def init_new_training(self):
if self.params.use_hf_model_:
if self.params.use_hf_electra_model_:
self.pretrained_bert = TFElectraModel.from_pretrained(
self.params.pretrained_hf_model_,cache_dir=self.params.hf_cache_dir_, return_dict=True
)
else:
self.pretrained_bert = TFBertModel.from_pretrained(self.params.pretrained_hf_model_,cache_dir=self.params.hf_cache_dir_, return_dict=True)
elif self.params.pretrained_bert:
logger.info(f"Attempt to load pre-trained bert from saved model: {self.params.pretrained_bert}")
if os.path.basename(self.params.pretrained_bert.strip(os.sep)) == "encoder_only":
saved_model_dir = self.params.pretrained_bert
elif os.path.isdir(os.path.join(self.params.pretrained_bert, "export", "additional", "encoder_only")):
saved_model_dir = os.path.join(self.params.pretrained_bert, "export", "additional", "encoder_only")
elif os.path.isdir(os.path.join(self.params.pretrained_bert, "additional", "encoder_only")):
saved_model_dir = os.path.join(self.params.pretrained_bert, "additional", "encoder_only")
elif os.path.basename(self.params.pretrained_bert.strip(os.sep)) == "best" and os.path.isdir(
os.path.join(self.params.pretrained_bert, "encoder_only")
):
saved_model_dir = os.path.join(self.params.pretrained_bert, "encoder_only")
else:
saved_model_dir = os.path.join(self.params.pretrained_bert, "best", "encoder_only")
self.pretrained_bert = keras.models.load_model(saved_model_dir)
logger.info(f"Saved model loaded from: {self.params.pretrained_bert}")
# else:
# logger.info(f"Attemed to load pre-trained bert from saved model: {self._params.pretrained_bert}")
# self.pretrained_bert = keras.models.load_model(self._params.pretrained_bert)
pass
def __init__(
self,
pretrained_model_name_or_path='google/electra-small-discriminator',
reduce_output='sum',
trainable=True,
num_tokens=None,
**kwargs
):
super(ELECTRAEncoder, self).__init__()
try:
from transformers import TFElectraModel
except ModuleNotFoundError:
logger.error(
' transformers is not installed. '
'In order to install all text feature dependencies run '
'pip install ludwig[text]'
)
sys.exit(-1)
self.transformer = TFElectraModel.from_pretrained(
pretrained_model_name_or_path
)
self.reduce_output = reduce_output
self.reduce_sequence = SequenceReducer(reduce_mode=reduce_output)
self.transformer.trainable = trainable
self.transformer.resize_token_embeddings(num_tokens)
def _test_Electra(self, size, large=False):
from transformers import ElectraTokenizer, TFElectraModel
tokenizer = ElectraTokenizer.from_pretrained(size)
model = TFElectraModel.from_pretrained(size)
input_dict = tokenizer("Hello, my dog is cute", return_tensors="tf")
spec, input_dict = self.spec_and_pad(input_dict)
outputs = ["last_hidden_state"]
self.run_test(model, input_dict, input_signature=spec, outputs=outputs, large=large)
def build_transformer(self, training, transformer):
if training:
self.config.n_words = len(self.transform.form_vocab)
self._init_config()
if isinstance(transformer, str):
if 'albert_chinese' in transformer:
tokenizer = BertTokenizerFast.from_pretrained(
transformer, add_special_tokens=False)
transformer: TFPreTrainedModel = TFAutoModel.from_pretrained(
transformer, name=transformer, from_pt=True)
elif transformer.startswith('albert') and transformer.endswith(
'zh'):
transformer, tokenizer, path = build_transformer(transformer)
transformer.config = AlbertConfig.from_json_file(
os.path.join(path, "albert_config.json"))
tokenizer = BertTokenizer.from_pretrained(
os.path.join(path, "vocab_chinese.txt"),
add_special_tokens=False)
elif 'chinese-roberta' in transformer:
tokenizer = BertTokenizer.from_pretrained(transformer)
transformer = TFBertModel.from_pretrained(transformer,
name=transformer,
from_pt=True)
elif 'electra' in transformer:
from transformers import TFElectraModel
tokenizer = BertTokenizer.from_pretrained(transformer)
