# encoding=utf8

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

import math

import paddle

import paddle.nn as nn

import paddle.nn.functional as F

from paddlenlp.transformers import PretrainedModel, register_base_model

from ..activations import get_activation

__all__ = [

"RemBertModel",

"RemBertForMaskedLM",

"RemBertForQuestionAnswering",

"RemBertForSequenceClassification",

"RemBertForMultipleChoice",

"RembertPretrainedModel",

"RemBertForTokenClassification",

]

class RembertPretrainedModel(PretrainedModel):

pretrained_init_configuration = {

"rembert": {

"attention_probs_dropout_prob": 0,

"input_embedding_size": 256,

"hidden_act": "gelu",

"hidden_dropout_prob": 0,

"hidden_size": 1152,

"initializer_range": 0.02,

"intermediate_size": 4608,

"max_position_embeddings": 512,

"num_attention_heads": 18,

"num_hidden_layers": 32,

"pad_token_id": 0,

"type_vocab_size": 2,

"vocab_size": 250300,

"layer_norm_eps": 1e-12,

}

}

pretrained_resource_files_map = {

"model_state": {

"rembert": "https://bj.bcebos.com/paddlenlp/models/transformers/rembert/model_state.pdparams",

}

}

base_model_prefix = "rembert"

def init_weights(self, layer):

"""Initialization hook"""

if isinstance(layer, (nn.Linear, nn.Embedding)):

# only support dygraph, use truncated_normal and make it inplace

# and configurable later

layer.weight.set_value(

paddle.tensor.normal(

mean=0.0,

std=self.initializer_range

if hasattr(self, "initializer_range")

else self.rembert.config["initializer_range"],

shape=layer.weight.shape,

)

)

elif isinstance(layer, nn.LayerNorm):

layer._epsilon = 1e-12

class RemBertEmbeddings(nn.Layer):

"""Construct the embeddings from word, position and token_type embeddings."""

def __init__(

self,

pad_token_id=0,

vocab_size=250300,

input_embedding_size=256,

max_position_embeddings=512,

type_vocab_size=2,

hidden_dropout_prob=0,

layer_norm_eps=1e-12,

):

super(RemBertEmbeddings, self).__init__()

self.word_embeddings = nn.Embedding(vocab_size, input_embedding_size)

self.position_embeddings = nn.Embedding(max_position_embeddings, input_embedding_size)

self.token_type_embeddings = nn.Embedding(type_vocab_size, input_embedding_size)

self.layer_norm = nn.LayerNorm(input_embedding_size, epsilon=layer_norm_eps)

self.dropout = nn.Dropout(hidden_dropout_prob)

# position_ids (1, len position emb) is contiguous in memory and exported when serialized

self.register_buffer("position_ids", paddle.arange(end=max_position_embeddings).expand((1, -1)))

def forward(

self,

input_ids=None,

token_type_ids=None,

position_ids=None,

):

input_shape = input_ids.shape

seq_length = input_shape[1]

if position_ids is None:

position_ids = self.position_ids[:, :seq_length]

if token_type_ids is None:

token_type_ids = paddle.zeros(input_shape, dtype="int64")

inputs_embeds = self.word_embeddings(input_ids)

token_type_embeddings = self.token_type_embeddings(token_type_ids)

embeddings = inputs_embeds + token_type_embeddings

position_embeddings = self.position_embeddings(position_ids)

embeddings += position_embeddings

embeddings = self.layer_norm(embeddings)

embeddings = self.dropout(embeddings)

return embeddings

class RemBertPooler(nn.Layer):

def __init__(self, hidden_size):

super(RemBertPooler, self).__init__()

self.dense = nn.Linear(hidden_size, hidden_size)

self.activation = nn.Tanh()

def forward(self, hidden_states):

