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# coding=utf-8 | |
# Copyright 2018 The OpenAI Team Authors and 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 ALBERT model.""" | |
from __future__ import annotations | |
import math | |
from dataclasses import dataclass | |
from typing import Dict, Optional, Tuple, Union | |
import numpy as np | |
import tensorflow as tf | |
from ...activations_tf import get_tf_activation | |
from ...modeling_tf_outputs import ( | |
TFBaseModelOutput, | |
TFBaseModelOutputWithPooling, | |
TFMaskedLMOutput, | |
TFMultipleChoiceModelOutput, | |
TFQuestionAnsweringModelOutput, | |
TFSequenceClassifierOutput, | |
TFTokenClassifierOutput, | |
) | |
from ...modeling_tf_utils import ( | |
TFMaskedLanguageModelingLoss, | |
TFModelInputType, | |
TFMultipleChoiceLoss, | |
TFPreTrainedModel, | |
TFQuestionAnsweringLoss, | |
TFSequenceClassificationLoss, | |
TFTokenClassificationLoss, | |
get_initializer, | |
keras_serializable, | |
unpack_inputs, | |
) | |
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax | |
from ...utils import ( | |
ModelOutput, | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
logging, | |
replace_return_docstrings, | |
) | |
from .configuration_albert import AlbertConfig | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "albert-base-v2" | |
_CONFIG_FOR_DOC = "AlbertConfig" | |
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"albert-base-v1", | |
"albert-large-v1", | |
"albert-xlarge-v1", | |
"albert-xxlarge-v1", | |
"albert-base-v2", | |
"albert-large-v2", | |
"albert-xlarge-v2", | |
"albert-xxlarge-v2", | |
# See all ALBERT models at https://huggingface.co/models?filter=albert | |
] | |
class TFAlbertPreTrainingLoss: | |
""" | |
Loss function suitable for ALBERT pretraining, that is, the task of pretraining a language model by combining SOP + | |
MLM. .. note:: Any label of -100 will be ignored (along with the corresponding logits) in the loss computation. | |
""" | |
def hf_compute_loss(self, labels: tf.Tensor, logits: tf.Tensor) -> tf.Tensor: | |
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( | |
from_logits=True, reduction=tf.keras.losses.Reduction.NONE | |
) | |
if self.config.tf_legacy_loss: | |
# make sure only labels that are not equal to -100 | |
# are taken into account as loss | |
masked_lm_active_loss = tf.not_equal(tf.reshape(tensor=labels["labels"], shape=(-1,)), -100) | |
masked_lm_reduced_logits = tf.boolean_mask( | |
tensor=tf.reshape(tensor=logits[0], shape=(-1, shape_list(logits[0])[2])), | |
mask=masked_lm_active_loss, | |
) | |
masked_lm_labels = tf.boolean_mask( | |
tensor=tf.reshape(tensor=labels["labels"], shape=(-1,)), mask=masked_lm_active_loss | |
) | |
sentence_order_active_loss = tf.not_equal( | |
tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), -100 | |
) | |
sentence_order_reduced_logits = tf.boolean_mask( | |
tensor=tf.reshape(tensor=logits[1], shape=(-1, 2)), mask=sentence_order_active_loss | |
) | |
sentence_order_label = tf.boolean_mask( | |
tensor=tf.reshape(tensor=labels["sentence_order_label"], shape=(-1,)), mask=sentence_order_active_loss | |
) | |
masked_lm_loss = loss_fn(y_true=masked_lm_labels, y_pred=masked_lm_reduced_logits) | |
sentence_order_loss = loss_fn(y_true=sentence_order_label, y_pred=sentence_order_reduced_logits) | |
masked_lm_loss = tf.reshape(tensor=masked_lm_loss, shape=(-1, shape_list(sentence_order_loss)[0])) | |
masked_lm_loss = tf.reduce_mean(input_tensor=masked_lm_loss, axis=0) | |
return masked_lm_loss + sentence_order_loss | |
# Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway | |
unmasked_lm_losses = loss_fn(y_true=tf.nn.relu(labels["labels"]), y_pred=logits[0]) | |
# make sure only labels that are not equal to -100 | |
# are taken into account for the loss computation | |
lm_loss_mask = tf.cast(labels["labels"] != -100, dtype=unmasked_lm_losses.dtype) | |
masked_lm_losses = unmasked_lm_losses * lm_loss_mask | |
reduced_masked_lm_loss = tf.reduce_sum(masked_lm_losses) / tf.reduce_sum(lm_loss_mask) | |
sop_logits = tf.reshape(logits[1], (-1, 2)) | |
# Clip negative labels to zero here to avoid NaNs and errors - those positions will get masked later anyway | |
unmasked_sop_loss = loss_fn(y_true=tf.nn.relu(labels["sentence_order_label"]), y_pred=sop_logits) | |
sop_loss_mask = tf.cast(labels["sentence_order_label"] != -100, dtype=unmasked_sop_loss.dtype) | |
masked_sop_loss = unmasked_sop_loss * sop_loss_mask | |
reduced_masked_sop_loss = tf.reduce_sum(masked_sop_loss) / tf.reduce_sum(sop_loss_mask) | |
