def train_and_eval(self, args):
self._initialize_run_env(args.device, args.seed)
self._initialize_model(args.model_type, args.pretrained_model_path)
self.data_helper = DataHelper(self.tokenizer, args.batch_size,
args.doc_stride, args.max_seq_length)
# start training
if args.do_train:
logging.info("start training...")
self._start_train(args)
logging.info("train success.")
# start evaluation
if args.do_eval:
logging.info("start evaluating...")
assert len(args.eval_files) == 1, "if do_eval, then eval_files must have one!!!"
eval_file_path = args.eval_files[0]
self.predict([eval_file_path], args.output_dir, args.max_answer_length,
args.cls_threshold, args.n_best_size)
file_name = os.path.basename(eval_file_path).replace(".json", "")
pred_file_path = os.path.join(args.output_dir, file_name + '_predictions.json')
self._evaluate(eval_file_path, pred_file_path, args.tag)
# confirm threshold
confirm_threshold(eval_file_path, args.output_dir, file_name)
logging.info("evaluate success.")
# start predicting
if args.do_predict:
logging.info("start predicting...")
self.predict(args.predict_files, args.output_dir, args.max_answer_length,
args.cls_threshold, args.n_best_size)
logging.info("predict success.")
def train_lstm():
batch_size = 100
num_layers = 3
num_directions = 2
embedding_size = 100
hidden_size = 64
learning_rate = 0.0001
num_epochs = 5
data_helper = DataHelper()
train_text, train_labels, ver_text, ver_labels, test_text, test_labels = data_helper.get_data_and_labels()
word_set = data_helper.get_word_set()
vocab = data_helper.get_word_dict()
words_length = len(word_set) + 2
lstm = LSTM(words_length, embedding_size, hidden_size, num_layers, num_directions, batch_size)
X = [[vocab[word] for word in sentence.split(' ')] for sentence in train_text]
X_lengths = [len(sentence) for sentence in X]
pad_token = vocab['<PAD>']
longest_sent = max(X_lengths)
b_size = len(X)
padded_X = np.ones((b_size, longest_sent)) * pad_token
for i, x_len in enumerate(X_lengths):
sequence = X[i]
padded_X[i, 0:x_len] = sequence[:x_len]
x = Variable(torch.tensor(padded_X)).long()
y = Variable(torch.tensor(list(int(i) for i in train_labels)))
dataset = Data.TensorDataset(x, y)
loader = Data.DataLoader(
dataset=dataset,
batch_size=batch_size,
shuffle=True,
num_workers=2
)
loss_func = nn.CrossEntropyLoss()
optimizer = optim.Adam(lstm.parameters(), lr=learning_rate)
for epoch in range(num_epochs):
for step, (batch_x, batch_y) in enumerate(loader):
output = lstm(batch_x)
temp = torch.argmax(output, dim=1)
correct = 0
for i in range(batch_size):
if batch_y[i] == temp[i]:
correct += 1
loss = loss_func(output, batch_y)
print('epoch: {0}, step: {1}, loss: {2}, train acc: {3}'.format(epoch, step, loss, correct / batch_size))
optimizer.zero_grad()
loss.backward()
optimizer.step()
ver_lstm(lstm, ver_text, ver_labels, vocab, batch_size)
test_lstm(lstm, test_text, test_labels, vocab, batch_size)
def train_svm():
data_helper = DataHelper()
train_text, train_labels, ver_text, ver_labels, test_text, test_labels = data_helper.get_data_and_labels()
stopwords = data_helper.get_stopwords()
svm = SVM(train_text, train_labels, ver_text, ver_labels, test_text, test_labels, stopwords)
svm.train()
svm.verification()
print('ver_acc: {:.3}'.format(svm.ver_acc))
svm.test()
print('test_acc: {:.3}'.format(svm.test_acc))
def run_generating(args):
# ----------------------------------------------------- #
log_path = os.path.join('./saved_models/pretrain_generator', 'run.log')
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(log_path, 'w')
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s: %(message)s', datefmt='%Y/%m/%d %H:%M:%S')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info('args: {}'.format(args))
# ----------------------------------------------------- #
# load data & init model and optimizer
logger.info('Loading data & model')
config = GPT2Config.from_pretrained(args.generator_type, cache_dir='../cache/')
datahelper = DataHelper(args)
path_embedding_file = os.path.join('./path_embeddings/', args.data_dir, 'path_embedding.pickle')
# self define lm head gpt2
gpt = GPT2Model.from_pretrained(args.generator_type, cache_dir='../cache/')
config.vocab_size = len(datahelper.gpt_tokenizer)
gpt.resize_token_embeddings(len(datahelper.gpt_tokenizer))
pretrain_generator_ckpt = os.path.join('./saved_models/pretrain_generator', 'model.ckpt')
generator = Generator(gpt, config, max_len=args.output_len).to(args.device)
generator.load_state_dict(torch.load(pretrain_generator_ckpt, map_location=args.device))
save_path_embedding(datahelper, generator, path_embedding_file, args)
print('Finish.')
