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xlnet.py
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xlnet.py
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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""XLNet models."""
# pylint: disable=g-classes-have-attributes
from typing import Any, Mapping, Optional, Union
import tensorflow as tf
from official.nlp.modeling import layers
from official.nlp.modeling import networks
class XLNetMaskedLM(tf.keras.layers.Layer):
"""XLNet pretraining head."""
def __init__(self,
vocab_size: int,
hidden_size: int,
initializer: str = 'glorot_uniform',
activation: str = 'gelu',
name=None,
**kwargs):
super().__init__(name=name, **kwargs)
self._vocab_size = vocab_size
self._hidden_size = hidden_size
self._initializer = initializer
self._activation = activation
def build(self, input_shape):
self.dense = tf.keras.layers.Dense(
units=self._hidden_size,
activation=self._activation,
kernel_initializer=self._initializer,
name='transform/dense')
self.layer_norm = tf.keras.layers.LayerNormalization(
axis=-1, epsilon=1e-12, name='transform/LayerNorm')
self.bias = self.add_weight(
'output_bias/bias',
shape=(self._vocab_size,),
initializer='zeros',
trainable=True)
super().build(input_shape)
def call(self,
sequence_data: tf.Tensor,
embedding_table: tf.Tensor):
lm_data = self.dense(sequence_data)
lm_data = self.layer_norm(lm_data)
lm_data = tf.matmul(lm_data, embedding_table, transpose_b=True)
logits = tf.nn.bias_add(lm_data, self.bias)
return logits
def get_config(self) -> Mapping[str, Any]:
config = {
'vocab_size':
self._vocab_size,
'hidden_size':
self._hidden_size,
'initializer':
self._initializer
}
base_config = super(XLNetMaskedLM, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
@tf.keras.utils.register_keras_serializable(package='Text')
class XLNetPretrainer(tf.keras.Model):
"""XLNet-based pretrainer.
This is an implementation of the network structure surrounding a
Transformer-XL encoder as described in "XLNet: Generalized Autoregressive
Pretraining for Language Understanding" (https://arxiv.org/abs/1906.08237).
Args:
network: An XLNet/Transformer-XL based network. This network should output a
sequence output and list of `state` tensors.
mlm_activation: The activation (if any) to use in the Masked LM network. If
None, then no activation will be used.
mlm_initializer: The initializer (if any) to use in the masked LM. Defaults
to a Glorot uniform initializer.
"""
def __init__(
self,
network: Union[tf.keras.layers.Layer, tf.keras.Model],
mlm_activation=None,
mlm_initializer='glorot_uniform',
name: Optional[str] = None,
**kwargs):
super().__init__(name=name, **kwargs)
self._config = {
'network': network,
'mlm_activation': mlm_activation,
'mlm_initializer': mlm_initializer,
}
self._network = network
self._hidden_size = network.get_config()['hidden_size']
self._vocab_size = network.get_config()['vocab_size']
self._activation = mlm_activation
self._initializer = mlm_initializer
self._masked_lm = XLNetMaskedLM(
vocab_size=self._vocab_size,
hidden_size=self._hidden_size,
initializer=self._initializer)
def call(self, inputs: Mapping[str, Any]): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
input_word_ids = inputs['input_word_ids']
input_type_ids = inputs['input_type_ids']
masked_tokens = inputs['masked_tokens']
permutation_mask = inputs['permutation_mask']
target_mapping = inputs['target_mapping']
state = inputs.get('state', None)
attention_output, state = self._network(
input_ids=input_word_ids,
segment_ids=input_type_ids,
input_mask=None,
state=state,
permutation_mask=permutation_mask,
target_mapping=target_mapping,
masked_tokens=masked_tokens)
embedding_table = self._network.get_embedding_lookup_table()
mlm_outputs = self._masked_lm(
sequence_data=attention_output,
embedding_table=embedding_table)
return mlm_outputs, state
def get_config(self) -> Mapping[str, Any]:
return self._config
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)
@property
def checkpoint_items(self):
return dict(encoder=self._network)
@tf.keras.utils.register_keras_serializable(package='Text')
class XLNetClassifier(tf.keras.Model):
"""Classifier model based on XLNet.
This is an implementation of the network structure surrounding a
Transformer-XL encoder as described in "XLNet: Generalized Autoregressive
Pretraining for Language Understanding" (https://arxiv.org/abs/1906.08237).
Note: This model does not use utilize the memory mechanism used in the
original XLNet Classifier.
Args:
network: An XLNet/Transformer-XL based network. This network should output a
sequence output and list of `state` tensors.
num_classes: Number of classes to predict from the classification network.
initializer: The initializer (if any) to use in the classification networks.
Defaults to a RandomNormal initializer.
summary_type: Method used to summarize a sequence into a compact vector.
dropout_rate: The dropout probability of the cls head.
head_name: Name of the classification head.
