def __init__(self,
pretrained_path='models/pubmed_pmc_470k/',
ffn_weight_file=None,
bert_ffn_weight_file='models/bertffn_crossentropy/bertffn',
gpt2_weight_file='models/gpt2',
embedding_file='qa_embeddings/bertffn_crossentropy.zip'
):
super(GenerateQADoc, self).__init__()
tf.compat.v1.disable_eager_execution()
session_config = tf.compat.v1.ConfigProto(
allow_soft_placement=True)
session_config.gpu_options.allow_growth = False
config = tf.estimator.RunConfig(
session_config=session_config)
self.batch_size = 1
self.gpt2_weight_file = gpt2_weight_file
gpt2_model_fn = gpt2_estimator.get_gpt2_model_fn(
accumulate_gradients=5,
learning_rate=0.1,
length=512,
batch_size=self.batch_size,
temperature=0.7,
top_k=0
)
hparams = gpt2_estimator.default_hparams()
with open(os.path.join(gpt2_weight_file, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
self.estimator = tf.estimator.Estimator(
gpt2_model_fn,
model_dir=gpt2_weight_file,
params=hparams,
config=config)
self.encoder = gpt2_estimator.encoder.get_encoder(gpt2_weight_file)
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
self.embed_sess = tf.compat.v1.Session(config=config)
with self.embed_sess.as_default():
self.qa_embed = QAEmbed(
pretrained_path=pretrained_path,
ffn_weight_file=ffn_weight_file,
bert_ffn_weight_file=bert_ffn_weight_file,
with_answer=False,
load_pretrain=False
)
self.faiss_topk = FaissTopK(embedding_file)
def train_gpt2(
model_dir='models/gpt2',
pretrained_path='models/117M',
steps=100000,
batch_size=2,
num_gpu=1,
learning_rate=0.0001):
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
mirrored_strategy = tf.distribute.MirroredStrategy(
devices=DEVICE[:num_gpu])
learning_rate = learning_rate*1.5**num_gpu
session_config = tf.compat.v1.ConfigProto(
allow_soft_placement=True)
session_config.gpu_options.allow_growth = True
config = tf_estimator.estimator.RunConfig(
session_config=session_config,
train_distribute=mirrored_strategy,
eval_distribute=mirrored_strategy,
log_step_count_steps=5)
gpt2_model_fn = gpt2_estimator.get_gpt2_model_fn(
accumulate_gradients=5,
learning_rate=learning_rate,
length=512,
batch_size=batch_size,
temperature=0.7,
top_k=0
)
hparams = gpt2_estimator.default_hparams()
with open(os.path.join(pretrained_path, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
estimator = tf_estimator.estimator.Estimator(
gpt2_model_fn,
model_dir=model_dir,
params=hparams,
config=config)
restore_hook = gpt2_estimator.RestoreCheckpointHook(pretrained_path)
estimator.train(
lambda: gpt2_estimator.train_input_fn(batch_size=batch_size), max_steps=steps, hooks=[restore_hook])
def train_gpt2(model_dir=FS.MODELS.GPT2,
pretrained_path=FS.PRE_TRAINED.GPT2,
steps=100000,
batch_size=1,
max_seq_len=1024,
num_gpu=3,
learning_rate=0.0001):
"""Function to train the GPT2 model.
For each question, we use topk qa pairs that retreived by FAISS and the question
as features, and correct answer as target to train GPT2.
Data: my eyes hurt, go see a doctor
Feature:
question: aaa, answer: bbb, question: ccc, answer: ddd, question: my eyes hurt, answer:
Target:
go see a doctor
Keyword Arguments:
model_dir {str} -- Path to save the GPT2 model (default: {'models/gpt2'})
pretrained_path {str} -- Pretrained GPT2 model path,
usually the output file of train_embedding_to_gpt2_data (default: {'models/117M'})
steps {int} -- Number of steps of training (default: {100000})
batch_size {int} -- Batch size per GPU (default: {4})
num_gpu {int} -- Number of GPU to use (default: {4})
learning_rate {float} -- Learning rate (default: {0.0001})
"""
# Create model_dir folders if it doesn't exist.
os.makedirs(model_dir, exist_ok=True)
tf.compat.v1.disable_eager_execution()
tf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.DEBUG)
learning_rate *= num_gpu
mirrored_strategy = tf.distribute.MirroredStrategy(
devices=DEVICE[:num_gpu])
session_config = tf.compat.v1.ConfigProto(allow_soft_placement=True)
session_config.gpu_options.allow_growth = False
config = tf_estimator.estimator.RunConfig(
session_config=session_config,
train_distribute=mirrored_strategy,
eval_distribute=mirrored_strategy,
log_step_count_steps=50)
gpt2_model_fn = gpt2_estimator.get_gpt2_model_fn(
accumulate_gradients=3,
learning_rate=learning_rate,
length=max_seq_len,
batch_size=batch_size,
temperature=0.7,
top_k=1)
# Copy configuration files to GPT-2 model dir.
copyfile(os.path.join(pretrained_path, 'hparams.json'),
os.path.join(model_dir, 'hparams.json'))
copyfile(os.path.join(pretrained_path, 'vocab.bpe'),
os.path.join(model_dir, 'vocab.bpe'))
copyfile(os.path.join(pretrained_path, 'encoder.json'),
os.path.join(model_dir, 'encoder.json'))
# GPT-2 Hyperparameters
hparams = gpt2_estimator.default_hparams()
with open(os.path.join(pretrained_path, 'hparams.json')) as f:
hparams.override_from_dict(json.load(f))
estimator = tf_estimator.estimator.Estimator(gpt2_model_fn,
model_dir=model_dir,
params=hparams,
config=config)
restore_hook = gpt2_estimator.RestoreCheckpointHook(pretrained_path)
estimator.train(
lambda: gpt2_estimator.train_input_fn(batch_size=batch_size,
dataset_load_fn=load_dataset,
sampler=Sampler,
max_seq_len=max_seq_len),
max_steps=steps,
hooks=[restore_hook])