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Unverified Commit 79465ac5 authored by Kamal Raj's avatar Kamal Raj Committed by GitHub
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TF clearer model variable naming: Tapas (#16145)

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src/transformers/models/tapas/modeling_tf_tapas.py
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...@@ -43,8 +43,8 @@ from ...modeling_tf_utils import ( ...@@ -43,8 +43,8 @@ from ...modeling_tf_utils import (
TFPreTrainedModel, TFPreTrainedModel,
TFSequenceClassificationLoss, TFSequenceClassificationLoss,
get_initializer, get_initializer,
input_processing,
keras_serializable, keras_serializable,
unpack_inputs,
) )
from ...tf_utils import shape_list from ...tf_utils import shape_list
from ...utils import logging from ...utils import logging
...@@ -757,6 +757,7 @@ class TFTapasMainLayer(tf.keras.layers.Layer): ...@@ -757,6 +757,7 @@ class TFTapasMainLayer(tf.keras.layers.Layer):
""" """
raise NotImplementedError raise NotImplementedError
@unpack_inputs
def call( def call(
self, self,
input_ids: Optional[TFModelInputType] = None, input_ids: Optional[TFModelInputType] = None,
...@@ -771,43 +772,28 @@ class TFTapasMainLayer(tf.keras.layers.Layer): ...@@ -771,43 +772,28 @@ class TFTapasMainLayer(tf.keras.layers.Layer):
training: bool = False, training: bool = False,
**kwargs, **kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]: ) -> Union[TFBaseModelOutputWithPooling, 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,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
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") raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif inputs["input_ids"] is not None: elif input_ids is not None:
input_shape = shape_list(inputs["input_ids"]) input_shape = shape_list(input_ids)
elif inputs["inputs_embeds"] is not None: elif inputs_embeds is not None:
input_shape = shape_list(inputs["inputs_embeds"])[:-1] input_shape = shape_list(inputs_embeds)[:-1]
else: else:
raise ValueError("You have to specify either input_ids or inputs_embeds") raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs["attention_mask"] is None: if attention_mask is None:
inputs["attention_mask"] = tf.fill(dims=input_shape, value=1) attention_mask = tf.fill(dims=input_shape, value=1)
if inputs["token_type_ids"] is None: if token_type_ids is None:
inputs["token_type_ids"] = tf.fill(dims=input_shape + [len(self.config.type_vocab_sizes)], value=0) token_type_ids = tf.fill(dims=input_shape + [len(self.config.type_vocab_sizes)], value=0)
embedding_output = self.embeddings( embedding_output = self.embeddings(
input_ids=inputs["input_ids"], input_ids=input_ids,
position_ids=inputs["position_ids"], position_ids=position_ids,
token_type_ids=inputs["token_type_ids"], token_type_ids=token_type_ids,
inputs_embeds=inputs["inputs_embeds"], inputs_embeds=inputs_embeds,
training=inputs["training"], training=training,
) )
