def authenticate(self, **kwargs):
result = None
tenantKey = kwargs.get('tenant')
userId = kwargs.get('userId')
if tenantKey != None and userId != None:
tenantPackages = get_subpackages(tenant)
logger.warn(str(tenantPackages))
if tenantKey in tenantPackages:
result = self.get_user(userId)
logger.warn(str(tenantKey))
if result == None:
if 'emailAddress'in kwargs.keys():
# 'Long' call has been issued
userId = kwargs['userId']
emailAddress = kwargs['emailAddress']
firstname = kwargs['firstname']
lastname = kwargs['lastname']
role = kwargs['role']
raw_password = userId[::-1]
user = User(username=userId, email=emailAddress, first_name=firstname,
last_name=lastname)
user.set_password(raw_password)
user.save()
result = user
return result
def __define_package(self):
logger.debug('')
for package_protocol in self.protocol['packages'].values():
if self.__define_request(package_protocol=package_protocol):
logger.info('GeneralProtocol package define as {}'.format(package_protocol['name']))
return package_protocol
logger.warn('GeneralProtocol can not define request')
def authenticate(self, **kwargs):
result = None
tenantKey = kwargs.get('tenant')
userId = kwargs.get('userId')
if tenantKey != None and userId != None:
tenantPackages = get_subpackages(tenant)
logger.warn(str(tenantPackages))
if tenantKey in tenantPackages:
result = self.get_user(userId)
logger.warn(str(tenantKey))
if result == None:
if 'emailAddress' in kwargs.keys():
# 'Long' call has been issued
userId = kwargs['userId']
emailAddress = kwargs['emailAddress']
firstname = kwargs['firstname']
lastname = kwargs['lastname']
role = kwargs['role']
raw_password = userId[::-1]
user = User(username=userId,
email=emailAddress,
first_name=firstname,
last_name=lastname)
user.set_password(raw_password)
user.save()
result = user
return result
def exec_query(self, sql_query):
if not sql_query:
logger.warn('sql error')
return
if not self.conn:
self.conn = self.connect()
self.cursor = self.conn.cursor()
try:
logger.info('executes sql: "%s"' % sql_query)
self.cursor.execute(sql_query)
result = self.cursor.fetchall()
except Exception as e:
logger.error(e)
return
if result:
logger.info('quering executed successfully')
else:
logger.info('quering executed with no result')
return result
def exec_query(self, sql_query):
if not sql_query:
logger.warn('sql error')
return
if not self.conn:
self.conn = self.connect()
self.cursor = self.conn.cursor()
try:
logger.info('executes sql: "%s"' % sql_query)
self.cursor.execute(sql_query)
result = self.cursor.fetchall()
except Exception as e:
logger.error(e)
return
if result:
logger.info('quering executed successfully')
else:
logger.info('quering executed with no result')
return result
def train(self, num_of_iters=1, data=None, hidden=None):
self.loss = 0.0
s = time.time()
# zero the parameter gradients
#self.optimizer.zero_grad()
for i in range(num_of_iters):
self.adjust_learning_rate(self.train_epoch, self.optimizer)
if self.train_iter % self.num_batches_per_epoch == 0 and self.train_iter > 0:
self.train_epoch += 1
logger.info('train iter: %d, num_batches_per_epoch: %d', self.train_iter, self.num_batches_per_epoch)
logger.info('Epoch %d, avg train acc: %f, lr: %f, avg loss: %f' % (self.train_iter//self.num_batches_per_epoch, np.mean(self.train_acc_top1), self.lr, self.avg_loss_per_epoch/self.num_batches_per_epoch))
if self.rank == 0 and self.writer is not None:
self.writer.add_scalar('cross_entropy', self.avg_loss_per_epoch/self.num_batches_per_epoch, self.train_epoch)
