# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
""" TF 2.0 MobileBERT model. """
from dataclasses import dataclass
from typing import Optional, Tuple
import tensorflow as tf
from . import MobileBertConfig
from .activations_tf import get_tf_activation
from .file_utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
replace_return_docstrings,
)
from .modeling_tf_outputs import (
TFBaseModelOutput,
TFBaseModelOutputWithPooling,
TFMaskedLMOutput,
TFMultipleChoiceModelOutput,
TFNextSentencePredictorOutput,
TFQuestionAnsweringModelOutput,
TFSequenceClassifierOutput,
TFTokenClassifierOutput,
)
from .modeling_tf_utils import (
TFMaskedLanguageModelingLoss,
TFMultipleChoiceLoss,
TFPreTrainedModel,
TFQuestionAnsweringLoss,
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
keras_serializable,
shape_list,
)
from .tokenization_utils import BatchEncoding
from .utils import logging
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "MobileBertConfig"
_TOKENIZER_FOR_DOC = "MobileBertTokenizer"
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [
"google/mobilebert-uncased",
# See all MobileBERT models at https://huggingface.co/models?filter=mobilebert
]
class TFMobileBertIntermediate(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.intermediate_size, name="dense")
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = get_tf_activation(config.hidden_act)
else:
self.intermediate_act_fn = config.hidden_act
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class TFLayerNorm(tf.keras.layers.LayerNormalization):
def __init__(self, feat_size, *args, **kwargs):
super().__init__(*args, **kwargs)
class TFNoNorm(tf.keras.layers.Layer):
def __init__(self, feat_size, epsilon=None, **kwargs):
super().__init__(**kwargs)
self.feat_size = feat_size
def build(self, input_shape):
self.bias = self.add_weight("bias", shape=[self.feat_size], initializer="zeros")
self.weight = self.add_weight("weight", shape=[self.feat_size], initializer="ones")
def call(self, inputs: tf.Tensor):
return inputs * self.weight + self.bias
NORM2FN = {"layer_norm": TFLayerNorm, "no_norm": TFNoNorm}
class TFMobileBertEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.trigram_input = config.trigram_input
self.embedding_size = config.embedding_size
self.vocab_size = config.vocab_size
self.hidden_size = config.hidden_size
self.initializer_range = config.initializer_range
self.position_embeddings = tf.keras.layers.Embedding(
config.max_position_embeddings,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="position_embeddings",
)
self.token_type_embeddings = tf.keras.layers.Embedding(
config.type_vocab_size,
config.hidden_size,
embeddings_initializer=get_initializer(self.initializer_range),
name="token_type_embeddings",
)
self.embedding_transformation = tf.keras.layers.Dense(config.hidden_size, name="embedding_transformation")
# self.LayerNorm is not snake-cased to stick with TensorFlow model variable name and be able to load
# any TensorFlow checkpoint file
self.LayerNorm = NORM2FN[config.normalization_type](
config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def build(self, input_shape):
"""Build shared word embedding layer """
with tf.name_scope("word_embeddings"):
# Create and initialize weights. The random normal initializer was chosen
# arbitrarily, and works well.
self.word_embeddings = self.add_weight(
"weight",
shape=[self.vocab_size, self.embedding_size],
initializer=get_initializer(self.initializer_range),
)
super().build(input_shape)
def call(
self,
input_ids=None,
position_ids=None,
token_type_ids=None,
inputs_embeds=None,
mode="embedding",
training=False,
):
"""Get token embeddings of inputs.
Args:
inputs: list of three int64 tensors with shape [batch_size, length]: (input_ids, position_ids, token_type_ids)
mode: string, a valid value is one of "embedding" and "linear".
Returns:
outputs: (1) If mode == "embedding", output embedding tensor, float32 with
shape [batch_size, length, embedding_size]; (2) mode == "linear", output
linear tensor, float32 with shape [batch_size, length, vocab_size].
