def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--bert_model_src", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--bert_model_mt", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--fc_model", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--output_dir_src",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--output_dir_mt",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--output_dir_fc",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--log_path', type=str, default="./log",
help="The path for saving tensorboard logs. Default is ./log")
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
args.train_batch_size = int(args.train_batch_size / args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
if n_gpu > 0:
torch.cuda.manual_seed_all(args.seed)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
if os.path.exists(args.output_dir_src) and os.listdir(args.output_dir_src):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir_src))
os.makedirs(args.output_dir_src, exist_ok=True)
if os.path.exists(args.output_dir_mt) and os.listdir(args.output_dir_mt):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir_mt))
os.makedirs(args.output_dir_mt, exist_ok=True)
if os.path.exists(args.output_dir_fc) and os.listdir(args.output_dir_fc):
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir_mt))
os.makedirs(args.output_dir_fc, exist_ok=True)
processors = {
"qe": MyProcessor
}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
model_collections = Collections()
# Prepare model
tokenizer_src = BertTokenizer.from_pretrained(args.bert_model_src, do_lower_case=args.do_lower_case)
tokenizer_mt = BertTokenizer.from_pretrained(args.bert_model_mt, do_lower_case=args.do_lower_case)
# model_src = BertModel.from_pretrained(args.bert_model_src)
# model_mt = BertModel.from_pretrained(args.bert_model_mt)
# model_src.to(device)
# model_mt.to(device)
# # load config
# # src.config==mt.config
# config_file = os.path.join(args.bert_model_src, CONFIG_NAME)
# config = BertConfig.from_json_file(config_file)
# # fnn
# full_connect = torch.nn.Linear(2 * config.hidden_size, 1)
# torch.nn.init.xavier_normal_(full_connect.weight)
# full_connect.to(device)
# fine-tuning fine-tuing model
# Load a trained model and config that you have fine-tuned
output_config_file_src = os.path.join(args.bert_model_src, CONFIG_NAME)
config_src = BertConfig(output_config_file_src)
model_src = BertModel(config_src)
output_model_file_src = os.path.join(args.bert_model_src, WEIGHTS_NAME)
model_state_dict_src = torch.load(output_model_file_src)
model_src.load_state_dict(model_state_dict_src)
# Load a trained model and config that you have fine-tuned
output_config_file_mt = os.path.join(args.bert_model_mt, CONFIG_NAME)
config_mt = BertConfig(output_config_file_mt)
model_mt = BertModel(config_mt)
output_model_file_mt = os.path.join(args.bert_model_mt, WEIGHTS_NAME)
model_state_dict_mt = torch.load(output_model_file_mt)
model_mt.load_state_dict(model_state_dict_mt)
model_src.to(device)
model_mt.to(device)
full_connect = torch.nn.Linear(2 * config_src.hidden_size, 1)
model_state_dict_fc = torch.load(args.fc_model)
full_connect.load_state_dict(model_state_dict_fc)
full_connect.to(device)
#---------------------------------------------
# # dropout
dropout = torch.nn.Dropout(config_src.hidden_dropout_prob)
# sigmoid
sigmoid = torch.nn.Sigmoid()
# loss
loss_fct = torch.nn.MSELoss()
# ---------------------------------------------------------------------------------------------#
train_examples=None
num_train_steps=None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare optimizer
param_optimizer = list(model_src.named_parameters(prefix='src')) + list(model_mt.named_parameters(prefix='mt')) \
+ list(full_connect.named_parameters())
# param_optimizer = list(full_connect.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
# optimizer.zero_grad()
global_step = 0
