MASR
/
transformers
/templates
/adding_a_new_model
/cookiecutter-template-{{cookiecutter.modelname}}
/modeling_tf_{{cookiecutter.lowercase_modelname}}.py
# coding=utf-8 | |
# Copyright 2022 {{cookiecutter.authors}} and The HuggingFace Inc. team. 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 {{cookiecutter.modelname}} model. """ | |
{% if cookiecutter.is_encoder_decoder_model == "False" %} | |
import math | |
from typing import Dict, Optional, Tuple, Union | |
import numpy as np | |
import tensorflow as tf | |
from ...activations_tf import get_tf_activation | |
from ...utils import ( | |
DUMMY_INPUTS, | |
MULTIPLE_CHOICE_DUMMY_INPUTS, | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
) | |
from ...modeling_tf_outputs import ( | |
TFBaseModelOutputWithPastAndCrossAttentions, | |
TFCausalLMOutputWithCrossAttentions, | |
TFMaskedLMOutput, | |
TFMultipleChoiceModelOutput, | |
TFQuestionAnsweringModelOutput, | |
TFSequenceClassifierOutput, | |
TFTokenClassifierOutput, | |
) | |
from ...modeling_tf_utils import ( | |
TFCausalLanguageModelingLoss, | |
TFMaskedLanguageModelingLoss, | |
TFModelInputType, | |
TFMultipleChoiceLoss, | |
TFPreTrainedModel, | |
TFQuestionAnsweringLoss, | |
TFSequenceClassificationLoss, | |
TFSequenceSummary, | |
TFTokenClassificationLoss, | |
get_initializer, | |
keras_serializable, | |
unpack_inputs, | |
) | |
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax | |
from ...utils import logging | |
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" | |
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" | |
TF_{{cookiecutter.uppercase_modelname}}_PRETRAINED_MODEL_ARCHIVE_LIST = [ | |
"{{cookiecutter.checkpoint_identifier}}", | |
# See all {{cookiecutter.modelname}} models at https://huggingface.co/models?filter={{cookiecutter.lowercase_modelname}} | |
] | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEmbeddings with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Embeddings(tf.keras.layers.Layer): | |
"""Construct the embeddings from word, position and token_type embeddings.""" | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.vocab_size = config.vocab_size | |
self.type_vocab_size = config.type_vocab_size | |
self.hidden_size = config.hidden_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.vocab_size, self.hidden_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.type_vocab_size, self.hidden_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.hidden_size], | |
initializer=get_initializer(self.initializer_range), | |
) | |
super().build(input_shape) | |
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. | |
""" | |
assert not (input_ids is None and inputs_embeds is None) | |
if input_ids is not None: | |
check_embeddings_within_bounds(input_ids, self.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 | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfAttention with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}SelfAttention(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **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.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.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob) | |
self.is_decoder = config.is_decoder | |
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, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
encoder_hidden_states: tf.Tensor, | |
encoder_attention_mask: tf.Tensor, | |
past_key_value: Tuple[tf.Tensor], | |
output_attentions: bool, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
batch_size = shape_list(hidden_states)[0] | |
mixed_query_layer = self.query(inputs=hidden_states) | |
# If this is instantiated as a cross-attention module, the keys | |
# and values come from an encoder; the attention mask needs to be | |
# such that the encoder's padding tokens are not attended to. | |
is_cross_attention = encoder_hidden_states is not None | |
if is_cross_attention and past_key_value is not None: | |
# reuse k,v, cross_attentions | |
key_layer = past_key_value[0] | |
value_layer = past_key_value[1] | |
attention_mask = encoder_attention_mask | |
elif is_cross_attention: | |
key_layer = self.transpose_for_scores(self.key(inputs=encoder_hidden_states), batch_size) | |
value_layer = self.transpose_for_scores(self.value(inputs=encoder_hidden_states), batch_size) | |
attention_mask = encoder_attention_mask | |
elif past_key_value is not None: | |
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) | |
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) | |
key_layer = tf.concat([past_key_value[0], key_layer], axis=2) | |
value_layer = tf.concat([past_key_value[1], value_layer], axis=2) | |
else: | |
key_layer = self.transpose_for_scores(self.key(inputs=hidden_states), batch_size) | |
value_layer = self.transpose_for_scores(self.value(inputs=hidden_states), batch_size) | |
query_layer = self.transpose_for_scores(mixed_query_layer, batch_size) | |
if self.is_decoder: | |
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. | |
# Further calls to cross_attention layer can then reuse all cross-attention | |
# key/value_states (first "if" case) | |
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of | |
# all previous decoder key/value_states. Further calls to uni-directional self-attention | |
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) | |
# if encoder bi-directional self-attention `past_key_value` is always `None` | |
past_key_value = (key_layer, value_layer) | |
# 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 TF{{cookiecutter.camelcase_modelname}}Model 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.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) | |
attention_output = tf.matmul(attention_probs, value_layer) | |
attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3]) | |
# (batch_size, seq_len_q, all_head_size) | |
attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size)) | |
outputs = (attention_output, attention_probs) if output_attentions else (attention_output,) | |
if self.is_decoder: | |
outputs = outputs + (past_key_value,) | |
return outputs | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertSelfOutput with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}SelfOutput(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
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") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.dropout(inputs=hidden_states, training=training) | |
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertAttention with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.self_attention = TF{{cookiecutter.camelcase_modelname}}SelfAttention(config, name="self") | |
self.dense_output = TF{{cookiecutter.camelcase_modelname}}SelfOutput(config, name="output") | |
def prune_heads(self, heads): | |
raise NotImplementedError | |
def call( | |
self, | |
input_tensor: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
encoder_hidden_states: tf.Tensor, | |
encoder_attention_mask: tf.Tensor, | |
past_key_value: Tuple[tf.Tensor], | |
output_attentions: bool, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
self_outputs = self.self_attention( | |
hidden_states=input_tensor, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
past_key_value=past_key_value, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
attention_output = self.dense_output( | |
hidden_states=self_outputs[0], input_tensor=input_tensor, training=training | |
) | |
# add attentions (possibly with past_key_value) if we output them | |
outputs = (attention_output,) + self_outputs[1:] | |
return outputs | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertIntermediate with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Intermediate(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense( | |
units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), 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: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.intermediate_act_fn(hidden_states) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertOutput with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Output(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
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") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.dropout(inputs=hidden_states, training=training) | |
hidden_states = self.LayerNorm(inputs=hidden_states + input_tensor) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLayer with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Layer(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.attention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="attention") | |
self.is_decoder = config.is_decoder | |
self.add_cross_attention = config.add_cross_attention | |
if self.add_cross_attention: | |
if not self.is_decoder: | |
raise ValueError(f"{self} should be used as a decoder model if cross attention is added") | |
self.crossattention = TF{{cookiecutter.camelcase_modelname}}Attention(config, name="crossattention") | |
self.intermediate = TF{{cookiecutter.camelcase_modelname}}Intermediate(config, name="intermediate") | |
self.bert_output = TF{{cookiecutter.camelcase_modelname}}Output(config, name="output") | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
encoder_hidden_states: tf.Tensor | None, | |
encoder_attention_mask: tf.Tensor | None, | |
past_key_value: Tuple[tf.Tensor] | None, | |
output_attentions: bool, | |
training: bool = False, | |
) -> Tuple[tf.Tensor]: | |
