leideng/QCFuse / srt /connector /remote_instance.py
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# SPDX-License-Identifier: Apache-2.0
import logging
from typing import Generator, Optional, Tuple
from urllib.parse import urlparse
import torch
import torch.distributed as dist
from sglang.srt.connector import BaseConnector
from sglang.srt.utils import init_custom_process_group
logger = logging.getLogger(__name__)
class RemoteInstanceConnector(BaseConnector):
def __init__(self, url: str, device: torch.device = "cpu"):
assert (
device.type == "cuda"
), "RemoteInstanceConnector only supports cuda device."
super().__init__(url)
self.url = url
self.device = device
def build_group(
self,
gpu_id: int = -1,
tp_rank: int = -1,
instance_ip: str = None,
group_rank: int = 1,
world_size: int = 2,
):
assert (
self.device.type == "cuda"
), "RemoteInstanceConnector only supports cuda device."
assert (
gpu_id != -1 and tp_rank != -1
), "gpu_id and tp_rank must be specified for RemoteInstanceConnector. "
self.device_id = torch.device(self.device.type, gpu_id)
parsed_url = urlparse(self.url)
master_address = parsed_url.hostname
master_port = parsed_url.port
group_name = f"send_weights_{instance_ip}_{master_port}_{tp_rank}"
backend = "nccl"
logger.info(
f"init custom process group: master_address={master_address}, master_port={master_port}, "
f"rank_offset={group_rank}, world_size={world_size}, group_name={group_name}, backend={backend}"
)
try:
self._model_update_group = init_custom_process_group(
backend=backend,
init_method=f"tcp://{master_address}:{master_port}",
world_size=world_size,
rank=group_rank,
group_name=group_name,
device_id=self.device_id,
)
dist.barrier(group=self._model_update_group)
return True, "Succeeded to initialize custom process group."
except Exception as e:
message = f"Failed to initialize custom process group: {e}."
logger.error(message)
return False, message
# Implemented as a no-op to make BaseConnector interface consistent.
def pull_files(
self,
allow_pattern: Optional[list[str]] = None,
ignore_pattern: Optional[list[str]] = None,
) -> None:
return
# Implemented as a no-op to make BaseConnector interface consistent.
def weight_iterator(
self, rank: int = 0
) -> Generator[Tuple[str, torch.Tensor], None, None]:
return

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