# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and 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.
"""Modeling classes for XLNet model."""
from dataclasses import dataclass
from typing import List, Optional, Tuple
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn import BCEWithLogitsLoss, CrossEntropyLoss, Layer, MSELoss
from ...utils.env import CONFIG_NAME
from .. import PretrainedModel, register_base_model
from ..activations import ACT2FN, get_activation
from ..model_outputs import ModelOutput, tuple_output
from .configuration import (
XLNET_PRETRAINED_INIT_CONFIGURATION,
XLNET_PRETRAINED_RESOURCE_FILES_MAP,
XLNetConfig,
)
__all__ = [
"XLNetPretrainedModel",
"XLNetModel",
"XLNetForSequenceClassification",
"XLNetForTokenClassification",
"XLNetLMHeadModel",
"XLNetForMultipleChoice",
"XLNetForQuestionAnswering",
"XLNetForCausalLM",
]
dtype_float = paddle.get_default_dtype()
class XLNetRelativeAttention(Layer):
def __init__(self, config: XLNetConfig):
super(XLNetRelativeAttention, self).__init__()
self.n_head = config.n_head
self.d_head = config.d_head
self.d_model = config.d_model
self.scale = 1 / (config.d_head**0.5)
self.q = self.create_parameter([self.d_model, self.n_head * self.d_head])
self.k = self.create_parameter([self.d_model, self.n_head * self.d_head])
self.v = self.create_parameter([self.d_model, self.n_head * self.d_head])
self.o = self.create_parameter([self.d_model, self.n_head * self.d_head])
self.r = self.create_parameter([self.d_model, self.n_head * self.d_head])
self.r_r_bias = self.create_parameter([self.n_head, self.d_head], is_bias=True)
self.r_s_bias = self.create_parameter([self.n_head, self.d_head], is_bias=True)
self.r_w_bias = self.create_parameter([self.n_head, self.d_head], is_bias=True)
self.seg_embed = self.create_parameter([2, self.n_head, self.d_head], is_bias=False)
self.layer_norm = nn.LayerNorm(config.d_model, epsilon=config.layer_norm_eps)
self.dropout = nn.Dropout(config.dropout)
def prune_heads(self, heads):
raise NotImplementedError
@staticmethod
def rel_shift_bnij(x, klen=-1):
# Relative shift of the attention matrix from bd~ to bd (refer to Appendix B in the Transformer-XL paper)
x_size = paddle.shape(x)
x = paddle.reshape(x, [x_size[0], x_size[1], x_size[3], x_size[2]])
x = x[:, :, 1:, :]
x = paddle.reshape(x, [x_size[0], x_size[1], x_size[2], x_size[3] - 1])
x = paddle.index_select(x, index=paddle.arange(klen, dtype="int64"), axis=3)
return x
def rel_attn_core(
self,
q_head,
k_head_h,
v_head_h,
k_head_r,
seg_mat=None,
attn_mask=None,
head_mask=None,
output_attentions=False,
):
"""Core relative positional attention operations."""
# Content based attention score (refer to the Transformer-XL paper)
# q_head = Exi * Wq; self.r_w_bias = u; k_head_h = Wke * Exj
# a = Exi * Wq * Wke * Exj; c = u * Wke * Exj; ac = a + c
ac = paddle.einsum("ibnd,jbnd->bnij", q_head + self.r_w_bias, k_head_h)
# Position based attention score (refer to the Transformer-XL paper)
# q_head = Exi * Wq; self.r_r_bias = v; k_head_r = Wkr * Rij
# b = Exi * Wq * Wkr * Rij; d = v * Wkr * Rij; bd = b + d
bd = paddle.einsum("ibnd,jbnd->bnij", q_head + self.r_r_bias, k_head_r)
bd = self.rel_shift_bnij(bd, klen=paddle.shape(ac)[3])
# Segment based attention score
if seg_mat is None:
ef = 0
else:
ef = paddle.einsum("ibnd,snd->ibns", q_head + self.r_s_bias, self.seg_embed)
ef = paddle.einsum("ijbs,ibns->bnij", seg_mat, ef)
# Merge attention scores and perform masking
attn_score = (ac + bd + ef) * self.scale
if attn_mask is not None:
attn_mask = attn_mask.transpose([2, 3, 0, 1])
attn_score = attn_score - 1e30 * attn_mask
# Attention probability
attn_prob = F.softmax(attn_score, axis=3)
attn_prob = self.dropout(attn_prob)
# Mask heads if we want to
if head_mask is not None:
attn_prob = attn_prob * head_mask.transpose([2, 3, 0, 1])
# Attention output
attn_vec = paddle.einsum("bnij,jbnd->ibnd", attn_prob, v_head_h)
if output_attentions:
return attn_vec, attn_prob.transpose([2, 3, 0, 1])
return attn_vec
def post_attention(self, h, attn_vec, residual=True):
"""Post-attention processing."""
