import torch import argparse import dgl import torch.multiprocessing as mp from torch.utils.data import DataLoader import os import random import time import numpy as np from reading_data import DeepwalkDataset from model import SkipGramModel from utils import thread_wrapped_func, shuffle_walks, sum_up_params class DeepwalkTrainer: def __init__(self, args): """ Initializing the trainer with the input arguments """ self.args = args self.dataset = DeepwalkDataset( net_file=args.data_file, map_file=args.map_file, walk_length=args.walk_length, window_size=args.window_size, num_walks=args.num_walks, batch_size=args.batch_size, negative=args.negative, gpus=args.gpus, fast_neg=args.fast_neg, ogbl_name=args.ogbl_name, load_from_ogbl=args.load_from_ogbl, ) self.emb_size = self.dataset.G.number_of_nodes() self.emb_model = None def init_device_emb(self): """ set the device before training will be called once in fast_train_mp / fast_train """ choices = sum([self.args.only_gpu, self.args.only_cpu, self.args.mix]) assert choices == 1, "Must choose only *one* training mode in [only_cpu, only_gpu, mix]" # initializing embedding on CPU self.emb_model = SkipGramModel( emb_size=self.emb_size, emb_dimension=self.args.dim, walk_length=self.args.walk_length, window_size=self.args.window_size, batch_size=self.args.batch_size, only_cpu=self.args.only_cpu, only_gpu=self.args.only_gpu, mix=self.args.mix, neg_weight=self.args.neg_weight, negative=self.args.negative, lr=self.args.lr, lap_norm=self.args.lap_norm, fast_neg=self.args.fast_neg, record_loss=self.args.print_loss, norm=self.args.norm, use_context_weight=self.args.use_context_weight, async_update=self.args.async_update, num_threads=self.args.num_threads, ) torch.set_num_threads(self.args.num_threads) if self.args.only_gpu: print("Run in 1 GPU") assert self.args.gpus[0] >= 0 self.emb_model.all_to_device(self.args.gpus[0]) elif self.args.mix: print("Mix CPU with %d GPU" % len(self.args.gpus)) if len(self.args.gpus) == 1: assert self.args.gpus[0] >= 0, 'mix CPU with GPU should have available GPU' self.emb_model.set_device(self.args.gpus[0]) else: print("Run in CPU process") self.args.gpus = [torch.device('cpu')] def train(self): """ train the embedding """ if len(self.args.gpus) > 1: self.fast_train_mp() else: self.fast_train() def fast_train_mp(self): """ multi-cpu-core or mix cpu & multi-gpu """ self.init_device_emb() self.emb_model.share_memory() if self.args.count_params: sum_up_params(self.emb_model) start_all = time.time() ps = [] for i in range(len(self.args.gpus)): p = mp.Process(target=self.fast_train_sp, args=(i, self.args.gpus[i])) ps.append(p) p.start() for p in ps: p.join() print("Used time: %.2fs" % (time.time()-start_all)) if self.args.save_in_txt: self.emb_model.save_embedding_txt(self.dataset, self.args.output_emb_file) elif self.args.save_in_pt: self.emb_model.save_embedding_pt(self.dataset, self.args.output_emb_file) else: self.emb_model.save_embedding(self.dataset, self.args.output_emb_file) @thread_wrapped_func def fast_train_sp(self, rank, gpu_id): """ a subprocess for fast_train_mp """ if self.args.mix: self.emb_model.set_device(gpu_id) torch.set_num_threads(self.args.num_threads) if self.args.async_update: self.emb_model.create_async_update() sampler = self.dataset.create_sampler(rank) dataloader = DataLoader( dataset=sampler.seeds, batch_size=self.args.batch_size, collate_fn=sampler.sample, shuffle=False, drop_last=False, num_workers=self.args.num_sampler_threads, ) num_batches = len(dataloader) print("num batchs: %d in process [%d] GPU [%d]" % (num_batches, rank, gpu_id)) # number of positive node pairs in a sequence num_pos = int(2 * self.args.walk_length * self.args.window_size\ - self.args.window_size * (self.args.window_size + 1)) start = time.time() with torch.no_grad(): for i, walks in enumerate(dataloader): if self.args.fast_neg: self.emb_model.fast_learn(walks) else: # do negative sampling bs = len(walks) neg_nodes = torch.LongTensor( np.random.choice(self.dataset.neg_table, bs * num_pos * self.args.negative, replace=True)) self.emb_model.fast_learn(walks, neg_nodes=neg_nodes) if i > 0 and i % self.args.print_interval == 0: if self.args.print_loss: print("GPU-[%d] batch %d time: %.2fs loss: %.4f" \ % (gpu_id, i, time.time()-start, -sum(self.emb_model.loss)/self.args.print_interval)) self.emb_model.loss = [] else: print("GPU-[%d] batch %d time: %.2fs" % (gpu_id, i, time.time()-start)) start = time.time() if self.args.async_update: self.emb_model.finish_async_update() def fast_train(self): """ fast train with dataloader with only gpu / only cpu""" # the number of postive node pairs of a node sequence num_pos = 2 * self.args.walk_length * self.args.window_size\ - self.args.window_size * (self.args.window_size + 1) num_pos = int(num_pos) self.init_device_emb() if self.args.async_update: self.emb_model.share_memory() self.emb_model.create_async_update() if self.args.count_params: sum_up_params(self.emb_model) sampler = self.dataset.create_sampler(0) dataloader = DataLoader( dataset=sampler.seeds, batch_size=self.args.batch_size, collate_fn=sampler.sample, shuffle=False, drop_last=False, num_workers=self.args.num_sampler_threads, ) num_batches = len(dataloader) print("num batchs: %d\n" % num_batches) start_all = time.time() start = time.time() with torch.no_grad(): max_i = num_batches for i, walks in enumerate(dataloader): if self.args.fast_neg: self.emb_model.fast_learn(walks) else: # do negative sampling bs = len(walks) neg_nodes = torch.LongTensor( np.random.choice(self.dataset.neg_table, bs * num_pos * self.args.negative, replace=True)) self.emb_model.fast_learn(walks, neg_nodes=neg_nodes) if i > 0 and i % self.args.print_interval == 0: if self.args.print_loss: print("Batch %d training time: %.2fs loss: %.4f" \ % (i, time.time()-start, -sum(self.emb_model.loss)/self.args.print_interval)) self.emb_model.loss = [] else: print("Batch %d, training time: %.2fs" % (i, time.time()-start)) start = time.time() if self.args.async_update: self.emb_model.finish_async_update() print("Training used time: %.2fs" % (time.time()-start_all)) if self.args.save_in_txt: self.emb_model.save_embedding_txt(self.dataset, self.args.output_emb_file) elif self.args.save_in_pt: self.emb_model.save_embedding_pt(self.dataset, self.args.output_emb_file) else: self.emb_model.save_embedding(self.dataset, self.args.output_emb_file) if __name__ == '__main__': parser = argparse.ArgumentParser(description="DeepWalk") # input files ## personal datasets parser.add_argument('--data_file', type=str, help="path of the txt network file, builtin dataset include youtube-net and blog-net") ## ogbl datasets parser.add_argument('--ogbl_name', type=str, help="name of ogbl dataset, e.g. ogbl-ddi") parser.add_argument('--load_from_ogbl', default=False, action="store_true", help="whether load dataset from ogbl") # output files parser.add_argument('--save_in_txt', default=False, action="store_true", help='Whether save dat in txt format or npy') parser.add_argument('--save_in_pt', default=False, action="store_true", help='Whether save dat in pt format or npy') parser.add_argument('--output_emb_file', type=str, default="emb.npy", help='path of the output npy embedding file') parser.add_argument('--map_file', type=str, default="nodeid_to_index.pickle", help='path of the mapping dict that maps node ids to embedding index') parser.add_argument('--norm', default=False, action="store_true", help="whether to do normalization over node embedding after training") # model parameters parser.add_argument('--dim', default=128, type=int, help="embedding dimensions") parser.add_argument('--window_size', default=5, type=int, help="context window size") parser.add_argument('--use_context_weight', default=False, action="store_true", help="whether to add weights over nodes in the context window") parser.add_argument('--num_walks', default=10, type=int, help="number of walks for each node") parser.add_argument('--negative', default=1, type=int, help="negative samples for each positve node pair") parser.add_argument('--batch_size', default=128, type=int, help="number of node sequences in each batch") parser.add_argument('--walk_length', default=80, type=int, help="number of nodes in a sequence") parser.add_argument('--neg_weight', default=1., type=float, help="negative weight") parser.add_argument('--lap_norm', default=0.01, type=float, help="weight of laplacian normalization, recommend to set as 0.1 / windoe_size") # training parameters parser.add_argument('--print_interval', default=100, type=int, help="number of batches between printing") parser.add_argument('--print_loss', default=False, action="store_true", help="whether print loss during training") parser.add_argument('--lr', default=0.2, type=float, help="learning rate") # optimization settings parser.add_argument('--mix', default=False, action="store_true", help="mixed training with CPU and GPU") parser.add_argument('--gpus', type=int, default=[-1], nargs='+', help='a list of active gpu ids, e.g. 0, used with --mix') parser.add_argument('--only_cpu', default=False, action="store_true", help="training with CPU") parser.add_argument('--only_gpu', default=False, action="store_true", help="training with GPU") parser.add_argument('--async_update', default=False, action="store_true", help="mixed training asynchronously, not recommended") parser.add_argument('--fast_neg', default=False, action="store_true", help="do negative sampling inside a batch") parser.add_argument('--num_threads', default=8, type=int, help="number of threads used for each CPU-core/GPU") parser.add_argument('--num_sampler_threads', default=2, type=int, help="number of threads used for sampling") parser.add_argument('--count_params', default=False, action="store_true", help="count the params, exit once counting over") args = parser.parse_args() if args.async_update: assert args.mix, "--async_update only with --mix" start_time = time.time() trainer = DeepwalkTrainer(args) trainer.train() print("Total used time: %.2f" % (time.time() - start_time))