# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Modeling classes for FNet model."""
import paddle
import paddle.nn as nn
from paddle.nn import Layer
from .. import PretrainedModel, register_base_model
from ..activations import ACT2FN
from .configuration import (
FNET_PRETRAINED_INIT_CONFIGURATION,
FNET_PRETRAINED_RESOURCE_FILES_MAP,
FNetConfig,
)
__all__ = [
"FNetPretrainedModel",
"FNetModel",
"FNetForSequenceClassification",
"FNetForPreTraining",
"FNetForMaskedLM",
"FNetForNextSentencePrediction",
"FNetForMultipleChoice",
"FNetForTokenClassification",
"FNetForQuestionAnswering",
]
class FNetBasicOutput(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
def forward(self, hidden_states, input_tensor):
hidden_states = self.layer_norm(input_tensor + hidden_states)
return hidden_states
class FNetOutput(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, hidden_states, input_tensor):
hidden_states = self.dense(hidden_states)
hidden_states = self.dropout(hidden_states)
hidden_states = self.layer_norm(input_tensor + hidden_states)
return hidden_states
class FNetIntermediate(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
if isinstance(config.hidden_act, str):
self.intermediate_act_fn = ACT2FN[config.hidden_act]
else:
self.intermediate_act_fn = config.hidden_act
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.intermediate_act_fn(hidden_states)
return hidden_states
class FNetLayer(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.fourier = FNetFourierTransform(config)
self.intermediate = FNetIntermediate(config)
self.output = FNetOutput(config)
def forward(self, hidden_states):
self_fourier_outputs = self.fourier(hidden_states)
fourier_output = self_fourier_outputs[0]
intermediate_output = self.intermediate(fourier_output)
layer_output = self.output(intermediate_output, fourier_output)
return (layer_output,)
class FNetEncoder(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.layers = nn.LayerList([FNetLayer(config) for _ in range(config.num_hidden_layers)])
self.gradient_checkpointing = False
def forward(self, hidden_states, output_hidden_states=False, return_dict=True):
all_hidden_states = () if output_hidden_states else None
for i, layer_module in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_outputs = layer_module(hidden_states)
hidden_states = layer_outputs[0]
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if return_dict:
return {"last_hidden_state": hidden_states, "all_hidden_states": all_hidden_states}
return (hidden_states,)
class FNetPooler(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
def forward(self, hidden_states):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token.
first_token_tensor = hidden_states[:, 0]
pooled_output = self.dense(first_token_tensor)
pooled_output = self.activation(pooled_output)
return pooled_output
class FNetEmbeddings(Layer):
"""Construct the embeddings from word, position and token_type embeddings."""
def __init__(self, config: FNetConfig):
super(FNetEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
# NOTE: This is the project layer and will be needed. The original code allows for different embedding and different model dimensions.
self.projection = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
# position_ids (1, len position emb) is contiguous in memory and exported when serialized
self.register_buffer(
"position_ids", paddle.arange(config.max_position_embeddings, dtype="int64").expand((1, -1))
)
def forward(
self,
input_ids,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
):
if input_ids is not None:
input_shape = input_ids.shape
else:
input_shape = inputs_embeds.shape[:-1]
seq_length = input_shape[1]
if position_ids is None:
position_ids = self.position_ids[:, :seq_length]
if token_type_ids is None:
token_type_ids = paddle.zeros(input_shape, dtype="int64")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + token_type_embeddings
position_embeddings = self.position_embeddings(position_ids)
embeddings += position_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.projection(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class FNetBasicFourierTransform(Layer):
def __init__(self):
super().__init__()
self.fourier_transform = paddle.fft.fftn
def forward(self, hidden_states):
outputs = self.fourier_transform(hidden_states).real()
return (outputs,)
class FNetFourierTransform(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.fourier_transform = FNetBasicFourierTransform()
self.output = FNetBasicOutput(config)
def forward(self, hidden_states):
self_outputs = self.fourier_transform(hidden_states)
fourier_output = self.output(self_outputs[0], hidden_states)
return (fourier_output,)
class FNetPredictionHeadTransform(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
