def __init__(self, sentence_tokenizer='normal', raw_reviews_path=None):
"""
Sentence Tokenizers: normal, punkt
"""
self.sent_tokenize = sent_tokenize
if sentence_tokenizer == 'normal':
pass
elif sentence_tokenizer == 'punkt':
self.sent_tokenize = nltk.data.load(
'tokenizers/punkt/english.pickle').tokenize
self.tokenizer = utils.get_tokenizer()
if utils.is_none(raw_reviews_path):
self.raw_reviews = utils.get_raw_test_reviews(review='tizi')
else:
with open(raw_reviews_path, 'r') as fi:
self.raw_reviews = [line.rstrip() for line in fi]
self.data = []
for _ in range(6):
self.data.append([])
self.categories = ['food', 'service', 'price', 'place']
self.conjunctions = [
"tetapi sayangnya", "namun", "tetapi", "walaupun", "akan tetapi",
"sayangnya", "hanya sayang", "sayang", "meski", "walau", "but"
]
def main():
tokenizer = get_tokenizer(args.bert_vocab_path)
train_data, dev_data, test_data, id2rel, rel2id, num_rels = load_data(
args.train_path, args.dev_path, args.test_path, args.rel_dict_path)
subject_model, object_model, hbt_model = E2EModel(
args.bert_config_path, args.bert_checkpoint_path, args.LR, num_rels)
# tensorflow
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if K.backend() == 'tensorflow':
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
STEPS = len(train_data) // args.BATCH_SIZE
data_manager = data_generator(train_data, tokenizer, rel2id, num_rels,
args.MAX_LEN, args.BATCH_SIZE)
evaluator = Evaluate(subject_model, object_model, tokenizer, id2rel,
dev_data, args.save_weights_path,
args.save_model_path)
hbt_model.fit_generator(data_manager.__iter__(),
steps_per_epoch=STEPS,
epochs=args.EPOCH,
callbacks=[evaluator])
print("model training finish")
def model_predict():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if K.backend() == 'tensorflow':
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tokenizer = get_tokenizer(args.bert_vocab_path)
# read data and relation
test_data, id2rel, rel2id, num_rels = load_data(args.test_path,
args.rel_dict_path)
# load model
subject_model, object_model, hbt_model = E2EModel(
args.bert_config_path, args.bert_checkpoint_path, args.LR, num_rels)
hbt_model.load_weights(args.save_weights_path)
isExactMatch = True if args.dataset == 'Wiki-KBP' else False
if isExactMatch:
print("Exact Match")
else:
print("Partial Match")
precision, recall, f1_score = metric(subject_model, object_model,
test_data, id2rel, tokenizer,
isExactMatch, args.test_result_path)
print(f'{precision}\t{recall}\t{f1_score}')
def predict():
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
if K.backend() == 'tensorflow':
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tokenizer = get_tokenizer(args.bert_vocab_path)
# read data and relation
# test_data, id2rel, rel2id, num_rels = load_data(args.test_path, args.rel_dict_path)
test_data = json.load(open(args.test_path, "r", encoding="utf-8"))
id2rel, rel2id = json.load(open(args.rel_dict_path, "r", encoding="utf-8"))
id2rel = {int(i): j for i, j in id2rel.items()}
num_rels = len(id2rel)
# load model
subject_model, object_model, hbt_model = E2EModel(
args.bert_config_path, args.bert_checkpoint_path, args.LR, num_rels)
hbt_model.load_weights(args.save_weights_path)
return_result = []
for line in test_data:
sent = line["text"]
result = extract_items(subject_model,
object_model,
tokenizer,
sent,
id2rel,
h_bar=0.5,
t_bar=0.5)
return_result.append({"text": sent, "relation": result})
with open("./results/baidurelation2020/test_data_pred.json",
"w",
encoding="utf-8") as f:
f.write(json.dumps(return_result, ensure_ascii=False, indent=2))
def load_pairs_text(filename, lowercase, language='en'):
"""
Read sent1 \t sent2 \t label
:param filename: path to the file
