# Copyright (c) 2020 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.
from __future__ import annotations
import warnings
from typing import Optional, Tuple
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import Tensor
from paddle.nn import Layer
try:
from paddle.incubate.nn import FusedTransformerEncoderLayer
except ImportError:
FusedTransformerEncoderLayer = None
from dataclasses import dataclass
from paddlenlp.transformers.model_utils import PretrainedModel, register_base_model
from ...layers import Linear as TransposedLinear
from ...utils.converter import StateDictNameMapping, init_name_mappings
from ...utils.env import CONFIG_NAME
from ..model_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
ModelOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
)
from .configuration import (
BERT_PRETRAINED_INIT_CONFIGURATION,
BERT_PRETRAINED_RESOURCE_FILES_MAP,
BertConfig,
)
__all__ = [
"BertModel",
"BertPretrainedModel",
"BertForPretraining",
"BertPretrainingCriterion",
"BertPretrainingHeads",
"BertForSequenceClassification",
"BertForTokenClassification",
"BertForQuestionAnswering",
"BertForMultipleChoice",
"BertForMaskedLM",
]
class BertEmbeddings(Layer):
"""
Include embeddings from word, position and token_type embeddings
"""
def __init__(self, config: BertConfig):
super(BertEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
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)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
past_key_values_length: Optional[int] = None,
):
if position_ids is None:
ones = paddle.ones_like(input_ids, dtype="int64")
seq_length = paddle.cumsum(ones, axis=-1)
position_ids = seq_length - ones
if past_key_values_length is not None:
position_ids += past_key_values_length
position_ids.stop_gradient = True
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
input_embedings = self.word_embeddings(input_ids)
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = input_embedings + position_embeddings + token_type_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class BertPooler(Layer):
"""
Pool the result of BertEncoder.
"""
def __init__(self, config: BertConfig):
"""init the bert pooler with config & args/kwargs
Args:
config (BertConfig): BertConfig instance. Defaults to None.
"""
super(BertPooler, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = nn.Tanh()
self.pool_act = config.pool_act
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)
if self.pool_act == "tanh":
pooled_output = self.activation(pooled_output)
return pooled_output
[文档]class BertPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained BERT models. It provides BERT 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 = BertConfig
resource_files_names = {"model_state": "model_state.pdparams"}
base_model_prefix = "bert"
pretrained_init_configuration = BERT_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = BERT_PRETRAINED_RESOURCE_FILES_MAP
@classmethod
def _get_name_mappings(cls, config: BertConfig) -> list[StateDictNameMapping]:
mappings: list[StateDictNameMapping] = []
model_mappings = [
"embeddings.word_embeddings.weight",
"embeddings.position_embeddings.weight",
"embeddings.token_type_embeddings.weight",
["embeddings.LayerNorm.weight", "embeddings.layer_norm.weight"],
["embeddings.LayerNorm.bias", "embeddings.layer_norm.bias"],
["pooler.dense.weight", None, "transpose"],
"pooler.dense.bias",
# for TokenClassification
]
for layer_index in range(config.num_hidden_layers):
layer_mappings = [
[
f"encoder.layer.{layer_index}.attention.self.query.weight",
f"encoder.layers.{layer_index}.self_attn.q_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.query.bias",
f"encoder.layers.{layer_index}.self_attn.q_proj.bias",
],
[
f"encoder.layer.{layer_index}.attention.self.key.weight",
f"encoder.layers.{layer_index}.self_attn.k_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.key.bias",
f"encoder.layers.{layer_index}.self_attn.k_proj.bias",
],
[
f"encoder.layer.{layer_index}.attention.self.value.weight",
f"encoder.layers.{layer_index}.self_attn.v_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.self.value.bias",
f"encoder.layers.{layer_index}.self_attn.v_proj.bias",
],
[
f"encoder.layer.{layer_index}.attention.output.dense.weight",
f"encoder.layers.{layer_index}.self_attn.out_proj.weight",
"transpose",
],
[
f"encoder.layer.{layer_index}.attention.output.dense.bias",
