[PD] Make bootstrap code common between NIXL and Mooncake (#6473)

docs/backend/pd_disaggregation.md

@@ -47,3 +47,44 @@ $ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --dis
 # decode 1
 $ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 --disaggregation-ib-device ${device_name} --disaggregation-mode decode --host ${local_ip} --port 30001 --trust-remote-code --dist-init-addr ${decode_master_ip}:5000 --nnodes 2 --node-rank 1 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode low_latency --mem-fraction-static 0.8 --max-running-requests 128
 ```
+
+## NIXL
+### Requirements
+
+Install via pip.
+
+```bash
+pip install nixl
+```
+
+Or build from source - may be required if you already have UCX installed.
+
+```bash
+git clone https://github.com/ai-dynamo/nixl.git
+cd nixl
+pip install . --config-settings=setup-args="-Ducx_path=/path/to/ucx"
+```
+
+
+### Usage
+
+### Llama Single Node
+
+```bash
+$ python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode prefill --disaggregation-transfer-backend nixl
+$ python -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --disaggregation-mode decode --port 30001 --base-gpu-id 1 --disaggregation-transfer-backend nixl
+$ python -m sglang.srt.disaggregation.mini_lb --prefill http://127.0.0.1:30000 --decode http://127.0.0.1:30001 --host 0.0.0.0 --port 8000
+```
+
+### DeepSeek Multi-Node
+
+```bash
+# prefill 0
+$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 ---disaggregation-transfer-backend nixl --disaggregation-mode prefill --host ${local_ip} --port 30000 --trust-remote-code --dist-init-addr ${prefill_master_ip}:5000 --nnodes 2 --node-rank 0 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode normal --mem-fraction-static 0.8
+# prefill 1
+$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 ---disaggregation-transfer-backend nixl --disaggregation-mode prefill --host ${local_ip} --port 30000 --trust-remote-code --dist-init-addr ${prefill_master_ip}:5000 --nnodes 2 --node-rank 1 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode normal --mem-fraction-static 0.8
+# decode 0
+$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 ---disaggregation-transfer-backend nixl --disaggregation-mode decode --host ${local_ip} --port 30001 --trust-remote-code --dist-init-addr ${decode_master_ip}:5000 --nnodes 2 --node-rank 0 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode low_latency --mem-fraction-static 0.8 --max-running-requests 128
+# decode 1
+$ python -m sglang.launch_server --model-path deepseek-ai/DeepSeek-V3-0324 ---disaggregation-transfer-backend nixl --disaggregation-mode decode --host ${local_ip} --port 30001 --trust-remote-code --dist-init-addr ${decode_master_ip}:5000 --nnodes 2 --node-rank 1 --tp-size 16 --dp-size 8 --enable-dp-attention --enable-deepep-moe --deepep-mode low_latency --mem-fraction-static 0.8 --max-running-requests 128
+```

python/sglang/srt/disaggregation/common/__init__.py

+from .conn import CommonKVBootstrapServer, CommonKVManager, CommonKVReceiver

python/sglang/srt/disaggregation/common/conn.py

+from __future__ import annotations
+
+import asyncio
+import logging
+import socket
+import threading
+from functools import cache
+from typing import Dict, List, Optional, Tuple, Union
+
+import numpy as np
+import numpy.typing as npt
+import requests
+import zmq
+from aiohttp import web
+
+from sglang.srt.disaggregation.base.conn import (
+    BaseKVBootstrapServer,
+    BaseKVManager,
+    BaseKVReceiver,
+    BaseKVSender,
+    KVArgs,
+    KVPoll,
+)
+from sglang.srt.disaggregation.utils import DisaggregationMode
+from sglang.srt.server_args import ServerArgs
+from sglang.srt.utils import get_free_port, get_ip, get_local_ip_by_remote
+
+logger = logging.getLogger(__name__)
+
+
+class CommonKVManager(BaseKVManager):
+    def __init__(
+        self,
+        args: KVArgs,
+        disaggregation_mode: DisaggregationMode,
+        server_args: ServerArgs,
+        is_mla_backend: Optional[bool] = False,
+    ):
+        self.kv_args = args
+        self.is_mla_backend = is_mla_backend
+        self.disaggregation_mode = disaggregation_mode
+        # for p/d multi node infer
+        self.bootstrap_port = server_args.disaggregation_bootstrap_port
+        self.dist_init_addr = server_args.dist_init_addr
+        self.tp_size = server_args.tp_size
+        self.dp_size = server_args.dp_size
+        self.enable_dp_attention = server_args.enable_dp_attention
+        if not server_args.enable_dp_attention and server_args.dp_size != 1:
+            raise ValueError(
+                "If dp_attention is not enabled, dp size must be 1 in disaggregation mode."