return tf.reshape(reduced_masked_lm_loss + reduced_masked_sop_loss, (1,)) | |
class TFAlbertEmbeddings(tf.keras.layers.Layer): | |
"""Construct the embeddings from word, position and token_type embeddings.""" | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embedding_size = config.embedding_size | |
self.max_position_embeddings = config.max_position_embeddings | |
self.initializer_range = config.initializer_range | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
def build(self, input_shape: tf.TensorShape): | |
with tf.name_scope("word_embeddings"): | |
self.weight = self.add_weight( | |
name="weight", | |
shape=[self.config.vocab_size, self.embedding_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
with tf.name_scope("token_type_embeddings"): | |
self.token_type_embeddings = self.add_weight( | |
name="embeddings", | |
shape=[self.config.type_vocab_size, self.embedding_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
with tf.name_scope("position_embeddings"): | |
self.position_embeddings = self.add_weight( | |
name="embeddings", | |
shape=[self.max_position_embeddings, self.embedding_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
super().build(input_shape) | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings.call | |
def call( | |
self, | |
input_ids: tf.Tensor = None, | |
position_ids: tf.Tensor = None, | |
token_type_ids: tf.Tensor = None, | |
inputs_embeds: tf.Tensor = None, | |
past_key_values_length=0, | |
training: bool = False, | |
) -> tf.Tensor: | |
""" | |
Applies embedding based on inputs tensor. | |
Returns: | |
final_embeddings (`tf.Tensor`): output embedding tensor. | |
""" | |
if input_ids is None and inputs_embeds is None: | |
raise ValueError("Need to provide either `input_ids` or `input_embeds`.") | |
if input_ids is not None: | |
check_embeddings_within_bounds(input_ids, self.config.vocab_size) | |
inputs_embeds = tf.gather(params=self.weight, indices=input_ids) | |
input_shape = shape_list(inputs_embeds)[:-1] | |
if token_type_ids is None: | |
token_type_ids = tf.fill(dims=input_shape, value=0) | |
if position_ids is None: | |
position_ids = tf.expand_dims( | |
tf.range(start=past_key_values_length, limit=input_shape[1] + past_key_values_length), axis=0 | |
) | |
position_embeds = tf.gather(params=self.position_embeddings, indices=position_ids) | |
token_type_embeds = tf.gather(params=self.token_type_embeddings, indices=token_type_ids) | |
final_embeddings = inputs_embeds + position_embeds + token_type_embeds | |
final_embeddings = self.LayerNorm(inputs=final_embeddings) | |
final_embeddings = self.dropout(inputs=final_embeddings, training=training) | |
return final_embeddings | |
class TFAlbertAttention(tf.keras.layers.Layer): | |
"""Contains the complete attention sublayer, including both dropouts and layer norm.""" | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
if config.hidden_size % config.num_attention_heads != 0: | |
raise ValueError( | |
f"The hidden size ({config.hidden_size}) is not a multiple of the number " | |
f"of attention heads ({config.num_attention_heads})" | |
) | |
self.num_attention_heads = config.num_attention_heads | |
self.attention_head_size = int(config.hidden_size / config.num_attention_heads) | |
self.all_head_size = self.num_attention_heads * self.attention_head_size | |
self.sqrt_att_head_size = math.sqrt(self.attention_head_size) | |
self.output_attentions = config.output_attentions | |
self.query = tf.keras.layers.Dense( | |
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query" | |
) | |
self.key = tf.keras.layers.Dense( | |
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key" | |
) | |
self.value = tf.keras.layers.Dense( | |
units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value" | |
) | |
self.dense = tf.keras.layers.Dense( | |
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" | |
) | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
# Two different dropout probabilities; see https://github.com/google-research/albert/blob/master/modeling.py#L971-L993 | |
self.attention_dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob) | |
self.output_dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor: | |
# Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size] | |
tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size)) | |
# Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size] | |
return tf.transpose(tensor, perm=[0, 2, 1, 3]) | |
def call( | |
