# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2021 The HuggingFace Inc. team.
#
# 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
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import Tensor, tensor
from paddle.nn import Layer
from .. import PretrainedModel, register_base_model
from ..activations import get_activation
from ..model_outputs import (
BaseModelOutputWithPoolingAndCrossAttentions,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
SequenceClassifierOutput,
TokenClassifierOutput,
tuple_output,
)
from .configuration import (
CONVBERT_PRETRAINED_INIT_CONFIGURATION,
CONVBERT_PRETRAINED_RESOURCE_FILES_MAP,
ConvBertConfig,
)
__all__ = [
"ConvBertModel",
"ConvBertForMaskedLM",
"ConvBertPretrainedModel",
"ConvBertForTotalPretraining",
"ConvBertDiscriminator",
"ConvBertGenerator",
"ConvBertClassificationHead",
"ConvBertForSequenceClassification",
"ConvBertForTokenClassification",
"ConvBertPretrainingCriterion",
"ConvBertForQuestionAnswering",
"ConvBertForMultipleChoice",
"ConvBertForPretraining",
]
dtype_float = paddle.get_default_dtype()
def _convert_attention_mask(attn_mask, dtype):
if attn_mask is not None and attn_mask.dtype != dtype:
attn_mask_dtype = attn_mask.dtype
if attn_mask_dtype in [paddle.bool, paddle.int8, paddle.int16, paddle.int32, paddle.int64]:
attn_mask = (paddle.cast(attn_mask, dtype) - 1.0) * 1e9
else:
attn_mask = paddle.cast(attn_mask, dtype)
return attn_mask
class GroupedLinear(nn.Layer):
def __init__(self, input_size, output_size, num_groups):
super().__init__()
self.input_size = input_size
self.output_size = output_size
self.num_groups = num_groups
self.group_in_dim = self.input_size // self.num_groups
self.group_out_dim = self.output_size // self.num_groups
self.weight = paddle.create_parameter(
shape=[self.num_groups, self.group_in_dim, self.group_out_dim], dtype=dtype_float
)
self.bias = paddle.create_parameter(shape=[output_size], dtype=dtype_float, is_bias=True)
def forward(self, hidden_states):
batch_size = hidden_states.shape[0]
x = tensor.reshape(hidden_states, [-1, self.num_groups, self.group_in_dim])
x = tensor.transpose(x, perm=[1, 0, 2])
x = tensor.matmul(x, self.weight)
x = tensor.transpose(x, perm=[1, 0, 2])
x = tensor.reshape(x, [batch_size, -1, self.output_size])
x = x + self.bias
return x
class SeparableConv1D(nn.Layer):
"""This class implements separable convolution, i.e. a depthwise and a pointwise layer"""
def __init__(self, input_filters, output_filters, kernel_size):
super().__init__()
self.depthwise = nn.Conv1D(
input_filters,
input_filters,
kernel_size=kernel_size,
groups=input_filters,
padding=kernel_size // 2,
bias_attr=False,
data_format="NLC",
)
self.pointwise = nn.Conv1D(
input_filters,
output_filters,
kernel_size=1,
bias_attr=False,
data_format="NLC",
)
self.bias = paddle.create_parameter(shape=[output_filters], dtype=dtype_float, is_bias=True)
def forward(self, hidden_states):
x = self.depthwise(hidden_states)
x = self.pointwise(x) + self.bias
return x
class MultiHeadAttentionWithConv(Layer):
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
kdim=None,
vdim=None,
need_weights=False,
conv_kernel_size=None,
head_ratio=None,
):
super(MultiHeadAttentionWithConv, self).__init__()
self.embed_dim = embed_dim
self.kdim = kdim if kdim is not None else embed_dim
self.vdim = vdim if vdim is not None else embed_dim
self.need_weights = need_weights
self.head_dim = embed_dim // num_heads
self.scale = self.head_dim**-0.5