self.writer.add_scalar('top-1_acc', np.mean(self.train_acc_top1), self.train_epoch)
if self.rank == 0:
self.test(self.train_epoch)
self.sparsities = []
self.compression_ratios = []
self.communication_sizes = []
self.train_acc_top1 = []
self.epochs_info.append(self.avg_loss_per_epoch/self.num_batches_per_epoch)
self.avg_loss_per_epoch = 0.0
# Save checkpoint
if self.train_iter > 0 and self.rank == 0:
state = {'iter': self.train_iter, 'epoch': self.train_epoch, 'state': self.get_model_state()}
if self.prefix:
relative_path = './weights/%s/%s-n%d-bs%d-lr%.4f' % (self.prefix, self.dnn, self.nworkers, self.batch_size, self.base_lr)
else:
relative_path = './weights/%s-n%d-bs%d-lr%.4f' % (self.dnn, self.nworkers, self.batch_size, self.base_lr)
utils.create_path(relative_path)
filename = '%s-rank%d-epoch%d.pth'%(self.dnn, self.rank, self.train_epoch)
fn = os.path.join(relative_path, filename)
if self.train_epoch % 2== 0:
self.save_checkpoint(state, fn)
self.remove_dict(state)
if self.train_sampler and (self.nworkers > 1):
self.train_sampler.set_epoch(self.train_epoch)
ss = time.time()
if data is None:
data = self.data_iter()
if self.dataset == 'an4':
inputs, labels_cpu, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
else:
inputs, labels_cpu = data
if self.is_cuda:
if self.dnn == 'lstm' :
inputs = Variable(inputs.transpose(0, 1).contiguous()).cuda()
labels = Variable(labels_cpu.transpose(0, 1).contiguous()).cuda()
else:
inputs, labels = inputs.cuda(non_blocking=True), labels_cpu.cuda(non_blocking=True)
else:
labels = labels_cpu
self.iotime += (time.time() - ss)
sforward = time.time()
if self.dnn == 'lstman4':
out, output_sizes = self.net(inputs, input_sizes)
out = out.transpose(0, 1) # TxNxH
loss = self.criterion(out, labels_cpu, output_sizes, target_sizes)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
loss = loss / inputs.size(0) # average the loss by minibatch
elif self.dnn == 'lstm' :
hidden = lstmpy.repackage_hidden(hidden)
outputs, hidden = self.net(inputs, hidden)
tt = torch.squeeze(labels.view(-1, self.net.batch_size * self.net.num_steps))
loss = self.criterion(outputs.view(-1, self.net.vocab_size), tt)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
else:
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
#torch.cuda.synchronize()
self.forwardtime += (time.time() - sforward)
sbackward = time.time()
if self.amp_handle is not None:
with apex.amp.scale_loss(loss, self.optimizer) as scaled_loss:
scaled_loss.backward()
loss = scaled_loss
else:
loss.backward()
loss_value = loss.item()
#torch.cuda.synchronize()
self.backwardtime += (time.time() - sbackward)
self.loss += loss_value
self.avg_loss_per_epoch += loss_value
if self.dnn not in ['lstm', 'lstman4']:
acc1, = self.cal_accuracy(outputs, labels, topk=(1,))
self.train_acc_top1.append(float(acc1))
self.train_iter += 1
self.num_of_updates_during_comm += 1
self.loss /= num_of_iters
self.timer += time.time() - s
display = 40
if self.train_iter % display == 0:
logger.warn('[%3d][%5d/%5d][rank:%d] loss: %.3f, average forward (%f) and backward (%f) time: %f, iotime: %f ' %
(self.train_epoch, self.train_iter, self.num_batches_per_epoch, self.rank, self.loss, self.forwardtime/display, self.backwardtime/display, self.timer/display, self.iotime/display))
self.timer = 0.0
self.iotime = 0.0
self.forwardtime = 0.0
self.backwardtime = 0.0
if self.dnn == 'lstm':
return num_of_iters, hidden
return num_of_iters
