add unit accessor (#23703)
* add unit accessor in fleet, support DownpourUnitAccessor * test=developrevert-23830-2.0-beta
xujiaqi01
5 years ago
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@ -0,0 +1,94 @@
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# Copyright (c) 2020 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 fleet."""
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from __future__ import print_function
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import os
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import unittest
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import paddle.fluid.incubate.fleet.base.role_maker as role_maker
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class TestFleet1(unittest.TestCase):
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"""
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Test cases for fleet minimize.
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"""
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def setUp(self):
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"""Set up, set envs."""
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os.environ["PADDLE_TRAINERS_NUM"] = "2"
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os.environ[
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"PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001,127.0.0.2:36001"
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def test_pslib_1(self):
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"""Test cases for pslib."""
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import paddle.fluid as fluid
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from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
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from paddle.fluid.incubate.fleet.parameter_server.pslib import PSLib
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from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
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try:
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import netifaces
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except:
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print("warning: no netifaces, skip test_pslib_1")
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return
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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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role_maker = GeneralRoleMaker()
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role_maker.generate_role()
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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fleet.init(role_maker)
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train_program = fluid.Program()
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startup_program = fluid.Program()
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scope = fluid.Scope()
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with fluid.program_guard(train_program, startup_program):
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show = fluid.layers.data(name="show", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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emb = fluid.layers.embedding(input=show, size=[1, 1], \
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is_sparse=True, is_distributed=True, \
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param_attr=fluid.ParamAttr(name="embedding"))
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fc = fluid.layers.fc(input=emb, size=1, act=None)
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label = fluid.layers.data(name="click", shape=[-1, 1], \
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dtype="int64", lod_level=1, append_batch_size=False)
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label_cast = fluid.layers.cast(label, dtype='float32')
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cost = fluid.layers.log_loss(fc, label_cast)
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strategy = {}
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strategy["embedding"] = {}
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strategy["embedding"]["sparse_accessor_class"] = "DownpourUnitAccessor"
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strategy["embedding"]["embed_sparse_optimizer"] = "naive"
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try:
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adam1 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam1 = fleet.distributed_optimizer(adam1, strategy=strategy)
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adam1.minimize([cost], [scope])
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strategy["embedding"]["embed_sparse_optimizer"] = "adagrad"
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adam2 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam2 = fleet.distributed_optimizer(adam2, strategy=strategy)
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adam2.minimize([cost], [scope])
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strategy["embedding"]["embed_sparse_optimizer"] = "adam"
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adam3 = fluid.optimizer.Adam(learning_rate=0.000005)
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adam3 = fleet.distributed_optimizer(adam3, strategy=strategy)
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adam3.minimize([cost], [scope])
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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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if __name__ == "__main__":
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unittest.main()
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