:param lowercase: whether to convert content to lower case
:param language: language to use tokenizer (only used if input is in
TSV format)
:return: a list of tuples (first_sent, second_sent, label)
"""
logging.info('Reading data from %s' % filename)
# we are only interested in the actual sentences + gold label
# the corpus files has a few more things
useful_data = []
# the SNLI corpus has one JSON object per line
with codecs.open(filename, 'r', 'utf-8') as f:
tokenize = utils.get_tokenizer(language)
for line in f:
line = line.strip()
if lowercase:
line = line.lower()
sent1, sent2, label = line.split('\t')
if label == '-':
continue
tokens1 = tokenize(sent1)
tokens2 = tokenize(sent2)
tokens1 = ['_BOS_'] + tokens1
tokens2 = ['_BOS_'] + tokens2
useful_data.append((tokens1, tokens2, label))
return useful_data
def model_predict():
max_len = 128
args = model_params()
test_data, test_label, _, _ = load_data(args["test_file"])
print("test data size: ", len(test_data))
tokenizer = get_tokenizer(args["pretrain_model_path"])
test_x, len_list = create_infer_inputs(test_data, max_len, tokenizer)
print("test data tokenizer: ", test_x[:3])
tag2id = {
'O': 0,
'B-LOC': 1,
'I-LOC': 2,
'B-PER': 3,
'I-PER': 4,
'B-ORG': 5,
'I-ORG': 6
}
model = create_model(args["pretrain_model_path"], len(tag2id),
args["dropout"])
model.load_weights("./output/ner_model.h5")
pred_logits = model.predict(test_x)
id2tag = {value: key for key, value in tag2id.items()}
# shape [batch_size, seq_len]
pred = np.argmax(pred_logits, axis=2).tolist()
predict_label = []
for i in range(len(len_list)):
temp = []
temp_pred = pred[i]
for j in range(min(len_list[i], max_len)):
temp.append(id2tag[temp_pred[j]])
predict_label.append(temp)
print("predict label: ", predict_label)
def read_corpus(filename, lowercase, language='en', ratio = None):
"""
Read a JSONL or TSV file with the SNLI corpus
:param filename: path to the file
:param lowercase: whether to convert content to lower case
:param language: language to use tokenizer (only used if input is in
TSV format)
:return: a list of tuples (first_sent, second_sent, label)
"""
# we are only interested in the actual sentences + gold label
# the corpus files has a few more things
useful_data = []
max_len = 0
# the SNLI corpus has one JSON object per line
with open(filename, 'rb') as f:
if filename.endswith('.tsv') or filename.endswith('.txt'):
tokenize = utils.get_tokenizer(language)
for line in f:
line = line.decode('utf-8').strip()
if lowercase:
line = line.lower()
sent1, sent2, label = line.split('\t')
if label == '-':
continue
tokens1 = tokenize(sent1)
tokens2 = tokenize(sent2)
if ratio:
if np.random.random() > np.float(ratio):
max_len = max([len(tokens1), len(tokens2), max_len])
useful_data.append((tokens1, tokens2, label))
else:
max_len = max([len(tokens1), len(tokens2), max_len])
useful_data.append((tokens1, tokens2, label))
else:
for line in f:
line = line.decode('utf-8')
if lowercase:
line = line.lower()
data = json.loads(line)
if data['gold_label'] == '-':
# ignore items without a gold label
continue
sentence1_parse = data['sentence1_parse']
sentence2_parse = data['sentence2_parse']
label = data['gold_label']
tree1 = nltk.Tree.fromstring(sentence1_parse)
tree2 = nltk.Tree.fromstring(sentence2_parse)
tokens1 = tree1.leaves()
tokens2 = tree2.leaves()
t = (tokens1, tokens2, label)
max_len = max([len(tokens1), len(tokens2), max_len])
useful_data.append(t)
return useful_data, max_len
def __init__(self, data: pd.DataFrame, augment: bool = False):
self._augment = augment
self._tokenizer = get_tokenizer('bert')
self._sentiment_ids = {'positive': 3893, 'negative': 4997, 'neutral': 8699}
self._data_df = data
self.exception_count = 0