def ssgd(dnn,
dataset,
data_dir,
nworkers,
lr,
batch_size,
nsteps_update,
max_epochs,
nwpernode,
pretrain,
num_steps,
compressor,
density,
threshold,
gradient_path=None):
rank = hvd.rank()
torch.cuda.set_device(rank % nwpernode)
if rank != 0:
pretrain = None
trainer = DLTrainer(rank,
nworkers,
dist=False,
batch_size=batch_size,
is_weak_scaling=True,
ngpus=1,
data_dir=data_dir,
dataset=dataset,
dnn=dnn,
lr=lr,
nworkers=nworkers,
prefix='allreduce',
pretrain=pretrain,
num_steps=num_steps,
tb_writer=writer)
init_epoch = torch.ones(1) * trainer.get_train_epoch()
init_iter = torch.ones(1) * trainer.get_train_iter()
trainer.set_train_epoch(int(hvd.broadcast(init_epoch, root_rank=0)[0]))
trainer.set_train_iter(int(hvd.broadcast(init_iter, root_rank=0)[0]))
is_sparse = density < 1
#if not is_sparse:
# compressor = None
if settings.ADAPTIVE_MERGE or settings.ADAPTIVE_SPARSE:
seq_layernames, layerwise_times, layerwise_sizes = benchmark(trainer)
layerwise_times = comm.bcast(layerwise_times, root=0)
if rank == 0:
logger.info('layerwise backward times: %s', list(layerwise_times))
logger.info('layerwise backward sizes: %s', list(layerwise_sizes))
logger.info('Bencharmked backward time: %f', np.sum(layerwise_times))
logger.info('Model size: %d', np.sum(layerwise_sizes))
else:
seq_layernames, layerwise_times, layerwise_sizes = None, None, None
norm_clip = None
if dnn == 'lstm':
norm_clip = 0.25
elif dnn == 'lstman4':
norm_clip = 400
optimizer = hvd.DistributedOptimizer(
trainer.optimizer,
named_parameters=trainer.net.named_parameters(),
compression=compressors[compressor],
is_sparse=is_sparse,
density=density,
seq_layernames=seq_layernames,
layerwise_times=layerwise_times,
norm_clip=norm_clip,
threshold=threshold,
writer=writer,
gradient_path=gradient_path)
hvd.broadcast_parameters(trainer.net.state_dict(), root_rank=0)
trainer.update_optimizer(optimizer)
iters_per_epoch = trainer.get_num_of_training_samples() // (
nworkers * batch_size * nsteps_update)
times = []
logger.info('max_epochs: %d', max_epochs)
display = 40 if iters_per_epoch > 40 else iters_per_epoch - 1
for epoch in range(max_epochs):
hidden = None
if dnn == 'lstm':
hidden = trainer.net.init_hidden()
for i in range(iters_per_epoch):
s = time.time()
optimizer.zero_grad()
for j in range(nsteps_update):
if j < nsteps_update - 1 and nsteps_update > 1:
optimizer.local = True
else:
optimizer.local = False
if dnn == 'lstm':
_, hidden = trainer.train(1, hidden=hidden)
else:
trainer.train(1)
if dnn == 'lstm':
optimizer.synchronize()
torch.nn.utils.clip_grad_norm_(trainer.net.parameters(), 0.25)
elif dnn == 'lstman4':
optimizer.synchronize()
torch.nn.utils.clip_grad_norm_(trainer.net.parameters(), 400)
trainer.update_model()
times.append(time.time() - s)
if i % display == 0 and i > 0:
time_per_iter = np.mean(times)
logger.warn(
'Time per iteration including communication: %f, Speed: %f images/s',
time_per_iter, batch_size * nsteps_update / time_per_iter)
times = []
optimizer.increase_one_epoch()
def _generate_groups_mgwfbp(self):
num_of_workers = size()
p_alpha_beta = {
16: (0.00010632079996292579, 1.5 * 3.2713239529771973e-10),
8: (9.75367204301171e-05, 3.0568230536676206e-10),
4: (4.204298980348825e-05, 2.0589360830118177e-10),
2: (2.554691138304671e-06, 9.837548167872609e-11)
}
alpha, beta = p_alpha_beta[num_of_workers]
def __calculate_comm_start(tc, tb, taob, L):
taoc = [0] * L
taoc[L - 1] = taob[L - 1] + tb[L - 1]
for l in range(L - 1)[::-1]:
taoc[l] = max(taoc[l + 1] + tc[l + 1], taob[l] + tb[l])
return taoc
def __merge(taob, tc, p, l):
tc[l] = 0
p[l - 1] = p[l - 1] + p[l]
p[l] = 0
tc[l - 1] = utils.predict_allreduce_time_with_size(
alpha, beta, p[l - 1] * 4, num_of_workers)
sizes = [
self._named_parameters[k].data.numel()
for k in self._seq_layernames
]
seq_layernames = self._seq_layernames
self._sizes = sizes
p = sizes[:]
L = len(sizes)
tc = [
utils.predict_allreduce_time_with_size(alpha, beta, s * 4,
num_of_workers)
for s in sizes
]
tb = list(self._layerwise_times)
taob = [0] * L
for l in range(0, L - 1)[::-1]:
taob[l] = taob[l + 1] + tb[l + 1]
taoc = __calculate_comm_start(tc, tb, taob, L)
if rank() == 0:
logger.warn('tc sum: %f', np.sum(tc))
logger.warn('tc: %s', tc)
logger.warn('taoc: %s', taoc)
groups = []
group = []
idx = 0
key_groupidx_maps = {}
l = L - 1
key = seq_layernames[l]
key_groupidx_maps[key] = idx
group.append(key)
for l in range(1, L - 1)[::-1]:
key = seq_layernames[l]
group.append(key)
key_groupidx_maps[key] = idx
current_taob = taob[l - 1] + tb[l - 1]
if current_taob < taoc[l + 1] + tc[l + 1]:
__merge(taob, tc, p, l)
taoc = __calculate_comm_start(tc, tb, taob, L)
elif current_taob > taoc[l + 1] + tc[l + 1] and current_taob < taoc[
l] + tc[l] and taoc[l] + alpha > current_taob:
__merge(taob, tc, p, l)
taoc = __calculate_comm_start(tc, tb, taob, L)
else:
idx += 1
groups.append(group)
group = []
l = 0
key = seq_layernames[l]
key_groupidx_maps[key] = idx
group.append(key)
logger.info('Predicted non-overlapped time: %f',
taoc[0] + tc[0] - (taob[0] + tb[0]))
logger.info('Predicted tb+tc= %f', taoc[0] + tc[0])
if len(group) > 0:
groups.append(group)
return groups, key_groupidx_maps
uid, gid = os.getuid(), os.getgid()
if uid != 0 and gid != 0:
logger.error("please use root privilege to exec.")
sys.exit(1)
return func(*args, **kargs)
return check
@check_permission
def directory(path="", mode=0755, recursive=False, action='create'):
if not path:
logger.warn("please sepecific the directory name.")
sys.exit(1)
if action == "create":
is_exists_dir = os.path.exists(path)
if is_exists_dir:
try:
shutil.rmtree(path)
except OSError, e:
logger.error("rm path: %s error" % (path) + str(e))
sys.exit(1)
if not recursive:
def train(self, num_of_iters=1, data=None, hidden=None):
self.loss = 0.0
s = time.time()
for i in range(num_of_iters):
self.adjust_learning_rate(self.train_epoch, self.optimizer)
if self.train_iter % self.num_batches_per_epoch == 0 and self.train_iter > 0:
logger.info('train iter: %d, num_batches_per_epoch: %d',
self.train_iter, self.num_batches_per_epoch)
logger.info(
'Epoch %d, avg train acc: %f, lr: %f, avg loss: %f' %
(self.train_iter // self.num_batches_per_epoch,
np.mean(self.train_acc_top1), self.lr,
self.avg_loss_per_epoch / self.num_batches_per_epoch))
mean_s = np.mean(self.sparsities)
if self.train_iter > 0 and np.isnan(mean_s):
logger.warn('NaN detected! sparsities: %s' %
self.sparsities)
logger.info(
'Average Sparsity: %f, compression ratio: %f, communication size: %f',
np.mean(self.sparsities), np.mean(self.compression_ratios),
np.mean(self.communication_sizes))
if self.rank == 0 and self.writer is not None:
self.writer.add_scalar(
'cross_entropy',
self.avg_loss_per_epoch / self.num_batches_per_epoch,
self.train_epoch)
self.writer.add_scalar('top-1 acc',
np.mean(self.train_acc_top1),
self.train_epoch)
if self.rank == 0:
self.test(self.train_epoch)
self.sparsities = []
self.compression_ratios = []