# Post-attention projection (back to 'd_model')
# Compute einsum4x4("ibnd,hnd->ibh", attn_vec, self.o)
shape = paddle.shape(attn_vec)
attn_vec = attn_vec.reshape([shape[0], shape[1], attn_vec.shape[2] * attn_vec.shape[3]])
attn_out = paddle.einsum("ibm,hm->ibh", attn_vec, self.o)
attn_out = self.dropout(attn_out)
if residual:
attn_out = attn_out + h
output = self.layer_norm(attn_out)
return output
def forward(
self,
h,
g,
attn_mask_h,
attn_mask_g,
r,
seg_mat,
mems=None,
target_mapping=None,
head_mask=None,
output_attentions=False,
):
if g is not None:
# Two-stream attention with relative positional encoding.
# Content based attention score
if mems is not None and mems.dim() > 1:
cat = paddle.concat([mems, h], axis=0)
else:
cat = h
# Content-based key head
# Compute k_head_h = einsum4x4("ibh,h(n*d)->ibnd", cat, self.k)
k_head_h = paddle.matmul(cat, self.k)
k_head_h = paddle.reshape(
k_head_h, shape=[paddle.shape(cat)[0], paddle.shape(cat)[1], self.n_head, self.d_head]
)
# Content-based value head
# Compute v_head_h = einsum4x4("ibh,h(n*d)->ibnd", cat, self.v)
v_head_h = paddle.matmul(cat, self.v)
v_head_h = paddle.reshape(
v_head_h, shape=[paddle.shape(cat)[0], paddle.shape(cat)[1], self.n_head, self.d_head]
)
# Position-based key head
# Compute k_head_r = einsum4x4("ibh,h(n*d)->ibnd", r, self.r)
k_head_r = paddle.matmul(r, self.r)
k_head_r = paddle.reshape(
k_head_r, shape=[paddle.shape(r)[0], paddle.shape(r)[1], self.n_head, self.d_head]
)
# H-stream
# Content-stream query head
# Compute q_head_h = einsum4x4("ibh,h(n*d)->ibnd", h, self.q)
q_head_h = paddle.matmul(h, self.q) # shape
q_head_h = paddle.reshape(
q_head_h, shape=[paddle.shape(h)[0], paddle.shape(h)[1], self.n_head, self.d_head]
)
# Core attention ops
attn_vec_h = self.rel_attn_core(
q_head_h,
k_head_h,
v_head_h,
k_head_r,
seg_mat=seg_mat,
attn_mask=attn_mask_h,
head_mask=head_mask,
output_attentions=output_attentions,
)
if output_attentions:
attn_vec_h, attn_prob_h = attn_vec_h
# Post processing
output_h = self.post_attention(h, attn_vec_h)
# G-stream
# Query-stream query head
# Compute q_head_g = einsum4x4("ibh,hnd->ibnd", g, self.q)
shape = g.shape
q_head_g = paddle.matmul(g, self.q).reshape([shape[0], shape[1], self.n_head, self.d_head])
# Core attention ops
if target_mapping is not None:
# Compute q_head_g = einsum4x4("mbnd,mlb->lbnd", q_head_g, target_mapping)
q_head_g = paddle.einsum("mbnd,mlb->lbnd", q_head_g, target_mapping)
attn_vec_g = self.rel_attn_core(
q_head_g,
k_head_h,
v_head_h,
k_head_r,
seg_mat=seg_mat,
attn_mask=attn_mask_g,
head_mask=head_mask,
output_attentions=output_attentions,
)
if output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
# Compute attn_vec_g = einsum4x4("lbnd,mlb->mbnd", attn_vec_g, target_mapping)
attn_vec_g = paddle.einsum("lbnd,mlb->mbnd", attn_vec_g, target_mapping)
else:
attn_vec_g = self.rel_attn_core(
q_head_g,
k_head_h,
v_head_h,
k_head_r,
seg_mat=seg_mat,
attn_mask=attn_mask_g,
head_mask=head_mask,
output_attentions=output_attentions,
)
if output_attentions:
attn_vec_g, attn_prob_g = attn_vec_g
# Post processing
output_g = self.post_attention(g, attn_vec_g)
if output_attentions:
attn_prob = attn_prob_h, attn_prob_g
else:
# Multi-head attention with relative positional encoding
if mems is not None and mems.dim() > 1:
cat = paddle.concat([mems, h], axis=0)
else:
cat = h
# Content heads
# Compute q_head_h = einsum4x4("ibh,hnd->ibnd", h, self.q)
q_head_h = paddle.matmul(h, self.q)
q_head_h = paddle.reshape(
q_head_h, shape=[paddle.shape(h)[0], paddle.shape(h)[1], self.n_head, self.d_head]
)
# Compute k_head_h = einsum4x4("ibh,hnd->ibnd", cat, self.k)
k_head_h = paddle.matmul(cat, self.k)
k_head_h = paddle.reshape(
k_head_h, shape=[paddle.shape(h)[0], paddle.shape(h)[1], self.n_head, self.d_head]
)
# Compute v_head_h = einsum4x4("ibh,hnd->ibnd", cat, self.v)
v_head_h = paddle.matmul(cat, self.v)
v_head_h = paddle.reshape(
v_head_h, shape=[paddle.shape(h)[0], paddle.shape(h)[1], self.n_head, self.d_head]
)
# Position-based key head
# Compute k_head_r = einsum4x4("ibh,hnd->ibnd", r, self.r)
k_head_r = paddle.matmul(r, self.r)
k_head_r = paddle.reshape(k_head_r, shape=[paddle.shape(k_head_r)[0], -1, self.n_head, self.d_head])
# Core attention ops
attn_vec = self.rel_attn_core(
q_head_h,
k_head_h,
v_head_h,
k_head_r,
seg_mat=seg_mat,
attn_mask=attn_mask_h,
head_mask=head_mask,
output_attentions=output_attentions,
)
if output_attentions:
attn_vec, attn_prob = attn_vec
# Post processing
output_h = self.post_attention(h, attn_vec)
output_g = None
outputs = (output_h, output_g)
if output_attentions:
outputs = outputs + (attn_prob,)
return outputs
class XLNetFeedForward(Layer):
def __init__(self, config: XLNetConfig):
super(XLNetFeedForward, self).__init__()
self.layer_norm = nn.LayerNorm(config.d_model, epsilon=config.layer_norm_eps)
self.layer_1 = nn.Linear(config.d_model, config.d_inner)
self.layer_2 = nn.Linear(config.d_inner, config.d_model)
self.dropout = nn.Dropout(config.dropout)
if isinstance(config.ff_activation, str):
self.activation_function = ACT2FN[config.ff_activation]
else:
self.activation_function = config.ff_activation
def forward(self, inp):
output = inp
output = self.layer_1(output)
output = self.activation_function(output)
output = self.dropout(output)
output = self.layer_2(output)
output = self.dropout(output)
output = self.layer_norm(output + inp)
return output
class XLNetLayer(Layer):
def __init__(self, config: XLNetConfig):
super(XLNetLayer, self).__init__()
self.rel_attn = XLNetRelativeAttention(config)
self.ff = XLNetFeedForward(config)
self.seq_len_dim = 1
def forward(
self,
output_h,
output_g,
attn_mask_h,
attn_mask_g,
r,
seg_mat,
mems=None,
target_mapping=None,
head_mask=None,
output_attentions=False,
):
outputs = self.rel_attn(
output_h,
output_g,
attn_mask_h,
attn_mask_g,
r,
seg_mat,
mems=mems,
target_mapping=target_mapping,
head_mask=head_mask,
output_attentions=output_attentions,
)
output_h, output_g = outputs[:2]
if output_g is not None:
output_g = self.ff(output_g)
output_h = self.ff(output_h)
outputs = (output_h, output_g) + outputs[2:] # Add again attentions if they are there
return outputs
[文档]
class XLNetPretrainedModel(PretrainedModel):
"""
An abstract class for pretrained XLNet models. It provides XLNet related
`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
`pretrained_init_configuration`, `base_model_prefix` for downloading
and loading pretrained models.