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2 | |
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None | |
self_attention_outputs = self.attention( | |
input_tensor=hidden_states, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
past_key_value=self_attn_past_key_value, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
attention_output = self_attention_outputs[0] | |
# if decoder, the last output is tuple of self-attn cache | |
if self.is_decoder: | |
outputs = self_attention_outputs[1:-1] | |
present_key_value = self_attention_outputs[-1] | |
else: | |
outputs = self_attention_outputs[1:] # add self attentions if we output attention weights | |
cross_attn_present_key_value = None | |
if self.is_decoder and encoder_hidden_states is not None: | |
if not hasattr(self, "crossattention"): | |
raise ValueError( | |
f"If `encoder_hidden_states` are passed, {self} has to be instantiated with cross-attention layers " | |
"by setting `config.add_cross_attention=True`" | |
) | |
# cross_attn cached key/values tuple is at positions 3,4 of past_key_value tuple | |
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None | |
cross_attention_outputs = self.crossattention( | |
input_tensor=attention_output, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
past_key_value=cross_attn_past_key_value, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
attention_output = cross_attention_outputs[0] | |
outputs = outputs + cross_attention_outputs[1:-1] # add cross attentions if we output attention weights | |
# add cross-attn cache to positions 3,4 of present_key_value tuple | |
cross_attn_present_key_value = cross_attention_outputs[-1] | |
present_key_value = present_key_value + cross_attn_present_key_value | |
intermediate_output = self.intermediate(hidden_states=attention_output) | |
layer_output = self.bert_output( | |
hidden_states=intermediate_output, input_tensor=attention_output, training=training | |
) | |
outputs = (layer_output,) + outputs # add attentions if we output them | |
# if decoder, return the attn key/values as the last output | |
if self.is_decoder: | |
outputs = outputs + (present_key_value,) | |
return outputs | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertEncoder with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.layer = [TF{{cookiecutter.camelcase_modelname}}Layer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)] | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
attention_mask: tf.Tensor, | |
head_mask: tf.Tensor, | |
encoder_hidden_states: tf.Tensor | None, | |
encoder_attention_mask: tf.Tensor | None, | |
past_key_values: Tuple[Tuple[tf.Tensor]] | None, | |
use_cache: Optional[bool], | |
output_attentions: bool, | |
output_hidden_states: bool, | |
return_dict: bool, | |
training: bool = False, | |
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: | |
all_hidden_states = () if output_hidden_states else None | |
all_attentions = () if output_attentions else None | |
all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None | |
next_decoder_cache = () if use_cache else None | |
for i, layer_module in enumerate(self.layer): | |
if output_hidden_states: | |
all_hidden_states = all_hidden_states + (hidden_states,) | |
past_key_value = past_key_values[i] if past_key_values is not None else None | |
layer_outputs = layer_module( | |
hidden_states=hidden_states, | |
attention_mask=attention_mask, | |
head_mask=head_mask[i], | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
past_key_value=past_key_value, | |
output_attentions=output_attentions, | |
training=training, | |
) | |
hidden_states = layer_outputs[0] | |
if use_cache: | |
next_decoder_cache += (layer_outputs[-1],) | |
if output_attentions: | |
all_attentions = all_attentions + (layer_outputs[1],) | |
if self.config.add_cross_attention and encoder_hidden_states is not None: | |
all_cross_attentions = all_cross_attentions + (layer_outputs[2],) | |
# 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, all_cross_attentions] if v is not None | |
) | |
return TFBaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
past_key_values=next_decoder_cache, | |
hidden_states=all_hidden_states, | |
attentions=all_attentions, | |
cross_attentions=all_cross_attentions, | |
) | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPredictionHeadTransform with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.dense = tf.keras.layers.Dense( | |
units=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 = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="LayerNorm") | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.transform_act_fn(hidden_states) | |
hidden_states = self.LayerNorm(inputs=hidden_states) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMPredictionHead with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs): | |
super().__init__(**kwargs) | |
self.vocab_size = config.vocab_size | |
self.hidden_size = config.hidden_size | |
self.transform = TF{{cookiecutter.camelcase_modelname}}PredictionHeadTransform(config, name="transform") | |
# The output weights are the same as the input embeddings, but there is | |
# an output-only bias for each token. | |
self.input_embeddings = input_embeddings | |
def build(self, input_shape: tf.TensorShape): | |
self.bias = self.add_weight(shape=(self.vocab_size,), initializer="zeros", trainable=True, name="bias") | |
super().build(input_shape) | |
def get_output_embeddings(self) -> tf.keras.layers.Layer: | |
return self.input_embeddings | |
def set_output_embeddings(self, value: tf.Variable): | |
self.input_embeddings.weight = value | |
self.input_embeddings.vocab_size = shape_list(value)[0] | |
def get_bias(self) -> Dict[str, tf.Variable]: | |
return {"bias": self.bias} | |
def set_bias(self, value: tf.Variable): | |
self.bias = value["bias"] | |
self.vocab_size = shape_list(value["bias"])[0] | |
def call(self, hidden_states: tf.Tensor) -> tf.Tensor: | |
hidden_states = self.transform(hidden_states=hidden_states) | |
seq_length = shape_list(hidden_states)[1] | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, self.hidden_size]) | |
hidden_states = tf.matmul(a=hidden_states, b=self.input_embeddings.weight, transpose_b=True) | |
hidden_states = tf.reshape(tensor=hidden_states, shape=[-1, seq_length, self.vocab_size]) | |
hidden_states = tf.nn.bias_add(value=hidden_states, bias=self.bias) | |
return hidden_states | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMLMHead with Bert->{{cookiecutter.camelcase_modelname}} | |
class TF{{cookiecutter.camelcase_modelname}}MLMHead(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, input_embeddings: tf.keras.layers.Layer, **kwargs): | |
super().__init__(**kwargs) | |
self.predictions = TF{{cookiecutter.camelcase_modelname}}LMPredictionHead(config, input_embeddings, name="predictions") | |
def call(self, sequence_output: tf.Tensor) -> tf.Tensor: | |
prediction_scores = self.predictions(hidden_states=sequence_output) | |
return prediction_scores | |
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer): | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, add_pooling_layer: bool = True, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.is_decoder = config.is_decoder | |
self.embeddings = TF{{cookiecutter.camelcase_modelname}}Embeddings(config, name="embeddings") | |
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, name="encoder") | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.get_input_embeddings | |
def get_input_embeddings(self) -> tf.keras.layers.Layer: | |
return self.embeddings | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer.set_input_embeddings | |
def set_input_embeddings(self, value: tf.Variable): | |
self.embeddings.weight = value | |
self.embeddings.vocab_size = shape_list(value)[0] | |
# Copied from transformers.models.bert.modeling_tf_bert.TFBertMainLayer._prune_heads | |
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, | |
encoder_hidden_states: np.ndarray | tf.Tensor | None = None, | |
encoder_attention_mask: np.ndarray | tf.Tensor | None = None, | |
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, | |
use_cache: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
training: bool = False, | |
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: | |
if not self.config.is_decoder: | |
use_cache = False | |
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") | |
batch_size, seq_length = input_shape | |
if past_key_values is None: | |
past_key_values_length = 0 | |
past_key_values = [None] * len(self.encoder.layer) | |
else: | |
past_key_values_length = shape_list(past_key_values[0][0])[-2] | |
if attention_mask is None: | |
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), 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, | |
past_key_values_length=past_key_values_length, | |
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. | |
attention_mask_shape = shape_list(attention_mask) | |
mask_seq_length = seq_length + past_key_values_length | |
# Copied from `modeling_tf_t5.py` | |
# Provided a padding mask of dimensions [batch_size, mask_seq_length] | |
# - if the model is a decoder, apply a causal mask in addition to the padding mask | |
# - if the model is an encoder, make the mask broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] | |
if self.is_decoder: | |
seq_ids = tf.range(mask_seq_length) | |
causal_mask = tf.less_equal( | |
tf.tile(seq_ids[None, None, :], (batch_size, mask_seq_length, 1)), | |