Raises:
ValueError: if mode is not valid.
Shared weights logic adapted from
https://github.com/tensorflow/models/blob/a009f4fb9d2fc4949e32192a944688925ef78659/official/transformer/v2/embedding_layer.py#L24
"""
if mode == "embedding":
return self._embedding(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
elif mode == "linear":
return self._linear(input_ids)
else:
raise ValueError("mode {} is not valid.".format(mode))
def _embedding(self, input_ids, position_ids, token_type_ids, inputs_embeds, training=False):
"""Applies embedding based on inputs tensor."""
assert not (input_ids is None and inputs_embeds is None)
if input_ids is not None:
input_shape = shape_list(input_ids)
else:
input_shape = shape_list(inputs_embeds)[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = tf.range(seq_length, dtype=tf.int32)[tf.newaxis, :]
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
if inputs_embeds is None:
inputs_embeds = tf.gather(self.word_embeddings, input_ids)
if self.trigram_input:
# From the paper MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited
# Devices (https://arxiv.org/abs/2004.02984)
#
# The embedding table in BERT models accounts for a substantial proportion of model size. To compress
# the embedding layer, we reduce the embedding dimension to 128 in MobileBERT.
# Then, we apply a 1D convolution with kernel size 3 on the raw token embedding to produce a 512
# dimensional output.
inputs_embeds = tf.concat(
[
tf.pad(inputs_embeds[:, 1:], ((0, 0), (0, 1), (0, 0))),
inputs_embeds,
tf.pad(inputs_embeds[:, :-1], ((0, 0), (1, 0), (0, 0))),
],
axis=2,
)
if self.trigram_input or self.embedding_size != self.hidden_size:
inputs_embeds = self.embedding_transformation(inputs_embeds)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.LayerNorm(embeddings)
embeddings = self.dropout(embeddings, training=training)
return embeddings
def _linear(self, inputs):
"""Computes logits by running inputs through a linear layer.
Args:
inputs: A float32 tensor with shape [batch_size, length, hidden_size]
Returns:
float32 tensor with shape [batch_size, length, vocab_size].
"""
batch_size = shape_list(inputs)[0]
length = shape_list(inputs)[1]
x = tf.reshape(inputs, [-1, self.hidden_size])
logits = tf.matmul(x, self.word_embeddings, transpose_b=True)
return tf.reshape(logits, [batch_size, length, self.vocab_size])
class TFMobileBertSelfAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
if config.hidden_size % config.num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (config.hidden_size, config.num_attention_heads)
)
self.num_attention_heads = config.num_attention_heads
self.output_attentions = config.output_attentions
assert config.hidden_size % config.num_attention_heads == 0
self.attention_head_size = int(config.true_hidden_size / config.num_attention_heads)
self.all_head_size = self.num_attention_heads * self.attention_head_size
self.query = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
)
self.key = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
)
self.value = tf.keras.layers.Dense(
self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
)
self.dropout = tf.keras.layers.Dropout(config.attention_probs_dropout_prob)
def transpose_for_scores(self, x, batch_size):
x = tf.reshape(x, (batch_size, -1, self.num_attention_heads, self.attention_head_size))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(
self, query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=False
):
batch_size = shape_list(attention_mask)[0]
mixed_query_layer = self.query(query_tensor)
mixed_key_layer = self.key(key_tensor)
mixed_value_layer = self.value(value_tensor)
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)
# Take the dot product between "query" and "key" to get the raw attention scores.
attention_scores = tf.matmul(
query_layer, key_layer, transpose_b=True
) # (batch size, num_heads, seq_len_q, seq_len_k)
dk = tf.cast(shape_list(key_layer)[-1], tf.float32) # scale attention_scores
attention_scores = attention_scores / tf.math.sqrt(dk)
if attention_mask is not None:
# Apply the attention mask is (precomputed for all layers in TFBertModel call() function)
attention_scores = attention_scores + attention_mask
# Normalize the attention scores to probabilities.
attention_probs = tf.nn.softmax(attention_scores, axis=-1)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = self.dropout(attention_probs, training=training)
# Mask heads if we want to
if head_mask is not None:
attention_probs = attention_probs * head_mask
context_layer = tf.matmul(attention_probs, value_layer)
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3])
context_layer = tf.reshape(
context_layer, (batch_size, -1, self.all_head_size)
) # (batch_size, seq_len_q, all_head_size)
outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
return outputs
class TFMobileBertSelfOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.dense = tf.keras.layers.Dense(
config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = NORM2FN[config.normalization_type](
config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
if not self.use_bottleneck:
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, residual_tensor, training=False):
hidden_states = self.dense(hidden_states)
if not self.use_bottleneck:
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + residual_tensor)
return hidden_states
class TFMobileBertAttention(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.self = TFMobileBertSelfAttention(config, name="self")
self.mobilebert_output = TFMobileBertSelfOutput(config, name="output")
def prune_heads(self, heads):
raise NotImplementedError
def call(
self,
query_tensor,
key_tensor,
value_tensor,
layer_input,
attention_mask,
head_mask,
output_attentions,
training=False,
):
self_outputs = self.self(
query_tensor, key_tensor, value_tensor, attention_mask, head_mask, output_attentions, training=training
)
attention_output = self.mobilebert_output(self_outputs[0], layer_input, training=training)
outputs = (attention_output,) + self_outputs[1:] # add attentions if we output them
return outputs
class TFOutputBottleneck(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.hidden_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
def call(self, hidden_states, residual_tensor, training=False):
layer_outputs = self.dense(hidden_states)
layer_outputs = self.dropout(layer_outputs, training=training)
layer_outputs = self.LayerNorm(layer_outputs + residual_tensor)
return layer_outputs
class TFMobileBertOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.dense = tf.keras.layers.Dense(
config.true_hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
self.LayerNorm = NORM2FN[config.normalization_type](
config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
if not self.use_bottleneck:
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
else:
self.bottleneck = TFOutputBottleneck(config, name="bottleneck")
def call(self, hidden_states, residual_tensor_1, residual_tensor_2, training=False):
hidden_states = self.dense(hidden_states)
if not self.use_bottleneck:
hidden_states = self.dropout(hidden_states, training=training)
hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
else:
hidden_states = self.LayerNorm(hidden_states + residual_tensor_1)
hidden_states = self.bottleneck(hidden_states, residual_tensor_2)
return hidden_states
class TFBottleneckLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.intra_bottleneck_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.intra_bottleneck_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
def call(self, inputs):
hidden_states = self.dense(inputs)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class TFBottleneck(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.key_query_shared_bottleneck = config.key_query_shared_bottleneck
self.use_bottleneck_attention = config.use_bottleneck_attention
self.bottleneck_input = TFBottleneckLayer(config, name="input")
if self.key_query_shared_bottleneck:
self.attention = TFBottleneckLayer(config, name="attention")
def call(self, hidden_states):
