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Unverified Commit 814de8fa authored by Matt's avatar Matt Committed by GitHub
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Overhaul TF serving signatures + dummy inputs (#23234) 

* Let's try autodetecting serving sigs

* Don't clobber existing sigs

* Change shapes for multiplechoice models

* Make default dummy inputs smarter too

* Fix missing f-string

* Let's YOLO a serving output too

* Read __class__.__name__ properly

* Don't just pass naked lists in there and expect it to be okay

* Code cleanup

* Update default serving sig

* Clearer error messages

* Further updates to the default serving output

* make fixup

* Update the serving output a bit more

* Cleanups and renames, raise errors appropriately when we can't infer inputs

* More renames

* we're building in a functional context again, yolo

* import DUMMY_INPUTS from the right place

* import DUMMY_INPUTS from the right place

* Support cross-attention in the dummies

* Support cross-attention in the dummies

* Complete removal of dummy/serving overrides in BERT

* Complete removal of dummy/serving overrides in RoBERTa

* Obliterate lots and lots of serving sig and dummy overrides

* merge type hint changes

* Fix for token_type_ids with vocab_size 1

* Add missing property decorator

* Fix T5 and hopefully some models that take conv inputs

* More signature pruning

* Fix T5's signature

* Fix Wav2Vec2 signature

* Fix LongformerForMultipleChoice input signature

* Fix BLIP and LED

* Better default serving output error handling

* Fix BART dummies

* Fix dummies for cross-attention, esp encoder-decoder models

* Fix visionencoderdecoder signature

* Fix BLIP serving output

* Small tweak to BART dummies

* Cleanup the ugly parameter inspection line that I used in a few places

* committed a breakpoint again

* Move the text_dims check

* Remove blip_text serving_output

* Add decoder_input_ids to the default input sig

* Remove all the manual overrides for encoder-decoder model signatures

* Tweak longformer/led input sigs

* Tweak default serving output

* output.keys() -> output

* make fixup
parent 3d7baef1
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Showing with 69 additions and 1124 deletions
src/transformers/models/opt/modeling_tf_opt.py
View file @ 814de8fa
......@@ -27,7 +27,6 @@ from ...modeling_tf_outputs import TFBaseModelOutputWithPast, TFCausalLMOutputWi
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
......@@ -413,29 +412,6 @@ class TFOPTPreTrainedModel(TFPreTrainedModel):
config_class = OPTConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.convert_to_tensor(DUMMY_INPUTS, dtype=tf.int32)
dummy_inputs = {
"attention_mask": tf.cast(input_ids != pad_token, tf.int32),
"input_ids": input_ids,
}
return dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
OPT_INPUTS_DOCSTRING = r"""
Args:
......
src/transformers/models/pegasus/modeling_tf_pegasus.py
View file @ 814de8fa
......@@ -33,7 +33,6 @@ from ...modeling_tf_outputs import (
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
......@@ -503,34 +502,6 @@ class TFPegasusPreTrainedModel(TFPreTrainedModel):
config_class = PegasusConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.convert_to_tensor(DUMMY_INPUTS, dtype=tf.int32)
decoder_input_ids = tf.convert_to_tensor(DUMMY_INPUTS, dtype=tf.int32)
dummy_inputs = {
"decoder_input_ids": decoder_input_ids,
"attention_mask": tf.cast(input_ids != pad_token, tf.int32),
"input_ids": input_ids,
}
return dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
# Copied from transformers.models.bart.modeling_tf_bart.TFBartPretrainedModel.serving
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
PEGASUS_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......
src/transformers/models/regnet/modeling_tf_regnet.py
View file @ 814de8fa
......@@ -14,7 +14,7 @@
# limitations under the License.
""" TensorFlow RegNet model."""
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import tensorflow as tf
......@@ -345,33 +345,8 @@ class TFRegNetPreTrainedModel(TFPreTrainedModel):
main_input_name = "pixel_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(shape=(3, self.config.num_channels, 224, 224), dtype=tf.float32)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
"""
Method used for serving the model.
Args:
inputs (`Dict[str, tf.Tensor]`):
The input of the saved model as a dictionary of tensors.
"""
output = self.call(inputs)
return self.serving_output(output)
def input_signature(self):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224), dtype=tf.float32)}
REGNET_START_DOCSTRING = r"""
......@@ -443,16 +418,6 @@ class TFRegNetModel(TFRegNetPreTrainedModel):
hidden_states=outputs.hidden_states,
)
def serving_output(
self, output: TFBaseModelOutputWithPoolingAndNoAttention
) -> TFBaseModelOutputWithPoolingAndNoAttention:
# hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=output.hidden_states,
)
@add_start_docstrings(
"""
......@@ -514,7 +479,3 @@ class TFRegNetForImageClassification(TFRegNetPreTrainedModel, TFSequenceClassifi
return ((loss,) + output) if loss is not None else output
return TFSequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
# hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=output.hidden_states)
src/transformers/models/rembert/modeling_tf_rembert.py
View file @ 814de8fa
......@@ -49,8 +49,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
DUMMY_INPUTS,
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -812,24 +810,6 @@ class TFRemBertPreTrainedModel(TFPreTrainedModel):
config_class = RemBertConfig
base_model_prefix = "rembert"
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
dummy = {"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32)}
# Add `encoder_hidden_states` to make the cross-attention layers' weights initialized
if self.config.add_cross_attention:
batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape
shape = (batch_size, seq_len) + (self.config.hidden_size,)
h = tf.random.uniform(shape=shape)
dummy["encoder_hidden_states"] = h
return dummy
REMBERT_START_DOCSTRING = r"""
......@@ -1002,27 +982,6 @@ class TFRemBertModel(TFRemBertPreTrainedModel):
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output
def serving_output(
self, output: TFBaseModelOutputWithPoolingAndCrossAttentions
) -> TFBaseModelOutputWithPoolingAndCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings("""RemBERT Model with a `language modeling` head on top.""", REMBERT_START_DOCSTRING)
class TFRemBertForMaskedLM(TFRemBertPreTrainedModel, TFMaskedLanguageModelingLoss):
......@@ -1095,12 +1054,6 @@ class TFRemBertForMaskedLM(TFRemBertPreTrainedModel, TFMaskedLanguageModelingLos
attentions=outputs.attentions,
)
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""RemBERT Model with a `language modeling` head on top for CLM fine-tuning.""", REMBERT_START_DOCSTRING
......@@ -1217,20 +1170,6 @@ class TFRemBertForCausalLM(TFRemBertPreTrainedModel, TFCausalLanguageModelingLos
cross_attentions=outputs.cross_attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
def serving_output(self, output: TFCausalLMOutputWithCrossAttentions) -> TFCausalLMOutputWithCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFCausalLMOutputWithCrossAttentions(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns
)
@add_start_docstrings(
"""
......@@ -1307,12 +1246,6 @@ class TFRemBertForSequenceClassification(TFRemBertPreTrainedModel, TFSequenceCla
attentions=outputs.attentions,
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1331,16 +1264,6 @@ class TFRemBertForMultipleChoice(TFRemBertPreTrainedModel, TFMultipleChoiceLoss)
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)}
@unpack_inputs
@add_start_docstrings_to_model_forward(REMBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
......@@ -1419,26 +1342,6 @@ class TFRemBertForMultipleChoice(TFRemBertPreTrainedModel, TFMultipleChoiceLoss)
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)
return self.serving_output(output)
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1512,12 +1415,6 @@ class TFRemBertForTokenClassification(TFRemBertPreTrainedModel, TFTokenClassific
attentions=outputs.attentions,
)
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1604,11 +1501,3 @@ class TFRemBertForQuestionAnswering(TFRemBertPreTrainedModel, TFQuestionAnswerin
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
src/transformers/models/resnet/modeling_tf_resnet.py
View file @ 814de8fa
......@@ -14,7 +14,7 @@
# limitations under the License.
""" TensorFlow ResNet model."""
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import tensorflow as tf
......@@ -276,24 +276,8 @@ class TFResNetPreTrainedModel(TFPreTrainedModel):
main_input_name = "pixel_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network. Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(shape=(3, self.config.num_channels, 224, 224), dtype=tf.float32)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
def input_signature(self):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224), dtype=tf.float32)}
RESNET_START_DOCSTRING = r"""
......@@ -419,16 +403,6 @@ class TFResNetModel(TFResNetPreTrainedModel):
)
return resnet_outputs
def serving_output(
self, output: TFBaseModelOutputWithPoolingAndNoAttention
) -> TFBaseModelOutputWithPoolingAndNoAttention:
# hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFBaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=output.hidden_states,
)
@add_start_docstrings(
"""
......@@ -492,7 +466,3 @@ class TFResNetForImageClassification(TFResNetPreTrainedModel, TFSequenceClassifi
return (loss,) + output if loss is not None else output
return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
def serving_output(self, output: TFImageClassifierOutputWithNoAttention) -> TFImageClassifierOutputWithNoAttention:
# hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFImageClassifierOutputWithNoAttention(logits=output.logits, hidden_states=output.hidden_states)
src/transformers/models/roberta/modeling_tf_roberta.py
View file @ 814de8fa
......@@ -51,8 +51,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
DUMMY_INPUTS,
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -777,38 +775,6 @@ class TFRobertaPreTrainedModel(TFPreTrainedModel):
config_class = RobertaConfig
base_model_prefix = "roberta"
@property
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainedModel.dummy_inputs
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