assert self.head_dim * num_heads == self.embed_dim, "embed_dim must be divisible by num_heads"
new_num_attention_heads = num_heads // head_ratio
if num_heads // head_ratio < 1:
self.num_heads = 1
self.conv_type = "noconv"
else:
self.num_heads = new_num_attention_heads
self.conv_type = "sdconv"
self.all_head_size = self.num_heads * self.head_dim
self.dropout = nn.Dropout(dropout)
self.q_proj = nn.Linear(embed_dim, self.all_head_size)
self.k_proj = nn.Linear(self.kdim, self.all_head_size)
self.v_proj = nn.Linear(self.vdim, self.all_head_size)
self.out_proj = nn.Linear(embed_dim, embed_dim)
if self.conv_type == "sdconv":
self.conv_kernel_size = conv_kernel_size
self.key_conv_attn_layer = SeparableConv1D(embed_dim, self.all_head_size, self.conv_kernel_size)
self.conv_kernel_layer = nn.Linear(self.all_head_size, self.num_heads * self.conv_kernel_size)
self.conv_out_layer = nn.Linear(embed_dim, self.all_head_size)
self.padding = (self.conv_kernel_size - 1) // 2
def forward(self, query, key=None, value=None, attn_mask=None, cache=None):
key = query if key is None else key
value = query if value is None else value
q = self.q_proj(query)
k = self.k_proj(key)
v = self.v_proj(value)
if self.conv_type == "sdconv":
bs = paddle.shape(q)[0]
seqlen = paddle.shape(q)[1]
mixed_key_conv_attn_layer = self.key_conv_attn_layer(query)
conv_attn_layer = mixed_key_conv_attn_layer * q
# conv_kernel_layer
conv_kernel_layer = self.conv_kernel_layer(conv_attn_layer)
conv_kernel_layer = tensor.reshape(conv_kernel_layer, shape=[-1, self.conv_kernel_size, 1])
conv_kernel_layer = F.softmax(conv_kernel_layer, axis=1)
conv_out_layer = self.conv_out_layer(query)
conv_out_layer = F.pad(conv_out_layer, pad=[self.padding, self.padding], data_format="NLC")
conv_out_layer = paddle.stack(
[
paddle.slice(conv_out_layer, axes=[1], starts=[i], ends=[i + seqlen])
for i in range(self.conv_kernel_size)
],
axis=-1,
)
conv_out_layer = tensor.reshape(conv_out_layer, shape=[-1, self.head_dim, self.conv_kernel_size])
conv_out_layer = tensor.matmul(conv_out_layer, conv_kernel_layer)
conv_out = tensor.reshape(conv_out_layer, shape=[bs, seqlen, self.num_heads, self.head_dim])
q = tensor.reshape(x=q, shape=[0, 0, self.num_heads, self.head_dim])
q = tensor.transpose(x=q, perm=[0, 2, 1, 3])
k = tensor.reshape(x=k, shape=[0, 0, self.num_heads, self.head_dim])
k = tensor.transpose(x=k, perm=[0, 2, 1, 3])
v = tensor.reshape(x=v, shape=[0, 0, self.num_heads, self.head_dim])
v = tensor.transpose(x=v, perm=[0, 2, 1, 3])
product = tensor.matmul(x=q, y=k, transpose_y=True) * self.scale
if attn_mask is not None:
attn_mask = _convert_attention_mask(attn_mask, product.dtype)
product = product + attn_mask
weights = F.softmax(product)
weights = self.dropout(weights)
out = tensor.matmul(weights, v)
# combine heads
out = tensor.transpose(out, perm=[0, 2, 1, 3])
if self.conv_type == "sdconv":
out = tensor.concat([out, conv_out], axis=2)
out = tensor.reshape(x=out, shape=[0, 0, out.shape[2] * out.shape[3]])
# project to output
out = self.out_proj(out)
outs = [out]
if self.need_weights:
outs.append(weights)
if cache is not None:
outs.append(cache)
return out if len(outs) == 1 else tuple(outs)
class TransformerEncoderLayerWithConv(nn.TransformerEncoderLayer):
def __init__(
self,
d_model,
nhead,
dim_feedforward,
dropout=0.1,
activation="relu",
attn_dropout=None,
act_dropout=None,
normalize_before=False,
conv_kernel_size=None,
head_ratio=None,
num_groups=None,
**kwargs
):
super().__init__(
d_model,
nhead,