transformer = TFElectraModel.from_pretrained(transformer,
name=transformer,
from_pt=True)
else:
tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(
transformer)
try:
transformer: TFPreTrainedModel = TFAutoModel.from_pretrained(
transformer, name=transformer)
except (TypeError, OSError):
transformer: TFPreTrainedModel = TFAutoModel.from_pretrained(
transformer, name=transformer, from_pt=True)
elif transformer[0] == 'AutoModelWithLMHead':
tokenizer: PreTrainedTokenizer = AutoTokenizer.from_pretrained(
transformer[1])
transformer: TFAutoModelWithLMHead = TFAutoModelWithLMHead.from_pretrained(
transformer[1])
else:
raise ValueError(f'Unknown identifier {transformer}')
self.transform.tokenizer = tokenizer
if self.config.get('fp16', None) or self.config.get('use_amp', None):
policy = tf.keras.mixed_precision.experimental.Policy(
'mixed_float16')
tf.keras.mixed_precision.experimental.set_policy(policy)
# tf.config.optimizer.set_experimental_options({"auto_mixed_precision": True})
transformer.set_weights(
[w.astype('float16') for w in transformer.get_weights()])
self.transform.transformer_config = transformer.config
return transformer
def build_model(num_labels, use_dropout=True, dropout_rate=0.15):
model = TFElectraModel.from_pretrained(model_name, cache_dir = cache_dir)
input_ids = tf.keras.layers.Input(shape=(model_meta.max_seq_length,), name='input_ids', dtype='int32')
attention_mask = tf.keras.layers.Input(shape=(model_meta.max_seq_length,), name='attention_mask', dtype='int32')
token_type_ids = tf.keras.layers.Input(shape=(model_meta.max_seq_length,), name='token_type_ids', dtype='int32')
model_inputs = [input_ids, attention_mask, token_type_ids]
outputs = model(model_inputs)
logits = outputs[0]
if use_dropout and dropout_rate>0:
logits = tf.keras.layers.Dropout(dropout_rate)(logits)
model_op = tf.keras.layers.Dense(num_labels, activation = 'softmax', kernel_initializer='glorot_uniform')(logits)
keras_model = tf.keras.Model(inputs= model_inputs, outputs = model_op)
def get_electra():
ids = keras.layers.Input(shape=(None, ), dtype=tf.int32, name='ids')
att = keras.layers.Input(shape=(None, ), dtype=tf.int32, name='att')
tok_type_ids = keras.layers.Input(shape=(None, ),
dtype=tf.int32,
name='tti')
config = ElectraConfig.from_pretrained(Config.Electra.config)
electra_model = TFElectraModel.from_pretrained(Config.Electra.model,
config=config)
x = electra_model(ids, attention_mask=att, token_type_ids=tok_type_ids)
x1 = keras.layers.Dropout(0.15)(x[0])
x1 = keras.layers.Conv1D(768, 2, padding='same')(x1)
x1 = keras.layers.LeakyReLU()(x1)
x1 = keras.layers.LayerNormalization()(x1)
x1 = keras.layers.Conv1D(64, 2, padding='same')(x1)
x1 = keras.layers.LeakyReLU()(x1)
x1 = keras.layers.LayerNormalization()(x1)
x1 = keras.layers.Conv1D(32, 2, padding='same')(x1)
x1 = keras.layers.Conv1D(1, 1)(x1)
x1 = keras.layers.Flatten()(x1)
x1 = keras.layers.Activation('softmax', dtype='float32', name='sts')(x1)
x2 = keras.layers.Dropout(0.15)(x[0])
x2 = keras.layers.Conv1D(768, 2, padding='same')(x2)
x2 = keras.layers.LeakyReLU()(x2)
x2 = keras.layers.LayerNormalization()(x2)
x2 = keras.layers.Conv1D(64, 2, padding='same')(x2)
x2 = keras.layers.LeakyReLU()(x2)
x2 = keras.layers.LayerNormalization()(x2)
x2 = keras.layers.Conv1D(32, 2, padding='same')(x2)
x2 = keras.layers.Conv1D(1, 1)(x2)
x2 = keras.layers.Flatten()(x2)
x2 = keras.layers.Activation('softmax', dtype='float32', name='ets')(x2)
model = keras.models.Model(inputs=[ids, att, tok_type_ids],
outputs=[x1, x2])
optimizer = keras.optimizers.Adam(learning_rate=6e-5)
if Config.Train.use_amp:
optimizer = keras.mixed_precision.experimental.LossScaleOptimizer(
optimizer, 'dynamic')
loss = keras.losses.CategoricalCrossentropy(
label_smoothing=Config.Train.label_smoothing)
model.compile(loss=loss, optimizer=optimizer)
return model
