Files
sglang/python/sglang/srt/managers/controller_multi.py
2024-08-20 22:35:05 -07:00

216 lines
6.4 KiB
Python

"""
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 manages multiple data parallel workers.
Each data parallel worker can manage multiple tensor parallel workers.
"""
import dataclasses
import logging
import multiprocessing
import os
from enum import Enum, auto
import numpy as np
import zmq
from sglang.srt.managers.controller_single import (
start_controller_process as start_controller_process_single,
)
from sglang.srt.managers.io_struct import (
AbortReq,
FlushCacheReq,
TokenizedGenerateReqInput,
)
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import kill_parent_process
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
@dataclasses.dataclass
class WorkerHandle:
"""Store the handle of a data parallel worker."""
proc: multiprocessing.Process
queue: multiprocessing.Queue
class ControllerMulti:
"""A controller that manages multiple data parallel workers."""
def __init__(
self,
server_args: ServerArgs,
port_args: PortArgs,
model_overide_args,
):
# Parse args
self.server_args = server_args
self.port_args = port_args
self.model_overide_args = model_overide_args
self.load_balance_method = LoadBalanceMethod.from_str(
server_args.load_balance_method
)
# Init communication
context = zmq.Context()
self.recv_from_tokenizer = context.socket(zmq.PULL)
self.recv_from_tokenizer.bind(f"tcp://127.0.0.1:{port_args.controller_port}")
# 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
self.workers = []
for i in range(server_args.dp_size):
self.start_dp_worker(i)
def start_dp_worker(self, dp_worker_id: int):
tp_size = self.server_args.tp_size
pipe_controller_reader, pipe_controller_writer = multiprocessing.Pipe(
duplex=False
)
gpu_ids = list(range(dp_worker_id * tp_size, (dp_worker_id + 1) * tp_size))
queue = multiprocessing.Queue()
proc = multiprocessing.Process(
target=start_controller_process_single,
args=(
self.server_args,
self.port_args,
pipe_controller_writer,
self.model_overide_args,
True,
gpu_ids,
dp_worker_id,
queue,
),
)
proc.start()
controller_init_state = pipe_controller_reader.recv()
if controller_init_state != "init ok":
raise RuntimeError(
f"Initialization failed. controller_init_state: {controller_init_state}"
)
self.workers.append(
WorkerHandle(
proc=proc,
queue=queue,
)
)
def round_robin_scheduler(self, input_requests):
for r in input_requests:
self.workers[self.round_robin_counter].queue.put(r)
self.round_robin_counter = (self.round_robin_counter + 1) % len(
self.workers
)
def shortest_queue_scheduler(self, input_requests):
for r in input_requests:
queue_sizes = [worker.queue.qsize() for worker in self.workers]
wid = np.argmin(queue_sizes)
self.workers[wid].queue.put(r)
def loop_for_forward(self):
while True:
recv_reqs = self.recv_requests()
self.dispatching(recv_reqs)
def recv_requests(self):
recv_reqs = []
while True:
try:
recv_req = self.recv_from_tokenizer.recv_pyobj(zmq.NOBLOCK)
except zmq.ZMQError:
break
if isinstance(recv_req, FlushCacheReq):
# TODO(lsyin): apply more specific flushCacheReq
for worker in self.workers:
worker.queue.put(recv_req)
elif isinstance(recv_req, AbortReq):
in_queue = False
for i, req in enumerate(recv_reqs):
if req.rid == recv_req.rid:
recv_reqs[i] = recv_req
in_queue = True
break
if not in_queue:
# Send abort req to all TP groups
for worker in self.workers:
worker.queue.put(recv_req)
elif isinstance(recv_req, TokenizedGenerateReqInput):
recv_reqs.append(recv_req)
else:
logger.error(f"Invalid object: {recv_req}")
return recv_reqs
def start_controller_process(
server_args: ServerArgs,
port_args: PortArgs,
pipe_writer,
model_overide_args: dict,
):
"""Start a controller process."""
logging.basicConfig(
level=getattr(logging, server_args.log_level.upper()),
format="%(message)s",
)
try:
controller = ControllerMulti(server_args, port_args, model_overide_args)
except Exception:
pipe_writer.send(get_exception_traceback())
raise
pipe_writer.send("init ok")
try:
controller.loop_for_forward()
except Exception:
logger.error("Exception in ControllerMulti:\n" + get_exception_traceback())
finally:
kill_parent_process()