self, | |
input_tensor: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
output_attentions: bool, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
batch_size = shape_list(input_tensor)[0] | |
mixed_query_layer = self.query(inputs=input_tensor) | |
mixed_key_layer = self.key(inputs=input_tensor) | |
mixed_value_layer = self.value(inputs=input_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. | |
# (batch size, num_heads, seq_len_q, seq_len_k) | |
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True) | |
dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype) | |
attention_scores = tf.divide(attention_scores, dk) | |
if attention_mask is not None: | |
# Apply the attention mask is (precomputed for all layers in TFAlbertModel call() function) | |
attention_scores = tf.add(attention_scores, attention_mask) | |
# Normalize the attention scores to probabilities. | |
attention_probs = stable_softmax(logits=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.attention_dropout(inputs=attention_probs, training=training) | |
# Mask heads if we want to | |
if head_mask is not None: | |
attention_probs = tf.multiply(attention_probs, head_mask) | |
context_layer = tf.matmul(attention_probs, value_layer) | |
context_layer = tf.transpose(context_layer, perm=[0, 2, 1, 3]) | |
# (batch_size, seq_len_q, all_head_size) | |
context_layer = tf.reshape(tensor=context_layer, shape=(batch_size, -1, self.all_head_size)) | |
self_outputs = (context_layer, attention_probs) if output_attentions else (context_layer,) | |
hidden_states = self_outputs[0] | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.output_dropout(inputs=hidden_states, training=training) | |
attention_output = self.LayerNorm(inputs=hidden_states + input_tensor) | |
# add attentions if we output them | |
outputs = (attention_output,) + self_outputs[1:] | |
return outputs | |
class TFAlbertLayer(tf.keras.layers.Layer): | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.attention = TFAlbertAttention(config, name="attention") | |
self.ffn = tf.keras.layers.Dense( | |
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn" | |
) | |
if isinstance(config.hidden_act, str): | |
self.activation = get_tf_activation(config.hidden_act) | |
else: | |
self.activation = config.hidden_act | |
self.ffn_output = tf.keras.layers.Dense( | |
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="ffn_output" | |
) | |
self.full_layer_layer_norm = tf.keras.layers.LayerNormalization( | |
epsilon=config.layer_norm_eps, name="full_layer_layer_norm" | |
) | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
output_attentions: bool, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
attention_outputs = self.attention( | |
input_tensor=hidden_states, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
ffn_output = self.ffn(inputs=attention_outputs[0]) | |
ffn_output = self.activation(ffn_output) | |
ffn_output = self.ffn_output(inputs=ffn_output) | |
ffn_output = self.dropout(inputs=ffn_output, training=training) | |
hidden_states = self.full_layer_layer_norm(inputs=ffn_output + attention_outputs[0]) | |
# add attentions if we output them | |
outputs = (hidden_states,) + attention_outputs[1:] | |
return outputs | |
class TFAlbertLayerGroup(tf.keras.layers.Layer): | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.albert_layers = [ | |
TFAlbertLayer(config, name=f"albert_layers_._{i}") for i in range(config.inner_group_num) | |
] | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
output_attentions: bool, | |
output_hidden_states: bool, | |
training: bool = False, | |
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: | |
layer_hidden_states = () if output_hidden_states else None | |
layer_attentions = () if output_attentions else None | |
for layer_index, albert_layer in enumerate(self.albert_layers): | |
if output_hidden_states: | |
layer_hidden_states = layer_hidden_states + (hidden_states,) | |
layer_output = albert_layer( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
head_mask=head_mask[layer_index], | |
output_attentions=output_attentions, | |
training=training, | |
) | |
hidden_states = layer_output[0] | |
if output_attentions: | |
layer_attentions = layer_attentions + (layer_output[1],) | |
# Add last layer | |
if output_hidden_states: | |
layer_hidden_states = layer_hidden_states + (hidden_states,) | |
return tuple(v for v in [hidden_states, layer_hidden_states, layer_attentions] if v is not None) | |