dim_feedforward,
dropout=dropout,
activation=activation,
attn_dropout=attn_dropout,
act_dropout=act_dropout,
normalize_before=normalize_before,
)
self.self_attn = MultiHeadAttentionWithConv(
d_model,
nhead,
dropout=attn_dropout,
conv_kernel_size=conv_kernel_size,
head_ratio=head_ratio,
)
if num_groups > 1:
self.linear1 = GroupedLinear(d_model, dim_feedforward, num_groups=num_groups)
self.linear2 = GroupedLinear(dim_feedforward, d_model, num_groups=num_groups)
self._config.update({"conv_kernel_size": conv_kernel_size, "head_ratio": head_ratio, "num_groups": num_groups})
class ConvBertEmbeddings(nn.Layer):
"""
Include embeddings from word, position and token_type embeddings
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertEmbeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
self.layer_norm = nn.LayerNorm(config.embedding_size, epsilon=config.layer_norm_eps)
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,
inputs_embeds: Optional[Tensor] = None,
):
if input_ids is not None:
inputs_embeds = self.word_embeddings(input_ids)
input_shape = paddle.shape(inputs_embeds)[:-1]
ones = paddle.ones(input_shape, dtype="int64")
seq_length = paddle.cumsum(ones, axis=1)
position_ids = seq_length - ones
position_ids.stop_gradient = True
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
position_embeddings = self.position_embeddings(position_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = inputs_embeds + position_embeddings + token_type_embeddings
embeddings = self.layer_norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
class ConvBertDiscriminatorPredictions(nn.Layer):
"""
Prediction layer for the discriminator.
"""
def __init__(self, hidden_size, hidden_act):
super(ConvBertDiscriminatorPredictions, self).__init__()
self.dense = nn.Linear(hidden_size, hidden_size)
self.dense_prediction = nn.Linear(hidden_size, 1)
self.act = get_activation(hidden_act)
def forward(self, discriminator_hidden_states):
hidden_states = self.dense(discriminator_hidden_states)
hidden_states = self.act(hidden_states)
logits = self.dense_prediction(hidden_states).squeeze()
return logits
class ConvBertGeneratorPredictions(nn.Layer):
"""
Prediction layer for the generator.
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertGeneratorPredictions, self).__init__()
self.layer_norm = nn.LayerNorm(config.embedding_size, epsilon=config.layer_norm_eps)
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
self.act = get_activation(config.hidden_act)
def forward(self, generator_hidden_states):
hidden_states = self.dense(generator_hidden_states)
hidden_states = self.act(hidden_states)
hidden_states = self.layer_norm(hidden_states)
return hidden_states
[文档]class ConvBertPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained ConvBert models. It provides ConvBert related
`model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
`pretrained_resource_files_map`, `base_model_prefix` for downloading and
loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
base_model_prefix = "convbert"
# pretrained general configuration
gen_weight = 1.0
disc_weight = 50.0
tie_word_embeddings = True
untied_generator_embeddings = False
use_softmax_sample = True
# model init configuration
pretrained_init_configuration = CONVBERT_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = CONVBERT_PRETRAINED_RESOURCE_FILES_MAP
config_class = ConvBertConfig
[文档] def tie_weights(self):
"""
Tie the weights between the input embeddings and the output embeddings.