if args.do_train:
train_features = convert_examples_to_features(
train_examples, args.max_seq_length, tokenizer_src, tokenizer_mt)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids_src = torch.tensor([f.input_ids_src for f in train_features], dtype=torch.long)
all_input_mask_src = torch.tensor([f.input_mask_src for f in train_features], dtype=torch.long)
all_segment_ids_src = torch.tensor([f.segment_ids_src for f in train_features], dtype=torch.long)
all_input_ids_mt = torch.tensor([f.input_ids_mt for f in train_features], dtype=torch.long)
all_input_mask_mt = torch.tensor([f.input_mask_mt for f in train_features], dtype=torch.long)
all_segment_ids_mt = torch.tensor([f.segment_ids_mt for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.float)
train_data = TensorDataset(all_input_ids_src, all_input_mask_src, all_segment_ids_src, all_input_ids_mt,
all_input_mask_mt, all_segment_ids_mt, all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
# Timer for computing speed
timer_for_speed = Timer()
timer_for_speed.tic()
summary_writer = SummaryWriter(log_dir=args.log_path)
is_early_stop = False
disp_freq = 100
loss_valid_freq = 100
early_stop_patience = 10
bad_count = 0
nb_tr_examples, nb_tr_steps = 0, 0
for eidx in trange(int(args.num_train_epochs), desc="Epoch"):
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
# optimizer.zero_grad()
try:
model_src.train()
model_mt.train()
full_connect.train()
# model_src.eval()
# model_mt.eval()
# full_connect.train()
batch = tuple(t.to(device) for t in batch)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt, label_ids = batch
with torch.enable_grad():
_, pooled_output_src = model_src(input_ids_src, segment_ids_src, input_mask_src,
output_all_encoded_layers=False)
# with torch.no_grad():
pooled_output_src = dropout(pooled_output_src)
_, pooled_output_mt = model_mt(input_ids_mt, segment_ids_mt, input_mask_mt,
output_all_encoded_layers=False)
pooled_output_mt = dropout(pooled_output_mt)
# pooled_output_mt = dropout(pooled_output_mt)
# pooled_output [batch_size,2*hidden_size]
pooled_output = torch.cat((pooled_output_src, pooled_output_mt), 1)
logits = sigmoid(full_connect(pooled_output))
loss = loss_fct(logits.view(-1), label_ids.view(-1))
# with torch.no_grad():
# _, pooled_output_src = model_src(input_ids_src, segment_ids_src, input_mask_src,
# output_all_encoded_layers=False)
#
# # pooled_output_src = dropout(pooled_output_src)
# _, pooled_output_mt = model_mt(input_ids_mt, segment_ids_mt, input_mask_mt,
# output_all_encoded_layers=False)
# # pooled_output_mt = dropout(pooled_output_mt)
#
# # pooled_output_mt = dropout(pooled_output_mt)
# # pooled_output [batch_size,2*hidden_size]
# pooled_output = torch.cat((pooled_output_src, pooled_output_mt), 1)
#
# logits = sigmoid(full_connect(pooled_output.detach()))
# loss = loss_fct(logits.view(-1), label_ids.view(-1))
if n_gpu > 1:
loss = loss.mean() # mean() to average on multi-gpu.
if args.gradient_accumulation_steps > 1:
loss = loss / args.gradient_accumulation_steps
if args.fp16:
optimizer.backward(loss)
else:
loss.backward()
# tr_loss += loss.item()
nb_tr_examples += input_ids_src.size(0)
nb_tr_steps += 1
# optimizer.step()
if (step + 1) % args.gradient_accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad()
global_step += 1
# display some information
if (nb_tr_steps % disp_freq == 0):
model_collections.add_to_collection("train_losses", loss.item())
summary_writer.add_scalar("train_losses", loss.item(), global_step=nb_tr_steps)
lrate = args.learning_rate * warmup_linear(
nb_tr_steps / t_total, args.warmup_proportion)
result = {'train_loss': loss.item(), 'lrate': lrate}
logger.info("***** train results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
except RuntimeError as e:
if 'out of memory' in str(e):
