def init_db():
import models
import helpers
engine, db_session = db_connect()
models.Base.metadata.create_all(bind=engine)
tag_highlight = models.Tag()
tag_highlight.name = "Tag 1"
tag_highlight.color = "#00aabb"
db_session.add(tag_highlight)
tag_exclude = models.Tag()
tag_exclude.name = "Tag 2"
tag_exclude.color = "#bc5a45"
db_session.add(tag_exclude)
config = models.Config()
config.key = "workdir"
config.set_value(str(settings.work_dir))
db_session.add(config)
db_session.commit()
user = models.User()
user.username = "******"
user.name = "local"
user.set_password("!@#$%¨&¨BFGFGBFffglkdfk*")
db_session.add(user)
db_session.commit()
def main(config='config/finetune/agnews/train.json'):
cfg = Config(**json.load(open(config, "r")))
cfg_data = data.Config(**json.load(open(cfg.cfg_data, "r")))
cfg_model = models.Config(**json.load(open(cfg.cfg_model, "r")))
cfg_optim = trainer.Config(**json.load(open(cfg.cfg_optim, "r")))
set_seeds(cfg.seed)
TaskDataset = data.get_class(
cfg_data.task) # task dataset class according to the task
tokenizer = tokenization.FullTokenizer(vocab_file=cfg_data.vocab_file,
do_lower_case=True)
dataset = TaskDataset(
cfg_data.data_file[cfg.mode],
pipelines=[
data.RemoveSymbols('\\'),
data.Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
data.AddSpecialTokensWithTruncation(cfg_data.max_len),
data.TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels, cfg_data.max_len)
],
n_data=None)
dataset = TensorDataset(*dataset.get_tensors()) # To Tensors
data_iter = DataLoader(dataset,
batch_size=cfg_optim.batch_size,
shuffle=True)
classifier = models.Classifier4Transformer(cfg_model,
len(TaskDataset.labels))
optimizer = optim.optim4GPU(cfg_optim, classifier)
train_loop = trainer.TrainLoop(cfg_optim, classifier, data_iter, optimizer,
cfg.save_dir, get_device())
def get_loss(model, batch,
global_step): # make sure loss is a scalar tensor
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
loss = nn.CrossEntropyLoss()(logits, label_id)
return loss
def evaluate(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
_, label_pred = logits.max(1)
result = (label_pred == label_id).float() #.cpu().numpy()
accuracy = result.mean()
return accuracy, result
if cfg.mode == "train":
train_loop.train(get_loss, cfg.model_file, cfg.pretrain_file)
print("Training has been done properly.")
elif cfg.mode == "eval":
results = train_loop.eval(evaluate, cfg.model_file)
total_accuracy = torch.cat(results).mean().item()
print(f"Accuracy: {total_accuracy}")
def test_get_model_raises_MultipleMatches_when_several_patterns_match(
self):
model1 = models.Model(name='switch1', pattern='foo', oids={})
model2 = models.Model(name='switch2', pattern='bar', oids={})
config = models.Config(models=(model1, model2), default_oids={})
with self.assertRaises(models.MultipleMatches):
config.get_model('sysDescr includes both foo and bar patterns')
def get_user_config():
"""Returns the current user-defined configuration from the database"""
config = models.Config.query.get(0)
if config is None:
config = models.Config()
config.id = 0
db.session.add(config)
return config
def post(self, user):
vc_sid = self.request.get('vc_sid')
yahoo_sid = self.request.get('yahoo_sid')
rakuten_aid = self.request.get('rakuten_aid')
amazon_aid = self.request.get('amazon_aid')
kumapon_aid = self.request.get('kumapon_aid')
conf = models.Config(
key_name=user['id'],
vc_sid=vc_sid,
yahoo_sid=yahoo_sid,
rakuten_aid=rakuten_aid,
amazon_aid=amazon_aid,
kumapon_aid=kumapon_aid,
)
conf.put()
return self.redirect(self.url_for('admin_conf'))
def main(config='config/finetune/agnews/train.json'):
cfg = Config(**json.load(open(config, "r")))
cfg_data = data.Config(**json.load(open(cfg.cfg_data, "r")))
cfg_model = models.Config(**json.load(open(cfg.cfg_model, "r")))
cfg_optim = trainer.Config(**json.load(open(cfg.cfg_optim, "r")))
set_seeds(cfg.seed)
### Prepare Dataset and Preprocessing ###
TaskDataset = data.get_class(cfg_data.task) # task dataset class according to the task
tokenizer = tokenization.FullTokenizer(vocab_file=cfg_data.vocab_file, do_lower_case=True)
dataset = TaskDataset(cfg_data.data_file[cfg.mode], pipelines=[
data.RemoveSymbols('\\'),
data.Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
data.AddSpecialTokensWithTruncation(cfg_data.max_len),
data.TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels,
cfg_data.max_len)
], n_data=None)
tensors = TensorDataset(*dataset.get_tensors()) # To Tensors
data_iter = DataLoader(tensors, batch_size=cfg_optim.batch_size, shuffle=False)
### Fetch Teacher's output and put it into the dataset ###
def fetch_logits(model):
def get_logits(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
return 0.0, logits
train_loop = trainer.TrainLoop(cfg_optim, model, data_iter, None, None, get_device())
results = torch.cat(train_loop.eval(get_logits, cfg.model_file))
return results
if cfg.mode == "train":
print("Fetching teacher's output...")
teacher = models.Classifier4Transformer(cfg_model, len(TaskDataset.labels))
teacher.load_state_dict(torch.load(cfg.model_file)) # use trained model
with torch.no_grad():
teacher_logits = fetch_logits(teacher)
tensors = TensorDataset(teacher_logits, *dataset.get_tensors()) # To Tensors
data_iter = DataLoader(tensors, batch_size=cfg_optim.batch_size, shuffle=False)
### Models ###
model = models.BlendCNN(cfg_model, len(TaskDataset.labels))
checkpoint.load_embedding(model.embed, cfg.pretrain_file)
optimizer = optim.optim4GPU(cfg_optim, model)
train_loop = trainer.TrainLoop(
cfg_optim, model, data_iter, optimizer, cfg.save_dir, get_device()
)
def get_loss(model, batch, global_step): # make sure loss is a scalar tensor
teacher_logits, input_ids, segment_ids, input_mask, label_id = batch
T = 1.0
logits = model(input_ids, segment_ids, input_mask)
loss = 0.1*nn.CrossEntropyLoss()(logits, label_id)
loss += 0.9*nn.KLDivLoss()(
F.log_softmax(logits/T, dim=1),
F.softmax(teacher_logits/T, dim=1)
)
#loss = 0.9*nn.MSELoss()(logits, teacher_logits)
return loss
def evaluate(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
_, label_pred = logits.max(1)
result = (label_pred == label_id).float() #.cpu().numpy()
accuracy = result.mean()
return accuracy, result
if cfg.mode == "train":
train_loop.train(get_loss, None, None) # not use pretrain file
print("Training has been done properly.")
elif cfg.mode == "eval":
results = train_loop.eval(evaluate, cfg.model_file)
total_accuracy = torch.cat(results).mean().item()
print(f"Accuracy: {total_accuracy}")
def main(train_cfg='config/pretrain.json',
model_cfg='config/bert_base.json',
data_file='../tbc/books_large_all.txt',
model_file=None,
data_parallel=True,
vocab='../uncased_L-12_H-768_A-12/vocab.txt',
save_dir='../exp/bert/pretrain',
log_dir='../exp/bert/pretrain/runs',
max_len=512,
max_pred=20,
mask_prob=0.15):
cfg = Config(**json.load(open(config, "r")))
cfg_optim = train.Config(**json.load(open(cfg.cfg_optim, "r")))
cfg_model = models.Config(**json.load(open(cfg.cfg_model, "r")))
set_seeds(cfg.seed)
tokenizer = tokenization.FullTokenizer(vocab_file=vocab,
do_lower_case=True)
tokenize = lambda x: tokenizer.tokenize(tokenizer.convert_to_unicode(x))
pipeline = [
Preprocess4Pretrain(max_pred, mask_prob, list(tokenizer.vocab.keys()),
tokenizer.convert_tokens_to_ids, max_len)
]
data_iter = SentPairDataLoader(data_file,
cfg.batch_size,
tokenize,
max_len,
pipeline=pipeline)
model = BertModel4Pretrain(model_cfg)
criterion1 = nn.CrossEntropyLoss(reduction='none')
criterion2 = nn.CrossEntropyLoss()
optimizer = optim.optim4GPU(cfg, model)
trainer = train.Trainer(cfg, model, data_iter, optimizer, save_dir,
get_device())
writer = SummaryWriter(log_dir=log_dir) # for tensorboardX
def get_loss(model, batch, global_step): # make sure loss is tensor
input_ids, segment_ids, input_mask, masked_ids, masked_pos, masked_weights, is_next = batch
logits_lm, logits_clsf = model(input_ids, segment_ids, input_mask,
masked_pos)
loss_lm = criterion1(logits_lm.transpose(1, 2),
masked_ids) # for masked LM
loss_lm = (loss_lm * masked_weights.float()).mean()
loss_clsf = criterion2(logits_clsf,
is_next) # for sentence classification
writer.add_scalars(
'data/scalar_group', {
'loss_lm': loss_lm.item(),
'loss_clsf': loss_clsf.item(),
'loss_total': (loss_lm + loss_clsf).item(),
'lr': optimizer.get_lr()[0],
}, global_step)
return loss_lm + loss_clsf
trainer.train(get_loss, model_file, None, data_parallel)
def main(config='config/blendcnn/mrpc/eval.json', args=None):
cfg = Config(**json.load(open(config, "r")))
cfg_data = data.Config(**json.load(open(cfg.cfg_data, "r")))
cfg_model = models.Config(**json.load(open(cfg.cfg_model, "r")))
cfg_optim = trainer.Config(**json.load(open(cfg.cfg_optim, "r")))
set_seeds(cfg.seed)
TaskDataset = data.get_class(
cfg_data.task) # task dataset class according to the task
tokenizer = tokenization.FullTokenizer(vocab_file=cfg_data.vocab_file,
do_lower_case=True)
dataset = TaskDataset(
args.dataset_location,
pipelines=[
data.RemoveSymbols('\\'),
data.Tokenizing(tokenizer.convert_to_unicode, tokenizer.tokenize),
data.AddSpecialTokensWithTruncation(cfg_data.max_len),
data.TokenIndexing(tokenizer.convert_tokens_to_ids,
TaskDataset.labels, cfg_data.max_len)
],
n_data=None)
dataset = TensorDataset(*dataset.get_tensors()) # To Tensors
data_iter = DataLoader(dataset, batch_size=args.batch_size, shuffle=False)
model = models.BlendCNN(cfg_model, len(TaskDataset.labels))
checkpoint.load_embedding(model.embed, cfg.pretrain_file)
optimizer = optim.optim4GPU(cfg_optim, model)
train_loop = trainer.TrainLoop(cfg_optim, model, data_iter, optimizer,
cfg.save_dir, get_device())
def get_loss(model, batch,
global_step): # make sure loss is a scalar tensor
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
loss = nn.CrossEntropyLoss()(logits, label_id)
return loss
def evaluate(model, batch):
input_ids, segment_ids, input_mask, label_id = batch
logits = model(input_ids, segment_ids, input_mask)
_, label_pred = logits.max(1)
result = (label_pred == label_id).float() #.cpu().numpy()
accuracy = result.mean()
return accuracy, result
class Bert_DataLoader(object):
def __init__(self,
loader=None,
model_type=None,
device='cpu',
batch_size=1):
self.loader = loader
self.model_type = model_type
self.device = device
self.batch_size = batch_size
def __iter__(self):
for batch in self.loader:
batch = tuple(t.to(self.device) for t in batch)
outputs = {
'output_all': (batch[0], batch[1], batch[2]),
'labels': batch[3]
}
yield outputs['output_all'], outputs['labels']
def benchmark(model):
total_samples = 0
total_time = 0
index = 0
class RandomDataset(object):
def __init__(self, size, shape):
self.len = size
self.input_ids = torch.randint(low=0,
high=30522,
size=(size, shape),
dtype=torch.int64)
self.segment_ids = torch.randint(low=0,
high=1,
size=(size, shape),
dtype=torch.int64)
self.input_mask = torch.randint(low=0,
high=1,
size=(size, shape),
dtype=torch.int64)
self.data = (self.input_ids, self.segment_ids, self.input_mask)
def __getitem__(self, index):
return (self.data[0][index], self.data[1][index],
self.data[2][index])
def __len__(self):
return self.len
rand_loader = DataLoader(dataset=RandomDataset(size=5000, shape=128),
batch_size=args.batch_size,
shuffle=True)
for batch in rand_loader:
index += 1
tic = time.time()
if os.environ.get('BLENDCNN_PROFILING') is not None:
with profiler.profile(record_shapes=True) as prof:
with torch.no_grad():
input_ids, segment_ids, input_mask = batch
_ = model(*batch)
else:
with torch.no_grad(
): # evaluation without gradient calculation
input_ids, segment_ids, input_mask = batch
_ = model(*batch)
if index > args.warmup:
total_samples += batch[0].size()[0]
total_time += time.time() - tic
throughput = total_samples / total_time
print('Latency: %.3f ms' % (1 / throughput * 1000))
print('Throughput: %.3f images/sec' % (throughput))
if os.environ.get('BLENDCNN_PROFILING') is not None:
print(prof.key_averages().table(sort_by="cpu_time_total",
row_limit=10))
def eval_func(model):
results = [] # prediction results
total_samples = 0
total_time = 0
index = 0
model.eval()
eval_dataloader = Bert_DataLoader(loader=data_iter,
batch_size=args.batch_size)
for batch, label in eval_dataloader:
index += 1
tic = time.time()
if os.environ.get('BLENDCNN_PROFILING') is not None:
with profiler.profile(record_shapes=True) as prof:
with torch.no_grad():
accuracy, result = evaluate(model, (*batch, label))
else:
with torch.no_grad(
): # evaluation without gradient calculation
accuracy, result = evaluate(model, (*batch, label))
results.append(result)
if index > args.warmup:
total_samples += batch[0].size()[0]
total_time += time.time() - tic
total_accuracy = torch.cat(results).mean().item()
throughput = total_samples / total_time
print('Latency: %.3f ms' % (1 / throughput * 1000))
print('Throughput: %.3f samples/sec' % (throughput))
print('Accuracy: %.3f ' % (total_accuracy))
if os.environ.get('BLENDCNN_PROFILING') is not None:
print(prof.key_averages().table(sort_by="cpu_time_total",
row_limit=10))
return total_accuracy
if cfg.mode == "train":
train_loop.train(get_loss, cfg.model_file,
None) # not use pretrain_file
print("Training has been done properly.")
elif cfg.mode == "eval":
# results = train_loop.eval(evaluate, cfg.model_file)
# total_accuracy = torch.cat(results).mean().item()
# print(f"Accuracy: {total_accuracy}")
if args.tune:
import lpot
from lpot import common
# lpot tune
model.load_state_dict(torch.load(args.input_model))
eval_dataloader = Bert_DataLoader(loader=data_iter,
batch_size=args.batch_size)
quantizer = lpot.Quantization(args.tuned_yaml)
quantizer.model = common.Model(model)
quantizer.calib_dataloader = eval_dataloader
quantizer.eval_func = eval_func
q_model = quantizer()
q_model.save(args.tuned_checkpoint)
elif args.int8:
from lpot.utils.pytorch import load
int8_model = load(
os.path.abspath(os.path.expanduser(args.tuned_checkpoint)),
model)
print(int8_model)
if args.accuracy_only:
eval_func(int8_model)
elif args.benchmark:
benchmark(int8_model)
else:
model.load_state_dict(torch.load(args.input_model))
print(model)
if args.accuracy_only:
eval_func(model)
elif args.benchmark:
benchmark(model)
def test_get_model_raises_UnknownSwitchModel_when_no_patterns_match(self):
model = models.Model(name='ok', pattern='foo', oids={})
config = models.Config(models=(model, ), default_oids={})
with self.assertRaises(models.UnknownSwitchModel):
config.get_model('sysDescr value that does not contain pattern')
def test_init_raises_BadConfiguration_when_parmaters_not_given(self):
with self.assertRaises(models.BadConfiguration):
models.Config(models=None, default_oids=None, line='none')
