# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2020 Huawei Technologies Co., Ltd.
#
# 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.
from typing import Optional, Tuple
import paddle
import paddle.nn as nn
from paddle import Tensor
from ...utils.env import CONFIG_NAME
from .. import PretrainedModel, register_base_model
from ..bert.modeling import BertEmbeddings, BertPooler
from ..model_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
tuple_output,
)
from .configuration import (
TINYBERT_PRETRAINED_INIT_CONFIGURATION,
TINYBERT_PRETRAINED_RESOURCE_FILES_MAP,
TinyBertConfig,
)
__all__ = [
"TinyBertModel",
"TinyBertPretrainedModel",
"TinyBertForPretraining",
"TinyBertForSequenceClassification",
"TinyBertForQuestionAnswering",
"TinyBertForMultipleChoice",
]
[docs]class TinyBertPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained TinyBERT models. It provides TinyBERT related
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading
and loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
model_config_file = CONFIG_NAME
config_class = TinyBertConfig
resource_files_names = {"model_state": "model_state.pdparams"}
pretrained_init_configuration = TINYBERT_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = TINYBERT_PRETRAINED_RESOURCE_FILES_MAP
base_model_prefix = "tinybert"
def _init_weights(self, layer):
"""Initialization hook"""
if isinstance(layer, (nn.Linear, nn.Embedding)):
# In the dygraph mode, use the `set_value` to reset the parameter directly,
# and reset the `state_dict` to update parameter in static mode.
if isinstance(layer.weight, paddle.Tensor):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.weight.shape,
)
)
elif isinstance(layer, nn.LayerNorm):
layer._epsilon = self.config.layer_norm_eps
[docs]@register_base_model
class TinyBertModel(TinyBertPretrainedModel):
"""
The bare TinyBERT Model transformer outputting raw hidden-states.
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
Refer to the superclass documentation for the generic methods.
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
/docs/en/api/paddle/fluid/dygraph/layers/Layer_en.html>`__ subclass. Use it as a regular Paddle Layer
and refer to the Paddle documentation for all matter related to general usage and behavior.
Args:
config (:class:`TinyBertConfig`):
An instance of TinyBertConfig used to construct TinyBertModel.
"""
def __init__(self, config: TinyBertConfig):
super(TinyBertModel, self).__init__(config)
self.pad_token_id = config.pad_token_id
self.initializer_range = config.initializer_range
self.embeddings = BertEmbeddings(config)
encoder_layer = nn.TransformerEncoderLayer(
config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
dropout=config.hidden_dropout_prob,
activation=config.hidden_act,
attn_dropout=config.attention_probs_dropout_prob,
act_dropout=0.0,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
self.pooler = BertPooler(config)
# fit_dense(s) means a hidden states' transformation from student to teacher.
# `fit_denses` is used in v2 model, and `fit_dense` is used in other pretraining models.
self.fit_denses = nn.LayerList(
[nn.Linear(config.hidden_size, config.fit_size) for i in range(config.num_hidden_layers + 1)]
)
self.fit_dense = nn.Linear(config.hidden_size, config.fit_size)
[docs] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
use_cache: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The TinyBertModel forward method, overrides the `__call__()` special 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`.
attention_mask (Tensor, optional):
Mask used in multi-head attention to avoid performing attention to some unwanted positions,
usually the paddings or the subsequent positions.
Its data type can be int, float and bool.
When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
[batch_size, num_attention_heads, sequence_length, sequence_length].
Defaults to `None`, which means nothing needed to be prevented attention to.
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`.
past_key_values (tuple(tuple(Tensor)), optional):
The length of tuple equals to the number of layers, and each inner
tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`)
which contains precomputed key and value hidden states of the attention blocks.
If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
`input_ids` of shape `(batch_size, sequence_length)`.
use_cache (`bool`, optional):
If set to `True`, `past_key_values` key value states are returned.
Defaults to `None`.
output_hidden_states (bool, optional):
Whether to return the hidden states of all layers.
