Source code for paddlenlp.transformers.roformerv2.modeling
# Copyright (c) 2022 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import tensor
from paddle.nn import Layer
from .. import PretrainedModel, register_base_model
from .configuration import (
ROFORMERV2_PRETRAINED_INIT_CONFIGURATION,
ROFORMERV2_PRETRAINED_RESOURCE_FILES_MAP,
RoFormerv2Config,
)
__all__ = [
"RoFormerv2Model",
"RoFormerv2ForMaskedLM",
"RoFormerv2PretrainedModel",
"RoFormerv2ForSequenceClassification",
"RoFormerv2ForTokenClassification",
"RoFormerv2ForQuestionAnswering",
"RoFormerv2ForMultipleChoice",
]
class Norm(Layer):
def __init__(self, epsilon=1e-12):
super().__init__()
self._epsilon = epsilon
def forward(self, x):
variance = paddle.mean(paddle.square(x), axis=-1, keepdim=True)
return x / paddle.sqrt(variance + self._epsilon)
def initializer(tensor, num_hidden_layers=12, order=2, gain=1.0):
"""
https://github.com/bojone/bert4keras/blob/5572ed481a14f5a62be7107e3846c88a5d6b617d/bert4keras/models.py#L1226-L1235
"""
shape = paddle.shape(tensor)
if shape[0] > 10000 or shape[0] < 10:
hidden_size = shape[1]
else:
hidden_size = shape[0]
gain *= num_hidden_layers ** (-1.0 / order)
std = 1.13684723 / hidden_size**0.5 * gain
return nn.initializer.TruncatedNormal(std=std)
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) * 1e4
else:
attn_mask = paddle.cast(attn_mask, dtype)
return attn_mask
class RotaryPositionEmbedding(Layer):
def __init__(self, dim, max_position_embeddings=512):
super().__init__()
inv_freq = 1.0 / (10000 ** (paddle.arange(0, dim, 2, dtype=paddle.get_default_dtype()) / dim))
t = paddle.arange(max_position_embeddings, dtype=paddle.get_default_dtype())
freqs = paddle.matmul(t.unsqueeze(1), inv_freq.unsqueeze(0))
self.register_buffer("sin", freqs.sin(), persistable=False)
self.register_buffer("cos", freqs.cos(), persistable=False)
def forward(self, x, offset=0):
# x shape [batch_size, num_heads, seqlen, head_dim]
seqlen = paddle.shape(x)[-2]
sin, cos = (
self.sin[offset : offset + seqlen, :],
self.cos[offset : offset + seqlen, :],
)
x1, x2 = x[..., 0::2], x[..., 1::2]
# [cos_nθ, -sin_nθ] [x1]
# [sin_nθ, cos_nθ] [x2]
# => [x1 * cos_nθ - x2 * sin_nθ, x1 * sin_nθ + x2 * cos_nθ]
return paddle.stack([x1 * cos - x2 * sin, x1 * sin + x2 * cos], axis=-1).flatten(-2, -1)
class MultiHeadAttentionWithRotary(Layer):
def __init__(
self,
embed_dim,
num_heads,
dropout=0.0,
kdim=None,
vdim=None,
need_weights=False,
rotary_value=False,
max_position_embeddings=512,
):
super(MultiHeadAttentionWithRotary, 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.num_heads = num_heads
self.need_weights = need_weights
self.rotary_value = rotary_value
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"
self.dropout = nn.Dropout(dropout)
self.q_proj = nn.Linear(embed_dim, embed_dim)
self.k_proj = nn.Linear(self.kdim, embed_dim)
self.v_proj = nn.Linear(self.vdim, embed_dim)
self.out_proj = nn.Linear(embed_dim, embed_dim)
self.rotary = RotaryPositionEmbedding(self.head_dim, max_position_embeddings)
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)
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])
q, k = self.rotary(q), self.rotary(k)
if self.rotary_value:
v = self.rotary(v)
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])
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 TransformerEncoderLayerWithRotary(nn.TransformerEncoderLayer):
def __init__(
self,
d_model,
nhead,
dim_feedforward,
dropout=0.1,
activation="relu",
attn_dropout=None,
act_dropout=None,
normalize_before=False,
rotary_value=False,
max_position_embeddings=512,
**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 = MultiHeadAttentionWithRotary(
d_model,
nhead,
dropout=attn_dropout,
rotary_value=rotary_value,
max_position_embeddings=max_position_embeddings,
)
self.norm1 = Norm()
self.norm2 = Norm()
self._config.update({"rotary_value": rotary_value, "max_position_embeddings": max_position_embeddings})
class RoFormerv2Embeddings(Layer):
"""
Include embeddings from word and token_type embeddings
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2Embeddings, self).__init__()
self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
self.norm = Norm(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
def forward(self, input_ids, token_type_ids=None):
if token_type_ids is None:
token_type_ids = paddle.zeros_like(input_ids)
input_embedings = self.word_embeddings(input_ids)
token_type_embeddings = self.token_type_embeddings(token_type_ids)
embeddings = input_embedings + token_type_embeddings
embeddings = self.norm(embeddings)
embeddings = self.dropout(embeddings)
return embeddings
[docs]
class RoFormerv2PretrainedModel(PretrainedModel):
"""
An abstract class for pretrained RoFormerv2 models. It provides RoFormerv2 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.