class TFAlbertTransformer(tf.keras.layers.Layer): | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.num_hidden_layers = config.num_hidden_layers | |
self.num_hidden_groups = config.num_hidden_groups | |
# Number of layers in a hidden group | |
self.layers_per_group = int(config.num_hidden_layers / config.num_hidden_groups) | |
self.embedding_hidden_mapping_in = tf.keras.layers.Dense( | |
units=config.hidden_size, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="embedding_hidden_mapping_in", | |
) | |
self.albert_layer_groups = [ | |
TFAlbertLayerGroup(config, name=f"albert_layer_groups_._{i}") for i in range(config.num_hidden_groups) | |
] | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
output_attentions: bool, | |
output_hidden_states: bool, | |
return_dict: bool, | |
training: bool = False, | |
) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]: | |
hidden_states = self.embedding_hidden_mapping_in(inputs=hidden_states) | |
all_attentions = () if output_attentions else None | |
all_hidden_states = (hidden_states,) if output_hidden_states else None | |
for i in range(self.num_hidden_layers): | |
# Index of the hidden group | |
group_idx = int(i / (self.num_hidden_layers / self.num_hidden_groups)) | |
layer_group_output = self.albert_layer_groups[group_idx]( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
head_mask=head_mask[group_idx * self.layers_per_group : (group_idx + 1) * self.layers_per_group], | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
training=training, | |
) | |
hidden_states = layer_group_output[0] | |
if output_attentions: | |
all_attentions = all_attentions + layer_group_output[-1] | |
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 TFAlbertPreTrainedModel(TFPreTrainedModel): | |
""" | |
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained | |
models. | |
""" | |
config_class = AlbertConfig | |
base_model_prefix = "albert" | |
class TFAlbertMLMHead(tf.keras.layers.Layer): | |
def __init__(self, config: AlbertConfig, input_embeddings: tf.keras.layers.Layer, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embedding_size = config.embedding_size | |
self.dense = tf.keras.layers.Dense( | |
config.embedding_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" | |
) | |
if isinstance(config.hidden_act, str): | |
self.activation = get_tf_activation(config.hidden_act) | |
else: | |
self.activation = config.hidden_act | |
self.LayerNorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
# The output weights are the same as the input embeddings, but there is | |
# an output-only bias for each token. | |
self.decoder = input_embeddings | |
def build(self, input_shape: tf.TensorShape): | |
self.bias = self.add_weight(shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="bias") | |
self.decoder_bias = self.add_weight( | |
shape=(self.config.vocab_size,), initializer="zeros", trainable=True, name="decoder/bias" | |
) | |
super().build(input_shape) | |
def get_output_embeddings(self) -> tf.keras.layers.Layer: | |
return self.decoder | |
def set_output_embeddings(self, value: tf.Variable): | |
self.decoder.weight = value | |
self.decoder.vocab_size = shape_list(value)[0] | |
def get_bias(self) -> Dict[str, tf.Variable]: | |
return {"bias": self.bias, "decoder_bias": self.decoder_bias} | |
def set_bias(self, value: tf.Variable): | |
self.bias = value["bias"] | |
self.decoder_bias = value["decoder_bias"] | |
self.config.vocab_size = shape_list(value["bias"])[0] | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.activation(hidden_states) | |
hidden_states = self.LayerNorm(inputs=hidden_states) | |
seq_length = shape_list(tensor=hidden_states)[1] | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.embedding_size]) | |
hidden_states = tf.matmul(a=hidden_states, b=self.decoder.weight, transpose_b=True) | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.config.vocab_size]) | |
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.decoder_bias) | |
return hidden_states | |
class TFAlbertMainLayer(tf.keras.layers.Layer): | |
config_class = AlbertConfig | |
def __init__(self, config: AlbertConfig, add_pooling_layer: bool = True, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.embeddings = TFAlbertEmbeddings(config, name="embeddings") | |
self.encoder = TFAlbertTransformer(config, name="encoder") | |
self.pooler = ( | |
tf.keras.layers.Dense( | |
units=config.hidden_size, | |
kernel_initializer=get_initializer(config.initializer_range), | |
activation="tanh", | |
name="pooler", | |
) | |
if add_pooling_layer | |
