wehub-resource-sync
300 lines
9.4 KiB
Python
300 lines
9.4 KiB
Python
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Test cases for role makers."""
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import os
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import tempfile
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import unittest
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import paddle
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class TestCloudRoleMaker2(unittest.TestCase):
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"""
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Test cases for paddle cloud role makers.
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"""
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def setUp(self):
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"""Set up, set envs."""
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self.temp_dir = tempfile.TemporaryDirectory()
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def tearDown(self):
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self.temp_dir.cleanup()
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def test_pslib_2(self):
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"""Test cases for pslib."""
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from paddle import base
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from paddle.incubate.distributed.fleet.parameter_server.distribute_transpiler import (
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fleet,
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)
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from paddle.incubate.distributed.fleet.role_maker import (
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GeneralRoleMaker,
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RoleMakerBase,
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)
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paddle.enable_static()
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os.environ["POD_IP"] = "127.0.0.1"
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os.environ["PADDLE_PORT"] = "36001"
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
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os.environ["PADDLE_TRAINER_ID"] = "0"
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os.environ["PADDLE_TRAINERS_NUM"] = "1"
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place = base.CPUPlace()
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exe = base.Executor(place)
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try:
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fleet.init(None)
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except:
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print("no mpi4py, skip test_pslib_2")
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return
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train_program = base.Program()
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startup_program = base.Program()
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scope = base.Scope()
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with base.program_guard(train_program, startup_program):
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show = paddle.static.data(
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name="show", shape=[-1, 1], dtype="float32"
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)
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fc = paddle.static.nn.fc(x=show, size=1, activation=None)
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label = paddle.static.data(
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name="click", shape=[-1, 1], dtype="int64"
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)
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label_cast = paddle.cast(label, dtype='float32')
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cost = paddle.nn.functional.log_loss(fc, label_cast)
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try:
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adam = paddle.optimizer.Adam(learning_rate=0.000005)
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adam = fleet.distributed_optimizer(adam)
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adam.minimize([cost], [scope])
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fleet.run_server()
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except:
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print("do not support pslib test, skip")
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return
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os.environ["TRAINING_ROLE"] = "wrong"
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try:
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role1 = GeneralRoleMaker(path="./test_gloo_1")
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role1.generate_role()
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except:
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print("catch expected error of wrong TRAINING_ROLE")
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os.environ["TRAINING_ROLE"] = "PSERVER"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
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role2 = GeneralRoleMaker(path="./test_gloo_2")
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role2._finalize()
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role2._all_gather(1)
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role2._all_gather(1)
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role2._barrier_server()
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role2._all_gather(1)
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role3 = GeneralRoleMaker(path="./test_gloo_3")
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role3._worker_gather(1)
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role3._worker_gather(1)
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os.environ["TRAINING_ROLE"] = "TRAINER"
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os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36002"
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role4 = GeneralRoleMaker(path="./test_gloo_4")
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role4._worker_gather(1)
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role4._get_rank()
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role4._get_size()
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role4._all_comm.init()
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role5 = GeneralRoleMaker(path="./test_gloo_5")
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role5.get_local_endpoint()
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role5.get_local_endpoint()
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role6 = GeneralRoleMaker(path="./test_gloo_6")
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role6.get_trainer_endpoints()
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role6.get_trainer_endpoints()
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role7 = GeneralRoleMaker(path="./test_gloo_7")
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role7.get_pserver_endpoints()
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role7.get_pserver_endpoints()
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role8 = GeneralRoleMaker(path="./test_gloo_8")
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role8.is_worker()
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role8.is_worker()
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role9 = GeneralRoleMaker(path="./test_gloo_9")
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role9.is_server()
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role9.is_server()
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role10 = GeneralRoleMaker(path="./test_gloo_10")
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role10.is_first_worker()
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role10.is_first_worker()
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role11 = GeneralRoleMaker(path="./test_gloo_11")
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role11.worker_index()
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role11.worker_index()
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role12 = GeneralRoleMaker(path="./test_gloo_12")
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role12.server_index()
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role12.server_index()
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role13 = GeneralRoleMaker(path="./test_gloo_13")
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role13.worker_num()
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role13.worker_num()
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role14 = GeneralRoleMaker(path="./test_gloo_14")
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role14.server_num()
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role14.server_num()
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role15 = GeneralRoleMaker(path="./test_gloo_15")
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role15._barrier_worker()
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role15._barrier_worker()
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role16 = GeneralRoleMaker(path="./test_gloo_16")
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role16._barrier_all()
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role16._barrier_all()
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role17 = GeneralRoleMaker(path="./test_gloo_17")
