[LLM] Update Multinode Deployment (#2830)

* [LLM] fix multinode bugs

* [LLM] update multinode deployment

* [LLM] update multinode deployment

* [LLM] update multinode deployment

* [LLM] update multinode deployment

* [LLM] update multinode deployment

* [LLM] fix ci bugs

* Update fastdeploy/engine/args_utils.py

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

* [LLM] update random port

* [LLM] update random port

* [LLM] fix ci bugs

* fix ci bugs

---------

Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>

fastdeploy/engine/args_utils.py

@@ -124,9 +124,19 @@ class EngineArgs:
     Ratio of tokens to process in a block.
     """
 
-    pod_ips: Optional[List[str]] = None
+    dist_init_ip: Optional[str] = None
     """
-    List of IP addresses for nodes in the cluster.
+    The master node ip of multinode deployment
+    """
+
+    nnodes: int = 1
+    """
+    The number of nodes in multinode deployment
+    """
+
+    node_rank: int = 0
+    """
+    The rank of the current node in multinode deployment
     """
 
     swap_space: float = None
@@ -485,11 +495,25 @@ class EngineArgs:
         # Cluster system parameters group
         system_group = parser.add_argument_group("System Configuration")
         system_group.add_argument(
-            "--pod-ips",
-            type=lambda s: s.split(",") if s else None,
-            default=EngineArgs.pod_ips,
+            "--dist-init-ip",
+            default=EngineArgs.dist_init_ip,
             help=
-            "List of IP addresses for nodes in the cluster (comma-separated).")
+            "IP addresses of master node.")
+
+        system_group.add_argument(
+            "--nnodes",
+            type=int,
+            default=EngineArgs.nnodes,
+            help=
+            "The number of all nodes.")
+
+        system_group.add_argument(
+            "--node-rank",
+            type=int,
+            default=EngineArgs.node_rank,
+            help=
+            "node rank id (range [0, nnodes)).")
+
 
 
         # Performance tuning parameters group
@@ -789,7 +813,9 @@ class EngineArgs:
             max_num_seqs=self.max_num_seqs,
             speculative_config=speculative_cfg,
             max_num_batched_tokens=self.max_num_batched_tokens,
-            pod_ips=self.pod_ips,
+            dist_init_ip=self.dist_init_ip,
+            nnodes=self.nnodes,
+            node_rank=self.node_rank,
             use_warmup=self.use_warmup,
             engine_worker_queue_port=self.engine_worker_queue_port,
             limit_mm_per_prompt=self.limit_mm_per_prompt,

fastdeploy/engine/config.py

@@ -6,7 +6,7 @@
 # You may obtain a copy of the License at
 #
 #     http://www.apache.org/licenses/LICENSE-2.0
-#
+#dist_init_ip
 # 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.
@@ -24,7 +24,7 @@ from fastdeploy import envs
 from fastdeploy.platforms import current_platform
 from fastdeploy.scheduler import SchedulerConfig
 from fastdeploy.utils import (ceil_div, check_unified_ckpt, get_host_ip,
-                              is_port_available, llm_logger)
+                              is_port_available, get_random_port, llm_logger)
 
 TaskOption = Literal["generate"]
 
