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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 71 additions and 1297 deletions
src/transformers/models/electra/modeling_tf_electra.py
View file @ 814de8fa
......@@ -20,7 +20,7 @@ from __future__ import annotations
import math
import warnings
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
......@@ -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,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
......@@ -596,25 +594,6 @@ class TFElectraPreTrainedModel(TFPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"generator_lm_head.weight"]
_keys_to_ignore_on_load_missing = [r"dropout"]
@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
@keras_serializable
class TFElectraMainLayer(tf.keras.layers.Layer):
......@@ -998,23 +977,6 @@ class TFElectraModel(TFElectraPreTrainedModel):
return outputs
def serving_output(self, output):
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 TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings(
"""
......@@ -1087,12 +1049,6 @@ class TFElectraForPreTraining(TFElectraPreTrainedModel):
attentions=discriminator_hidden_states.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 TFElectraForPreTrainingOutput(logits=output.logits, hidden_states=hs, attentions=attns)
class TFElectraMaskedLMHead(tf.keras.layers.Layer):
def __init__(self, config, input_embeddings, **kwargs):
......@@ -1221,13 +1177,6 @@ class TFElectraForMaskedLM(TFElectraPreTrainedModel, TFMaskedLanguageModelingLos
attentions=generator_hidden_states.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 TFElectraClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
......@@ -1329,13 +1278,6 @@ class TFElectraForSequenceClassification(TFElectraPreTrainedModel, 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(
"""
......@@ -1356,16 +1298,6 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, 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(ELECTRA_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
......@@ -1438,28 +1370,6 @@ class TFElectraForMultipleChoice(TFElectraPreTrainedModel, 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"),
}
]
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving
def serving(self, inputs: Dict[str, tf.Tensor]):
output = self.call(input_ids=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(
"""
......@@ -1538,13 +1448,6 @@ class TFElectraForTokenClassification(TFElectraPreTrainedModel, TFTokenClassific
attentions=discriminator_hidden_states.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(
"""
......@@ -1638,12 +1541,3 @@ class TFElectraForQuestionAnswering(TFElectraPreTrainedModel, TFQuestionAnswerin
hidden_states=discriminator_hidden_states.hidden_states,
attentions=discriminator_hidden_states.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/encoder_decoder/modeling_tf_encoder_decoder.py
View file @ 814de8fa
......@@ -36,7 +36,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import shape_list
from ...utils import (
DUMMY_INPUTS,
ModelOutput,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -276,19 +275,6 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
"following discussion on GitHub: https://github.com/huggingface/transformers/issues/23350"
)
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
# Add `decoder_input_ids` because `self.decoder` requires it.
input_ids = tf.constant(DUMMY_INPUTS, dtype=tf.int32)
dummy = {"input_ids": input_ids, "decoder_input_ids": input_ids}
return dummy
def get_encoder(self):
return self.encoder
......@@ -642,33 +628,6 @@ class TFEncoderDecoderModel(TFPreTrainedModel, TFCausalLanguageModelingLoss):
encoder_attentions=encoder_outputs.attentions,
)
def serving_output(self, output):
pkv = tf.tuple(output.past_key_values)[1] if self.config.decoder.use_cache else None
dec_hs = (
tf.convert_to_tensor(output.decoder_hidden_states) if self.config.decoder.output_hidden_states else None
)
dec_attns = tf.convert_to_tensor(output.decoder_attentions) if self.config.decoder.output_attentions else None
enc_hs = (
tf.convert_to_tensor(output.encoder_hidden_states) if self.config.encoder.output_hidden_states else None
)
enc_attns = tf.convert_to_tensor(output.encoder_attentions) if self.config.encoder.output_attentions else None
cross_attns = (
tf.convert_to_tensor(output.cross_attentions)
if self.config.decoder.output_attentions and output.cross_attentions is not None
else None
)
return TFSeq2SeqLMOutput(
logits=output.logits,
past_key_values=pkv,
decoder_hidden_states=dec_hs,
decoder_attentions=dec_attns,
encoder_last_hidden_state=output.encoder_last_hidden_state,
encoder_hidden_states=enc_hs,
encoder_attentions=enc_attns,
cross_attentions=cross_attns,
)
def prepare_inputs_for_generation(
self, input_ids, past_key_values=None, attention_mask=None, use_cache=None, encoder_outputs=None, **kwargs
):
......
