def test_google_weights(self):
albert_model_name = "albert_base"
albert_dir = bert.fetch_tfhub_albert_model(albert_model_name,
".models")
albert_params = bert.albert_params(albert_model_name)
l_bert = bert.BertModelLayer.from_params(albert_params, name="albert")
l_input_ids = keras.layers.Input(shape=(128, ),
dtype='int32',
name="input_ids")
l_token_type_ids = keras.layers.Input(shape=(128, ),
dtype='int32',
name="token_type_ids")
output = l_bert([l_input_ids, l_token_type_ids])
output = keras.layers.Lambda(lambda x: x[:, 0, :])(output)
output = keras.layers.Dense(2)(output)
model = keras.Model(inputs=[l_input_ids, l_token_type_ids],
outputs=output)
model.build(input_shape=(None, 128))
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")])
for weight in l_bert.weights:
print(weight.name)
bert.load_albert_weights(l_bert, albert_dir)
model.summary()
def test_albert_load_base_google_weights(self): # for coverage mainly
albert_model_name = "albert_base"
albert_dir = bert.fetch_tfhub_albert_model(albert_model_name,
".models")
model_params = bert.albert_params(albert_model_name)
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
model = keras.models.Sequential([
keras.layers.InputLayer(input_shape=(8, ),
dtype=tf.int32,
name="input_ids"),
l_bert,
keras.layers.Lambda(lambda x: x[:, 0, :]),
keras.layers.Dense(2),
])
model.build(input_shape=(None, 8))
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")])
bert.load_albert_weights(l_bert, albert_dir)
model.summary()
def create_model(l_bert, model_ckpt, max_seq_len, num_labels,
label_threshold_less, model_type):
"""
Wrapper function to return keras learning rate scheduler callback
Args:
l_bert (bert.model.BertModelLayer): BERT layer
model_ckpt (str): path to best model checkpoint
max_seq_length (int): maximum sequence length for training data
num_labels (int): final output dimensionality per token
label_threshold_less (int): all label IDs strictly less than this number
will be ignored in class accuracy calculations
model_type (str): type of model decoder to use, see
'./utils/model_utils.py'
Returns:
model (tensorflow.python.keras.engine.training.Model): final compiled
model which can be used for fine-tuning
"""
input_ids = Input(shape=(max_seq_len, ), dtype='int32')
output = l_bert(input_ids)
if model_type == "TD_Dense":
output = TimeDistributed(Dense(512))(output)
output = Activation("relu")(output)
output = TimeDistributed(Dense(256))(output)
output = Activation("relu")(output)
output = TimeDistributed(Dense(128))(output)
output = Activation("relu")(output)
output = TimeDistributed(Dense(64))(output)
output = Activation("relu")(output)
output = TimeDistributed(Dense(num_labels))(output)
elif model_type == "1D_CNN":
output = Conv1D(512, 3, padding="same")(output)
output = Activation("relu")(output)
output = Conv1D(256, 3, padding="same")(output)
output = Activation("relu")(output)
output = Conv1D(128, 3, padding="same")(output)
output = Activation("relu")(output)
output = Conv1D(64, 3, padding="same")(output)
output = Activation("relu")(output)
output = Conv1D(num_labels, 3, padding="same")(output)
elif model_type == "Stacked_LSTM":
output = LSTM(512, return_sequences=True)(output)
output = LSTM(256, return_sequences=True)(output)
output = LSTM(128, return_sequences=True)(output)
output = TimeDistributed(Dense(64))(output)
output = Activation("relu")(output)
output = TimeDistributed(Dense(num_labels))(output)
prob = Activation("softmax")(output)
model = tf.keras.Model(inputs=input_ids, outputs=prob)
model.build(input_shape=(None, max_seq_len))
bert.load_albert_weights(l_bert, model_ckpt)
model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(),
metrics=[class_acc(label_threshold_less)])
model.summary()
return model
def create_model(
model_dir, model_type, max_seq_len, n_classes, load_pretrained_weights=True, summary=False,
):
"""Creates keras model with pretrained BERT/ALBERT layer.
Args:
model_dir: String. Path to model.
model_type: String. Expects either "albert" or "bert"
max_seq_len: Int. Maximum length of a classificaton example.
n_classes: Int. Number of training classes.
load_pretrained_weights: Boolean. Load pretrained model weights.
summary: Boolean. Print model summary.