# We "pool" the model by simply taking the hidden state corresponding

# to the first token.

first_token_tensor = hidden_states[:, 0]

pooled_output = self.dense(first_token_tensor)

pooled_output = self.activation(pooled_output)

return pooled_output

class RemBertSelfAttention(nn.Layer):

def __init__(self, hidden_size, num_attention_heads, attention_probs_dropout_prob):

super(RemBertSelfAttention, self).__init__()

self.num_attention_heads = num_attention_heads

self.attention_head_size = int(hidden_size / num_attention_heads)

self.all_head_size = self.num_attention_heads * self.attention_head_size

self.query = nn.Linear(hidden_size, self.all_head_size)

self.key = nn.Linear(hidden_size, self.all_head_size)

self.value = nn.Linear(hidden_size, self.all_head_size)

self.dropout = nn.Dropout(attention_probs_dropout_prob)

def transpose_for_scores(self, x):

new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]

x = x.reshape(new_x_shape)

return x.transpose((0, 2, 1, 3))

def forward(self, hidden_states, attention_mask=None):

mixed_query_layer = self.query(hidden_states)

key_layer = self.transpose_for_scores(self.key(hidden_states))

value_layer = self.transpose_for_scores(self.value(hidden_states))

query_layer = self.transpose_for_scores(mixed_query_layer)

# Take the dot product between "query" and "key" to get the raw attention scores.

attention_scores = paddle.matmul(query_layer, key_layer.transpose((0, 1, 3, 2)))

attention_scores = attention_scores / math.sqrt(self.attention_head_size)

if attention_mask is not None:

# Apply the attention mask is (precomputed for all layers in RemBertModel forward() function)

attention_scores = attention_scores + attention_mask

# Normalize the attention scores to probabilities.

attention_probs = F.softmax(attention_scores, axis=-1)

# This is actually dropping out entire tokens to attend to, which might

# seem a bit unusual, but is taken from the original Transformer paper.

attention_probs = self.dropout(attention_probs)

context_layer = paddle.matmul(attention_probs, value_layer)

context_layer = context_layer.transpose((0, 2, 1, 3))

new_context_layer_shape = context_layer.shape[:-2] + [self.all_head_size]

context_layer = context_layer.reshape(new_context_layer_shape)

outputs = (context_layer, attention_probs)

return outputs

class RemBertSelfOutput(nn.Layer):

def __init__(self, hidden_size, hidden_dropout_prob, layer_norm_eps=1e-12):

super(RemBertSelfOutput, self).__init__()

self.dense = nn.Linear(hidden_size, hidden_size)

self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps)

self.dropout = nn.Dropout(hidden_dropout_prob)

def forward(self, hidden_states, input_tensor):

hidden_states = self.dense(hidden_states)

hidden_states = self.dropout(hidden_states)

hidden_states = self.layer_norm(hidden_states + input_tensor)

return hidden_states

class RemBertAttention(nn.Layer):

def __init__(

self, hidden_size, num_attention_heads, attention_probs_dropout_prob, hidden_dropout_prob, layer_norm_eps

):

super(RemBertAttention, self).__init__()

self.self = RemBertSelfAttention(

hidden_size=hidden_size,

num_attention_heads=num_attention_heads,

attention_probs_dropout_prob=attention_probs_dropout_prob,

)

self.output = RemBertSelfOutput(

hidden_size=hidden_size, hidden_dropout_prob=hidden_dropout_prob, layer_norm_eps=layer_norm_eps

)

def forward(

self,

hidden_states,

attention_mask=None,

):

self_outputs = self.self(hidden_states, attention_mask)

attention_output = self.output(self_outputs, hidden_states)

return attention_output

class RemBertIntermediate(nn.Layer):

def __init__(self, hidden_size, intermediate_size, hidden_act):

super(RemBertIntermediate, self).__init__()

self.dense = nn.Linear(hidden_size, intermediate_size)

self.intermediate_act_fn = get_activation(hidden_act)

def forward(self, hidden_states):

hidden_states = self.dense(hidden_states)

hidden_states = self.intermediate_act_fn(hidden_states)

return hidden_states

class RemBertOutput(nn.Layer):

def __init__(self, hidden_size, hidden_dropout_prob, intermediate_size, layer_norm_eps=1e-12):

super(RemBertOutput, self).__init__()

self.dense = nn.Linear(intermediate_size, hidden_size)

self.layer_norm = nn.LayerNorm(hidden_size, epsilon=layer_norm_eps)

self.dropout = nn.Dropout(hidden_dropout_prob)

def forward(self, hidden_states, input_tensor):

hidden_states = self.dense(hidden_states)

hidden_states = self.dropout(hidden_states)

hidden_states = self.layer_norm(hidden_states + input_tensor)

return hidden_states

class RemBertLayer(nn.Layer):

def __init__(

self,

hidden_size,

num_attention_heads,

attention_probs_dropout_prob,

hidden_dropout_prob,

hidden_act,

intermediate_size,

layer_norm_eps,

):

super(RemBertLayer, self).__init__()

self.attention = RemBertAttention(

hidden_size=hidden_size,

num_attention_heads=num_attention_heads,

attention_probs_dropout_prob=attention_probs_dropout_prob,

hidden_dropout_prob=hidden_dropout_prob,

layer_norm_eps=layer_norm_eps,

)

self.intermediate = RemBertIntermediate(

hidden_size=hidden_size, intermediate_size=intermediate_size, hidden_act=hidden_act