# This method can return three different tuples of values. These different values make use of bottlenecks,
# which are linear layers used to project the hidden states to a lower-dimensional vector, reducing memory
# usage. These linear layer have weights that are learned during training.
#
# If `config.use_bottleneck_attention`, it will return the result of the bottleneck layer four times for the
# key, query, value, and "layer input" to be used by the attention layer.
# This bottleneck is used to project the hidden. This last layer input will be used as a residual tensor
# in the attention self output, after the attention scores have been computed.
#
# If not `config.use_bottleneck_attention` and `config.key_query_shared_bottleneck`, this will return
# four values, three of which have been passed through a bottleneck: the query and key, passed through the same
# bottleneck, and the residual layer to be applied in the attention self output, through another bottleneck.
#
# Finally, in the last case, the values for the query, key and values are the hidden states without bottleneck,
# and the residual layer will be this value passed through a bottleneck.
bottlenecked_hidden_states = self.bottleneck_input(hidden_states)
if self.use_bottleneck_attention:
return (bottlenecked_hidden_states,) * 4
elif self.key_query_shared_bottleneck:
shared_attention_input = self.attention(hidden_states)
return (shared_attention_input, shared_attention_input, hidden_states, bottlenecked_hidden_states)
else:
return (hidden_states, hidden_states, hidden_states, bottlenecked_hidden_states)
class TFFFNOutput(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(config.true_hidden_size, name="dense")
self.LayerNorm = NORM2FN[config.normalization_type](
config.true_hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm"
)
def call(self, hidden_states, residual_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.LayerNorm(hidden_states + residual_tensor)
return hidden_states
class TFFFNLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
self.mobilebert_output = TFFFNOutput(config, name="output")
def call(self, hidden_states):
intermediate_output = self.intermediate(hidden_states)
layer_outputs = self.mobilebert_output(intermediate_output, hidden_states)
return layer_outputs
class TFMobileBertLayer(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.use_bottleneck = config.use_bottleneck
self.num_feedforward_networks = config.num_feedforward_networks
self.attention = TFMobileBertAttention(config, name="attention")
self.intermediate = TFMobileBertIntermediate(config, name="intermediate")
self.mobilebert_output = TFMobileBertOutput(config, name="output")
if self.use_bottleneck:
self.bottleneck = TFBottleneck(config, name="bottleneck")
if config.num_feedforward_networks > 1:
self.ffn = [
TFFFNLayer(config, name="ffn.{}".format(i)) for i in range(config.num_feedforward_networks - 1)
]
def call(self, hidden_states, attention_mask, head_mask, output_attentions, training=False):
if self.use_bottleneck:
query_tensor, key_tensor, value_tensor, layer_input = self.bottleneck(hidden_states)
else:
query_tensor, key_tensor, value_tensor, layer_input = [hidden_states] * 4
attention_outputs = self.attention(
query_tensor,
key_tensor,
value_tensor,
layer_input,
attention_mask,
head_mask,
output_attentions,
training=training,
)
attention_output = attention_outputs[0]
s = (attention_output,)
if self.num_feedforward_networks != 1:
for i, ffn_module in enumerate(self.ffn):
attention_output = ffn_module(attention_output)
s += (attention_output,)
intermediate_output = self.intermediate(attention_output)
layer_output = self.mobilebert_output(intermediate_output, attention_output, hidden_states, training=training)
outputs = (
(layer_output,)
+ attention_outputs[1:]
+ (
tf.constant(0),
query_tensor,
key_tensor,
value_tensor,
layer_input,
attention_output,
intermediate_output,
)
+ s
) # add attentions if we output them
return outputs
class TFMobileBertEncoder(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.layer = [TFMobileBertLayer(config, name="layer_._{}".format(i)) for i in range(config.num_hidden_layers)]
def call(
self,
hidden_states,
attention_mask,
head_mask,
output_attentions,
output_hidden_states,
return_dict,
training=False,
):
all_hidden_states = () if output_hidden_states else None
all_attentions = () if output_attentions else None
for i, layer_module in enumerate(self.layer):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(