"""
pretrained_init_configuration = ROFORMERV2_PRETRAINED_INIT_CONFIGURATION
pretrained_resource_files_map = ROFORMERV2_PRETRAINED_RESOURCE_FILES_MAP
base_model_prefix = "roformerv2"
config_class = RoFormerv2Config
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):
num_hidden_layers = self.config.num_hidden_layers
initializer(layer.weight, num_hidden_layers, order=2, gain=1.0)
if isinstance(layer, nn.Linear):
use_bias = self.config.use_bias
if layer.bias is not None and not use_bias:
layer.bias = None
elif isinstance(layer, Norm):
layer._epsilon = 1e-12
[docs]
@register_base_model
class RoFormerv2Model(RoFormerv2PretrainedModel):
"""
The bare RoFormerv2 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:
vocab_size (int):
Vocabulary size of `inputs_ids` in `RoFormerv2Model`. 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 `RoFormerv2Model`.
hidden_size (int, optional):
Dimensionality of the, encoder layers and pooler layer. Defaults to `768`.
num_hidden_layers (int, optional):
Number of hidden layers in the Transformer encoder. Defaults to `12`.
num_attention_heads (int, optional):
Number of attention heads for each attention layer in the Transformer encoder.
Defaults to `12`.
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 `"relu"`.
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 `2`.
pad_token_id (int, optional):
The index of padding token in the token vocabulary.
Defaults to `0`.
rotary_value (`bool`, optional):
Whether or not apply rotay position embeddings to value.
Defaults to `False`.
use_bias (`bool`, optional):
Whether or not use bias.
Defaults to `False`.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2Model, self).__init__(config)
self.pad_token_id = config.pad_token_id
self.num_hidden_layers = config.num_hidden_layers
self.use_bias = config.use_bias
self.embeddings = RoFormerv2Embeddings(config)
encoder_layer = TransformerEncoderLayerWithRotary(
d_model=config.hidden_size,
nhead=config.num_attention_heads,
dim_feedforward=config.intermediate_size,
dropout=config.hidden_dropout_prob,
activation=config.hidden_act,
attn_dropout=config.attention_probs_dropout_prob,
act_dropout=0,
rotary_value=config.rotary_value,
max_position_embeddings=config.max_position_embeddings,
)
self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None, output_hidden_states=False):
r"""
The RoFormerv2Model 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.
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 `0.0` values and the others have `1.0` values.
It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
Currently, we only support 2D attention_mask.
Defaults to `None`, which means `pad_token_id` will be ignored.
output_hidden_states (bool, optional):
Whether to return the output of each hidden layers.
Defaults to `False`.
Returns:
tuple: Returns `sequence_output` or `encoder_outputs`.
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].
- `encoder_outputs` (List(Tensor)):
A list of Tensor containing hidden-states of the model at each hidden layer in the Transformer encoder.
The length of the list is `num_hidden_layers`.
Each Tensor has a data type of float32 and its shape is [batch_size, sequence_length, hidden_size].
Example:
.. code-block::
import paddle
from paddlenlp.transformers import RoFormerv2Model, RoFormerv2Tokenizer
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2Model.from_pretrained('roformer_v2_chinese_char_base')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
output = model(**inputs)
"""
if attention_mask is None:
attention_mask = paddle.unsqueeze(
(input_ids == self.pad_token_id).astype(paddle.get_default_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
else:
raise ValueError("Currently we only support 2D attention_mask.")
attention_mask.stop_gradient = True
embedding_output = self.embeddings(input_ids=input_ids, token_type_ids=token_type_ids)
if output_hidden_states:
output = embedding_output
encoder_outputs = []
for mod in self.encoder.layers:
output = mod(output, src_mask=attention_mask)
encoder_outputs.append(output)
if self.encoder.norm is not None:
encoder_outputs[-1] = self.encoder.norm(encoder_outputs[-1])
else:
sequence_output = self.encoder(embedding_output, attention_mask)
outputs = encoder_outputs if output_hidden_states else sequence_output
return outputs
[docs]
def set_input_embeddings(self, embedding: nn.Embedding):
self.embeddings.word_embeddings = embedding
[docs]
class RoFormerv2ForQuestionAnswering(RoFormerv2PretrainedModel):
"""
RoFormerv2 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:
roformerv2 (:class:`RoFormerv2Model`):
An instance of RoFormerv2Model.
dropout (float, optional):
The dropout probability for output of RoFormerv2.
