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deepwalk.py
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deepwalk.py
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import torch
import argparse
import dgl
import dgl.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 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)
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('--true_neg', default=False, action="store_true",
help="If not specified, this program will use "
"a faster negative sampling method, "
"but the samples might be false negative "
"with a small probability. If specified, "
"this program will generate a true negative sample table,"
"and select from it when doing negative samling")
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()
args.fast_neg = not args.true_neg
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))