【cherry-pick 1.8】fix ports conflict when use paddlecloud to launch analogue multi-nodes (#27117)

* add DISTRIBUTED_TRAINER_ENDPOINTS env for cloud

python/paddle/distributed/cloud_utils.py

@@ -19,7 +19,7 @@ from paddle.distributed.utils import get_cluster, logger
 
 def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_gpus):
     """
-    args_node_ips, args_node_ip:string
+    args_node_ips:string, args_node_ip:string, args_port: int, selected_gpus:list
     """
     #you can automatically get ip info while using paddlecloud multi nodes mode.
     node_ips = os.getenv("PADDLE_TRAINERS")
@@ -31,6 +31,9 @@ def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_gpus):
     node_rank = os.getenv("PADDLE_TRAINER_ID")
     assert node_rank is not None, "PADDLE_TRAINER_ID should not be None"
 
+    paddle_ports_num = int(os.getenv("TRAINER_PORTS_NUM"))
+    assert paddle_ports_num is not None, "TRAINER_PORTS_NUM should not be None"
+
     node_ips = node_ips.split(",")
     num_nodes = len(node_ips)
     node_rank = int(node_rank)
@@ -47,14 +50,16 @@ automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
 Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
 paddlecloud environment.".format(args_node_ips, node_ips))
 
+    # DISTRIBUTED_TRAINER_ENDPOINTS: new environment since paddlecloud 1.8.4
+    # e.g: DISTRIBUTED_TRAINER_ENDPOINTS="ip1:port1,ip1:port2,ip1:port3,ip1:port4,ip2:port5,ip2:port6,ip2:port7,ip2:port8"
+    trainer_endpoints = os.getenv("DISTRIBUTED_TRAINER_ENDPOINTS")
+    if trainer_endpoints is None:
         started_port = args_port
-    print("num_nodes:", num_nodes)
         if num_nodes > 1:
             try:
                 paddle_port = int(os.getenv("PADDLE_PORT", ""))
-            paddle_port_num = int(os.getenv("TRAINER_PORTS_NUM", ""))
 
-            if paddle_port_num >= len(
+                if paddle_ports_num >= len(
                         selected_gpus) and paddle_port != args_port:
                     logger.warning("Use Cloud specified port:{}.".format(
                         paddle_port))
@@ -66,13 +71,26 @@ paddlecloud environment.".format(args_node_ips, node_ips))
 
         if started_port is None:
             started_port = 6170
-
-    logger.debug("parsed from args:node_ips:{} \
-        node_ip:{} node_rank:{} started_port:{}"
-                 .format(node_ips, node_ip, node_rank, started_port))
-
-    ports = [x for x in range(started_port, started_port + len(selected_gpus))]
-    cluster, pod = get_cluster(node_ips, node_ip, ports, selected_gpus)
+        ports = [
+            x for x in range(started_port, started_port + len(selected_gpus))
+        ]
+        trainer_endpoints = []
+        for ip in node_ips:
+            trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports])
+    else:
+        trainer_endpoints_ori = trainer_endpoints.split(",")
+        trainer_endpoints = []
+        assert num_nodes * paddle_ports_num == len(trainer_endpoints_ori)
+        for i in range(num_nodes):
+            trainer_endpoints.append(trainer_endpoints_ori[
+                i * paddle_ports_num:(i + 1) * paddle_ports_num])
+
+    logger.debug("parsed from args: node_ips:{} \
+        node_ip:{} node_rank:{} trainer_endpoints:{}"
+                 .format(node_ips, node_ip, node_rank, trainer_endpoints))
+
+    cluster, pod = get_cluster(node_ips, node_ip, trainer_endpoints,
+                               selected_gpus)
     return cluster, cluster.pods[node_rank]
 
 

python/paddle/distributed/launch.py

@@ -162,7 +162,10 @@ def get_cluster_from_args(args, selected_gpus):
             x for x in range(started_port, started_port + len(selected_gpus))
         ]
 
-    return get_cluster(node_ips, node_ip, free_ports, selected_gpus)
+    trainer_endpoints = []
+    for ip in node_ips:
+        trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports])
+    return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus)
 
 
 def get_gpus(selected_gpus):

python/paddle/distributed/utils.py

@@ -227,18 +227,23 @@ def get_logger(log_level, name="root"):
     return logger
 
 
-def get_cluster(node_ips, node_ip, paddle_ports, selected_gpus):
-    assert type(paddle_ports) is list, "paddle_ports must be list"
+def get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus):
+    assert type(trainer_endpoints) is list, "trainer_endpoints must be list"
     cluster = Cluster(hdfs=None)
     trainer_rank = 0
     for node_rank, ip in enumerate(node_ips):
         pod = Pod()
         pod.rank = node_rank
         pod.addr = ip
+        cur_node_endpoints = trainer_endpoints[node_rank]
+        # when use paddlecloud, endpoints may > selected_gpus(user_defined)
+        assert len(cur_node_endpoints) >= len(
+            selected_gpus
+        ), "current trainer_endpoints size should be greater equal than selected_gpus size."
         for i in range(len(selected_gpus)):
             trainer = Trainer()
             trainer.gpus.append(selected_gpus[i])
-            trainer.endpoint = "%s:%d" % (ip, paddle_ports[i])
+            trainer.endpoint = "%s" % (cur_node_endpoints[i])
             trainer.rank = trainer_rank
             trainer_rank += 1
 

python/paddle/fluid/tests/unittests/test_launch.sh

@@ -48,9 +48,9 @@ if [ -f $file_1 ]; then
     rm $file_1
 fi
 
-
+# test use DISTRIBUTED_TRAINER_ENDPOINTS env in paddlecloud
 unset PADDLE_PORT
-unset TRAINER_PORTS_NUM
+export DISTRIBUTED_TRAINER_ENDPOINTS=127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171
 
 echo ""
 echo "paddle.distributed.launch async poll process test"