# We create a 3D attention mask from a 2D tensor mask. # We create a 3D attention mask from a 2D tensor mask.
...@@ -815,7 +801,7 @@ class TFTapasMainLayer(tf.keras.layers.Layer): ...@@ -815,7 +801,7 @@ class TFTapasMainLayer(tf.keras.layers.Layer):
# So we can broadcast to [batch_size, num_heads, from_seq_length, 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 # 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. # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(inputs["attention_mask"], (input_shape[0], 1, 1, input_shape[1])) extended_attention_mask = tf.reshape(attention_mask, (input_shape[0], 1, 1, input_shape[1]))
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for # 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 # masked positions, this operation will create a tensor which is 0.0 for
...@@ -832,29 +818,29 @@ class TFTapasMainLayer(tf.keras.layers.Layer): ...@@ -832,29 +818,29 @@ class TFTapasMainLayer(tf.keras.layers.Layer):
# attention_probs has shape bsz x n_heads x N x N # 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] # 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] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
if inputs["head_mask"] is not None: if head_mask is not None:
raise NotImplementedError raise NotImplementedError
else: else:
inputs["head_mask"] = [None] * self.config.num_hidden_layers head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder( encoder_outputs = self.encoder(
hidden_states=embedding_output, hidden_states=embedding_output,
attention_mask=extended_attention_mask, attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"], head_mask=head_mask,
encoder_hidden_states=None, encoder_hidden_states=None,
encoder_attention_mask=None, encoder_attention_mask=None,
past_key_values=None, past_key_values=None,
use_cache=None, use_cache=None,
output_attentions=inputs["output_attentions"], output_attentions=output_attentions,
output_hidden_states=inputs["output_hidden_states"], output_hidden_states=output_hidden_states,
return_dict=inputs["return_dict"], return_dict=return_dict,
training=inputs["training"], training=training,
) )
sequence_output = encoder_outputs[0] sequence_output = encoder_outputs[0]
pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None pooled_output = self.pooler(hidden_states=sequence_output) if self.pooler is not None else None
if not inputs["return_dict"]: if not return_dict:
return ( return (
sequence_output, sequence_output,
pooled_output, pooled_output,
...@@ -979,6 +965,7 @@ class TFTapasModel(TFTapasPreTrainedModel): ...@@ -979,6 +965,7 @@ class TFTapasModel(TFTapasPreTrainedModel):
self.tapas = TFTapasMainLayer(config, name="tapas") self.tapas = TFTapasMainLayer(config, name="tapas")
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=_CONFIG_FOR_DOC)
def call( def call(
...@@ -1020,9 +1007,7 @@ class TFTapasModel(TFTapasPreTrainedModel): ...@@ -1020,9 +1007,7 @@ class TFTapasModel(TFTapasPreTrainedModel):
>>> last_hidden_states = outputs.last_hidden_state >>> last_hidden_states = outputs.last_hidden_state
```""" ```"""
inputs = input_processing( outputs = self.tapas(
func=self.call,
config=self.config,
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
token_type_ids=token_type_ids, token_type_ids=token_type_ids,
...@@ -1033,19 +1018,6 @@ class TFTapasModel(TFTapasPreTrainedModel): ...@@ -1033,19 +1018,6 @@ class TFTapasModel(TFTapasPreTrainedModel):
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=return_dict,
training=training, training=training,
kwargs_call=kwargs,
)
outputs = self.tapas(
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"],
) )
return outputs return outputs
...@@ -1079,6 +1051,7 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss): ...@@ -1079,6 +1051,7 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
def get_lm_head(self) -> tf.keras.layers.Layer: def get_lm_head(self) -> tf.keras.layers.Layer:
return self.lm_head.predictions return self.lm_head.predictions
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call( def call(
...@@ -1130,9 +1103,7 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss): ...@@ -1130,9 +1103,7 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
>>> outputs = model(**inputs, labels=labels) >>> outputs = model(**inputs, labels=labels)
>>> logits = outputs.logits >>> logits = outputs.logits
```""" ```"""
inputs = input_processing( outputs = self.tapas(
func=self.call,
config=self.config,
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
token_type_ids=token_type_ids, token_type_ids=token_type_ids,
...@@ -1142,31 +1113,13 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss): ...@@ -1142,31 +1113,13 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=return_dict,
labels=labels,
training=training, training=training,
kwargs_call=kwargs,
)
outputs = self.tapas(
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"],
) )