)

self.output = RemBertOutput(

hidden_size=hidden_size,

hidden_dropout_prob=hidden_dropout_prob,

intermediate_size=intermediate_size,

layer_norm_eps=layer_norm_eps,

)

def forward(self, hidden_states, attention_mask=None):

self_attention_outputs = self.attention(

hidden_states,

attention_mask,

)

layer_output = self.feed_forward_chunk(self_attention_outputs)

return layer_output

def feed_forward_chunk(self, attention_output):

intermediate_output = self.intermediate(attention_output)

layer_output = self.output(intermediate_output, attention_output)

return layer_output

class RemBertEncoder(nn.Layer):

def __init__(

self,

input_embedding_size,

hidden_size,

hidden_act,

num_hidden_layers,

num_attention_heads,

attention_probs_dropout_prob,

hidden_dropout_prob,

intermediate_size,

layer_norm_eps,

):

super(RemBertEncoder, self).__init__()

self.embedding_hidden_mapping_in = nn.Linear(input_embedding_size, hidden_size)

self.layer = nn.LayerList(

[

RemBertLayer(

hidden_size=hidden_size,

num_attention_heads=num_attention_heads,

attention_probs_dropout_prob=attention_probs_dropout_prob,

hidden_dropout_prob=hidden_dropout_prob,

intermediate_size=intermediate_size,

layer_norm_eps=layer_norm_eps,

hidden_act=hidden_act,

)

for _ in range(num_hidden_layers)

]

)

def forward(self, hidden_states, attention_mask=None):

hidden_states = self.embedding_hidden_mapping_in(hidden_states)

for i, layer_module in enumerate(self.layer):

layer_outputs = layer_module(hidden_states, attention_mask)

hidden_states = layer_outputs

return hidden_states

@register_base_model

class RemBertModel(RembertPretrainedModel):

"""

The bare RemBERT 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:

vocab_size (int):

Vocabulary size of `inputs_ids` in `RemBertModel`. Also is the vocab size of token embedding matrix.

Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling `RemBertModel`.

input_embedding_size (int, optional):

Dimensionality of the embedding layer. Defaults to `256`.

hidden_size (int, optional):

Dimensionality of the encoder layer and pooler layer. Defaults to `1152`.

num_hidden_layers (int, optional):

Number of hidden layers in the Transformer encoder. Defaults to `32`.

num_attention_heads (int, optional):

Number of attention heads for each attention layer in the Transformer encoder.

Defaults to `18`.

intermediate_size (int, optional):

Dimensionality of the feed-forward (ff) layer in the encoder. Input tensors

to ff layers are firstly projected from `hidden_size` to `intermediate_size`,

and then projected back to `hidden_size`. Typically `intermediate_size` is larger than `hidden_size`.

Defaults to `3072`.

hidden_act (str, optional):

The non-linear activation function in the feed-forward layer.

``"gelu"``, ``"relu"`` and any other paddle supported activation functions

are supported. Defaults to `"gelu"`.

hidden_dropout_prob (float, optional):

The dropout probability for all fully connected layers in the embeddings and encoder.

Defaults to `0.1`.

attention_probs_dropout_prob (float, optional):

The dropout probability used in MultiHeadAttention in all encoder layers to drop some attention target.

Defaults to `0.1`.

max_position_embeddings (int, optional):

The maximum value of the dimensionality of position encoding, which dictates the maximum supported length of an input

sequence. Defaults to `512`.

type_vocab_size (int, optional):

The vocabulary size of `token_type_ids`.

Defaults to `16`.

initializer_range (float, optional):

The standard deviation of the normal initializer.

Defaults to 0.02.

.. note::

A normal_initializer initializes weight matrices as normal distributions.