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role17._barrier_server()
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role17._barrier_server()
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role18 = GeneralRoleMaker(path="./test_gloo_18")
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role18._worker_num()
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role18._worker_num()
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role19 = GeneralRoleMaker(path="./test_gloo_19")
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role19._server_num()
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role19._server_num()
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role20 = GeneralRoleMaker(path="./test_gloo_20")
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a = [1]
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b = [0]
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role20._all_reduce(a, b)
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role21 = GeneralRoleMaker(path="./test_gloo_21")
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role21.all_reduce_worker([], [])
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role21.all_reduce_worker([], [])
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role21.barrier_worker()
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role21.barrier_all()
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role22 = GeneralRoleMaker(path="./test_gloo_22")
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role22._get_rank()
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role22._get_rank()
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os.environ["PADDLE_PSERVER_ID"] = "0"
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role23 = GeneralRoleMaker(path="./test_gloo_23")
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role23._get_size()
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role23._get_size()
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path = os.path.join(
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self.temp_dir.name, "test_fleet_gloo_role_maker_1.txt"
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)
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with open(path, "w") as f:
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data = "1 1 1 1\n"
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f.write(data)
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dataset = paddle.distributed.InMemoryDataset()
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dataset.set_filelist([path])
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dataset._set_use_var([show, label])
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dataset.load_into_memory()
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dataset.get_memory_data_size(fleet)
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dataset.get_shuffle_data_size(fleet)
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class TmpClass:
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"""
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dummy tmp class
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"""
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def __init__(self):
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pass
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def all_reduce_worker(self, input, output):
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"""
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dummy all reduce worker
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Args:
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input(None): fake input
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output(None): fake output
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"""
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pass
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def barrier_worker(self):
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"""
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dummy barrier worker
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"""
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pass
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from paddle.incubate.distributed.fleet.base import Fleet
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class TmpFleet(Fleet):
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"""
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dummy tmp fleet
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"""
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def __init__(self):
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super().__init__()
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self._role_maker = None
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def init_worker(self):
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"""
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dummy init worker
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"""
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pass
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def init_server(self, model_dir=None):
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"""
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dummy init server
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Args:
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model_dir(None): fake model_dir
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"""
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pass
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def run_server(self):
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"""
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dummy run server
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"""
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pass
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def stop_worker(self):
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"""
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dummy stop worker
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"""
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pass
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def distributed_optimizer(self, optimizer, strategy=None):
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"""
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dummy distributed optimizer
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Args:
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optimizer(None): fake optimizer
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strategy(None): fake strategy
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"""
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pass
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def save_inference_model(self):
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"""
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dummy save inference model
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"""
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pass
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def save_persistables(self):
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"""
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dummy save persistables
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"""
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pass
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os.environ["TRAINING_ROLE"] = "TRAINER"
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tmp = TmpFleet()
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tmp._role_maker = TmpClass()
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tmp.all_reduce_worker([], [])
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tmp.barrier_worker()
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from paddle.incubate.distributed.fleet.role_maker import (
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GeneralRoleMaker,
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)
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tmp = RoleMakerBase()
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tmp.all_gather(1)
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tmp.all_reduce_worker([], [])
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tmp.barrier_worker()
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tmp.barrier_all()
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from paddle.incubate.distributed.fleet.role_maker import (
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MPISymmetricRoleMaker,
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)
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tmp1 = MPISymmetricRoleMaker()
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tmp1.all_gather(1)
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tmp1.all_gather(1)
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tmp2 = MPISymmetricRoleMaker()
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tmp2.all_reduce_worker([], [])
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tmp3 = MPISymmetricRoleMaker()
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tmp3.barrier_worker()
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tmp3.barrier_worker()
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tmp4 = MPISymmetricRoleMaker()
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tmp4.barrier_all()
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tmp4.barrier_all()
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if __name__ == "__main__":
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unittest.main()
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