seq_ids[None, :, None], | |
) | |
causal_mask = tf.cast(causal_mask, dtype=attention_mask.dtype) | |
extended_attention_mask = causal_mask * attention_mask[:, None, :] | |
attention_mask_shape = shape_list(extended_attention_mask) | |
extended_attention_mask = tf.reshape( | |
extended_attention_mask, (attention_mask_shape[0], 1, attention_mask_shape[1], attention_mask_shape[2]) | |
) | |
if past_key_values[0] is not None: | |
# attention_mask needs to be sliced to the shape `[batch_size, 1, from_seq_length - cached_seq_length, to_seq_length] | |
extended_attention_mask = extended_attention_mask[:, :, -seq_length:, :] | |
else: | |
extended_attention_mask = tf.reshape( | |
attention_mask, (attention_mask_shape[0], 1, 1, attention_mask_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) | |
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000 | |
if self.is_decoder and encoder_attention_mask is not None: | |
# If a 2D ou 3D attention mask is provided for the cross-attention | |
# we need to make broadcastable to [batch_size, num_heads, mask_seq_length, mask_seq_length] | |
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] | |
encoder_attention_mask = tf.cast( | |
encoder_attention_mask, dtype=extended_attention_mask.dtype | |
) | |
num_dims_encoder_attention_mask = len(shape_list(encoder_attention_mask)) | |
if num_dims_encoder_attention_mask == 3: | |
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :] | |
if num_dims_encoder_attention_mask == 2: | |
encoder_extended_attention_mask = encoder_attention_mask[:, None, None, :] | |
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition | |
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow/transformer/transformer_layers.py#L270 | |
# encoder_extended_attention_mask = tf.math.equal(encoder_extended_attention_mask, | |
# tf.transpose(encoder_extended_attention_mask, perm=(-1, -2))) | |
encoder_extended_attention_mask = (1.0 - encoder_extended_attention_mask) * -10000.0 | |
else: | |
encoder_extended_attention_mask = None | |
# 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, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_extended_attention_mask, | |
past_key_values=past_key_values, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = encoder_outputs[0] | |
if not return_dict: | |
return ( | |
sequence_output, | |
) + encoder_outputs[1:] | |
return TFBaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=sequence_output, | |
past_key_values=encoder_outputs.past_key_values, | |
hidden_states=encoder_outputs.hidden_states, | |
attentions=encoder_outputs.attentions, | |
cross_attentions=encoder_outputs.cross_attentions, | |
) | |
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel): | |
"""An abstract class to handle weights initialization and | |
a simple interface for downloading and loading pretrained models. | |
""" | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
base_model_prefix = "{{cookiecutter.lowercase_modelname}}" | |
{{cookiecutter.uppercase_modelname}}_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 ([`~{{cookiecutter.camelcase_modelname}}Config`]): 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. | |
""" | |
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`np.ndarray`, `tf.Tensor`, `List[tf.Tensor]`, `Dict[str, tf.Tensor]` or `Dict[str, np.ndarray]` and each example must have the 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 (`np.ndarray` 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 (`np.ndarray` 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 (`np.ndarray` 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 (`np.ndarray` 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 (`np.ndarray` or `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 TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
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, | |
encoder_hidden_states: np.ndarray | tf.Tensor | None = None, | |
encoder_attention_mask: np.ndarray | tf.Tensor | None = None, | |
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, | |
use_cache: Optional[bool] = None, | |
output_attentions: Optional[bool] = None, | |
output_hidden_states: Optional[bool] = None, | |
return_dict: Optional[bool] = None, | |
training: Optional[bool] = False, | |
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]: | |
r""" | |
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if | |
the model is configured as a decoder. | |
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in | |
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) | |
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. | |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` | |
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)` | |
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up | |
decoding (see `past_key_values`). Set to `False` during training, `True` during generation | |
""" | |
outputs = self.{{cookiecutter.lowercase_modelname}}( | |
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, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
past_key_values=past_key_values, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
return outputs | |
class TF{{cookiecutter.camelcase_modelname}}ForMaskedLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMaskedLanguageModelingLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
if config.is_decoder: | |
logger.warning( | |
"If you want to use `TF{{cookiecutter.camelcase_modelname}}ForMaskedLM` make sure `config.is_decoder=False` for " | |
"bi-directional self-attention." | |
) | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls") | |
def get_lm_head(self) -> tf.keras.layers.Layer: | |
return self.mlm.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` or `np.ndarray` 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]` | |
""" | |
outputs = self.{{cookiecutter.lowercase_modelname}}( | |
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.mlm(sequence_output=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 TF{{cookiecutter.camelcase_modelname}}ForCausalLM(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFCausalLanguageModelingLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
if not config.is_decoder: | |
logger.warning("If you want to use `TF{{cookiecutter.camelcase_modelname}}ForCausalLM` as a standalone, add `is_decoder=True.`") | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
self.mlm = TF{{cookiecutter.camelcase_modelname}}MLMHead(config, input_embeddings=self.{{cookiecutter.lowercase_modelname}}.embeddings, name="mlm___cls") | |
def get_lm_head(self) -> tf.keras.layers.Layer: | |
return self.mlm.predictions | |
def prepare_inputs_for_generation(self, inputs, past_key_values=None, attention_mask=None, **model_kwargs): | |
# cut decoder_input_ids if past is used | |
if past_key_values: | |
inputs = tf.expand_dims(inputs[:, -1], -1) | |
return { | |
"input_ids": inputs, | |
"attention_mask": attention_mask, | |
"past_key_values": past_key_values, | |
"use_cache": model_kwargs["use_cache"], | |
} | |
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, | |
encoder_hidden_states: np.ndarray | tf.Tensor | None = None, | |
encoder_attention_mask: np.ndarray | tf.Tensor | None = None, | |
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None, | |
use_cache: Optional[bool] = 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[TFCausalLMOutputWithCrossAttentions, Tuple[tf.Tensor]]: | |
r""" | |
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if | |
the model is configured as a decoder. | |
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*): | |
Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in | |
the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`: | |
- 1 for tokens that are **not masked**, | |
- 0 for tokens that are **masked**. | |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) | |
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. | |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` | |
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)` | |
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up | |
decoding (see `past_key_values`). Set to `False` during training, `True` during generation | |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*): | |
Labels for computing the cross entropy classification loss. Indices should be in `[0, ..., config.vocab_size - 1]`. | |
""" | |
outputs = self.{{cookiecutter.lowercase_modelname}}( | |
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, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
past_key_values=past_key_values, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
sequence_output = outputs[0] | |
logits = self.mlm(sequence_output=sequence_output, training=training) | |
loss = None | |
if labels is not None: | |
# shift labels to the left and cut last logit token | |
shifted_logits = logits[:, :-1] | |
labels = labels[:, 1:] | |
loss = self.hf_compute_loss(labels=labels, logits=shifted_logits) | |
if not return_dict: | |
output = (logits,) + outputs[2:] | |
return ((loss,) + output) if loss is not None else output | |
return TFCausalLMOutputWithCrossAttentions( | |
loss=loss, | |
logits=logits, | |
past_key_values=outputs.past_key_values, | |
hidden_states=outputs.hidden_states, | |
attentions=outputs.attentions, | |
cross_attentions=outputs.cross_attentions, | |
) | |
class TF{{cookiecutter.camelcase_modelname}}ClassificationHead(tf.keras.layers.Layer): | |