f"encoder.layers.{layer_index}.self_attn.out_proj.bias",
],
[
f"encoder.layer.{layer_index}.intermediate.dense.weight",
f"encoder.layers.{layer_index}.linear1.weight",
"transpose",
],
[f"encoder.layer.{layer_index}.intermediate.dense.bias", f"encoder.layers.{layer_index}.linear1.bias"],
[
f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
f"encoder.layers.{layer_index}.norm1.weight",
],
[
f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
f"encoder.layers.{layer_index}.norm1.bias",
],
[
f"encoder.layer.{layer_index}.output.dense.weight",
f"encoder.layers.{layer_index}.linear2.weight",
"transpose",
],
[f"encoder.layer.{layer_index}.output.dense.bias", f"encoder.layers.{layer_index}.linear2.bias"],
[f"encoder.layer.{layer_index}.output.LayerNorm.weight", f"encoder.layers.{layer_index}.norm2.weight"],
[f"encoder.layer.{layer_index}.output.LayerNorm.bias", f"encoder.layers.{layer_index}.norm2.bias"],
]
model_mappings.extend(layer_mappings)
init_name_mappings(model_mappings)
# base-model prefix "BertModel"
if "BertModel" not in config.architectures:
for mapping in model_mappings:
mapping[0] = "bert." + mapping[0]
mapping[1] = "bert." + mapping[1]
# downstream mappings
if "BertForQuestionAnswering" in config.architectures:
model_mappings.extend(
[["qa_outputs.weight", "classifier.weight", "transpose"], ["qa_outputs.bias", "classifier.bias"]]
)
if (
"BertForMultipleChoice" in config.architectures
or "BertForSequenceClassification" in config.architectures
or "BertForTokenClassification" in config.architectures
):
model_mappings.extend([["classifier.weight", "classifier.weight", "transpose"]])
mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
return mappings
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
[文档]@register_base_model
class BertModel(BertPretrainedModel):
"""
The bare BERT 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/zh/api/paddle/nn/Layer_cn.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:`BertConfig`):
An instance of BertConfig used to construct BertModel.
"""
def __init__(self, config: BertConfig):
super(BertModel, self).__init__(config)
self.pad_token_id = config.pad_token_id
self.initializer_range = config.initializer_range
self.embeddings = BertEmbeddings(config)
if config.fuse and FusedTransformerEncoderLayer is None:
warnings.warn(
"FusedTransformerEncoderLayer is not supported by the running Paddle. "
"The flag fuse_transformer will be ignored. Try Paddle >= 2.3.0"
)
self.fuse = config.fuse and FusedTransformerEncoderLayer is not None
if self.fuse:
self.encoder = nn.LayerList(
[
FusedTransformerEncoderLayer(
config.hidden_size,
config.num_attention_heads,
config.intermediate_size,
dropout_rate=config.hidden_dropout_prob,
activation=config.hidden_act,
attn_dropout_rate=config.attention_probs_dropout_prob,
act_dropout_rate=0.0,
)
for _ in range(config.num_hidden_layers)
]
)
else:
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,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
self.pooler = BertPooler(config)
[文档] def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: 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 BertModel 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 on 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]`.
Defaults to `None`, which means nothing needed to be prevented attention to.
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 `None`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `None`.
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 `None`.
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`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import BertModel, BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-wwm-chinese')
model = BertModel.from_pretrained('bert-wwm-chinese')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
use_cache = use_cache if use_cache is not None else self.config.use_cache
past_key_values_length = None
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)
else:
if 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
embedding_output = self.embeddings(
input_ids=input_ids,
position_ids=position_ids,
token_type_ids=token_type_ids,
past_key_values_length=past_key_values_length,
)
if self.fuse:
assert not output_attentions, "Not support attentions output currently."
assert past_key_values is None, "Not support past_key_values currently."