+            )
+
+        self.rank_port = get_free_port()
+        if self.disaggregation_mode == DisaggregationMode.PREFILL:
+            self._register_to_bootstrap()
+        elif self.disaggregation_mode == DisaggregationMode.DECODE:
+            self.connection_pool: Dict[str, Dict[str, Union[str, int]]] = {}
+            self.prefill_tp_size_table: Dict[str, int] = {}
+            self.prefill_dp_size_table: Dict[str, int] = {}
+        else:
+            raise ValueError(
+                f"Unsupported DisaggregationMode: {self.disaggregation_mode}"
+            )
+
+    def _register_to_bootstrap(self):
+        """Register KVSender to bootstrap server via HTTP POST."""
+        if self.dist_init_addr:
+            ip_address = socket.gethostbyname(self.dist_init_addr.split(":")[0])
+        else:
+            ip_address = get_ip()
+
+        bootstrap_server_url = f"{ip_address}:{self.bootstrap_port}"
+        url = f"http://{bootstrap_server_url}/route"
+        payload = {
+            "role": "Prefill",
+            "tp_size": self.tp_size,
+            "dp_size": self.dp_size,
+            "rank_ip": get_local_ip_by_remote(),
+            "rank_port": self.rank_port,
+            "engine_rank": self.kv_args.engine_rank,
+        }
+
+        try:
+            response = requests.put(url, json=payload)
+            if response.status_code == 200:
+                logger.debug("Prefill successfully registered to bootstrap server.")
+            else:
+                logger.error(
+                    f"Prefill Failed to connect to bootstrap server: {response.status_code}, {response.text}"
+                )
+        except Exception as e:
+            logger.error(f"Prefill Failed to register to bootstrap server: {e}")
+
+    @cache
+    def _connect(self, endpoint: str):
+        socket = zmq.Context().socket(zmq.PUSH)
+        socket.connect(endpoint)
+        return socket
+
+
+class CommonKVReceiver(BaseKVReceiver):
+    _ctx = zmq.Context()
+    _socket_cache = {}
+    _socket_locks = {}
+    _global_lock = threading.Lock()
+
+    def __init__(
+        self,
+        mgr: BaseKVManager,
+        bootstrap_addr: str,
+        bootstrap_room: Optional[int] = None,
+    ):
+        self.bootstrap_room = bootstrap_room
+        self.bootstrap_addr = bootstrap_addr
+        self.kv_mgr = mgr
+
+        if self.bootstrap_addr not in self.kv_mgr.prefill_dp_size_table:
+            self.prefill_tp_size, self.prefill_dp_size = (
+                self._get_prefill_dp_size_from_server()
+            )
+            if self.prefill_tp_size is None or self.prefill_dp_size is None:
+                logger.error(
+                    f"Could not fetch prefill parallel info for bootstrap_addr: {self.bootstrap_addr}"
+                )
+            else:
+                self.kv_mgr.prefill_tp_size_table[self.bootstrap_addr] = (
+                    self.prefill_tp_size
+                )
+                self.kv_mgr.prefill_dp_size_table[self.bootstrap_addr] = (
+                    self.prefill_dp_size
+                )
+        else:
+            self.prefill_tp_size = self.kv_mgr.prefill_tp_size_table[
+                self.bootstrap_addr
+            ]
+            self.prefill_dp_size = self.kv_mgr.prefill_dp_size_table[
+                self.bootstrap_addr
+            ]
+
+        # Currently, we don't allow prefill instance and decode instance to
+        # have different TP sizes per DP rank, except for models using MLA.
