# Copyright (c) 2018 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. from __future__ import print_function import paddle.fluid as fluid import paddle.distributed.fleet.base.role_maker as role_maker import paddle.distributed.fleet as fleet import unittest import paddle import os paddle.enable_static() # For Net base_lr = 0.2 emb_lr = base_lr * 3 dict_dim = 1500 emb_dim = 128 hid_dim = 128 margin = 0.1 sample_rate = 1 batch_size = 4 class TestExponentialDecay(unittest.TestCase): def net(self): input_data = paddle.static.data( name="sparse_input", shape=[None, 1], dtype="int64") input_label = paddle.static.data( name="label", shape=[None, 1], dtype="int64") label = paddle.cast(input_label, dtype="float32") embedding = paddle.static.nn.embedding( input_data, is_sparse=True, size=[1000, 128]) fc1 = paddle.static.nn.fc(embedding, size=1024, activation="relu") fc2 = paddle.static.nn.fc(fc1, size=512, activation="relu") fc3 = paddle.static.nn.fc(fc2, size=256, activation="relu") predict = paddle.static.nn.fc(fc3, size=2, activation="softmax") label = paddle.cast(label, dtype="int64") cost = paddle.nn.functional.cross_entropy(input=predict, label=label) return cost def test(self): endpoints = [ "127.0.0.1:36004", "127.0.0.1:36005", "127.0.0.1:36006", "127.0.0.1:36007" ] role = role_maker.UserDefinedRoleMaker( current_id=0, role=role_maker.Role.SERVER, worker_num=2, server_endpoints=endpoints) fleet.init(role) loss = self.net() scheduler = paddle.optimizer.lr.InverseTimeDecay( learning_rate=base_lr, gamma=0.999, verbose=True) optimizer = fluid.optimizer.Adam(scheduler) strategy = paddle.distributed.fleet.DistributedStrategy() strategy.a_sync = True optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(loss) fleet.init_server() if __name__ == '__main__': os.environ["GLOG_v"] = "4" os.environ["GLOG_logtostderr"] = "1" unittest.main()