src/transformers/models/esm/modeling_tf_esm.py
View file @ 814de8fa
......@@ -1038,39 +1038,6 @@ class TFEsmModel(TFEsmPreTrainedModel):
)
return outputs
@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)
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,
)
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask)
......@@ -1170,26 +1137,6 @@ class TFEsmForMaskedLM(TFEsmPreTrainedModel, TFMaskedLanguageModelingLoss):
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)
@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)
def predict_contacts(self, tokens, attention_mask):
return self.esm.predict_contacts(tokens, attention_mask)
......@@ -1310,26 +1257,6 @@ class TFEsmForSequenceClassification(TFEsmPreTrainedModel, TFSequenceClassificat
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)
@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)
@add_start_docstrings(
"""
......@@ -1406,26 +1333,6 @@ class TFEsmForTokenClassification(TFEsmPreTrainedModel, TFTokenClassificationLos
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)
@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)
class TFEsmClassificationHead(Layer):
"""Head for sentence-level classification tasks."""
......
src/transformers/models/flaubert/modeling_tf_flaubert.py
View file @ 814de8fa
......@@ -290,13 +290,6 @@ class TFFlaubertModel(TFFlaubertPreTrainedModel):
return outputs
# 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)
# Copied from transformers.models.xlm.modeling_tf_xlm.TFXLMMultiHeadAttention with XLM->Flaubert
class TFFlaubertMultiHeadAttention(tf.keras.layers.Layer):
......@@ -845,12 +838,6 @@ class TFFlaubertWithLMHeadModel(TFFlaubertPreTrainedModel):
logits=outputs, 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 TFFlaubertWithLMHeadModelOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -930,13 +917,6 @@ class TFFlaubertForSequenceClassification(TFFlaubertPreTrainedModel, TFSequenceC
attentions=transformer_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(
"""
......@@ -1029,15 +1009,6 @@ class TFFlaubertForQuestionAnsweringSimple(TFFlaubertPreTrainedModel, TFQuestion
attentions=transformer_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
)
@add_start_docstrings(
"""
......@@ -1119,13 +1090,6 @@ class TFFlaubertForTokenClassification(TFFlaubertPreTrainedModel, TFTokenClassif
attentions=transformer_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(
"""
......@@ -1247,25 +1211,3 @@ class TFFlaubertForMultipleChoice(TFFlaubertPreTrainedModel, TFMultipleChoiceLos
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_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"),
}
]
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving
def serving(self, inputs: Dict[str, tf.Tensor]):
output = self.call(input_ids=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)
src/transformers/models/funnel/modeling_tf_funnel.py
View file @ 814de8fa
......@@ -19,7 +19,7 @@ from __future__ import annotations
import warnings
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
......@@ -47,7 +47,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
......@@ -1425,16 +1424,6 @@ class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
self.funnel = TFFunnelBaseLayer(config, name="funnel")
self.classifier = TFFunnelClassificationHead(config, 1, 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(FUNNEL_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
......@@ -1504,20 +1493,6 @@ class TFFunnelForMultipleChoice(TFFunnelPreTrainedModel, TFMultipleChoiceLoss):
attentions=outputs.attentions,
)
@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), 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=output)
def serving_output(self, output: TFMultipleChoiceModelOutput) -> TFMultipleChoiceModelOutput:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of
# different dimensions
......