"""
def __init__(
self,
network: Union[tf.keras.layers.Layer, tf.keras.Model],
num_classes: int,
initializer: tf.keras.initializers.Initializer = 'random_normal',
summary_type: str = 'last',
dropout_rate: float = 0.1,
head_name: str = 'sentence_prediction', # pytype: disable=annotation-type-mismatch # typed-keras
**kwargs):
super().__init__(**kwargs)
self._network = network
self._initializer = initializer
self._summary_type = summary_type
self._num_classes = num_classes
self._config = {
'network': network,
'initializer': initializer,
'num_classes': num_classes,
'summary_type': summary_type,
'dropout_rate': dropout_rate,
'head_name': head_name,
}
if summary_type == 'last':
cls_token_idx = -1
elif summary_type == 'first':
cls_token_idx = 0
else:
raise ValueError('Invalid summary type provided: %s.' % summary_type)
self.classifier = layers.ClassificationHead(
inner_dim=network.get_config()['hidden_size'],
num_classes=num_classes,
initializer=initializer,
dropout_rate=dropout_rate,
cls_token_idx=cls_token_idx,
name=head_name)
def call(self, inputs: Mapping[str, Any]): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
input_ids = inputs['input_word_ids']
segment_ids = inputs['input_type_ids']
input_mask = tf.cast(inputs['input_mask'], tf.float32)
state = inputs.get('mems', None)
attention_output, _ = self._network(
input_ids=input_ids,
segment_ids=segment_ids,
input_mask=input_mask,
state=state)
logits = self.classifier(attention_output)
return logits
def get_config(self):
return self._config
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)
@property
def checkpoint_items(self):
items = dict(encoder=self._network)
if hasattr(self.classifier, 'checkpoint_items'):
for key, item in self.classifier.checkpoint_items.items():
items['.'.join([self.classifier.name, key])] = item
return items
@tf.keras.utils.register_keras_serializable(package='Text')
class XLNetSpanLabeler(tf.keras.Model):
"""Span labeler model based on XLNet.
This is an implementation of the network structure surrounding a
Transformer-XL encoder as described in "XLNet: Generalized Autoregressive
Pretraining for Language Understanding" (https://arxiv.org/abs/1906.08237).
Args:
network: A transformer network. This network should output a sequence output
and a classification output. Furthermore, it should expose its embedding
table via a "get_embedding_table" method.
start_n_top: Beam size for span start.
end_n_top: Beam size for span end.
dropout_rate: The dropout rate for the span labeling layer.
span_labeling_activation: The activation for the span labeling head.
initializer: The initializer (if any) to use in the span labeling network.
Defaults to a Glorot uniform initializer.
"""
def __init__(
self,
network: Union[tf.keras.layers.Layer, tf.keras.Model],
start_n_top: int = 5,
end_n_top: int = 5,
dropout_rate: float = 0.1,
span_labeling_activation: tf.keras.initializers.Initializer = 'tanh',
initializer: tf.keras.initializers.Initializer = 'glorot_uniform', # pytype: disable=annotation-type-mismatch # typed-keras
**kwargs):
super().__init__(**kwargs)
self._config = {
'network': network,
'start_n_top': start_n_top,
'end_n_top': end_n_top,
'dropout_rate': dropout_rate,
'span_labeling_activation': span_labeling_activation,
'initializer': initializer,
}
network_config = network.get_config()
try:
input_width = network_config['inner_size']
self._xlnet_base = True
except KeyError:
# BertEncoder uses 'intermediate_size' due to legacy naming.
input_width = network_config['intermediate_size']
self._xlnet_base = False
self._network = network
self._initializer = initializer
self._start_n_top = start_n_top
self._end_n_top = end_n_top
self._dropout_rate = dropout_rate
self._activation = span_labeling_activation
self.span_labeling = networks.XLNetSpanLabeling(
input_width=input_width,
start_n_top=self._start_n_top,
end_n_top=self._end_n_top,
activation=self._activation,
dropout_rate=self._dropout_rate,
initializer=self._initializer)
def call(self, inputs: Mapping[str, Any]): # pytype: disable=signature-mismatch # overriding-parameter-count-checks
input_word_ids = inputs['input_word_ids']
input_type_ids = inputs['input_type_ids']
input_mask = inputs['input_mask']
class_index = inputs['class_index']
paragraph_mask = inputs['paragraph_mask']
start_positions = inputs.get('start_positions', None)
if self._xlnet_base:
attention_output, _ = self._network(
input_ids=input_word_ids,
segment_ids=input_type_ids,
input_mask=input_mask)
else:
network_output_dict = self._network(dict(
input_word_ids=input_word_ids,
input_type_ids=input_type_ids,
input_mask=input_mask))
attention_output = network_output_dict['sequence_output']
outputs = self.span_labeling(
sequence_data=attention_output,
class_index=class_index,
paragraph_mask=paragraph_mask,
start_positions=start_positions)
return outputs
@property
def checkpoint_items(self):
return dict(encoder=self._network)
def get_config(self):
return self._config
@classmethod
def from_config(cls, config, custom_objects=None):
return cls(**config)