If None, use the same value as `hidden_dropout_prob` of `RoFormerv2Model`
instance `roformerv2`. Defaults to `None`.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2ForQuestionAnswering, self).__init__(config)
self.roformerv2 = RoFormerv2Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 2)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
r"""
The RoFormerv2ForQuestionAnswering forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`RoFormerv2Model`.
token_type_ids (Tensor, optional):
See :class:`RoFormerv2Model`.
attention_mask (Tensor, optional):
See :class:`RoFormerv2Model`.
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 RoFormerv2ForQuestionAnswering, RoFormerv2Tokenizer
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2ForQuestionAnswering.from_pretrained('roformer_v2_chinese_char_base')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
outputs = model(**inputs)
start_logits = outputs[0]
end_logits = outputs[1]
"""
sequence_output = self.roformerv2(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
logits = self.classifier(sequence_output)
start_logits, end_logits = paddle.unstack(logits, axis=-1)
return start_logits, end_logits
[docs]
class RoFormerv2ForSequenceClassification(RoFormerv2PretrainedModel):
"""
RoFormerv2 Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
roformerv2 (`RoFormerv2Model`):
An instance of `paddlenlp.transformers.RoFormerv2Model`.
num_labels (int, optional):
The number of classes. Default to `2`.
dropout (float, optional):
The dropout probability for output of RoFormerv2.
If None, use the same value as `hidden_dropout_prob`
of `paddlenlp.transformers.RoFormerv2Model` instance. Defaults to `None`.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2ForSequenceClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roformerv2 = RoFormerv2Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
r"""
Args:
input_ids (Tensor):
See :class:`RoFormerv2Model`.
token_type_ids (Tensor, optional):
See :class:`RoFormerv2Model`.
attention_mask (Tensor, optional):
See :class:`RoFormerv2Model`.
Returns:
Tensor: Returns tensor `logits`, a tensor of the input text classification logits.
Shape as `[batch_size, num_labels]` and dtype as float32.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import RoFormerv2ForSequenceClassification, RoFormerv2Tokenizer
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2ForSequenceClassification.from_pretrained('roformer_v2_chinese_char_base')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
logits = model(**inputs)
"""
sequence_output = self.roformerv2(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
pooled_output = sequence_output[:, 0]
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits
[docs]
class RoFormerv2ForTokenClassification(RoFormerv2PretrainedModel):
"""
RoFormerv2 Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
roformerv2 (`RoFormerv2Model`):
An instance of `paddlenlp.transformers.RoFormerv2Model`.
num_labels (int, optional):
The number of classes. Default to `2`.
dropout (float, optional):
The dropout probability for output of RoFormerv2.
If None, use the same value as `hidden_dropout_prob`
of `paddlenlp.transformers.RoFormerv2Model` instance. Defaults to `None`.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2ForTokenClassification, self).__init__(config)
self.num_labels = config.num_labels
self.roformerv2 = RoFormerv2Model(config) # allow roformerv2 to be config
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
r"""
Args:
input_ids (Tensor):
See :class:`RoFormerv2Model`.
token_type_ids (Tensor, optional):
See :class:`RoFormerv2Model`.
attention_mask (Tensor, optional):
See :class:`RoFormerv2Model`.
Returns:
Tensor: Returns tensor `logits`, a tensor of the input token classification logits.
Shape as `[batch_size, sequence_length, num_labels]` and dtype as `float32`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers import RoFormerv2ForTokenClassification, RoFormerv2Tokenizer
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2ForTokenClassification.from_pretrained('roformer_v2_chinese_char_base')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
logits = model(**inputs)
"""
sequence_output = self.roformerv2(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
sequence_output = self.dropout(sequence_output)
logits = self.classifier(sequence_output)
return logits
[docs]
class RoFormerv2ForMultipleChoice(RoFormerv2PretrainedModel):
"""
RoFormerv2 Model with a linear layer on top of the hidden-states output layer,
designed for multiple choice tasks like RocStories/SWAG tasks.
Args:
roformerv2 (:class:`RoFormerv2Model`):
An instance of RoFormerv2Model.
num_choices (int, optional):
The number of choices. Defaults to `2`.
dropout (float, optional):
The dropout probability for output of RoFormerv2.