"""
if hasattr(self, "get_output_embeddings") and hasattr(self, "get_input_embeddings"):
output_embeddings = self.get_output_embeddings()
if output_embeddings is not None:
self._tie_or_clone_weights(output_embeddings, self.get_input_embeddings())
def _init_weights(self, layer):
"""Initialize the weights"""
if isinstance(layer, (nn.Linear, nn.Embedding, GroupedLinear)):
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.bias.set_value(paddle.zeros_like(layer.bias))
layer.weight.set_value(paddle.full_like(layer.weight, 1.0))
layer._epsilon = self.config.layer_norm_eps
elif isinstance(layer, SeparableConv1D):
layer.depthwise.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.depthwise.weight.shape,
)
)
layer.pointwise.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.config.initializer_range,
shape=layer.pointwise.weight.shape,
)
)
if isinstance(layer, (nn.Linear, GroupedLinear, SeparableConv1D)) and layer.bias is not None:
layer.bias.set_value(paddle.zeros_like(layer.bias))
def _tie_or_clone_weights(self, output_embeddings, input_embeddings):
"""Tie or clone layer weights"""
if output_embeddings.weight.shape == input_embeddings.weight.shape:
output_embeddings.weight = input_embeddings.weight
elif output_embeddings.weight.shape == input_embeddings.weight.t().shape:
output_embeddings.weight.set_value(input_embeddings.weight.t())
else:
raise ValueError(
"when tie input/output embeddings, the shape of output embeddings: {}"
"should be equal to shape of input embeddings: {}"
"or should be equal to the shape of transpose input embeddings: {}".format(
output_embeddings.weight.shape,
input_embeddings.weight.shape,
input_embeddings.weight.t().shape,
)
)
if getattr(output_embeddings, "bias", None) is not None:
if output_embeddings.weight.shape[-1] != output_embeddings.bias.shape[0]:
raise ValueError(
"the weight lase shape: {} of output_embeddings is not equal to the bias shape: {}"
"please check output_embeddings configuration".format(
output_embeddings.weight.shape[-1],
output_embeddings.bias.shape[0],
)
)
[文档]@register_base_model
class ConvBertModel(ConvBertPretrainedModel):
"""
The bare ConvBert 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:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertModel, self).__init__(config)
self.pad_token_id = config.pad_token_id
self.initializer_range = config.initializer_range
self.embeddings = ConvBertEmbeddings(config)
if config.embedding_size != config.hidden_size:
self.embeddings_project = nn.Linear(config.embedding_size, config.hidden_size)
encoder_layer = TransformerEncoderLayerWithConv(
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,
conv_kernel_size=config.conv_kernel_size,
head_ratio=config.head_ratio,
num_groups=config.num_groups,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
# self.config = config
self.pooler = ConvBertPooler(config)
[文档] 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 ConvBertModel 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.
If its data type is int, the values should be either 0 or 1.
- **1** for tokens that **not masked**,
- **0** for tokens that **masked**.
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.
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`.
inputs_embeds (Tensor, optional):
Instead of passing input_ids you can choose to directly pass an embedded representation.
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`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import ConvBertModel, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertModel.from_pretrained('convbert-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
output = model(**inputs)
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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")
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids)
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]
)
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, inputs_embeds=inputs_embeds
)
if hasattr(self, "embeddings_project"):
embedding_output = self.embeddings_project(embedding_output)
encoder_outputs = self.encoder(
embedding_output,
src_mask=attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
# output_attentions may be False
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,
hidden_states=encoder_outputs.hidden_states,
attentions=encoder_outputs.attentions,
)
[文档]class ConvBertDiscriminator(ConvBertPretrainedModel):
"""
ConvBert Model with a discriminator prediction head on top.
Args:
config (:class:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertDiscriminator, self).__init__(config)
self.convbert = ConvBertModel(config)
self.discriminator_predictions = ConvBertDiscriminatorPredictions(config.hidden_size, config.hidden_act)
[文档] def forward(
self,
input_ids,
token_type_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
):
r"""
The ConvBertDiscriminator 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.
If its data type is int, the values should be either 0 or 1.
- **1** for tokens that **not masked**,
- **0** for tokens that **masked**.
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.
inputs_embeds (Tensor, optional):
Instead of passing input_ids you can choose to directly pass an embedded representation.
Returns:
Tensor: Returns tensor `logits`, a tensor of the discriminator prediction logits.
Shape as `[batch_size, sequence_length]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import ConvBertDiscriminatorPredictions, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertDiscriminator.from_pretrained('convbert-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
discriminator_sequence_output = self.convbert(
input_ids=input_ids,
token_type_ids=token_type_ids,
position_ids=position_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
)
logits = self.discriminator_predictions(discriminator_sequence_output)
return logits
[文档]class ConvBertGenerator(ConvBertPretrainedModel):
"""
ConvBert Model with a generator prediction head on top.