hidden_states, attention_mask, head_mask[i], output_attentions, training=training
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
# Add last layer
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
return TFBaseModelOutput(
last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
)
class TFMobileBertPooler(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.do_activate = config.classifier_activation
if self.do_activate:
self.dense = tf.keras.layers.Dense(
config.hidden_size,
kernel_initializer=get_initializer(config.initializer_range),
activation="tanh",
name="dense",
)
def call(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
if not self.do_activate:
return first_token_tensor
else:
pooled_output = self.dense(first_token_tensor)
return pooled_output
class TFMobileBertPredictionHeadTransform(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(
config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
)
if isinstance(config.hidden_act, str):
self.transform_act_fn = get_tf_activation(config.hidden_act)
else:
self.transform_act_fn = config.hidden_act
self.LayerNorm = NORM2FN["layer_norm"](config.hidden_size, epsilon=config.layer_norm_eps, name="LayerNorm")
def call(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.LayerNorm(hidden_states)
return hidden_states
class TFMobileBertLMPredictionHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.transform = TFMobileBertPredictionHeadTransform(config, name="transform")
self.vocab_size = config.vocab_size
self.config = config
def build(self, input_shape):
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias")
self.dense = self.add_weight(
shape=(self.config.hidden_size - self.config.embedding_size, self.vocab_size),
initializer="zeros",
trainable=True,
name="dense/weight",
)
self.decoder = self.add_weight(
shape=(self.config.vocab_size, self.config.embedding_size),
initializer="zeros",
trainable=True,
name="decoder/weight",
)
super().build(input_shape)
def call(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = tf.matmul(hidden_states, tf.concat([tf.transpose(self.decoder), self.dense], axis=0))
hidden_states = hidden_states + self.bias
return hidden_states
class TFMobileBertMLMHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.predictions = TFMobileBertLMPredictionHead(config, name="predictions")
def call(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
@keras_serializable
class TFMobileBertMainLayer(tf.keras.layers.Layer):
config_class = MobileBertConfig
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.num_hidden_layers = config.num_hidden_layers
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.return_dict = config.use_return_dict
self.embeddings = TFMobileBertEmbeddings(config, name="embeddings")
self.encoder = TFMobileBertEncoder(config, name="encoder")
self.pooler = TFMobileBertPooler(config, name="pooler")
def get_input_embeddings(self):
return self.embeddings
def _resize_token_embeddings(self, new_num_tokens):
raise NotImplementedError
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
See base class PreTrainedModel
"""
raise NotImplementedError
def call(
self,
inputs,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
if isinstance(inputs, (tuple, list)):
input_ids = inputs[0]
attention_mask = inputs[1] if len(inputs) > 1 else attention_mask
token_type_ids = inputs[2] if len(inputs) > 2 else token_type_ids
position_ids = inputs[3] if len(inputs) > 3 else position_ids
head_mask = inputs[4] if len(inputs) > 4 else head_mask
inputs_embeds = inputs[5] if len(inputs) > 5 else inputs_embeds
output_attentions = inputs[6] if len(inputs) > 6 else output_attentions
output_hidden_states = inputs[7] if len(inputs) > 7 else output_hidden_states
return_dict = inputs[8] if len(inputs) > 8 else return_dict
assert len(inputs) <= 9, "Too many inputs."
elif isinstance(inputs, (dict, BatchEncoding)):
input_ids = inputs.get("input_ids")
attention_mask = inputs.get("attention_mask", attention_mask)
token_type_ids = inputs.get("token_type_ids", token_type_ids)
position_ids = inputs.get("position_ids", position_ids)
head_mask = inputs.get("head_mask", head_mask)
inputs_embeds = inputs.get("inputs_embeds", inputs_embeds)
output_attentions = inputs.get("output_attentions", output_attentions)
output_hidden_states = inputs.get("output_hidden_states", output_hidden_states)
return_dict = inputs.get("return_dict", return_dict)
assert len(inputs) <= 9, "Too many inputs."