if isinstance(config.hidden_act, str):
self.transform_act_fn = ACT2FN[config.hidden_act]
else:
self.transform_act_fn = config.hidden_act
self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
def forward(self, hidden_states):
hidden_states = self.dense(hidden_states)
hidden_states = self.transform_act_fn(hidden_states)
hidden_states = self.layer_norm(hidden_states)
return hidden_states
class FNetLMPredictionHead(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.transform = FNetPredictionHeadTransform(config)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(config.vocab_size, config.hidden_size)
self.bias = self.create_parameter(
[config.vocab_size], is_bias=True, default_initializer=nn.initializer.Constant(value=0)
)
self.decoder.bias = self.bias
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
hidden_states = paddle.matmul(hidden_states, self.decoder.weight, transpose_y=True) + self.bias
return hidden_states
class FNetOnlyMLMHead(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
return prediction_scores
class FNetOnlyNSPHead(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, pooled_output):
seq_relationship_score = self.seq_relationship(pooled_output)
return seq_relationship_score
class FNetPreTrainingHeads(Layer):
def __init__(self, config: FNetConfig):
super().__init__()
self.predictions = FNetLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
prediction_scores = self.predictions(sequence_output)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
[docs]class FNetPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained FNet models. It provides FNet related
`model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
`pretrained_resource_files_map`, `base_model_prefix` for downloading and
loading pretrained models. See `PretrainedModel` for more details.
"""
pretrained_init_configuration = FNET_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = FNET_PRETRAINED_RESOURCE_FILES_MAP
base_model_prefix = "fnet"
config_class = FNetConfig
def _init_weights(self, layer):
# Initialize the weights.
if isinstance(layer, nn.Linear):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
if layer.bias is not None:
layer.bias.set_value(paddle.zeros_like(layer.bias))
elif isinstance(layer, nn.Embedding):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
if layer._padding_idx is not None:
layer.weight[layer._padding_idx].set_value(paddle.zeros_like(layer.weight[layer._padding_idx]))
elif isinstance(layer, nn.LayerNorm):
layer.bias.set_value(paddle.zeros_like(layer.bias))
layer.weight.set_value(paddle.ones_like(layer.weight))
[docs]@register_base_model
class FNetModel(FNetPretrainedModel):
"""
The model can behave as an encoder, following the architecture described in `FNet: Mixing Tokens with Fourier
Transforms <https://arxiv.org/abs/2105.03824>`__ by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago
Ontanon.
"""
def __init__(self, config: FNetConfig):
super(FNetModel, self).__init__(config)
self.initializer_range = config.initializer_range
self.num_hidden_layers = config.num_hidden_layers
self.embeddings = FNetEmbeddings(config)
self.encoder = FNetEncoder(config)
self.pooler = FNetPooler(config) if config.add_pooling_layer else None
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
output_hidden_states=None,
return_dict=None,
):
r"""
The FNetModel forward method.
Args:
input_ids (Tensor):
Indices of input sequence tokens in the vocabulary. They are
numerical representations of tokens that build the input sequence.
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
token_type_ids (Tensor, optional):
Segment token indices to indicate different portions of the inputs.
Selected in the range ``[0, type_vocab_size - 1]``.
If `type_vocab_size` is 2, which means the inputs have two portions.
Indices can either be 0 or 1:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
Defaults to `None`, which means we don't add segment embeddings.
position_ids(Tensor, optional):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
max_position_embeddings - 1]``.
Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
inputs_embeds (Tensor, optional):
If you want to control how to convert `inputs_ids` indices into associated vectors, you can
pass an embedded representation directly instead of passing `inputs_ids`.
output_hidden_states (bool, optional):
Whether or not to return all hidden states. Default to `None`.
return_dict (bool, optional):
Whether or not to return a dict instead of a plain tuple. Default to `None`.
Returns:
tuple or Dict: Returns tuple (`sequence_output`, `pooled_output`, `encoder_outputs[1:]`)
or a dict with last_hidden_state`, `pooled_output`, `all_hidden_states`, fields.
With the fields:
- `sequence_output` (Tensor):
Sequence of hidden-states at the last layer of the model.