else None | |
) | |
def get_input_embeddings(self) -> tf.keras.layers.Layer: | |
return self.embeddings | |
def set_input_embeddings(self, value: tf.Variable): | |
self.embeddings.weight = value | |
self.embeddings.vocab_size = shape_list(value)[0] | |
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, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
training: bool = False, | |
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: | |
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(dims=input_shape, value=1) | |
if token_type_ids is None: | |
token_type_ids = tf.fill(dims=input_shape, value=0) | |
embedding_output = self.embeddings( | |
input_ids=input_ids, | |
position_ids=position_ids, | |
token_type_ids=token_type_ids, | |
inputs_embeds=inputs_embeds, | |
training=training, | |
) | |
# 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 = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1])) | |
# 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, dtype=embedding_output.dtype) | |
one_cst = tf.constant(1.0, dtype=embedding_output.dtype) | |
ten_thousand_cst = tf.constant(-10000.0, dtype=embedding_output.dtype) | |
extended_attention_mask = tf.multiply(tf.subtract(one_cst, extended_attention_mask), ten_thousand_cst) | |
# 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.config.num_hidden_layers | |
encoder_outputs = self.encoder( | |
hidden_states=embedding_output, | |
attention_mask=extended_attention_mask, | |
head_mask=head_mask, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = encoder_outputs[0] | |
pooled_output = self.pooler(inputs=sequence_output[:, 0]) if self.pooler is not None else None | |
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 TFAlbertForPreTrainingOutput(ModelOutput): | |
""" | |
Output type of [`TFAlbertForPreTraining`]. | |
Args: | |
prediction_logits (`tf.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`): | |
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). | |
sop_logits (`tf.Tensor` of shape `(batch_size, 2)`): | |
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation | |
before SoftMax). | |
hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): | |
Tuple of `tf.Tensor` (one for the output of the embeddings + one for the output of each layer) of shape | |
`(batch_size, sequence_length, hidden_size)`. | |
Hidden-states of the model at the output of each layer plus the initial embedding outputs. | |
attentions (`tuple(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): | |
Tuple of `tf.Tensor` (one for each layer) of shape `(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: tf.Tensor = None | |
prediction_logits: tf.Tensor = None | |
sop_logits: tf.Tensor = None | |
hidden_states: Tuple[tf.Tensor] | None = None | |
attentions: Tuple[tf.Tensor] | None = None | |
ALBERT_START_DOCSTRING = r""" | |
This model inherits from [`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. | |
<Tip> | |
TensorFlow models and layers in `transformers` accept two formats as input: | |
- having all inputs as keyword arguments (like PyTorch models), or | |
- having all inputs as a list, tuple or dict in the first positional argument. | |
The reason the second format is supported is that Keras methods prefer this format when passing inputs to models | |
and layers. Because of this support, when using methods like `model.fit()` things should "just work" for you - just | |
pass your inputs and labels in any format that `model.fit()` supports! If, however, you want to use the second | |
format outside of Keras methods like `fit()` and `predict()`, such as when creating your own layers or models with | |
the Keras `Functional` API, there are three possibilities you can use to gather all the input Tensors in the first | |
positional argument: | |
- a single Tensor with `input_ids` only and nothing else: `model(input_ids)` | |
- a list of varying length with one or several input Tensors IN THE ORDER given in the docstring: | |
`model([input_ids, attention_mask])` or `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: | |
`model({"input_ids": input_ids, "token_type_ids": token_type_ids})` | |
Note that when creating models and layers with | |
[subclassing](https://keras.io/guides/making_new_layers_and_models_via_subclassing/) then you don't need to worry | |
about any of this, as you can just pass inputs like you would to any other Python function! | |
</Tip> | |
Args: | |
config ([`AlbertConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. | |