See :meth:`BertPretrainedModel.init_weights()` for how weights are initialized in `BertModel`.

pad_token_id (int, optional):

The index of padding token in the token vocabulary.

Defaults to `0`.

"""

def __init__(

self,

vocab_size,

input_embedding_size=256,

hidden_size=1152,

num_hidden_layers=32,

num_attention_heads=18,

intermediate_size=4608,

hidden_act="gelu",

hidden_dropout_prob=0,

attention_probs_dropout_prob=0,

max_position_embeddings=512,

type_vocab_size=2,

initializer_range=0.02,

pad_token_id=0,

layer_norm_eps=1e-12,

):

super(RemBertModel, self).__init__()

self.pad_token_id = pad_token_id

self.num_hidden_layers = num_hidden_layers

self.initializer_range = initializer_range

self.layer_norm_eps = layer_norm_eps

self.embeddings = RemBertEmbeddings(

vocab_size=vocab_size,

layer_norm_eps=layer_norm_eps,

pad_token_id=pad_token_id,

input_embedding_size=input_embedding_size,

max_position_embeddings=max_position_embeddings,

type_vocab_size=type_vocab_size,

hidden_dropout_prob=hidden_dropout_prob,

)

self.encoder = RemBertEncoder(

input_embedding_size=input_embedding_size,

hidden_size=hidden_size,

num_hidden_layers=num_hidden_layers,

num_attention_heads=num_attention_heads,

attention_probs_dropout_prob=attention_probs_dropout_prob,

hidden_dropout_prob=hidden_dropout_prob,

intermediate_size=intermediate_size,

hidden_act=hidden_act,

layer_norm_eps=layer_norm_eps,

)

self.pooler = RemBertPooler(hidden_size)

self.apply(self.init_weights)

def get_input_embeddings(self):

return self.embeddings.word_embeddings

def set_input_embeddings(self, value):

self.embeddings.word_embeddings = value

def forward(self, input_ids=None, token_type_ids=None, position_ids=None, attention_mask=None):

r"""

The RemBertModel 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.

Returns:

tuple: Returns tuple (`sequence_output`, `pooled_output`)

With the fields:

- `sequence_output` (Tensor):

Sequence of hidden-states at the last layer of the model.

It's data type should be float32 and 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 RemBertModel, RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertModel.from_pretrained('rembert')

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)

"""

input_shape = input_ids.shape

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])

if token_type_ids is None:

token_type_ids = paddle.zeros(input_shape, dtype="int64")

embedding_output = self.embeddings(

input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids

)

encoder_outputs = self.encoder(

embedding_output,

attention_mask=attention_mask,

)

sequence_output = encoder_outputs

pooled_output = self.pooler(sequence_output)

return sequence_output, pooled_output

class RemBertForSequenceClassification(RembertPretrainedModel):

"""

RemBert Model with a linear layer on top of the output layer,

designed for sequence classification/regression tasks like GLUE tasks.

Args:

rembert (:class:`RemBertModel`):

An instance of RemBertModel.

num_classes (int, optional):

The number of classes.

"""

def __init__(self, rembert, num_classes):

super(RemBertForSequenceClassification, self).__init__()

self.rembert = rembert

self.dense = nn.Linear(self.rembert.config["hidden_size"], num_classes)

self.dropout = nn.Dropout(self.rembert.config["hidden_dropout_prob"])

self.apply(self.init_weights)

def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):

r"""

The RemBertForSequenceClassification forward method, overrides the __call__() special method.

Args:

input_ids (Tensor):

See :class:`RemBertModel`.

token_type_ids (Tensor, optional):

See :class:`RemBertModel`.

position_ids (Tensor, optional):

See :class:`RemBertModel`.

attention_mask (Tensor, optional):

See :class:`RemBertModel`.

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 RemBertForSequenceClassification

from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertForQuestionAnswering.from_pretrained('rembert', num_classes=2)

inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")

inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}

outputs = model(**inputs)

"""

pool_output = self.rembert(

input_ids=input_ids,

position_ids=position_ids,

attention_mask=attention_mask,

token_type_ids=token_type_ids,

)[1]

pool_output = self.dropout(pool_output)

logits = self.dense(pool_output)

return logits

class RemBertForQuestionAnswering(RembertPretrainedModel):

"""

RemBert 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:

rembert (:class:`RemBertModel`):

An instance of RemBertModel.