See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
"""
pretrained_init_configuration = XLNET_PRETRAINED_INIT_CONFIGURATION
resource_files_names = {"model_state": "model_state.pdparams"}
pretrained_resource_files_map = XLNET_PRETRAINED_RESOURCE_FILES_MAP
model_config_file = CONFIG_NAME
config_class = XLNetConfig
base_model_prefix = "transformer"
def _init_weights(self, layer):
# Initialize the weights.
if isinstance(layer, (nn.Linear, nn.Embedding)):
if isinstance(layer.weight, paddle.Tensor):
layer.weight.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.initializer_range
if hasattr(self, "initializer_range")
else self.transformer.config["initializer_range"],
shape=layer.weight.shape,
)
)
if isinstance(layer, nn.Linear) and layer.bias is not None:
layer.bias.set_value(paddle.zeros_like(layer.bias))
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))
elif isinstance(layer, XLNetRelativeAttention):
for param in [
layer.q,
layer.k,
layer.v,
layer.o,
layer.r,
layer.r_r_bias,
layer.r_s_bias,
layer.r_w_bias,
layer.seg_embed,
]:
param.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.initializer_range
if hasattr(self, "initializer_range")
else self.transformer.config["initializer_range"],
shape=param.shape,
)
)
elif isinstance(layer, XLNetModel):
layer.mask_emb.set_value(
paddle.tensor.normal(
mean=0.0,
std=self.initializer_range
if hasattr(self, "initializer_range")
else self.transformer.config["initializer_range"],
shape=layer.mask_emb.shape,
)
)
@dataclass
class XLNetModelOutput(ModelOutput):
"""
Output type of [`XLNetModel`].
Args:
last_hidden_state (`paddle.Tensor` of shape `(batch_size, num_predict, hidden_size)`):
Sequence of hidden-states at the last layer of the model.
`num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict`
corresponds to `sequence_length`.
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
last_hidden_state: paddle.Tensor
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetLMHeadModelOutput(ModelOutput):
"""
Output type of [`XLNetLMHeadModel`].
Args:
loss (`paddle.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided)
Language modeling loss (for next-token prediction).
logits (`paddle.Tensor` of shape `(batch_size, num_predict, config.vocab_size)`):
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
`num_predict` corresponds to `target_mapping.shape[1]`. If `target_mapping` is `None`, then `num_predict`
corresponds to `sequence_length`.
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
logits: paddle.Tensor = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetForSequenceClassificationOutput(ModelOutput):
"""
Output type of [`XLNetForSequenceClassification`].
Args:
loss (`paddle.Tensor` of shape `(1,)`, *optional*, returned when `label` is provided):
Classification (or regression if config.num_labels==1) loss.
logits (`paddle.Tensor` of shape `(batch_size, config.num_labels)`):
Classification (or regression if config.num_labels==1) scores (before SoftMax).
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
logits: paddle.Tensor = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetForTokenClassificationOutput(ModelOutput):
"""
Output type of [`XLNetForTokenClassificationOutput`].
Args:
loss (`paddle.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided) :
Classification loss.
logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.num_labels)`):
Classification scores (before SoftMax).
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
logits: paddle.Tensor = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetForMultipleChoiceOutput(ModelOutput):
"""
Output type of [`XLNetForMultipleChoice`].
Args:
loss (`paddle.Tensor` of shape *(1,)*, *optional*, returned when `labels` is provided):
Classification loss.
logits (`paddle.Tensor` of shape `(batch_size, num_choices)`):
*num_choices* is the second dimension of the input tensors. (see *input_ids* above).
Classification scores (before SoftMax).
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
logits: paddle.Tensor = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetForQuestionAnsweringSimpleOutput(ModelOutput):
"""
Output type of [`XLNetForQuestionAnsweringSimple`].
Args:
loss (`paddle.Tensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Total span extraction loss is the sum of a Cross-Entropy for the start and end positions.
start_logits (`paddle.Tensor` of shape `(batch_size, sequence_length,)`):
Span-start scores (before SoftMax).
end_logits (`paddle.Tensor` of shape `(batch_size, sequence_length,)`):
Span-end scores (before SoftMax).
