dmlc--dgl
Chao Ma
635dfb4a59
* update metis * update * update dataloader * update dataloader * new script * update * update * update * update * update * update * update * update dataloader * update * update * update * update * update * update * update * Add license to every filer header
402 行
22 KiB
Python
402 行
22 KiB
Python
# -*- coding: utf-8 -*-
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#
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# setup.py
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#
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# Copyright 2018 Amazon.com, Inc. or its affiliates. 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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#
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from dataloader import EvalDataset, TrainDataset, NewBidirectionalOneShotIterator
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from dataloader import get_dataset
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import argparse
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import os
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import logging
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import time
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import json
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from utils import get_compatible_batch_size
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backend = os.environ.get('DGLBACKEND', 'pytorch')
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if backend.lower() == 'mxnet':
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import multiprocessing as mp
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from train_mxnet import load_model
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from train_mxnet import train
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from train_mxnet import test
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else:
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import torch.multiprocessing as mp
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from train_pytorch import load_model
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from train_pytorch import train, train_mp
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from train_pytorch import test, test_mp
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class ArgParser(argparse.ArgumentParser):
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def __init__(self):
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super(ArgParser, self).__init__()
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self.add_argument('--model_name', default='TransE',
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choices=['TransE', 'TransE_l1', 'TransE_l2', 'TransR',
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'RESCAL', 'DistMult', 'ComplEx', 'RotatE'],
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help='model to use')
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self.add_argument('--data_path', type=str, default='data',
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help='root path of all dataset')
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self.add_argument('--dataset', type=str, default='FB15k',
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help='dataset name, under data_path')
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self.add_argument('--format', type=str, default='built_in',
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help='the format of the dataset, it can be built_in,'\
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'raw_udd_{htr} and udd_{htr}')
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self.add_argument('--data_files', type=str, default=None, nargs='+',
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help='a list of data files, e.g. entity relation train valid test')
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self.add_argument('--save_path', type=str, default='ckpts',
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help='place to save models and logs')
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self.add_argument('--save_emb', type=str, default=None,
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help='save the embeddings in the specific location.')
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self.add_argument('--max_step', type=int, default=80000,
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help='train xx steps')
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self.add_argument('--batch_size', type=int, default=1024,
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help='batch size')
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self.add_argument('--batch_size_eval', type=int, default=8,
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help='batch size used for eval and test')
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self.add_argument('--neg_sample_size', type=int, default=128,
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help='negative sampling size')
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self.add_argument('--neg_deg_sample', action='store_true',
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help='negative sample proportional to vertex degree in the training')
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self.add_argument('--neg_deg_sample_eval', action='store_true',
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help='negative sampling proportional to vertex degree in the evaluation')
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self.add_argument('--neg_sample_size_eval', type=int, default=-1,
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help='negative sampling size for evaluation')
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self.add_argument('--eval_percent', type=float, default=1,
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help='sample some percentage for evaluation.')
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self.add_argument('--hidden_dim', type=int, default=256,
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help='hidden dim used by relation and entity')
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self.add_argument('--lr', type=float, default=0.0001,
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help='learning rate')
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self.add_argument('-g', '--gamma', type=float, default=12.0,
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help='margin value')
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self.add_argument('--no_eval_filter', action='store_true',
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help='do not filter positive edges among negative edges for evaluation')
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self.add_argument('--gpu', type=int, default=[-1], nargs='+',
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help='a list of active gpu ids, e.g. 0 1 2 4')
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self.add_argument('--mix_cpu_gpu', action='store_true',
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help='mix CPU and GPU training')
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self.add_argument('-de', '--double_ent', action='store_true',
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help='double entitiy dim for complex number')
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self.add_argument('-dr', '--double_rel', action='store_true',
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help='double relation dim for complex number')
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self.add_argument('-log', '--log_interval', type=int, default=1000,
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help='do evaluation after every x steps')
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self.add_argument('--eval_interval', type=int, default=10000,
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help='do evaluation after every x steps')
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self.add_argument('-adv', '--neg_adversarial_sampling', action='store_true',
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help='if use negative adversarial sampling')
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self.add_argument('-a', '--adversarial_temperature', default=1.0, type=float,
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help='adversarial_temperature')
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self.add_argument('--valid', action='store_true',
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help='if valid a model')
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self.add_argument('--test', action='store_true',
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help='if test a model')
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self.add_argument('-rc', '--regularization_coef', type=float, default=0.000002,
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help='set value > 0.0 if regularization is used')
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self.add_argument('-rn', '--regularization_norm', type=int, default=3,
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help='norm used in regularization')
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self.add_argument('--non_uni_weight', action='store_true',
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help='if use uniform weight when computing loss')
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self.add_argument('--pickle_graph', action='store_true',
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help='pickle built graph, building a huge graph is slow.')