"""Head for sentence-level classification tasks.""" | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.dense = tf.keras.layers.Dense( | |
units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense" | |
) | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob) | |
self.out_proj = tf.keras.layers.Dense( | |
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="out_proj" | |
) | |
if isinstance(config.hidden_act, str): | |
self.classifier_act_fn = get_tf_activation(config.hidden_act) | |
else: | |
self.classifier_act_fn = config.hidden_act | |
def call(self, hidden_states: tf.Tensor, training: bool = False) -> tf.Tensor: | |
hidden_states = hidden_states[:, 0, :] # take <s> token (equiv. to [CLS]) | |
hidden_states = self.dropout(inputs=hidden_states, training=training) | |
hidden_states = self.dense(inputs=hidden_states) | |
hidden_states = self.classifier_act_fn(hidden_states) | |
hidden_states = self.dropout(inputs=hidden_states, training=training) | |
hidden_states = self.out_proj(hidden_states) | |
return hidden_states | |
class TF{{cookiecutter.camelcase_modelname}}ForSequenceClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFSequenceClassificationLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
self.classifier = TF{{cookiecutter.camelcase_modelname}}ClassificationHead(config, 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` or `np.ndarray` 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.{{cookiecutter.lowercase_modelname}}( | |
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, | |
) | |
logits = self.classifier(hidden_states=outputs[0], training=training) | |
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits) | |
if not return_dict: | |
output = (logits,) + outputs[1:] | |
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 TF{{cookiecutter.camelcase_modelname}}ForMultipleChoice(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFMultipleChoiceLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
self.sequence_summary = TFSequenceSummary( | |
config, config.initializer_range, name="sequence_summary" | |
) | |
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` or `np.ndarray` 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(tensor=input_ids, shape=(-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.{{cookiecutter.lowercase_modelname}}( | |
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, | |
) | |
logits = self.sequence_summary(inputs=outputs[0], training=training) | |
logits = self.classifier(inputs=logits) | |
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[1:] | |
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, | |
) | |
class TF{{cookiecutter.camelcase_modelname}}ForTokenClassification(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFTokenClassificationLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
self.dropout = tf.keras.layers.Dropout(rate=config.hidden_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` or `np.ndarray` 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.{{cookiecutter.lowercase_modelname}}( | |
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[1:] | |
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 TF{{cookiecutter.camelcase_modelname}}ForQuestionAnswering(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel, TFQuestionAnsweringLoss): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.num_labels = config.num_labels | |
self.{{cookiecutter.lowercase_modelname}} = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="{{cookiecutter.lowercase_modelname}}") | |
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` or `np.ndarray` 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` or `np.ndarray` 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.{{cookiecutter.lowercase_modelname}}( | |
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, | |
) | |
{% else %} | |
import random | |
from typing import Optional, Tuple, Union | |
import tensorflow as tf | |
from ...activations_tf import get_tf_activation | |
from ...utils import ( | |
add_code_sample_docstrings, | |
add_start_docstrings, | |
add_start_docstrings_to_model_forward, | |
replace_return_docstrings, | |
) | |
from ...modeling_tf_outputs import ( | |
TFBaseModelOutput, | |
TFBaseModelOutputWithPastAndCrossAttentions, | |
TFSeq2SeqLMOutput, | |
TFSeq2SeqModelOutput, | |
) | |
# Public API | |
from ...modeling_tf_utils import ( | |
DUMMY_INPUTS, | |
TFPreTrainedModel, | |
keras_serializable, | |
unpack_inputs, | |
) | |
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax | |
from ...utils import ContextManagers, logging | |
from .configuration_{{cookiecutter.lowercase_modelname}} import {{cookiecutter.camelcase_modelname}}Config | |
logger = logging.get_logger(__name__) | |
_CHECKPOINT_FOR_DOC = "{{cookiecutter.checkpoint_identifier}}" | |
_CONFIG_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Config" | |
_TOKENIZER_FOR_DOC = "{{cookiecutter.camelcase_modelname}}Tokenizer" | |
LARGE_NEGATIVE = -1e8 | |
# Copied from transformers.models.bart.modeling_tf_bart.shift_tokens_right | |
def shift_tokens_right(input_ids: tf.Tensor, pad_token_id: int, decoder_start_token_id: int): | |
pad_token_id = tf.cast(pad_token_id, input_ids.dtype) | |
decoder_start_token_id = tf.cast(decoder_start_token_id, input_ids.dtype) | |
start_tokens = tf.fill((shape_list(input_ids)[0], 1), tf.convert_to_tensor(decoder_start_token_id, input_ids.dtype)) | |
shifted_input_ids = tf.concat([start_tokens, input_ids[:, :-1]], -1) | |
# replace possible -100 values in labels by `pad_token_id` | |
shifted_input_ids = tf.where( | |
shifted_input_ids == -100, | |
tf.fill(shape_list(shifted_input_ids), tf.convert_to_tensor(pad_token_id, input_ids.dtype)), | |
shifted_input_ids, | |
) | |
# "Verify that `labels` has only positive values and -100" | |
assert_gte0 = tf.debugging.assert_greater_equal(shifted_input_ids, tf.constant(0, dtype=shifted_input_ids.dtype)) | |
# Make sure the assertion op is called by wrapping the result in an identity no-op | |
with tf.control_dependencies([assert_gte0]): | |
shifted_input_ids = tf.identity(shifted_input_ids) | |
return shifted_input_ids | |
def _make_causal_mask(input_ids_shape: tf.TensorShape, past_key_values_length: int = 0): | |
""" | |
Make causal mask used for bi-directional self-attention. | |
""" | |
bsz, tgt_len = input_ids_shape | |
mask = tf.ones((tgt_len, tgt_len)) * LARGE_NEGATIVE | |
mask_cond = tf.range(shape_list(mask)[-1]) | |
mask = tf.where(mask_cond < tf.reshape(mask_cond + 1, (shape_list(mask)[-1], 1)), 0.0, mask) | |
if past_key_values_length > 0: | |
mask = tf.concat([tf.zeros((tgt_len, past_key_values_length)), mask], axis=-1) | |
return tf.tile(mask[None, None, :, :], (bsz, 1, 1, 1)) | |
def _expand_mask(mask: tf.Tensor, tgt_len: Optional[int] = None): | |
""" | |
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`. | |
""" | |
src_len = shape_list(mask)[1] | |
tgt_len = tgt_len if tgt_len is not None else src_len | |
one_cst = tf.constant(1.0) | |
mask = tf.cast(mask, dtype=one_cst.dtype) | |
expanded_mask = tf.tile(mask[:, None, None, :], (1, 1, tgt_len, 1)) | |
return (one_cst - expanded_mask) * LARGE_NEGATIVE | |
class TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding(tf.keras.layers.Embedding): | |
""" | |
This module learns positional embeddings up to a fixed maximum size. | |
""" | |
def __init__(self, num_embeddings: int, embedding_dim: int, **kwargs): | |
super().__init__(num_embeddings, embedding_dim, **kwargs) | |
def call(self, input_shape: tf.TensorShape, past_key_values_length: int = 0): | |
"""Input is expected to be of size [bsz x seqlen].""" | |
seq_len = input_shape[1] | |
position_ids = tf.range(seq_len, delta=1, name="range") | |
position_ids += past_key_values_length | |
return super().call(tf.cast(position_ids, dtype=tf.int32)) | |
class TF{{cookiecutter.camelcase_modelname}}Attention(tf.keras.layers.Layer): | |
"""Multi-headed attention from "Attention Is All You Need""" | |
def __init__( | |
self, | |
embed_dim: int, | |
num_heads: int, | |
dropout: float = 0.0, | |
is_decoder: bool = False, | |
bias: bool = True, | |
**kwargs, | |
): | |
super().__init__(**kwargs) | |
self.embed_dim = embed_dim | |
self.num_heads = num_heads | |
self.dropout = tf.keras.layers.Dropout(dropout) | |
self.head_dim = embed_dim // num_heads | |
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads" | |
self.scaling = self.head_dim ** -0.5 | |
self.is_decoder = is_decoder | |
self.k_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="k_proj") | |
self.q_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="q_proj") | |
self.v_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="v_proj") | |
self.out_proj = tf.keras.layers.Dense(embed_dim, use_bias=bias, name="out_proj") | |
def _shape(self, tensor: tf.Tensor, seq_len: int, bsz: int): | |
return tf.transpose(tf.reshape(tensor, (bsz, seq_len, self.num_heads, self.head_dim)), (0, 2, 1, 3)) | |
def call( | |
self, | |
hidden_states: tf.Tensor, | |
key_value_states: tf.Tensor | None = None, | |
past_key_value: Tuple[Tuple[tf.Tensor]] | None = None, | |
attention_mask: tf.Tensor | None = None, | |
layer_head_mask: tf.Tensor | None = None, | |
training=False, | |
) -> Tuple[tf.Tensor, tf.Tensor | None]: | |
"""Input shape: Batch x Time x Channel""" | |
# if key_value_states are provided this layer is used as a cross-attention layer | |
# for the decoder | |
is_cross_attention = key_value_states is not None | |
