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TF Electra - clearer model variable naming (#16143)

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Kamal Raj 2022-03-14 23:40:07 +05:30 committed by GitHub
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1 changed files with 81 additions and 216 deletions
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src/transformers/models/electra/modeling_tf_electra.py
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@ -50,8 +50,8 @@ from ...modeling_tf_utils import (
TFSequenceSummary,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list
from ...utils import logging
@ -702,6 +702,7 @@ class TFElectraMainLayer(tf.keras.layers.Layer):
return head_mask
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
@ -720,77 +721,55 @@ class TFElectraMainLayer(tf.keras.layers.Layer):
training: Optional[bool] = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPastAndCrossAttentions, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
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,
kwargs_call=kwargs,
)
if not self.config.is_decoder:
inputs["use_cache"] = False
use_cache = False
if inputs["input_ids"] is not None and inputs["inputs_embeds"] is not None:
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 inputs["input_ids"] is not None:
input_shape = shape_list(inputs["input_ids"])
elif inputs["inputs_embeds"] is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1]
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 inputs["past_key_values"] is None:
if past_key_values is None:
past_key_values_length = 0
inputs["past_key_values"] = [None] * len(self.encoder.layer)
past_key_values = [None] * len(self.encoder.layer)
else:
past_key_values_length = shape_list(inputs["past_key_values"][0][0])[-2]
past_key_values_length = shape_list(past_key_values[0][0])[-2]
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
if attention_mask is None:
attention_mask = tf.fill(dims=(batch_size, seq_length + past_key_values_length), value=1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
hidden_states = self.embeddings(
input_ids=inputs["input_ids"],
position_ids=inputs["position_ids"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
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=inputs["training"],
training=training,
)
extended_attention_mask = self.get_extended_attention_mask(
inputs["attention_mask"], input_shape, hidden_states.dtype, past_key_values_length
attention_mask, input_shape, hidden_states.dtype, past_key_values_length
)
# Copied from `modeling_tf_t5.py` with -1e9 -> -10000
if self.is_decoder and inputs["encoder_attention_mask"] is not None:
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]
inputs["encoder_attention_mask"] = tf.cast(
inputs["encoder_attention_mask"], dtype=extended_attention_mask.dtype
)
num_dims_encoder_attention_mask = len(shape_list(inputs["encoder_attention_mask"]))
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 = inputs["encoder_attention_mask"][:, None, :, :]
encoder_extended_attention_mask = encoder_attention_mask[:, None, :, :]
if num_dims_encoder_attention_mask == 2:
encoder_extended_attention_mask = inputs["encoder_attention_mask"][:, None, None, :]
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
@ -801,23 +780,23 @@ class TFElectraMainLayer(tf.keras.layers.Layer):
else:
encoder_extended_attention_mask = None
inputs["head_mask"] = self.get_head_mask(inputs["head_mask"])
head_mask = self.get_head_mask(head_mask)
if hasattr(self, "embeddings_project"):
hidden_states = self.embeddings_project(hidden_states, training=inputs["training"])
hidden_states = self.embeddings_project(hidden_states, training=training)
hidden_states = self.encoder(
hidden_states=hidden_states,
attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"],
encoder_hidden_states=inputs["encoder_hidden_states"],
head_mask=head_mask,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_extended_attention_mask,
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
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 hidden_states
@ -950,6 +929,7 @@ class TFElectraModel(TFElectraPreTrainedModel):
self.electra = TFElectraMainLayer(config, name="electra")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -995,40 +975,21 @@ class TFElectraModel(TFElectraPreTrainedModel):
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
"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.electra(
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,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
encoder_hidden_states=inputs["encoder_hidden_states"],
encoder_attention_mask=inputs["encoder_attention_mask"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
return outputs
@ -1067,6 +1028,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
self.electra = TFElectraMainLayer(config, name="electra")
self.discriminator_predictions = TFElectraDiscriminatorPredictions(config, name="discriminator_predictions")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFElectraForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
def call(
@ -1098,9 +1060,7 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
>>> outputs = model(input_ids)
>>> scores = outputs[0]
```"""
inputs = input_processing(
func=self.call,
config=self.config,
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
@ -1111,24 +1071,11 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
discriminator_hidden_states = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.discriminator_predictions(discriminator_sequence_output)
if not inputs["return_dict"]:
if not return_dict:
return (logits,) + discriminator_hidden_states[1:]
return TFElectraForPreTrainingOutput(
@ -1212,6 +1159,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.generator_lm_head.name
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -1240,9 +1188,7 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
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]`
"""
inputs = input_processing(
func=self.call,
config=self.config,
generator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
@ -1252,28 +1198,14 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
generator_hidden_states = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
generator_sequence_output = generator_hidden_states[0]
prediction_scores = self.generator_predictions(generator_sequence_output, training=inputs["training"])
prediction_scores = self.generator_lm_head(prediction_scores, training=inputs["training"])
loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], prediction_scores)
prediction_scores = self.generator_predictions(generator_sequence_output, training=training)
prediction_scores = self.generator_lm_head(prediction_scores, training=training)
loss = None if labels is None else self.hf_compute_loss(labels, prediction_scores)
if not inputs["return_dict"]:
if not return_dict:
output = (prediction_scores,) + generator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
@ -1337,6 +1269,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
self.electra = TFElectraMainLayer(config, name="electra")
self.classifier = TFElectraClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -1365,9 +1298,7 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
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).