print('| WARNING: ran out of memory, skipping batch')
# optimizer.zero_grad()
if hasattr(torch.cuda, 'empty_cache'):
torch.cuda.empty_cache()
else:
raise e
# calculate dev loss
if (nb_tr_steps % loss_valid_freq == 0):
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_dev_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, args.max_seq_length, tokenizer_src, tokenizer_mt)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids_src = torch.tensor([f.input_ids_src for f in eval_features], dtype=torch.long)
all_input_mask_src = torch.tensor([f.input_mask_src for f in eval_features], dtype=torch.long)
all_segment_ids_src = torch.tensor([f.segment_ids_src for f in eval_features], dtype=torch.long)
all_input_ids_mt = torch.tensor([f.input_ids_mt for f in eval_features], dtype=torch.long)
all_input_mask_mt = torch.tensor([f.input_mask_mt for f in eval_features], dtype=torch.long)
all_segment_ids_mt = torch.tensor([f.segment_ids_mt for f in eval_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids_src, all_input_mask_src, all_segment_ids_src,
all_input_ids_mt, all_input_mask_mt, all_segment_ids_mt,
all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model_src.eval()
model_mt.eval()
full_connect.eval()
eval_loss = 0
nb_eval_steps, nb_eval_examples = 0, 0
for batch_eval in eval_dataloader:
batch_eval = tuple(t.to(device) for t in batch_eval)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt, label_ids=batch_eval
with torch.no_grad():
_, pooled_output_src = model_src(input_ids_src, segment_ids_src, input_mask_src,
output_all_encoded_layers=False)
_, pooled_output_mt = model_mt(input_ids_mt, segment_ids_mt, input_mask_mt,
output_all_encoded_layers=False)
# pooled_output [batch_size,2*hidden_size]
pooled_output = torch.cat((pooled_output_src, pooled_output_mt), 1)
logits = sigmoid(full_connect(pooled_output.detach()))
tmp_eval_loss = loss_fct(logits.view(-1), label_ids.view(-1))
eval_loss += tmp_eval_loss.mean().item()
nb_eval_examples += input_ids_src.size(0)
nb_eval_steps += 1
eval_loss = eval_loss / nb_eval_steps
model_collections.add_to_collection("history_losses", eval_loss)
min_history_loss = np.array(model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", eval_loss, global_step=nb_tr_steps)
summary_writer.add_scalar("best_loss", min_history_loss, global_step=nb_tr_steps)
lrate = args.learning_rate * warmup_linear(
nb_tr_steps / t_total, args.warmup_proportion)
summary_writer.add_scalar("lrate", scalar_value=lrate, global_step=nb_tr_steps)
best_eval_loss = min_history_loss
# If model get new best valid loss
# save model & early stop
if eval_loss <= best_eval_loss:
bad_count = 0
if is_early_stop is False:
# Save a trained model
# Only save the model it-self
# # Save a trained model and the associated configuration
model_to_save_src = model_src.module if hasattr(model_src, 'module') else model_src
output_model_file_src = os.path.join(args.output_dir_src, WEIGHTS_NAME)
torch.save(model_to_save_src.state_dict(), output_model_file_src)
output_config_file_src = os.path.join(args.output_dir_src, CONFIG_NAME)
with open(output_config_file_src, 'w') as f:
f.write(model_to_save_src.config.to_json_string())
model_to_save_mt = model_mt.module if hasattr(model_mt, 'module') else model_mt
output_model_file_mt = os.path.join(args.output_dir_mt, WEIGHTS_NAME)
torch.save(model_to_save_mt.state_dict(), output_model_file_mt)
output_config_file_mt = os.path.join(args.output_dir_mt, CONFIG_NAME)
with open(output_config_file_mt, 'w') as f:
f.write(model_to_save_mt.config.to_json_string())
output_model_file_fc = os.path.join(args.output_dir_fc, "fnn.best." + str(nb_tr_steps))
torch.save(full_connect.state_dict(), output_model_file_fc)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= early_stop_patience and eidx > 0:
is_early_stop = True
logger.info("Early Stop!")