Defaults to `False`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `False`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
will be a tuple of tensors. Defaults to `False`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if
`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
to ordered and not None (depending on the input arguments) fields of
:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`.
tuple: Returns tuple (`encoder_output`, `pooled_output`).
With the fields:
- `encoder_output` (Tensor):
Sequence of hidden-states at the last layer of the model.
It's data type should be float32 and its shape is [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 its shape is [batch_size, hidden_size].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import TinyBertModel, TinyBertTokenizer
tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
model = TinyBertModel.from_pretrained('tinybert-4l-312d')
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.")
# init the default bool value
output_attentions = output_attentions if output_attentions is not None else False
output_hidden_states = output_hidden_states if output_hidden_states is not None else False
return_dict = return_dict if return_dict is not None else False
use_cache = use_cache if use_cache is not None else False
past_key_values_length = 0
if past_key_values is not None:
past_key_values_length = past_key_values[0][0].shape[2]
if attention_mask is None:
attention_mask = paddle.unsqueeze(
(input_ids == self.pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
)
if past_key_values is not None:
batch_size = past_key_values[0][0].shape[0]
past_mask = paddle.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
attention_mask = paddle.concat([past_mask, attention_mask], axis=-1)
elif attention_mask.ndim == 2:
# attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
attention_mask = attention_mask.unsqueeze(axis=[1, 2]).astype(paddle.get_default_dtype())
attention_mask = (1.0 - attention_mask) * -1e4
# TODO(wj-Mcat): in current branch, not support `inputs_embeds`
embedding_output = self.embeddings(
input_ids, token_type_ids, position_ids, past_key_values_length=past_key_values_length
)
self.encoder._use_cache = use_cache # To be consistent with HF
encoder_outputs = self.encoder(
embedding_output,
attention_mask,
cache=past_key_values,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
if isinstance(encoder_outputs, type(embedding_output)):
sequence_output = encoder_outputs
pooled_output = self.pooler(sequence_output)
return (sequence_output, pooled_output)
sequence_output = encoder_outputs[0]
pooled_output = self.pooler(sequence_output)
if not return_dict:
return (sequence_output, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=sequence_output,
pooler_output=pooled_output,
past_key_values=encoder_outputs.past_key_values,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
[docs]class TinyBertForPretraining(TinyBertPretrainedModel):
"""
TinyBert Model with pretraining tasks on top.
Args:
config (:class:`TinyBertConfig`):
An instance of TinyBertConfig used to construct TinyBertForPretraining.
"""
def __init__(self, config: TinyBertConfig):
super(TinyBertForPretraining, self).__init__(config)
self.tinybert = TinyBertModel(config)
[docs] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The TinyBertForPretraining forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`TinyBertModel`.
token_type_ids (Tensor, optional):
See :class:`TinyBertModel`.
position_ids (Tensor, optional):
See :class:`TinyBertModel`.
attention_mask (Tensor, optional):
See :class:`TinyBertModel`.
Returns:
Tensor: Returns tensor `sequence_output`, sequence of hidden-states at the last layer of the model.
It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
Example:
.. code-block::
import paddle
from paddlenlp.transformers.tinybert.modeling import TinyBertForPretraining
from paddlenlp.transformers.tinybert.tokenizer import TinyBertTokenizer
tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
model = TinyBertForPretraining.from_pretrained('tinybert-4l-312d')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
logits = outputs[0]
"""
outputs = self.tinybert(
input_ids,
token_type_ids,
position_ids,
attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# return the sequence presentation
if not return_dict:
return outputs[0]
return outputs
[docs]class TinyBertForSequenceClassification(TinyBertPretrainedModel):
"""
TinyBert Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
config (:class:`TinyBertConfig`):
An instance of TinyBertConfig used to construct TinyBertForSequenceClassification.