self.exceptions = []
self.exception_mask = []
def __init__(self, data: pd.DataFrame, augment: bool = False):
self._augment = augment
self._tokenizer = get_tokenizer('xlnet')
self._sentiment_ids = {'positive': 1654, 'negative': 2981, 'neutral': 9201}
self._data_df = data
self.exception_count = 0
self.exceptions = []
self.exception_mask = []
def __init__(self, data: pd.DataFrame, augment: bool = False):
self._augment = augment
self._tokenizer = get_tokenizer('roberta')
self._sentiment_ids = {'positive': 1313, 'negative': 2430, 'neutral': 7974}
self._data_df = data
self.exception_count = 0
self.exceptions = []
self.exception_mask = []
def main():
args = params()
tag2id_path = os.path.join(args["output_path"], args["tag2id"])
if not os.path.exists(args["output_path"]):
os.makedirs(args["output_path"])
if not os.path.join(args["pb_path"]):
os.makedirs(args["pb_path"])
tag2id = {"体育": 0, "健康": 1, "军事": 2, "教育": 3, "汽车": 4}
max_len = args["max_len"]
batch_size = args["batch_size"]
epoch = args["epoch"]
# load data
data, label = load_data(args["data_file"], tag2id)
logger.info("total data size: {}".format(len(data)))
logger.info("total label size: {}".format(len(label)))
# random 乱序
data, label = random_shuffle(data, label)
# save tag2id
save_dict(tag2id, tag2id_path)
# label encoder
total_label = label_encoder(label, len(tag2id))
# get train test data
train_data, dev_data, train_label, dev_label = train_test_split(
data, total_label, test_size=0.2)
logger.info("train data size: {}".format(len(train_data)))
logger.info("dev data size: {}".format(len(dev_data)))
# bert tokenizer
tokenizer = get_tokenizer()
# tokenizer = get_roberta_tokenizer()
# 准备模型数据
train_x, train_y = create_inputs_targets(train_data, train_label, max_len,
tokenizer)
dev_x, dev_y = create_inputs_targets(dev_data, dev_label, max_len,
tokenizer)
# create model bert
# model = create_model(len(tag2id))
model = create_model(args["bert_model_name"], len(tag2id))
# model.summary()
model.fit(train_x,
train_y,
epochs=epoch,
verbose=1,
batch_size=batch_size,
validation_data=(dev_x, dev_y),
validation_batch_size=batch_size) # , validation_split=0.1
# model save
model_path = os.path.join(args["output_path"], "classification_model.h5")
model.save_weights(model_path, overwrite=True)
# save pb model
tf.keras.models.save_model(model,
args["pb_path"],
save_format="tf",
overwrite=True)
def main():
args = get_args()
label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
df_train = pd.read_csv(args["train_file"])
train_datas = df_train['comment_text'].tolist()
train_labels = df_train[label_cols].values.tolist()
print("train data size: ", len(train_datas))
print("train label size: ", len(train_labels))
train_data, val_data, train_label, val_label = train_test_split(train_datas,
train_labels,
test_size=0.2,
random_state=0)
tokenizer = get_tokenizer(args["bert_model_name"],
args["pretrain_model_path"])
train_x, train_y = get_model_data(train_data, train_label, tokenizer,
args["max_length"])
val_x, val_y = get_model_data(val_data, val_label, tokenizer, args["max_length"])
label_cols = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate']
model = create_model(args["bert_model_name"], len(label_cols))
# 自定义计算f1 score
# metrics = Metrics(val_x, val_y)
# callbacks = [metrics]
# 设置保存最优的模型,保存的是pb模型
callbacks = [
tf.keras.callbacks.ModelCheckpoint(
# Path where to save the model
# The two parameters below mean that we will overwrite
# the current checkpoint if and only if
# the `val_loss` score has improved.
# The saved model name will include the current epoch.
filepath="./output/model/1", # {epoch}
save_best_only=True, # Only save a model if `val_loss` has improved.
