chore: import upstream snapshot with attribution

test/legacy_test/test_launch_coverage.py

@@ -0,0 +1,128 @@
+# Copyright (c) 2020 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 unittest
+from argparse import REMAINDER, ArgumentParser
+
+from paddle.distributed.fleet.launch_utils import find_free_ports
+from paddle.distributed.utils.launch_utils import (
+    _print_arguments,
+    get_cluster_from_args,
+    get_gpus,
+)
+from paddle.distributed.utils.process_utils import set_affinity
+
+
+def _parse_args():
+    parser = ArgumentParser(
+        description='''start paddle training using multi-process mode.
+NOTE: your train program ***must*** run as distributed nccl2 mode,
+see: http://www.paddlepaddle.org/documentation/docs/zh/1.6/user_guides/howto/training/cluster_howto.html#permalink-8--nccl2-
+And your train program must read environment variables below in order to let different
+process init properly:
+FLAGS_selected_gpus
+PADDLE_TRAINER_ID
+PADDLE_CURRENT_ENDPOINT
+PADDLE_TRAINERS_NUM
+PADDLE_TRAINER_ENDPOINTS
+POD_IP (current node ip address, not needed for local training)
+'''
+    )
+
+    # Optional arguments for the launch helper
+    parser.add_argument(
+        "--cluster_node_ips",
+        type=str,
+        default="127.0.0.1",
+        help="Paddle cluster nodes ips, such as 192.168.0.16,192.168.0.17..",
+    )
+    parser.add_argument(
+        "--node_ip", type=str, default="127.0.0.1", help="The current node ip. "
+    )
+    parser.add_argument(
+        "--use_paddlecloud",
+        action='store_true',
+        help="whether to use paddle cloud platform to run your multi-process job. If false, no need to set this argument.",
+    )
+    parser.add_argument(
+        "--started_port",
+        type=int,
+        default=None,
+        help="The trainer's started port on a single node",
+    )
+
+    parser.add_argument(
+        "--print_config",
+        type=bool,
+        default=True,
+        help="Print the config or not",
+    )
+
+    parser.add_argument(
+        "--selected_gpus",
+        type=str,
+        default=None,
+        help="It's for gpu training and the training process will run on the selected_gpus,"
+        "each process is bound to a single GPU. And if it's not set, this module will use all the gpu cards for training.",
+    )
+
+    parser.add_argument(
+        "--log_level",
+        type=int,
+        default=20,  # logging.INFO, details are here:https://docs.python.org/3/library/logging.html#levels
+        help="Logging level, default is logging.INFO",
+    )
+
+    parser.add_argument(
+        "--log_dir",
+        type=str,
+        help="The path for each process's log.If it's not set, the log will printed to default pipe.",
+    )
+
+    # positional
+    parser.add_argument(
+        "training_script",
+        type=str,
+        help="The full path to the single GPU training "
+        "program/script to be launched in parallel, "
+        "followed by all the arguments for the "
+        "training script",
+    )
+
+    # rest from the training program
+    parser.add_argument('training_script_args', nargs=REMAINDER)
+    return parser.parse_args()
+
+
+class TestCoverage(unittest.TestCase):
+    def test_gpus(self):
+        args = _parse_args()
+
+        if args.print_config:
+            _print_arguments(args)
+
+        gpus = get_gpus(None)
+
+        args.use_paddlecloud = True
+        cluster, pod = get_cluster_from_args(args, "0")
+
+    def test_find_free_ports(self):
+        find_free_ports(2)
+
+    def test_set_affinity(self):
+        set_affinity()
+
+
+if __name__ == '__main__':
+    unittest.main()