src/transformers/models/gpt2/modeling_tf_gpt2.py
View file @ 814de8fa
......@@ -42,7 +42,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
......@@ -522,37 +521,6 @@ class TFGPT2PreTrainedModel(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"h.\d+.attn.bias", r"h.\d+.crossattention.bias"]
@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
@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)
@dataclass
class TFGPT2DoubleHeadsModelOutput(ModelOutput):
......@@ -773,26 +741,6 @@ class TFGPT2Model(TFGPT2PreTrainedModel):
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
and output.cross_attentions is not None
else None
)
return TFBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
cross_attentions=cross_attns,
)
@add_start_docstrings(
"""
......@@ -925,22 +873,6 @@ class TFGPT2LMHeadModel(TFGPT2PreTrainedModel, TFCausalLanguageModelingLoss):
cross_attentions=transformer_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
and output.cross_attentions is not None
else None
)
return TFCausalLMOutputWithCrossAttentions(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns, cross_attentions=cross_attns
)
@add_start_docstrings(
"""
......@@ -1062,32 +994,13 @@ class TFGPT2DoubleHeadsModel(TFGPT2PreTrainedModel):
attentions=transformer_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"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
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
return TFGPT2DoubleHeadsModelOutput(
logits=output.logits,
mc_logits=output.mc_logits,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
)
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="mc_token_ids"),
}
@add_start_docstrings(
......@@ -1210,12 +1123,3 @@ class TFGPT2ForSequenceClassification(TFGPT2PreTrainedModel, TFSequenceClassific
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.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
return TFSequenceClassifierOutputWithPast(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns
)
src/transformers/models/gptj/modeling_tf_gptj.py
View file @ 814de8fa
......@@ -23,7 +23,6 @@ import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...file_utils import (
DUMMY_INPUTS,
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
......@@ -513,30 +512,6 @@ class TFGPTJPreTrainedModel(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"h.\d+.attn.bias"]
@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)}
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)
GPTJ_START_DOCSTRING = r"""
......@@ -697,18 +672,6 @@ class TFGPTJModel(TFGPTJPreTrainedModel):
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
return TFBaseModelOutputWithPast(
last_hidden_state=output.last_hidden_state,
past_key_values=pkv,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings(
"""
......@@ -821,13 +784,6 @@ class TFGPTJForCausalLM(TFGPTJPreTrainedModel, TFCausalLanguageModelingLoss):
attentions=transformer_outputs.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
return TFCausalLMOutputWithPast(logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -952,15 +908,6 @@ class TFGPTJForSequenceClassification(TFGPTJPreTrainedModel, TFSequenceClassific
attentions=transformer_outputs.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
return TFSequenceClassifierOutputWithPast(
logits=output.logits, past_key_values=pkv, hidden_states=hs, attentions=attns
)
@add_start_docstrings(
"""
......@@ -1051,11 +998,3 @@ class TFGPTJForQuestionAnswering(TFGPTJPreTrainedModel, TFQuestionAnsweringLoss)
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_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/groupvit/modeling_tf_groupvit.py
View file @ 814de8fa
......@@ -20,7 +20,7 @@ from __future__ import annotations
import collections.abc
import math
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
......@@ -28,7 +28,6 @@ import tensorflow as tf
from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import TFBaseModelOutput, TFBaseModelOutputWithPooling
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFModelInputType,
TFPreTrainedModel,
get_initializer,
......@@ -1608,30 +1607,6 @@ class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
self.groupvit = TFGroupViTTextMainLayer(config, name="groupvit")
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network.
Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
return {
"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
}
@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: Dict[str, tf.Tensor]) -> TFBaseModelOutputWithPooling:
output = self.call(inputs)
return self.serving_output(output)
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTTextConfig)
......@@ -1675,17 +1650,6 @@ class TFGroupViTTextModel(TFGroupViTPreTrainedModel):
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 TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
config_class = GroupViTVisionConfig
......@@ -1696,38 +1660,6 @@ class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
self.groupvit = TFGroupViTVisionMainLayer(config, name="groupvit")
@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=(len(DUMMY_INPUTS), 3, self.config.image_size, self.config.image_size), dtype=tf.float32
)
return {"pixel_values": VISION_DUMMY_INPUTS}
@tf.function(
input_signature=[
{
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFBaseModelOutputWithPooling:
"""
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)
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_VISION_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=TFBaseModelOutputWithPooling, config_class=GroupViTVisionConfig)
......@@ -1772,15 +1704,6 @@ class TFGroupViTVisionModel(TFGroupViTPreTrainedModel):
return outputs
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
# hidden_states and attentions not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=output.hidden_states,
attentions=output.attentions,
)
@add_start_docstrings(GROUPVIT_START_DOCSTRING)
class TFGroupViTModel(TFGroupViTPreTrainedModel):
......@@ -1791,44 +1714,6 @@ class TFGroupViTModel(TFGroupViTPreTrainedModel):
self.groupvit = TFGroupViTMainLayer(config, name="groupvit")
@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=(len(DUMMY_INPUTS), 3, self.config.vision_config.image_size, self.config.vision_config.image_size),
dtype=tf.float32,
)
return {
"input_ids": tf.constant(DUMMY_INPUTS, dtype=tf.int32),
"pixel_values": VISION_DUMMY_INPUTS,
}
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float64, name="pixel_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
def serving(self, inputs: Dict[str, tf.Tensor]) -> TFGroupViTModelOutput:
"""
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)
@unpack_inputs
@add_start_docstrings_to_model_forward(GROUPVIT_TEXT_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
def get_text_features(
......