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self.add_argument('--num_proc', type=int, default=1,
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help='number of process used')
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self.add_argument('--num_thread', type=int, default=1,
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help='number of thread used')
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self.add_argument('--rel_part', action='store_true',
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help='enable relation partitioning')
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self.add_argument('--soft_rel_part', action='store_true',
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help='enable soft relation partition')
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self.add_argument('--async_update', action='store_true',
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help='allow async_update on node embedding')
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self.add_argument('--force_sync_interval', type=int, default=-1,
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help='We force a synchronization between processes every x steps')
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def get_logger(args):
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if not os.path.exists(args.save_path):
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os.mkdir(args.save_path)
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folder = '{}_{}_'.format(args.model_name, args.dataset)
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n = len([x for x in os.listdir(args.save_path) if x.startswith(folder)])
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folder += str(n)
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args.save_path = os.path.join(args.save_path, folder)
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if not os.path.exists(args.save_path):
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os.makedirs(args.save_path)
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log_file = os.path.join(args.save_path, 'train.log')
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logging.basicConfig(
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format='%(asctime)s %(levelname)-8s %(message)s',
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level=logging.INFO,
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datefmt='%Y-%m-%d %H:%M:%S',
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filename=log_file,
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filemode='w'
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)
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logger = logging.getLogger(__name__)
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print("Logs are being recorded at: {}".format(log_file))
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return logger
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def run(args, logger):
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init_time_start = time.time()
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# load dataset and samplers
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dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)
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if args.neg_sample_size_eval < 0:
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args.neg_sample_size_eval = dataset.n_entities
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args.batch_size = get_compatible_batch_size(args.batch_size, args.neg_sample_size)
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args.batch_size_eval = get_compatible_batch_size(args.batch_size_eval, args.neg_sample_size_eval)
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args.eval_filter = not args.no_eval_filter
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if args.neg_deg_sample_eval:
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assert not args.eval_filter, "if negative sampling based on degree, we can't filter positive edges."
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train_data = TrainDataset(dataset, args, ranks=args.num_proc)
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# if there is no cross partition relaiton, we fall back to strict_rel_part
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args.strict_rel_part = args.mix_cpu_gpu and (train_data.cross_part == False)
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args.soft_rel_part = args.mix_cpu_gpu and args.soft_rel_part and train_data.cross_part
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args.num_workers = 8 # fix num_worker to 8
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if args.num_proc > 1:
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train_samplers = []
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for i in range(args.num_proc):
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train_sampler_head = train_data.create_sampler(args.batch_size,
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args.neg_sample_size,
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args.neg_sample_size,
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mode='head',
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num_workers=args.num_workers,
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shuffle=True,
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exclude_positive=False,
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rank=i)
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train_sampler_tail = train_data.create_sampler(args.batch_size,
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args.neg_sample_size,
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args.neg_sample_size,
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mode='tail',
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num_workers=args.num_workers,
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shuffle=True,
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exclude_positive=False,
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rank=i)
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train_samplers.append(NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
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args.neg_sample_size, args.neg_sample_size,
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True, dataset.n_entities))
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train_sampler = NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
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args.neg_sample_size, args.neg_sample_size,
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True, dataset.n_entities)
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else: # This is used for debug
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train_sampler_head = train_data.create_sampler(args.batch_size,
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args.neg_sample_size,
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args.neg_sample_size,
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mode='head',
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num_workers=args.num_workers,
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shuffle=True,
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exclude_positive=False)
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train_sampler_tail = train_data.create_sampler(args.batch_size,
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args.neg_sample_size,
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args.neg_sample_size,
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mode='tail',
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num_workers=args.num_workers,
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shuffle=True,
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exclude_positive=False)
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train_sampler = NewBidirectionalOneShotIterator(train_sampler_head, train_sampler_tail,
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args.neg_sample_size, args.neg_sample_size,
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True, dataset.n_entities)
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if args.valid or args.test:
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if len(args.gpu) > 1:
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args.num_test_proc = args.num_proc if args.num_proc < len(args.gpu) else len(args.gpu)
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else:
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args.num_test_proc = args.num_proc
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eval_dataset = EvalDataset(dataset, args)
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if args.valid:
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if args.num_proc > 1:
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valid_sampler_heads = []
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valid_sampler_tails = []
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for i in range(args.num_proc):
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valid_sampler_head = eval_dataset.create_sampler('valid', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_proc)
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valid_sampler_tail = eval_dataset.create_sampler('valid', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_proc)
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valid_sampler_heads.append(valid_sampler_head)
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valid_sampler_tails.append(valid_sampler_tail)
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else: # This is used for debug
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valid_sampler_head = eval_dataset.create_sampler('valid', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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valid_sampler_tail = eval_dataset.create_sampler('valid', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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if args.test:
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if args.num_test_proc > 1:
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test_sampler_tails = []
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test_sampler_heads = []
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for i in range(args.num_test_proc):
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test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_test_proc)
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test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_test_proc)
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test_sampler_heads.append(test_sampler_head)
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test_sampler_tails.append(test_sampler_tail)
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else:
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test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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# load model
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model = load_model(logger, args, dataset.n_entities, dataset.n_relations)
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if args.num_proc > 1 or args.async_update:
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model.share_memory()
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# We need to free all memory referenced by dataset.
