# 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 unittest import paddle.fluid as fluid import paddle.fluid.incubate.fleet.base.role_maker as role_maker from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory # 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.layers.embedding( input=q, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr), is_sparse=is_sparse) 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.layers.embedding( input=pt, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr), is_sparse=is_sparse) 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.layers.embedding( input=nt, is_distributed=is_distributed, size=[dict_dim, emb_dim], param_attr=fluid.ParamAttr( initializer=fluid.initializer.Constant(value=0.01), name="__emb__", learning_rate=emb_lr), is_sparse=is_sparse) 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): 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, acc, _ = self.net() optimizer = fluid.optimizer.SGD(base_lr) strategy = StrategyFactory.create_sync_strategy() optimizer = fleet.distributed_optimizer(optimizer, strategy) optimizer.minimize(loss) if __name__ == '__main__': unittest.main()