src/transformers/models/hubert/modeling_tf_hubert.py
View file @ 814de8fa
......@@ -17,7 +17,7 @@
from __future__ import annotations
import warnings
from typing import Any, Dict, Optional, Tuple, Union
from typing import Any, Optional, Tuple, Union
import numpy as np
import tensorflow as tf
......@@ -1157,14 +1157,12 @@ class TFHubertPreTrainedModel(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, 16000), 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"),
}
return dummy_inputs
def __init__(self, config, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
......@@ -1173,20 +1171,6 @@ class TFHubertPreTrainedModel(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)
HUBERT_START_DOCSTRING = r"""
......@@ -1359,13 +1343,6 @@ class TFHubertModel(TFHubertPreTrainedModel):
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 TFBaseModelOutput(
last_hidden_state=output.last_hidden_state, hidden_states=hidden_states, attentions=attentions
)
@add_start_docstrings(
"""TFHubert Model with a `language modeling` head on top for Connectionist Temporal Classification (CTC).""",
......@@ -1518,8 +1495,3 @@ class TFHubertForCTC(TFHubertPreTrainedModel):
hidden_states=outputs.hidden_states,
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)
src/transformers/models/layoutlm/modeling_tf_layoutlm.py
View file @ 814de8fa
......@@ -986,27 +986,6 @@ class TFLayoutLMModel(TFLayoutLMPreTrainedModel):
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("""LayoutLM Model with a `language modeling` head on top.""", LAYOUTLM_START_DOCSTRING)
class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, TFMaskedLanguageModelingLoss):
......@@ -1128,12 +1107,6 @@ class TFLayoutLMForMaskedLM(TFLayoutLMPreTrainedModel, 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(
"""
......@@ -1252,12 +1225,6 @@ class TFLayoutLMForSequenceClassification(TFLayoutLMPreTrainedModel, 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(
"""
......@@ -1380,12 +1347,6 @@ class TFLayoutLMForTokenClassification(TFLayoutLMPreTrainedModel, 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(
"""
......@@ -1524,11 +1485,3 @@ class TFLayoutLMForQuestionAnswering(TFLayoutLMPreTrainedModel, TFQuestionAnswer
hidden_states=outputs.hidden_states,
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 TFQuestionAnsweringModelOutput(
start_logits=output.start_logits, end_logits=output.end_logits, hidden_states=hs, attentions=attns
)
src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py
View file @ 814de8fa
......@@ -19,7 +19,7 @@ from __future__ import annotations
import collections
import math
from typing import Dict, List, Optional, Tuple, Union
from typing import List, Optional, Tuple, Union
import tensorflow as tf
......@@ -980,37 +980,10 @@ class TFLayoutLMv3PreTrainedModel(TFPreTrainedModel):
base_model_prefix = "layoutlmv3"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
size = self.config.input_size
image_shape = (2, self.config.num_channels, size, size)
pixel_values = tf.random.uniform(shape=image_shape, minval=-1, maxval=1)
return {
"input_ids": tf.constant(_DUMMY_INPUT_IDS, dtype=tf.int32),
"bbox": tf.constant(_DUMMY_BBOX, dtype=tf.int32),
"pixel_values": pixel_values,
}
@tf.function(
input_signature=[
{
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"bbox": tf.TensorSpec((None, None, 4), tf.int32, name="bbox"),
"pixel_values": tf.TensorSpec((None, None, None, None), tf.float32, name="pixel_values"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
}
]
)
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):
sig = super().input_signature
sig["bbox"] = tf.TensorSpec((None, None, 4), tf.int32, name="bbox")
return sig
LAYOUTLMV3_START_DOCSTRING = r"""
......@@ -1207,16 +1180,6 @@ class TFLayoutLMv3Model(TFLayoutLMv3PreTrainedModel):
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,
)
class TFLayoutLMv3ClassificationHead(tf.keras.layers.Layer):
"""
......@@ -1354,13 +1317,6 @@ class TFLayoutLMv3ForSequenceClassification(TFLayoutLMv3PreTrainedModel, TFSeque
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(
"""
......@@ -1484,13 +1440,6 @@ class TFLayoutLMv3ForTokenClassification(TFLayoutLMv3PreTrainedModel, TFTokenCla
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(
"""
......@@ -1618,12 +1567,3 @@ class TFLayoutLMv3ForQuestionAnswering(TFLayoutLMv3PreTrainedModel, TFQuestionAn
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/led/modeling_tf_led.py
View file @ 814de8fa
......@@ -1323,33 +1323,10 @@ class TFLEDPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "led"
@property
def dummy_inputs(self):
input_ids = tf.convert_to_tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0]], dtype=tf.int32)
# make sure global layers are initialized
attention_mask = tf.convert_to_tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0]], dtype=tf.int32)
global_attention_mask = tf.convert_to_tensor([[0, 0, 0, 0, 1], [0, 0, 1, 0, 0]], dtype=tf.int32)
dummy_inputs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"global_attention_mask": global_attention_mask,
"decoder_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"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(output)
def input_signature(self):
sig = super().input_signature
sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
return sig
@dataclass
......