hidden_states = embedding_output
all_hidden_states = [] if output_hidden_states else None
for layer in self.encoder:
hidden_states = layer(hidden_states, attention_mask)
if output_hidden_states:
all_hidden_states.append(hidden_states)
pooled_output = self.pooler(hidden_states)
if return_dict:
return BaseModelOutputWithPoolingAndCrossAttentions(
last_hidden_state=hidden_states, pooler_output=pooled_output, hidden_states=all_hidden_states
)
else:
return (
(hidden_states, pooled_output, all_hidden_states)
if output_hidden_states
else (hidden_states, pooled_output)
)
else:
self.encoder._use_cache = use_cache # To be consistent with HF
encoder_outputs = self.encoder(
embedding_output,
src_mask=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)
else:
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,
)
[文档]class BertForQuestionAnswering(BertPretrainedModel):
"""
Bert 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:
config (:class:`BertConfig`):
An instance of BertConfig used to construct BertForQuestionAnswering.
"""
def __init__(self, config: BertConfig):
super(BertForQuestionAnswering, self).__init__(config)
self.bert = BertModel(config)
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, 2)
[文档] def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: 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"""
The BertForQuestionAnswering forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`BertModel`.
token_type_ids (Tensor, optional):
See :class:`BertModel`.
position_ids(Tensor, optional):
See :class:`BertModel`.
attention_mask (Tensor, optional):
See :class:`BertModel`.
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 `None`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `None`.
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 `None`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` 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.QuestionAnsweringModelOutput`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.bert.modeling import BertForQuestionAnswering
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForQuestionAnswering.from_pretrained('bert-base-cased')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
start_logits = outputs[0]
end_logits = outputs[1]
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
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 ((total_loss,) + output) if total_loss is not None else output
return QuestionAnsweringModelOutput(
loss=total_loss,
start_logits=start_logits,
end_logits=end_logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class BertForSequenceClassification(BertPretrainedModel):
"""
Bert Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
config (:class:`BertConfig`):
An instance of BertConfig used to construct BertForSequenceClassification.
"""
def __init__(self, config: BertConfig):
super(BertForSequenceClassification, self).__init__(config)
self.bert = BertModel(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)
[文档] def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The BertForSequenceClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`BertModel`.
token_type_ids (Tensor, optional):
See :class:`BertModel`.
position_ids(Tensor, optional):
See :class:`BertModel`.
attention_mask (Tensor, optional):
See :class:`BertModel`.
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 `None`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `None`.
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 `None`.
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.bert.modeling import BertForSequenceClassification
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForSequenceClassification.from_pretrained('bert-base-cased', num_labels=2)
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
print(logits.shape)
# [1, 2]
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
pooled_output = outputs[1]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
loss = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
self.config.problem_type = "regression"
elif self.num_labels > 1 and (labels.dtype == paddle.int64 or labels.dtype == paddle.int32):
self.config.problem_type = "single_label_classification"
else:
self.config.problem_type = "multi_label_classification"
if self.config.problem_type == "regression":
loss_fct = paddle.nn.MSELoss()
if self.num_labels == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif self.config.problem_type == "single_label_classification":
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
elif self.config.problem_type == "multi_label_classification":
loss_fct = paddle.nn.BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class BertForTokenClassification(BertPretrainedModel):
"""
Bert Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
config (:class:`BertConfig`):
An instance of BertConfig used to construct BertForTokenClassification.
"""
def __init__(self, config: BertConfig):
super().__init__(config)
self.bert = BertModel(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)
[文档] def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The BertForTokenClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`BertModel`.
token_type_ids (Tensor, optional):
See :class:`BertModel`.
position_ids(Tensor, optional):
See :class:`BertModel`.
attention_mask (list, optional):
See :class:`BertModel`.
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
Labels for computing the token classification loss. Indices should be in `[0, ..., num_labels - 1]`.
output_hidden_states (bool, optional):
Whether to return the hidden states of all layers.
Defaults to `None`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `None`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `None`.