It's data type should be float32 and has a shape of [`batch_size, sequence_length, hidden_size`].
- `pooled_output` (Tensor):
The output of first token (`[CLS]`) in sequence.
We "pool" the model by simply taking the hidden state corresponding to the first token.
Its data type should be float32 and
has a shape of [batch_size, hidden_size].
- `last_hidden_state` (Tensor):
The output of the last encoder layer, it is also the `sequence_output`.
It's data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].
- `all_hidden_states` (Tensor):
Hidden_states of all layers in the Transformer encoder. The length of `all_hidden_states` is `num_hidden_layers + 1`.
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
Example:
.. code-block::
import paddle
from paddlenlp.transformers.fnet.modeling import FNetModel
from paddlenlp.transformers.fnet.tokenizer import FNetTokenizer
tokenizer = FNetTokenizer.from_pretrained('fnet-base')
model = FNetModel.from_pretrained('fnet-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
"""
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
input_shape = input_ids.shape
elif inputs_embeds is not None:
input_shape = inputs_embeds.shape[:-1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if token_type_ids is None:
token_type_ids = paddle.zeros(shape=input_shape, dtype="int64")
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
inputs_embeds=inputs_embeds,
)
encoder_outputs = self.encoder(
embedding_output,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = encoder_outputs["last_hidden_state"] if return_dict else encoder_outputs[0]
pooler_output = self.pooler(sequence_output) if self.pooler is not None else None
if return_dict:
return {
"last_hidden_state": sequence_output,
"pooler_output": pooler_output,
"all_hidden_states": encoder_outputs["all_hidden_states"],
}
return (sequence_output, pooler_output) + encoder_outputs[1:]
[docs]class FNetForSequenceClassification(FNetPretrainedModel):
"""
FNet Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
fnet (:class:`FNetModel`):
An instance of FNetModel.
num_classes (int, optional):
The number of classes. Defaults to `2`.
"""
def __init__(self, config: FNetConfig, num_classes=2):
super(FNetForSequenceClassification, self).__init__(config)
self.num_classes = num_classes
self.fnet = FNetModel(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, num_classes)
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_hidden_states=None,
return_dict=None,
):
r"""
The FNetForSequenceClassification forward method.
Args:
input_ids (Tensor):
Indices of input sequence tokens in the vocabulary. They are
numerical representations of tokens that build the input sequence.
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
token_type_ids (Tensor, optional):
Segment token indices to indicate different portions of the inputs.
Selected in the range ``[0, type_vocab_size - 1]``.
If `type_vocab_size` is 2, which means the inputs have two portions.
Indices can either be 0 or 1:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
Defaults to `None`, which means we don't add segment embeddings.
position_ids(Tensor, optional):
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
max_position_embeddings - 1]``.
Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
inputs_embeds (Tensor, optional):
If you want to control how to convert `inputs_ids` indices into associated vectors, you can
pass an embedded representation directly instead of passing `inputs_ids`.
output_hidden_states (bool, optional):
Whether or not to return all hidden states. Default to `None`.
return_dict (bool, optional):
Whether or not to return a dict instead of a plain tuple. Default to `None`.
Returns:
Tensor or Dict: Returns tensor `logits`, or a dict with `logits`, `hidden_states`, `attentions` fields.
With the fields:
- `logits` (Tensor):
A tensor of the input text classification logits.
Shape as `[batch_size, num_classes]` and dtype as float32.
- `hidden_states` (Tensor):
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
Example:
.. code-block::
import paddle
from paddlenlp.transformers.fnet.modeling import FNetForSequenceClassification
from paddlenlp.transformers.fnet.tokenizer import FNetTokenizer
tokenizer = FNetTokenizer.from_pretrained('fnet-base')
model = FNetModel.from_pretrained('fnet-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
"""
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs["pooler_output"] if return_dict else outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
if return_dict:
return {
"logits": logits,
"hidden_states": outputs["all_hidden_states"],
}
return logits
[docs]class FNetForPreTraining(FNetPretrainedModel):
"""
FNet Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
sentence prediction (classification)` head.