from models.conv_model import ConvModel
if __name__ == "__main__":
config = ConvConfig()
NEG_DATA_PATH = 'data/sentiment/neg.txt'
POS_DATA_PATH = 'data/sentiment/pos.txt'
WORD2VEC_PATH = '/data/pretrained_model/word_embedding/glove.6B/glove.6B.%sd.txt' % config.EMBEDDING_DIM
SAVE_PATH = 'models/models/model_w{}_e{}_c{}.h5'.format(
config.EMBEDDING_DIM, config.LATENT_DIM, config.CLASS_NUM)
print(config)
print("Data Path: ", NEG_DATA_PATH, POS_DATA_PATH)
print("Word2Vec Path: ", WORD2VEC_PATH)
print("Save Path: ", SAVE_PATH)
data_helper = DataHelper(config)
#### load the data ####
input_texts, target_texts, target_texts_inputs, classes = data_helper.read_txt_sentiment(
NEG_DATA_PATH, POS_DATA_PATH)
#### tokenize the inputs, outputs ####
input_sequences, word2idx_inputs, max_len_input = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### load word2vec pretrained model ####
word2vec = data_helper.load_word2vec(WORD2VEC_PATH)
#### create embedding matrix ####
embedding_matrix = data_helper.create_embedding_matrix(
word2vec, word2idx_inputs, WORD2VEC_PATH)
class ModelOperation(object):
"""ModelTrain"""
def __init__(self):
self.cur_process_num = paddle.distributed.get_world_size() # PADDLE_TRAINERS_NUM 的值，默认值为1
self.cur_process_rank = paddle.distributed.get_rank() # PADDLE_TRAINER_ID 的值，默认值为0
self.model_class = {
"ernie": (ErnieForQuestionAnswering, ErnieTokenizer),
"bert": (BertForQuestionAnswering, BertTokenizer),
"roberta": (RobertaForQuestionAnswering, RobertaTokenizer)
}
self.data_helper = None
def _initialize_run_env(self, device, seed):
assert device in ("cpu", "gpu", "xpu"), \
f"param device({device}) must be in ('cpu', 'gpu', 'xpu')!!!"
paddle.set_device(device)
if self.cur_process_num > 1:
paddle.distributed.init_parallel_env()
if seed:
self.set_seed(seed)
def _initialize_model(self, model_type, pretrained_model_path):
assert os.path.exists(pretrained_model_path), \
f"model path {pretrained_model_path} must exists!!!"