If None, use the same value as `hidden_dropout_prob` of `RoFormerv2Model`
instance `roformerv2`. Defaults to None.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2ForMultipleChoice, self).__init__(config)
self.num_choices = config.num_choices
self.roformerv2 = RoFormerv2Model(config)
self.dropout = nn.Dropout(config.hidden_dropout_prob)
self.classifier = nn.Linear(config.hidden_size, 1)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
r"""
The RoFormerv2ForMultipleChoice forward method, overrides the __call__() special method.
Args:
input_ids (Tensor):
See :class:`RoFormerv2Model` and shape as [batch_size, num_choice, sequence_length].
token_type_ids(Tensor, optional):
See :class:`RoFormerv2Model` and shape as [batch_size, num_choice, sequence_length].
attention_mask (list, optional):
See :class:`RoFormerv2Model` 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 RoFormerv2ForMultipleChoice, RoFormerv2Tokenizer
from paddlenlp.data import Pad
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2ForMultipleChoice.from_pretrained('roformer_v2_chinese_char_base', num_choices=2)
data = [
{
"question": "如何打开ipad屏幕？",
"answer1": "按音量按钮。",
"answer2": "按下锁定按钮。",
"label": 1,
},
{
"question": "如何缩进一些文本？",
"answer1": "在开始写之前留一些空格。",
"answer2": "按空格键。",
"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)
input_ids = Pad(axis=0, pad_val=tokenizer.pad_token_id)(inputs["input_ids"])
token_type_ids = Pad(axis=0, pad_val=tokenizer.pad_token_type_id)(inputs["token_type_ids"])
reshaped_logits = model(
input_ids=paddle.to_tensor(input_ids, dtype="int64"),
token_type_ids=paddle.to_tensor(token_type_ids, dtype="int64"),
)
print(reshaped_logits.shape)
# [2, 2]
"""
# input_ids: [bs, num_choice, seq_l]
input_ids = input_ids.reshape(shape=(-1, paddle.shape(input_ids)[-1])) # flat_input_ids: [bs*num_choice,seq_l]
if token_type_ids is not None:
token_type_ids = token_type_ids.reshape(shape=(-1, paddle.shape(token_type_ids)[-1]))
if attention_mask is not None:
attention_mask = attention_mask.reshape(shape=(-1, paddle.shape(attention_mask)[-1]))
sequence_output = self.roformerv2(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
pooled_output = sequence_output[:, 0]
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
class RoFormerv2LMPredictionHead(Layer):
def __init__(self, config: RoFormerv2Config, embedding_weights=None):
super(RoFormerv2LMPredictionHead, self).__init__()
self.use_bias = config.use_bias
self.decoder_weight = (
self.create_parameter(shape=[config.vocab_size, config.hidden_size], dtype=self.transform.weight.dtype)
if embedding_weights is None
else embedding_weights
)
if config.use_bias:
self.decoder_bias = self.create_parameter(
shape=[config.vocab_size], dtype=self.decoder_weight.dtype, is_bias=True
)
def forward(self, hidden_states):
hidden_states = paddle.matmul(hidden_states, self.decoder_weight, transpose_y=True)
if self.use_bias:
hidden_states = hidden_states + self.decoder_bias
return hidden_states
[docs]
class RoFormerv2ForMaskedLM(RoFormerv2PretrainedModel):
"""
RoFormerv2 Model with a `masked language modeling` head on top.
Args:
roformerv2 (:class:`RoFormerv2Model`):
An instance of :class:`RoFormerv2Model`.
"""
def __init__(self, config: RoFormerv2Config):
super(RoFormerv2ForMaskedLM, self).__init__(config)
self.roformerv2 = RoFormerv2Model(config)
self.cls = RoFormerv2LMPredictionHead(
config, embedding_weights=self.roformerv2.embeddings.word_embeddings.weight
)
[docs]
def forward(self, input_ids, token_type_ids=None, attention_mask=None):
r"""
Args:
input_ids (Tensor):
See :class:`RoFormerv2Model`.
token_type_ids (Tensor, optional):
See :class:`RoFormerv2Model`.
attention_mask (Tensor, optional):
See :class:`RoFormerv2Model`.
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 RoFormerv2ForMaskedLM, RoFormerv2Tokenizer
tokenizer = RoFormerv2Tokenizer.from_pretrained('roformer_v2_chinese_char_base')
model = RoFormerv2ForMaskedLM.from_pretrained('roformer_v2_chinese_char_base')
inputs = tokenizer("欢迎使用百度飞桨!")
inputs = {k:paddle.to_tensor([v], dtype="int64") for (k, v) in inputs.items()}
logits = model(**inputs)
print(logits.shape)
# [1, 11, 12000]
"""
sequence_output = self.roformerv2(input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)
prediction_scores = self.cls(sequence_output)
return prediction_scores