src/transformers/models/longformer/modeling_tf_longformer.py
View file @ 814de8fa
......@@ -39,7 +39,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
......@@ -1874,31 +1873,10 @@ class TFLongformerPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "longformer"
@property
def dummy_inputs(self):
input_ids = tf.convert_to_tensor([[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]], dtype=tf.int32)
# make sure global layers are initialized
attention_mask = tf.convert_to_tensor([[1, 1, 0, 0, 1], [1, 1, 1, 0, 0], [1, 0, 0, 1, 1]], dtype=tf.int32)
global_attention_mask = tf.convert_to_tensor(
[[0, 0, 0, 0, 1], [0, 0, 1, 0, 0], [0, 0, 0, 0, 1]], dtype=tf.int32
)
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"global_attention_mask": global_attention_mask,
}
@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)
def input_signature(self):
sig = super().input_signature
sig["global_attention_mask"] = tf.TensorSpec((None, None), tf.int32, name="global_attention_mask")
return sig
LONGFORMER_START_DOCSTRING = r"""
......@@ -2069,19 +2047,6 @@ class TFLongformerModel(TFLongformerPreTrainedModel):
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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=hs,
attentions=attns,
global_attentions=g_attns,
)
@add_start_docstrings(
"""Longformer Model with a `language modeling` head on top.""",
......@@ -2166,15 +2131,6 @@ class TFLongformerForMaskedLM(TFLongformerPreTrainedModel, TFMaskedLanguageModel
global_attentions=outputs.global_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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerMaskedLMOutput(
logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns
)
@add_start_docstrings(
"""
......@@ -2305,19 +2261,6 @@ class TFLongformerForQuestionAnswering(TFLongformerPreTrainedModel, TFQuestionAn
global_attentions=outputs.global_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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerQuestionAnsweringModelOutput(
start_logits=output.start_logits,
end_logits=output.end_logits,
hidden_states=hs,
attentions=attns,
global_attentions=g_attns,
)
class TFLongformerClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
......@@ -2446,15 +2389,6 @@ class TFLongformerForSequenceClassification(TFLongformerPreTrainedModel, TFSeque
global_attentions=outputs.global_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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerSequenceClassifierOutput(
logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns
)
@add_start_docstrings(
"""
......@@ -2477,11 +2411,12 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic
)
@property
def dummy_inputs(self):
input_ids = tf.convert_to_tensor(MULTIPLE_CHOICE_DUMMY_INPUTS, dtype=tf.int32)
# make sure global layers are initialized
global_attention_mask = tf.convert_to_tensor([[[0, 0, 0, 1], [0, 0, 0, 1]]] * 2, dtype=tf.int32)
return {"input_ids": input_ids, "global_attention_mask": global_attention_mask}
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"global_attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="global_attention_mask"),
}
@unpack_inputs
@add_start_docstrings_to_model_forward(
......@@ -2568,28 +2503,6 @@ class TFLongformerForMultipleChoice(TFLongformerPreTrainedModel, TFMultipleChoic
global_attentions=outputs.global_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)
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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerMultipleChoiceModelOutput(
logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns
)
@add_start_docstrings(
"""
......@@ -2669,12 +2582,3 @@ class TFLongformerForTokenClassification(TFLongformerPreTrainedModel, TFTokenCla
attentions=outputs.attentions,
global_attentions=outputs.global_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
g_attns = tf.convert_to_tensor(output.global_attentions) if self.config.output_attentions else None
return TFLongformerTokenClassifierOutput(
logits=output.logits, hidden_states=hs, attentions=attns, global_attentions=g_attns
)
src/transformers/models/lxmert/modeling_tf_lxmert.py
View file @ 814de8fa
......@@ -636,26 +636,6 @@ class TFLxmertEncoder(tf.keras.layers.Layer):
class TFLxmertMainLayer(tf.keras.layers.Layer):
config_class = LxmertConfig
@property
def dummy_inputs(self):
"""
Dummy inputs to build the network.