@@ -642,7 +642,9 @@ class Config:
         max_model_len: int = 8192,
         max_num_seqs: int = 8,
         max_num_batched_tokens: Optional[int] = None,
-        pod_ips: Optional[List[str]] = None,
+        dist_init_ip: str = None,
+        nnodes: int = 1,
+        node_rank: int = 0,
         speculative_config: Optional[Dict[str, Any]] = None,
         graph_optimization_config: Optional[Dict[str, Any]] = None,
         use_warmup: bool = False,
@@ -675,7 +677,6 @@ class Config:
             max_model_len (int): Maximum model length. Default is 8192.
             max_num_seqs (int): Maximum number of sequences. Default is 8.
             max_num_batched_tokens (Optional[int]): Maximum number of batched tokens. Default is None.
-            pod_ips (Optional[List[str]]): List of POD IPs. Default is None.
             mm_processor_kwargs (Optional[Dict[str, Any]]): Additional arguments for multi-modal processor. Default is None.
             speculative_config (Optional[Dict[str, Any]]): Speculative execution configuration. Default is None.
             graph_optimization_config (Optional[Dict[str, Any]]): Graph optimizaion backend execution configuration. Default is None.
@@ -699,7 +700,16 @@ class Config:
         self.tokenizer = tokenizer
         self.max_num_batched_tokens = max_num_batched_tokens
         self.tensor_parallel_size = tensor_parallel_size
-        self.pod_ips = pod_ips
+        self.dist_init_ip = dist_init_ip
+        
+        self.nnode = nnodes
+        self.node_rank = node_rank
+        if self.dist_init_ip is None:
+            self.master_ip = "0.0.0.0"
+        else:
+            self.master_ip = self.dist_init_ip
+            self.dist_init_addr = f"{self.dist_init_ip}:{get_random_port()}"
+
         self.max_model_len = max_model_len
         self.max_num_seqs = max_num_seqs
         self.limit_mm_per_prompt = limit_mm_per_prompt
@@ -716,14 +726,8 @@ class Config:
         self.graph_optimization_config = graph_optimization_config
         self.guided_decoding_backend = guided_decoding_backend
         self.disable_any_whitespace = disable_any_whitespace
-        self.is_master = True
         self._str_to_list("innode_prefill_ports", int)
-        self._str_to_list("pod_ips", str)
 
-        if self.pod_ips is None:
-            self.nnode = 1
-        else:
-            self.nnode = len(self.pod_ips)
 
         assert self.splitwise_role in ["mixed", "prefill", "decode"]
 
@@ -778,9 +782,9 @@ class Config:
 
         self.host_ip = get_host_ip()
 
-        if self.pod_ips is None:
-            self.pod_ips = ["0.0.0.0"]
-        elif self.host_ip != self.pod_ips[0]:
+        if self.dist_init_ip is None or self.host_ip == self.master_ip:
+            self.is_master = True
+        else:
             self.is_master = False
 
         import paddle

fastdeploy/engine/engine.py

@@ -174,7 +174,7 @@ class LLMEngine(object):
                 cache_config=self.cfg.cache_config,
                 tensor_parallel_size=self.cfg.tensor_parallel_size,
                 device_ids=device_ids,
-                pod_ip=self.cfg.pod_ips[0],
+                pod_ip=self.cfg.master_ip,
                 engine_worker_queue_port=self.cfg.engine_worker_queue_port,
                 pid_suffix=self.ipc_signal_suffix)
 
@@ -239,11 +239,12 @@ class LLMEngine(object):
 
             if self.cfg.parallel_config.enable_expert_parallel and self.cfg.parallel_config.data_parallel_size > 1:
                 self.dp_processed = []
-                for i in range(1, self.cfg.parallel_config.data_parallel_size):
+                for i in range(1, self.cfg.parallel_config.data_parallel_size // self.cfg.nnode):
                     time.sleep(1)
                     self.dp_processed.append(
                         multiprocessing.Process(target=start_expert_service,
-                                                args=(self.cfg, i,
+                                                args=(self.cfg,
+                                                      i + self.cfg.node_rank * self.cfg.worker_num_per_node,
                                                       self.ipc_signal_suffix)))
                     llm_logger.info(f"Engine is initialized successfully with {self.cfg.tensor_parallel_size}" \
                 + " data parallel id {}".format(i))
@@ -1007,8 +1008,6 @@ class LLMEngine(object):
         )
 
