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Unverified Commit 611d3a09 authored by Minh Chien Vu's avatar Minh Chien Vu Committed by GitHub
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Change unpacking of TF inputs: layoutlm, mpnet, rag, and roformer (#16112) 


Co-authored-by: default avatarChienVM <chien_vm@detomo.co.jp>
parent 0d7322c1
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Showing with 223 additions and 626 deletions
src/transformers/models/layoutlm/modeling_tf_layoutlm.py
View file @ 611d3a09
......@@ -37,8 +37,8 @@ from ...modeling_tf_utils import (
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list
from ...utils import logging
......@@ -691,6 +691,7 @@ class TFLayoutLMMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
......@@ -708,47 +709,31 @@ class TFLayoutLMMainLayer(tf.keras.layers.Layer):
training: bool = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPoolingAndCrossAttentions, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
bbox=bbox,
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")
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")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, value=1)
if inputs["token_type_ids"] is None:
inputs["token_type_ids"] = tf.fill(dims=input_shape, value=0)
if inputs["bbox"] is None:
inputs["bbox"] = tf.fill(dims=input_shape + [4], value=0)
if token_type_ids is None:
token_type_ids = tf.fill(dims=input_shape, value=0)
if bbox is None:
bbox = tf.fill(dims=input_shape + [4], value=0)
embedding_output = self.embeddings(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
position_ids=inputs["position_ids"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
training=inputs["training"],
input_ids=input_ids,
bbox=bbox,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
......@@ -756,7 +741,7 @@ class TFLayoutLMMainLayer(tf.keras.layers.Layer):
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(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
# masked positions, this operation will create a tensor which is 0.0 for
......@@ -773,30 +758,30 @@ class TFLayoutLMMainLayer(tf.keras.layers.Layer):
# 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 inputs["head_mask"] is not None:
if head_mask is not None:
raise NotImplementedError
else:
inputs["head_mask"] = [None] * self.config.num_hidden_layers
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"],
head_mask=head_mask,
# Need to pass these required positional arguments to `Encoder`
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=None,
past_key_values=None,
use_cache=False,
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,
)
sequence_output = encoder_outputs[0]
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 (
sequence_output,
pooled_output,
......@@ -924,6 +909,7 @@ class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
self.layoutlm = TFLayoutLMMainLayer(config, name="layoutlm")
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(
output_type=TFBaseModelOutputWithPoolingAndCrossAttentions, config_class=_CONFIG_FOR_DOC
......@@ -979,9 +965,7 @@ class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
>>> last_hidden_states = outputs.last_hidden_state
```"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
......@@ -989,26 +973,10 @@ class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.layoutlm(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
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
......@@ -1064,6 +1032,7 @@ class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingL
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.mlm.name + "/" + self.mlm.predictions.name
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFMaskedLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -1127,9 +1096,7 @@ class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingL
>>> loss = outputs.loss
```"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
......@@ -1140,32 +1107,13 @@ class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingL
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.layoutlm(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
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]
prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
loss = (
None
if inputs["labels"] is None
else self.hf_compute_loss(labels=inputs["labels"], logits=prediction_scores)
)
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 inputs["return_dict"]:
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......@@ -1208,6 +1156,7 @@ class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceC
name="classifier",
)
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFSequenceClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -1271,9 +1220,7 @@ class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceC
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
......@@ -1284,29 +1231,14 @@ class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, TFSequenceC
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.layoutlm(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
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 = self.dropout(inputs=pooled_output, training=inputs["training"])
pooled_output = self.dropout(inputs=pooled_output, training=training)
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:]
return ((loss,) + output) if loss is not None else output
......@@ -1355,6 +1287,7 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassif
name="classifier",
)
@unpack_inputs
@add_start_docstrings_to_model_forward(LAYOUTLM_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFTokenClassifierOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -1416,9 +1349,7 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassif
>>> loss = outputs.loss
>>> logits = outputs.logits
```"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.layoutlm(
input_ids=input_ids,
bbox=bbox,
attention_mask=attention_mask,
......@@ -1429,29 +1360,14 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, TFTokenClassif
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.layoutlm(
input_ids=inputs["input_ids"],
bbox=inputs["bbox"],
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 = self.dropout(inputs=sequence_output, training=inputs["training"])
sequence_output = self.dropout(inputs=sequence_output, training=training)
logits = self.classifier(inputs=sequence_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:]
return ((loss,) + output) if loss is not None else output
......