Args:
config (:class:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertGenerator, self).__init__(config)
self.config = config
self.convbert = ConvBertModel(config)
self.generator_predictions = ConvBertGeneratorPredictions(config)
if not self.tie_word_embeddings:
self.generator_lm_head = nn.Linear(config.embedding_size, config.vocab_size)
else:
self.generator_lm_head_bias = paddle.create_parameter(
shape=[config.vocab_size],
dtype=dtype_float,
is_bias=True,
)
[文档] def forward(
self,
input_ids=None,
token_type_ids=None,
position_ids=None,
attention_mask=None,
inputs_embeds=None,
labels=None,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The ConvBertGenerator forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
See :class:`ConvBertModel`.
token_type_ids (Tensor, optional):
See :class:`ConvBertModel`.
position_ids (Tensor, optional):
See :class:`ConvBertModel`.
attention_mask (Tensor, optional):
See :class:`ConvBertModel`.
output_hidden_states (bool, optional):
See :class:`ConvBertModel`.
output_attentions (bool, optional):
See :class:`ConvBertModel`.
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:
Tensor: Returns tensor `prediction_scores`, a tensor of the generator prediction scores.
Shape as `[batch_size, sequence_length, vocab_size]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import ConvBertGenerator, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertGenerator.from_pretrained('convbert-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
prediction_scores = model(**inputs)
"""
convbert_outputs = self.convbert(
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,
)
prediction_scores = self.generator_predictions(convbert_outputs[0])
if not self.tie_word_embeddings:
prediction_scores = self.generator_lm_head(prediction_scores)
else:
prediction_scores = paddle.add(
paddle.matmul(prediction_scores, self.get_input_embeddings().weight, transpose_y=True),
self.generator_lm_head_bias,
)
loss = None
# # Masked language modeling softmax layer
if labels is not None:
loss_fct = nn.CrossEntropyLoss() # -100 index = padding token
loss = loss_fct(prediction_scores.reshape([-1, self.config.vocab_size]), labels.reshape([-1]))
if not return_dict:
output = (prediction_scores,) + convbert_outputs[1:]
return tuple_output(output, loss)
return MaskedLMOutput(
loss=loss,
logits=prediction_scores,
hidden_states=convbert_outputs.hidden_states,
attentions=convbert_outputs.attentions,
)
[文档]class ConvBertClassificationHead(nn.Layer):
"""
ConvBert head for sentence-level classification tasks.
Args:
config (:class:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertClassificationHead, self).__init__()
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
self.act = get_activation(config.hidden_act)
[文档] def forward(self, features, **kwargs):
x = self.dropout(features)
x = self.dense(x)
x = self.act(x) # ConvBert paper used gelu here
x = self.dropout(x)
x = self.out_proj(x)
return x
[文档]class ConvBertForSequenceClassification(ConvBertPretrainedModel):
"""
ConvBert Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
config (:class:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertForSequenceClassification, self).__init__(config)
self.convbert = ConvBertModel(config)
self.num_labels = config.num_labels
self.classifier = ConvBertClassificationHead(config)
[文档] 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,
labels: Optional[Tensor] = None,
output_hidden_states: Optional[bool] = None,
output_attentions: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
r"""
The ConvBertForSequenceClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`ConvBertModel`.
token_type_ids (Tensor, optional):
See :class:`ConvBertModel`.
position_ids (Tensor, optional):
See :class:`ConvBertModel`.
attention_mask (Tensor, optional):
See :class:`ConvBertModel`.
inputs_embeds (Tensor, optional):
Instead of passing input_ids you can choose to directly pass an embedded representation.
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):
See :class:`ConvBertModel`.
output_attentions (bool, optional):
See :class:`ConvBertModel`.
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:
Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
Shape as `[batch_size, num_classes]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import ConvBertForSequenceClassification, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertForSequenceClassification.from_pretrained('convbert-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.convbert(
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,
)
pooled_output = outputs[1]
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 tuple_output(output, loss)
return SequenceClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class ConvBertForTokenClassification(ConvBertPretrainedModel):
"""
ConvBert Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
config (:class:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertForTokenClassification, self).__init__(config)
self.convbert = ConvBertModel(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: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
inputs_embeds: 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 ConvBertForTokenClassification forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`ConvBertModel`.
token_type_ids (Tensor, optional):
See :class:`ConvBertModel`.
position_ids (Tensor, optional):
See :class:`ConvBertModel`.
attention_mask (Tensor, optional):
See :class:`ConvBertModel`.
inputs_embeds (Tensor, optional):
See :class:`ConvBertModel`.