monitor='auc', # 'accuracy',
verbose=1,
)
]
model.fit(train_x, train_y, epochs=args["epoch"], verbose=1,
batch_size=args["batch_size"],
callbacks=callbacks,
validation_data=(val_x, val_y),
validation_batch_size=args["batch_size"])
if not os.path.exists(args["model_path"]):
os.makedirs(args["model_path"])
model.save_weights(args["model_path"])
if not os.path.exists(args["pbmodel_path"]):
os.makedirs(args["pbmodel_path"])
tf.keras.models.save_model(model, args["pbmodel_path"], save_format="tf")
def __init__(self, df: pd.DataFrame):
self._data_df = df
self._tokenizer = get_tokenizer('roberta')
self._sentiment_ids = {'positive': 1313, 'negative': 2430, 'neutral': 7974}
n_data = self._data_df.shape[0]
self._input_ids = np.ones((n_data, Config.Train.max_len), dtype='int32')
self._attention_mask = np.zeros((n_data, Config.Train.max_len), dtype='int32')
self._token_type_ids = np.zeros((n_data, Config.Train.max_len), dtype='int32')
self._start_tokens = np.zeros((n_data, Config.Train.max_len), dtype='int32')
self._end_tokens = np.zeros((n_data, Config.Train.max_len), dtype='int32')
def __init__(self, sentence, word_dict, lowercase, language='en'):
self.sentence = sentence
tokenize = utils.get_tokenizer(language)
if lowercase:
pre_tokenize = sentence.lower()
else:
pre_tokenize = sentence
self.tokens = tokenize(pre_tokenize)
self.indices = [word_dict[token] for token in self.tokens_with_null]
self.padding_index = word_dict[utils.PADDING]
def __init__(self, ckpt_path, max_seq_length=128, batch_size=32):
print('load gpt2 scorer from', ckpt_path)
ckpt_dir = os.path.dirname(ckpt_path)
self.tokenizer = get_tokenizer('gpt2', ckpt_dir)
self.criterion = torch.nn.CrossEntropyLoss(reduction='none', ignore_index=self.tokenizer.pad_token_id)
self.criterion.cuda()
self.model = torch.load(ckpt_path)
self.model.eval()
self.model.cuda()
self.max_seq_length = max_seq_length
self.batch_size = batch_size
def __init__(self, ckpt_path, max_seq_length=128, batch_size=32):
print('load bert proposal from', ckpt_path)
# config_class, model_class, tokenizer_class = MODEL_CLASSES['bert']
ckpt_dir = os.path.dirname(ckpt_path)
# config = config_class.from_pretrained(ckpt_dir)
self.tokenizer = get_tokenizer('bert', ckpt_dir)
self.model = torch.load(ckpt_path)
self.model.eval()
self.max_seq_length = max_seq_length
self.batch_size = batch_size
self.model.cuda()
def create_keras_sequences(data_path='../text/cleaned/'):
"""Previous stuff before running model train."""
logging.info('Loading and wrangling data.')
lines = get_lines(data_path)
tokenizer = get_tokenizer(lines)
all_words_list = ordereddict_to_list(tokenizer.word_counts)
p = p_distribution(lines)
index = int(len(lines) * VAL_RATIO)
return (TextSequence(lines[:-index], tokenizer, all_words_list, BATCH_SIZE,
8, 20, p),
TextSequence(lines[-index:], tokenizer, all_words_list, BATCH_SIZE,
8, 20, p), len(tokenizer.word_index) + 1)
def predict_test():
print('\n>> Predicting on test')
max_l = Config.Train.max_len
test_df = pd.read_csv(Config.test_path)
_test_generator = RobertaTestDataGenerator(test_df)
test_dataset = tf.data.Dataset.from_generator(_test_generator.generate,
output_types=({
'ids': tf.int32,
'att': tf.int32,
'tti': tf.int32
}))
test_dataset = test_dataset.padded_batch(Config.Train.batch_size,
padded_shapes=({
'ids': [max_l],
'att': [max_l],
'tti': [max_l]
}),
padding_values=({
'ids': 1,
'att': 0,
'tti': 0
}))
test_dataset = test_dataset.prefetch(tf.data.experimental.AUTOTUNE)
model_dir = Config.Train.checkpoint_dir / Config.model_type
start_idx = 0
end_idx = 0
model_count = len(list(model_dir.iterdir()))
for i in range(model_count):
model_path = model_dir / f'weights_{Config.version}_{i}.h5'
model = get_roberta()
model.load_weights(str(model_path))