bsz, tgt_len, embed_dim = shape_list(hidden_states) | |
# get query proj | |
query_states = self.q_proj(hidden_states) * self.scaling | |
# get key, value proj | |
if is_cross_attention and past_key_value is not None: | |
# reuse k,v, cross_attentions | |
key_states = past_key_value[0] | |
value_states = past_key_value[1] | |
elif is_cross_attention: | |
# cross_attentions | |
key_states = self._shape(self.k_proj(key_value_states), -1, bsz) | |
value_states = self._shape(self.v_proj(key_value_states), -1, bsz) | |
elif past_key_value is not None: | |
# reuse k, v, self_attention | |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) | |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | |
key_states = tf.concat([past_key_value[0], key_states], axis=2) | |
value_states = tf.concat([past_key_value[1], value_states], axis=2) | |
else: | |
# self_attention | |
key_states = self._shape(self.k_proj(hidden_states), -1, bsz) | |
value_states = self._shape(self.v_proj(hidden_states), -1, bsz) | |
if self.is_decoder: | |
# if cross_attention save Tuple(tf.Tensor, tf.Tensor) of all cross attention key/value_states. | |
# Further calls to cross_attention layer can then reuse all cross-attention | |
# key/value_states (first "if" case) | |
# if uni-directional self-attention (decoder) save Tuple(tf.Tensor, tf.Tensor) of | |
# all previous decoder key/value_states. Further calls to uni-directional self-attention | |
# can concat previous decoder key/value_states to current projected key/value_states (third "elif" case) | |
# if encoder bi-directional self-attention `past_key_value` is always `None` | |
past_key_value = (key_states, value_states) | |
proj_shape = (bsz * self.num_heads, -1, self.head_dim) | |
query_states = tf.reshape(self._shape(query_states, tgt_len, bsz), proj_shape) | |
key_states = tf.reshape(key_states, proj_shape) | |
value_states = tf.reshape(value_states, proj_shape) | |
src_len = shape_list(key_states)[1] | |
attn_weights = tf.matmul(query_states, key_states, transpose_b=True) | |
tf.debugging.assert_equal( | |
shape_list(attn_weights), | |
[bsz * self.num_heads, tgt_len, src_len], | |
message=f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {shape_list(attn_weights)}", | |
) | |
if attention_mask is not None: | |
tf.debugging.assert_equal( | |
shape_list(attention_mask), | |
[bsz, 1, tgt_len, src_len], | |
message=f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {shape_list(attention_mask)}", | |
) | |
attn_weights = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) + attention_mask | |
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) | |
attn_weights = stable_softmax(attn_weights, axis=-1) | |
if layer_head_mask is not None: | |
tf.debugging.assert_equal( | |
shape_list(layer_head_mask), | |
[self.num_heads], | |
message=f"Head mask for a single layer should be of size {(self.num_heads)}, but is {shape_list(layer_head_mask)}", | |
) | |
attn_weights = tf.reshape(layer_head_mask, (1, -1, 1, 1)) * tf.reshape( | |
attn_weights, (bsz, self.num_heads, tgt_len, src_len) | |
) | |
attn_weights = tf.reshape(attn_weights, (bsz * self.num_heads, tgt_len, src_len)) | |
attn_probs = self.dropout(attn_weights, training=training) | |
attn_output = tf.matmul(attn_probs, value_states) | |
tf.debugging.assert_equal( | |
shape_list(attn_output), | |
[bsz * self.num_heads, tgt_len, self.head_dim], | |
message=f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {shape_list(attn_output)}", | |
) | |
attn_output = tf.transpose( | |
tf.reshape(attn_output, (bsz, self.num_heads, tgt_len, self.head_dim)), (0, 2, 1, 3) | |
) | |
attn_output = tf.reshape(attn_output, (bsz, tgt_len, embed_dim)) | |
attn_output = self.out_proj(attn_output) | |
attn_weights: tf.Tensor = tf.reshape(attn_weights, (bsz, self.num_heads, tgt_len, src_len)) | |
return attn_output, attn_weights, past_key_value | |
class TF{{cookiecutter.camelcase_modelname}}EncoderLayer(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.embed_dim = config.d_model | |
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention( | |
self.embed_dim, config.encoder_attention_heads, dropout=config.attention_dropout, name="self_attn" | |
) | |
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") | |
self.dropout = tf.keras.layers.Dropout(config.dropout) | |
self.activation_fn = get_tf_activation(config.activation_function) | |
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) | |
self.fc1 = tf.keras.layers.Dense(config.encoder_ffn_dim, name="fc1") | |
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") | |
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") | |
def call(self, hidden_states: tf.Tensor, attention_mask: tf.Tensor, layer_head_mask: tf.Tensor, training=False): | |
""" | |
Args: | |
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)* | |
attention_mask (`tf.Tensor`): attention mask of size | |
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. | |
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size | |
*(encoder_attention_heads,)* | |
""" | |
residual = hidden_states | |
hidden_states, self_attn_weights, _ = self.self_attn( | |
hidden_states=hidden_states, attention_mask=attention_mask, layer_head_mask=layer_head_mask | |
) | |
tf.debugging.assert_equal( | |
shape_list(hidden_states), | |
shape_list(residual), | |
message=f"Self attn modified the shape of query {shape_list(residual)} to {shape_list(hidden_states)}", | |
) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = self.activation_dropout(hidden_states, training=training) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
return hidden_states, self_attn_weights | |
class TF{{cookiecutter.camelcase_modelname}}DecoderLayer(tf.keras.layers.Layer): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.embed_dim = config.d_model | |
self.self_attn = TF{{cookiecutter.camelcase_modelname}}Attention( | |
embed_dim=self.embed_dim, | |
num_heads=config.decoder_attention_heads, | |
dropout=config.attention_dropout, | |
name="self_attn", | |
is_decoder=True, | |
) | |
self.dropout = tf.keras.layers.Dropout(config.dropout) | |
self.activation_fn = get_tf_activation(config.activation_function) | |
self.activation_dropout = tf.keras.layers.Dropout(config.activation_dropout) | |
self.self_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="self_attn_layer_norm") | |
self.encoder_attn = TF{{cookiecutter.camelcase_modelname}}Attention( | |
self.embed_dim, | |
config.decoder_attention_heads, | |
dropout=config.attention_dropout, | |
name="encoder_attn", | |
is_decoder=True, | |
) | |
self.encoder_attn_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="encoder_attn_layer_norm") | |
self.fc1 = tf.keras.layers.Dense(config.decoder_ffn_dim, name="fc1") | |
self.fc2 = tf.keras.layers.Dense(self.embed_dim, name="fc2") | |
self.final_layer_norm = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="final_layer_norm") | |
def call( | |
self, | |
hidden_states, | |
attention_mask: tf.Tensor | None = None, | |
encoder_hidden_states: tf.Tensor | None = None, | |
encoder_attention_mask: tf.Tensor | None = None, | |
layer_head_mask: tf.Tensor | None = None, | |
cross_attn_layer_head_mask: tf.Tensor | None = None, | |
past_key_value: Tuple[tf.Tensor] | None = None, | |
training=False, | |
) -> Tuple[tf.Tensor, tf.Tensor, Tuple[Tuple[tf.Tensor]]]: | |
""" | |
Args: | |
hidden_states (`tf.Tensor`): input to the layer of shape *(batch, seq_len, embed_dim)* | |
attention_mask (`tf.Tensor`): attention mask of size | |
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. | |
encoder_hidden_states (`tf.Tensor`): cross attention input to the layer of shape *(batch, seq_len, embed_dim)* | |
encoder_attention_mask (`tf.Tensor`): encoder attention mask of size | |
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values. | |
layer_head_mask (`tf.Tensor`): mask for attention heads in a given layer of size | |
*(decoder_attention_heads,)* | |
cross_attn_layer_head_mask (`tf.Tensor`): mask for heads of the cross-attention module. | |
*(decoder_attention_heads,)* | |
past_key_value (`Tuple(tf.Tensor)`): cached past key and value projection states | |
""" | |
residual = hidden_states | |
# Self Attention | |
# decoder uni-directional self-attention cached key/values tuple is at positions 1,2 | |
self_attn_past_key_value = past_key_value[:2] if past_key_value is not None else None | |
# add present self-attn cache to positions 1,2 of present_key_value tuple | |
hidden_states, self_attn_weights, present_key_value = self.self_attn( | |
hidden_states=hidden_states, | |
past_key_value=self_attn_past_key_value, | |
attention_mask=attention_mask, | |
layer_head_mask=layer_head_mask, | |
) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.self_attn_layer_norm(hidden_states) | |
# Cross-Attention Block | |
cross_attn_present_key_value = None | |
cross_attn_weights = None | |
if encoder_hidden_states is not None: | |
residual = hidden_states | |
# cross_attn cached key/values tuple is at positions 3,4 of present_key_value tuple | |
cross_attn_past_key_value = past_key_value[-2:] if past_key_value is not None else None | |
hidden_states, cross_attn_weights, cross_attn_present_key_value = self.encoder_attn( | |
hidden_states=hidden_states, | |
key_value_states=encoder_hidden_states, | |
attention_mask=encoder_attention_mask, | |
layer_head_mask=cross_attn_layer_head_mask, | |
past_key_value=cross_attn_past_key_value, | |
) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.encoder_attn_layer_norm(hidden_states) | |