         arguments = (
-            f" --nnodes {str(self.cfg.nnode)}"
-            f" --ips {','.join(self.cfg.pod_ips)}"
             f" --devices {self.cfg.device_ids} {py_script}"
             f" --max_num_seqs {self.cfg.max_num_seqs} --max_model_len {self.cfg.max_model_len}"
             f" --gpu_memory_utilization {self.cfg.cache_config.gpu_memory_utilization}"
@@ -1016,7 +1015,7 @@ class LLMEngine(object):
             f" --device_ids {self.cfg.device_ids}"
             f" --tensor_parallel_size {self.cfg.tensor_parallel_size}"
             f" --engine_worker_queue_port {str(self.cfg.engine_worker_queue_port)}"
-            f" --pod_ip {self.cfg.pod_ips[0]}"
+            f" --pod_ip {self.cfg.master_ip}"
             f" --total_block_num {self.cfg.cache_config.total_block_num}"
             f" --block_size {self.cfg.cache_config.block_size}"
             f" --enc_dec_block_num {self.cfg.cache_config.enc_dec_block_num}"
@@ -1057,7 +1056,11 @@ class LLMEngine(object):
             if value:
                 arguments = arguments + f" --{worker_flag}"
         if self.cfg.nnode > 1:
-            pd_cmd = pd_cmd + f" --ips {self.cfg.ips}"
+            pd_cmd = pd_cmd + (
+                f" --master {self.cfg.dist_init_addr}"
+                f" --nnodes {str(self.cfg.nnode)}"
+                f" --rank {str(self.cfg.node_rank)}"
+            )
         pd_cmd = pd_cmd + arguments + f" 2>{log_dir}/launch_worker.log"
         llm_logger.info("Launch worker service command: {}".format(pd_cmd))
         p = subprocess.Popen(
@@ -1158,7 +1161,7 @@ class LLMEngine(object):
                 cache_config=self.cfg.cache_config,
                 tensor_parallel_size=self.cfg.tensor_parallel_size,
                 device_ids=device_ids,
-                pod_ip=self.cfg.pod_ips[0],
+                pod_ip=self.cfg.master_ip,
                 engine_worker_queue_port=self.cfg.engine_worker_queue_port,
                 pid_suffix=self.ipc_signal_suffix)
     def check_health(self, time_interval_threashold=30):
@@ -1245,8 +1248,9 @@ class LLMEngine(object):
         """
         start queue service for engine worker communication
         """
-        address = (self.cfg.pod_ips[0], self.cfg.engine_worker_queue_port)
-        if self.cfg.host_ip == self.cfg.pod_ips[0] or self.cfg.pod_ips[0] == "0.0.0.0":
+        address = (self.cfg.master_ip, self.cfg.engine_worker_queue_port)
+        if self.cfg.host_ip == self.cfg.master_ip or self.cfg.master_ip == "0.0.0.0":
+            llm_logger.info(f"Starting engine worker queue server service at {address}")
             self.engine_worker_queue_server = EngineWorkerQueue(
                 address=address,
                 is_server=True,
@@ -1256,7 +1260,7 @@ class LLMEngine(object):
 
             if self.cfg.cache_config.enable_prefix_caching or self.cfg.splitwise_role != 'mixed':
                 self.cache_task_queue = EngineCacheQueue(
-                    address=(self.cfg.pod_ips[0], self.cfg.cache_config.cache_queue_port),
+                    address=(self.cfg.master_ip, self.cfg.cache_config.cache_queue_port),
                     authkey=b'cache_queue_service',
                     is_server=True,
                     num_client=self.cfg.tensor_parallel_size,
@@ -1270,4 +1274,6 @@ class LLMEngine(object):
             is_server=False,
             num_client=self.cfg.tensor_parallel_size,
             client_id=0,
-            local_data_parallel_id=0)
+            local_data_parallel_size=self.cfg.parallel_config.data_parallel_size,
+            local_data_parallel_id= min(self.cfg.worker_num_per_node * self.cfg.node_rank,
+                                       self.cfg.parallel_config.data_parallel_size - 1))