start_idx, end_idx = model.predict(test_dataset, verbose=1)
start_idx += start_idx
end_idx += end_idx
start_idx /= model_count
end_idx /= model_count
start_idx = np.argmax(start_idx, axis=-1)
end_idx = np.argmax(end_idx, axis=-1)
end_idx = np.where(start_idx > end_idx, start_idx, end_idx)
tokenizer = get_tokenizer('roberta')
selected_texts = []
for i, row in enumerate(test_df.itertuples(index=False, name='tweet')):
a = start_idx[i]
b = end_idx[i]
text = ' ' + ' '.join(row.text.split())
encoded_text = tokenizer.encode(text)
selected_text = tokenizer.decode(encoded_text.ids[a - 1:b])
selected_texts.append(selected_text)
test_df['selected_text'] = selected_texts
test_df.to_csv('test_predictions.csv', index=False)
test_df[['textID', 'selected_text']].to_csv('submission.csv', index=False)
def main(test_data, args, label_num):
# test_steps_per_epoch = len(test_data) // args["batch_size"]
tokenizer = get_tokenizer(args['bert_model_name'],
args['pretrain_model_path'])
testdata = get_model_data(test_data, tokenizer, args["max_length"])
print("testdata: ", testdata)
model = create_model(args['bert_model_name'], label_num)
model.load_weights("./output/model/mulclassifition.h5")
pred_logits = model.predict(testdata, batch_size=args["batch_size"])
pred = np.where(pred_logits >= 0.5, 1, 0).tolist()
# pred = np.where(pred < 0.5, pred, 1).tolist()
return pred
def text_classifier_predict(sentences, max_len, tag2id, bert_model_name,
model_path):
# get tokenizer
tokenizer = get_tokenizer()
test_x = create_infer_inputs(sentences, max_len, tokenizer)
# id2tag
id2tag = {value: key for key, value in tag2id.items()}
# model
model = create_model(bert_model_name, len(tag2id))
model.load_weights(model_path)
logits = model.predict(test_x)
pred = np.argmax(logits, axis=1).tolist()
pred_label = [id2tag[i] for i in pred]
print("preict label: ", pred_label)
def main():
args = get_args()
df_test = pd.read_csv(args["test_file"])
test_data = df_test['comment_text'].values.tolist()
label_cols = [
'toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate'
]
tokenizer = get_tokenizer(args['bert_model_name'],
args['pretrain_model_path'])
testdata = get_model_data(test_data, tokenizer, args["max_length"])
model = create_model(args["bert_model_name"], len(label_cols))
model.load_weights(args["model_path"])
pred_logits = model.predict(testdata)
pred = np.where(pred_logits > 0.15, 1, 0).tolist()
print(pred)
def __init__(self, verbose=False, rnn_type="gru"):
self.max_nr_utterances = config.data["max_nr_utterances"]
self.max_nr_words = config.data["max_nr_words"]
self.corpus = config.corpus["corpus"]
self.detail_level = config.corpus["detail_level"]
self.verbose = verbose
self.id2tag = get_id2tag(self.corpus, detail_level=self.detail_level)
self.tag2id = {t: id for id, t in self.id2tag.items()}
self.tag2full = get_tag2full_label(self.corpus, self.detail_level)
self.n_tags = len(self.tag2id.keys())
self.tokenizer = get_tokenizer(rebuild_from_all_words=False)
word2id = self.tokenizer.word_index
# WARNING: if you force rebuild, the embedding matrix
# may change and you may need to retrain the Neural Network!
# set force rebuild to False when not changing total vocabulary
self.embedding_matrix = get_embedding_matrix(word2id,
force_rebuild=False)
# use GPU
os.environ["TF_FORCE_GPU_ALLOW_GROWTH"] = "true"
factory = BiRNN_CRF_factory(self.embedding_matrix, self.n_tags,
rnn_type)
self.model = factory.get()
data_name = self.corpus + "_detail_" + str(self.detail_level)
checkpoint_path = "../trained_model/bilstm_crf/ckpt_" + data_name + ".hdf5"
if os.path.exists(checkpoint_path):
if self.verbose:
print("loading trained weights...")
self.model.load_weights(checkpoint_path)
if self.verbose:
print("Done!")
else:
print("WARNING: no model found in path, using untrained model!")