+        local_tp_size_per_dp_rank = self.kv_mgr.tp_size // self.kv_mgr.dp_size
+        prefill_tp_size_per_dp_rank = self.prefill_tp_size // self.prefill_dp_size
+        if local_tp_size_per_dp_rank == prefill_tp_size_per_dp_rank:
+            self.target_tp_rank = (
+                self.kv_mgr.kv_args.engine_rank % local_tp_size_per_dp_rank
+            )
+            self.required_dst_info_num = 1
+            self.target_tp_ranks = [self.target_tp_rank]
+        elif local_tp_size_per_dp_rank > prefill_tp_size_per_dp_rank:
+            assert (
+                self.kv_mgr.is_mla_backend
+            ), "PD with different TP sizes per DP rank is not yet supported for non-MLA models"
+            self.target_tp_rank = (
+                self.kv_mgr.kv_args.engine_rank % local_tp_size_per_dp_rank
+            ) // (local_tp_size_per_dp_rank // prefill_tp_size_per_dp_rank)
+            self.required_dst_info_num = (
+                local_tp_size_per_dp_rank // prefill_tp_size_per_dp_rank
+            )
+            self.target_tp_ranks = [self.target_tp_rank]
+        else:
+            assert (
+                self.kv_mgr.is_mla_backend
+            ), "PD with different TP sizes per DP rank is not yet supported for non-MLA models"
+
+            # For non-MLA models, one decode rank needs to retrieve KVCache from multiple prefill ranks for non MLA models;
+            self.target_tp_ranks = [
+                rank
+                for rank in range(
+                    (self.kv_mgr.kv_args.engine_rank % local_tp_size_per_dp_rank)
+                    * (prefill_tp_size_per_dp_rank // local_tp_size_per_dp_rank),
+                    (self.kv_mgr.kv_args.engine_rank % local_tp_size_per_dp_rank + 1)
+                    * (prefill_tp_size_per_dp_rank // local_tp_size_per_dp_rank),
+                )
+            ]
+
+            # For MLA models, we can retrieve KVCache from only one prefill rank, but we still need to maintain
+            # multiple connections in the connection pool and have to send dummy requests to other prefill ranks,
+            # or the KVPoll will never be set correctly
+            self.target_tp_rank = self.target_tp_ranks[0]
+            self.required_dst_info_num = 1
+
+        self.target_dp_group = bootstrap_room % self.prefill_dp_size
+
+        # NOTE: key distinguished by bootstrap_addr, target_dp_group, and target_tp_rank
+        bootstrap_key = (
+            f"{self.bootstrap_addr}_{self.target_dp_group}_{self.target_tp_rank}"
+        )
+
+        if bootstrap_key not in self.kv_mgr.connection_pool:
+            bootstrap_infos = []
+            for target_tp_rank in self.target_tp_ranks:
+                bootstrap_info = self._get_bootstrap_info_from_server(
+                    target_tp_rank,
+                    self.target_dp_group,
+                )
+                if bootstrap_info is not None:
+                    # NOTE: only support MLA for now: select one prefill rank as real rank
+                    bootstrap_info["is_dummy"] = not bool(
+                        target_tp_rank == self.target_tp_rank
+                        or self.target_tp_rank is None
+                    )
+                    bootstrap_infos.append(bootstrap_info)
+                else:
+                    logger.error(
+                        f"Could not fetch bootstrap info for engine rank: {self.kv_mgr.kv_args.engine_rank} and target_dp_group: {self.target_dp_group}"
+                    )
+            self.bootstrap_infos = bootstrap_infos
+
+            if len(self.bootstrap_infos) == 0:
+                logger.error(
+                    f"Could not fetch bootstrap info for engine rank: {self.kv_mgr.kv_args.engine_rank}"
+                )
+            else:
+                self.kv_mgr.connection_pool[bootstrap_key] = self.bootstrap_infos
+                # Register kv_args only once to prefill KVManager according to the info fetched from the bootstrap server
+                self._register_kv_args()
+        else:
+            self.bootstrap_infos = self.kv_mgr.connection_pool[bootstrap_key]
+
+        assert len(self.bootstrap_infos) > 0
+
+    def _get_bootstrap_info_from_server(self, engine_rank, target_dp_group):
+        """Fetch the bootstrap info from the bootstrap server."""
+        try:
+            url = f"http://{self.bootstrap_addr}/route?engine_rank={engine_rank}&target_dp_group={target_dp_group}"
+            response = requests.get(url)
+            if response.status_code == 200:
+                bootstrap_info = response.json()
+                return bootstrap_info
+            else:
+                logger.error(
+                    f"Failed to get prefill server info: {response.status_code}, {response.text}"
+                )
+                return None
+        except Exception as e:
+            logger.error(f"Error fetching prefill info from bootstrap: {e}")
+            return None
+
+    def _get_prefill_dp_size_from_server(self) -> int:
+        """Fetch the prefill parallel info from the bootstrap server."""