fastdeploy/engine/expert_service.py

@@ -49,8 +49,8 @@ class ExpertService(object):
             cfg (Config): Config object containing all the configuration parameters.
         """
         self.cfg = cfg
-        start_pos = local_data_parallel_id * self.cfg.tensor_parallel_size
-        end_pos = (local_data_parallel_id + 1) * self.cfg.tensor_parallel_size
+        start_pos = (local_data_parallel_id * self.cfg.tensor_parallel_size) % self.cfg.worker_num_per_node
+        end_pos = ((local_data_parallel_id + 1) * self.cfg.tensor_parallel_size) % self.cfg.worker_num_per_node
         self.cfg.cache_config.rdma_comm_ports = self.cfg.cache_config.rdma_comm_ports[
             start_pos:end_pos]
         self.cfg.local_device_ids = self.cfg.device_ids.split(
@@ -65,7 +65,7 @@ class ExpertService(object):
 
         self.cfg.parallel_config.local_data_parallel_id = local_data_parallel_id
 
-        address = (cfg.pod_ips[0], cfg.engine_worker_queue_port)
+        address = (cfg.master_ip, cfg.engine_worker_queue_port)
         self.engine_worker_queue = EngineWorkerQueue(
             address=address,
             is_server=False,
@@ -118,7 +118,7 @@ class ExpertService(object):
             cache_config=self.cfg.cache_config,
             tensor_parallel_size=self.cfg.tensor_parallel_size,
             device_ids=self.cfg.local_device_ids,
-            pod_ip=self.cfg.pod_ips[0],
+            pod_ip=self.cfg.master_ip,
             engine_worker_queue_port=self.cfg.engine_worker_queue_port,
             pid_suffix=f"{local_data_parallel_id}_{ipc_signal_suffix}"
         )

fastdeploy/entrypoints/llm.py

@@ -85,7 +85,7 @@ class LLM:
 
         self.mutex = threading.Lock()
         self.req_output = dict()
-        self.master_node_ip = self.llm_engine.cfg.pod_ips[0]
+        self.master_node_ip = self.llm_engine.cfg.master_ip
         self._receive_output_thread = threading.Thread(
             target=self._receive_output, daemon=True)
         self._receive_output_thread.start()

fastdeploy/entrypoints/openai/api_server.py

@@ -122,8 +122,8 @@ async def lifespan(app: FastAPI):
                                  args.mm_processor_kwargs, args.enable_mm,
                                  args.reasoning_parser)
     app.state.dynamic_load_weight = args.dynamic_load_weight
-    chat_handler = OpenAIServingChat(engine_client, pid, args.pod_ips)
-    completion_handler = OpenAIServingCompletion(engine_client, pid, args.pod_ips)
+    chat_handler = OpenAIServingChat(engine_client, pid, args.dist_init_ip)
+    completion_handler = OpenAIServingCompletion(engine_client, pid, args.dist_init_ip)
     engine_client.create_zmq_client(model=pid, mode=zmq.PUSH)
     engine_client.pid = pid
     app.state.engine_client = engine_client

fastdeploy/entrypoints/openai/serving_chat.py

@@ -40,16 +40,16 @@ class OpenAIServingChat:
     OpenAI-style chat completions serving
     """
 
-    def __init__(self, engine_client, pid, pod_ips):
+    def __init__(self, engine_client, pid, dist_init_ip):
         self.engine_client = engine_client
         self.pid = pid
-        self.pod_ips = pod_ips
+        self.master_ip = dist_init_ip
         self.host_ip = get_host_ip()
 
     def _check_master(self):
-        if self.pod_ips is None:
+        if self.master_ip is None:
             return True
-        if self.host_ip == self.pod_ips[0]:
+        if self.host_ip == self.master_ip:
             return True
         return False
 

fastdeploy/entrypoints/openai/serving_completion.py

@@ -45,16 +45,16 @@ from fastdeploy.engine.request import RequestOutput
 
 
 class OpenAIServingCompletion:
-    def __init__(self, engine_client, pid, pod_ips):
+    def __init__(self, engine_client, pid, dist_init_ip):
         self.engine_client = engine_client
         self.pid = pid
-        self.pod_ips = pod_ips
+        self.master_ip = dist_init_ip
         self.host_ip = get_host_ip()
 
     def _check_master(self):
-        if self.pod_ips is None:
+        if self.master_ip is None:
             return True
-        if self.host_ip == self.pod_ips[0]:
+        if self.host_ip == self.master_ip:
             return True
         return False
 

fastdeploy/utils.py

@@ -27,7 +27,8 @@ from datetime import datetime
 from logging.handlers import BaseRotatingHandler
 from pathlib import Path
 from typing import Literal, TypeVar, Union
-
+import random
+import socket
 import requests
 import yaml
 from aistudio_sdk.snapshot_download import snapshot_download
@@ -421,6 +422,19 @@ def get_host_ip():
     return ip
 