dummy = {"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32)}
# Add `encoder_hidden_states` to make the cross-attention layers' weights initialized
if self.config.add_cross_attention:
batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape
shape = (batch_size, seq_len) + (self.config.hidden_size,)
h = tf.random.uniform(shape=shape)
dummy["encoder_hidden_states"] = h
return dummy
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
ROBERTA_START_DOCSTRING = r"""
......@@ -980,27 +946,6 @@ class TFRobertaModel(TFRobertaPreTrainedModel):
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output
def serving_output(
self, output: TFBaseModelOutputWithPoolingAndCrossAttentions
) -> TFBaseModelOutputWithPoolingAndCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
class TFRobertaLMHead(tf.keras.layers.Layer):
"""Roberta Head for masked language modeling."""
......@@ -1131,13 +1076,6 @@ class TFRobertaForMaskedLM(TFRobertaPreTrainedModel, TFMaskedLanguageModelingLos
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLoss):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
......@@ -1260,20 +1198,6 @@ class TFRobertaForCausalLM(TFRobertaPreTrainedModel, TFCausalLanguageModelingLos
cross_attentions=outputs.cross_attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
def serving_output(self, output: TFCausalLMOutputWithCrossAttentions) -> TFCausalLMOutputWithCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFCausalLMOutputWithCrossAttentions(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns
)
class TFRobertaClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
......@@ -1378,13 +1302,6 @@ class TFRobertaForSequenceClassification(TFRobertaPreTrainedModel, TFSequenceCla
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1407,16 +1324,6 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)}
@unpack_inputs
@add_start_docstrings_to_model_forward(ROBERTA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
......@@ -1485,26 +1392,6 @@ class TFRobertaForMultipleChoice(TFRobertaPreTrainedModel, TFMultipleChoiceLoss)
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1588,13 +1475,6 @@ class TFRobertaForTokenClassification(TFRobertaPreTrainedModel, TFTokenClassific
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1686,12 +1566,3 @@ class TFRobertaForQuestionAnswering(TFRobertaPreTrainedModel, TFQuestionAnswerin
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
src/transformers/models/roberta_prelayernorm/modeling_tf_roberta_prelayernorm.py
View file @ 814de8fa
......@@ -51,8 +51,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
DUMMY_INPUTS,
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -778,38 +776,6 @@ class TFRobertaPreLayerNormPreTrainedModel(TFPreTrainedModel):
config_class = RobertaPreLayerNormConfig
base_model_prefix = "roberta_prelayernorm"
@property
# Copied from transformers.models.bert.modeling_tf_bert.TFBertPreTrainedModel.dummy_inputs
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
dummy = {"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32)}
# Add `encoder_hidden_states` to make the cross-attention layers' weights initialized
if self.config.add_cross_attention:
batch_size, seq_len = tf.constant(DUMMY_INPUTS).shape
shape = (batch_size, seq_len) + (self.config.hidden_size,)
h = tf.random.uniform(shape=shape)
dummy["encoder_hidden_states"] = h
return dummy
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
ROBERTA_PRELAYERNORM_START_DOCSTRING = r"""
......@@ -982,27 +948,6 @@ class TFRobertaPreLayerNormModel(TFRobertaPreLayerNormPreTrainedModel):
return outputs
# Copied from transformers.models.bert.modeling_tf_bert.TFBertModel.serving_output
def serving_output(
self, output: TFBaseModelOutputWithPoolingAndCrossAttentions
) -> TFBaseModelOutputWithPoolingAndCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFBaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaLMHead with Roberta->RobertaPreLayerNorm
class TFRobertaPreLayerNormLMHead(tf.keras.layers.Layer):
......@@ -1140,13 +1085,6 @@ class TFRobertaPreLayerNormForMaskedLM(TFRobertaPreLayerNormPreTrainedModel, TFM
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMaskedLM.serving_output
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaForCausalLM with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta->roberta_prelayernorm
class TFRobertaPreLayerNormForCausalLM(TFRobertaPreLayerNormPreTrainedModel, TFCausalLanguageModelingLoss):
......@@ -1276,20 +1214,6 @@ class TFRobertaPreLayerNormForCausalLM(TFRobertaPreLayerNormPreTrainedModel, TFC
cross_attentions=outputs.cross_attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertLMHeadModel.serving_output
def serving_output(self, output: TFCausalLMOutputWithCrossAttentions) -> TFCausalLMOutputWithCrossAttentions:
output_cache = self.config.use_cache and self.config.is_decoder
pkv = tf.convert_to_tensor(output.past_key_values) if output_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if output.cross_attentions is not None else None
if not (self.config.output_attentions and self.config.add_cross_attention):
cross_attns = None
return TFCausalLMOutputWithCrossAttentions(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns
)