logging.info(f"initialize model from {pretrained_model_path}")
model_class, tokenizer_class = self.model_class[model_type]
self.tokenizer = tokenizer_class.from_pretrained(pretrained_model_path)
self.model = model_class.from_pretrained(pretrained_model_path)
if self.cur_process_num > 1:
self.model = paddle.DataParallel(self.model)
def _initialize_optimizer(self, args, num_training_steps):
self.lr_scheduler = LinearDecayWithWarmup(
args.learning_rate, num_training_steps, args.warmup_proportion)
self.optimizer = paddle.optimizer.AdamW(
learning_rate=self.lr_scheduler,
epsilon=args.adam_epsilon,
parameters=self.model.parameters(),
weight_decay=args.weight_decay,
apply_decay_param_fun=lambda x: x in [
p.name for n, p in self.model.named_parameters()
if not any(nd in n for nd in ["bias", "norm"])
])
def _start_train(self, args):
# get train data loader
train_data_loader = self.data_helper.get_iterator(args.train_data_path, shuffle=True)
num_training_steps = args.max_train_steps if args.max_train_steps > 0 else \
len(train_data_loader) * args.train_epochs
logging.info("Num train examples: %d" % len(train_data_loader.dataset.data))
logging.info("Max train steps: %d" % num_training_steps)
# initialize optimizer
self._initialize_optimizer(args, num_training_steps)
# define loss function
criterion = CrossEntropyLossForQA()
global_step = 0
tic_train = time.time()
for epoch in range(args.train_epochs):
for step, batch in enumerate(train_data_loader):
global_step += 1
input_ids, segment_ids, start_positions, end_positions, answerable_label = batch
logits = self.model(input_ids=input_ids, token_type_ids=segment_ids)
loss = criterion(logits, (start_positions, end_positions, answerable_label))
if global_step % args.logging_steps == 0:
logging.info(
"global step %d, epoch: %d, batch: %d, loss: %f, speed: %.2f step/s"
% (global_step, epoch, step, loss,
args.logging_steps / (time.time() - tic_train)))
tic_train = time.time()
loss.backward()
self.optimizer.step()
self.lr_scheduler.step()
self.optimizer.clear_gradients()
if global_step % args.save_steps == 0 or global_step == num_training_steps:
if self.cur_process_rank == 0:
output_dir = \
os.path.join(args.output_dir, "model_{}".format(global_step))
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# need better way to get inner model of DataParallel
model_to_save = \
self.model._layers if isinstance(self.model, paddle.DataParallel) else self.model
model_to_save.save_pretrained(output_dir)
self.tokenizer.save_pretrained(output_dir)
logging.info('Saving checkpoint to:', output_dir)
@staticmethod
def _evaluate(raw_data_path, pred_data_path, tag=None):
ref_ans = read_mrc_dataset(raw_data_path, tag=tag)
assert len(ref_ans) > 0, 'Find no sample with tag - {}'.format(tag)
pred_ans = read_model_prediction(pred_data_path)
F1, EM, ans_score, TOTAL, SKIP = evaluate(ref_ans, pred_ans, verbose=False)
print_metrics(F1, EM, ans_score, TOTAL, SKIP, tag)
def train_and_eval(self, args):
self._initialize_run_env(args.device, args.seed)
self._initialize_model(args.model_type, args.pretrained_model_path)
self.data_helper = DataHelper(self.tokenizer, args.batch_size,
args.doc_stride, args.max_seq_length)
# start training
if args.do_train:
logging.info("start training...")
self._start_train(args)
logging.info("train success.")
# start evaluation
if args.do_eval:
logging.info("start evaluating...")
assert len(args.eval_files) == 1, "if do_eval, then eval_files must have one!!!"
eval_file_path = args.eval_files[0]
self.predict([eval_file_path], args.output_dir, args.max_answer_length,
args.cls_threshold, args.n_best_size)
file_name = os.path.basename(eval_file_path).replace(".json", "")
pred_file_path = os.path.join(args.output_dir, file_name + '_predictions.json')
self._evaluate(eval_file_path, pred_file_path, args.tag)
# confirm threshold
confirm_threshold(eval_file_path, args.output_dir, file_name)
logging.info("evaluate success.")
# start predicting
if args.do_predict:
logging.info("start predicting...")
self.predict(args.predict_files, args.output_dir, args.max_answer_length,
args.cls_threshold, args.n_best_size)
logging.info("predict success.")
