Add back data parallelism (#1635)

python/sglang/srt/managers/data_parallel_controller.py

@@ -0,0 +1,177 @@
+"""
+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 logging
+import multiprocessing as mp
+from enum import Enum, auto
+
+import zmq
+
+from sglang.srt.managers.io_struct import (
+    TokenizedEmbeddingReqInput,
+    TokenizedGenerateReqInput,
+    TokenizedRewardReqInput,
+)
+from sglang.srt.managers.scheduler import run_scheduler_process
+from sglang.srt.server_args import PortArgs, ServerArgs
+from sglang.srt.utils import (
+    configure_logger,
+    kill_parent_process,
+    suppress_other_loggers,
+)
+from sglang.utils import get_exception_traceback
+
+logger = logging.getLogger(__name__)
+
+
+class LoadBalanceMethod(Enum):
+    """Load balance method."""
+
+    ROUND_ROBIN = auto()
+    SHORTEST_QUEUE = 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 DataParallelController:
+    """A controller that dispatches requests to multiple data parallel workers."""
+
+    def __init__(self, server_args, port_args) -> 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
+        )
+
+        # Init inter-process communication
+        self.context = zmq.Context(1 + server_args.dp_size)
+        self.recv_from_tokenizer = self.context.socket(zmq.PULL)
+        self.recv_from_tokenizer.bind(f"ipc://{port_args.scheduler_input_ipc_name}")
+
+        # Dispatch method
+        self.round_robin_counter = 0
+        dispatch_lookup = {
+            LoadBalanceMethod.ROUND_ROBIN: self.round_robin_scheduler,
+            LoadBalanceMethod.SHORTEST_QUEUE: self.shortest_queue_scheduler,
+        }
+        self.dispatching = dispatch_lookup[self.load_balance_method]
+
+        # Start data parallel workers
+        base_gpu_id = 0
+        self.workers = []
+        for dp_rank in range(server_args.dp_size):
+            tmp_port_args = PortArgs.init_new(server_args)
+            tmp_port_args.detokenizer_ipc_name = port_args.detokenizer_ipc_name
+
+            send_to = self.launch_tensor_parallel_group(
+                server_args,
+                tmp_port_args,
+                base_gpu_id,
+                dp_rank,
+            )
+
+            self.workers.append(send_to)
+            base_gpu_id += server_args.tp_size
+
+    def launch_tensor_parallel_group(
+        self,
+        server_args: ServerArgs,
+        port_args: PortArgs,
+        base_gpu_id: int,
+        dp_rank: int,
+    ):
+        # Launch tensor parallel scheduler processes
+        scheduler_procs = []
+        scheduler_pipe_readers = []
+        tp_size_per_node = server_args.tp_size // server_args.nnodes
+        tp_rank_range = range(
+            tp_size_per_node * server_args.node_rank,
+            tp_size_per_node * (server_args.node_rank + 1),
+        )
+        for tp_rank in tp_rank_range:
+            reader, writer = mp.Pipe(duplex=False)
+            gpu_id = base_gpu_id + tp_rank % tp_size_per_node
+            proc = mp.Process(
+                target=run_scheduler_process,
+                args=(server_args, port_args, gpu_id, tp_rank, dp_rank, writer),
+            )
+            proc.start()
+            scheduler_procs.append(proc)
+            scheduler_pipe_readers.append(reader)
+
+        send_to = self.context.socket(zmq.PUSH)
+        send_to.connect(f"ipc://{port_args.scheduler_input_ipc_name}")
+
+        # Wait for model to finish loading
+        for i in range(len(scheduler_pipe_readers)):
+            scheduler_pipe_readers[i].recv()
+
+        return send_to
+
+    def round_robin_scheduler(self, req):
+        self.workers[self.round_robin_counter].send_pyobj(req)
+        self.round_robin_counter = (self.round_robin_counter + 1) % len(self.workers)
+
+    def shortest_queue_scheduler(self, input_requests):
+        raise NotImplementedError()
+
+    def event_loop(self):
+        while True:
+            while True:
+                try:
+                    recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
+                except zmq.ZMQError:
+                    break
+
+                if isinstance(
+                    recv_req,
+                    (
+                        TokenizedGenerateReqInput,
+                        TokenizedEmbeddingReqInput,
+                        TokenizedRewardReqInput,
+                    ),
+                ):
+                    self.dispatching(recv_req)
+                else:
+                    # Send other control messages to all workers
+                    for worker in self.workers:
+                        worker.queue.put(recv_req)
+
+
+def run_data_parallel_controller_process(
+    server_args: ServerArgs,
+    port_args: PortArgs,
+    pipe_writer,
+):
+    configure_logger(server_args)
+    suppress_other_loggers()
+
+    try:
+        controller = DataParallelController(server_args, port_args)
+        pipe_writer.send("ready")
+        controller.event_loop()
+    except Exception:
+        msg = get_exception_traceback()
+        logger.error(msg)
+        kill_parent_process()

python/sglang/srt/managers/scheduler.py

@@ -142,7 +142,7 @@ class Scheduler:
             gpu_id=gpu_id,
             tp_rank=tp_rank,
             server_args=server_args,
-            nccl_port=port_args.nccl_ports[0],
+            nccl_port=port_args.nccl_port,
         )
         self.tp_cpu_group = self.tp_worker.model_runner.tp_group.cpu_group
 
@@ -1042,9 +1042,14 @@ def run_scheduler_process(
     port_args: PortArgs,
     gpu_id: int,
     tp_rank: int,
+    dp_rank: Optional[int],
     pipe_writer,
 ):
-    configure_logger(server_args, prefix=f" TP{tp_rank}")
+    if dp_rank is None:
+        configure_logger(server_args, prefix=f" TP{tp_rank}")
+    else:
+        configure_logger(server_args, prefix=f" DP{dp_rank} TP{tp_rank}")
+
     suppress_other_loggers()
 
     try: