def __init__(self, opt, bert_config=None, initial_from_local=False):
super(SANBertNetwork, self).__init__()
self.dropout_list = nn.ModuleList()
if opt['encoder_type'] not in EncoderModelType._value2member_map_:
raise ValueError("encoder_type is out of pre-defined types")
self.encoder_type = opt['encoder_type']
self.preloaded_config = None
literal_encoder_type = EncoderModelType(self.encoder_type).name.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_encoder_type]
self.preloaded_config = config_class.from_dict(
opt) # load config from opt
self.preloaded_config.output_hidden_states = True # return all hidden states
self.bert = model_class(self.preloaded_config)
hidden_size = self.bert.config.hidden_size
if opt.get('dump_feature', False):
self.opt = opt
return
if opt['update_bert_opt'] > 0:
for p in self.bert.parameters():
p.requires_grad = False
task_def_list = opt['task_def_list']
self.task_def_list = task_def_list
self.decoder_opt = []
self.task_types = []
for task_id, task_def in enumerate(task_def_list):
self.decoder_opt.append(
generate_decoder_opt(task_def.enable_san, opt['answer_opt']))
self.task_types.append(task_def.task_type)
# create output header
self.scoring_list = nn.ModuleList()
self.dropout_list = nn.ModuleList()
for task_id in range(len(task_def_list)):
task_def: TaskDef = task_def_list[task_id]
lab = task_def.n_class
decoder_opt = self.decoder_opt[task_id]
task_type = self.task_types[task_id]
task_dropout_p = opt[
'dropout_p'] if task_def.dropout_p is None else task_def.dropout_p
dropout = DropoutWrapper(task_dropout_p, opt['vb_dropout'])
self.dropout_list.append(dropout)
task_obj = tasks.get_task_obj(task_def)
if task_obj is not None:
out_proj = task_obj.train_build_task_layer(decoder_opt,
hidden_size,
lab,
opt,
prefix='answer',
dropout=dropout)
elif task_type == TaskType.Span:
assert decoder_opt != 1
out_proj = nn.Linear(hidden_size, 2)
elif task_type == TaskType.SeqenceLabeling:
out_proj = nn.Linear(hidden_size, lab)
elif task_type == TaskType.MaskLM:
if opt['encoder_type'] == EncoderModelType.ROBERTA:
# TODO: xiaodl
out_proj = MaskLmHeader(
self.bert.embeddings.word_embeddings.weight)
else:
out_proj = MaskLmHeader(
self.bert.embeddings.word_embeddings.weight)
else:
if decoder_opt == 1:
out_proj = SANClassifier(hidden_size,
hidden_size,
lab,
opt,
prefix='answer',
dropout=dropout)
else:
out_proj = nn.Linear(hidden_size, lab)
self.scoring_list.append(out_proj)
self.opt = opt
self._my_init()
# if not loading from local, loading model weights from pre-trained model, after initialization
if not initial_from_local:
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_encoder_type]
self.bert = model_class.from_pretrained(
opt['init_checkpoint'], config=self.preloaded_config)
def main():
# set up dist
device = torch.device("cuda")
if args.local_rank > -1:
device = initialize_distributed(args)
elif torch.cuda.is_available():
device = torch.device("cuda")
else:
device = torch.device("cpu")
opt = vars(args)
# update data dir
opt['data_dir'] = data_dir
batch_size = args.batch_size
print_message(logger, 'Launching the MT-DNN training')
#return
tasks = {}
task_def_list = []
dropout_list = []
printable = args.local_rank in [-1, 0]
train_datasets = []
for dataset in args.train_datasets:
prefix = dataset.split('_')[0]
if prefix in tasks:
continue
task_id = len(tasks)
tasks[prefix] = task_id
task_def = task_defs.get_task_def(prefix)
task_def_list.append(task_def)
train_path = os.path.join(data_dir, '{}_train.json'.format(dataset))
print_message(logger,
'Loading {} as task {}'.format(train_path, task_id))
train_data_set = SingleTaskDataset(train_path,
True,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def,
printable=printable)
train_datasets.append(train_data_set)
train_collater = Collater(dropout_w=args.dropout_w,
encoder_type=encoder_type,
soft_label=args.mkd_opt > 0,
max_seq_len=args.max_seq_len,
do_padding=args.do_padding)
multi_task_train_dataset = MultiTaskDataset(train_datasets)
if args.local_rank != -1:
multi_task_batch_sampler = DistMultiTaskBatchSampler(
train_datasets,
args.batch_size,
args.mix_opt,
args.ratio,
rank=args.local_rank,
world_size=args.world_size)
else:
multi_task_batch_sampler = MultiTaskBatchSampler(
train_datasets,
args.batch_size,
args.mix_opt,
args.ratio,
bin_on=args.bin_on,
bin_size=args.bin_size,
bin_grow_ratio=args.bin_grow_ratio)
multi_task_train_data = DataLoader(multi_task_train_dataset,
batch_sampler=multi_task_batch_sampler,
collate_fn=train_collater.collate_fn,
pin_memory=args.cuda)
opt['task_def_list'] = task_def_list
dev_data_list = []
test_data_list = []
test_collater = Collater(is_train=False,
encoder_type=encoder_type,
max_seq_len=args.max_seq_len,
do_padding=args.do_padding)
for dataset in args.test_datasets:
prefix = dataset.split('_')[0]
task_def = task_defs.get_task_def(prefix)
task_id = tasks[prefix]
task_type = task_def.task_type
data_type = task_def.data_type
dev_path = os.path.join(data_dir, '{}_dev.json'.format(dataset))
dev_data = None
if os.path.exists(dev_path):
dev_data_set = SingleTaskDataset(dev_path,
False,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def,
printable=printable)
if args.local_rank != -1:
dev_data_set = DistTaskDataset(dev_data_set, task_id)
single_task_batch_sampler = DistSingleTaskBatchSampler(
dev_data_set,
args.batch_size_eval,
rank=args.local_rank,
world_size=args.world_size)
dev_data = DataLoader(dev_data_set,
batch_sampler=single_task_batch_sampler,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
else:
dev_data = DataLoader(dev_data_set,
batch_size=args.batch_size_eval,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
dev_data_list.append(dev_data)
test_path = os.path.join(data_dir, '{}_test.json'.format(dataset))
test_data = None
if os.path.exists(test_path):
test_data_set = SingleTaskDataset(test_path,
False,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def,
printable=printable)
if args.local_rank != -1:
test_data_set = DistTaskDataset(test_data_set, task_id)
single_task_batch_sampler = DistSingleTaskBatchSampler(
test_data_set,
args.batch_size_eval,
rank=args.local_rank,
world_size=args.world_size)
test_data = DataLoader(test_data_set,
batch_sampler=single_task_batch_sampler,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
else:
test_data = DataLoader(test_data_set,
batch_size=args.batch_size_eval,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
test_data_list.append(test_data)
print_message(logger, '#' * 20)
print_message(logger, opt)
print_message(logger, '#' * 20)
# div number of grad accumulation.
num_all_batches = args.epochs * len(
multi_task_train_data) // args.grad_accumulation_step
print_message(logger,
'############# Gradient Accumulation Info #############')
print_message(
logger,
'number of step: {}'.format(args.epochs * len(multi_task_train_data)))
print_message(
logger, 'number of grad grad_accumulation step: {}'.format(
args.grad_accumulation_step))
print_message(logger,
'adjusted number of step: {}'.format(num_all_batches))
print_message(logger,
'############# Gradient Accumulation Info #############')
init_model = args.init_checkpoint
state_dict = None
if os.path.exists(init_model):
if encoder_type == EncoderModelType.BERT or \
encoder_type == EncoderModelType.DEBERTA or \
encoder_type == EncoderModelType.ELECTRA:
state_dict = torch.load(init_model, map_location=device)
config = state_dict['config']
elif encoder_type == EncoderModelType.ROBERTA or encoder_type == EncoderModelType.XLM:
model_path = '{}/model.pt'.format(init_model)
state_dict = torch.load(model_path, map_location=device)
arch = state_dict['args'].arch
arch = arch.replace('_', '-')
if encoder_type == EncoderModelType.XLM:
arch = "xlm-{}".format(arch)
# convert model arch
from data_utils.roberta_utils import update_roberta_keys
from data_utils.roberta_utils import patch_name_dict
state = update_roberta_keys(
state_dict['model'], nlayer=state_dict['args'].encoder_layers)
state = patch_name_dict(state)
literal_encoder_type = EncoderModelType(
opt['encoder_type']).name.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_encoder_type]
config = config_class.from_pretrained(arch).to_dict()
state_dict = {'state': state}
else:
if opt['encoder_type'] not in EncoderModelType._value2member_map_:
raise ValueError("encoder_type is out of pre-defined types")
literal_encoder_type = EncoderModelType(
opt['encoder_type']).name.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_encoder_type]
config = config_class.from_pretrained(init_model).to_dict()
config['attention_probs_dropout_prob'] = args.bert_dropout_p
config['hidden_dropout_prob'] = args.bert_dropout_p
config['multi_gpu_on'] = opt["multi_gpu_on"]
if args.num_hidden_layers > 0:
config['num_hidden_layers'] = args.num_hidden_layers
opt.update(config)
model = MTDNNModel(opt,
device=device,
state_dict=state_dict,
num_train_step=num_all_batches)
if args.resume and args.model_ckpt:
print_message(logger, 'loading model from {}'.format(args.model_ckpt))
model.load(args.model_ckpt)
#### model meta str
headline = '############# Model Arch of MT-DNN #############'
### print network
print_message(logger, '\n{}\n{}\n'.format(headline, model.network))
# dump config
config_file = os.path.join(output_dir, 'config.json')
with open(config_file, 'w', encoding='utf-8') as writer:
writer.write('{}\n'.format(json.dumps(opt)))
writer.write('\n{}\n{}\n'.format(headline, model.network))
print_message(logger,
"Total number of params: {}".format(model.total_param))
# tensorboard
tensorboard = None
if args.tensorboard:
args.tensorboard_logdir = os.path.join(args.output_dir,
args.tensorboard_logdir)
tensorboard = SummaryWriter(log_dir=args.tensorboard_logdir)
if args.encode_mode:
for idx, dataset in enumerate(args.test_datasets):
prefix = dataset.split('_')[0]
test_data = test_data_list[idx]
with torch.no_grad():
encoding = extract_encoding(model,
test_data,
use_cuda=args.cuda)
torch.save(
encoding,
os.path.join(output_dir, '{}_encoding.pt'.format(dataset)))
return
for epoch in range(0, args.epochs):
print_message(logger, 'At epoch {}'.format(epoch), level=1)
start = datetime.now()
for i, (batch_meta, batch_data) in enumerate(multi_task_train_data):
batch_meta, batch_data = Collater.patch_data(
device, batch_meta, batch_data)
task_id = batch_meta['task_id']
model.update(batch_meta, batch_data)
if (model.updates) % (
args.log_per_updates) == 0 or model.updates == 1:
ramaining_time = str(
(datetime.now() - start) / (i + 1) *
(len(multi_task_train_data) - i - 1)).split('.')[0]
if args.adv_train and args.debug:
debug_info = ' basic loss[%.5f] adv loss[%.5f] emb val[%.8f] noise val[%.8f] noise grad val[%.8f] no proj noise[%.8f] ' % (
model.basic_loss.avg, model.adv_loss.avg,
model.emb_val.avg, model.noise_val.avg,
model.noise_grad_val.avg, model.no_proj_noise_val.avg)
else:
debug_info = ' '
print_message(
logger,
'Task [{0:2}] updates[{1:6}] train loss[{2:.5f}]{3}remaining[{4}]'
.format(task_id, model.updates, model.train_loss.avg,
debug_info, ramaining_time))
if args.tensorboard:
tensorboard.add_scalar('train/loss',
model.train_loss.avg,
global_step=model.updates)
if args.save_per_updates_on and (
(model.local_updates) %
(args.save_per_updates * args.grad_accumulation_step)
== 0) and args.local_rank in [-1, 0]:
model_file = os.path.join(
output_dir, 'model_{}_{}.pt'.format(epoch, model.updates))
evaluation(model,
args.test_datasets,
dev_data_list,
task_defs,
output_dir,
epoch,
n_updates=args.save_per_updates,
with_label=True,
tensorboard=tensorboard,
glue_format_on=args.glue_format_on,
test_on=False,
device=device,
logger=logger)
evaluation(model,
args.test_datasets,
test_data_list,
task_defs,
output_dir,
epoch,
n_updates=args.save_per_updates,
with_label=False,
tensorboard=tensorboard,
glue_format_on=args.glue_format_on,
test_on=True,
device=device,
logger=logger)
print_message(logger,
'Saving mt-dnn model to {}'.format(model_file))
model.save(model_file)
evaluation(model,
args.test_datasets,
dev_data_list,
task_defs,
output_dir,
epoch,
with_label=True,
tensorboard=tensorboard,
glue_format_on=args.glue_format_on,
test_on=False,
device=device,
logger=logger)
evaluation(model,
args.test_datasets,
test_data_list,
task_defs,
output_dir,
epoch,
with_label=False,
tensorboard=tensorboard,
glue_format_on=args.glue_format_on,
test_on=True,
device=device,
logger=logger)
print_message(logger, '[new test scores at {} saved.]'.format(epoch))
if args.local_rank in [-1, 0]:
model_file = os.path.join(output_dir, 'model_{}.pt'.format(epoch))
model.save(model_file)
if args.tensorboard:
tensorboard.close()
def main():
logger.info('Launching the MT-DNN training')
opt = vars(args)
# update data dir
opt['data_dir'] = data_dir
batch_size = args.batch_size
tasks = {}
task_def_list = []
dropout_list = []
train_datasets = []
for dataset in args.train_datasets:
prefix = dataset.split('_')[0]
if prefix in tasks:
continue
task_id = len(tasks)
tasks[prefix] = task_id
task_def = task_defs.get_task_def(prefix)
task_def_list.append(task_def)
train_path = os.path.join(data_dir, '{}_train.json'.format(dataset))
logger.info('Loading {} as task {}'.format(train_path, task_id))
train_data_set = SingleTaskDataset(train_path,
True,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def)
train_datasets.append(train_data_set)
train_collater = Collater(dropout_w=args.dropout_w,
encoder_type=encoder_type,
soft_label=args.mkd_opt > 0)
multi_task_train_dataset = MultiTaskDataset(train_datasets)
multi_task_batch_sampler = MultiTaskBatchSampler(train_datasets,
args.batch_size,
args.mix_opt, args.ratio)
multi_task_train_data = DataLoader(multi_task_train_dataset,
batch_sampler=multi_task_batch_sampler,
collate_fn=train_collater.collate_fn,
pin_memory=args.cuda)
opt['task_def_list'] = task_def_list
dev_data_list = []
test_data_list = []
test_collater = Collater(is_train=False, encoder_type=encoder_type)
for dataset in args.test_datasets:
prefix = dataset.split('_')[0]
task_def = task_defs.get_task_def(prefix)
task_id = tasks[prefix]
task_type = task_def.task_type
data_type = task_def.data_type
dev_path = os.path.join(data_dir, '{}_dev.json'.format(dataset))
dev_data = None
if os.path.exists(dev_path):
dev_data_set = SingleTaskDataset(dev_path,
False,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def)
dev_data = DataLoader(dev_data_set,
batch_size=args.batch_size_eval,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
dev_data_list.append(dev_data)
test_path = os.path.join(data_dir, '{}_test.json'.format(dataset))
test_data = None
if os.path.exists(test_path):
test_data_set = SingleTaskDataset(test_path,
False,
maxlen=args.max_seq_len,
task_id=task_id,
task_def=task_def)
test_data = DataLoader(test_data_set,
batch_size=args.batch_size_eval,
collate_fn=test_collater.collate_fn,
pin_memory=args.cuda)
test_data_list.append(test_data)
logger.info('#' * 20)
logger.info(opt)
logger.info('#' * 20)
# div number of grad accumulation.
num_all_batches = args.epochs * len(
multi_task_train_data) // args.grad_accumulation_step
logger.info('############# Gradient Accumulation Info #############')
logger.info('number of step: {}'.format(args.epochs *
len(multi_task_train_data)))
logger.info('number of grad grad_accumulation step: {}'.format(
args.grad_accumulation_step))
logger.info('adjusted number of step: {}'.format(num_all_batches))
logger.info('############# Gradient Accumulation Info #############')
init_model = args.init_checkpoint
state_dict = None
if os.path.exists(init_model):
state_dict = torch.load(init_model)
config = state_dict['config']
else:
if opt['encoder_type'] not in EncoderModelType._value2member_map_:
raise ValueError("encoder_type is out of pre-defined types")
literal_encoder_type = EncoderModelType(
opt['encoder_type']).name.lower()
config_class, model_class, tokenizer_class = MODEL_CLASSES[
literal_encoder_type]
config = config_class.from_pretrained(
init_model, output_hidden_states=True).to_dict(
) # change here to enable multi-layer output
config['output_hidden_states'] = True
config['attention_probs_dropout_prob'] = args.bert_dropout_p
config['hidden_dropout_prob'] = args.bert_dropout_p
config['multi_gpu_on'] = opt["multi_gpu_on"]
if args.num_hidden_layers != -1:
config['num_hidden_layers'] = args.num_hidden_layers
opt.update(config)
model = MTDNNModel(opt,
state_dict=state_dict,
num_train_step=num_all_batches)
if args.resume and args.model_ckpt:
logger.info('loading model from {}'.format(args.model_ckpt))
model.load(args.model_ckpt)
#### model meta str
headline = '############# Model Arch of MT-DNN #############'
### print network
logger.info('\n{}\n{}\n'.format(headline, model.network))
# dump config
config_file = os.path.join(output_dir, 'config.json')
with open(config_file, 'w', encoding='utf-8') as writer:
writer.write('{}\n'.format(json.dumps(opt)))
writer.write('\n{}\n{}\n'.format(headline, model.network))
logger.info("Total number of params: {}".format(model.total_param))
# tensorboard
if args.tensorboard:
args.tensorboard_logdir = os.path.join(args.output_dir,
args.tensorboard_logdir)
tensorboard = SummaryWriter(log_dir=args.tensorboard_logdir)
if args.encode_mode:
for idx, dataset in enumerate(args.test_datasets):
prefix = dataset.split('_')[0]
test_data = test_data_list[idx]
with torch.no_grad():
encoding = extract_encoding(model,
test_data,
use_cuda=args.cuda)
torch.save(
encoding,
os.path.join(output_dir, '{}_encoding.pt'.format(dataset)))
return
for epoch in range(0, args.epochs):
logger.warning('At epoch {}'.format(epoch))
start = datetime.now()
for i, (batch_meta, batch_data) in enumerate(multi_task_train_data):
batch_meta, batch_data = Collater.patch_data(
args.cuda, batch_meta, batch_data)
task_id = batch_meta['task_id']
model.update(batch_meta, batch_data)
if (model.local_updates) % (args.log_per_updates *
args.grad_accumulation_step
) == 0 or model.local_updates == 1:
ramaining_time = str(
(datetime.now() - start) / (i + 1) *
(len(multi_task_train_data) - i - 1)).split('.')[0]
logger.info(
'Task [{0:2}] updates[{1:6}] train loss[{2:.5f}] remaining[{3}]'
.format(task_id, model.updates, model.train_loss.avg,
ramaining_time))
if args.tensorboard:
tensorboard.add_scalar('train/loss',
model.train_loss.avg,
global_step=model.updates)
if args.save_per_updates_on and (
(model.local_updates) %
(args.save_per_updates * args.grad_accumulation_step) == 0):
model_file = os.path.join(
output_dir, 'model_{}_{}.pt'.format(epoch, model.updates))
logger.info('Saving mt-dnn model to {}'.format(model_file))
model.save(model_file)
for idx, dataset in enumerate(args.test_datasets):
prefix = dataset.split('_')[0]
task_def = task_defs.get_task_def(prefix)
label_dict = task_def.label_vocab
dev_data = dev_data_list[idx]
if dev_data is not None:
with torch.no_grad():
dev_metrics, dev_predictions, scores, golds, dev_ids = eval_model(
model,
dev_data,
metric_meta=task_def.metric_meta,
use_cuda=args.cuda,
label_mapper=label_dict,
task_type=task_def.task_type)
for key, val in dev_metrics.items():
if args.tensorboard:
tensorboard.add_scalar('dev/{}/{}'.format(
dataset, key),
val,
global_step=epoch)
if isinstance(val, str):
logger.warning(
'Task {0} -- epoch {1} -- Dev {2}:\n {3}'.format(
dataset, epoch, key, val))
else:
logger.warning(
'Task {0} -- epoch {1} -- Dev {2}: {3:.3f}'.format(
dataset, epoch, key, val))
score_file = os.path.join(
output_dir, '{}_dev_scores_{}.json'.format(dataset, epoch))
results = {
'metrics': dev_metrics,
'predictions': dev_predictions,
'uids': dev_ids,
'scores': scores
}
dump(score_file, results)
if args.glue_format_on:
from experiments.glue.glue_utils import submit
official_score_file = os.path.join(
output_dir,
'{}_dev_scores_{}.tsv'.format(dataset, epoch))
submit(official_score_file, results, label_dict)
# test eval
test_data = test_data_list[idx]
if test_data is not None:
with torch.no_grad():
test_metrics, test_predictions, scores, golds, test_ids = eval_model(
model,
test_data,
metric_meta=task_def.metric_meta,
use_cuda=args.cuda,
with_label=False,
label_mapper=label_dict,
task_type=task_def.task_type)
score_file = os.path.join(
output_dir,
'{}_test_scores_{}.json'.format(dataset, epoch))
results = {
'metrics': test_metrics,
'predictions': test_predictions,
'uids': test_ids,
'scores': scores
}
dump(score_file, results)
if args.glue_format_on:
from experiments.glue.glue_utils import submit
official_score_file = os.path.join(
output_dir,
'{}_test_scores_{}.tsv'.format(dataset, epoch))
submit(official_score_file, results, label_dict)
logger.info('[new test scores saved.]')
model_file = os.path.join(output_dir, 'model_{}.pt'.format(epoch))
model.save(model_file)
if args.tensorboard:
tensorboard.close()
def __init__(self, opt, bert_config=None, initial_from_local=False):
super(SANBertNetwork, self).__init__()
self.dropout_list = nn.ModuleList()
if opt["encoder_type"] not in EncoderModelType._value2member_map_:
raise ValueError("encoder_type is out of pre-defined types")
self.encoder_type = opt["encoder_type"]
self.preloaded_config = None
literal_encoder_type = EncoderModelType(self.encoder_type).name.lower()
config_class, model_class, _ = MODEL_CLASSES[literal_encoder_type]
if not initial_from_local:
# self.bert = model_class.from_pretrained(opt['init_checkpoint'], config=self.preloaded_config)
self.bert = model_class.from_pretrained(
opt["init_checkpoint"], cache_dir=opt["transformer_cache"])
else:
self.preloaded_config = config_class.from_dict(
opt) # load config from opt
self.preloaded_config.output_hidden_states = (
True # return all hidden states
)
self.bert = model_class(self.preloaded_config)
hidden_size = self.bert.config.hidden_size
if opt.get("dump_feature", False):
self.config = opt
return
if opt["update_bert_opt"] > 0:
for p in self.bert.parameters():
p.requires_grad = False
task_def_list = opt["task_def_list"]
self.task_def_list = task_def_list
self.decoder_opt = []
self.task_types = []
for task_id, task_def in enumerate(task_def_list):
self.decoder_opt.append(
generate_decoder_opt(task_def.enable_san, opt["answer_opt"]))
self.task_types.append(task_def.task_type)
# create output header
self.scoring_list = nn.ModuleList()
self.dropout_list = nn.ModuleList()
for task_id in range(len(task_def_list)):
task_def: TaskDef = task_def_list[task_id]
lab = task_def.n_class
decoder_opt = self.decoder_opt[task_id]
task_type = self.task_types[task_id]
task_dropout_p = (opt["dropout_p"] if task_def.dropout_p is None
else task_def.dropout_p)
dropout = DropoutWrapper(task_dropout_p, opt["vb_dropout"])
self.dropout_list.append(dropout)
task_obj = tasks.get_task_obj(task_def)
if task_obj is not None:
# Move this to task_obj
self.pooler = Pooler(hidden_size,
dropout_p=opt["dropout_p"],
actf=opt["pooler_actf"])
out_proj = task_obj.train_build_task_layer(decoder_opt,
hidden_size,
lab,
opt,
prefix="answer",
dropout=dropout)
elif task_type == TaskType.Span:
assert decoder_opt != 1
out_proj = nn.Linear(hidden_size, 2)
elif task_type == TaskType.SpanYN:
assert decoder_opt != 1
out_proj = nn.Linear(hidden_size, 2)
elif task_type == TaskType.SeqenceLabeling:
out_proj = nn.Linear(hidden_size, lab)
# elif task_type == TaskType.MaskLM:
# if opt["encoder_type"] == EncoderModelType.ROBERTA:
# # TODO: xiaodl
# out_proj = MaskLmHeader(self.bert.embeddings.word_embeddings.weight)
# else:
# out_proj = MaskLmHeader(self.bert.embeddings.word_embeddings.weight)
elif task_type == TaskType.SeqenceGeneration:
# use orginal header
out_proj = None
elif task_type == TaskType.ClozeChoice:
self.pooler = Pooler(hidden_size,
dropout_p=opt["dropout_p"],
actf=opt["pooler_actf"])
out_proj = nn.Linear(hidden_size, lab)
else:
if decoder_opt == 1:
out_proj = SANClassifier(
hidden_size,
hidden_size,
lab,
opt,
prefix="answer",
dropout=dropout,
)
else:
out_proj = nn.Linear(hidden_size, lab)
self.scoring_list.append(out_proj)
self.config = opt