@paddle.no_grad()
def _predict(self, data_loader, output_dir, max_answer_length, cls_threshold,
n_best_size=10, prefix=""):
self.model.eval()
all_start_logits, all_end_logits = [], []
all_cls_logits = []
tic_eval = time.time()
for batch in data_loader:
input_ids, segment_ids = batch
start_logits_tensor, end_logits_tensor, cls_logits_tensor = \
self.model(input_ids, segment_ids)
for idx in range(start_logits_tensor.shape[0]):
if len(all_start_logits) % 1000 == 0 and len(all_start_logits):
logging.info("Processing example: %d" % len(all_start_logits))
logging.info('time per 1000:', time.time() - tic_eval)
tic_eval = time.time()
all_start_logits.append(start_logits_tensor.numpy()[idx])
all_end_logits.append(end_logits_tensor.numpy()[idx])
all_cls_logits.append(cls_logits_tensor.numpy()[idx])
all_predictions, all_nbest_json, all_cls_predictions = \
compute_prediction_span(
examples=data_loader.dataset.data,
features=data_loader.dataset.new_data,
predictions=(all_start_logits, all_end_logits, all_cls_logits),
version_2_with_negative=True,
n_best_size=n_best_size,
max_answer_length=max_answer_length,
cls_threshold=cls_threshold)
# start save inference result
if not os.path.exists(output_dir):
os.makedirs(output_dir)
with open(os.path.join(output_dir, prefix + '_predictions.json'), "w", encoding='utf-8') as f:
f.write(json.dumps(all_predictions, ensure_ascii=False, indent=4) + "\n")
with open(os.path.join(output_dir, prefix + '_nbest_predictions.json'), "w",
encoding="utf8") as f:
f.write(json.dumps(all_nbest_json, indent=4, ensure_ascii=False) + u"\n")
if all_cls_predictions:
with open(os.path.join(output_dir, prefix + "_cls_preditions.json"), "w") as f:
for cls_predictions in all_cls_predictions:
qas_id, pred_cls_label, no_answer_prob, answerable_prob = cls_predictions
f.write('{}\t{}\t{}\t{}\n'.format(qas_id, pred_cls_label, no_answer_prob, answerable_prob))
self.model.train()
def predict(self, predict_files, output_dir, max_answer_length, cls_threshold, n_best_size):
assert predict_files is not None, "param predict_files should be set when predicting!"
input_files = []
for input_pattern in predict_files:
input_files.extend(glob.glob(input_pattern))
assert len(input_files) > 0, 'Can not find predict file in {}'.format(predict_files)
for input_file in input_files:
file_name = os.path.basename(input_file).replace(".json", "")
data_loader = \
self.data_helper.get_iterator(input_file, part_feature=True) # no need extract position info
self._predict(data_loader, output_dir, max_answer_length,
cls_threshold, n_best_size, prefix=file_name)
@staticmethod
def set_seed(random_seed):
random.seed(random_seed)
np.random.seed(random_seed)
paddle.seed(random_seed)
config = configparser.ConfigParser()
config.read(CONFIG_PATH)
print("Config file read")
#load embedding, read data and tokenize
if args.emb_type == 'keras':
raise NotImplementedError(
"Keras Embedding is not applicable to linear model.",
self.emb_type)
io = IOHelper(size=args.size,
emb_type=args.emb_type,
preprocess=args.preprocess)
dh = DataHelper(io,
ln=(int)(config['MODEL']['LENGTH']),
nr_words=(int)(config['MODEL']['NUM_WORDS']),
dim=(int)(config['MODEL']['DIM']))
#read data
X_raw, Y_raw, X_test_raw = io.readData()
#prepare data for training
emb, emb_dim = dh.getEmbedding()
#compute feature vectors
X_feat = []
X_feat = [embed2featureWords(row, emb, emb_dim) for row in X_raw]
X_feat = pd.DataFrame(X_feat)
y = pd.DataFrame(Y_raw)
tf.flags.DEFINE_boolean("allow_soft_placement", True,