Returns:
An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` 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.TokenClassifierOutput`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.bert.modeling import BertForTokenClassification
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = BertForTokenClassification.from_pretrained('bert-base-cased', num_labels=2)
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
print(logits.shape)
# [1, 13, 2]
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.bert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = paddle.nn.CrossEntropyLoss()
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
if not return_dict:
output = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
class BertLMPredictionHead(Layer):
"""
Bert Model with a `language modeling` head on top for CLM fine-tuning.
"""
def __init__(self, config: BertConfig):
super(BertLMPredictionHead, self).__init__()
self.transform = nn.Linear(config.hidden_size, config.hidden_size)
self.activation = getattr(nn.functional, config.hidden_act)
self.layer_norm = nn.LayerNorm(config.hidden_size)
self.decoder = TransposedLinear(config.hidden_size, config.vocab_size)
# link bias to load pretrained weights
self.decoder_bias = self.decoder.bias
def forward(self, hidden_states, masked_positions=None):
if masked_positions is not None:
hidden_states = paddle.reshape(hidden_states, [-1, hidden_states.shape[-1]])
hidden_states = paddle.tensor.gather(hidden_states, masked_positions)
# gather masked tokens might be more quick
hidden_states = self.transform(hidden_states)
hidden_states = self.activation(hidden_states)
hidden_states = self.layer_norm(hidden_states)
hidden_states = self.decoder(hidden_states)
return hidden_states
[文档]class BertPretrainingHeads(Layer):
"""
Perform language modeling task and next sentence classification task.
Args:
config (:class:`BertConfig`):
An instance of BertConfig used to construct BertForPretraining.
"""
def __init__(self, config: BertConfig):
super(BertPretrainingHeads, self).__init__()
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
[文档] def forward(self, sequence_output, pooled_output, masked_positions=None):
"""
Args:
sequence_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].
masked_positions(Tensor, optional):
A tensor indicates positions to be masked in the position embedding.
Its data type should be int64 and its shape is [batch_size, mask_token_num].
`mask_token_num` is the number of masked tokens. It should be no bigger than `sequence_length`.
Defaults to `None`, which means we output hidden-states of all tokens in masked token prediction.
Returns:
tuple: Returns tuple (``prediction_scores``, ``seq_relationship_score``).
With the fields:
- `prediction_scores` (Tensor):
The scores of masked token prediction. Its data type should be float32.
If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
- `seq_relationship_score` (Tensor):
The scores of next sentence prediction.
Its data type should be float32 and its shape is [batch_size, 2].
"""
prediction_scores = self.predictions(sequence_output, masked_positions)
seq_relationship_score = self.seq_relationship(pooled_output)
return prediction_scores, seq_relationship_score
@dataclass
class BertForPreTrainingOutput(ModelOutput):
"""
Output type of [`BertForPreTraining`].
Args:
loss (*optional*, returned when `labels` is provided, `paddle.Tensor` of shape `(1,)`):
Total loss as the sum of the masked language modeling loss and the next sequence prediction
(classification) loss.
prediction_logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
seq_relationship_logits (`paddle.Tensor` of shape `(batch_size, 2)`):
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
before SoftMax).
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
prediction_logits: paddle.Tensor = None
seq_relationship_logits: paddle.Tensor = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
[文档]class BertForPretraining(BertPretrainedModel):
"""
Bert Model with pretraining tasks on top.
Args:
config (:class:`BertConfig`):
An instance of BertConfig used to construct BertForPretraining.
"""
def __init__(self, config: BertConfig):
super(BertForPretraining, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertPretrainingHeads(config)
self.tie_weights()
[文档] def get_output_embeddings(self):
return self.cls.predictions.decoder
[文档] def forward(
self,
input_ids: Tensor,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
masked_positions: Optional[Tensor] = None,
labels: Optional[Tensor] = None,
next_sentence_label: 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:`BertModel`.
token_type_ids (Tensor, optional):
See :class:`BertModel`.
position_ids (Tensor, optional):
See :class:`BertModel`.
attention_mask (Tensor, optional):
See :class:`BertModel`.
masked_positions(Tensor, optional):
See :class:`BertPretrainingHeads`.