+        try:
+            url = f"http://{self.bootstrap_addr}/route?engine_rank={-1}&target_dp_group={-1}"
+            response = requests.get(url)
+            if response.status_code == 200:
+                prefill_parallel_info = response.json()
+                return int(prefill_parallel_info["prefill_tp_size"]), int(
+                    prefill_parallel_info["prefill_dp_size"]
+                )
+            else:
+                logger.error(
+                    f"Failed to get prefill parallel info: {response.status_code}, {response.text}"
+                )
+                return None
+        except Exception as e:
+            logger.error(f"Error fetching prefill parallel info from bootstrap: {e}")
+            return None
+
+    @classmethod
+    def _connect(cls, endpoint: str):
+        with cls._global_lock:
+            if endpoint not in cls._socket_cache:
+                sock = cls._ctx.socket(zmq.PUSH)
+                sock.connect(endpoint)
+                cls._socket_cache[endpoint] = sock
+                cls._socket_locks[endpoint] = threading.Lock()
+            return cls._socket_cache[endpoint], cls._socket_locks[endpoint]
+
+    def _register_kv_args(self):
+        pass
+
+    def failure_exception(self):
+        raise Exception("Fake KVReceiver Exception")
+
+
+class CommonKVBootstrapServer(BaseKVBootstrapServer):
+    def __init__(self, port: int):
+        self.port = port
+        self.app = web.Application()
+        self.store = dict()
+        self.lock = asyncio.Lock()
+        self._setup_routes()
+        self.tp_size = None
+        self.dp_size = None
+        self.tp_size_per_dp_rank = None
+        self.prefill_port_table: Dict[int, Dict[int, Dict[str, Union[str, int]]]] = {}
+
+        # Start bootstrap server
+        self.thread = threading.Thread(target=self._run_server, daemon=True)
+        self.run()
+
+    def run(self):
+        self.thread.start()
+
+    def _setup_routes(self):
+        self.app.router.add_route("*", "/route", self._handle_route)
+
+    async def _handle_route(self, request: web.Request):
+        method = request.method
+        if method == "PUT":
+            return await self._handle_route_put(request)
+        elif method == "GET":
+            return await self._handle_route_get(request)
+        else:
+            return web.Response(
+                text="Method not allowed", status=405, content_type="application/json"
+            )
+
+    async def _handle_route_put(self, request: web.Request):
+        data = await request.json()
+        role = data["role"]
+        tp_size = data["tp_size"]
+        dp_size = data["dp_size"]
+        rank_ip = data["rank_ip"]
+        rank_port = int(data["rank_port"])
+        engine_rank = int(data["engine_rank"])
+
+        if self.tp_size is None:
+            self.tp_size = tp_size
+
+        if self.dp_size is None:
+            self.dp_size = dp_size
+
+        tp_size_per_dp_rank = tp_size // dp_size
+        if self.tp_size_per_dp_rank == None:
+            self.tp_size_per_dp_rank = tp_size_per_dp_rank
+
+        # Add lock to make sure thread-safe
+        if role == "Prefill":
+            dp_group = engine_rank // tp_size_per_dp_rank
+            tp_rank_in_dp_group = engine_rank % tp_size_per_dp_rank
+
+            async with self.lock:
+                if dp_group not in self.prefill_port_table:
+                    self.prefill_port_table[dp_group] = {}
+
+            self.prefill_port_table[dp_group][tp_rank_in_dp_group] = {
+                "rank_ip": rank_ip,
+                "rank_port": rank_port,
+            }
+            logger.debug(
+                f"Register Prefill bootstrap: {engine_rank} with rank_ip: {rank_ip} and rank_port: {rank_port}"
+            )
+
+        return web.Response(text="OK", status=200)
+
+    async def _handle_route_get(self, request: web.Request):
+        engine_rank = request.query.get("engine_rank")
+        target_dp_group = request.query.get("target_dp_group")
+        if not engine_rank or not target_dp_group:
+            return web.Response(text="Missing inputs for bootstrap server.", status=400)
+