# add cross-attn to positions 3,4 of present_key_value tuple | |
present_key_value = present_key_value + cross_attn_present_key_value | |
# Fully Connected | |
residual = hidden_states | |
hidden_states = self.activation_fn(self.fc1(hidden_states)) | |
hidden_states = self.activation_dropout(hidden_states, training=training) | |
hidden_states = self.fc2(hidden_states) | |
hidden_states = self.dropout(hidden_states, training=training) | |
hidden_states = residual + hidden_states | |
hidden_states = self.final_layer_norm(hidden_states) | |
return ( | |
hidden_states, | |
self_attn_weights, | |
cross_attn_weights, | |
present_key_value, | |
) | |
class TF{{cookiecutter.camelcase_modelname}}PreTrainedModel(TFPreTrainedModel): | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
base_model_prefix = "model" | |
{{cookiecutter.uppercase_modelname}}_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 ([`~{{cookiecutter.camelcase_modelname}}Config`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the | |
model weights. | |
""" | |
{{cookiecutter.uppercase_modelname}}_INPUTS_DOCSTRING = r""" | |
Args: | |
input_ids (`tf.Tensor` of shape `({0})`): | |
Indices of input sequence tokens in the vocabulary. | |
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See | |
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for | |
details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`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) | |
decoder_input_ids (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
Indices of decoder input sequence tokens in the vocabulary. | |
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See | |
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for | |
details. | |
[What are input IDs?](../glossary#input-ids) | |
{{cookiecutter.camelcase_modelname}} uses the `eos_token_id` as the starting token for | |
`decoder_input_ids` generation. If `past_key_values` is used, optionally only the last | |
`decoder_input_ids` have to be input (see `past_key_values`). | |
For translation and summarization training, `decoder_input_ids` should be provided. If no | |
`decoder_input_ids` is provided, the model will create this tensor by shifting the `input_ids` to | |
the right for denoising pre-training following the paper. | |
decoder_attention_mask (`tf.Tensor` of shape `(batch_size, target_sequence_length)`, *optional*): | |
will be made by default and ignore pad tokens. It is not recommended to set this for most use cases. | |
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, *optional*): | |
Mask to nullify selected heads of the attention modules in the encoder. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
decoder_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): | |
Mask to nullify selected heads of the attention modules in the decoder. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): | |
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
encoder_outputs (`tf.FloatTensor`, *optional*): | |
hidden states at the output of the last layer of the encoder. Used in the cross-attention of the decoder. | |
of shape `(batch_size, sequence_length, hidden_size)` is a sequence of | |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers`) | |
contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. | |
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` | |
(those that don't have their past key value states given to this model) of shape `(batch_size, 1)` | |
instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. | |
use_cache (`bool`, *optional*, defaults to `True`): | |
If set to `True`, `past_key_values` key value states are returned and can be used to speed up | |
decoding (see `past_key_values`). Set to `False` during training, `True` during generation | |
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 TF{{cookiecutter.camelcase_modelname}}Encoder(tf.keras.layers.Layer): | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
""" | |
Transformer encoder consisting of *config.encoder_layers* self attention layers. Each layer is a | |
[`TF{{cookiecutter.camelcase_modelname}}EncoderLayer`]. | |
Args: | |
config: {{cookiecutter.camelcase_modelname}}Config | |
""" | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.dropout = tf.keras.layers.Dropout(config.dropout) | |
self.layerdrop = config.encoder_layerdrop | |
self.padding_idx = config.pad_token_id | |
self.max_source_positions = config.max_position_embeddings | |
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 | |
self.embed_tokens = embed_tokens | |
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding( | |
config.max_position_embeddings, | |
config.d_model, | |
name="embed_positions", | |
) | |
self.layers = [TF{{cookiecutter.camelcase_modelname}}EncoderLayer(config, name=f"layers.{i}") for i in range(config.encoder_layers)] | |
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding") | |
def get_embed_tokens(self): | |
return self.embed_tokens | |
def set_embed_tokens(self, embed_tokens): | |
self.embed_tokens = embed_tokens | |
def call( | |
self, | |
input_ids=None, | |
inputs_embeds=None, | |
attention_mask=None, | |
head_mask=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
training=False, | |
): | |
""" | |
Args: | |
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you | |
provide it. | |
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See | |
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] | |
for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *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) | |
head_mask (`tf.Tensor` of shape `(encoder_layers, encoder_attention_heads)`, `optional): Mask to nullify selected heads of the 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 `(batch_size, sequence_length, 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). | |
""" | |
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 inputs_embeds is None: | |
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name | |
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope` | |
# is used with a name ending in `/`, that name replaces the current name scope. | |
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0) | |
context = [] | |
if hasattr(self.embed_tokens, "load_weight_prefix"): | |
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) | |
with ContextManagers(context): | |
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim) | |
inputs_embeds = self.embed_tokens(input_ids) * self.embed_scale | |
embed_pos = self.embed_positions(input_shape) | |
hidden_states = inputs_embeds + embed_pos | |
hidden_states = self.layernorm_embedding(hidden_states) | |
hidden_states = self.dropout(hidden_states, training=training) | |
# check attention mask and invert | |
if attention_mask is not None: | |
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] | |
attention_mask = _expand_mask(attention_mask) | |
encoder_states = () if output_hidden_states else None | |
all_attentions = () if output_attentions else None | |
# check if head_mask has a correct number of layers specified if desired | |
if head_mask is not None: | |
tf.debugging.assert_equal( | |
shape_list(head_mask)[0], | |
len(self.layers), | |
message=f"The head_mask should be specified for {len(self.layers)} layers, but it is for {shape_list(head_mask)[0]}.", | |
) | |
# encoder layers | |
for idx, encoder_layer in enumerate(self.layers): | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
dropout_probability = random.uniform(0, 1) | |
if training and (dropout_probability < self.layerdrop): # skip the layer | |
continue | |
hidden_states, attn = encoder_layer( | |
hidden_states, | |
attention_mask, | |
head_mask[idx] if head_mask is not None else None, | |
) | |
if output_attentions: | |
all_attentions += (attn,) | |
if output_hidden_states: | |
encoder_states = encoder_states + (hidden_states,) | |
if not return_dict: | |
return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) | |
return TFBaseModelOutput( | |
last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions | |
) | |
class TF{{cookiecutter.camelcase_modelname}}Decoder(tf.keras.layers.Layer): | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
""" | |
Transformer decoder consisting of *config.decoder_layers* layers. Each layer is a [`TF{{cookiecutter.camelcase_modelname}}DecoderLayer`] | |
Args: | |
config: {{cookiecutter.camelcase_modelname}}Config | |
embed_tokens: output embedding | |
""" | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, embed_tokens: Optional[tf.keras.layers.Embedding] = None, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.padding_idx = config.pad_token_id | |
self.embed_tokens = embed_tokens | |
self.layerdrop = config.decoder_layerdrop | |
self.embed_positions = TF{{cookiecutter.camelcase_modelname}}LearnedPositionalEmbedding( | |
config.max_position_embeddings, | |
config.d_model, | |
name="embed_positions", | |
) | |
self.embed_scale = tf.math.sqrt(float(config.d_model)) if config.scale_embedding else 1.0 | |
self.layers = [TF{{cookiecutter.camelcase_modelname}}DecoderLayer(config, name=f"layers.{i}") for i in range(config.decoder_layers)] | |
self.layernorm_embedding = tf.keras.layers.LayerNormalization(epsilon=1e-5, name="layernorm_embedding") | |
self.dropout = tf.keras.layers.Dropout(config.dropout) | |
def get_embed_tokens(self): | |
return self.embed_tokens | |
def set_embed_tokens(self, embed_tokens): | |
self.embed_tokens = embed_tokens | |