def __init__(self,
vocab=None,
tokenizer=None,
maxlen=30,
model_dir=Path('data_in')):
if vocab is None or tokenizer is None:
tok_path = get_tokenizer()
self.ptr_tokenizer = SentencepieceTokenizer(tok_path)
self.ptr_detokenizer = SentencepieceDetokenizer(tok_path)
_, vocab_of_gluonnlp = get_kobert_model()
token2idx = vocab_of_gluonnlp.token_to_idx
self.vocab = Vocabulary(token2idx=token2idx)
self.tokenizer = Tokenizer(vocab=self.vocab,
split_fn=self.ptr_tokenizer,
pad_fn=keras_pad_fn,
maxlen=maxlen)
else:
self.vocab = vocab
self.tokenizer = tokenizer
self.maxlen = maxlen
self.model_dir = model_dir
def run_squad_and_get_results(
run_name: str,
fsx_prefix: str,
pre_layer_norm: bool,
model_size: str,
load_from: Union[str, tf.keras.Model],
load_step: int,
batch_size: int,
checkpoint_frequency: Optional[int],
validate_frequency: Optional[int],
learning_rate: float,
warmup_steps: int,
total_steps: int,
dataset: str,
dummy_eval: bool = False,
config: Optional[PretrainedConfig] = None,
) -> Dict:
checkpoint_frequency = checkpoint_frequency or 1000000
validate_frequency = validate_frequency or 1000000
if isinstance(load_from, tf.keras.Model):
config = load_from.config
assert config is not None, "config may not be None"
# Instantiate QuestionAnswering model
if isinstance(load_from, TFPreTrainedModel):
model = load_qa_from_pretrained(model=load_from)
elif load_from == "scratch":
model = TFAutoModelForQuestionAnswering.from_config(config)
elif load_from == "huggingface":
model = load_qa_from_pretrained(name=f"albert-{model_size}-v2")
else:
raise ValueError(
f"'load_from' is '{load_from}'; must be in ['scratch', 'huggingface', 'amazon']"
)
tokenizer = get_tokenizer()
schedule = LinearWarmupPolyDecaySchedule(
max_learning_rate=learning_rate,
end_learning_rate=0,
warmup_steps=warmup_steps,
total_steps=total_steps,
)
optimizer = tfa.optimizers.AdamW(weight_decay=0.0, learning_rate=schedule)
optimizer = tf.train.experimental.enable_mixed_precision_graph_rewrite(
optimizer, loss_scale="dynamic"
) # AMP
model.call = wrap_tf_function_idempotent(model.call)
if dataset == "squadv1":
train_filename = "train-v1.1.json"
val_filename = "dev-v1.1.json"
processor = SquadV1Processor()
elif dataset == "squadv2":
train_filename = "train-v2.0.json"
val_filename = "dev-v2.0.json"
processor = SquadV2Processor()
elif dataset == "debug":
train_filename = "dev-v2.0.json"
val_filename = "dev-v2.0.json"
processor = SquadV2Processor()
else:
assert False, "--dataset must be one of ['squadv1', 'squadv2', 'debug']"
data_dir = f"{fsx_prefix}/squad_data"
train_dataset = get_dataset(
tokenizer=tokenizer,
processor=processor,
data_dir=data_dir,
filename=train_filename,
batch_size=batch_size,
shard=True,
shuffle=True,
repeat=True,
drop_remainder=True,
)
if hvd.rank() == 0:
print("Starting finetuning")
pbar = tqdm.tqdm(total_steps)
summary_writer = None # Only create a writer if we make it through a successful step
val_dataset = get_dataset(
tokenizer=tokenizer,
processor=processor,
data_dir=data_dir,
filename=val_filename,
batch_size=batch_size,
shard=False,
shuffle=True,
drop_remainder=False,
)
# Need to re-wrap every time this function is called
# Wrapping train_step gives an error with optimizer initialization on the second pass
# of run_squad_and_get_results(). Bug report at https://github.com/tensorflow/tensorflow/issues/38875
# Discussion at https://github.com/tensorflow/tensorflow/issues/27120
wrapped_train_step = tf.function(train_step)
for step, batch in enumerate(train_dataset):
learning_rate = schedule(step=tf.constant(step, dtype=tf.float32))
loss, acc, exact_match, f1, precision, recall = wrapped_train_step(
model=model, optimizer=optimizer, batch=batch
)
# Broadcast model after the first step so parameters and optimizer are initialized
if step == 0:
hvd.broadcast_variables(model.variables, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
is_final_step = step >= total_steps - 1
if hvd.rank() == 0:
do_checkpoint = (step % checkpoint_frequency == 0) or is_final_step
do_validate = (step % validate_frequency == 0) or is_final_step
pbar.update(1)
description = f"Loss: {loss:.3f}, Acc: {acc:.3f}, EM: {exact_match:.3f}, F1: {f1:.3f}"
pbar.set_description(description)
if do_validate:
print("Running validation")
(
val_loss,
val_acc,
val_exact_match,
val_f1,
val_precision,
val_recall,