sequence_output = outputs[0] sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output) prediction_scores = self.lm_head(sequence_output)
loss = ( loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=prediction_scores)
None
if inputs["labels"] is None
else self.hf_compute_loss(labels=inputs["labels"], logits=prediction_scores)
)
if not inputs["return_dict"]: if not return_dict:
output = (prediction_scores,) + outputs[2:] output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output return ((loss,) + output) if loss is not None else output
...@@ -1311,6 +1264,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1311,6 +1264,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
) )
self.config = config self.config = config
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length")) @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFTableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=TFTableQuestionAnsweringOutput, config_class=_CONFIG_FOR_DOC)
def call( def call(
...@@ -1385,38 +1339,17 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1385,38 +1339,17 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
>>> logits_aggregation = outputs.logits_aggregation >>> logits_aggregation = outputs.logits_aggregation
```""" ```"""
inputs = input_processing( outputs = self.tapas(
func=self.call,
config=self.config,
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
token_type_ids=token_type_ids, token_type_ids=token_type_ids,
position_ids=position_ids, position_ids=position_ids,
head_mask=head_mask, head_mask=head_mask,
inputs_embeds=inputs_embeds, inputs_embeds=inputs_embeds,
table_mask=table_mask,
aggregation_labels=aggregation_labels,
float_answer=float_answer,
numeric_values=numeric_values,
numeric_values_scale=numeric_values_scale,
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=return_dict,
labels=labels,
training=training, training=training,
kwargs_call=kwargs,
)
outputs = self.tapas(
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"],
) )
sequence_output = outputs[0] sequence_output = outputs[0]
...@@ -1424,14 +1357,14 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1424,14 +1357,14 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
sequence_output = self.dropout(sequence_output) sequence_output = self.dropout(sequence_output)
if inputs["input_ids"] is not None: if input_ids is not None:
input_shape = shape_list(inputs["input_ids"]) input_shape = shape_list(input_ids)
else: else:
input_shape = shape_list(inputs["inputs_embeds"])[:-1] input_shape = shape_list(inputs_embeds)[:-1]
# Construct indices for the table. # Construct indices for the table.
if inputs["token_type_ids"] is None: if token_type_ids is None:
inputs["token_type_ids"] = tf.fill(input_shape + [len(self.config.type_vocab_sizes)], 0) token_type_ids = tf.fill(input_shape + [len(self.config.type_vocab_sizes)], 0)
token_types = [ token_types = [
"segment_ids", "segment_ids",
...@@ -1443,8 +1376,8 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1443,8 +1376,8 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
"numeric_relations", "numeric_relations",
] ]
row_ids = inputs["token_type_ids"][:, :, token_types.index("row_ids")] row_ids = token_type_ids[:, :, token_types.index("row_ids")]
column_ids = inputs["token_type_ids"][:, :, token_types.index("column_ids")] column_ids = token_type_ids[:, :, token_types.index("column_ids")]
# Construct indices for the table. # Construct indices for the table.
row_index = IndexMap( row_index = IndexMap(
...@@ -1460,19 +1393,15 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1460,19 +1393,15 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
cell_index = ProductIndexMap(row_index, col_index) cell_index = ProductIndexMap(row_index, col_index)
# Masks. # Masks.
input_shape = ( input_shape = shape_list(input_ids) if input_ids is not None else shape_list(inputs_embeds)[:-1]
shape_list(inputs["input_ids"]) if attention_mask is None:
if inputs["input_ids"] is not None attention_mask = tf.ones(input_shape)
else shape_list(inputs["inputs_embeds"])[:-1]
)
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.ones(input_shape)
# Table cells only, without question tokens and table headers. # Table cells only, without question tokens and table headers.
if inputs["table_mask"] is None: if table_mask is None:
inputs["table_mask"] = tf.where(row_ids > 0, tf.ones_like(row_ids), tf.zeros_like(row_ids)) table_mask = tf.where(row_ids > 0, tf.ones_like(row_ids), tf.zeros_like(row_ids))
# <float32>[batch_size, seq_length] # <float32>[batch_size, seq_length]
input_mask_float = tf.cast(inputs["attention_mask"], tf.float32) input_mask_float = tf.cast(attention_mask, tf.float32)
table_mask_float = tf.cast(inputs["table_mask"], tf.float32) table_mask_float = tf.cast(table_mask, tf.float32)
# Mask for cells that exist in the table (i.e. that are not padding). # Mask for cells that exist in the table (i.e. that are not padding).