def call( | |
self, | |
input_ids=None, | |
inputs_embeds=None, | |
attention_mask=None, | |
encoder_hidden_states=None, | |
encoder_attention_mask=None, | |
head_mask=None, | |
cross_attn_head_mask=None, | |
past_key_values=None, | |
use_cache=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
training=False, | |
): | |
r""" | |
Args: | |
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`): | |
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you | |
provide it. | |
Indices can be obtained using [`~{{cookiecutter.camelcase_modelname}}Tokenizer`]. See | |
[`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] | |
for details. | |
[What are input IDs?](../glossary#input-ids) | |
attention_mask (`tf.Tensor` of shape `(batch_size, sequence_length)`, *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) | |
encoder_hidden_states (`tf.Tensor` of shape `(batch_size, encoder_sequence_length, hidden_size)`, *optional*): | |
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention | |
of the decoder. | |
encoder_attention_mask (`tf.Tensor` of shape `(batch_size, encoder_sequence_length)`, *optional*): | |
Mask to avoid performing cross-attention on padding tokens indices of encoder input_ids. 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) | |
head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): | |
Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
cross_attn_head_mask (`tf.Tensor` of shape `(decoder_layers, decoder_attention_heads)`, *optional*): | |
Mask to nullify selected heads of the cross-attention modules. Mask values selected in `[0, 1]`: | |
- 1 indicates the head is **not masked**, | |
- 0 indicates the head is **masked**. | |
past_key_values (`Tuple[Tuple[tf.Tensor]]` of length `config.n_layers` with each tuple having 2 tuples each of which has 2 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): | |
Contains precomputed key and value hidden-states of the attention blocks. Can be used to speed up | |
decoding. | |
If `past_key_values` are used, the user can optionally input only the last | |
`decoder_input_ids` (those that don't have their past key value states given to this model) of | |
shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, | |
sequence_length)`. | |
inputs_embeds (`tf.Tensor` of shape `(batch_size, sequence_length, 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). | |
""" | |
if input_ids is not None and inputs_embeds is not None: | |
raise ValueError("You cannot specify both decoder_input_ids and decoder_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 decoder_input_ids or decoder_inputs_embeds") | |
past_key_values_length = ( | |
shape_list(past_key_values[0][0])[2] if past_key_values is not None else 0 | |
) | |
# embed positions | |
positions = self.embed_positions(input_shape, past_key_values_length) | |
if inputs_embeds is None: | |
# if `self.embed_tokens.load_weight_prefix` is set, runs the embedding operation with the correct name | |
# scope, so that its weights are registered with the desired name for loading/storing. When `tf.name_scope` | |
# is used with a name ending in `/`, that name replaces the current name scope. | |
# (embeddings with tf.name_scope: self.embed_tokens.load_weight_prefix/self.embed_tokens.name/embeddings:0) | |
context = [] | |
if hasattr(self.embed_tokens, "load_weight_prefix"): | |
context.append(tf.name_scope(self.embed_tokens.load_weight_prefix + "/")) | |
with ContextManagers(context): | |
check_embeddings_within_bounds(input_ids, self.embed_tokens.input_dim) | |
inputs_embeds = self.embed_tokens(input_ids) | |
hidden_states = inputs_embeds | |
attention_mask, combined_attention_mask = self.compute_combined_attns_mask( | |
input_ids, attention_mask, input_shape, past_key_values_length | |
) | |
if encoder_hidden_states is not None and encoder_attention_mask is not None: | |
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] | |
encoder_attention_mask = _expand_mask(encoder_attention_mask, tgt_len=input_shape[-1]) | |
hidden_states = self.layernorm_embedding(hidden_states + positions) | |
hidden_states = self.dropout(hidden_states, training=training) | |
# decoder layers | |
all_hidden_states = () if output_hidden_states else None | |
all_self_attns = () if output_attentions else None | |
all_cross_attns = () if (output_attentions and encoder_hidden_states is not None) else None | |
present_key_values = () if use_cache else None | |
# check if head_mask and cross_attn_head_mask have a correct number of layers specified if desired | |
for attn_mask_name, attn_mask in [("head_mask", head_mask), ("cross_attn_head_mask", cross_attn_head_mask)]: | |
if attn_mask is not None: | |
tf.debugging.assert_equal( | |
shape_list(attn_mask)[0], | |
len(self.layers), | |
message=f"The {attn_mask_name} should be specified for {len(self.layers)} layers, but it is for {shape_list(attn_mask)[0]}.", | |
) | |
for idx, decoder_layer in enumerate(self.layers): | |
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description) | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
dropout_probability = random.uniform(0, 1) | |
if training and (dropout_probability < self.layerdrop): | |
continue | |
past_key_value = past_key_values[idx] if past_key_values is not None else None | |
hidden_states, layer_self_attn, layer_cross_attn, present_key_value = decoder_layer( | |
hidden_states, | |
attention_mask=combined_attention_mask, | |
encoder_hidden_states=encoder_hidden_states, | |
encoder_attention_mask=encoder_attention_mask, | |
layer_head_mask=head_mask[idx] if head_mask is not None else None, | |
cross_attn_layer_head_mask=cross_attn_head_mask[idx] | |
if cross_attn_head_mask is not None | |
else None, | |
past_key_value=past_key_value, | |
) | |
if use_cache: | |
present_key_values += (present_key_value,) | |
if output_attentions: | |
all_self_attns += (layer_self_attn,) | |
if encoder_hidden_states is not None: | |
all_cross_attns += (layer_cross_attn,) | |
if output_hidden_states: | |
all_hidden_states += (hidden_states,) | |
if not return_dict: | |
return hidden_states, present_key_values, all_hidden_states, all_self_attns, all_cross_attns | |
else: | |
return TFBaseModelOutputWithPastAndCrossAttentions( | |
last_hidden_state=hidden_states, | |
past_key_values=present_key_values, | |
hidden_states=all_hidden_states, | |
attentions=all_self_attns, | |
cross_attentions=all_cross_attns, | |
) | |
def compute_combined_attns_mask(self, input_ids, attention_mask, input_shape, past_key_values_length): | |
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len] | |
combined_attention_mask = None | |
if input_shape[-1] > 1: | |
combined_attention_mask = _make_causal_mask(input_shape, past_key_values_length=past_key_values_length) | |
else: | |
combined_attention_mask = _expand_mask( | |
tf.ones((input_shape[0], input_shape[1] + past_key_values_length)), tgt_len=input_shape[-1] | |
) | |
if attention_mask is None and input_ids is not None and input_shape[-1] > 1: | |
attention_mask = tf.cast( | |
tf.math.not_equal(input_ids, self.config.pad_token_id), input_ids.dtype | |
) | |
attention_mask = tf.concat( | |
[ | |
tf.ones((input_shape[0], past_key_values_length), dtype=attention_mask.dtype), | |
attention_mask, | |
], | |
axis=-1, | |
) | |
else: | |
attention_mask = tf.ones((input_shape[0], input_shape[1] + past_key_values_length)) | |
return attention_mask, combined_attention_mask | |
class TF{{cookiecutter.camelcase_modelname}}MainLayer(tf.keras.layers.Layer): | |
config_class = {{cookiecutter.camelcase_modelname}}Config | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, **kwargs): | |
super().__init__(**kwargs) | |
self.config = config | |
self.shared = tf.keras.layers.Embedding( | |
input_dim=config.vocab_size, | |
output_dim=config.d_model, | |
embeddings_initializer=tf.keras.initializers.TruncatedNormal(stddev=self.config.init_std), | |
name="model.shared" | |
) | |
# Additional attribute to specify the expected name scope of the layer (for loading/storing weights) | |
self.shared.load_weight_prefix = "model.shared" | |
self.encoder = TF{{cookiecutter.camelcase_modelname}}Encoder(config, self.shared, name="encoder") | |
self.decoder = TF{{cookiecutter.camelcase_modelname}}Decoder(config, self.shared, name="decoder") | |
def get_input_embeddings(self): | |
return self.shared | |
def set_input_embeddings(self, new_embeddings): | |
self.shared = new_embeddings | |
self.encoder.embed_tokens = self.shared | |
self.decoder.embed_tokens = self.shared | |
def call( | |
self, | |
input_ids=None, | |
attention_mask=None, | |
decoder_input_ids=None, | |
decoder_attention_mask=None, | |
head_mask=None, | |
decoder_head_mask=None, | |
cross_attn_head_mask=None, | |
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, | |
past_key_values=None, | |
inputs_embeds=None, | |
decoder_inputs_embeds=None, | |
use_cache=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
training=False, | |
**kwargs | |
): | |
if decoder_input_ids is None and decoder_inputs_embeds is None: | |
use_cache = False | |
if encoder_outputs is None: | |
encoder_outputs = self.encoder( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
head_mask=head_mask, | |
inputs_embeds=inputs_embeds, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
# If the user passed a tuple for encoder_outputs, we wrap it in a TFBaseModelOutput when return_dict=True | |
elif return_dict and not isinstance(encoder_outputs, TFBaseModelOutput): | |
