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,
)
self.emb_size = len(self.dataset.net)
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]"
choices = sum([self.args.sgd, self.args.adam, self.args.avg_sgd])
assert choices == 1, "Must choose only *one* gradient descent strategy in [sgd, avg_sgd, adam]"
# 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,
adam=self.args.adam,
sgd=self.args.sgd,
avg_sgd=self.args.avg_sgd,
fast_neg=self.args.fast_neg,
)
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 abaliable 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()
start_all = time.time()
ps = []
for i in range(len(self.args.gpus)):
p = mp.Process(target=self.fast_train_sp,
args=(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)
else:
self.emb_model.save_embedding(self.dataset,
self.args.output_emb_file)
@thread_wrapped_func
def fast_train_sp(self, 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)
sampler = self.dataset.create_sampler(gpu_id)
dataloader = DataLoader(
dataset=sampler.seeds,
batch_size=self.args.batch_size,
collate_fn=sampler.sample,
shuffle=False,
drop_last=False,
num_workers=4,
)
num_batches = len(dataloader)
print("num batchs: %d in subprocess [%d]" % (num_batches, 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():
max_i = self.args.iterations * num_batches
for i, walks in enumerate(dataloader):
# decay learning rate for SGD
lr = self.args.lr * (max_i - i) / max_i
if lr < 0.00001:
lr = 0.00001
if self.args.fast_neg:
self.emb_model.fast_learn(walks, lr)
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, lr, neg_nodes=neg_nodes)
if i > 0 and i % self.args.print_interval == 0:
print("Solver [%d] batch %d tt: %.2fs" %
(gpu_id, i, time.time() - start))
start = time.time()
def fast_train(self):
""" fast train with dataloader """
# 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()
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=4,
)
num_batches = len(dataloader)
print("num batchs: %d" % num_batches)
start_all = time.time()
start = time.time()
with torch.no_grad():
max_i = self.args.iterations * num_batches
for iteration in range(self.args.iterations):
print("\nIteration: " + str(iteration + 1))
for i, walks in enumerate(dataloader):
# decay learning rate for SGD
lr = self.args.lr * (max_i - i) / max_i
if lr < 0.00001:
lr = 0.00001
if self.args.fast_neg:
self.emb_model.fast_learn(walks, lr)
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,
lr,
neg_nodes=neg_nodes)
if i > 0 and i % self.args.print_interval == 0:
print("Batch %d, training time: %.2fs" %
(i, time.time() - start))
start = time.time()
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)
else:
self.emb_model.save_embedding(self.dataset,
self.args.output_emb_file)
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)