# Copied from transformers.models.roberta.modeling_tf_roberta.TFRobertaClassificationHead with Roberta->RobertaPreLayerNorm
class TFRobertaPreLayerNormClassificationHead(tf.keras.layers.Layer):
......@@ -1398,13 +1322,6 @@ class TFRobertaPreLayerNormForSequenceClassification(
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForSequenceClassification.serving_output
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1428,16 +1345,6 @@ class TFRobertaPreLayerNormForMultipleChoice(TFRobertaPreLayerNormPreTrainedMode
1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)}
@unpack_inputs
@add_start_docstrings_to_model_forward(
ROBERTA_PRELAYERNORM_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
......@@ -1508,26 +1415,6 @@ class TFRobertaPreLayerNormForMultipleChoice(TFRobertaPreLayerNormPreTrainedMode
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving_output
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1612,13 +1499,6 @@ class TFRobertaPreLayerNormForTokenClassification(TFRobertaPreLayerNormPreTraine
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForTokenClassification.serving_output
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1711,12 +1591,3 @@ class TFRobertaPreLayerNormForQuestionAnswering(TFRobertaPreLayerNormPreTrainedM
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForQuestionAnswering.serving_output
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
src/transformers/models/roformer/modeling_tf_roformer.py
View file @ 814de8fa
......@@ -50,7 +50,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -835,12 +834,6 @@ class TFRoFormerModel(TFRoFormerPreTrainedModel):
return outputs
def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
@add_start_docstrings("""RoFormer Model with a `language modeling` head on top.""", ROFORMER_START_DOCSTRING)
class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingLoss):
......@@ -911,12 +904,6 @@ class TFRoFormerForMaskedLM(TFRoFormerPreTrainedModel, TFMaskedLanguageModelingL
attentions=outputs.attentions,
)
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""RoFormer Model with a `language modeling` head on top for CLM fine-tuning.""", ROFORMER_START_DOCSTRING
......@@ -990,12 +977,6 @@ class TFRoFormerForCausalLM(TFRoFormerPreTrainedModel, TFCausalLanguageModelingL
attentions=outputs.attentions,
)
def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFCausalLMOutput(logits=output.logits, hidden_states=hs, attentions=attns)
class TFRoFormerClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
......@@ -1094,12 +1075,6 @@ class TFRoFormerForSequenceClassification(TFRoFormerPreTrainedModel, TFSequenceC
attentions=outputs.attentions,
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1118,17 +1093,6 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
units=1, kernel_initializer=get_initializer(config.initializer_range), name="classifier"
)
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
return {"input_ids": tf.constant(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)}
@unpack_inputs
@add_start_docstrings_to_model_forward(
ROFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
......@@ -1203,26 +1167,6 @@ class TFRoFormerForMultipleChoice(TFRoFormerPreTrainedModel, TFMultipleChoiceLos
attentions=outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFMultipleChoiceModelOutput:
output = self.call(input_ids=inputs)
return self.serving_output(output)
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMultipleChoiceModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1294,12 +1238,6 @@ class TFRoFormerForTokenClassification(TFRoFormerPreTrainedModel, TFTokenClassif
attentions=outputs.attentions,
)
def serving_output(self, output: TFTokenClassifierOutput) -> TFTokenClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTokenClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1383,11 +1321,3 @@ class TFRoFormerForQuestionAnswering(TFRoFormerPreTrainedModel, TFQuestionAnswer
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFQuestionAnsweringModelOutput) -> TFQuestionAnsweringModelOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
src/transformers/models/segformer/modeling_tf_segformer.py
View file @ 814de8fa
......@@ -18,7 +18,7 @@
from __future__ import annotations
import math
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import tensorflow as tf
......@@ -521,34 +521,8 @@ class TFSegformerPreTrainedModel(TFPreTrainedModel):
main_input_name = "pixel_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(shape=(3, self.config.num_channels, 512, 512), dtype=tf.float32)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
"""
Method used for serving the model.
Args:
inputs (`Dict[str, tf.Tensor]`):
The input of the saved model as a dictionary of tensors.
"""
output = self.call(inputs)
return self.serving_output(output)
def input_signature(self):
return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 512, 512), dtype=tf.float32)}
SEGFORMER_START_DOCSTRING = r"""
......@@ -631,14 +605,6 @@ class TFSegformerModel(TFSegformerPreTrainedModel):
)
return outputs
def serving_output(self, output: TFBaseModelOutput) -> TFBaseModelOutput:
# hidden_states and attention not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFBaseModelOutput(
last_hidden_state=output.last_hidden_state,