 
+
+
+def get_random_port():
+    while True:
+        port = random.randint(49152, 65535)
+        with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+            try:
+                s.bind(("0.0.0.0", port)) 
+                return port 
+            except OSError:
+                continue
+
+
 def is_port_available(host, port):
     """
     Check the port is available

fastdeploy/worker/gpu_worker.py

@@ -23,6 +23,7 @@ import pynvml
 
 from fastdeploy.config import FDConfig
 from fastdeploy.engine.request import Request
+from fastdeploy.platforms import current_platform
 from fastdeploy.utils import get_logger
 from fastdeploy.worker.gpu_model_runner import GPUModelRunner
 from fastdeploy.worker.output import ModelRunnerOutput
@@ -50,11 +51,12 @@ class GpuWorker(WorkerBase):
         """
         Initialize device and construct model runner
         """
+        self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
         if self.device_config.device_type == "cuda" and paddle.device.is_compiled_with_cuda(
         ):
             # Set evironment variable
             self.device_ids = self.parallel_config.device_ids.split(",")
-            self.device = f"gpu:{self.local_rank}"
+            self.device = f"gpu:{self.local_rank % self.max_chips_per_node}"
             paddle.device.set_device(self.device)
             paddle.set_default_dtype(self.parallel_config.dtype)
 
@@ -72,7 +74,7 @@ class GpuWorker(WorkerBase):
         self.model_runner: GPUModelRunner = GPUModelRunner(
             fd_config=self.fd_config,
             device=self.device,
-            device_id=self.device_ids[self.local_rank],
+            device_id=self.device_ids[self.local_rank % self.max_chips_per_node],
             rank=self.rank,
             local_rank=self.local_rank)
 

fastdeploy/worker/worker_process.py

@@ -136,9 +136,9 @@ class PaddleDisWorkerProc():
             model_weights_status:
         """
         # init worker_ready_signal
-        max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
+        self.max_chips_per_node = 16 if current_platform.is_iluvatar() else 8
         array_size = min(
-            max_chips_per_node, self.parallel_config.tensor_parallel_size *
+            self.max_chips_per_node, self.parallel_config.tensor_parallel_size *
             self.parallel_config.expert_parallel_size)
         workers_ready = np.zeros(shape=[array_size], dtype=np.int32)
         self.worker_ready_signal = IPCSignal(
@@ -148,10 +148,10 @@ class PaddleDisWorkerProc():
             suffix=self.parallel_config.engine_pid,
             create=False)
         self.worker_ready_signal.value[self.local_rank %
-                                       max_chips_per_node] = 1
+                                       self.max_chips_per_node] = 1
 
         # init worker_healthy_live_signal
-        workers_alive = np.zeros(shape=[self.ranks], dtype=np.int32)
+        workers_alive = np.zeros(shape=[array_size], dtype=np.int32)
         self.worker_healthy_live_signal = IPCSignal(
             name="worker_healthy_live_signal",
             array=workers_alive,
@@ -205,7 +205,7 @@ class PaddleDisWorkerProc():
         Tmp loop function for ep utill DP is supported
         """
         while True:
-            self.worker_healthy_live_signal.value[self.local_rank] = int(
+            self.worker_healthy_live_signal.value[self.local_rank % self.max_chips_per_node] = int(
                 time.time())
 
             if self.fd_config.parallel_config.tensor_parallel_rank == 0 and self.task_queue.num_tasks(