self.communication_sizes = []
self.train_acc_top1 = []
self.epochs_info.append(self.avg_loss_per_epoch /
self.num_batches_per_epoch)
self.avg_loss_per_epoch = 0.0
if self.train_iter > 0 and self.rank == 0:
state = {
'iter': self.train_iter,
'epoch': self.train_epoch,
'state': self.get_model_state()
}
if self.prefix:
relative_path = './weights/%s/%s-n%d-bs%d-lr%.4f' % (
self.prefix, self.dnn, self.nworkers,
self.batch_size, self.base_lr)
else:
relative_path = './weights/%s-n%d-bs%d-lr%.4f' % (
self.dnn, self.nworkers, self.batch_size,
self.base_lr)
if settings.SPARSE:
relative_path += '-s%.5f' % self.sparsity
utils.create_path(relative_path)
filename = '%s-rank%d-epoch%d.pth' % (self.dnn, self.rank,
self.train_epoch)
fn = os.path.join(relative_path, filename)
#self.save_checkpoint(state, fn)
#self.remove_dict(state)
self.train_epoch += 1
if self.train_sampler and (self.nworkers > 1):
self.train_sampler.set_epoch(self.train_epoch)
ss = time.time()
if data is None:
data = self.data_iter()
if self.dataset == 'an4':
inputs, labels_cpu, input_percentages, target_sizes = data
input_sizes = input_percentages.mul_(int(inputs.size(3))).int()
else:
inputs, labels_cpu = data
if self.is_cuda:
if self.dnn == 'lstm':
inputs = Variable(inputs.transpose(0,
1).contiguous()).cuda()
labels = Variable(labels_cpu.transpose(
0, 1).contiguous()).cuda()
else:
inputs, labels = inputs.cuda(
non_blocking=True), labels_cpu.cuda(non_blocking=True)
else:
labels = labels_cpu
self.iotime += (time.time() - ss)
if self.dnn == 'lstman4':
out, output_sizes = self.net(inputs, input_sizes)
out = out.transpose(0, 1) # TxNxH
loss = self.criterion(out, labels_cpu, output_sizes,
target_sizes)
loss = loss / inputs.size(0) # average the loss by minibatch
loss.backward()
elif self.dnn == 'lstm':
hidden = lstmpy.repackage_hidden(hidden)
outputs, hidden = self.net(inputs, hidden)
tt = torch.squeeze(
labels.view(-1, self.net.batch_size * self.net.num_steps))
loss = self.criterion(outputs.view(-1, self.net.vocab_size),
tt)
loss.backward()
else:
# forward + backward + optimize
outputs = self.net(inputs)
loss = self.criterion(outputs, labels)
loss.backward()
loss_value = loss.item()
# logger.info statistics
self.loss += loss_value
self.avg_loss_per_epoch += loss_value
if self.dnn not in ['lstm', 'lstman4']:
acc1, = self.cal_accuracy(outputs, labels, topk=(1, ))
self.train_acc_top1.append(acc1)
self.train_iter += 1
self.num_of_updates_during_comm += 1
self.loss /= num_of_iters
self.timer += time.time() - s
display = 100
if self.train_iter % display == 0:
logger.info(
'[%3d][%5d/%5d][rank:%d] loss: %.3f, average forward and backward time: %f, iotime: %f '
% (self.train_epoch, self.train_iter,
self.num_batches_per_epoch, self.rank, self.loss,
self.timer / display, self.iotime / display))
mbytes = 1024. * 1024
logger.info(
'GPU memory usage memory_allocated: %d MBytes, max_memory_allocated: %d MBytes, memory_cached: %d MBytes, max_memory_cached: %d MBytes, CPU memory usage: %d MBytes',
ct.memory_allocated() / mbytes,
ct.max_memory_allocated() / mbytes,
ct.memory_cached() / mbytes,
ct.max_memory_cached() / mbytes,
process.memory_info().rss / mbytes)
self.timer = 0.0
self.iotime = 0.0
if self.is_cuda:
torch.cuda.empty_cache()
if self.dnn == 'lstm':
return num_of_iters, hidden
return num_of_iters
def singleIpScanner(ip):
logger.info("Start to scan %s" % ip.strip())
ret = checkIP(ip)
if ret:
logger.warn("Found wormhole vuln in %s : %s" % (ip, dumps(ret)))