Returns:
tf.Tensor with dummy inputs
"""
batch_size = 2
num_visual_features = 10
input_ids = tf.constant([[3, 5, 6], [2, 3, 4]], dtype=tf.int32)
visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
return {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": visual_pos,
}
def __init__(self, config, **kwargs):
super().__init__(**kwargs)
......@@ -802,25 +782,35 @@ class TFLxmertPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "lxmert"
@property
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
return getattr(self, self.base_model_prefix).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"),
"visual_feats": tf.TensorSpec((None, None, None), tf.float32, name="visual_feats"),
"visual_pos": tf.TensorSpec((None, None, None), tf.float32, name="visual_pos"),
"visual_attention_mask": tf.TensorSpec((None, None), tf.int32, name="visual_attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
def dummy_inputs(self):
"""
Dummy inputs to build the network.
return self.serving_output(output)
Returns:
tf.Tensor with dummy inputs
"""
batch_size = 2
num_visual_features = 10
input_ids = tf.constant([[3, 5, 6], [2, 3, 4]], dtype=tf.int32)
visual_feats = tf.random.uniform((batch_size, num_visual_features, self.config.visual_feat_dim))
visual_pos = tf.random.uniform((batch_size, num_visual_features, 4))
return {
"input_ids": input_ids,
"visual_feats": visual_feats,
"visual_pos": visual_pos,
}
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None), tf.int32, name="attention_mask"),
"visual_feats": tf.TensorSpec((None, None, self.config.visual_feat_dim), tf.float32, name="visual_feats"),
"visual_pos": tf.TensorSpec((None, None, 4), tf.float32, name="visual_pos"),
"visual_attention_mask": tf.TensorSpec((None, None), tf.int32, name="visual_attention_mask"),
"token_type_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
LXMERT_START_DOCSTRING = r"""
......@@ -976,24 +966,6 @@ class TFLxmertModel(TFLxmertPreTrainedModel):
return outputs
def serving_output(self, output):
l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None
v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None
l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None
v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None
c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None
return TFLxmertModelOutput(
pooled_output=output.pooled_output,
language_output=output.language_output,
vision_output=output.vision_output,
language_hidden_states=l_hs,
vision_hidden_states=v_hs,
language_attentions=l_attns,
vision_attentions=v_attns,
cross_encoder_attentions=c_enc_attns,
)
class TFLxmertPooler(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
......@@ -1415,21 +1387,3 @@ class TFLxmertForPreTraining(TFLxmertPreTrainedModel):
vision_attentions=lxmert_output.vision_attentions,
cross_encoder_attentions=lxmert_output.cross_encoder_attentions,
)
def serving_output(self, output):
l_hs = tf.convert_to_tensor(output.language_hidden_states) if self.config.output_hidden_states else None
v_hs = tf.convert_to_tensor(output.vision_hidden_states) if self.config.output_hidden_states else None
l_attns = tf.convert_to_tensor(output.language_attentions) if self.config.output_attentions else None
v_attns = tf.convert_to_tensor(output.vision_attentions) if self.config.output_attentions else None
c_enc_attns = tf.convert_to_tensor(output.cross_encoder_attentions) if self.config.output_attentions else None
return TFLxmertForPreTrainingOutput(
prediction_logits=output.prediction_logits,
cross_relationship_score=output.cross_relationship_score,
question_answering_score=output.question_answering_score,
language_hidden_states=l_hs,
vision_hidden_states=v_hs,
language_attentions=l_attns,
vision_attentions=v_attns,
cross_encoder_attentions=c_enc_attns,
)
src/transformers/models/marian/modeling_tf_marian.py
View file @ 814de8fa
......@@ -33,7 +33,6 @@ from ...modeling_tf_outputs import (
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFCausalLanguageModelingLoss,
TFPreTrainedModel,
keras_serializable,
......@@ -501,34 +500,6 @@ class TFMarianPreTrainedModel(TFPreTrainedModel):
config_class = MarianConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), 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)
MARIAN_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......
src/transformers/models/mbart/modeling_tf_mbart.py
View file @ 814de8fa
......@@ -32,7 +32,6 @@ from ...modeling_tf_outputs import (
# Public API
from ...modeling_tf_utils import (
DUMMY_INPUTS,
TFCausalLanguageModelingLoss,
TFModelInputType,
TFPreTrainedModel,
......@@ -468,34 +467,6 @@ class TFMBartPreTrainedModel(TFPreTrainedModel):
config_class = MBartConfig
base_model_prefix = "model"
@property
def dummy_inputs(self):
pad_token = 1
input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), tf.int32)
decoder_input_ids = tf.cast(tf.convert_to_tensor(DUMMY_INPUTS), 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)
MBART_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......