else:
input_ids = inputs
output_attentions = output_attentions if output_attentions is not None else self.output_attentions
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.output_hidden_states
return_dict = return_dict if return_dict is not None else self.return_dict
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = shape_list(input_ids)
elif inputs_embeds is not None:
input_shape = shape_list(inputs_embeds)[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
if token_type_ids is None:
token_type_ids = tf.fill(input_shape, 0)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = attention_mask[:, tf.newaxis, tf.newaxis, :]
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
extended_attention_mask = tf.cast(extended_attention_mask, tf.float32)
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if head_mask is not None:
raise NotImplementedError
else:
head_mask = [None] * self.num_hidden_layers
embedding_output = self.embeddings(input_ids, position_ids, token_type_ids, inputs_embeds, training=training)
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
head_mask,
output_attentions,
output_hidden_states,
return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not return_dict:
return (
sequence_output,
pooled_output,
) + encoder_outputs[1:]
return TFBaseModelOutputWithPooling(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
class TFMobileBertPreTrainedModel(TFPreTrainedModel):
"""An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
config_class = MobileBertConfig
base_model_prefix = "mobilebert"
[docs]@dataclass
class TFMobileBertForPreTrainingOutput(ModelOutput):
"""
Output type of :class:`~transformers.TFMobileBertForPreTrainingModel`.
Args:
prediction_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (:obj:`tf.Tensor` of shape :obj:`(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False
continuation before SoftMax).
hidden_states (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_hidden_states=True`` is passed or when ``config.output_hidden_states=True``):
Tuple of :obj:`tf.Tensor` (one for the output of the embeddings + one for the output of each layer)
of shape :obj:`(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (:obj:`tuple(tf.Tensor)`, `optional`, returned when ``output_attentions=True`` is passed or when ``config.output_attentions=True``):
Tuple of :obj:`tf.Tensor` (one for each layer) of shape
:obj:`(batch_size, num_heads, sequence_length, sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[tf.Tensor] = None
prediction_logits: tf.Tensor = None
seq_relationship_logits: tf.Tensor = None
hidden_states: Optional[Tuple[tf.Tensor]] = None
attentions: Optional[Tuple[tf.Tensor]] = None
MOBILEBERT_START_DOCSTRING = r"""
This model inherits from :class:`~transformers.TFPreTrainedModel`. Check the superclass documentation for the
generic methods the library implements for all its model (such as downloading or saving, resizing the input
embeddings, pruning heads etc.)
This model is also a `tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ subclass.
Use it as a regular TF 2.0 Keras Model and refer to the TF 2.0 documentation for all matter related to general
usage and behavior.
.. note::
TF 2.0 models accepts two formats as inputs:
- having all inputs as keyword arguments (like PyTorch models), or
- having all inputs as a list, tuple or dict in the first positional arguments.
This second option is useful when using :meth:`tf.keras.Model.fit` method which currently requires having
all the tensors in the first argument of the model call function: :obj:`model(inputs)`.
If you choose this second option, there are three possibilities you can use to gather all the input Tensors
in the first positional argument :
- a single Tensor with :obj:`input_ids` only and nothing else: :obj:`model(inputs_ids)`
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring:
:obj:`model([input_ids, attention_mask])` or :obj:`model([input_ids, attention_mask, token_type_ids])`
- a dictionary with one or several input Tensors associated to the input names given in the docstring:
:obj:`model({"input_ids": input_ids, "token_type_ids": token_type_ids})`
Parameters:
config (:class:`~transformers.MobileBertConfig`): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the :meth:`~transformers.PreTrainedModel.from_pretrained` method to load the model weights.
"""
MOBILEBERT_INPUTS_DOCSTRING = r"""
Args:
input_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using :class:`~transformers.MobileBertTokenizer`.
See :func:`transformers.PreTrainedTokenizer.__call__` and
:func:`transformers.PreTrainedTokenizer.encode` for details.
`What are input IDs? <../glossary.html#input-ids>`__
attention_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Mask to avoid performing attention on padding token indices.
Mask values selected in ``[0, 1]``:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **maked**.
`What are attention masks? <../glossary.html#attention-mask>`__
token_type_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Segment token indices to indicate first and second portions of the inputs.
Indices are selected in ``[0, 1]``:
- 0 corresponds to a `sentence A` token,
- 1 corresponds to a `sentence B` token.