"allow device soft device placement")
tf.flags.DEFINE_boolean("log_device_placement", False,
"log placement of ops on devices")
tf.flags.DEFINE_boolean("gpu_options_allow_growth", True,
"allow gpu options growth")
FLAGS = tf.flags.FLAGS
print("parameters info:")
for attr, value in tf.flags.FLAGS.__flags.items():
print("{0}: {1}".format(attr, value.value))
# 2. load data
## get train_x, train_y, dev_x, dev_y
data_helper = DataHelper(FLAGS.data_path,
FLAGS.vocab_path,
fields=['y', 'x'],
startline=1)
data_list = data_helper.get_data(id_fields=['x'])
x, y = data_list['x'], data_list['y']
padding_x, max_document_length = padding(x, maxlen=FLAGS.pad_seq_len)
int_y = [int(_y) for _y in y]
encoded_y = one_hot_encode(int_y)
train_x, test_x, train_y, test_y = train_test_data_split(padding_x, encoded_y)
# 3. define session
with tf.Graph().as_default():
# session_config=tf.ConfigProto(allow_soft_placement=True,log_device_placement=False)
# sess=tf.Session(config=session_config)
session_config = tf.compat.v1.ConfigProto(allow_soft_placement=True,
log_device_placement=False)
sess = tf.compat.v1.Session(config=session_config)
from models.config import AttnConfig
from models.seq2seq_attn_model import Seq2SeqAttnModel
if __name__ == "__main__":
config = AttnConfig()
DATA_PATH = 'toy_data/translation/kor.txt'
WORD2VEC_PATH = '/data/pretrained_model/word_embedding/glove.6B/glove.6B.%sd.txt' % config.EMBEDDING_DIM
LOAD_PATH = 'bin/checkpoints/seq2seq_model.h5'
print(config)
print("Data Path: ", DATA_PATH)
print("Word2Vec Path: ", WORD2VEC_PATH)
print("Save Path: ", LOAD_PATH)
data_helper = DataHelper(config)
#### load the data ####
input_texts, target_texts, target_texts_inputs = data_helper.read_txt_translation(
DATA_PATH)
#### tokenize the inputs, outputs ####
encoder_inputs, decoder_inputs, decoder_targets, \
word2idx_inputs, word2idx_outputs, \
max_len_input, max_len_target, num_words_output = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### set data of model ####
model = Seq2SeqAttnModel(config)
model.set_data(encoder_inputs, decoder_inputs, decoder_targets,
max_len_input, max_len_target, num_words_output,
config = AdvStyleConfig()
NEG_DATA_PATH = 'toy_data/sentiment/neg.txt'
POS_DATA_PATH = 'toy_data/sentiment/pos.txt'
WORD2VEC_PATH = '/data/pretrained_model/word_embedding/glove.6B/glove.6B.%sd.txt' % config.EMBEDDING_DIM
LOAD_PATH = 'bin/checkpoints/seq2seq_adv_style_model.h5'
SAVE_RESULT_PATH = 'results/sample_result.csv'
config.MODE = 'inference'
print(config)
print("Data Path: ", NEG_DATA_PATH, POS_DATA_PATH)
print("Word2Vec Path: ", WORD2VEC_PATH)
print("Save Path: ", LOAD_PATH)
data_helper = DataHelper(config)
#### load the data ####
input_texts, target_texts, target_texts_inputs, styles = data_helper.read_txt_sentiment(
NEG_DATA_PATH, POS_DATA_PATH)
#### tokenize the inputs, outputs ####
encoder_inputs, decoder_inputs, decoder_targets, \
word2idx_inputs, word2idx_outputs, \
max_len_input, max_len_target, num_words_output = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### set data of model ####
model = Seq2SeqAdvStyleModel(config)
model.set_data(encoder_inputs, decoder_inputs, decoder_targets, styles,
max_len_input, max_len_target, num_words_output,
import os, sys
import numpy as np
from utils.data_helper import DataHelper
from models.config import PtrNetworkConfig
from models.ptr_network_model import PtrNetworkModel
if __name__ == "__main__":
config = PtrNetworkConfig()