src/transformers/models/mpnet/modeling_tf_mpnet.py
View file @ 611d3a09
......@@ -45,8 +45,8 @@ from ...modeling_tf_utils import (
TFSequenceClassificationLoss,
TFTokenClassificationLoss,
get_initializer,
input_processing,
keras_serializable,
unpack_inputs,
)
from ...tf_utils import shape_list
from ...utils import logging
......@@ -485,6 +485,7 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids=None,
......@@ -498,38 +499,24 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
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")
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")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(input_shape, 1)
if attention_mask is None:
attention_mask = tf.fill(input_shape, 1)
embedding_output = self.embeddings(
inputs["input_ids"],
inputs["position_ids"],
inputs["inputs_embeds"],
training=inputs["training"],
input_ids,
position_ids,
inputs_embeds,
training=training,
)
# We create a 3D attention mask from a 2D tensor mask.
......@@ -537,7 +524,7 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(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
# masked positions, this operation will create a tensor which is 0.0 for
......@@ -554,25 +541,25 @@ class TFMPNetMainLayer(tf.keras.layers.Layer):
# 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 inputs["head_mask"] is not None:
if head_mask is not None:
raise NotImplementedError
else:
inputs["head_mask"] = [None] * self.num_hidden_layers
head_mask = [None] * self.num_hidden_layers
encoder_outputs = self.encoder(
embedding_output,
extended_attention_mask,
inputs["head_mask"],
inputs["output_attentions"],
inputs["output_hidden_states"],
inputs["return_dict"],
training=inputs["training"],
head_mask,
output_attentions,
output_hidden_states,
return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not inputs["return_dict"]:
if not return_dict:
return (
sequence_output,
pooled_output,
......@@ -680,6 +667,7 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
super().__init__(config, *inputs, **kwargs)
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -700,9 +688,7 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
training=False,
**kwargs,
):
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.mpnet(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
......@@ -712,18 +698,6 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.mpnet(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
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
......@@ -809,6 +783,7 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `get_bias` instead.", FutureWarning)
return self.name + "/" + self.lm_head.name
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -836,11 +811,8 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
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,
input_ids=input_ids,
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
......@@ -848,27 +820,14 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.mpnet(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
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]
prediction_scores = self.lm_head(sequence_output)
loss = None if inputs["labels"] is None else self.hf_compute_loss(inputs["labels"], prediction_scores)
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,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......@@ -930,6 +889,7 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
self.mpnet = TFMPNetMainLayer(config, name="mpnet")
self.classifier = TFMPNetClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -957,11 +917,8 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
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,
input_ids=input_ids,
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
......@@ -969,28 +926,15 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.mpnet(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
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]
logits = self.classifier(sequence_output, training=training)
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[2:]
return ((loss,) + output) if loss is not None else output
......@@ -1036,6 +980,7 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, TFMultipleChoiceLoss):
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1062,59 +1007,39 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, 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,
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_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_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.mpnet(
flat_input_ids,
flat_attention_mask,
flat_position_ids,
inputs["head_mask"],
head_mask,
flat_inputs_embeds,
inputs["output_attentions"],
inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
output_attentions,
output_hidden_states,
return_dict=return_dict,
training=training,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output, training=inputs["training"])
pooled_output = self.dropout(pooled_output, training=training)
logits = self.classifier(pooled_output)
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[2:]
return ((loss,) + output) if loss is not None else output
......@@ -1167,6 +1092,7 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1192,10 +1118,7 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
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,
outputs = self.mpnet(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
......@@ -1204,29 +1127,16 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.mpnet(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
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 = self.dropout(sequence_output, training=inputs["training"])
sequence_output = self.dropout(sequence_output, training=training)
logits = self.classifier(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,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
......@@ -1265,6 +1175,7 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(MPNET_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1297,11 +1208,8 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
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,
input_ids=input_ids,
outputs = self.mpnet(
input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
head_mask=head_mask,
......@@ -1309,21 +1217,7 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
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,
)
outputs = self.mpnet(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
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]
......@@ -1333,12 +1227,11 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
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, "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) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......