"""
def __init__(self, config: TinyBertConfig):
super(TinyBertForSequenceClassification, self).__init__(config)
self.tinybert = TinyBertModel(config)
self.num_labels = config.num_labels
self.dropout = nn.Dropout(
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
self.activation = nn.ReLU()
[docs] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The TinyBertForSequenceClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`TinyBertModel`.
token_type_ids (Tensor, optional):
See :class:`TinyBertModel`.
position_ids (Tensor, optional):
See :class:`TinyBertModel`.
attention_mask_list (list, optional):
See :class:`TinyBertModel`.
labels (Tensor of shape `(batch_size,)`, optional):
Labels for computing the sequence classification/regression loss.
Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
a regression loss is computed (Mean-Square loss), If `num_labels > 1`
a classification loss is computed (Cross-Entropy).
output_hidden_states (bool, optional):
Whether to return the hidden states of all layers.
Defaults to `False`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `False`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`.
Otherwise it returns a tuple of tensors corresponding to ordered and
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.tinybert.modeling import TinyBertForSequenceClassification
from paddlenlp.transformers.tinybert.tokenizer import TinyBertTokenizer
tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
model = TinyBertForSequenceClassification.from_pretrained('tinybert-4l-312d')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
logits = outputs[0]
"""
outputs = self.tinybert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.classifier(self.activation(outputs[1]))
loss = None
if labels is not None:
if self.num_labels == 1:
loss_fct = paddle.nn.MSELoss()
loss = loss_fct(logits, labels)
elif labels.dtype == paddle.int64 or labels.dtype == paddle.int32:
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
else:
loss_fct = paddle.nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return tuple_output(output, loss)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[docs]class TinyBertForQuestionAnswering(TinyBertPretrainedModel):
"""
TinyBert 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:
Args:
config (:class:`TinyBertConfig`):
An instance of TinyBertConfig used to construct TinyBertForQuestionAnswering.
"""
def __init__(self, config: TinyBertConfig):
super(TinyBertForQuestionAnswering, self).__init__(config)
self.tinybert = TinyBertModel(config)
self.classifier = nn.Linear(config.hidden_size, 2)
[docs] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
inputs_embeds: Optional[Tensor] = None,
start_positions: Optional[Tensor] = None,
end_positions: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
Args:
input_ids (Tensor):
See :class:`TinyBertModel`.
token_type_ids (Tensor, optional):
See :class:`TinyBertModel`.
position_ids (Tensor, optional):
See :class:`TinyBertModel`.
attention_mask (Tensor, optional):
See :class:`TinyBertModel`.
start_positions (Tensor of shape `(batch_size,)`, optional):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
end_positions (Tensor of shape `(batch_size,)`, optional):
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
are not taken into account for computing the loss.
output_hidden_states (bool, optional):
Whether to return the hidden states of all layers.
Defaults to `False`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `False`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `False`.
Returns:
tuple: Returns tuple (`start_logits`, `end_logits`).
With the fields:
- `start_logits` (Tensor):
A tensor of the input token classification logits, indicates the start position of the labelled span.
Its data type should be float32 and its shape is [batch_size, sequence_length].
- `end_logits` (Tensor):
A tensor of the input token classification logits, indicates the end position of the labelled span.
Its data type should be float32 and its shape is [batch_size, sequence_length].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import TinyBertForQuestionAnswering, TinyBertTokenizer
tokenizer = TinyBertTokenizer.from_pretrained('tinybert-6l-768d-zh')
model = TinyBertForQuestionAnswering.from_pretrained('tinybert-6l-768d-zh')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
outputs = self.tinybert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = self.classifier(outputs[0])
logits = paddle.transpose(logits, perm=[2, 0, 1])
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
total_loss = None
if start_positions is not None and end_positions is not None:
# If we are on multi-GPU, split add a dimension
if start_positions.ndim > 1:
start_positions = start_positions.squeeze(-1)
if start_positions.ndim > 1:
end_positions = end_positions.squeeze(-1)
# sometimes the start/end positions are outside our model inputs, we ignore these terms
ignored_index = paddle.shape(start_logits)[1]
start_positions = start_positions.clip(0, ignored_index)
end_positions = end_positions.clip(0, ignored_index)
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
start_loss = loss_fct(start_logits, start_positions)
end_loss = loss_fct(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + outputs[2:]
return tuple_output(output, total_loss)
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