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eval_dataset = None
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dataset = None
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print('Total initialize time {:.3f} seconds'.format(time.time() - init_time_start))
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# train
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start = time.time()
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rel_parts = train_data.rel_parts if args.strict_rel_part or args.soft_rel_part else None
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cross_rels = train_data.cross_rels if args.soft_rel_part else None
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if args.num_proc > 1:
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procs = []
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barrier = mp.Barrier(args.num_proc)
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for i in range(args.num_proc):
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valid_sampler = [valid_sampler_heads[i], valid_sampler_tails[i]] if args.valid else None
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proc = mp.Process(target=train_mp, args=(args,
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model,
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train_samplers[i],
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valid_sampler,
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i,
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rel_parts,
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cross_rels,
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barrier))
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procs.append(proc)
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proc.start()
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for proc in procs:
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proc.join()
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else:
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valid_samplers = [valid_sampler_head, valid_sampler_tail] if args.valid else None
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train(args, model, train_sampler, valid_samplers, rel_parts=rel_parts)
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print('training takes {} seconds'.format(time.time() - start))
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if args.save_emb is not None:
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if not os.path.exists(args.save_emb):
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os.mkdir(args.save_emb)
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model.save_emb(args.save_emb, args.dataset)
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# We need to save the model configurations as well.
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conf_file = os.path.join(args.save_emb, 'config.json')
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with open(conf_file, 'w') as outfile:
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json.dump({'dataset': args.dataset,
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'model': args.model_name,
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'emb_size': args.hidden_dim,
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'max_train_step': args.max_step,
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'batch_size': args.batch_size,
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'neg_sample_size': args.neg_sample_size,
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'lr': args.lr,
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'gamma': args.gamma,
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'double_ent': args.double_ent,
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'double_rel': args.double_rel,
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'neg_adversarial_sampling': args.neg_adversarial_sampling,
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'adversarial_temperature': args.adversarial_temperature,
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'regularization_coef': args.regularization_coef,
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'regularization_norm': args.regularization_norm},
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outfile, indent=4)
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# test
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if args.test:
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start = time.time()
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if args.num_test_proc > 1:
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queue = mp.Queue(args.num_test_proc)
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procs = []
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for i in range(args.num_test_proc):
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proc = mp.Process(target=test_mp, args=(args,
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model,
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[test_sampler_heads[i], test_sampler_tails[i]],
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i,
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'Test',
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queue))
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procs.append(proc)
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proc.start()
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total_metrics = {}
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metrics = {}
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logs = []
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for i in range(args.num_test_proc):
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log = queue.get()
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logs = logs + log
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for metric in logs[0].keys():
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metrics[metric] = sum([log[metric] for log in logs]) / len(logs)
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for k, v in metrics.items():
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print('Test average {} : {}'.format(k, v))
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for proc in procs:
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proc.join()
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else:
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test(args, model, [test_sampler_head, test_sampler_tail])
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print('testing takes {:.3f} seconds'.format(time.time() - start))
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if __name__ == '__main__':
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args = ArgParser().parse_args()
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logger = get_logger(args)
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run(args, logger)
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