"""
def __init__(self, config: FNetConfig):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetPreTrainingHeads(config)
[docs] def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_hidden_states=None,
return_dict=None,
):
r"""
The FNetForPretraining forward method.
Args:
input_ids (Tensor):
See :class:`FNetModel`.
token_type_ids (Tensor, optional):
See :class:`FNetModel`.
position_ids(Tensor, optional):
See :class:`FNetModel`.
labels (LongTensor of shape (batch_size, sequence_length), optional):
Labels for computing the masked language modeling loss.
inputs_embeds(Tensor, optional):
See :class:`FNetModel`.
next_sentence_labels(Tensor):
The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels`
is equal to `seq_relation_labels`. Its data type should be int64 and
its shape is [batch_size, 1]
output_hidden_states (bool, optional):
See :class:`FNetModel`.
return_dict (bool, optional):
See :class:`FNetModel`.
Returns:
tuple or Dict: Returns tuple (`prediction_scores`, `seq_relationship_score`) or a dict with
`prediction_logits`, `seq_relationship_logits`, `hidden_states` fields.
"""
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
pooled_output = outputs[1] if not return_dict else outputs["pooler_output"]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output)
if return_dict:
return {
"prediction_logits": prediction_scores,
"seq_relationship_logits": seq_relationship_score,
"hidden_states": outputs["all_hidden_states"],
}
return prediction_scores, seq_relationship_score, outputs["all_hidden_states"]
[docs]class FNetForNextSentencePrediction(FNetPretrainedModel):
"""
FNet Model with a `next sentence prediction` head on top.
Args:
fnet (:class:`FNetModel`):
An instance of :class:`FNetModel`.
"""
def __init__(self, config: FNetConfig):
super().__init__(config)
self.fnet = FNetModel(config)
self.cls = FNetOnlyNSPHead(config)
[docs] def get_output_embeddings(self):
return self.cls.predictions.decoder
def set_output_embeddings(self, new_embeddings):
self.cls.predictions.decoder = new_embeddings
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
next_sentence_label=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1] if not return_dict else outputs["pooler_output"]
seq_relationship_score = self.cls(pooled_output)
if return_dict:
return {"seq_relationship_logits": seq_relationship_score, "hidden_states": outputs["all_hidden_states"]}
return seq_relationship_score, outputs["all_hidden_states"]
[docs]class FNetForTokenClassification(FNetPretrainedModel):
"""
FNet Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
fnet (:class:`FNetModel`):
An instance of FNetModel.
num_classes (int, optional):
The number of classes. Defaults to `2`.
"""
def __init__(self, config: FNetConfig, num_classes=2):
super(FNetForTokenClassification, self).__init__(config)
self.fnet = FNetModel(config)
self.num_classes = num_classes
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, self.num_classes)
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
labels=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
if return_dict:
return {
"logits": logits,
"hidden_states": outputs["all_hidden_states"],
}
return logits
[docs]class FNetForQuestionAnswering(FNetPretrainedModel):
"""
FNet Model with a linear layer on top of the hidden-states output to compute `span_start_logits`
and `span_end_logits`, designed for question-answering tasks like SQuAD.
Args:
fnet (:class:`FNetModel`):
An instance of FNetModel.
num_labels (int):
The number of labels.
"""
def __init__(self, config: FNetConfig, num_labels):
super(FNetForQuestionAnswering, self).__init__(config)
self.num_labels = num_labels
self.fnet = FNetModel(config)
self.qa_outputs = nn.Linear(config.hidden_size, self.num_labels)
[docs] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
inputs_embeds=None,
start_positions=None,
end_positions=None,
output_hidden_states=None,
return_dict=None,
):
outputs = self.fnet(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
inputs_embeds=inputs_embeds,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0] if not return_dict else outputs["last_hidden_state"]
logits = self.qa_outputs(sequence_output)
start_logits, end_logits = paddle.split(logits, num_or_sections=2, axis=-1)
start_logits = start_logits.squeeze(axis=-1)
end_logits = start_logits.squeeze(axis=-1)
if return_dict:
return {
"start_logits": start_logits,
"end_logits": end_logits,
"hidden_states": outputs["all_hidden_states"],
}
return start_logits, end_logits
