116 lines
3.7 KiB
Python
116 lines
3.7 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Ray actor wrapper for SGLang Scheduler."""
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from __future__ import annotations
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import logging
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from typing import TYPE_CHECKING, Any, Dict, Optional
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import ray
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if TYPE_CHECKING:
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from sglang.srt.server_args import PortArgs, ServerArgs
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logger = logging.getLogger(__name__)
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@ray.remote
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class SchedulerActor:
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"""Ray actor wrapper for SGLang Scheduler.
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Each actor manages one GPU and runs the Scheduler + TpModelWorker stack.
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Ray is used for process lifecycle; ZMQ handles request/response communication.
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"""
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def __init__(
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self,
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server_args: ServerArgs,
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port_args: PortArgs,
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gpu_id: int,
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tp_rank: int,
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attn_cp_rank: int,
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moe_dp_rank: int,
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moe_ep_rank: int,
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pp_rank: int,
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dp_rank: Optional[int],
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dist_init_addr: Optional[str] = None,
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):
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import dataclasses
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from sglang.srt.managers.scheduler import Scheduler, configure_scheduler
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# Override dist_init_addr if provided (for multi-node)
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if dist_init_addr:
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server_args = dataclasses.replace(
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server_args, dist_init_addr=dist_init_addr
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)
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# Get actual GPU IDs from Ray runtime context
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accelerator_ids = ray.get_runtime_context().get_accelerator_ids()
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assigned_gpus = accelerator_ids.get("GPU", [])
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if assigned_gpus:
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# Ray assigned specific GPU(s), use the first one
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actual_gpu_id = int(assigned_gpus[0])
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logger.info(f"[TP{tp_rank}] Ray assigned GPU: {actual_gpu_id}")
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else:
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# Fallback to passed gpu_id
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actual_gpu_id = gpu_id
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logger.info(f"[TP{tp_rank}] Using passed gpu_id: {gpu_id}")
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# Configure worker (logging, process title, etc.)
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dp_rank = configure_scheduler(
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server_args,
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tp_rank,
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attn_cp_rank,
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moe_dp_rank,
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moe_ep_rank,
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pp_rank,
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dp_rank,
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)
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# Create scheduler (loads model into GPU, initializes NCCL)
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self.scheduler = Scheduler(
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server_args=server_args,
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port_args=port_args,
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gpu_id=actual_gpu_id,
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tp_rank=tp_rank,
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moe_ep_rank=moe_ep_rank,
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pp_rank=pp_rank,
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attn_cp_rank=attn_cp_rank,
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moe_dp_rank=moe_dp_rank,
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dp_rank=dp_rank,
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)
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self._tp_rank = tp_rank
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self._pp_rank = pp_rank
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def get_info(self) -> Dict[str, Any]:
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"""Return scheduler initialization info for handshake."""
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return self.scheduler.get_init_info()
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def run_event_loop(self) -> None:
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"""Run the scheduler's event loop. Blocks until shutdown."""
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try:
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import torch
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# Need to set the GPU id for the event loop for nccl to work
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torch.cuda.set_device(self.scheduler.gpu_id)
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self.scheduler.run_event_loop()
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except Exception as e:
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logger.error(f"Scheduler PP{self._pp_rank} TP{self._tp_rank} crashed: {e}")
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raise
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