`What are token type IDs? <../glossary.html#token-type-ids>`__
position_ids (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`({0})`, `optional`):
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range ``[0, config.max_position_embeddings - 1]``.
`What are position IDs? <../glossary.html#position-ids>`__
head_mask (:obj:`Numpy array` or :obj:`tf.Tensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`):
Mask to nullify selected heads of the self-attention modules.
Mask values selected in ``[0, 1]``:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (:obj:`tf.Tensor` of shape :obj:`({0}, hidden_size)`, `optional`):
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert :obj:`input_ids` indices into associated
vectors than the model's internal embedding lookup matrix.
output_attentions (:obj:`bool`, `optional`):
Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned
tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`):
Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for
more detail.
return_dict (:obj:`bool`, `optional`):
Whether or not to return a :class:`~transformers.file_utils.ModelOutput` instead of a plain tuple.
training (:obj:`bool`, `optional`, defaults to :obj:`False`):
Whether or not to use the model in training mode (some modules like dropout modules have different
behaviors between training and evaluation).
"""
[docs]@add_start_docstrings(
"The bare MobileBert Model transformer outputing raw hidden-states without any specific head on top.",
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertModel(TFMobileBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/mobilebert-uncased",
output_type=TFBaseModelOutputWithPooling,
config_class=_CONFIG_FOR_DOC,
)
def call(self, inputs, **kwargs):
outputs = self.mobilebert(inputs, **kwargs)
return outputs
[docs]@add_start_docstrings(
"""MobileBert Model with two heads on top as done during the pre-training:
a `masked language modeling` head and a `next sentence prediction (classification)` head. """,
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.predictions = TFMobileBertMLMHead(config, name="predictions___cls")
self.seq_relationship = TFMobileBertOnlyNSPHead(2, name="seq_relationship___cls")
def get_output_embeddings(self):
return self.mobilebert.embeddings
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMobileBertForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(self, inputs, **kwargs):
r"""
Return:
Examples::
>>> import tensorflow as tf
>>> from transformers import MobileBertTokenizer, TFMobileBertForPreTraining
>>> tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
>>> model = TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased')
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute"))[None, :] # Batch size 1
>>> outputs = model(input_ids)
>>> prediction_scores, seq_relationship_scores = outputs[:2]
"""
return_dict = kwargs.get("return_dict")
return_dict = return_dict if return_dict is not None else self.mobilebert.return_dict
outputs = self.mobilebert(inputs, **kwargs)
sequence_output, pooled_output = outputs[:2]
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
if not return_dict:
return (prediction_scores, seq_relationship_score) + outputs[2:]
return TFMobileBertForPreTrainingOutput(
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class TFMobileBertOnlyNSPHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
self.seq_relationship = tf.keras.layers.Dense(2, name="seq_relationship")
def call(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
[docs]@add_start_docstrings(
"""MobileBert Model with a `next sentence prediction (classification)` head on top. """,
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.cls = TFMobileBertOnlyNSPHead(config, name="seq_relationship___cls")
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFNextSentencePredictorOutput, config_class=_CONFIG_FOR_DOC)
def call(self, inputs, **kwargs):
r"""
Return:
Examples::
>>> import tensorflow as tf
>>> from transformers import MobileBertTokenizer, TFMobileBertForNextSentencePrediction
>>> tokenizer = MobileBertTokenizer.from_pretrained('google/mobilebert-uncased')
>>> model = TFMobileBertForNextSentencePrediction.from_pretrained('google/mobilebert-uncased')
>>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced."
>>> next_sentence = "The sky is blue due to the shorter wavelength of blue light."