cell_mask, _ = reduce_mean(input_mask_float, cell_index) cell_mask, _ = reduce_mean(input_mask_float, cell_index)
...@@ -1495,7 +1424,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1495,7 +1424,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
# Total loss calculation # Total loss calculation
total_loss = 0.0 total_loss = 0.0
calculate_loss = False calculate_loss = False
if inputs["labels"] is not None: if labels is not None:
calculate_loss = True calculate_loss = True
is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision is_supervised = not self.config.num_aggregation_labels > 0 or not self.config.use_answer_as_supervision
...@@ -1509,16 +1438,16 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1509,16 +1438,16 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
if is_supervised: if is_supervised:
aggregate_mask = None aggregate_mask = None
else: else:
if inputs["float_answer"] is not None: if float_answer is not None:
assert ( assert (
shape_list(inputs["labels"])[0] == shape_list(inputs["float_answer"])[0] shape_list(labels)[0] == shape_list(float_answer)[0]
), "Make sure the answers are a FloatTensor of shape (batch_size,)" ), "Make sure the answers are a FloatTensor of shape (batch_size,)"
# <float32>[batch_size] # <float32>[batch_size]
aggregate_mask = _calculate_aggregate_mask( aggregate_mask = _calculate_aggregate_mask(
inputs["float_answer"], float_answer,
pooled_output, pooled_output,
self.config.cell_selection_preference, self.config.cell_selection_preference,
inputs["labels"], labels,
self.aggregation_classifier, self.aggregation_classifier,
) )
else: else:
...@@ -1535,17 +1464,17 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1535,17 +1464,17 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
selection_loss_per_example = None selection_loss_per_example = None
if not self.config.select_one_column: if not self.config.select_one_column:
weight = tf.where( weight = tf.where(
inputs["labels"] == 0, labels == 0,
tf.ones_like(inputs["labels"], dtype=tf.float32), tf.ones_like(labels, dtype=tf.float32),
self.config.positive_label_weight * tf.ones_like(inputs["labels"], dtype=tf.float32), self.config.positive_label_weight * tf.ones_like(labels, dtype=tf.float32),
) )
selection_loss_per_token = -dist_per_token.log_prob(inputs["labels"]) * weight selection_loss_per_token = -dist_per_token.log_prob(labels) * weight
selection_loss_per_example = tf.reduce_sum(selection_loss_per_token * input_mask_float, axis=1) / ( selection_loss_per_example = tf.reduce_sum(selection_loss_per_token * input_mask_float, axis=1) / (
tf.reduce_sum(input_mask_float, axis=1) + EPSILON_ZERO_DIVISION tf.reduce_sum(input_mask_float, axis=1) + EPSILON_ZERO_DIVISION
) )
else: else:
selection_loss_per_example, logits = _single_column_cell_selection_loss( selection_loss_per_example, logits = _single_column_cell_selection_loss(
logits, column_logits, inputs["labels"], cell_index, col_index, cell_mask logits, column_logits, labels, cell_index, col_index, cell_mask
) )
dist_per_token = tfp.distributions.Bernoulli(logits=logits) dist_per_token = tfp.distributions.Bernoulli(logits=logits)
...@@ -1562,14 +1491,14 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1562,14 +1491,14 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
if self.config.num_aggregation_labels > 0: if self.config.num_aggregation_labels > 0:
if is_supervised: if is_supervised:
# Note that `aggregate_mask` is None if the setting is supervised. # Note that `aggregate_mask` is None if the setting is supervised.