src/transformers/models/rag/modeling_tf_rag.py
View file @ 611d3a09
......@@ -23,7 +23,7 @@ import tensorflow as tf
from ...configuration_utils import PretrainedConfig
from ...file_utils import ModelOutput, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, input_processing, shape_list
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, shape_list, unpack_inputs
from ...utils import logging
from .configuration_rag import RagConfig
from .retrieval_rag import RagRetriever
......@@ -532,6 +532,7 @@ class TFRagModel(TFRagPreTrainedModel):
def set_retriever(self, retriever: RagRetriever):
self.retriever = retriever
@unpack_inputs
@add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFRetrievAugLMOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -580,46 +581,8 @@ class TFRagModel(TFRagPreTrainedModel):
"decoder_cached_states" not in kwargs
), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
attention_mask=attention_mask,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
doc_scores=doc_scores,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_retrieved=output_retrieved,
return_dict=return_dict,
n_docs=n_docs,
training=training,
kwargs_call=kwargs,
)
# aliasing to minimize code changing
input_ids = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
decoder_input_ids = inputs["decoder_input_ids"]
decoder_attention_mask = inputs["decoder_attention_mask"]
encoder_outputs = inputs["encoder_outputs"]
past_key_values = inputs["past_key_values"]
doc_scores = inputs["doc_scores"]
context_input_ids = inputs["context_input_ids"]
context_attention_mask = inputs["context_attention_mask"]
use_cache = inputs["use_cache"]
output_attentions = inputs["output_attentions"]
output_hidden_states = inputs["output_hidden_states"]
return_dict = inputs["return_dict"]
n_docs = inputs["n_docs"] if inputs["n_docs"] is not None else self.config.n_docs
output_retrieved = inputs["output_retrieved"]
training = inputs["training"]
n_docs = n_docs if n_docs is not None else self.config.n_docs
# whether retriever has to be used
has_to_retrieve = (
......@@ -855,6 +818,7 @@ class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss
log_prob_sum = seq_logprobs + doc_logprobs
return tf.reduce_logsumexp(log_prob_sum, axis=1)
@unpack_inputs
@add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -948,72 +912,47 @@ class TFRagTokenForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingLoss
"decoder_cached_states" not in kwargs
), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
do_marginalize = do_marginalize if do_marginalize else self.config.do_marginalize
reduce_loss = reduce_loss if reduce_loss else self.config.reduce_loss
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = labels
use_cache = False
outputs = self.rag(
input_ids,
attention_mask=attention_mask,
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
doc_scores=doc_scores,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
doc_scores=doc_scores,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_retrieved=output_retrieved,
n_docs=n_docs,
do_marginalize=do_marginalize,
labels=labels,
reduce_loss=reduce_loss,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
inputs["do_marginalize"] = inputs["do_marginalize"] if inputs["do_marginalize"] else self.config.do_marginalize
inputs["reduce_loss"] = inputs["reduce_loss"] if inputs["reduce_loss"] else self.config.reduce_loss
if inputs["labels"] is not None:
if inputs["decoder_input_ids"] is None:
inputs["decoder_input_ids"] = inputs["labels"]
inputs["use_cache"] = False
outputs = self.rag(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
encoder_outputs=inputs["encoder_outputs"],
decoder_input_ids=inputs["decoder_input_ids"],
decoder_attention_mask=inputs["decoder_attention_mask"],
