dist_simnet_bow.py

#   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(op="sgd"):
    if op.upper() == "sgd".upper():
        optimizer = fluid.optimizer.SGD(learning_rate=base_lr)
    elif op.upper() == "adam".upper():
        optimizer = fluid.optimizer.Adam(learning_rate=base_lr)
    else:
        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.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)
    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.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)
    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.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)
    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 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 = os.getenv('OPTIMIZER', 'sgd')
        opt = get_optimizer(opt)
        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)