""" | |
ALBERT_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`Numpy array` or `tf.Tensor` of shape `({0})`): | |
Indices of input sequence tokens in the vocabulary. | |
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.__call__`] and | |
[`PreTrainedTokenizer.encode`] for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`Numpy array` or `tf.Tensor` of shape `({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 **masked**. | |
[What are attention masks?](../glossary#attention-mask) | |
token_type_ids (`Numpy array` or `tf.Tensor` of shape `({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#token-type-ids) | |
position_ids (`Numpy array` or `tf.Tensor` of shape `({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#position-ids) | |
head_mask (`Numpy array` or `tf.Tensor` of shape `(num_heads,)` or `(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 (`tf.Tensor` of shape `({0}, hidden_size)`, *optional*): | |
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This | |
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the | |
model's internal embedding lookup matrix. | |
output_attentions (`bool`, *optional*): | |
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned | |
tensors for more detail. This argument can be used only in eager mode, in graph mode the value in the | |
config will be used instead. | |
output_hidden_states (`bool`, *optional*): | |
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for | |
more detail. This argument can be used only in eager mode, in graph mode the value in the config will be | |
used instead. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. This argument can be used in | |
eager mode, in graph mode the value will always be set to True. | |
training (`bool`, *optional*, defaults to `False`): | |
Whether or not to use the model in training mode (some modules like dropout modules have different | |
behaviors between training and evaluation). | |
""" | |
class TFAlbertModel(TFAlbertPreTrainedModel): | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.albert = TFAlbertMainLayer(config, name="albert") | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
training: Optional[bool] = False, | |
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: | |
outputs = self.albert( | |
input_ids=input_ids, | |
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, | |
) | |
return outputs | |
class TFAlbertForPreTraining(TFAlbertPreTrainedModel, TFAlbertPreTrainingLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"predictions.decoder.weight"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.albert = TFAlbertMainLayer(config, name="albert") | |
self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions") | |
self.sop_classifier = TFAlbertSOPHead(config, name="sop_classifier") | |
def get_lm_head(self) -> tf.keras.layers.Layer: | |
return self.predictions | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
sentence_order_label: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFAlbertForPreTrainingOutput, Tuple[tf.Tensor]]: | |
r""" | |
Return: | |
Example: | |
```python | |
>>> import tensorflow as tf | |
>>> from transformers import AutoTokenizer, TFAlbertForPreTraining | |
>>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") | |
>>> model = TFAlbertForPreTraining.from_pretrained("albert-base-v2") | |
>>> input_ids = tf.constant(tokenizer.encode("Hello, my dog is cute", add_special_tokens=True))[None, :] | |
>>> # Batch size 1 | |
>>> outputs = model(input_ids) | |
>>> prediction_logits = outputs.prediction_logits | |
>>> sop_logits = outputs.sop_logits | |
```""" | |
outputs = self.albert( | |
input_ids=input_ids, | |
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, pooled_output = outputs[:2] | |
prediction_scores = self.predictions(hidden_states=sequence_output) | |
sop_scores = self.sop_classifier(pooled_output=pooled_output, training=training) | |
total_loss = None | |
if labels is not None and sentence_order_label is not None: | |
d_labels = {"labels": labels} | |
d_labels["sentence_order_label"] = sentence_order_label | |
total_loss = self.hf_compute_loss(labels=d_labels, logits=(prediction_scores, sop_scores)) | |
if not return_dict: | |
output = (prediction_scores, sop_scores) + outputs[2:] | |
return ((total_loss,) + output) if total_loss is not None else output | |
return TFAlbertForPreTrainingOutput( | |
loss=total_loss, | |
prediction_logits=prediction_scores, | |
