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