X_DATA_PATH = 'toy_data/number_ordering/x.txt'
Y_DATA_PATH = 'toy_data/number_ordering/y.txt'
SAVE_PATH = 'bin/checkpoints/seq2seq_adv_style_model.h5'
print(config)
print("Data Path: ", X_DATA_PATH, Y_DATA_PATH)
print("Save Path: ", SAVE_PATH)
data_helper = DataHelper(config)
#### create number ordering data ####
data_helper.create_txt_number_ordering(X_DATA_PATH, Y_DATA_PATH)
#### load the data ####
parser.add_argument('--1', dest='cross_valid', action='store_true')
args = parser.parse_args()
#parse config file
config = configparser.ConfigParser()
config.read(CONFIG_PATH)
print("Config file read")
#initialize IO- and Data-Helper
io = IOHelper(size=args.size,
emb_type=args.emb_type,
preprocess=args.preprocess)
dh = DataHelper(io,
ln=(int)(config['MODEL']['LENGTH']),
nr_words=(int)(config['MODEL']['NUM_WORDS']),
dim=(int)(config['MODEL']['DIM']))
#initialize model class for training
model = Model(io,
dh,
arch_name=args.arch_name,
epochs=(int)(config['MODEL']['EPOCHS']),
ln=(int)(config['MODEL']['LENGTH']),
batch_size=(int)(config['MODEL']['BATCH_SIZE']))
if args.cross_valid:
#print("Cross Validation")
model.crossValidate(kfold=(int)(config['DEFAULT']['KFOLD']))
print("Train on full dataset")
from models.config import AttnConfig
from models.seq2seq_attn_model import Seq2SeqAttnModel
if __name__ == "__main__":
config = AttnConfig()
DATA_PATH = 'toy_data/translation/kor.txt'
WORD2VEC_PATH = '/data/pretrained_model/word_embedding/glove.6B/glove.6B.%sd.txt' % config.EMBEDDING_DIM
SAVE_PATH = 'bin/checkpoints/seq2seq_model.h5'
print(config)
print("Data Path: ", DATA_PATH)
print("Word2Vec Path: ", WORD2VEC_PATH)
print("Save Path: ", SAVE_PATH)
data_helper = DataHelper(config)
#### load the data ####
input_texts, target_texts, target_texts_inputs = data_helper.read_txt_translation(
DATA_PATH)
#### tokenize the inputs, outputs ####
encoder_inputs, decoder_inputs, decoder_targets, \
word2idx_inputs, word2idx_outputs, \
max_len_input, max_len_target, num_words_output = \
data_helper.create_vocab(input_texts, target_texts, target_texts_inputs)
#### load word2vec pretrained model ####
word2vec = data_helper.load_word2vec(WORD2VEC_PATH)
#### create embedding matrix ####
def test():
data_helper = DataHelper()
train_text, train_labels, ver_text, ver_labels, test_text, test_labels = data_helper.get_data_and_labels()
labels = list(int(i) for i in train_labels)
wts = np.bincount(labels)
print(wts)
def run_training(args):
# ----------------------------------------------------- #
# checkpoint directory
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
model_ckpt = os.path.join(args.save_dir, 'model.ckpt')
# log file
if args.num_epoch == 0:
log_path = os.path.join(args.save_dir, 'test.log')
else:
log_path = os.path.join(args.save_dir, 'train.log')
logger.setLevel(logging.DEBUG)
handler = logging.FileHandler(log_path, 'w')
handler.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s: %(message)s',
datefmt='%Y/%m/%d %H:%M:%S')
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.info('args: {}'.format(args))
writer = SummaryWriter(log_dir=args.save_dir)
# ----------------------------------------------------- #
# load data & init model and optimizer
logger.info('Loading data & model')
config = GPT2Config.from_pretrained(args.model, cache_dir='../cache/')
tokenizer = GPT2Tokenizer.from_pretrained(args.model,
cache_dir='../cache/')
gpt = GPT2Model.from_pretrained(args.model, cache_dir='../cache/')
logger.info('Old vocab size: {}'.format(config.vocab_size))