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):
See :class:`ConvBertModel`.
output_attentions (bool, optional):
See :class:`ConvBertModel`.
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 `False`.
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 import ConvBertForTokenClassification, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertForTokenClassification.from_pretrained('convbert-base')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
logits = model(**inputs)
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.convbert(
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,
)
sequence_output = self.dropout(outputs[0])
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 tuple_output(output, loss)
return TokenClassifierOutput(
loss=loss,
logits=logits,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]class ConvBertForTotalPretraining(ConvBertPretrainedModel):
"""
Combine generator with discriminator for Replaced Token Detection (RTD) pretraining.
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertForTotalPretraining, self).__init__(config)
self.generator = ConvBertGenerator(config)
self.discriminator = ConvBertDiscriminator(config)
self.initializer_range = config.initializer_range
self.tie_weights()
[文档] def get_output_embeddings(self):
if not self.untied_generator_embeddings:
return self.discriminator.convbert.embeddings.word_embeddings
else:
return None
def sample_from_softmax(self, logits, use_softmax_sample=True):
if use_softmax_sample:
# uniform_noise = paddle.uniform(logits.shape, dtype="float32", min=0, max=1)
uniform_noise = paddle.rand(logits.shape, dtype=paddle.get_default_dtype())
gumbel_noise = -paddle.log(-paddle.log(uniform_noise + 1e-9) + 1e-9)
else:
gumbel_noise = paddle.zeros_like(logits)
# softmax_sample equal to sampled_tokids.unsqueeze(-1)
softmax_sample = paddle.argmax(F.softmax(logits + gumbel_noise), axis=-1)
# one hot
return F.one_hot(softmax_sample, logits.shape[-1])
def update_inputs(self, sequence, updates, positions):
shape = sequence.shape
assert len(shape) == 2, "the dimension of inputs should be [batch_size, sequence_length]"
B, L = shape
N = positions.shape[1]
assert N == L, "the dimension of inputs and mask should be same as [batch_size, sequence_length]"
updated_sequence = ((paddle.ones_like(sequence) - positions) * sequence) + (positions * updates)
return updated_sequence
[文档] def forward(
self,
input_ids: Optional[Tensor] = None,
token_type_ids: Optional[Tensor] = None,
position_ids: Optional[Tensor] = None,
attention_mask: Optional[Tensor] = None,
raw_input_ids: Optional[Tensor] = None,
generator_labels: Optional[Tensor] = None,
):
r"""
Args:
input_ids (Tensor):
See :class:`ConvBertModel`.
token_type_ids (Tensor, optional):
See :class:`ConvBertModel`.
position_ids (Tensor, optional):
See :class:`ConvBertModel`.
attention_mask (Tensor, optional):
See :class:`ConvBertModel`.
raw_input_ids(Tensor, optional):
The raw input_ids. Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
generator_labels(Tensor, optional):
The generator labels. Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
Returns:
tuple: Returns tuple (``generator_logits``, ``disc_logits``, ``disc_labels``, ``attention_mask``).
With the fields:
- `generator_logits` (Tensor):
a tensor of the generator prediction logits. Shape as `[batch_size, sequence_length, vocab_size]` and dtype as float32.
- `disc_logits` (Tensor):
a tensor of the discriminator prediction logits. Shape as `[batch_size, sequence_length]` and dtype as float32.
- `disc_labels` (Tensor):
a tensor of the discriminator prediction labels. Shape as `[batch_size, sequence_length]` and dtype as int64.
- `attention_mask` (Tensor):
See :class:`ConvBertModel`.
