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182 lines
6.2 KiB
182 lines
6.2 KiB
# Copyright (c) 2018 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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from __future__ import print_function
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import os
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import unittest
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import paddle
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paddle.enable_static()
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import paddle.fluid as fluid
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import paddle.distributed.fleet.base.role_maker as role_maker
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import paddle.distributed.fleet as fleet
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# For Net
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base_lr = 0.2
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emb_lr = base_lr * 3
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dict_dim = 1500
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emb_dim = 128
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hid_dim = 128
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margin = 0.1
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sample_rate = 1
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batch_size = 4
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class TestPSPassWithBow(unittest.TestCase):
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def net(self):
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def get_acc(cos_q_nt, cos_q_pt, batch_size):
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cond = fluid.layers.less_than(cos_q_nt, cos_q_pt)
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cond = fluid.layers.cast(cond, dtype='float64')
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cond_3 = fluid.layers.reduce_sum(cond)
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acc = fluid.layers.elementwise_div(
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cond_3,
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fluid.layers.fill_constant(
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shape=[1], value=batch_size * 1.0, dtype='float64'),
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name="simnet_acc")
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return acc
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def get_loss(cos_q_pt, cos_q_nt):
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loss_op1 = fluid.layers.elementwise_sub(
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fluid.layers.fill_constant_batch_size_like(
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input=cos_q_pt,
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shape=[-1, 1],
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value=margin,
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dtype='float32'),
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cos_q_pt)
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loss_op2 = fluid.layers.elementwise_add(loss_op1, cos_q_nt)
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loss_op3 = fluid.layers.elementwise_max(
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fluid.layers.fill_constant_batch_size_like(
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input=loss_op2, shape=[-1, 1], value=0.0, dtype='float32'),
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loss_op2)
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avg_cost = fluid.layers.mean(loss_op3)
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return avg_cost
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is_distributed = False
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is_sparse = True
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# query
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q = fluid.layers.data(
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name="query_ids", shape=[1], dtype="int64", lod_level=1)
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# embedding
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q_emb = fluid.layers.embedding(
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input=q,
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is_distributed=is_distributed,
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size=[dict_dim, emb_dim],
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__emb__",
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learning_rate=emb_lr),
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is_sparse=is_sparse)
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q_emb = fluid.layers.reshape(q_emb, [-1, emb_dim])
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# vsum
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q_sum = fluid.layers.sequence_pool(input=q_emb, pool_type='sum')
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q_ss = fluid.layers.softsign(q_sum)
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# fc layer after conv
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q_fc = fluid.layers.fc(
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input=q_ss,
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size=hid_dim,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__q_fc__",
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learning_rate=base_lr))
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# label data
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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# pt
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pt = fluid.layers.data(
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name="pos_title_ids", shape=[1], dtype="int64", lod_level=1)
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# embedding
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pt_emb = fluid.layers.embedding(
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input=pt,
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is_distributed=is_distributed,
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size=[dict_dim, emb_dim],
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__emb__",
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learning_rate=emb_lr),
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is_sparse=is_sparse)
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pt_emb = fluid.layers.reshape(pt_emb, [-1, emb_dim])
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# vsum
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pt_sum = fluid.layers.sequence_pool(input=pt_emb, pool_type='sum')
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pt_ss = fluid.layers.softsign(pt_sum)
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# fc layer
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pt_fc = fluid.layers.fc(
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input=pt_ss,
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size=hid_dim,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__fc__",
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learning_rate=base_lr),
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bias_attr=fluid.ParamAttr(name="__fc_b__"))
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# nt
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nt = fluid.layers.data(
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name="neg_title_ids", shape=[1], dtype="int64", lod_level=1)
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# embedding
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nt_emb = fluid.layers.embedding(
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input=nt,
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is_distributed=is_distributed,
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size=[dict_dim, emb_dim],
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__emb__",
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learning_rate=emb_lr),
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is_sparse=is_sparse)
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nt_emb = fluid.layers.reshape(nt_emb, [-1, emb_dim])
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# vsum
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nt_sum = fluid.layers.sequence_pool(input=nt_emb, pool_type='sum')
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nt_ss = fluid.layers.softsign(nt_sum)
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# fc layer
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nt_fc = fluid.layers.fc(
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input=nt_ss,
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size=hid_dim,
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param_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=0.01),
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name="__fc__",
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learning_rate=base_lr),
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bias_attr=fluid.ParamAttr(name="__fc_b__"))
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cos_q_pt = fluid.layers.cos_sim(q_fc, pt_fc)
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cos_q_nt = fluid.layers.cos_sim(q_fc, nt_fc)
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# loss
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avg_cost = get_loss(cos_q_pt, cos_q_nt)
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# acc
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acc = get_acc(cos_q_nt, cos_q_pt, batch_size)
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return [avg_cost, acc, cos_q_pt]
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def test(self):
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endpoints = [
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"127.0.0.1:36004", "127.0.0.1:36005", "127.0.0.1:36006",
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"127.0.0.1:36007"
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]
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role = fleet.UserDefinedRoleMaker(
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current_id=0,
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role=role_maker.Role.SERVER,
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worker_num=2,
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server_endpoints=endpoints)
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fleet.init(role)
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loss, acc, _ = self.net()
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optimizer = fluid.optimizer.SGD(base_lr)
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strategy = paddle.distributed.fleet.DistributedStrategy()
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strategy.a_sync = True
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optimizer = fleet.distributed_optimizer(optimizer, strategy)
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optimizer.minimize(loss)
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if __name__ == '__main__':
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unittest.main()
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