src/transformers/models/mobilebert/modeling_tf_mobilebert.py
View file @ 814de8fa
......@@ -20,7 +20,7 @@ from __future__ import annotations
import warnings
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
......@@ -51,7 +51,6 @@ from ...modeling_tf_utils import (
)
from ...tf_utils import check_embeddings_within_bounds, shape_list, stable_softmax
from ...utils import (
MULTIPLE_CHOICE_DUMMY_INPUTS,
ModelOutput,
add_code_sample_docstrings,
add_start_docstrings,
......@@ -998,17 +997,6 @@ class TFMobileBertModel(TFMobileBertPreTrainedModel):
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,
)
@add_start_docstrings(
"""
......@@ -1099,17 +1087,6 @@ class TFMobileBertForPreTraining(TFMobileBertPreTrainedModel, TFMobileBertPreTra
attentions=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 TFMobileBertForPreTrainingOutput(
prediction_logits=output.prediction_logits,
seq_relationship_logits=output.seq_relationship_logits,
hidden_states=hs,
attentions=attns,
)
@add_start_docstrings("""MobileBert Model with a `language modeling` head on top.""", MOBILEBERT_START_DOCSTRING)
class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModelingLoss):
......@@ -1190,13 +1167,6 @@ class TFMobileBertForMaskedLM(TFMobileBertPreTrainedModel, TFMaskedLanguageModel
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 TFMobileBertOnlyNSPHead(tf.keras.layers.Layer):
def __init__(self, config, **kwargs):
......@@ -1289,13 +1259,6 @@ class TFMobileBertForNextSentencePrediction(TFMobileBertPreTrainedModel, TFNextS
attentions=outputs.attentions,
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForNextSentencePrediction.serving_output
def serving_output(self, output: TFNextSentencePredictorOutput) -> TFNextSentencePredictorOutput:
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 TFNextSentencePredictorOutput(logits=output.logits, hidden_states=hs, attentions=attns)
@add_start_docstrings(
"""
......@@ -1386,13 +1349,6 @@ class TFMobileBertForSequenceClassification(TFMobileBertPreTrainedModel, TFSeque
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(
"""
......@@ -1492,15 +1448,6 @@ class TFMobileBertForQuestionAnswering(TFMobileBertPreTrainedModel, TFQuestionAn
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
)
@add_start_docstrings(
"""
......@@ -1528,16 +1475,6 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
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(
MOBILEBERT_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
......@@ -1612,28 +1549,6 @@ class TFMobileBertForMultipleChoice(TFMobileBertPreTrainedModel, TFMultipleChoic
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"),
}
]
)
# Copied from transformers.models.bert.modeling_tf_bert.TFBertForMultipleChoice.serving
def serving(self, inputs: Dict[str, tf.Tensor]):
output = self.call(input_ids=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(
"""
......@@ -1722,10 +1637,3 @@ class TFMobileBertForTokenClassification(TFMobileBertPreTrainedModel, TFTokenCla
hidden_states=outputs.hidden_states,
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)
src/transformers/models/mobilevit/modeling_tf_mobilevit.py
View file @ 814de8fa
......@@ -735,38 +735,6 @@ class TFMobileViTPreTrainedModel(TFPreTrainedModel):
base_model_prefix = "mobilevit"
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)
MOBILEVIT_START_DOCSTRING = r"""
This model inherits from [`TFPreTrainedModel`]. Check the superclass documentation for the generic methods the
......@@ -856,14 +824,6 @@ class TFMobileViTModel(TFMobileViTPreTrainedModel):
output = self.mobilevit(pixel_values, output_hidden_states, return_dict, training=training)
return output
def serving_output(self, output: TFBaseModelOutputWithPooling) -> TFBaseModelOutputWithPooling:
# hidden_states not converted to Tensor with tf.convert_to_tensor as they are all of different dimensions
return TFBaseModelOutputWithPooling(
last_hidden_state=output.last_hidden_state,
pooler_output=output.pooler_output,
hidden_states=output.hidden_states,
)
@add_start_docstrings(
"""
......@@ -924,10 +884,6 @@ class TFMobileViTForImageClassification(TFMobileViTPreTrainedModel, TFSequenceCl
return TFImageClassifierOutputWithNoAttention(loss=loss, logits=logits, hidden_states=outputs.hidden_states)
def serving_output(self, output: TFImageClassifierOutputWithNoAttention) -> TFImageClassifierOutputWithNoAttention:
# hidden_states and attention 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)
class TFMobileViTASPPPooling(tf.keras.layers.Layer):
def __init__(self, config: MobileViTConfig, out_channels: int, **kwargs) -> None:
......@@ -1157,8 +1113,3 @@ class TFMobileViTForSemanticSegmentation(TFMobileViTPreTrainedModel):
logits=logits,
hidden_states=outputs.hidden_states if output_hidden_states else None,
)
def serving_output(
self, output: TFSemanticSegmenterOutputWithNoAttention
) -> TFSemanticSegmenterOutputWithNoAttention:
return TFSemanticSegmenterOutputWithNoAttention(logits=output.logits, hidden_states=output.hidden_states)
src/transformers/models/mpnet/modeling_tf_mpnet.py
View file @ 814de8fa
......@@ -49,7 +49,6 @@ 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,
......@@ -77,19 +76,6 @@ class TFMPNetPreTrainedModel(TFPreTrainedModel):
config_class = MPNetConfig
base_model_prefix = "mpnet"
@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)
class TFMPNetEmbeddings(tf.keras.layers.Layer):
"""Construct the embeddings from word, position embeddings."""
......@@ -707,17 +693,6 @@ class TFMPNetModel(TFMPNetPreTrainedModel):
)
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 TFMPNetLMHead(tf.keras.layers.Layer):
"""MPNet head for masked and permuted language modeling"""
......@@ -841,13 +816,6 @@ class TFMPNetForMaskedLM(TFMPNetPreTrainedModel, TFMaskedLanguageModelingLoss):
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 TFMPNetClassificationHead(tf.keras.layers.Layer):
"""Head for sentence-level classification tasks."""
......@@ -945,13 +913,6 @@ class TFMPNetForSequenceClassification(TFMPNetPreTrainedModel, TFSequenceClassif
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(
"""
......@@ -970,16 +931,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, 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(MPNET_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
@add_code_sample_docstrings(
......@@ -1048,26 +999,6 @@ class TFMPNetForMultipleChoice(TFMPNetPreTrainedModel, 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(
"""
......@@ -1142,13 +1073,6 @@ class TFMPNetForTokenClassification(TFMPNetPreTrainedModel, TFTokenClassificatio
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(
"""
......@@ -1235,12 +1159,3 @@ class TFMPNetForQuestionAnswering(TFMPNetPreTrainedModel, TFQuestionAnsweringLos
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/openai/modeling_tf_openai.py
View file @ 814de8fa
......@@ -357,19 +357,6 @@ class TFOpenAIGPTPreTrainedModel(TFPreTrainedModel):
config_class = OpenAIGPTConfig
base_model_prefix = "transformer"
@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)
@dataclass
class TFOpenAIGPTDoubleHeadsModelOutput(ModelOutput):
......@@ -541,13 +528,6 @@ class TFOpenAIGPTModel(TFOpenAIGPTPreTrainedModel):
)
return outputs
# 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)
@add_start_docstrings(
"""
......@@ -630,12 +610,6 @@ class TFOpenAIGPTLMHeadModel(TFOpenAIGPTPreTrainedModel, TFCausalLanguageModelin
attentions=transformer_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)
def prepare_inputs_for_generation(self, inputs, **kwargs):
return {"input_ids": inputs}
......@@ -752,27 +726,13 @@ class TFOpenAIGPTDoubleHeadsModel(TFOpenAIGPTPreTrainedModel):
attentions=transformer_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"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
]
)
def serving(self, inputs):
output = self.call(inputs)
return self.serving_output(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 TFOpenAIGPTDoubleHeadsModelOutput(
logits=output.logits, mc_logits=output.mc_logits, hidden_states=hs, attentions=attns
)
@property
def input_signature(self):
return {
"input_ids": tf.TensorSpec((None, None, None), tf.int32, name="input_ids"),
"attention_mask": tf.TensorSpec((None, None, None), tf.int32, name="attention_mask"),
"mc_token_ids": tf.TensorSpec((None, None), tf.int32, name="token_type_ids"),
}
@add_start_docstrings(
......@@ -894,10 +854,3 @@ class TFOpenAIGPTForSequenceClassification(TFOpenAIGPTPreTrainedModel, TFSequenc
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_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)
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