class BertMultiTask:
def __init__(self, job_config, use_pretrain, tokenizer, cache_dir, device,
write_log, summary_writer):
self.job_config = job_config
if not use_pretrain:
model_config = self.job_config.get_model_config()
bert_config = BertConfig(**model_config)
bert_config.vocab_size = len(tokenizer.vocab)
self.bert_encoder = BertModel(bert_config)
# Use pretrained bert weights
else:
self.bert_encoder = BertModel.from_pretrained(
self.job_config.get_model_file_type(), cache_dir=cache_dir)
bert_config = self.bert_encoder.config
self.network = MTLRouting(self.bert_encoder,
write_log=write_log,
summary_writer=summary_writer)
#config_data=self.config['data']
# Pretrain Dataset
self.network.register_batch(BatchType.PRETRAIN_BATCH,
"pretrain_dataset",
loss_calculation=BertPretrainingLoss(
self.bert_encoder, bert_config))
self.device = device
# self.network = self.network.float()
# print(f"Bert ID: {id(self.bert_encoder)} from GPU: {dist.get_rank()}")
def save(self, filename: str):
network = self.network.module
return torch.save(network.state_dict(), filename)
def load(self, model_state_dict: str):
return self.network.module.load_state_dict(
torch.load(model_state_dict,
map_location=lambda storage, loc: storage))
def move_batch(self, batch, non_blocking=False):
return batch.to(self.device, non_blocking)
def eval(self):
self.network.eval()
def train(self):
self.network.train()
def save_bert(self, filename: str):
return torch.save(self.bert_encoder.state_dict(), filename)
def to(self, device):
assert isinstance(device, torch.device)
self.network.to(device)
def half(self):
self.network.half()
def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str,
model_name: str):
"""
:param model:BertModel Pytorch model instance to be converted
:param ckpt_dir: Tensorflow model directory
:param model_name: model name
:return:
Currently supported HF models:
Y BertModel
N BertForMaskedLM
N BertForPreTraining
N BertForMultipleChoice
N BertForNextSentencePrediction
N BertForSequenceClassification
N BertForQuestionAnswering
"""
tensors_to_transpose = ("dense.weight", "attention.self.query",
"attention.self.key", "attention.self.value")
var_map = (('layer.', 'layer_'), ('word_embeddings.weight',
'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'), ('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'), ('weight', 'kernel'))
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
state_dict = model.state_dict()
def to_tf_var_name(name: str):
for patt, repl in iter(var_map):
name = name.replace(patt, repl)
return 'bert/{}'.format(name)
def create_tf_var(tensor: np.ndarray, name: str, session: tf.Session):
tf_dtype = tf.dtypes.as_dtype(tensor.dtype)
tf_var = tf.get_variable(dtype=tf_dtype,
shape=tensor.shape,
name=name,
initializer=tf.zeros_initializer())
session.run(tf.variables_initializer([tf_var]))
session.run(tf_var)
return tf_var
tf.reset_default_graph()
with tf.Session() as session:
for var_name in state_dict:
tf_name = to_tf_var_name(var_name)
torch_tensor = state_dict[var_name].numpy()
if any([x in var_name for x in tensors_to_transpose]):
torch_tensor = torch_tensor.T
tf_var = create_tf_var(tensor=torch_tensor,
name=tf_name,
session=session)
tf.keras.backend.set_value(tf_var, torch_tensor)
tf_weight = session.run(tf_var)
print("Successfully created {}: {}".format(
tf_name, np.allclose(tf_weight, torch_tensor)))
saver = tf.train.Saver(tf.trainable_variables())
saver.save(
session,
os.path.join(ckpt_dir,
model_name.replace("-", "_") + ".ckpt"))
def convert_pytorch_checkpoint_to_tf(model: BertModel, ckpt_dir: str,
model_name: str):
"""
:param model:BertModel Pytorch model instance to be converted
:param ckpt_dir: Tensorflow model directory
:param model_name: model name
:return:
Currently supported HF models:
Y BertModel
N BertForMaskedLM
N BertForPreTraining
N BertForMultipleChoice
N BertForNextSentencePrediction
N BertForSequenceClassification
N BertForQuestionAnswering
"""
tensors_to_transopse = ("dense.weight", "attention.self.query",
"attention.self.key", "attention.self.value")
var_map = (('layer.', 'layer_'), ('word_embeddings.weight',
'word_embeddings'),
('position_embeddings.weight', 'position_embeddings'),
('token_type_embeddings.weight', 'token_type_embeddings'),
('.', '/'), ('LayerNorm/weight', 'LayerNorm/gamma'),
('LayerNorm/bias', 'LayerNorm/beta'), ('weight', 'kernel'))
if not os.path.isdir(ckpt_dir):
os.makedirs(ckpt_dir)
session = tf.Session()
state_dict = model.state_dict()
tf_vars = []
def to_tf_var_name(name: str):
for patt, repl in iter(var_map):
name = name.replace(patt, repl)
return 'bert/{}'.format(name)
def assign_tf_var(tensor: np.ndarray, name: str):
tmp_var = tf.Variable(initial_value=tensor)
tf_var = tf.get_variable(dtype=tmp_var.dtype,
shape=tmp_var.shape,
name=name)
op = tf.assign(ref=tf_var, value=tmp_var)
session.run(tf.variables_initializer([tmp_var, tf_var]))
session.run(fetches=[op, tf_var])
return tf_var
for var_name in state_dict:
tf_name = to_tf_var_name(var_name)
torch_tensor = state_dict[var_name].numpy()
if any([x in var_name for x in tensors_to_transopse]):
torch_tensor = torch_tensor.T
tf_tensor = assign_tf_var(tensor=torch_tensor, name=tf_name)
tf_vars.append(tf_tensor)
print("{0}{1}initialized".format(tf_name, " " * (60 - len(tf_name))))
saver = tf.train.Saver(tf_vars)
saver.save(session,
os.path.join(ckpt_dir,
model_name.replace("-", "_") + ".ckpt"))