hidden_states=output.hidden_states,
attentions=output.attentions,
)
@add_start_docstrings(
"""
......@@ -702,12 +668,6 @@ class TFSegformerForImageClassification(TFSegformerPreTrainedModel, TFSequenceCl
loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
# hidden_states and attention not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSequenceClassifierOutput(
logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
)
class TFSegformerMLP(tf.keras.layers.Layer):
"""
......@@ -892,9 +852,3 @@ class TFSegformerForSemanticSegmentation(TFSegformerPreTrainedModel):
hidden_states=outputs.hidden_states if output_hidden_states else None,
attentions=outputs.attentions,
)
def serving_output(self, output: TFSemanticSegmenterOutput) -> TFSemanticSegmenterOutput:
# hidden_states and attention not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSemanticSegmenterOutput(
logits=output.logits, hidden_states=output.hidden_states, attentions=output.attentions
)
src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
View file @ 814de8fa
......@@ -18,7 +18,7 @@
from __future__ import annotations
import random
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
......@@ -563,26 +563,6 @@ class TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "model"
main_input_name = "input_features"
# Overwritten property due to different expected input shape and type
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
return {
self.main_input_name: tf.random.uniform(
[
1,
random.randint(1, self.config.max_source_positions), # time
self.config.input_feat_per_channel * self.config.input_channels, # input channels
]
),
"decoder_input_ids": tf.constant([[2, 3]], dtype=tf.int32),
}
def _get_feat_extract_output_lengths(self, input_lengths: tf.Tensor):
"""
Computes the output length of the convolutional layers
......@@ -592,20 +572,18 @@ class TFSpeech2TextPreTrainedModel(TFPreTrainedModel):
return input_lengths
@tf.function(
input_signature=[
{
"input_features": tf.TensorSpec((None, None, None), tf.float32, name="input_features"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
@property
def input_signature(self):
return {
"input_features": tf.TensorSpec(
(None, None, self.config.input_feat_per_channel * self.config.input_channels),
tf.float32,
name="input_features",
),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
SPEECH_TO_TEXT_START_DOCSTRING = r"""
......
src/transformers/models/swin/modeling_tf_swin.py
View file @ 814de8fa
......@@ -957,29 +957,6 @@ class TFSwinPreTrainedModel(TFPreTrainedModel):
if isinstance(module, TFSwinEncoder):
module.gradient_checkpointing = value
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network. Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(3, self.config.num_channels, self.config.image_size, self.config.image_size),
dtype=tf.float32,
)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
SWIN_START_DOCSTRING = r"""
This model is a Tensorflow
......@@ -1245,16 +1222,6 @@ class TFSwinModel(TFSwinPreTrainedModel):
return swin_outputs
def serving_output(self, output: TFSwinModelOutput) -> TFSwinModelOutput:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSwinModelOutput(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=output.hidden_states,
attentions=output.attentions,
reshaped_hidden_states=output.reshaped_hidden_states,
)
class TFSwinPixelShuffle(tf.keras.layers.Layer):
"""TF layer implementation of torch.nn.PixelShuffle"""
......@@ -1410,15 +1377,6 @@ class TFSwinForMaskedImageModeling(TFSwinPreTrainedModel):
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
def serving_output(self, output: TFSwinMaskedImageModelingOutput) -> TFSwinMaskedImageModelingOutput:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSwinMaskedImageModelingOutput(
reconstruction=output.reconstruction,
hidden_states=output.hidden_states,
attentions=output.attentions,
reshaped_hidden_states=output.reshaped_hidden_states,
)
@add_start_docstrings(
"""
......@@ -1493,12 +1451,3 @@ class TFSwinForImageClassification(TFSwinPreTrainedModel, TFSequenceClassificati
attentions=outputs.attentions,
reshaped_hidden_states=outputs.reshaped_hidden_states,
)
def serving_output(self, output: TFSwinImageClassifierOutput) -> TFSwinImageClassifierOutput:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFSwinImageClassifierOutput(
logits=output.logits,
hidden_states=output.hidden_states,
attentions=output.attentions,
reshaped_hidden_states=output.reshaped_hidden_states,
)
src/transformers/models/t5/modeling_tf_t5.py
View file @ 814de8fa
......@@ -45,8 +45,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
DUMMY_INPUTS,
DUMMY_MASK,
ContextManagers,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -867,32 +865,6 @@ class TFT5PreTrainedModel(TFPreTrainedModel):
# names with a '.' represents the authorized unexpected/missing layers when a TF model is loaded from a PT model
_keys_to_ignore_on_load_unexpected = [r"decoder\Wblock[\W_0]+layer[\W_1]+EncDecAttention\Wrelative_attention_bias"]
@property
def dummy_inputs(self):
inputs = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
input_mask = tf.constant(DUMMY_MASK, dtype=tf.int32)
dummy_inputs = {