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
start_logits: paddle.Tensor = None
end_logits: paddle.Tensor = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
@dataclass
class XLNetForQuestionAnsweringOutput(ModelOutput):
"""
Output type of [`XLNetForQuestionAnswering`].
Args:
loss (`paddle.Tensor` of shape `(1,)`, *optional*, returned if both `start_positions` and `end_positions` are provided):
Classification loss as the sum of start token, end token (and is_impossible if provided) classification
losses.
start_top_log_probs (`paddle.Tensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the top config.start_n_top start token possibilities (beam-search).
start_top_index (`paddle.Tensor` of shape `(batch_size, config.start_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top config.start_n_top start token possibilities (beam-search).
end_top_log_probs (`paddle.Tensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the top `config.start_n_top * config.end_n_top` end token possibilities
(beam-search).
end_top_index (`paddle.Tensor` of shape `(batch_size, config.start_n_top * config.end_n_top)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Indices for the top `config.start_n_top * config.end_n_top` end token possibilities (beam-search).
cls_logits (`paddle.Tensor` of shape `(batch_size,)`, *optional*, returned if `start_positions` or `end_positions` is not provided):
Log probabilities for the `is_impossible` label of the answers.
mems (`List[paddle.Tensor]` of length `config.n_layers`):
Contains pre-computed hidden-states. Can be used (see `mems` input) to speed up sequential decoding. The
token ids which have their past given to this model should not be passed as `input_ids` as they have
already been computed.
hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
shape `(batch_size, sequence_length, hidden_size)`.
Hidden-states of the model at the output of each layer plus the initial embedding outputs.
attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
sequence_length)`.
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
"""
loss: Optional[paddle.Tensor] = None
start_top_log_probs: Optional[paddle.Tensor] = None
start_top_index: Optional[paddle.Tensor] = None
end_top_log_probs: Optional[paddle.Tensor] = None
end_top_index: Optional[paddle.Tensor] = None
cls_logits: Optional[paddle.Tensor] = None
mems: Optional[List[paddle.Tensor]] = None
hidden_states: Optional[Tuple[paddle.Tensor]] = None
attentions: Optional[Tuple[paddle.Tensor]] = None
[文档]
@register_base_model
class XLNetModel(XLNetPretrainedModel):
"""
The bare XLNet Model 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.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:`XLNetConfig`):
An instance of :class:`XLNetConfig`.
.. note::
A normal_initializer initializes weight matrices as normal distributions.
See :meth:`XLNetPretrainedModel._init_weights()` for how weights are initialized in `XLNetModel`.
"""
def __init__(self, config: XLNetConfig):
super(XLNetModel, self).__init__(config)
self.initializer_range = config.initializer_range
self.mem_len = config.mem_len
self.reuse_len = config.reuse_len
self.d_model = config.d_model
self.same_length = config.same_length
self.attn_type = config.attn_type
self.bi_data = config.bi_data
self.clamp_len = config.clamp_len
self.n_layer = config.n_layer
self.dropout = nn.Dropout(config.dropout)
self.word_embedding = nn.Embedding(config.vocab_size, config.d_model)
self.mask_emb = self.create_parameter([1, 1, config.d_model])
self.layer = nn.LayerList([XLNetLayer(config) for _ in range(config.n_layer)])
def _prune_heads(self, heads_to_prune):
raise NotImplementedError
def create_mask(self, qlen, mlen):
# Creates causal attention mask. Float mask where 1.0 indicates masked, 0.0 indicates not-masked.
attn_mask = paddle.ones([qlen, qlen])
mask_up = paddle.triu(attn_mask, diagonal=1)
attn_mask_pad = paddle.zeros([qlen, mlen])
ret = paddle.concat([attn_mask_pad, mask_up], axis=1)
if self.same_length:
mask_lo = paddle.tril(attn_mask, diagonal=-1)
ret = paddle.concat([ret[:, :qlen] + mask_lo, ret[:, qlen:]], axis=1)
return ret
def cache_mem(self, curr_out, prev_mem):
# Cache hidden states into memory.
if self.reuse_len is not None and self.reuse_len > 0:
curr_out = curr_out[: self.reuse_len]
if self.mem_len is None or self.mem_len == 0:
# If `use_mems` is active but no `mem_len` is defined, the model behaves like GPT-2 at inference time
# and returns all of the past and current hidden states.
cutoff = 0
else:
# If :obj:`use_mems` is active and `mem_len` is defined, the model returns the last `mem_len` hidden
# states. This is the preferred setting for training and long-form generation.
cutoff = -self.mem_len
if prev_mem is None:
# If :obj:`use_mems` is active and `mem_len` is defined, the model
new_mem = curr_out[cutoff:]
else:
new_mem = paddle.concat([prev_mem, curr_out], axis=0)[cutoff:]
return new_mem.detach()
@staticmethod
def positional_embedding(pos_seq, inv_freq, bsz=None):
# Compute sinusoid_inp = einsum4x4("i,d->id", pos_seq, inv_freq)
sinusoid_inp = paddle.einsum("i,d->id", pos_seq, inv_freq)
pos_emb = paddle.concat([paddle.sin(sinusoid_inp), paddle.cos(sinusoid_inp)], axis=-1)
pos_emb = paddle.unsqueeze(pos_emb, axis=1)
if bsz is not None:
pos_emb = pos_emb.expand([-1, bsz, -1])
pos_emb.stop_gradient = True
pos_emb.stop_gradient = True
return pos_emb
def relative_positional_encoding(self, qlen, klen, bsz=None):
# Create relative positional encoding.
freq_seq = paddle.arange(0, self.d_model, 2.0, dtype=dtype_float)
inv_freq = 1 / 10000 ** (freq_seq / self.d_model)
if self.attn_type == "bi":
beg, end = klen, -qlen
elif self.attn_type == "uni":
beg, end = klen, -1
else:
raise ValueError("Unknown `attn_type` {}.".format(self.attn_type))
if self.bi_data:
fwd_pos_seq = paddle.arange(beg, end, -1.0, dtype=dtype_float)
bwd_pos_seq = paddle.arange(-beg, -end, 1.0, dtype=dtype_float)
if self.clamp_len > 0:
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
bwd_pos_seq = bwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
if bsz is not None:
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz // 2)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq, bsz // 2)
else:
fwd_pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq)
bwd_pos_emb = self.positional_embedding(bwd_pos_seq, inv_freq)
pos_emb = paddle.concat([fwd_pos_emb, bwd_pos_emb], axis=1)
else:
fwd_pos_seq = paddle.arange(beg, end, -1.0, dtype=dtype_float)
if self.clamp_len > 0:
fwd_pos_seq = fwd_pos_seq.clamp(-self.clamp_len, self.clamp_len)
pos_emb = self.positional_embedding(fwd_pos_seq, inv_freq, bsz)
return pos_emb
[文档]
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems_train=False,
use_mems_eval=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The XLNetModel 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.
It's data type should be `int64` and has a shape of [batch_size, sequence_length].
token_type_ids (Tensor, optional):
Segment token indices to indicate first and second portions of the inputs.
Indices can be either 0 or 1:
- 0 corresponds to a **sentence A** token,
- 1 corresponds to a **sentence B** token.
It's data type should be `int64` and has a shape of [batch_size, sequence_length].
Defaults to None, which means no segment embeddings is added to token embeddings.
attention_mask (Tensor, optional):
Mask to indicate whether to perform attention on each input token or not.
The values should be either 0 or 1. The attention scores will be set
to **-infinity** for any positions in the mask that are **0**, and will be
**unchanged** for positions that are **1**.
- **1** for tokens that are **not masked**,
- **0** for tokens that are **masked**.
It's data type should be `float32` and has a shape of [batch_size, sequence_length].
Defaults to `None`.
mems (List[Tensor], optional):
A list of length `n_layers` with each Tensor being a pre-computed hidden-state for each layer.
Each Tensor has a dtype `float32` and a shape of [batch_size, sequence_length, hidden_size].
Defaults to None, and we don't use mems.
.. note::
`use_mems` has to be set to `True` in order to make use of `mems`.
perm_mask (Tensor, optional):
Mask to indicate the permutation pattern of the input sequence with values being either 0 or 1.
- if ``perm_mask[k, i, j] = 0``, i **attend** to j in batch k;
- if ``perm_mask[k, i, j] = 1``, i **does not attend** to j in batch k.
Only used during pretraining (to define factorization order) or
for sequential decoding (generation). It's data type should be `float32` and
has a shape of [batch_size, sequence_length, sequence_length].