"""
assert (
generator_labels is not None
), "generator_labels should not be None, please check DataCollatorForLanguageModeling"
generator_logits = self.generator(input_ids, token_type_ids, position_ids, attention_mask)[0]
disc_inputs, disc_labels, generator_predict_tokens = self.get_discriminator_inputs(
input_ids, raw_input_ids, generator_logits, generator_labels, self.use_softmax_sample
)
disc_logits = self.discriminator(disc_inputs, token_type_ids, position_ids, attention_mask)
if attention_mask is None:
attention_mask = input_ids != self.discriminator.convbert.config.pad_token_id
else:
attention_mask = attention_mask.astype("bool")
return generator_logits, disc_logits, disc_labels, attention_mask
[文档]class ConvBertPretrainingCriterion(nn.Layer):
"""
Args:
vocab_size(int):
Vocabulary size of `inputs_ids` in `ConvBertModel`. Defines the number of different tokens that can
be represented by the `inputs_ids` passed when calling `ConvBertModel`.
gen_weight(float):
This is the generator weight.
disc_weight(float):
This is the discriminator weight.
"""
def __init__(self, vocab_size, gen_weight, disc_weight):
super(ConvBertPretrainingCriterion, self).__init__()
self.vocab_size = vocab_size
self.gen_weight = gen_weight
self.disc_weight = disc_weight
self.gen_loss_fct = nn.CrossEntropyLoss(reduction="none")
self.disc_loss_fct = nn.BCEWithLogitsLoss(reduction="none")
[文档] def forward(
self,
generator_prediction_scores,
discriminator_prediction_scores,
generator_labels,
discriminator_labels,
attention_mask,
):
# generator loss
gen_loss = self.gen_loss_fct(
paddle.reshape(generator_prediction_scores, [-1, self.vocab_size]),
paddle.reshape(generator_labels, [-1]),
)
# todo: we can remove 4 lines after when CrossEntropyLoss(reduction='mean') improved
umask_positions = paddle.zeros_like(generator_labels).astype(dtype_float)
mask_positions = paddle.ones_like(generator_labels).astype(dtype_float)
mask_positions = paddle.where(generator_labels == -100, umask_positions, mask_positions)
if mask_positions.sum() == 0:
gen_loss = paddle.to_tensor([0.0])
else:
gen_loss = gen_loss.sum() / mask_positions.sum()
# discriminator loss
seq_length = discriminator_labels.shape[1]
disc_loss = self.disc_loss_fct(
paddle.reshape(discriminator_prediction_scores, [-1, seq_length]),
discriminator_labels.astype(dtype_float),
)
if attention_mask is not None:
umask_positions = paddle.ones_like(discriminator_labels).astype(dtype_float)
mask_positions = paddle.zeros_like(discriminator_labels).astype(dtype_float)
use_disc_loss = paddle.where(attention_mask, disc_loss, mask_positions)
umask_positions = paddle.where(attention_mask, umask_positions, mask_positions)
disc_loss = use_disc_loss.sum() / umask_positions.sum()
else:
total_positions = paddle.ones_like(discriminator_labels).astype(dtype_float)
disc_loss = disc_loss.sum() / total_positions.sum()
return self.gen_weight * gen_loss + self.disc_weight * disc_loss
class ConvBertPooler(Layer):
def __init__(self, config: ConvBertConfig):
super(ConvBertPooler, 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 ConvBertForQuestionAnswering(ConvBertPretrainedModel):
"""
ConvBert 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:`ConvBertConfig`):
An instance of ConvBertConfig
"""
def __init__(self, config: ConvBertConfig):
super(ConvBertForQuestionAnswering, self).__init__(config)
self.convbert = ConvBertModel(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: 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"""
The ConvBertForQuestionAnswering forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`ConvBertModel`.
token_type_ids (Tensor, optional):
See :class:`ConvBertModel`.
position_ids(Tensor, optional):
See :class:`ConvBertModel`.
attention_mask (Tensor, optional):
See :class:`ConvBertModel`.
inputs_embeds (Tensor, optional):
See :class:`ConvBertModel`.
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):
See :class:`ConvBertModel`.
output_attentions (bool, optional):
See :class:`ConvBertModel`.
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 ConvBertForQuestionAnswering, ConvBertTokenizer
tokenizer = ConvBertTokenizer.from_pretrained('convbert-base')
model = ConvBertForQuestionAnswering.from_pretrained('convbert-base')
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.convbert(
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,
)
# ConvBertForMaskedLM is the same as ConvBertGenerator
ConvBertForMaskedLM = ConvBertGenerator
ConvBertForPretraining = ConvBertForTotalPretraining