"input_ids": inputs,
"decoder_input_ids": inputs,
"decoder_attention_mask": input_mask,
}
return dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
def get_input_embeddings(self):
return self.shared
......@@ -1249,25 +1221,6 @@ class TFT5Model(TFT5PreTrainedModel):
encoder_attentions=encoder_outputs.attentions,
)
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values[1:]) if self.config.use_cache else None
dec_hs = tf.convert_to_tensor(output.decoder_hidden_states) if self.config.output_hidden_states else None
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.output_attentions else None
cross_attns = tf.convert_to_tensor(output.cross_attentions) if self.config.output_attentions else None
enc_hs = tf.convert_to_tensor(output.encoder_hidden_states) if self.config.output_hidden_states else None
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.output_attentions else None
return TFSeq2SeqModelOutput(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
cross_attentions=cross_attns,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
)
@add_start_docstrings("""T5 Model with a `language modeling` head on top.""", T5_START_DOCSTRING)
class TFT5ForConditionalGeneration(TFT5PreTrainedModel, TFCausalLanguageModelingLoss):
......@@ -1539,10 +1492,6 @@ class TFT5EncoderModel(TFT5PreTrainedModel):
encoder_config.use_cache = False
self.encoder = TFT5MainLayer(encoder_config, self.shared, name="encoder")
@property
def dummy_inputs(self):
return {"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32)}
def get_encoder(self):
return self.encoder
......@@ -1600,23 +1549,3 @@ class TFT5EncoderModel(TFT5PreTrainedModel):
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
# Copied from transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertModel.serving_output
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutput(last_hidden_state=output.last_hidden_state, hidden_states=hs, attentions=attns)
src/transformers/models/tapas/modeling_tf_tapas.py
View file @ 814de8fa
......@@ -862,18 +862,13 @@ class TFTapasPreTrainedModel(TFPreTrainedModel):
config_class = TapasConfig
base_model_prefix = "tapas"
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None, 7), tf.int32, name="token_type_ids"),
}
TAPAS_START_DOCSTRING = r"""
......@@ -1038,17 +1033,6 @@ class TFTapasModel(TFTapasPreTrainedModel):
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hidden_states,
attentions=attentions,
)
@add_start_docstrings("""Tapas Model with a `language modeling` head on top.""", TAPAS_START_DOCSTRING)
class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
......@@ -1145,12 +1129,6 @@ class TFTapasForMaskedLM(TFTapasPreTrainedModel, TFMaskedLanguageModelingLoss):
attentions=outputs.attentions,
)
def serving_output(self, output: TFMaskedLMOutput) -> TFMaskedLMOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFMaskedLMOutput(logits=output.logits, hidden_states=hidden_states, attentions=attentions)
class TFTapasComputeTokenLogits(tf.keras.layers.Layer):
def __init__(self, config: TapasConfig, **kwargs):
......@@ -1574,17 +1552,6 @@ class TFTapasForQuestionAnswering(TFTapasPreTrainedModel):
attentions=outputs.attentions,
)
def serving_output(self, output: TFTableQuestionAnsweringOutput) -> TFTableQuestionAnsweringOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTableQuestionAnsweringOutput(
logits=output.logits,
logits_aggregation=output.logits_aggregation,
hidden_states=hidden_states,
attentions=attentions,
)
@add_start_docstrings(
"""
......@@ -1687,12 +1654,6 @@ class TFTapasForSequenceClassification(TFTapasPreTrainedModel, TFSequenceClassif
attentions=outputs.attentions,
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hidden_states, attentions=attentions)
""" TAPAS utilities."""
......
src/transformers/models/transfo_xl/modeling_tf_transfo_xl.py
View file @ 814de8fa
......@@ -684,18 +684,6 @@ class TFTransfoXLPreTrainedModel(TFPreTrainedModel):
config_class = TransfoXLConfig
base_model_prefix = "transformer"
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
@dataclass
class TFTransfoXLModelOutput(ModelOutput):
......@@ -916,17 +904,6 @@ class TFTransfoXLModel(TFTransfoXLPreTrainedModel):
return outputs
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTransfoXLModelOutput(
last_hidden_state=output.last_hidden_state,
mems=tf.convert_to_tensor(output.mems),
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings(
"""
......@@ -1015,17 +992,6 @@ class TFTransfoXLLMHeadModel(TFTransfoXLPreTrainedModel):
attentions=transformer_outputs.attentions,
)
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTransfoXLLMHeadModelOutput(
prediction_scores=output.prediction_scores,
mems=tf.convert_to_tensor(output.mems),
hidden_states=hs,
attentions=attns,
)
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **model_kwargs):
inputs = {}
......@@ -1157,11 +1123,3 @@ class TFTransfoXLForSequenceClassification(TFTransfoXLPreTrainedModel, TFSequenc
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
def serving_output(self, output):
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFTransfoXLSequenceClassifierOutputWithPast(
logits=output.logits, mems=tf.convert_to_tensor(output.mems), hidden_states=hs, attentions=attns
)