+        # Currently we use engine_rank == -1 and target_dp_group == -1 to sync dp size
+        if int(engine_rank) == -1 and int(target_dp_group) == -1:
+            prefill_parallel_info = {
+                "prefill_tp_size": self.tp_size,
+                "prefill_dp_size": self.dp_size,
+            }
+            return web.json_response(prefill_parallel_info, status=200)
+
+        # Find corresponding prefill info
+        async with self.lock:
+            bootstrap_info = self.prefill_port_table[int(target_dp_group)][
+                int(engine_rank)
+            ]
+
+        if bootstrap_info is not None:
+            return web.json_response(bootstrap_info, status=200)
+        else:
+            return web.Response(text="Bootstrap info not Found", status=404)
+
+    def _run_server(self):
+        try:
+            # Event Loop
+            self._loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(self._loop)
+
+            self._runner = web.AppRunner(self.app)
+            self._loop.run_until_complete(self._runner.setup())
+
+            site = web.TCPSite(self._runner, port=self.port)
+            self._loop.run_until_complete(site.start())
+            self._loop.run_forever()
+        except Exception as e:
+            logger.error(f"Server error: {str(e)}")
+        finally:
+            # Cleanup
+            self._loop.run_until_complete(self._runner.cleanup())
+            self._loop.close()
+
+    def close(self):
+        """Shutdown"""
+        if self._loop is not None and self._loop.is_running():
+            self._loop.call_soon_threadsafe(self._loop.stop)
+            logger.info("Stopping server loop...")
+
+        if self.thread.is_alive():
+            self.thread.join(timeout=2)
+            logger.info("Server thread stopped")
+
+    def poll(self) -> KVPoll: ...

python/sglang/srt/disaggregation/mooncake/conn.py

@@ -29,7 +29,10 @@ from sglang.srt.disaggregation.base.conn import (
     KVPoll,
 )
 from sglang.srt.disaggregation.mooncake.transfer_engine import MooncakeTransferEngine
-from sglang.srt.disaggregation.utils import DisaggregationMode
+from sglang.srt.disaggregation.utils import (
+    DisaggregationMode,
+    group_concurrent_contiguous,
+)
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_free_port,
@@ -41,23 +44,6 @@ from sglang.srt.utils import (
 logger = logging.getLogger(__name__)
 
 
-def group_concurrent_contiguous(
-    src_indices: npt.NDArray[np.int64], dst_indices: npt.NDArray[np.int64]
-) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]:
-    """Vectorised NumPy implementation."""
-    if src_indices.size == 0:
-        return [], []
-
-    brk = np.where((np.diff(src_indices) != 1) | (np.diff(dst_indices) != 1))[0] + 1
-    src_groups = np.split(src_indices, brk)
-    dst_groups = np.split(dst_indices, brk)
-
-    src_groups = [g.tolist() for g in src_groups]
-    dst_groups = [g.tolist() for g in dst_groups]
-
-    return src_groups, dst_groups
-
-
 class KVTransferError(Exception):
     def __init__(self, bootstrap_room: int, failure_reason: str):
         super().__init__(failure_reason)

python/sglang/srt/disaggregation/utils.py

@@ -13,7 +13,7 @@ import requests
 import torch
 import torch.distributed as dist
 
-from sglang.srt.utils import get_ip
+from sglang.srt.utils import get_ip, get_local_ip_by_remote
 
 if TYPE_CHECKING:
     from sglang.srt.managers.schedule_batch import Req
@@ -279,3 +279,20 @@ class MetadataBuffers:
                 ] = torch.tensor(
                     req.output_top_logprobs_idx[0], dtype=torch.int32, device="cpu"
                 )
+
+
+def group_concurrent_contiguous(
+    src_indices: npt.NDArray[np.int64], dst_indices: npt.NDArray[np.int64]
+) -> Tuple[List[npt.NDArray[np.int64]], List[npt.NDArray[np.int64]]]:
+    """Vectorised NumPy implementation."""
+    if src_indices.size == 0:
+        return [], []
+
+    brk = np.where((np.diff(src_indices) != 1) | (np.diff(dst_indices) != 1))[0] + 1
+    src_groups = np.split(src_indices, brk)
+    dst_groups = np.split(dst_indices, brk)
+
+    src_groups = [g.tolist() for g in src_groups]
+    dst_groups = [g.tolist() for g in dst_groups]
+
+    return src_groups, dst_groups