) = run_validation(model=model, val_dataset=val_dataset)
description = (
f"Step {step} validation - Loss: {val_loss:.3f}, Acc: {val_acc:.3f}, "
f"EM: {val_exact_match:.3f}, F1: {val_f1:.3f}"
)
print(description)
print("Running evaluation")
if dummy_eval:
results = {
"exact": 0.8169797018445212,
"f1": 4.4469722448269335,
"total": 11873,
"HasAns_exact": 0.15182186234817813,
"HasAns_f1": 7.422216845956518,
"HasAns_total": 5928,
"NoAns_exact": 1.4802354920100924,
"NoAns_f1": 1.4802354920100924,
"NoAns_total": 5945,
"best_exact": 50.07159100480081,
"best_exact_thresh": 0.0,
"best_f1": 50.0772059855695,
"best_f1_thresh": 0.0,
}
else:
results: Dict = get_evaluation_metrics(
model=model, data_dir=data_dir, filename=val_filename, batch_size=32,
)
print_eval_metrics(results=results, step=step)
if do_checkpoint:
checkpoint_path = (
f"{fsx_prefix}/checkpoints/albert-squad/{run_name}-step{step}.ckpt"
)
print(f"Saving checkpoint at {checkpoint_path}")
model.save_weights(checkpoint_path)
if summary_writer is None:
summary_writer = tf.summary.create_file_writer(
f"{fsx_prefix}/logs/albert-squad/{run_name}"
)
with summary_writer.as_default():
tf.summary.scalar("learning_rate", learning_rate, step=step)
tf.summary.scalar("train_loss", loss, step=step)
tf.summary.scalar("train_acc", acc, step=step)
tf.summary.scalar("train_exact", exact_match, step=step)
tf.summary.scalar("train_f1", f1, step=step)
tf.summary.scalar("train_precision", precision, step=step)
tf.summary.scalar("train_recall", recall, step=step)
if do_validate:
tf.summary.scalar("val_loss", val_loss, step=step)
tf.summary.scalar("val_acc", val_acc, step=step)
tf.summary.scalar("val_exact", val_exact_match, step=step)
tf.summary.scalar("val_f1", val_f1, step=step)
tf.summary.scalar("val_precision", val_precision, step=step)
tf.summary.scalar("val_recall", val_recall, step=step)
# And the eval metrics
tensorboard_eval_metrics(
summary_writer=summary_writer, results=results, step=step
)
if is_final_step:
break
# Can we return a value only on a single rank?
if hvd.rank() == 0:
pbar.close()
print(f"Finished finetuning, job name {run_name}")
return results
)
g.set_title(title, fontsize=16)
g.set_xlabel("Predicted Label", fontsize=14)
g.set_ylabel("True Label", fontsize=14)
plt.savefig(save_path, bbox_inches="tight")
plt.show()
conversations, labels = load_corpus_data(corpus, detail_level)
conversations = chunk(conversations, max_nr_utterances)
labels = chunk(labels, max_nr_utterances)
n_tags = len(get_id2tag(corpus, detail_level=detail_level))
tokenizer = get_tokenizer(rebuild_from_all_words=False)
word2id = tokenizer.word_index
X, y = make_model_readable_data(conversations, labels, tokenizer,
max_nr_utterances, max_nr_words)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
shuffle=True)
# import pretrained embeddings
# set force rebuild to False when not changing total vocabulary
embedding_matrix = get_embedding_matrix(word2id, force_rebuild=False)
# model = get_bilstm_crf_model(embedding_matrix, n_tags)
vocab_path = '../kogpt2/kogpt2_news_wiki_ko_cased_818bfa919d.spiece'
return get_kogpt2_model(model_path, vocab_path, ctx)
def load_kogpt2_model_from_checkpoint(kogpt2, load_path, device, ctx='cpu'):
try:
checkpoint = torch.load(load_path, map_location=device)
kogpt2model = GPT2LMHeadModel(config=GPT2Config.from_dict(kogpt2_config))
kogpt2model.load_state_dict(checkpoint['model_state_dict'])
kogpt2model.eval()
except:
count = 0
kogpt2model, _ = load_kogpt2_model()
else:
count = int(re.findall("\d+", load_path)[1])
print(count)
return kogpt2model, count
if __name__ == "__main__":
tok_path = get_tokenizer()
model, vocab = get_pytorch_kogpt2_model()
tok = SentencepieceTokenizer(tok_path, num_best=0, alpha=0)
PATH = '../model/pretrained_kogpt2.pth'
torch.save(model.state_dict(), PATH)
import requests
import traceback
import numpy as np
from flask import request
from flask import Flask, jsonify
from tensorflow.keras.preprocessing.sequence import pad_sequences
from config import get_args
from utils import get_tokenizer
app = Flask(__name__)
app.config["JSON_AS_ASCII"] = False
args = get_args()
tokenizer = get_tokenizer(args['bert_model_name'], args['pretrain_model_path'])
def get_model_data(sentence, tokenizer, max_seq_len=128):
dataset_dict = {
"input_ids": [],
"attention_mask": [],
}
input_ids = tokenizer.encode(
sentence, # Sentence to encode.