context_input_ids=inputs["context_input_ids"],
context_attention_mask=inputs["context_attention_mask"],
doc_scores=inputs["doc_scores"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
output_retrieved=inputs["output_retrieved"],
n_docs=inputs["n_docs"],
training=inputs["training"],
)
loss = None
logits = outputs.logits
if inputs["labels"] is not None:
assert inputs["decoder_input_ids"] is not None
if labels is not None:
assert decoder_input_ids is not None
loss = self.get_nll(
outputs.logits,
outputs.doc_scores,
inputs["labels"],
reduce_loss=inputs["reduce_loss"],
labels,
reduce_loss=reduce_loss,
epsilon=self.config.label_smoothing,
n_docs=inputs["n_docs"],
n_docs=n_docs,
)
if inputs["do_marginalize"]:
logits = self.marginalize(logits, outputs.doc_scores, inputs["n_docs"])
if do_marginalize:
logits = self.marginalize(logits, outputs.doc_scores, n_docs)
return TFRetrievAugLMMarginOutput(
loss=loss,
......@@ -1465,6 +1404,7 @@ class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingL
def question_encoder(self):
return self.rag.question_encoder
@unpack_inputs
@add_start_docstrings_to_model_forward(RAG_FORWARD_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFRetrievAugLMMarginOutput, config_class=_CONFIG_FOR_DOC)
def call(
......@@ -1559,68 +1499,41 @@ class TFRagSequenceForGeneration(TFRagPreTrainedModel, TFCausalLanguageModelingL
"decoder_cached_states" not in kwargs
), "Please use past_key_values to cache intermediate outputs" # from modeling_tf_bart.py
inputs = input_processing(
func=self.call,
config=self.config,
input_ids=input_ids,
exclude_bos_score = exclude_bos_score if exclude_bos_score else self.config.exclude_bos_score
reduce_loss = reduce_loss if reduce_loss else self.config.reduce_loss
if labels is not None:
if decoder_input_ids is None:
decoder_input_ids = labels
use_cache = False
outputs = self.rag(
input_ids,
attention_mask=attention_mask,
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
encoder_outputs=encoder_outputs,
past_key_values=past_key_values,
doc_scores=doc_scores,
context_input_ids=context_input_ids,
context_attention_mask=context_attention_mask,
doc_scores=doc_scores,
past_key_values=past_key_values,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_retrieved=output_retrieved,
n_docs=n_docs,
exclude_bos_score=exclude_bos_score,
labels=labels,
reduce_loss=reduce_loss,
training=training,
return_dict=return_dict,
kwargs_call=kwargs,
)
inputs["exclude_bos_score"] = (
inputs["exclude_bos_score"] if inputs["exclude_bos_score"] else self.config.exclude_bos_score
)
inputs["reduce_loss"] = inputs["reduce_loss"] if inputs["reduce_loss"] else self.config.reduce_loss
if inputs["labels"] is not None:
if inputs["decoder_input_ids"] is None:
inputs["decoder_input_ids"] = inputs["labels"]
inputs["use_cache"] = False
outputs = self.rag(
inputs["input_ids"],
attention_mask=inputs["attention_mask"],
encoder_outputs=inputs["encoder_outputs"],
decoder_input_ids=inputs["decoder_input_ids"],
decoder_attention_mask=inputs["decoder_attention_mask"],
context_input_ids=inputs["context_input_ids"],
context_attention_mask=inputs["context_attention_mask"],
doc_scores=inputs["doc_scores"],
past_key_values=inputs["past_key_values"],
use_cache=inputs["use_cache"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
output_retrieved=inputs["output_retrieved"],
n_docs=inputs["n_docs"],
training=inputs["training"],
)
loss = None
if inputs["labels"] is not None:
if labels is not None:
loss = self.get_nll(
outputs.logits,
outputs.doc_scores,
inputs["labels"],
reduce_loss=inputs["reduce_loss"],
labels,
reduce_loss=reduce_loss,
epsilon=self.config.label_smoothing,
n_docs=inputs["n_docs"],
n_docs=n_docs,
)
return TFRetrievAugLMMarginOutput(
......