Returns:
Keras model
"""
if model_type == "albert":
model_ckpt = os.path.join(model_dir, "model.ckpt-best")
model_params = bert.albert_params(model_dir)
elif model_type == "bert":
model_ckpt = os.path.join(model_dir, "bert_model.ckpt")
model_params = bert.params_from_pretrained_ckpt(model_dir)
layer_bert = bert.BertModelLayer.from_params(model_params, name=model_type)
input_ids = keras.layers.Input(shape=(max_seq_len,), dtype="int32", name="input_ids")
output = layer_bert(input_ids)
cls_out = keras.layers.Lambda(lambda seq: seq[:, 0, :])(output)
cls_out = keras.layers.Dropout(0.5)(cls_out)
logits = keras.layers.Dense(units=model_params["hidden_size"], activation="relu")(cls_out)
logits = keras.layers.Dropout(0.5)(logits)
logits = keras.layers.Dense(units=n_classes, activation="softmax")(logits)
model = keras.Model(inputs=input_ids, outputs=logits)
model.build(input_shape=(None, max_seq_len))
if load_pretrained_weights:
if model_type == "albert":
bert.load_albert_weights(layer_bert, model_ckpt)
elif model_type == "bert":
bert.load_bert_weights(layer_bert, model_ckpt)
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")],
)
if summary:
model.summary()
return model
def test_albert_params(self):
albert_model_name = "albert_base"
albert_dir = bert.fetch_tfhub_albert_model(albert_model_name,
".models")
dir_params = bert.albert_params(albert_dir)
dir_params.attention_dropout = 0.1 # diff between README and assets/albert_config.json
dir_params.hidden_dropout = 0.1
name_params = bert.albert_params(albert_model_name)
self.assertEqual(name_params, dir_params)
# coverage
model_params = dir_params
model_params.vocab_size = model_params.vocab_size + 2
model_params.adapter_size = 1
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
l_bert(tf.zeros((1, 128)))
bert.load_albert_weights(l_bert, albert_dir)
def test_albert_google_weights(self):
albert_model_name = "albert_base"
albert_dir = bert.fetch_tfhub_albert_model(albert_model_name, ".models")
albert_params = bert.albert_params(albert_model_name)
model, l_bert = self.build_model(albert_params)
skipped_weight_value_tuples = bert.load_albert_weights(l_bert, albert_dir)
self.assertEqual(0, len(skipped_weight_value_tuples))
model.summary()
def Albert_model(max_seq_len):
model_name = "albert_large"
model_dir = bert.fetch_tfhub_albert_model(model_name, ".models")
model_params = bert.albert_params(model_name)
model_params.shared_layer = True
model_params.embedding_size = 1024
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
l_input_ids = keras.layers.Input(shape=(max_seq_len, ), dtype='int32')
# using the default token_type/segment id 0
output = l_bert(l_input_ids) # output: [batch_size, max_seq_len, hidden_size]
output = keras.layers.GlobalAveragePooling1D()(output)
model = keras.Model(inputs=l_input_ids, outputs=output)
model.build(input_shape=(None, max_seq_len))
# use in a Keras Model here, and call model.build()
bert.load_albert_weights(l_bert, model_dir) # should be called after model.build()
return model, model_dir
def test_albert_google_weights_non_tfhub(self):
albert_model_name = "albert_base_v2"
albert_dir = bert.fetch_google_albert_model(albert_model_name, ".models")
model_ckpt = os.path.join(albert_dir, "model.ckpt-best")
albert_params = bert.albert_params(albert_dir)
model, l_bert = self.build_model(albert_params)
skipped_weight_value_tuples = bert.load_albert_weights(l_bert, model_ckpt)
self.assertEqual(0, len(skipped_weight_value_tuples))
model.summary()
def test_albert_chinese_weights(self):
albert_model_name = "albert_base"
albert_dir = bert.fetch_brightmart_albert_model(albert_model_name, ".models")
albert_ckpt = os.path.join(albert_dir, "albert_model.ckpt")
albert_params = bert.params_from_pretrained_ckpt(albert_dir)