summary_writer.add_scalar("bad_count", bad_count, nb_tr_steps)
logger.info("{0} Loss: {1:.4f} patience: {2}".format(
nb_tr_steps, eval_loss, bad_count))
if is_early_stop == True:
break
def test_rnn(args):
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {} distributed training: {}".format(
device, n_gpu, bool(args.local_rank != -1)))
layer_indexes = [int(x) for x in args.layers.split(",")]
processors = {"qe": MyProcessor}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
model_collections = Collections()
# 0. load bert
tokenizer_src = BertTokenizer.from_pretrained(
args.bert_model_src, do_lower_case=args.do_lower_case)
tokenizer_mt = BertTokenizer.from_pretrained(
args.bert_model_mt, do_lower_case=args.do_lower_case)
# Load a trained model and config that you have fine-tuned
output_config_file_src = os.path.join(args.output_dir_src, CONFIG_NAME)
config_src = BertConfig(output_config_file_src)
model_src = BertModel(config_src)
output_model_file_src = os.path.join(args.output_dir_src, WEIGHTS_NAME)
model_state_dict_src = torch.load(output_model_file_src)
model_src.load_state_dict(model_state_dict_src)
# Load a trained model and config that you have fine-tuned
output_config_file_mt = os.path.join(args.output_dir_mt, CONFIG_NAME)
config_mt = BertConfig(output_config_file_mt)
model_mt = BertModel(config_mt)
output_model_file_mt = os.path.join(args.output_dir_mt, WEIGHTS_NAME)
model_state_dict_mt = torch.load(output_model_file_mt)
model_mt.load_state_dict(model_state_dict_mt)
model_src.to(device)
model_mt.to(device)
model_src.eval()
model_mt.eval()
# 1. load Bi-LSTM Model
qe_model = QE(feature_size=768, hidden_size=512, dropout_rate=0.0)
model_state_dict = torch.load(args.output_dir_qe_model)
qe_model.load_state_dict(model_state_dict)
qe_model.to(device)
qe_model.eval()
# 2. prepare test data
eval_examples = processor.get_test_examples(args.input_file)
eval_features = convert_examples_to_features(eval_examples,
args.max_seq_length,
tokenizer_src, tokenizer_mt)
all_input_ids_src = torch.tensor([f.input_ids_src for f in eval_features],
dtype=torch.long)
all_input_mask_src = torch.tensor(
[f.input_mask_src for f in eval_features], dtype=torch.long)
all_segment_ids_src = torch.tensor(
[f.segment_ids_src for f in eval_features], dtype=torch.long)
all_input_ids_mt = torch.tensor([f.input_ids_mt for f in eval_features],
dtype=torch.long)
all_input_mask_mt = torch.tensor([f.input_mask_mt for f in eval_features],
dtype=torch.long)
all_segment_ids_mt = torch.tensor(
[f.segment_ids_mt for f in eval_features], dtype=torch.long)
test_data = TensorDataset(all_input_ids_src, all_input_mask_src,
all_segment_ids_src, all_input_ids_mt,
all_input_mask_mt, all_segment_ids_mt)
# Run prediction for full data
test_sampler = SequentialSampler(test_data)
test_dataloader = DataLoader(test_data,
sampler=test_sampler,
batch_size=args.test_batch_size)
result = []
for batch_test in test_dataloader:
batch_test = tuple(t.to(device) for t in batch_test)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt = batch_test
with torch.no_grad():
all_encoder_layers_test_src, _ = model_src(
input_ids_src,
token_type_ids=segment_ids_src,
attention_mask=input_mask_src)
encoder_layers_test_src = all_encoder_layers_test_src[-1]
all_encoder_layers_test_mt, _ = model_mt(
input_ids_mt,
token_type_ids=segment_ids_mt,
attention_mask=input_mask_mt)
encoder_layers_test_mt = all_encoder_layers_test_mt[-1]
outputs = qe_model(encoder_layers_test_src, input_ids_src,
encoder_layers_test_mt, input_ids_mt)
outputs.squeeze(1).cpu().numpy().tolist()
for val in outputs:
result.append(float(val))
with open(args.saveto, 'w') as f:
for val in result:
f.write('%.6f\n' % val)
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument("--data_dir",
default=None,
type=str,
required=True,
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
parser.add_argument("--saveto", type=str,
help="""Result prefix.""")
parser.add_argument("--bert_model_src", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--bert_model_mt", default=None, type=str, required=True,
help="Bert pre-trained model selected in the list: bert-base-uncased, "
"bert-large-uncased, bert-base-cased, bert-base-multilingual, bert-base-chinese.")
parser.add_argument("--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train.")
parser.add_argument("--output_dir_src",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--output_dir_mt",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
parser.add_argument("--output_dir_fc",
default=None,
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.")
## Other parameters
parser.add_argument("--max_seq_length",
default=128,
type=int,
help="The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, and sequences shorter \n"
"than this will be padded.")
parser.add_argument("--do_train",
default=False,
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
default=False,
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument("--do_lower_case",
default=False,
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--train_batch_size",
default=32,
type=int,
help="Total batch size for training.")
parser.add_argument("--eval_batch_size",
default=8,
type=int,
help="Total batch size for eval.")
