# 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 numpy as np
import argparse
import time
import math
import random
import paddle
import paddle.fluid as fluid
import paddle.fluid.profiler as profiler
from paddle.fluid import core
import unittest
from multiprocessing import Process
import os
import signal
from functools import reduce
from test_dist_base import TestDistRunnerBase, runtime_main
DTYPE = "int64"
DATA_URL = 'http://paddle-dist-ce-data.bj.bcebos.com/simnet.train.1000'
DATA_MD5 = '24e49366eb0611c552667989de2f57d5'
# 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
# Fix seed for test
fluid.default_startup_program().random_seed = 1
fluid.default_main_program().random_seed = 1
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
def get_optimizer():
# SGD optimizer
optimizer = fluid.optimizer.SGD(learning_rate=base_lr)
return optimizer
def train_network(batch_size,
is_distributed=False,
is_sparse=False,
is_self_contained_lr=False):
# 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) if is_self_contained_lr else fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__"),
is_sparse=is_sparse)
## 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) if is_self_contained_lr else fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__"),
is_sparse=is_sparse)
## 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) if is_self_contained_lr else fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.01),
name="__emb__"),
is_sparse=is_sparse)
## 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 combination(x, y):
res = [[[xi, yi] for yi in y] for xi in x]
return res[0]
def get_one_data(file_list):
for file in file_list:
contents = []
with open(file, "r") as fin:
for i in fin:
contents.append(i.strip())
for index, q in enumerate(contents):
try:
one_data = [[int(j) for j in i.split(" ")]
for i in q.split(";")[:-1]]
if one_data[1][0] + one_data[1][1] != len(one_data) - 3:
q = fin.readline()
continue
tmp = combination(one_data[3:3 + one_data[1][0]],
one_data[3 + one_data[1][0]:])
except Exception as e:
continue
for each in tmp:
yield [one_data[2], 0, each[0], each[1]]
def get_batch_reader(file_list, batch_size):
def batch_reader():
res = []
for i in get_one_data(file_list):
if random.random() <= sample_rate:
res.append(i)
if len(res) >= batch_size:
yield res
res = []
return batch_reader
def get_train_reader(batch_size):
# The training data set.
train_file = os.path.join(paddle.dataset.common.DATA_HOME, "simnet",
"train")
train_reader = get_batch_reader([train_file], batch_size)
train_feed = ["query_ids", "pos_title_ids", "neg_title_ids", "label"]
return train_reader, train_feed
class TestDistSimnetBow2x2(TestDistRunnerBase):
def get_model(self, batch_size=2):
# Train program
avg_cost, acc, predict = \
train_network(batch_size,
bool(int(os.environ["IS_DISTRIBUTED"])),
bool(int(os.environ["IS_SPARSE"])),
bool(int(os.environ["IS_SELF_CONTAINED_LR"])))
inference_program = fluid.default_main_program().clone()
# Optimization
opt = get_optimizer()
opt.minimize(avg_cost)
# Reader
train_reader, _ = get_train_reader(batch_size)
return inference_program, avg_cost, train_reader, train_reader, acc, predict
if __name__ == "__main__":
paddle.dataset.common.download(DATA_URL, 'simnet', DATA_MD5, "train")
runtime_main(TestDistSimnetBow2x2)