sop_logits=sop_scores, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class TFAlbertSOPHead(tf.keras.layers.Layer): | |
def __init__(self, config: AlbertConfig, **kwargs): | |
super().__init__(**kwargs) | |
self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob) | |
self.classifier = tf.keras.layers.Dense( | |
units=config.num_labels, | |
kernel_initializer=get_initializer(config.initializer_range), | |
name="classifier", | |
) | |
def call(self, pooled_output: tf.Tensor, training: bool) -> tf.Tensor: | |
dropout_pooled_output = self.dropout(inputs=pooled_output, training=training) | |
logits = self.classifier(inputs=dropout_pooled_output) | |
return logits | |
class TFAlbertForMaskedLM(TFAlbertPreTrainedModel, TFMaskedLanguageModelingLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions.decoder.weight"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert") | |
self.predictions = TFAlbertMLMHead(config, input_embeddings=self.albert.embeddings, name="predictions") | |
def get_lm_head(self) -> tf.keras.layers.Layer: | |
return self.predictions | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFMaskedLMOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ..., | |
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the | |
loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]` | |
Returns: | |
Example: | |
```python | |
>>> import tensorflow as tf | |
>>> from transformers import AutoTokenizer, TFAlbertForMaskedLM | |
>>> tokenizer = AutoTokenizer.from_pretrained("albert-base-v2") | |
>>> model = TFAlbertForMaskedLM.from_pretrained("albert-base-v2") | |
>>> # add mask_token | |
>>> inputs = tokenizer(f"The capital of [MASK] is Paris.", return_tensors="tf") | |
>>> logits = model(**inputs).logits | |
>>> # retrieve index of [MASK] | |
>>> mask_token_index = tf.where(inputs.input_ids == tokenizer.mask_token_id)[0][1] | |
>>> predicted_token_id = tf.math.argmax(logits[0, mask_token_index], axis=-1) | |
>>> tokenizer.decode(predicted_token_id) | |
'france' | |
``` | |
```python | |
>>> labels = tokenizer("The capital of France is Paris.", return_tensors="tf")["input_ids"] | |
>>> labels = tf.where(inputs.input_ids == tokenizer.mask_token_id, labels, -100) | |
>>> outputs = model(**inputs, labels=labels) | |
>>> round(float(outputs.loss), 2) | |
0.81 | |
``` | |
""" | |
outputs = self.albert( | |
input_ids=input_ids, | |
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] | |
prediction_scores = self.predictions(hidden_states=sequence_output, training=training) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores) | |
if not return_dict: | |
output = (prediction_scores,) + outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TFMaskedLMOutput( | |
loss=loss, | |
logits=prediction_scores, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |
class TFAlbertForSequenceClassification(TFAlbertPreTrainedModel, TFSequenceClassificationLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"predictions"] | |
_keys_to_ignore_on_load_missing = [r"dropout"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.albert = TFAlbertMainLayer(config, name="albert") | |
self.dropout = tf.keras.layers.Dropout(rate=config.classifier_dropout_prob) | |
self.classifier = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): | |
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., | |
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If | |
`config.num_labels > 1` a classification loss is computed (Cross-Entropy). | |
""" | |
outputs = self.albert( | |
input_ids=input_ids, | |
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(inputs=pooled_output, training=training) | |
logits = self.classifier(inputs=pooled_output) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=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, | |
) | |
class TFAlbertForTokenClassification(TFAlbertPreTrainedModel, TFTokenClassificationLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"] | |
_keys_to_ignore_on_load_missing = [r"dropout"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert") | |
classifier_dropout_prob = ( | |
config.classifier_dropout_prob | |
if config.classifier_dropout_prob is not None | |
else config.hidden_dropout_prob | |
) | |
self.dropout = tf.keras.layers.Dropout(rate=classifier_dropout_prob) | |
self.classifier = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFTokenClassifierOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. | |
""" | |
outputs = self.albert( | |
input_ids=input_ids, | |
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(inputs=sequence_output, training=training) | |