if inputs["aggregation_labels"] is not None: if aggregation_labels is not None:
assert ( assert (
shape_list(inputs["labels"])[0] == shape_list(inputs["aggregation_labels"])[0] shape_list(labels)[0] == shape_list(aggregation_labels)[0]
), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)" ), "Make sure the aggregation labels are a LongTensor of shape (batch_size,)"
per_example_additional_loss = _calculate_aggregation_loss( per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation, logits_aggregation,
aggregate_mask, aggregate_mask,
inputs["aggregation_labels"], aggregation_labels,
self.config.use_answer_as_supervision, self.config.use_answer_as_supervision,
self.config.num_aggregation_labels, self.config.num_aggregation_labels,
self.config.aggregation_loss_weight, self.config.aggregation_loss_weight,
...@@ -1579,7 +1508,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1579,7 +1508,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
"You have to specify aggregation labels in order to calculate the aggregation loss" "You have to specify aggregation labels in order to calculate the aggregation loss"
) )
else: else:
aggregation_labels = tf.zeros(shape_list(inputs["labels"])[0], dtype=tf.int32) aggregation_labels = tf.zeros(shape_list(labels)[0], dtype=tf.int32)
per_example_additional_loss = _calculate_aggregation_loss( per_example_additional_loss = _calculate_aggregation_loss(
logits_aggregation, logits_aggregation,
aggregate_mask, aggregate_mask,
...@@ -1590,15 +1519,15 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1590,15 +1519,15 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
) )
if self.config.use_answer_as_supervision: if self.config.use_answer_as_supervision:
if inputs["numeric_values"] is not None and inputs["numeric_values_scale"] is not None: if numeric_values is not None and numeric_values_scale is not None:
assert shape_list(inputs["numeric_values"]) == shape_list(inputs["numeric_values_scale"]) assert shape_list(numeric_values) == shape_list(numeric_values_scale)
# Add regression loss for numeric answers which require aggregation. # Add regression loss for numeric answers which require aggregation.
answer_loss, large_answer_loss_mask = _calculate_regression_loss( answer_loss, large_answer_loss_mask = _calculate_regression_loss(
inputs["float_answer"], float_answer,
aggregate_mask, aggregate_mask,
dist_per_token, dist_per_token,
inputs["numeric_values"], numeric_values,
inputs["numeric_values_scale"], numeric_values_scale,
table_mask_float, table_mask_float,
logits_aggregation, logits_aggregation,
self.config, self.config,
...@@ -1618,7 +1547,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel): ...@@ -1618,7 +1547,7 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
_, logits = _single_column_cell_selection_loss( _, logits = _single_column_cell_selection_loss(
logits, column_logits, labels, cell_index, col_index, cell_mask logits, column_logits, labels, cell_index, col_index, cell_mask
) )
if not inputs["return_dict"]: if not return_dict:
output = (logits, logits_aggregation) + outputs[2:] output = (logits, logits_aggregation) + outputs[2:]
return ((total_loss,) + output) if calculate_loss else output return ((total_loss,) + output) if calculate_loss else output
...@@ -1657,6 +1586,7 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif ...@@ -1657,6 +1586,7 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier" config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
) )
@unpack_inputs
@add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")) @add_start_docstrings_to_model_forward(TAPAS_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC) @replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call( def call(
...@@ -1712,9 +1642,7 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif ...@@ -1712,9 +1642,7 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif
>>> logits = outputs.logits >>> logits = outputs.logits
```""" ```"""
inputs = input_processing( outputs = self.tapas(
func=self.call,
config=self.config,
input_ids=input_ids, input_ids=input_ids,
attention_mask=attention_mask, attention_mask=attention_mask,
token_type_ids=token_type_ids, token_type_ids=token_type_ids,
...@@ -1724,28 +1652,14 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif ...@@ -1724,28 +1652,14 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif
output_attentions=output_attentions, output_attentions=output_attentions,
output_hidden_states=output_hidden_states, output_hidden_states=output_hidden_states,
return_dict=return_dict, return_dict=return_dict,
labels=labels,
training=training, training=training,
kwargs_call=kwargs,
)
outputs = self.tapas(
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"],
) )
pooled_output = outputs[1] pooled_output = outputs[1]
pooled_output = self.dropout(inputs=pooled_output, training=inputs["training"]) pooled_output = self.dropout(inputs=pooled_output, training=training)
logits = self.classifier(inputs=pooled_output) logits = self.classifier(inputs=pooled_output)
loss = None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=logits) loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=logits)
if not inputs["return_dict"]: if not return_dict:
output = (logits,) + outputs[2:] output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output return ((loss,) + output) if loss is not None else output
......
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