src/transformers/models/roformer/modeling_tf_roformer.py
View file @ 611d3a09
......@@ -49,8 +49,8 @@ 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
......@@ -602,6 +602,7 @@ class TFRoFormerMainLayer(tf.keras.layers.Layer):
"""
raise NotImplementedError
@unpack_inputs
def call(
self,
input_ids: Optional[TFModelInputType] = None,
......@@ -615,51 +616,37 @@ class TFRoFormerMainLayer(tf.keras.layers.Layer):
training: bool = False,
**kwargs,
) -> Union[TFBaseModelOutput, 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,
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")
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")
if inputs["attention_mask"] is None:
inputs["attention_mask"] = tf.fill(dims=input_shape, value=1)
if attention_mask is None:
attention_mask = tf.fill(dims=input_shape, 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)
embedding_output = self.embeddings(
input_ids=inputs["input_ids"],
token_type_ids=inputs["token_type_ids"],
inputs_embeds=inputs["inputs_embeds"],
training=inputs["training"],
input_ids=input_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
training=training,
)
if hasattr(self, "embeddings_project"):
embedding_output = self.embeddings_project(embedding_output, training=inputs["training"])
embedding_output = self.embeddings_project(embedding_output, training=training)
# We create a 3D attention mask from a 2D tensor mask.
# Sizes are [batch_size, 1, 1, to_seq_length]
# So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
# this attention mask is more simple than the triangular masking of causal attention
# used in OpenAI GPT, we just need to prepare the broadcast dimension here.
extended_attention_mask = tf.reshape(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
# masked positions, this operation will create a tensor which is 0.0 for
......@@ -676,24 +663,24 @@ class TFRoFormerMainLayer(tf.keras.layers.Layer):
# 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 inputs["head_mask"] is not None:
if head_mask is not None:
raise NotImplementedError
else:
inputs["head_mask"] = [None] * self.config.num_hidden_layers
head_mask = [None] * self.config.num_hidden_layers
encoder_outputs = self.encoder(
hidden_states=embedding_output,
attention_mask=extended_attention_mask,
head_mask=inputs["head_mask"],
output_attentions=inputs["output_attentions"],
output_hidden_states=inputs["output_hidden_states"],
return_dict=inputs["return_dict"],
training=inputs["training"],
head_mask=head_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
)
sequence_output = encoder_outputs[0]
if not inputs["return_dict"]:
if not return_dict:
return (sequence_output,) + encoder_outputs[1:]
return TFBaseModelOutput(
......@@ -811,6 +798,7 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):
self.roformer = TFRoFormerMainLayer(config, name="roformer")
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -831,9 +819,7 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):
training: Optional[bool] = False,
**kwargs,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -843,18 +829,6 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):
output_hidden_states=output_hidden_states,
return_dict=return_dict,
training=training,
kwargs_call=kwargs,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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
......@@ -883,6 +857,7 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -910,9 +885,7 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL
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,
outputs = self.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -921,30 +894,13 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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]
prediction_scores = self.mlm(sequence_output=sequence_output, training=inputs["training"])
loss = (
None
if inputs["labels"] is None
else self.hf_compute_loss(labels=inputs["labels"], logits=prediction_scores)
)
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 inputs["return_dict"]:
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......@@ -978,6 +934,7 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
def get_lm_head(self) -> tf.keras.layers.Layer:
return self.mlm.predictions
@unpack_inputs
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
checkpoint=_CHECKPOINT_FOR_DOC,
......@@ -1003,9 +960,7 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -1014,32 +969,19 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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]
logits = self.mlm(sequence_output=sequence_output, training=inputs["training"])
logits = self.mlm(sequence_output=sequence_output, training=training)
loss = None
if inputs["labels"] is not None:
if labels is not None:
# shift labels to the left and cut last logit token
shifted_logits = logits[:, :-1]
labels = inputs["labels"][:, 1:]
labels = labels[:, 1:]
loss = self.hf_compute_loss(labels=labels, logits=shifted_logits)
if not inputs["return_dict"]:
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......@@ -1102,6 +1044,7 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC
self.roformer = TFRoFormerMainLayer(config, name="roformer")
self.classifier = TFRoFormerClassificationHead(config, name="classifier")
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1129,9 +1072,7 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC
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.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -1140,25 +1081,12 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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(hidden_states=outputs[0], training=inputs["training"])
loss = None if inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=logits)
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 inputs["return_dict"]:
if not return_dict:
output = (logits,) + outputs[1:]
return ((loss,) + output) if loss is not None else output
......@@ -1205,6 +1133,7 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS)}
@unpack_inputs
@add_start_docstrings_to_model_forward(
ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
......@@ -1233,66 +1162,42 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
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,
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(tensor=inputs["input_ids"], shape=(-1, seq_length)) if inputs["input_ids"] is not None else None
)
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=inputs["attention_mask"], shape=(-1, seq_length))
if inputs["attention_mask"] is not None
else None
tf.reshape(tensor=attention_mask, shape=(-1, seq_length)) if attention_mask is not None else None
)
flat_token_type_ids = (
tf.reshape(tensor=inputs["token_type_ids"], shape=(-1, seq_length))
if inputs["token_type_ids"] is not None
else None
tf.reshape(tensor=token_type_ids, shape=(-1, seq_length)) if token_type_ids is not None else None
)
flat_inputs_embeds = (
tf.reshape(tensor=inputs["inputs_embeds"], shape=(-1, seq_length, shape_list(inputs["inputs_embeds"])[3]))
if inputs["inputs_embeds"] is not None
tf.reshape(tensor=inputs_embeds, shape=(-1, seq_length, shape_list(inputs_embeds)[3]))
if inputs_embeds is not None
else None
)
outputs = self.roformer(
input_ids=flat_input_ids,
attention_mask=flat_attention_mask,
token_type_ids=flat_token_type_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(inputs=outputs[0], training=inputs["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 inputs["labels"] is None else self.hf_compute_loss(labels=inputs["labels"], logits=reshaped_logits)
)
loss = None if labels is None else self.hf_compute_loss(labels=labels, logits=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
......@@ -1344,6 +1249,7 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1369,9 +1275,7 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif
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]`.
"""
inputs = input_processing(
func=self.call,
config=self.config,
outputs = self.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -1380,27 +1284,14 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
labels=labels,
training=training,
kwargs_call=kwargs,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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 = self.dropout(inputs=sequence_output, training=inputs["training"])
sequence_output = self.dropout(inputs=sequence_output, training=training)
logits = self.classifier(inputs=sequence_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[1:]
return ((loss,) + output) if loss is not None else output
......@@ -1436,6 +1327,7 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
units=config.num_labels, kernel_initializer=get_initializer(config.initializer_range), name="qa_outputs"
)
@unpack_inputs
@add_start_docstrings_to_model_forward(ROFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
processor_class=_TOKENIZER_FOR_DOC,
......@@ -1468,9 +1360,7 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
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,
outputs = self.roformer(
input_ids=input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
......@@ -1479,21 +1369,7 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
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,
)
outputs = self.roformer(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
token_type_ids=inputs["token_type_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]
logits = self.qa_outputs(inputs=sequence_output)
......@@ -1502,12 +1378,11 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
end_logits = tf.squeeze(input=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, "end_position": end_positions}
loss = self.hf_compute_loss(labels=labels, logits=(start_logits, end_logits))
if not inputs["return_dict"]:
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return ((loss,) + output) if loss is not None else output
......
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