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument("--warmup_proportion",
default=0.1,
type=float,
help="Proportion of training to perform linear learning rate warmup for. "
"E.g., 0.1 = 10%% of training.")
parser.add_argument("--no_cuda",
default=False,
action='store_true',
help="Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="local_rank for distributed training on gpus")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument('--gradient_accumulation_steps',
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.")
parser.add_argument('--fp16',
default=False,
action='store_true',
help="Whether to use 16-bit float precision instead of 32-bit")
parser.add_argument('--loss_scale',
type=float, default=0,
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
"0 (default value): dynamic loss scaling.\n"
"Positive power of 2: static loss scaling value.\n")
parser.add_argument('--log_path', type=str, default="./log",
help="The path for saving tensorboard logs. Default is ./log")
args = parser.parse_args()
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
device, n_gpu, bool(args.local_rank != -1), args.fp16))
if args.gradient_accumulation_steps < 1:
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
args.gradient_accumulation_steps))
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
processors = {
"qe": MyProcessor
}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
model_collections = Collections()
tokenizer_src = BertTokenizer.from_pretrained(args.bert_model_src, do_lower_case=args.do_lower_case)
tokenizer_mt = BertTokenizer.from_pretrained(args.bert_model_mt, do_lower_case=args.do_lower_case)
# Load a trained model and config that you have fine-tuned
output_config_file_src = os.path.join(args.output_dir_src, CONFIG_NAME)
config_src = BertConfig(output_config_file_src)
model_src = BertModel(config_src)
output_model_file_src = os.path.join(args.output_dir_src, WEIGHTS_NAME)
model_state_dict_src = torch.load(output_model_file_src)
model_src.load_state_dict(model_state_dict_src)
# Load a trained model and config that you have fine-tuned
output_config_file_mt = os.path.join(args.output_dir_mt, CONFIG_NAME)
config_mt = BertConfig(output_config_file_mt)
model_mt = BertModel(config_mt)
output_model_file_mt = os.path.join(args.output_dir_mt, WEIGHTS_NAME)
model_state_dict_mt = torch.load(output_model_file_mt)
model_mt.load_state_dict(model_state_dict_mt)
# Prepare model
# model_src = BertModel.from_pretrained(args.bert_model_src)
# model_mt = BertModel.from_pretrained(args.bert_model_mt)
# load config
# src.config==mt.config
config_file = os.path.join(args.bert_model_src, CONFIG_NAME)
config_src = BertConfig.from_json_file(config_file)
model_src.to(device)
model_mt.to(device)
model_src.eval()
model_mt.eval()
# fnn
full_connect = torch.nn.Linear(2 * config_src.hidden_size, 1)
model_state_dict_fc = torch.load(args.output_dir_fc)
full_connect.load_state_dict(model_state_dict_fc)
full_connect.to(device)
full_connect.eval()
# sigmoid
sigmoid = torch.nn.Sigmoid()
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
eval_examples = processor.get_test_examples(args.data_dir)
eval_features = convert_examples_to_features(
eval_examples, args.max_seq_length, tokenizer_src, tokenizer_mt)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids_src = torch.tensor([f.input_ids_src for f in eval_features], dtype=torch.long)
all_input_mask_src = torch.tensor([f.input_mask_src for f in eval_features], dtype=torch.long)
all_segment_ids_src = torch.tensor([f.segment_ids_src for f in eval_features], dtype=torch.long)
all_input_ids_mt = torch.tensor([f.input_ids_mt for f in eval_features], dtype=torch.long)
all_input_mask_mt = torch.tensor([f.input_mask_mt for f in eval_features], dtype=torch.long)
all_segment_ids_mt = torch.tensor([f.segment_ids_mt for f in eval_features], dtype=torch.long)
eval_data = TensorDataset(all_input_ids_src, all_input_mask_src, all_segment_ids_src,