logits = self.classifier(inputs=sequence_output) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=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, | |
) | |
class TFAlbertForQuestionAnswering(TFAlbertPreTrainedModel, TFQuestionAnsweringLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.albert = TFAlbertMainLayer(config, add_pooling_layer=False, name="albert") | |
self.qa_outputs = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
start_positions: np.ndarray | tf.Tensor | None = None, | |
end_positions: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFQuestionAnsweringModelOutput, Tuple[tf.Tensor]]: | |
r""" | |
start_positions (`tf.Tensor` of shape `(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 (`sequence_length`). Position outside of the sequence | |
are not taken into account for computing the loss. | |
end_positions (`tf.Tensor` of shape `(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 (`sequence_length`). Position outside of the sequence | |
are not taken into account for computing the loss. | |
""" | |
outputs = self.albert( | |
input_ids=input_ids, | |
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(inputs=sequence_output) | |
start_logits, end_logits = tf.split(value=logits, num_or_size_splits=2, axis=-1) | |
start_logits = tf.squeeze(input=start_logits, axis=-1) | |
end_logits = tf.squeeze(input=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.hf_compute_loss(labels=labels, logits=(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, | |
) | |
class TFAlbertForMultipleChoice(TFAlbertPreTrainedModel, TFMultipleChoiceLoss): | |
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model | |
_keys_to_ignore_on_load_unexpected = [r"pooler", r"predictions"] | |
_keys_to_ignore_on_load_missing = [r"dropout"] | |
def __init__(self, config: AlbertConfig, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.albert = TFAlbertMainLayer(config, name="albert") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
self.classifier = tf.keras.layers.Dense( | |
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier" | |
) | |
def call( | |
self, | |
input_ids: TFModelInputType | None = None, | |
attention_mask: np.ndarray | tf.Tensor | None = None, | |
token_type_ids: np.ndarray | tf.Tensor | None = None, | |
position_ids: np.ndarray | tf.Tensor | None = None, | |
head_mask: np.ndarray | tf.Tensor | None = None, | |
inputs_embeds: np.ndarray | tf.Tensor | None = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
labels: np.ndarray | tf.Tensor | None = None, | |
training: Optional[bool] = False, | |
) -> Union[TFMultipleChoiceModelOutput, Tuple[tf.Tensor]]: | |
r""" | |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*): | |
Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices]` | |
where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) | |
""" | |
if input_ids is not None: | |
num_choices = shape_list(input_ids)[1] | |
seq_length = shape_list(input_ids)[2] | |
else: | |
num_choices = shape_list(inputs_embeds)[1] | |
seq_length = shape_list(inputs_embeds)[2] | |
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None | |
flat_attention_mask = ( | |
tf.reshape(tensor=attention_mask, shape=(-1, seq_length)) if attention_mask is not None else None | |
) | |
flat_token_type_ids = ( | |
tf.reshape(tensor=token_type_ids, shape=(-1, seq_length)) if token_type_ids is not None else None | |
) | |
flat_position_ids = ( | |
tf.reshape(tensor=position_ids, shape=(-1, seq_length)) if position_ids is not None else None | |
) | |
flat_inputs_embeds = ( | |
tf.reshape(tensor=inputs_embeds, shape=(-1, seq_length, shape_list(inputs_embeds)[3])) | |
if inputs_embeds is not None | |
else None | |
) | |
outputs = self.albert( | |
input_ids=flat_input_ids, | |
attention_mask=flat_attention_mask, | |
token_type_ids=flat_token_type_ids, | |
position_ids=flat_position_ids, | |
head_mask=head_mask, | |
inputs_embeds=flat_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(inputs=pooled_output, training=training) | |
logits = self.classifier(inputs=pooled_output) | |
reshaped_logits = tf.reshape(tensor=logits, shape=(-1, num_choices)) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=reshaped_logits) | |
if not return_dict: | |
output = (reshaped_logits,) + outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TFMultipleChoiceModelOutput( | |
loss=loss, | |
logits=reshaped_logits, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
) | |