datahelper = DataHelper(os.path.join('./data', args.data_dir),
tokenizer=tokenizer)
config.vocab_size = len(tokenizer)
logger.info('New vocab size: {}'.format(config.vocab_size))
gpt.resize_token_embeddings(len(tokenizer))
model = GPT2LM(gpt, config)
model.to(args.device)
train_sampler = RandomSampler(datahelper.trainset)
train_dataloader = DataLoader(datahelper.trainset,
sampler=train_sampler,
batch_size=args.batch_size)
logger.info('Num of samples: {}, steps: {}'.format(
len(datahelper.trainset),
len(datahelper.trainset) // args.batch_size))
t_total = len(train_dataloader) * args.num_epoch
no_decay = ['bias', 'LayerNorm.weight']
optimizer_grouped_parameters = [{
'params': [
p for n, p in model.named_parameters()
if not any(nd in n for nd in no_decay)
],
'weight_decay':
args.weight_decay
}, {
'params': [
p for n, p in model.named_parameters()
if any(nd in n for nd in no_decay)
],
'weight_decay':
0.0
}]
optimizer = AdamW(optimizer_grouped_parameters,
lr=args.learning_rate,
eps=args.adam_epsilon)
scheduler = get_linear_schedule_with_warmup(
optimizer,
num_warmup_steps=args.warmup_steps,
num_training_steps=t_total)
# ----------------------------------------------------- #
# training
best_dev_loss = 1e19
train_iterator = trange(int(args.num_epoch), desc="Epoch")
step_nogress = 0
global_step = 0
save_id = 0
tr_loss, logging_loss = 0.0, 0.0
for epoch in train_iterator:
train_loss = 0.0
num_steps = 0
model.train()
epoch_iterator = tqdm(train_dataloader,
desc="Train Iteration at Epoch {}".format(epoch))
for step, batch in enumerate(epoch_iterator):
inputs = batch.to(args.device)
labels = batch.clone()[:, 16:].to(args.device)
optimizer.zero_grad()
outputs = model(inputs)
outputs = outputs[:, 15:-1]
outputs = outputs.contiguous()
labels = labels.contiguous()
loss = F.nll_loss(outputs.view(-1, config.vocab_size),
labels.view(-1))
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(),
args.max_grad_norm)
optimizer.step()
scheduler.step() # Update learning rate schedule
train_loss += loss.item()
tr_loss += loss.item()
num_steps += 1 # len(batch)
log = 'Epoch: {:03d}, Iter: {:03d}, step loss: {:.4f}'
logger.info(log.format(epoch, step, loss.item()))
writer.add_scalar('Train/nll', loss.item(), global_step)
# writer.add_scalar('Train/nll_no_pad', loss_no_pad.item(), global_step)
global_step += 1
train_loss /= num_steps
log = 'Epoch: {:03d} Train loss: {:.4f}'
logger.info(log.format(epoch, train_loss))
result_dev = evaluation(datahelper, model, config, args, test=False)
log = 'Epoch: {:03d}, Dev ppl: {:.4f} loss: {:.4f}'
if result_dev['loss_no_pad'] <= best_dev_loss:
best_dev_loss = result_dev['loss_no_pad']
torch.save(model.state_dict(), '{}'.format(model_ckpt))
step_nogress = 0
logger.info(log.format(epoch, result_dev['ppl'], result_dev['loss']))
writer.add_scalar('Dev/nll', result_dev['loss'], epoch)
writer.add_scalar('Dev/nll_no_pad', result_dev['loss_no_pad'], epoch)
writer.add_scalar('Dev/ppl', result_dev['ppl'], epoch)
step_nogress += 1
if step_nogress > 2:
break
# testing
model.load_state_dict(torch.load('{}'.format(model_ckpt)))
result_test = evaluation(datahelper, model, config, args, test=True)
log = 'Epoch: {:03d}, Test ppl: {:.4f} loss: {:.4f}'
logger.info(log.format(-1, result_test['ppl'], result_test['loss']))
writer.add_scalar('Test/nll', result_test['loss'], 0)
writer.add_scalar('Test/nll_no_pad', result_test['loss_no_pad'], 0)
writer.add_scalar('Test/ppl', result_test['ppl'], 0)
