# 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)