distribute.launch use poll to query subprocess (#19853)

distribute.launch use poll to query subprocess

python/paddle/distributed/launch.py

@@ -36,16 +36,25 @@ launch a process on each of the given gpu card.
 """
 
 from __future__ import print_function
+import logging
 import sys
 from sys import version
 import subprocess
 import os
-import warnings
+import time
 import six
 import copy
 from argparse import ArgumentParser, REMAINDER
 import paddle.fluid as fluid
 
+logger = logging.getLogger()
+logger.setLevel(logging.INFO)
+log_handler = logging.StreamHandler()
+log_format = logging.Formatter(
+    '%(asctime)s - %(filename)s:%(lineno)d - %(levelname)s: %(message)s')
+log_handler.setFormatter(log_format)
+logger.addHandler(log_handler)
+
 
 def _print_arguments(args):
     print("-----------  Configuration Arguments -----------")
@@ -129,6 +138,12 @@ POD_IP (current node ip address, not needed for local training)
     return parser.parse_args()
 
 
+def terminate_procs(procs):
+    for p in procs:
+        if p.poll() is None:
+            p.terminate()
+
+
 def start_procs(args):
     """
     """
@@ -154,14 +169,14 @@ def start_procs(args):
             node_id = int(node_id)
 
             if args.node_ip != "127.0.0.1" and current_node_ip != args.node_ip:
-                warnings.warn(
+                logger.warning(
                     "Please NOTE: When using paddlecloud, current_node_ip is \
 automatically got from POD_IP. Your input node_ip: {} doesn't equals to \
 current_node_ip: {} from paddlecloud environment."
                     .format(args.node_ip, current_node_ip))
             if args.cluster_node_ips != "127.0.0.1" and args.cluster_node_ips != ",".join(
                     node_ips):
-                warnings.warn(
+                logger.warning(
                     "Please NOTE: When using paddlecloud, cluster_node_ips is \
 automatically got from PADDLE_TRAINERS(multi nodes) or POD_IP(single node).\
 Your input cluster_node_ips: {} doesn't equals to IPs: {} from \
@@ -228,16 +243,39 @@ paddlecloud environment.".format(args.cluster_node_ips, node_ips))
 
         procs.append(proc)
 
-    for i in range(0, len(procs)):
-        proc = procs[i]
-
-        proc.wait()
-        if len(log_fns) > 0:
-            log_fns[i].close()
-
-        if proc.returncode != 0:
-            raise subprocess.CalledProcessError(
-                returncode=procs[i].returncode, cmd=cmds[i])
+    try:
+        alive = True
+        error = False
+        # wait all process finish or one error
+        while alive and not error:
+            alive = False
+            for p in procs:
+                ret = p.poll()
+                if ret is None:
+                    alive = True
+                elif ret != 0:
+                    error = True
+            time.sleep(1)
+
+        if error:
+            terminate_procs(procs)
+            exit(1)
+
+    except KeyboardInterrupt:
+        logger.warning("KeyboardInterrupt, exit")
+        terminate_procs(procs)
+        raise
+    except SystemExit:
+        logger.error("One trainer process abort, exit")
+        terminate_procs(procs)
+        raise
+    except:
+        logger.error("Trainer process abort, exit")
+        terminate_procs(procs)
+        raise
+    finally:
+        for fn in log_fns:
+            fn.close()
 
 
 def launch():

python/paddle/fluid/tests/unittests/multi_process.py

@@ -13,6 +13,8 @@
 # limitations under the License.
 
 import os
+import sys
+import time
 
 
 def train():
@@ -31,5 +33,39 @@ def train():
         f.write(name)
 
 
+def train_abort():
+    selected_gpus = os.getenv("FLAGS_selected_gpus")
+    trainer_id = int(os.getenv("PADDLE_TRAINER_ID"))
+    worker_endpoints_env = os.getenv("PADDLE_TRAINER_ENDPOINTS")
+    current_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
+    worker_endpoints = worker_endpoints_env
+    trainers_num = len(worker_endpoints.split(','))
+
+    if trainer_id == 0:
+        try:
+            # train abort 
+            exit(1)
+        except SystemExit:
+            name = "abort>>> selected_gpus:{} worker_endpoints:{} trainers_num:{} current_endpoint:{} trainer_id:{}"\
+                .format(selected_gpus, worker_endpoints, trainers_num, current_endpoint,trainer_id)
+            print(name)
+            with open("multi_process.check_{}.log".format(trainer_id),
+                      "w") as f:
+                f.write(name)
+            raise
+    else:
+        # sleep 30s to make sure paddle.distributed.launch will terminate this process
+        time.sleep(30)
+        name = "selected_gpus:{} worker_endpoints:{} trainers_num:{} current_endpoint:{} trainer_id:{}"\
+            .format(selected_gpus, worker_endpoints, trainers_num, current_endpoint,trainer_id)
+
+        print(name)
+        with open("multi_process.check_{}.log".format(trainer_id), "w") as f:
+            f.write(name)
+
+
 if __name__ == '__main__':
+    if len(sys.argv) == 2 and sys.argv[1] == "abort":
+        train_abort()
+    else:
         train()

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

 #!/bin/bash
-set -ex
+set -e
 # use default values
 python -m paddle.distributed.launch multi_process.py
 
@@ -33,3 +33,33 @@ else
     echo "not find trainer 1"
     exit -1
 fi
+
+# test async poll process
+if [ -f $file_0 ]; then
+    rm $file_0
+fi
+if [ -f $file_1 ]; then
+    rm $file_1
+fi
+
+echo ""
+echo "paddle.distributed.launch async poll process test"
+if ! python -m paddle.distributed.launch ${distributed_args} multi_process.py abort; then
+    echo "train abort as planned"
+fi
+
+abort_str1="abort>>> selected_gpus:0 worker_endpoints:127.0.0.1:6170,127.0.0.1:6171,127.0.0.2:6170,127.0.0.2:6171 trainers_num:4 current_endpoint:127.0.0.1:6170 trainer_id:0"
+
+if grep -q "$abort_str1" "$file_0"; then
+    echo "trainer 0 abort as planned"
+else
+    echo "trainer 0 not abort as planned"
+    exit -1
+fi
+
+if [ ! -f $file_1 ]; then
+    echo "trainer 1 terminate as planned"
+else
+    echo "trainer 1 not terminate as planned"
+    exit -1
+fi