src/transformers/models/vision_encoder_decoder/modeling_tf_vision_encoder_decoder.py
View file @ 814de8fa
......@@ -29,7 +29,6 @@ from ...modeling_tf_outputs import TFBaseModelOutput, TFSeq2SeqLMOutput
from ...modeling_tf_utils import TFCausalLanguageModelingLoss, TFPreTrainedModel, get_initializer, unpack_inputs
from ...tf_utils import shape_list
from ...utils import (
DUMMY_INPUTS,
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -254,29 +253,26 @@ class TFVisionEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLos
)
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
decoder_input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
batch_size, seq_len = decoder_input_ids.shape
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(
batch_size,
self.config.encoder.num_channels,
self.config.encoder.image_size,
self.config.encoder.image_size,
def input_signature(self):
vision_config = self.config.encoder
if hasattr(vision_config, "vision_config"):
vision_config = vision_config.vision_config
if hasattr(vision_config, "image_size"):
image_size = vision_config.image_size
else:
image_size = vision_config.input_size
return {
"pixel_values": tf.TensorSpec(
shape=(
None,
vision_config.num_channels,
image_size,
image_size,
),
dtype=tf.float32,
),
dtype=tf.float32,
)
pixel_values = tf.constant(VISION_DUMMY_INPUTS)
# Add `decoder_input_ids` because `self.decoder` requires it.
dummy = {"pixel_values": pixel_values, "decoder_input_ids": decoder_input_ids}
return dummy
"decoder_input_ids": tf.TensorSpec(shape=(None, None), dtype=tf.int32, name="decoder_input_ids"),
}
def get_encoder(self):
return self.encoder
......
src/transformers/models/vit/modeling_tf_vit.py
View file @ 814de8fa
......@@ -19,7 +19,7 @@ from __future__ import annotations
import collections.abc
import math
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
......@@ -550,38 +550,6 @@ class TFViTPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "vit"
main_input_name = "pixel_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(3, self.config.num_channels, self.config.image_size, self.config.image_size), dtype=tf.float32
)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
"""
Method used for serving the model.
Args:
inputs (`Dict[str, tf.Tensor]`):
The input of the saved model as a dictionary of tensors.
"""
output = self.call(inputs)
return self.serving_output(output)
VIT_START_DOCSTRING = r"""
......@@ -697,17 +665,6 @@ class TFViTModel(TFViTPreTrainedModel):
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
)
class TFViTPooler(tf.keras.layers.Layer):
def __init__(self, config: ViTConfig, **kwargs):
......@@ -807,9 +764,3 @@ class TFViTForImageClassification(TFViTPreTrainedModel, TFSequenceClassification
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFSequenceClassifierOutput) -> TFSequenceClassifierOutput:
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(logits=output.logits, hidden_states=hs, attentions=attns)
src/transformers/models/vit_mae/modeling_tf_vit_mae.py
View file @ 814de8fa
......@@ -21,7 +21,7 @@ import collections.abc
import math
from copy import deepcopy
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
from typing import Optional, Tuple, Union
import numpy as np
import tensorflow as tf
......@@ -698,36 +698,6 @@ class TFViTMAEPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "vit"
main_input_name = "pixel_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network. Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(3, self.config.num_channels, self.config.image_size, self.config.image_size),
dtype=tf.float32,
)
return {"pixel_values": tf.constant(VISION_DUMMY_INPUTS)}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs):
"""
Method used for serving the model.
Args:
inputs (`Dict[str, tf.Tensor]`):
The input of the saved model as a dictionary of tensors.
"""
output = self.call(inputs)
return self.serving_output(output)
VIT_MAE_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......@@ -859,18 +829,6 @@ class TFViTMAEModel(TFViTMAEPreTrainedModel):
return outputs
def serving_output(self, output: TFViTMAEModelOutput) -> TFViTMAEModelOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFViTMAEModelOutput(
last_hidden_state=output.last_hidden_state,
mask=output.mask,
ids_restore=output.ids_restore,
hidden_states=hidden_states,
attentions=attentions,
)
class TFViTMAEDecoder(tf.keras.layers.Layer):
def __init__(self, config, num_patches, **kwargs):
......@@ -1173,15 +1131,3 @@ class TFViTMAEForPreTraining(TFViTMAEPreTrainedModel):
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output: TFViTMAEForPreTrainingOutput) -> TFViTMAEForPreTrainingOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFViTMAEForPreTrainingOutput(
logits=output.logits,
mask=output.mask,
ids_restore=output.ids_restore,
hidden_states=hidden_states,
attentions=attentions,
)
src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
View file @ 814de8fa
......@@ -19,7 +19,7 @@ from __future__ import annotations
import warnings
from dataclasses import dataclass
from typing import Any, Dict, Optional, Tuple, Union
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
......@@ -1185,14 +1185,18 @@ class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
