# 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 os import unittest import tempfile import shutil import paddle import paddle.fluid as fluid import paddle.fluid.incubate.fleet.base.role_maker as role_maker import paddle.fleet as fleet # 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 TestPSPassWithBow(unittest.TestCase): def net(self): def get_acc(cos_q_nt, cos_q_pt, batch_size): cond = fluid.layers.less_than(cos_q_nt, cos_q_pt) cond = fluid.layers.cast(cond, dtype='float64') cond_3 = fluid.layers.reduce_sum(cond) acc = fluid.layers.elementwise_div( cond_3, fluid.layers.fill_constant( shape=[1], value=batch_size * 1.0, dtype='float64'), name="simnet_acc") return acc def get_loss(cos_q_pt, cos_q_nt): loss_op1 = fluid.layers.elementwise_sub( fluid.layers.fill_constant_batch_size_like( input=cos_q_pt, shape=[-1, 1], value=margin, dtype='float32'), cos_q_pt) loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt) loss_op3 = fluid.layers.elementwise_max( fluid.layers.fill_constant_batch_size_like( input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'), loss_op2) avg_cost = fluid.layers.mean(loss_op3) return avg_cost is_distributed = False is_sparse = True # query q = fluid.layers.data( name="query_ids", shape=[1], dtype="int64", lod_level=1) # embedding q_emb = fluid.contrib.layers.sparse_embedding( input=q, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr)) q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim]) # vsum q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum') q_ss = fluid.layers.softsign(q_sum) # fc layer after conv q_fc = fluid.layers.fc( input=q_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__q_fc__", learning_rate=base_lr)) # label data label = fluid.layers.data(name="label", shape=[1], dtype="int64") # pt pt = fluid.layers.data( name="pos_title_ids", shape=[1], dtype="int64", lod_level=1) # embedding pt_emb = fluid.contrib.layers.sparse_embedding( input=pt, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr)) pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim]) # vsum pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum') pt_ss = fluid.layers.softsign(pt_sum) # fc layer pt_fc = fluid.layers.fc( input=pt_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__fc__", learning_rate=base_lr), bias_attr=fluid.ParamAttr(name="__fc_b__")) # nt nt = fluid.layers.data( name="neg_title_ids", shape=[1], dtype="int64", lod_level=1) # embedding nt_emb = fluid.contrib.layers.sparse_embedding( input=nt, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr)) nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim]) # vsum nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum') nt_ss = fluid.layers.softsign(nt_sum) # fc layer nt_fc = fluid.layers.fc( input=nt_ss, size=hid_dim, param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__fc__", learning_rate=base_lr), bias_attr=fluid.ParamAttr(name="__fc_b__")) cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc) cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc) # loss avg_cost = get_loss(cos_q_pt, cos_q_nt) # acc acc = get_acc(cos_q_nt, cos_q_pt, batch_size) return [avg_cost, acc, cos_q_pt] def test(self): os.environ["PADDLE_PSERVER_NUMS"] = "2" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["POD_IP"] = "127.0.0.1" os.environ["PADDLE_PORT"] = "36001" os.environ["PADDLE_TRAINER_ID"] = "0" os.environ["PADDLE_TRAINERS_NUM"] = "2" os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \ "127.0.0.1:36001,127.0.0.2:36001" os.environ["TRAINING_ROLE"] = "PSERVER" role = role_maker.PaddleCloudRoleMaker() fleet.init(role) loss, acc, _ = self.net() strategy = paddle.fleet.DistributedStrategy() strategy.a_sync = True optimizer = paddle.optimizer.SGD(learning_rate=0.01) optimizer = fleet.distributed_optimizer(optimizer, strategy=strategy) optimizer.minimize(loss) model_dir = tempfile.mkdtemp() with self.assertRaises(ValueError): fleet.init_server(os.path.join(model_dir, "temp"), "xxxx") with self.assertRaises(ValueError): fleet.init_server(os.path.join(model_dir, "temp")) fleet.init_server() from paddle.fluid.communicator import LargeScaleKV kv = LargeScaleKV() kv.save("__emb__.block0", os.path.join(model_dir, "__emb__", "__emb__.block0")) fluid.framework.switch_main_program(fluid.Program()) fleet.init_server(model_dir) shutil.rmtree(model_dir) if __name__ == '__main__': unittest.main()