encoder_outputs = TFBaseModelOutput( | |
last_hidden_state=encoder_outputs[0], | |
hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, | |
attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None, | |
) | |
# If the user passed a TFBaseModelOutput for encoder_outputs, we wrap it in a tuple when return_dict=False | |
elif not return_dict and not isinstance(encoder_outputs, tuple): | |
encoder_outputs = encoder_outputs.to_tuple() | |
decoder_outputs = self.decoder( | |
decoder_input_ids, | |
attention_mask=decoder_attention_mask, | |
encoder_hidden_states=encoder_outputs[0], | |
encoder_attention_mask=attention_mask, | |
head_mask=decoder_head_mask, | |
cross_attn_head_mask=cross_attn_head_mask, | |
past_key_values=past_key_values, | |
inputs_embeds=decoder_inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
if not return_dict: | |
return decoder_outputs + encoder_outputs | |
return TFSeq2SeqModelOutput( | |
last_hidden_state=decoder_outputs.last_hidden_state, | |
past_key_values=decoder_outputs.past_key_values, | |
decoder_hidden_states=decoder_outputs.hidden_states, | |
decoder_attentions=decoder_outputs.attentions, | |
cross_attentions=decoder_outputs.cross_attentions, | |
encoder_last_hidden_state=encoder_outputs.last_hidden_state, | |
encoder_hidden_states=encoder_outputs.hidden_states, | |
encoder_attentions=encoder_outputs.attentions, | |
) | |
class TF{{cookiecutter.camelcase_modelname}}Model(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel): | |
def __init__(self, config: {{cookiecutter.camelcase_modelname}}Config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model") | |
def get_encoder(self): | |
return self.model.encoder | |
def get_decoder(self): | |
return self.model.decoder | |
def call( | |
self, | |
input_ids=None, | |
attention_mask=None, | |
decoder_input_ids=None, | |
decoder_attention_mask=None, | |
head_mask=None, | |
decoder_head_mask=None, | |
cross_attn_head_mask=None, | |
encoder_outputs: Optional[Union[Tuple, TFBaseModelOutput]] = None, | |
past_key_values=None, | |
inputs_embeds=None, | |
decoder_inputs_embeds=None, | |
use_cache=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
training=False, | |
**kwargs | |
): | |
outputs = self.model( | |
input_ids=input_ids, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
decoder_attention_mask=decoder_attention_mask, | |
head_mask=head_mask, | |
decoder_head_mask=decoder_head_mask, | |
cross_attn_head_mask=cross_attn_head_mask, | |
encoder_outputs=encoder_outputs, | |
past_key_values=past_key_values, | |
inputs_embeds=inputs_embeds, | |
decoder_inputs_embeds=decoder_inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training, | |
) | |
return outputs | |
# Copied from transformers.models.bart.modeling_tf_bart.BiasLayer | |
class BiasLayer(tf.keras.layers.Layer): | |
""" | |
Bias as a layer. It is used for serialization purposes: `tf.keras.Model.save_weights` stores on a per-layer basis, | |
so all weights have to be registered in a layer. | |
""" | |
def __init__(self, shape, initializer, trainable, name, **kwargs): | |
super().__init__(name=name, **kwargs) | |
# Note: the name of this variable will NOT be scoped when serialized, i.e. it will not be in the format of | |
# "outer_layer/inner_layer/.../name:0". Instead, it will be "name:0". For further details, see: | |
# https://github.com/huggingface/transformers/pull/18833#issuecomment-1233090214 | |
self.bias = self.add_weight(name=name, shape=shape, initializer=initializer, trainable=trainable) | |
def call(self, x): | |
return x + self.bias | |
class TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration(TF{{cookiecutter.camelcase_modelname}}PreTrainedModel): | |
_keys_to_ignore_on_load_unexpected = [ | |
r"model.encoder.embed_tokens.weight", | |
r"model.decoder.embed_tokens.weight", | |
] | |
def __init__(self, config, *inputs, **kwargs): | |
super().__init__(config, *inputs, **kwargs) | |
self.model = TF{{cookiecutter.camelcase_modelname}}MainLayer(config, name="model") | |
self.use_cache = config.use_cache | |
# final_bias_logits is registered as a buffer in pytorch, so not trainable for the sake of consistency. | |
self.bias_layer = BiasLayer( | |
name="final_logits_bias", shape=[1, config.vocab_size], initializer="zeros", trainable=False | |
) | |
def get_decoder(self): | |
return self.model.decoder | |
def get_encoder(self): | |
return self.model.encoder | |
def get_bias(self): | |
return {"final_logits_bias": self.bias_layer.bias} | |
def set_bias(self, value): | |
# Replaces the existing layers containing bias for correct (de)serialization. | |
vocab_size = value["final_logits_bias"].shape[-1] | |
self.bias_layer = BiasLayer( | |
name="final_logits_bias", shape=[1, vocab_size], initializer="zeros", trainable=False | |
) | |
self.bias_layer.bias.assign(value["final_logits_bias"]) | |
def get_output_embeddings(self): | |
return self.get_input_embeddings() | |
def set_output_embeddings(self, value): | |
self.set_input_embeddings(value) | |
def call( | |
self, | |
input_ids=None, | |
attention_mask=None, | |
decoder_input_ids=None, | |
decoder_attention_mask=None, | |
head_mask=None, | |
decoder_head_mask=None, | |
cross_attn_head_mask=None, | |
encoder_outputs: Optional[TFBaseModelOutput] = None, | |
past_key_values=None, | |
inputs_embeds=None, | |
decoder_inputs_embeds=None, | |
use_cache=None, | |
output_attentions=None, | |
output_hidden_states=None, | |
return_dict=None, | |
labels=None, | |
training=False, | |
): | |
""" | |
Returns: | |
Examples: | |
```python | |
>>> from transformers import {{cookiecutter.camelcase_modelname}}Tokenizer, TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration | |
>>> import tensorflow as tf | |
>>> mname = '{{cookiecutter.checkpoint_identifier}}' | |
>>> tokenizer = {{cookiecutter.camelcase_modelname}}Tokenizer.from_pretrained(mname) | |
>>> TXT = "My friends are <mask> but they eat too many carbs." | |
>>> model = TF{{cookiecutter.camelcase_modelname}}ForConditionalGeneration.from_pretrained(mname) | |
>>> batch = tokenizer([TXT], return_tensors='tf') | |
>>> logits = model(inputs=batch.input_ids).logits | |
>>> probs = tf.nn.softmax(logits[0]) | |
>>> # probs[5] is associated with the mask token | |
```""" | |
if labels is not None: | |
use_cache = False | |
if decoder_input_ids is None and decoder_inputs_embeds is None: | |
decoder_input_ids = shift_tokens_right( | |
labels, self.config.pad_token_id, self.config.decoder_start_token_id | |
) | |
outputs = self.model( | |
input_ids, | |
attention_mask=attention_mask, | |
decoder_input_ids=decoder_input_ids, | |
encoder_outputs=encoder_outputs, | |
decoder_attention_mask=decoder_attention_mask, | |
head_mask=head_mask, | |
decoder_head_mask=decoder_head_mask, | |
cross_attn_head_mask=cross_attn_head_mask, | |
past_key_values=past_key_values, | |
inputs_embeds=inputs_embeds, | |
decoder_inputs_embeds=decoder_inputs_embeds, | |
use_cache=use_cache, | |
output_attentions=output_attentions, | |
output_hidden_states=output_hidden_states, | |
return_dict=return_dict, | |
training=training | |
) | |
lm_logits = tf.matmul(outputs[0], self.model.shared.weights, transpose_b=True) | |
lm_logits = self.bias_layer(lm_logits) | |
masked_lm_loss = None if labels is None else self.hf_compute_loss(labels, lm_logits) | |
if not return_dict: | |
output = (lm_logits,) + outputs[1:] | |
return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output | |
return TFSeq2SeqLMOutput( | |
loss=masked_lm_loss, | |
logits=lm_logits, | |
past_key_values=outputs.past_key_values, # index 1 of d outputs | |
decoder_hidden_states=outputs.decoder_hidden_states, # index 2 of d outputs | |
decoder_attentions=outputs.decoder_attentions, # index 3 of d outputs | |
cross_attentions=outputs.cross_attentions, # index 4 of d outputs | |
encoder_last_hidden_state=outputs.encoder_last_hidden_state, # index 0 of encoder outputs | |
encoder_hidden_states=outputs.encoder_hidden_states, # 1 of e out | |
encoder_attentions=outputs.encoder_attentions, # 2 of e out | |
) | |
def prepare_inputs_for_generation( | |
self, | |
decoder_input_ids, | |
past_key_values=None, | |
attention_mask=None, | |
head_mask=None, | |
decoder_head_mask=None, | |
cross_attn_head_mask=None, | |
use_cache=None, | |
encoder_outputs=None, | |
**kwargs | |
): | |
# cut decoder_input_ids if past is used | |
if past_key_values is not None: | |
decoder_input_ids = decoder_input_ids[:, -1:] | |
return { | |
"input_ids": None, # needs to be passed to make Keras.layer.__call__ happy | |
"encoder_outputs": encoder_outputs, | |
"past_key_values": past_key_values, | |
"decoder_input_ids": decoder_input_ids, | |
"attention_mask": attention_mask, | |
"head_mask": head_mask, | |
"decoder_head_mask": decoder_head_mask, | |
"cross_attn_head_mask": cross_attn_head_mask, | |
"use_cache": use_cache, # change this to avoid caching (presumably for debugging) | |
} | |
def hf_compute_loss(self, labels, logits): | |
"""CrossEntropyLoss that ignores pad tokens""" | |
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy( | |
from_logits=True, | |
reduction=tf.keras.losses.Reduction.NONE, | |
) | |
melted_labels = tf.reshape(labels, (-1,)) | |
active_loss = tf.not_equal(melted_labels, self.config.pad_token_id) | |
reduced_logits = tf.boolean_mask(tf.reshape(logits, (-1, shape_list(logits)[2])), active_loss) | |
labels = tf.boolean_mask(melted_labels, active_loss) | |
return loss_fn(labels, reduced_logits) | |
{% endif -%} | |