model, l_bert = self.build_model(albert_params)
skipped_weight_value_tuples = bert.load_albert_weights(l_bert, albert_ckpt)
self.assertEqual(0, len(skipped_weight_value_tuples))
model.summary()
def test_albert_zh_fetch_and_load(self):
albert_model_name = "albert_tiny"
albert_dir = bert.fetch_brightmart_albert_model(
albert_model_name, ".models")
model_params = bert.params_from_pretrained_ckpt(albert_dir)
model_params.vocab_size = model_params.vocab_size + 2
model_params.adapter_size = 1
l_bert = bert.BertModelLayer.from_params(model_params, name="albert")
l_bert(tf.zeros((1, 128)))
res = bert.load_albert_weights(l_bert, albert_dir)
self.assertTrue(len(res) > 0)
def test_chinese_weights(self):
#bert_ckpt_dir = ".models/albert_base_zh/"
#bert_ckpt_file = bert_ckpt_dir + "albert_model.ckpt"
#bert_config_file = bert_ckpt_dir + "albert_config_base.json"
print("Eager Execution:", tf.executing_eagerly())
albert_model_name = "albert_base"
albert_dir = bert.fetch_brightmart_albert_model(
albert_model_name, ".models")
albert_ckpt = os.path.join(albert_dir, "albert_model.ckpt")
bert_params = bert.params_from_pretrained_ckpt(albert_dir)
l_bert = bert.BertModelLayer.from_params(bert_params, name="bert")
l_input_ids = keras.layers.Input(shape=(128, ),
dtype='int32',
name="input_ids")
l_token_type_ids = keras.layers.Input(shape=(128, ),
dtype='int32',
name="token_type_ids")
output = l_bert([l_input_ids, l_token_type_ids])
output = keras.layers.Lambda(lambda x: x[:, 0, :])(output)
output = keras.layers.Dense(2)(output)
model = keras.Model(inputs=[l_input_ids, l_token_type_ids],
outputs=output)
model.build(input_shape=(None, 128))
model.compile(
optimizer=keras.optimizers.Adam(),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[keras.metrics.SparseCategoricalAccuracy(name="acc")])
for weight in l_bert.weights:
print(weight.name)
bert.load_albert_weights(l_bert, albert_ckpt)
model.summary()
def load_bert_model(name_model, max_seq_len, trainable=False):
"""
models name supported, same as tf-2.0-bert
"""
model_name = name_model
model_dir = bert.fetch_tfhub_albert_model(model_name, ".models")
model_params = bert.albert_params(model_name)
l_bert = bert.BertModelLayer.from_params(model_params, name=name_model)
l_input_ids = tf.keras.layers.Input(shape=(max_seq_len, ), dtype='int32')
output = l_bert(
l_input_ids) # output: [batch_size, max_seq_len, hidden_size]
model = tf.keras.Model(inputs=l_input_ids, outputs=output)
model.build(input_shape=(None, max_seq_len))
# load google albert original weights after the build
bert.load_albert_weights(l_bert, model_dir)
model.trainable = trainable
return model
def build_transformer(transformer, max_seq_length=None, num_labels=None, tagging=True, tokenizer_only=False):
spm_model_file = None
if transformer in zh_albert_models_google:
from bert.tokenization.albert_tokenization import FullTokenizer
model_url = zh_albert_models_google[transformer]
albert = True
elif transformer in albert_models_tfhub:
from edparser.layers.transformers.albert_tokenization import FullTokenizer
with stdout_redirected(to=os.devnull):
model_url = fetch_tfhub_albert_model(transformer,
os.path.join(hanlp_home(), 'thirdparty', 'tfhub.dev', 'google',
transformer))
albert = True
spm_model_file = glob.glob(os.path.join(model_url, 'assets', '*.model'))
assert len(spm_model_file) == 1, 'No vocab found or unambiguous vocabs found'
spm_model_file = spm_model_file[0]
elif transformer in bert_models_google:
from bert.tokenization.bert_tokenization import FullTokenizer
model_url = bert_models_google[transformer]
albert = False
else:
raise ValueError(
f'Unknown model {transformer}, available ones: {list(bert_models_google.keys()) + list(zh_albert_models_google.keys()) + list(albert_models_tfhub.keys())}')