add_special_tokens=True, # Add '[CLS]' and '[SEP]'
max_length=max_seq_len, # Truncate all sentences.
)
sentence_length = len(input_ids)
input_ids = pad_sequences([input_ids],
config = json.load(f)
model_config = config["model"]
pretraining_config = config["pretraining_setting"]
gpu_config = config["gpu_setting"]
checkpoint_dir = config["model_checkpoints"]
glue_dataset_folder = config["glue_dataset_folder"]
device_ids = list(range(torch.cuda.device_count()))
print(f"GPU list: {device_ids}")
print(json.dumps([model_config, pretraining_config], indent=4))
########################### Loading Datasets ###########################
tokenizer = utils.get_tokenizer(model_config["max_seq_len"])
model_config["vocab_size"] = len(tokenizer.get_vocab())
data_args = GlueDataTrainingArguments(
task_name=args.task,
data_dir=os.path.join(glue_dataset_folder, args.task),
max_seq_length=model_config["max_seq_len"],
overwrite_cache=True)
train_dataset = GlueDataset(data_args, tokenizer=tokenizer)
data_loader = DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
collate_fn=default_data_collator)
num_steps_per_epoch = len(data_loader)
print(f"num_steps_per_epoch: {num_steps_per_epoch}", flush=True)
@classmethod
def get_true_length(cls, examples):
assert cls.tokenizer is not None
print(f'Tokenizer_type: {cls.tokenizer.name_or_path}, should check the n_real method.')
examples['n'] = [sum(i) - 2 for i in examples['attention_mask']]
examples['n_real'] = [sum([0 if cls.tokenizer.convert_ids_to_tokens(i).startswith('##')
else 1 for i in line]) - 2 for line in examples['input_ids']]
return examples
if __name__ == '__main__':
from utils import get_tokenizer
from copy import deepcopy
t=get_tokenizer('bert-base-chinese', is_zh=True)
ds = get_tokenized_ds('hfds_scripts/atec_dataset.py', '../sentence-embedding/data/ATEC/atec_nlp_sim_train.csv', t, tokenize_type='with_prefix')
ds = ds['atec']
ds2=deepcopy(ds)
for index, ds_ in enumerate([ds, ds2]):
features=list(ds_.features)
for feature in features:
if index:
if feature.startswith('textb') or feature == 'label':
ds_.remove_columns_(feature)
else:
ds_.rename_column_(feature, feature[6:])
else:
if feature.startswith('texta') or feature == 'label':
data_folder = os.path.join(curr_path, "datasets", config["data_folder"])
if args.batch_size is not None:
pretraining_config["batch_size"] = args.batch_size
if args.num_batch is not None:
pretraining_config["validate_batches_per_epoch"] = args.num_batch
device_ids = list(range(torch.cuda.device_count()))
print(f"GPU list: {device_ids}")
print(json.dumps([model_config, pretraining_config], indent=4))
########################### Loading Dataset ###########################
tokenizer = utils.get_tokenizer(os.path.join(curr_path, 'roberta-base'),
model_config["max_seq_len"])
model_config["vocab_size"] = len(tokenizer.get_vocab())
if "dataset" not in config:
config["dataset"] = None
dataset = CorpusDataset(folder_path=data_folder,
file_json="dev.json",
option=config["dataset"])
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer,
mlm=True,
mlm_probability=0.15)
data_loader = DataLoader(dataset,
batch_size=pretraining_config["batch_size"],
collate_fn=data_collator)
pretrain_dataloader_iter = enumerate(data_loader)