all_input_ids_mt, all_input_mask_mt, all_segment_ids_mt)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
model_src.eval()
model_mt.eval()
full_connect.eval()
predict_hter = []
for bacth_eval in eval_dataloader:
bacth_eval = tuple(t.to(device) for t in bacth_eval)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt = bacth_eval
with torch.no_grad():
_, pooled_output_src = model_src(input_ids_src, segment_ids_src, input_mask_src,
output_all_encoded_layers=False)
_, pooled_output_mt = model_mt(input_ids_mt, segment_ids_mt, input_mask_mt,
output_all_encoded_layers=False)
# pooled_output [batch_size,2*hidden_size]
pooled_output = torch.cat((pooled_output_src, pooled_output_mt), 1)
logits = sigmoid(full_connect(pooled_output.detach()))
logits = logits.detach().squeeze(1).cpu().numpy().tolist()
for val in logits:
predict_hter.append(float(val))
def auto_mkdir(path):
if not os.path.exists(path):
os.makedirs(path, exist_ok=True)
auto_mkdir('/'.join(args.saveto.split('/')[0:2]))
with open(args.saveto, 'w') as f:
for val in predict_hter:
f.write('%.6f\n' % val)
def train_rnn(args):
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.distributed.init_process_group(backend='nccl')
logger.info("device: {} n_gpu: {} distributed training: {}".format(
device, n_gpu, bool(args.local_rank != -1)))
args.train_batch_size = int(args.train_batch_size /
args.gradient_accumulation_steps)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
processors = {"qe": MyProcessor}
task_name = args.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
model_collections = Collections()
# optimizer = "sgd"
# lrate = 0.01
# grad_clip = 1.0
layer_indexes = [int(x) for x in args.layers.split(",")]
tokenizer_src = BertTokenizer.from_pretrained(
args.bert_model_src, do_lower_case=args.do_lower_case)
tokenizer_mt = BertTokenizer.from_pretrained(
args.bert_model_mt, do_lower_case=args.do_lower_case)
# # 0. load bert
# model_src = BertModel.from_pretrained(args.bert_model_src)
# model_mt = BertModel.from_pretrained(args.bert_model_mt)
# model_src.to(device)
# model_mt.to(device)
# # 1. Build Bi-LSTM Model & Criterion
# qe_model = QE(feature_size=768, hidden_size=512, dropout_rate=0.1)
# qe_model.to(device)
# fine-tuning fine-tuing model
# Load a trained model and config that you have fine-tuned
output_config_file_src = os.path.join(args.bert_model_src, CONFIG_NAME)
config_src = BertConfig(output_config_file_src)
model_src = BertModel(config_src)
output_model_file_src = os.path.join(args.bert_model_src, WEIGHTS_NAME)
model_state_dict_src = torch.load(output_model_file_src)
model_src.load_state_dict(model_state_dict_src)
# Load a trained model and config that you have fine-tuned
output_config_file_mt = os.path.join(args.bert_model_mt, CONFIG_NAME)
config_mt = BertConfig(output_config_file_mt)
model_mt = BertModel(config_mt)
output_model_file_mt = os.path.join(args.bert_model_mt, WEIGHTS_NAME)
model_state_dict_mt = torch.load(output_model_file_mt)
model_mt.load_state_dict(model_state_dict_mt)
model_src.to(device)
model_mt.to(device)
qe_model = QE(feature_size=768, hidden_size=512, dropout_rate=0.1)
model_state_dict = torch.load(args.qe_model)
qe_model.load_state_dict(model_state_dict)
qe_model.to(device)
# ---------------------------------------------
critic = torch.nn.MSELoss()
critic.to(device)
# 2. Build optimizer
# optim = Optimizer(name=optimizer, model=qe_model, lr=lrate,
# grad_clip=grad_clip, optim_args=None)
train_examples = processor.get_train_examples(args.input_file)
num_train_steps = int(
len(train_examples) / args.train_batch_size /
args.gradient_accumulation_steps * args.num_train_epochs)
# Prepare optimizer
param_optimizer = list(model_src.named_parameters(prefix='src')) + list(model_mt.named_parameters(prefix='mt')) \
+ list(qe_model.named_parameters())
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params':