"""

def __init__(self, rembert):

super(RemBertForQuestionAnswering, self).__init__()

self.rembert = rembert

self.qa_outputs = nn.Linear(self.rembert.config["hidden_size"], 2)

self.apply(self.init_weights)

def forward(

self,

input_ids=None,

token_type_ids=None,

position_ids=None,

attention_mask=None,

):

r"""

The RemBertForQuestionAnswering forward method, overrides the __call__() special method.

Args:

input_ids (Tensor):

See :class:`RemBertModel`.

token_type_ids (Tensor, optional):

See :class:`RemBertModel`.

position_ids (Tensor, optional):

See :class:`RemBertModel`.

attention_mask (Tensor, optional):

See :class:`RemBertModel`.

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 RemBertForQuestionAnswering

from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertForQuestionAnswering.from_pretrained('rembert')

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]

"""

outputs = self.rembert(

input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids

)

sequence_output = outputs[0]

logits = self.qa_outputs(sequence_output)

start_logits, end_logits = paddle.split(logits, num_or_sections=2, axis=-1)

return start_logits, end_logits

class RemBertLMPredictionHead(nn.Layer):

"""

RemBert Model with a `language modeling` head on top for CLM fine-tuning.

"""

def __init__(self, hidden_size, vocab_size, activation, embedding_weights=None):

super(RemBertLMPredictionHead, self).__init__()

self.transform = nn.Linear(hidden_size, hidden_size)

self.activation = get_activation(activation)

self.layer_norm = nn.LayerNorm(hidden_size)

self.decoder = nn.Linear(hidden_size, hidden_size)

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

class RemBertOnlyMLMHead(nn.Layer):

def __init__(self, hidden_size, vocab_size, activation, embedding_weights):

super(RemBertOnlyMLMHead, self).__init__()

self.predictions = RemBertLMPredictionHead(

hidden_size=hidden_size, vocab_size=vocab_size, activation=activation, embedding_weights=embedding_weights

)

def forward(self, sequence_output, masked_positions=None):

prediction_scores = self.predictions(sequence_output, masked_positions)

return prediction_scores

class RemBertForMaskedLM(RembertPretrainedModel):

"""

RemBert Model with a `masked language modeling` head on top.

Args:

rembert (:class:`RemBertModel`):

An instance of :class:`RemBertModel`.

"""

def __init__(self, rembert):

super(RemBertForMaskedLM, self).__init__()

self.rembert = rembert

self.cls = RemBertOnlyMLMHead(

self.rembert.config["hidden_size"],

self.rembert.config["vocab_size"],

self.rembert.config["hidden_act"],

embedding_weights=self.rembert.embeddings.word_embeddings.weight,

)

self.apply(self.init_weights)

def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):

r"""

Args:

input_ids (Tensor):

See :class:`RemBertModel`.

token_type_ids (Tensor, optional):

See :class:`RemBertModel`.

position_ids (Tensor, optional):

See :class:`RemBertModel`.

attention_mask (Tensor, optional):

See :class:`RemBertModel`.

Returns:

Tensor: Returns tensor `prediction_scores`, The scores of masked token prediction.

Its data type should be float32 and shape is [batch_size, sequence_length, vocab_size].

Example:

.. code-block::

import paddle

from paddlenlp.transformers import RemBertForMaskedLM, RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertForMaskedLM.from_pretrained('rembert')

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.rembert(

input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask

)

sequence_output = outputs[0]

prediction_scores = self.cls(sequence_output, masked_positions=None)

return prediction_scores

class RemBertForTokenClassification(RembertPretrainedModel):

"""

RemBert Model with a linear layer on top of the hidden-states output layer,

designed for token classification tasks like NER tasks.

Args:

rembert (:class:`RemBertModel`):

An instance of RemBertModel.

num_classes (int):

The number of classes.

"""

def __init__(self, rembert, num_classes=2):

super(RemBertForTokenClassification, self).__init__()

self.num_classes = num_classes

self.rembert = rembert # allow rembert to be config

self.dropout = nn.Dropout(self.rembert.config["hidden_dropout_prob"])

self.classifier = nn.Linear(self.rembert.config["hidden_size"], num_classes)

self.apply(self.init_weights)

def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):

r"""

The RemBertForTokenClassification forward method, overrides the __call__() special method.