def make_model():
transformer = TFElectraModel.from_pretrained(r'models\electra')
input_ids = L.Input(shape=(512, ), dtype=tf.int32)
x = transformer(input_ids)[0]
x = x[:, 0, :]
x = L.Dense(1, activation='sigmoid', name='sigmoid')(x)
model = Model(inputs=input_ids, outputs=x)
model.compile(loss='binary_crossentropy',
metrics=['accuracy'],
optimizer=Adam(lr=1e-5))
last_layer = pickle.load(open('models\sigmoid.pickle', 'rb'))
model.get_layer('sigmoid').set_weights(last_layer)
return model
def __init__(self, extractor, config, *args, **kwargs):
super(TFParade_Class, self).__init__(*args, **kwargs)
self.extractor = extractor
self.config = config
if config["pretrained"] == "electra-base-msmarco":
self.bert = TFElectraModel.from_pretrained(
"Capreolus/electra-base-msmarco")
elif config["pretrained"] == "bert-base-msmarco":
self.bert = TFBertModel.from_pretrained(
"Capreolus/bert-base-msmarco")
elif config["pretrained"] == "bert-base-uncased":
self.bert = TFBertModel.from_pretrained("bert-base-uncased")
else:
raise ValueError(
f"unsupported model: {config['pretrained']}; need to ensure correct tokenizers will be used before arbitrary hgf models are supported"
)
self.transformer_layer_1 = TFBertLayer(self.bert.config)
self.transformer_layer_2 = TFBertLayer(self.bert.config)
self.num_passages = extractor.config["numpassages"]
self.maxseqlen = extractor.config["maxseqlen"]
self.linear = tf.keras.layers.Dense(
1, input_shape=(self.bert.config.hidden_size, ), dtype=tf.float32)
if config["aggregation"] == "maxp":
self.aggregation = self.aggregate_using_maxp
elif config["aggregation"] == "transformer":
self.aggregation = self.aggregate_using_transformer
input_embeddings = self.bert.get_input_embeddings()
cls_token_id = tf.convert_to_tensor([101])
cls_token_id = tf.reshape(cls_token_id, [1, 1])
self.initial_cls_embedding = input_embeddings(
input_ids=cls_token_id)
self.initial_cls_embedding = tf.reshape(
self.initial_cls_embedding, [1, self.bert.config.hidden_size])
initializer = tf.random_normal_initializer(stddev=0.02)
full_position_embeddings = tf.Variable(
initial_value=initializer(shape=[
self.num_passages + 1, self.bert.config.hidden_size
]),
name="passage_position_embedding",
)
self.full_position_embeddings = tf.expand_dims(
full_position_embeddings, axis=0)
def load_model(
sigmoid_dir, transformer_dir="transformer", architecture="distilbert", max_len=256
):
"""
Special function to load a keras model that uses a transformer layer
"""
sigmoid_path = os.path.join(sigmoid_dir, "sigmoid.pickle")
if architecture == "electra":
transformer = TFElectraModel.from_pretrained(transformer_dir)
else:
transformer = TFAutoModel.from_pretrained(transformer_dir)
model = build_model(transformer, max_len=max_len)
sigmoid = pickle.load(open(sigmoid_path, "rb"))
model.get_layer("sigmoid").set_weights(sigmoid)
return model
def test_load_tf_model():
for model_name_or_path in ALL_MODEL_NAME_OR_PATH_LST:
TFElectraModel.from_pretrained(model_name_or_path, from_pt=True)
embedding_weights.append(np.zeros(vocab_size))
for char, i in tk.word_index.items():
onehot = np.zeros(vocab_size)
onehot[i - 1] = 1
embedding_weights.append(onehot)
embedding_weights = np.array(embedding_weights)
### Ko-ELECTRA
from transformers import TFElectraModel
from transformers import ElectraTokenizer
tokenizer = ElectraTokenizer.from_pretrained(
"monologg/koelectra-base-v3-discriminator")
model = TFElectraModel.from_pretrained(
"monologg/koelectra-base-v3-discriminator", from_pt=True)
SEQ_LEN = 128
BATCH_SIZE = 16
token_inputs = tf.keras.layers.Input((SEQ_LEN, ),
dtype=tf.int32,
name='input_word_ids')
mask_inputs = tf.keras.layers.Input((SEQ_LEN, ),
dtype=tf.int32,
name='input_masks')
segment_inputs = tf.keras.layers.Input((SEQ_LEN, ),
dtype=tf.int32,
name='input_segment')
ELEC_outputs = model([token_inputs, mask_inputs, segment_inputs])