[p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
'weight_decay':
0.01
}, {
'params':
[p for n, p in param_optimizer if any(nd in n for nd in no_decay)],
'weight_decay':
0.0
}]
t_total = num_train_steps
if args.local_rank != -1:
t_total = t_total // torch.distributed.get_world_size()
optimizer = BertAdam(optimizer_grouped_parameters,
lr=args.learning_rate,
warmup=args.warmup_proportion,
t_total=t_total)
# 3. prepare training data
train_features = convert_examples_to_features(train_examples,
args.max_seq_length,
tokenizer_src, tokenizer_mt)
logger.info("***** Running training *****")
logger.info(" Num examples = %d", len(train_examples))
logger.info(" Batch size = %d", args.train_batch_size)
logger.info(" Num steps = %d", num_train_steps)
all_input_ids_src = torch.tensor([f.input_ids_src for f in train_features],
dtype=torch.long)
all_input_mask_src = torch.tensor(
[f.input_mask_src for f in train_features], dtype=torch.long)
all_segment_ids_src = torch.tensor(
[f.segment_ids_src for f in train_features], dtype=torch.long)
all_input_ids_mt = torch.tensor([f.input_ids_mt for f in train_features],
dtype=torch.long)
all_input_mask_mt = torch.tensor([f.input_mask_mt for f in train_features],
dtype=torch.long)
all_segment_ids_mt = torch.tensor(
[f.segment_ids_mt for f in train_features], dtype=torch.long)
all_label_ids = torch.tensor([f.label_id for f in train_features],
dtype=torch.float)
train_data = TensorDataset(all_input_ids_src, all_input_mask_src,
all_segment_ids_src, all_input_ids_mt,
all_input_mask_mt, all_segment_ids_mt,
all_label_ids)
if args.local_rank == -1:
train_sampler = RandomSampler(train_data)
else:
train_sampler = DistributedSampler(train_data)
train_dataloader = DataLoader(train_data,
sampler=train_sampler,
batch_size=args.train_batch_size)
# Timer for computing speed
summary_writer = SummaryWriter(log_dir=args.log_path)
is_early_stop = False
disp_freq = 100
loss_valid_freq = 100
early_stop_patience = 10
bad_count = 0
# 3. begin training
eidx = 0
uidx = 0
while True:
summary_writer.add_scalar("Epoch", (eidx + 1), uidx)
training_progress_bar = tqdm(desc=' - (Epoch %d) ' % eidx,
total=len(train_dataloader),
unit="sents")
for batch_train in train_dataloader:
model_src.train()
model_mt.train()
qe_model.train()
uidx += 1
batch_train = tuple(t.to(device) for t in batch_train)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt, label_ids = batch_train
n_samples_t = len(input_ids_src)
training_progress_bar.update(n_samples_t)
optimizer.zero_grad()
with torch.enable_grad():
all_encoder_layers_src, _ = model_src(
input_ids_src,
token_type_ids=segment_ids_src,
attention_mask=input_mask_src)
encoder_layers_src = all_encoder_layers_src[-1]
all_encoder_layers_mt, _ = model_mt(
input_ids_mt,
token_type_ids=segment_ids_mt,
attention_mask=input_mask_mt)
encoder_layers_mt = all_encoder_layers_mt[-1]
loss = compute_forward_qe_bt(model=qe_model,
critic=critic,
feature_src=encoder_layers_src,
feature_mt=encoder_layers_mt,
src=input_ids_src,
mt=input_ids_mt,
label=label_ids,
eval=False)
optimizer.step()
# display some information
if (uidx % disp_freq == 0):
lrate = args.learning_rate * warmup_linear(
uidx / t_total, args.warmup_proportion)
result = {'train_loss': loss, 'lrate': lrate}
logger.info("***** train results *****")
for key in sorted(result.keys()):
logger.info(" %s = %s", key, str(result[key]))
# calculate dev loss
if (uidx % loss_valid_freq == 0):
eval_examples = processor.get_dev_examples(args.input_file)
eval_features = convert_examples_to_features(
eval_examples, args.max_seq_length, tokenizer_src,
tokenizer_mt)
logger.info("***** Running evaluation *****")
logger.info(" Num examples = %d", len(eval_examples))
logger.info(" Batch size = %d", args.eval_batch_size)
all_input_ids_src = torch.tensor(
[f.input_ids_src for f in eval_features], dtype=torch.long)
all_input_mask_src = torch.tensor(
[f.input_mask_src for f in eval_features],