Args:

input_ids (Tensor):

See :class:`RemBertModel`.

token_type_ids (Tensor, optional):

See :class:`RemBertModel`.

position_ids(Tensor, optional):

See :class:`RemBertModel`.

attention_mask (list, optional):

See :class:`RemBertModel`.

Returns:

Tensor: Returns tensor `logits`, a tensor of the input token classification logits.

Shape as `[batch_size, sequence_length, num_classes]` and dtype as `float32`.

Example:

.. code-block::

import paddle

from paddlenlp.transformers import RemBertForTokenClassification

from paddlenlp.transformers import RemBertTokenizer

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertForTokenClassification.from_pretrained('rembert')

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)

"""

sequence_output, _ = self.rembert(

input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask

)

sequence_output = self.dropout(sequence_output)

logits = self.classifier(sequence_output)

return logits

class RemBertForMultipleChoice(RembertPretrainedModel):

"""

RemBert Model with a linear layer on top of the hidden-states output layer,

designed for multiple choice tasks like RocStories/SWAG tasks.

Args:

rembert (:class:`RemBertModel`):

An instance of RemBertModel.

num_choices (int):

The number of choices.

"""

def __init__(self, rembert, num_choices):

super(RemBertForMultipleChoice, self).__init__()

self.num_choices = num_choices

self.rembert = rembert

self.dropout = nn.Dropout(self.rembert.config["hidden_dropout_prob"])

self.classifier = nn.Linear(self.rembert.config["hidden_size"], 1)

self.apply(self.init_weights)

def forward(self, input_ids, token_type_ids=None, position_ids=None, attention_mask=None):

r"""

The BertForMultipleChoice forward method, overrides the __call__() special method.

Args:

input_ids (Tensor):

See :class:`RemBertModel` and shape as [batch_size, num_choice, sequence_length].

token_type_ids(Tensor, optional):

See :class:`RemBertModel` and shape as [batch_size, num_choice, sequence_length].

position_ids(Tensor, optional):

See :class:`RemBertModel` and shape as [batch_size, num_choice, sequence_length].

attention_mask (list, optional):

See :class:`RemBertModel` and shape as [batch_size, num_choice, sequence_length].

Returns:

Tensor: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits.

Shape as `[batch_size, num_choice]` and dtype as `float32`.

Example:

.. code-block::

import paddle

from paddlenlp.transformers import RemBertForMultipleChoice, RemBertTokenizer

from paddlenlp.data import Pad, Dict

tokenizer = RemBertTokenizer.from_pretrained('rembert')

model = RemBertForMultipleChoice.from_pretrained('rembert', num_choices=2)

data = [

{

"question": "how do you turn on an ipad screen?",

"answer1": "press the volume button.",

"answer2": "press the lock button.",

"label": 1,

},

{

"question": "how do you indent something?",

"answer1": "leave a space before starting the writing",

"answer2": "press the spacebar",

"label": 0,

},

]

text = []

text_pair = []

for d in data:

text.append(d["question"])

text_pair.append(d["answer1"])

text.append(d["question"])

text_pair.append(d["answer2"])

inputs = tokenizer(text, text_pair)

batchify_fn = lambda samples, fn=Dict(

{

"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids

"token_type_ids": Pad(

axis=0, pad_val=tokenizer.pad_token_type_id

), # token_type_ids

}

): fn(samples)

inputs = batchify_fn(inputs)

reshaped_logits = model(

input_ids=paddle.to_tensor(inputs[0], dtype="int64"),

token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"),

)

"""

# input_ids: [bs, num_choice, seq_l]

input_ids = input_ids.reshape(shape=(-1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l]

if position_ids is not None:

position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1]))

if token_type_ids is not None:

token_type_ids = token_type_ids.reshape(shape=(-1, token_type_ids.shape[-1]))

if attention_mask is not None:

attention_mask = attention_mask.reshape(shape=(-1, attention_mask.shape[-1]))

_, pooled_output = self.rembert(

input_ids, token_type_ids=token_type_ids, position_ids=position_ids, attention_mask=attention_mask

)

pooled_output = self.dropout(pooled_output)

logits = self.classifier(pooled_output) # logits: (bs*num_choice,1)

reshaped_logits = logits.reshape(shape=(-1, self.num_choices)) # logits: (bs, num_choice)

return reshaped_logits