finally: req_socket.close() def launch_dp_attention_schedulers( self, server_args: ServerArgs, port_args: PortArgs ): # Pre-allocate worker ports on node 0 to avoid conflicts worker_ports = [] if server_args.node_rank == 0: for dp_rank in range(server_args.dp_size): port_and_socket = get_zmq_socket(self.context, zmq.PUSH) worker_ports.append(port_and_socket[0]) self.workers[dp_rank] = port_and_socket[1] logger.debug(f"Assigned port {port_and_socket[0]} to worker {dp_rank}") broadcasted_ports = self._broadcast_worker_ports( server_args, worker_ports if worker_ports else None ) self.launch_tensor_parallel_group( server_args, port_args, 0, None, broadcasted_ports ) def launch_tensor_parallel_group( self, server_args: ServerArgs, port_args: PortArgs, base_gpu_id: int, dp_rank: Optional[int], worker_ports: Optional[List[int]] = None, ): if not server_args.enable_dp_attention: logger.info(f"Launch DP{dp_rank} starting at GPU #{base_gpu_id}.") memory_saver_adapter = TorchMemorySaverAdapter.create( enable=server_args.enable_memory_saver ) scheduler_pipe_readers = [] pp_size_per_node = max(server_args.pp_size // server_args.nnodes, 1) nnodes_per_pp_rank = max(server_args.nnodes // server_args.pp_size, 1) pp_rank_range = range( pp_size_per_node * (server_args.node_rank // nnodes_per_pp_rank), pp_size_per_node * (server_args.node_rank // nnodes_per_pp_rank + 1), ) nnodes_per_tp_group = nnodes_per_pp_rank tp_size_per_node = server_args.tp_size // nnodes_per_tp_group tp_rank_range = range( tp_size_per_node * (server_args.node_rank % nnodes_per_tp_group), tp_size_per_node * (server_args.node_rank % nnodes_per_tp_group + 1), ) attn_cp_rank = 0 moe_dp_rank = 0 for pp_rank in pp_rank_range: for tp_rank in tp_rank_range: rank_port_args = port_args if server_args.enable_dp_attention: # dp attention has different sharding logic _, _, dp_rank = compute_dp_attention_world_info( server_args.enable_dp_attention, tp_rank, server_args.tp_size, server_args.dp_size, server_args.attn_cp_size, ) # compute zmq ports for this dp rank rank_port_args = PortArgs.init_new( server_args, dp_rank, worker_ports ) # Data parallelism reuses the tensor parallelism group, # so all dp ranks should use the same nccl port. rank_port_args.nccl_port = port_args.nccl_port reader, writer = mp.Pipe(duplex=False) gpu_id = ( server_args.base_gpu_id + base_gpu_id + ((pp_rank % pp_size_per_node) * tp_size_per_node) + (tp_rank % tp_size_per_node) * server_args.gpu_id_step ) attn_dp_size = ( server_args.dp_size if server_args.enable_dp_attention else 1 ) # Parallelism hierarchy (outermost to innermost): # - Attention: Global(TP) -> DP -> ATTN_CP -> ATTN_TP (innermost) # - MoE: Global(TP) -> MOE_DP -> EP -> MOE_TP (innermost) attn_tp_size = ( server_args.tp_size // attn_dp_size // server_args.attn_cp_size ) attn_cp_rank = (tp_rank // attn_tp_size) % server_args.attn_cp_size moe_dp_rank = tp_rank // ( server_args.tp_size // server_args.moe_dp_size ) moe_ep_rank = ( tp_rank % (server_args.tp_size // server_args.moe_dp_size) // ( server_args.tp_size // server_args.moe_dp_size // server_args.ep_size ) ) with self.env_lock, maybe_reindex_device_id(gpu_id) as gpu_id: proc = mp.Process( target=self.run_scheduler_process_func, args=( server_args, rank_port_args, gpu_id, tp_rank, attn_cp_rank, moe_dp_rank, moe_ep_rank, pp_rank, dp_rank, writer, ), ) with memory_saver_adapter.configure_subprocess(), numa_utils.configure_subprocess( server_args, gpu_id ): proc.start() self.scheduler_procs.append(proc) scheduler_pipe_readers.append(reader) # Wait for