dtype=torch.long)
all_segment_ids_src = torch.tensor(
[f.segment_ids_src for f in eval_features],
dtype=torch.long)
all_input_ids_mt = torch.tensor(
[f.input_ids_mt for f in eval_features], dtype=torch.long)
all_input_mask_mt = torch.tensor(
[f.input_mask_mt for f in eval_features], dtype=torch.long)
all_segment_ids_mt = torch.tensor(
[f.segment_ids_mt for f in eval_features],
dtype=torch.long)
all_label_ids = torch.tensor(
[f.label_id for f in eval_features], dtype=torch.float)
eval_data = TensorDataset(all_input_ids_src,
all_input_mask_src,
all_segment_ids_src,
all_input_ids_mt, all_input_mask_mt,
all_segment_ids_mt, all_label_ids)
# Run prediction for full data
eval_sampler = SequentialSampler(eval_data)
eval_dataloader = DataLoader(eval_data,
sampler=eval_sampler,
batch_size=args.eval_batch_size)
model_src.eval()
model_mt.eval()
qe_model.eval()
sum_loss = 0.0
nb_eval_steps = 0
for batch_eval in eval_dataloader:
batch_eval = tuple(t.to(device) for t in batch_eval)
input_ids_src, input_mask_src, segment_ids_src, input_ids_mt, \
input_mask_mt, segment_ids_mt, label_ids = batch_eval
with torch.no_grad():
all_encoder_layers_src, _ = model_src(
input_ids_src,
token_type_ids=segment_ids_src,
attention_mask=input_mask_src)
encoder_layers_src = all_encoder_layers_src[-1]
all_encoder_layers_mt, _ = model_mt(
input_ids_mt,
token_type_ids=segment_ids_mt,
attention_mask=input_mask_mt)
encoder_layers_mt = all_encoder_layers_mt[-1]
eval_loss_batch = compute_forward_qe_bt(
model=qe_model,
critic=critic,
feature_src=encoder_layers_src,
feature_mt=encoder_layers_mt,
src=input_ids_src,
mt=input_ids_mt,
label=label_ids,
eval=True)
# print(eval_loss_batch)
if np.isnan(eval_loss_batch):
logging.info("NaN detected!")
sum_loss += float(eval_loss_batch)
nb_eval_steps += 1
eval_loss = sum_loss / nb_eval_steps
model_collections.add_to_collection("history_losses",
eval_loss)
min_history_loss = np.array(
model_collections.get_collection("history_losses")).min()
summary_writer.add_scalar("loss", eval_loss, global_step=uidx)
summary_writer.add_scalar("best_loss",
min_history_loss,
global_step=uidx)
lrate = args.learning_rate * warmup_linear(
uidx / t_total, args.warmup_proportion)
summary_writer.add_scalar("lrate",
scalar_value=lrate,
global_step=uidx)
best_eval_loss = min_history_loss
# If model get new best valid loss
# save model & early stop
if eval_loss <= best_eval_loss:
bad_count = 0
if is_early_stop is False:
# Save a trained model
# Only save the model it-self
model_to_save_src = model_src.module if hasattr(
model_src, 'module') else model_src
output_model_file_src = os.path.join(
args.output_dir_src, WEIGHTS_NAME)
torch.save(model_to_save_src.state_dict(),
output_model_file_src)
output_config_file_src = os.path.join(
args.output_dir_src, CONFIG_NAME)
with open(output_config_file_src, 'w') as f:
f.write(model_to_save_src.config.to_json_string())
model_to_save_mt = model_mt.module if hasattr(
model_mt, 'module') else model_mt
output_model_file_mt = os.path.join(
args.output_dir_mt, WEIGHTS_NAME)
torch.save(model_to_save_mt.state_dict(),
output_model_file_mt)
output_config_file_mt = os.path.join(
args.output_dir_mt, CONFIG_NAME)
with open(output_config_file_mt, 'w') as f:
f.write(model_to_save_mt.config.to_json_string())
# Save a qe_model
output_qe_model_file = os.path.join(
args.output_dir_qe_model,
"qe_bert.best." + str(uidx))
torch.save(qe_model.state_dict(), output_qe_model_file)
else:
bad_count += 1
# At least one epoch should be traversed
if bad_count >= early_stop_patience and eidx > 0:
is_early_stop = True
logger.info("Early Stop!")
summary_writer.add_scalar("bad_count", bad_count, uidx)
logger.info("{0} Loss: {1:.14f} patience: {2}".format(
uidx, eval_loss, bad_count))
print("{0} Loss: {1:.14f} patience: {2}".format(
uidx, eval_loss, bad_count))
if is_early_stop == True:
break
training_progress_bar.close()
eidx += 1
if eidx > args.num_train_epochs:
break