bert_dir = get_resource(model_url)
if spm_model_file:
vocab = glob.glob(os.path.join(bert_dir, 'assets', '*.vocab'))
else:
vocab = glob.glob(os.path.join(bert_dir, '*vocab*.txt'))
assert len(vocab) == 1, 'No vocab found or unambiguous vocabs found'
vocab = vocab[0]
lower_case = any(key in transformer for key in ['uncased', 'multilingual', 'chinese', 'albert'])
if spm_model_file:
# noinspection PyTypeChecker
tokenizer = FullTokenizer(vocab_file=vocab, spm_model_file=spm_model_file, do_lower_case=lower_case)
else:
tokenizer = FullTokenizer(vocab_file=vocab, do_lower_case=lower_case)
if tokenizer_only:
return tokenizer
if spm_model_file:
bert_params = albert_params(bert_dir)
else:
bert_params = bert.params_from_pretrained_ckpt(bert_dir)
l_bert = bert.BertModelLayer.from_params(bert_params, name='albert' if albert else "bert")
if not max_seq_length:
return l_bert, tokenizer, bert_dir
l_input_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name="input_ids")
l_mask_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name="mask_ids")
l_token_type_ids = tf.keras.layers.Input(shape=(max_seq_length,), dtype='int32', name="token_type_ids")
output = l_bert([l_input_ids, l_token_type_ids], mask=l_mask_ids)
if not tagging:
output = tf.keras.layers.Lambda(lambda seq: seq[:, 0, :])(output)
if bert_params.hidden_dropout:
output = tf.keras.layers.Dropout(bert_params.hidden_dropout, name='hidden_dropout')(output)
logits = tf.keras.layers.Dense(num_labels, kernel_initializer=tf.keras.initializers.TruncatedNormal(
bert_params.initializer_range))(output)
model = tf.keras.Model(inputs=[l_input_ids, l_mask_ids, l_token_type_ids], outputs=logits)
model.build(input_shape=(None, max_seq_length))
if not spm_model_file:
ckpt = glob.glob(os.path.join(bert_dir, '*.index'))
assert ckpt, f'No checkpoint found under {bert_dir}'
ckpt, _ = os.path.splitext(ckpt[0])
with stdout_redirected(to=os.devnull):
if albert:
if spm_model_file:
skipped_weight_value_tuples = bert.load_albert_weights(l_bert, bert_dir)
else:
# noinspection PyUnboundLocalVariable
skipped_weight_value_tuples = load_stock_weights(l_bert, ckpt)
else:
# noinspection PyUnboundLocalVariable
skipped_weight_value_tuples = bert.load_bert_weights(l_bert, ckpt)
assert 0 == len(skipped_weight_value_tuples), f'failed to load pretrained {transformer}'
return model, tokenizer
def load_pretrained(self, pretrained_ckpt):
self.build(input_shape=(None, 2, self._max_seq_len))
bert.load_albert_weights(self.l_bert, pretrained_ckpt)
def load_pretrained(self, pretrained_ckpt):
# self.call(np.zeros((16, 2, self.max_seq_len)))
self.build(input_shape=(None, 2, self._max_seq_len))
bert.load_albert_weights(self.l_bert, pretrained_ckpt)
# TODO: Try with more regularisation
# cls_out = keras.layers.Dropout(0.5)(cls_out)
logits = keras.layers.Dense(units=256, activation='relu')(cls_out)
logits = keras.layers.Dropout(0.5)(logits)
# NOTE: Alternative to the Lambda layer
# bgru_layer = keras.layers.Bidirectional(keras.layers.GRU(64))(output)
output = keras.layers.Dense(units=1, activation='sigmoid')(logits)
model = keras.Model(inputs=input_ids, outputs=output)
# Freeze all non-trainable layers
freeze_layers(bert_layer, exclude=['LayerNorm'])
# Originally from tutorial: ['LayerNorm', 'adapter-down', 'adapter-up']
# Build model and load pre-trained weights
model.build(input_shape=(None, MAX_SEQ_LEN))
bert.load_albert_weights(bert_layer, MODEL_DIR)
model.compile(
loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy', f1_score],
)
model.summary()
# Alternative for loading weights from checkpoint file
# from bert.loader import (StockBertConfig, map_stock_config_to_params,
# load_stock_weights)
# bert_ckpt_dir="gs://bert_models/2018_10_18/uncased_L-12_H-768_A-12/"