"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
@ -1377,26 +1308,12 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, TFSequenceCla
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
logits = self.classifier(outputs[0])
loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], logits)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not inputs["return_dict"]:
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
@ -1445,6 +1362,7 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -1472,43 +1390,21 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
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)
"""
inputs = input_processing(
func=self.call,
config=self.config,
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,
labels=labels,
training=training,
kwargs_call=kwargs,
)
if inputs["input_ids"] is not None:
num_choices = shape_list(inputs["input_ids"])[1]
seq_length = shape_list(inputs["input_ids"])[2]
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["inputs_embeds"])[1]
seq_length = shape_list(inputs["inputs_embeds"])[2]
num_choices = shape_list(inputs_embeds)[1]
seq_length = shape_list(inputs_embeds)[2]
flat_input_ids = tf.reshape(inputs["input_ids"], (-1, seq_length)) if inputs["input_ids"] is not None else None
flat_attention_mask = (
tf.reshape(inputs["attention_mask"], (-1, seq_length)) if inputs["attention_mask"] is not None else None
)
flat_token_type_ids = (
tf.reshape(inputs["token_type_ids"], (-1, seq_length)) if inputs["token_type_ids"] is not None else None
)
flat_position_ids = (
tf.reshape(inputs["position_ids"], (-1, seq_length)) if inputs["position_ids"] is not None else None
)
flat_input_ids = tf.reshape(input_ids, (-1, seq_length)) if input_ids is not None else None
flat_attention_mask = tf.reshape(attention_mask, (-1, seq_length)) if attention_mask is not None else None
flat_token_type_ids = tf.reshape(token_type_ids, (-1, seq_length)) if token_type_ids is not None else None
flat_position_ids = tf.reshape(position_ids, (-1, seq_length)) if position_ids is not None else None
flat_inputs_embeds = (
tf.reshape(inputs["inputs_embeds"], (-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
if inputs["inputs_embeds"] is not None
tf.reshape(inputs_embeds, (-1, seq_length, shape_list(inputs_embeds)[3]))
if inputs_embeds is not None
else None
)
outputs = self.electra(
@ -1516,19 +1412,19 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, TFMultipleChoiceLoss)
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_ids,
position_ids=flat_position_ids,
head_mask=inputs["head_mask"],
head_mask=head_mask,
inputs_embeds=flat_inputs_embeds,
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
logits = self.sequence_summary(outputs[0])
logits = self.classifier(logits)
reshaped_logits = tf.reshape(logits, (-1, num_choices))
loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], reshaped_logits)
loss = None if labels is None else self.hf_compute_loss(labels, reshaped_logits)
if not inputs["return_dict"]:
if not return_dict:
output = (reshaped_logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
@ -1584,6 +1480,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -1610,9 +1507,7 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
"""
inputs = input_processing(
func=self.call,
config=self.config,
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
@ -1622,28 +1517,14 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
discriminator_hidden_states = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
discriminator_sequence_output = discriminator_hidden_states[0]
discriminator_sequence_output = self.dropout(discriminator_sequence_output)
logits = self.classifier(discriminator_sequence_output)
loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], logits)
loss = None if labels is None else self.hf_compute_loss(labels, logits)
if not inputs["return_dict"]:
if not return_dict:
output = (logits,) + discriminator_hidden_states[1:]
return ((loss,) + output) if loss is not None else output
@ -1680,6 +1561,7 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(ELECTRA_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
@ -1713,9 +1595,7 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
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.
"""
inputs = input_processing(
func=self.call,
config=self.config,
discriminator_hidden_states = self.electra(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
@ -1725,22 +1605,7 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
start_positions=start_positions,
end_positions=end_positions,
training=training,
kwargs_call=kwargs,
)
discriminator_hidden_states = self.electra(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_ids"],
position_ids=inputs["position_ids"],
head_mask=inputs["head_mask"],
inputs_embeds=inputs["inputs_embeds"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
)
discriminator_sequence_output = discriminator_hidden_states[0]
logits = self.qa_outputs(discriminator_sequence_output)
@ -1749,12 +1614,12 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
end_logits = tf.squeeze(end_logits, axis=-1)
loss = None
if inputs["start_positions"] is not None and inputs["end_positions"] is not None:
labels = {"start_position": inputs["start_positions"]}
labels["end_position"] = inputs["end_positions"]
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, (start_logits, end_logits))
if not inputs["return_dict"]:
if not return_dict:
output = (
start_logits,
end_logits,