model to finish loading scheduler_info = [] for i in range(len(scheduler_pipe_readers)): scheduler_info.append(scheduler_pipe_readers[i].recv()) self.max_total_num_tokens = scheduler_info[0]["max_total_num_tokens"] self.max_req_input_len = scheduler_info[0]["max_req_input_len"] def maybe_external_dp_rank_routing(self, req: Req): if req.data_parallel_rank is not None: logger.debug(f"Direct routing to DP rank {req.data_parallel_rank}") self.workers[req.data_parallel_rank].send_pyobj(req) return True return False def round_robin_scheduler(self, req: Req): if self.maybe_external_dp_rank_routing(req): return while True: if self.status[self.round_robin_counter]: logger.debug(f"Choose worker {self.round_robin_counter}") self.workers[self.round_robin_counter].send_pyobj(req) self.round_robin_counter = (self.round_robin_counter + 1) % len( self.workers ) break self.round_robin_counter = (self.round_robin_counter + 1) % len( self.workers ) def follow_bootstrap_room_scheduler(self, req: Req): if self.maybe_external_dp_rank_routing(req): return # Set default bootstrap_room if in FAKE auto mode and room is None if ( req.bootstrap_room is None and self.server_args.disaggregation_transfer_backend == "fake" ): req.bootstrap_room = self.round_robin_counter self.round_robin_counter = (self.round_robin_counter + 1) % len( self.workers ) assert req.bootstrap_room is not None, ( "req.bootstrap_room should not be None. Do not send requests directly to " "prefill or decode instances; send to the router instead." ) target_rank = req.bootstrap_room % len(self.workers) self.workers[target_rank].send_pyobj(req) def total_requests_scheduler(self, req: Req): if self.maybe_external_dp_rank_routing(req): return target_worker = self.dp_budget.dispatch(LoadBalanceMethod.TOTAL_REQUESTS) self.workers[target_worker].send_pyobj(req) def total_tokens_scheduler(self, req: Req): if self.maybe_external_dp_rank_routing(req): return target_worker = self.dp_budget.dispatch(LoadBalanceMethod.TOTAL_TOKENS) self.workers[target_worker].send_pyobj(req) def event_loop(self): while True: while True: self.soft_watchdog.feed() try: recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK) except zmq.ZMQError: break self._request_dispatcher(recv_req) def run_data_parallel_controller_process( server_args: ServerArgs, port_args: PortArgs, pipe_writer, run_scheduler_process_func: Callable = run_scheduler_process, ): setproctitle.setproctitle("sglang::data_parallel_controller") faulthandler.enable() kill_itself_when_parent_died() parent_process = psutil.Process().parent() configure_logger(server_args) if server_args.enable_trace: process_tracing_init(server_args.otlp_traces_endpoint, "sglang") thread_label = "DP Controller" if server_args.disaggregation_mode == "prefill": thread_label = "Prefill DP Controller" elif server_args.disaggregation_mode == "decode": thread_label = "Decode DP Controller" trace_set_thread_info(thread_label) try: controller = DataParallelController( server_args, port_args, run_scheduler_process_func ) pipe_writer.send( { "status": "ready", "max_total_num_tokens": controller.max_total_num_tokens, "max_req_input_len": controller.max_req_input_len, } ) if server_args.node_rank == 0: controller.event_loop() for proc in controller.scheduler_procs: proc.join() logger.error( f"Scheduler or DataParallelController {proc.pid} terminated with {proc.exitcode}" ) except Exception: traceback = get_exception_traceback() logger.error(f"DataParallelController hit an exception: {traceback}") parent_process.send_signal(signal.SIGQUIT)