main_input_name = "input_values"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
pad_token = 0.0
input_values = tf.convert_to_tensor(np.random.rand(1, 16000), tf.float32)
dummy_inputs = {
"input_values": input_values,
"attention_mask": tf.cast(tf.not_equal(input_values, pad_token), tf.float32),
def input_signature(self):
return {
"input_values": tf.TensorSpec((None, None), tf.float32, name="input_values"),
"attention_mask": tf.TensorSpec((None, None), tf.float32, name="attention_mask"),
}
@property
def dummy_inputs(self):
return {
"input_values": tf.random.uniform(shape=(1, 16000), dtype=tf.float32),
"attention_mask": tf.ones(shape=(1, 16000), dtype=tf.float32),
}
return dummy_inputs
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
......@@ -1201,20 +1205,6 @@ class TFWav2Vec2PreTrainedModel(TFPreTrainedModel):
"to train/fine-tine this model, you need a GPU or a TPU"
)
@tf.function(
input_signature=[
{
"input_values": tf.TensorSpec((None, None), tf.float32, name="input_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(input_values=inputs, training=False)
return self.serving_output(output)
def _get_feat_extract_output_lengths(self, input_lengths, add_adapter=None):
"""
Computes the output length of the convolutional layers
......@@ -1427,17 +1417,6 @@ class TFWav2Vec2Model(TFWav2Vec2PreTrainedModel):
return outputs
def serving_output(self, output):
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFWav2Vec2BaseModelOutput(
last_hidden_state=output.last_hidden_state,
extract_features=output.extract_features,
hidden_states=hidden_states,
attentions=attentions,
)
@add_start_docstrings(
"""TFWav2Vec2 Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
......@@ -1591,11 +1570,6 @@ class TFWav2Vec2ForCTC(TFWav2Vec2PreTrainedModel):
attentions=outputs.attentions,
)
def serving_output(self, output: TFCausalLMOutput) -> TFCausalLMOutput:
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFCausalLMOutput(logits=output.logits, hidden_states=hidden_states, attentions=attentions)
class TFWav2Vec2ForSequenceClassification(TFWav2Vec2PreTrainedModel):
def __init__(self, config):
......@@ -1693,27 +1667,3 @@ class TFWav2Vec2ForSequenceClassification(TFWav2Vec2PreTrainedModel):
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
def serving_output(self, output):
hidden_states = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attentions = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
return TFSequenceClassifierOutput(
logits=output.logits,
hidden_states=hidden_states,
attentions=attentions,
)
@tf.function(
input_signature=[
{
"input_values": tf.TensorSpec((None, None), tf.float32, name="input_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(input_values=inputs)
return self.serving_output(output)
src/transformers/models/whisper/modeling_tf_whisper.py
View file @ 814de8fa
......@@ -486,18 +486,13 @@ class TFWhisperPreTrainedModel(TFPreTrainedModel):
"decoder_input_ids": tf.constant([[2, 3]], dtype=tf.int32),
}
@tf.function(
input_signature=[
{
"input_features": tf.TensorSpec((None, None, None), tf.float32, name="input_features"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
@property
def input_signature(self):
return {
"input_features": tf.TensorSpec((None, self.config.num_mel_bins, None), tf.float32, name="input_features"),
"decoder_input_ids": tf.TensorSpec((None, None), tf.int32, name="decoder_input_ids"),
"decoder_attention_mask": tf.TensorSpec((None, None), tf.int32, name="decoder_attention_mask"),
}
WHISPER_START_DOCSTRING = r"""
......
src/transformers/models/xglm/modeling_tf_xglm.py
View file @ 814de8fa
......@@ -28,7 +28,6 @@ from ...activations_tf import get_tf_activation
# Public API
from ...file_utils import (
DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -620,29 +619,6 @@ class TFXGLMPreTrainedModel(TFPreTrainedModel):
config_class = XGLMConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
dummy_inputs = {
"input_ids": input_ids,
"attention_mask": tf.cast(input_ids != pad_token, tf.int32),
}
return dummy_inputs
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
XGLM_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......@@ -821,24 +797,6 @@ class TFXGLMModel(TFXGLMPreTrainedModel):
return outputs
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.output_attentions and self.config.add_cross_attention
else None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output.hidden_states,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings(
"""
......@@ -971,22 +929,3 @@ class TFXGLMForCausalLM(TFXGLMPreTrainedModel, TFCausalLanguageModelingLoss):
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
def serving_output(self, output):
pkv = tf.convert_to_tensor(output.past_key_values) if self.config.use_cache else None
hs = tf.convert_to_tensor(output.hidden_states) if self.config.output_hidden_states else None
attns = tf.convert_to_tensor(output.attentions) if self.config.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.output_attentions and self.config.add_cross_attention
else None
)
return TFCausalLMOutputWithCrossAttentions(
loss=output.loss,
logits=output.logits,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
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