>>> encoding = tokenizer(prompt, next_sentence, return_tensors='tf')
>>> logits = model(encoding['input_ids'], token_type_ids=encoding['token_type_ids'])[0]
"""
return_dict = kwargs.get("return_dict")
return_dict = return_dict if return_dict is not None else self.mobilebert.return_dict
outputs = self.mobilebert(inputs, **kwargs)
pooled_output = outputs[1]
seq_relationship_score = self.cls(pooled_output)
if not return_dict:
return (seq_relationship_score,) + outputs[2:]
return TFNextSentencePredictorOutput(
logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]@add_start_docstrings(
"""MobileBert Model transformer with a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. """,
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSequenceClassificationLoss):
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/mobilebert-uncased",
output_type=TFSequenceClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
inputs=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for computing the sequence classification/regression loss.
Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
"""
return_dict = return_dict if return_dict is not None else self.mobilebert.return_dict
if isinstance(inputs, (tuple, list)):
labels = inputs[9] if len(inputs) > 9 else labels
if len(inputs) > 9:
inputs = inputs[:9]
elif isinstance(inputs, (dict, BatchEncoding)):
labels = inputs.pop("labels", labels)
outputs = self.mobilebert(
inputs,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=training)
logits = self.classifier(pooled_output)
loss = None if labels is None else self.compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]@add_start_docstrings(
"""MobileBert Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of
the hidden-states output to compute `span start logits` and `span end logits`). """,
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAnsweringLoss):
authorized_missing_keys = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.qa_outputs = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/mobilebert-uncased",
output_type=TFQuestionAnsweringModelOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
inputs=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
start_positions=None,
end_positions=None,
training=False,
):
r"""
start_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
end_positions (:obj:`tf.Tensor` of shape :obj:`(batch_size,)`, `optional`):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (:obj:`sequence_length`).
Position outside of the sequence are not taken into account for computing the loss.
"""
return_dict = return_dict if return_dict is not None else self.mobilebert.return_dict
if isinstance(inputs, (tuple, list)):
start_positions = inputs[9] if len(inputs) > 9 else start_positions
end_positions = inputs[10] if len(inputs) > 10 else end_positions
if len(inputs) > 9:
inputs = inputs[:9]
elif isinstance(inputs, (dict, BatchEncoding)):
start_positions = inputs.pop("start_positions", start_positions)
end_positions = inputs.pop("end_positions", start_positions)
outputs = self.mobilebert(
inputs,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = tf.split(logits, 2, axis=-1)
start_logits = tf.squeeze(start_logits, axis=-1)
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if start_positions is not None and end_positions is not None:
labels = {"start_position": start_positions}
labels["end_position"] = end_positions
loss = self.compute_loss(labels, (start_logits, end_logits))
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFQuestionAnsweringModelOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]@add_start_docstrings(
"""MobileBert Model with a token classification head on top (a linear layer on top of
the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks. """,
MOBILEBERT_START_DOCSTRING,
)
class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenClassificationLoss):
authorized_missing_keys = [r"pooler"]
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.num_labels = config.num_labels
self.mobilebert = TFMobileBertMainLayer(config, name="mobilebert")
self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob)
self.classifier = tf.keras.layers.Dense(
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
[docs] @add_start_docstrings_to_callable(MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
tokenizer_class=_TOKENIZER_FOR_DOC,
checkpoint="google/mobilebert-uncased",
output_type=TFTokenClassifierOutput,
config_class=_CONFIG_FOR_DOC,
)
def call(
self,
inputs=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
labels=None,
training=False,
):
r"""
labels (:obj:`tf.Tensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
Labels for computing the token classification loss.
Indices should be in ``[0, ..., config.num_labels - 1]``.
"""
return_dict = return_dict if return_dict is not None else self.mobilebert.return_dict
if isinstance(inputs, (tuple, list)):
labels = inputs[9] if len(inputs) > 9 else labels
if len(inputs) > 9:
inputs = inputs[:9]
elif isinstance(inputs, (dict, BatchEncoding)):
labels = inputs.pop("labels", labels)
outputs = self.mobilebert(
inputs,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output, training=training)
logits = self.classifier(sequence_output)
loss = None if labels is None else self.compute_loss(labels, logits)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return TFTokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)