chore: import upstream snapshot with attribution

test/legacy_test/multi_process.py

@@ -0,0 +1,71 @@
+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import sys
+import time
+
+
+def train(prefix):
+    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(','))
+
+    name = f"selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
+
+    print(name)
+    with open(f"multi_process_{prefix}.check_{trainer_id}.log", "w") as f:
+        f.write(name)
+
+
+def train_abort(prefix):
+    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
+            sys.exit(1)
+        except SystemExit:
+            name = f"abort>>> selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
+            print(name)
+            with open(
+                f"multi_process_{prefix}.check_{trainer_id}.log", "w"
+            ) as f:
+                f.write(name)
+            raise
+    else:
+        # sleep 30s to make sure paddle.distributed.launch will terminate this process
+        time.sleep(30)
+        name = f"selected_gpus:{selected_gpus} worker_endpoints:{worker_endpoints} trainers_num:{trainers_num} current_endpoint:{current_endpoint} trainer_id:{trainer_id}"
+
+        print(name)
+        with open(f"multi_process_{prefix}.check_{trainer_id}.log", "w") as f:
+            f.write(name)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) == 3 and sys.argv[2] == "abort":
+        prefix = sys.argv[1]
+        train_abort(prefix)
+    else:
+        prefix = sys.argv[1]
+        train(prefix)