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked),
the loss is only computed for the tokens with labels in `[0, ..., vocab_size]`.
next_sentence_label (Tensor of shape `(batch_size,)`, optional):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation of sequence A,
- 1 indicates sequence B is a random sequence.
output_hidden_states (bool, optional):
Whether to return the hidden states of all layers.
Defaults to `None`.
output_attentions (bool, optional):
Whether to return the attentions tensors of all attention layers.
Defaults to `None`.
return_dict (bool, optional):
Whether to return a :class:`~paddlenlp.transformers.bert.BertForPreTrainingOutput` object. If
`False`, the output will be a tuple of tensors. Defaults to `None`.
Returns:
An instance of :class:`~paddlenlp.transformers.bert.BertForPreTrainingOutput` 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.bert.BertForPreTrainingOutput`.
"""
with paddle.static.amp.fp16_guard():
outputs = self.bert(
input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output, pooled_output = outputs[:2]
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output, masked_positions)
total_loss = None
if labels is not None and next_sentence_label is not None:
loss_fct = paddle.nn.CrossEntropyLoss()
masked_lm_loss = loss_fct(
prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1,))
)
next_sentence_loss = loss_fct(
seq_relationship_score.reshape((-1, 2)), next_sentence_label.reshape((-1,))
)
total_loss = masked_lm_loss + next_sentence_loss
if not return_dict:
output = (prediction_scores, seq_relationship_score) + outputs[2:]
return ((total_loss,) + output) if total_loss is not None else output
return BertForPreTrainingOutput(
loss=total_loss,
prediction_logits=prediction_scores,
seq_relationship_logits=seq_relationship_score,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class BertPretrainingCriterion(paddle.nn.Layer):
"""
Args:
vocab_size(int):
Vocabulary size of `inputs_ids` in `BertModel`. Defines the number of different tokens that can
be represented by the `inputs_ids` passed when calling `BertModel`.
"""
def __init__(self, vocab_size):
super(BertPretrainingCriterion, self).__init__()
# CrossEntropyLoss is expensive since the inner reshape (copy)
self.loss_fn = paddle.nn.loss.CrossEntropyLoss(ignore_index=-1)
self.vocab_size = vocab_size
[文档] def forward(
self, prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels, masked_lm_scale
):
"""
Args:
prediction_scores(Tensor):
The scores of masked token prediction. Its data type should be float32.
If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
Otherwise, its shape is [batch_size, mask_token_num, vocab_size]
seq_relationship_score(Tensor):
The scores of next sentence prediction. Its data type should be float32 and
its shape is [batch_size, 2]
masked_lm_labels(Tensor):
The labels of the masked language modeling, its dimensionality is equal to `prediction_scores`.
Its data type should be int64. If `masked_positions` is None, its shape is [batch_size, sequence_length, 1].
Otherwise, its shape is [batch_size, mask_token_num, 1]
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]
masked_lm_scale(Tensor or int):
The scale of masked tokens. Used for the normalization of masked language modeling loss.
If it is a `Tensor`, its data type should be int64 and its shape is equal to `prediction_scores`.
Returns:
Tensor: The pretraining loss, equals to the sum of `masked_lm_loss` plus the mean of `next_sentence_loss`.
Its data type should be float32 and its shape is [1].
"""
with paddle.static.amp.fp16_guard():
masked_lm_loss = F.cross_entropy(prediction_scores, masked_lm_labels, reduction="none", ignore_index=-1)
masked_lm_loss = masked_lm_loss / masked_lm_scale
next_sentence_loss = F.cross_entropy(seq_relationship_score, next_sentence_labels, reduction="none")
return paddle.sum(masked_lm_loss) + paddle.mean(next_sentence_loss)
class BertOnlyMLMHead(nn.Layer):
def __init__(self, config: BertConfig):
super().__init__()
self.predictions = BertLMPredictionHead(config=config)
def forward(self, sequence_output, masked_positions=None):
prediction_scores = self.predictions(sequence_output, masked_positions)
return prediction_scores