Defaults to `None`, then each token attends to all the other tokens (full bidirectional attention).
target_mapping (Tensor, optional):
Mask to indicate the output tokens to use with values being either 0 or 1.
If ``target_mapping[k, i, j] = 1``, the i-th predict in batch k is on the j-th token.
It's data type should be `float32` and has a shape of [batch_size, num_predict, sequence_length].
Only used during pretraining for partial prediction or for sequential decoding (generation).
Defaults to `None`.
input_mask (Tensor, optional):
Mask to avoid performing attention on padding token with values being either 0 or 1.
It's data type should be `float32` and it has a shape of [batch_size, sequence_length].
This mask is negative of `attention_mask`:
- 1 for tokens that are **masked**,
- 0 for tokens that are **not masked**.
You should use only one of `input_mask` and `attention_mask`. Defaults to `None`.
head_mask (Tensor, optional):
Mask to nullify selected heads of the self-attention layers with values being either 0 or 1.
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
It's data type should be `float32` and has a shape of [num_heads] or [num_layers, num_heads].
Defaults to `None`, which means we keep all heads.
inputs_embeds (Tensor, optional):
An embedded representation tensor which is an alternative of `input_ids`.
You should specify only either one of them to avoid contradiction.
It's data type should be `float32` and has a shape of [batch_size, sequence_length, hidden_size].
Defaults to `None`, which means we only specify `input_ids`.
use_mems_train (bool, optional):
Whether or not to use recurrent memory mechanism during training.
Defaults to `False` and we don't use recurrent memory mechanism in training mode.
use_mems_eval (bool, optional):
Whether or not to use recurrent memory mechanism during evaluation.
Defaults to `False` and we don't use recurrent memory mechanism in evaluation mode.
return_dict (bool, optional):
Whether or not to return additional information other than the output tensor.
If True, then returns information about `output`, `new_mems`, `hidden_states` and `attentions`
which will also be formatted as a dict. Else only returns the output tensor.
Defaults to False.
Returns:
Tensor or dict: Returns tensor `output` or a dict with key-value pairs:
{"last_hidden_state": `output`, "mems": `mems`,
"hidden_states": `hidden_states`, "attentions": `attentions`}.
With the corresponding fields:
- `output` (Tensor):
Output of the final layer of the model.
It's a Tensor of dtype `float32` and has a shape of [batch_size, num_predict, hidden_size].
.. note::
`num_predict` corresponds to `target_mapping.shape[1]`.
If `target_mapping` is `None`, then `num_predict` equals to `sequence_length`.
- `mems` (List[Tensor]):
A list of pre-computed hidden-states. The length of the list is `n_layers`.
Each element in the list is a Tensor with dtype `float32` and has a shape of
[batch_size, sequence_length, hidden_size].
- `hidden_states` (List[Tensor], optional):
A list of Tensor containing hidden-states of the model at the output of each layer
plus the initial embedding outputs. Each Tensor has a data type of `float32` and
has a shape of [batch_size, sequence_length, hidden_size].
Being returned when `output_hidden_states` is set to `True`.
- `attentions` (List[Tensor], optional):
A list of Tensor containing attentions weights of each hidden layer.
Each Tensor (one for each layer) has a data type of `float32` and
has a shape of [batch_size, num_heads, sequence_length, sequence_length].
Being returned when `output_attentions` is set to `True`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.xlnet.modeling import XLNetModel
from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetModel.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hey, Paddle-paddle is awesome !")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
last_hidden_states = outputs[0]
"""
if self.training:
use_mems = use_mems_train
else:
use_mems = use_mems_eval
# The original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
# but we want a unified interface in the library with the batch size on the first dimension
# so we move here the first dimension (batch) to the end
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")
elif input_ids is not None:
input_ids = paddle.transpose(input_ids, perm=[1, 0])
qlen, bsz = paddle.shape(input_ids)[0], paddle.shape(input_ids)[1]
elif inputs_embeds is not None:
inputs_embeds = paddle.transpose(inputs_embeds, perm=[1, 0])
qlen, bsz = paddle.shape(inputs_embeds)[0], paddle.shape(inputs_embeds)[1]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
token_type_ids = token_type_ids.transpose([1, 0]) if token_type_ids is not None else None
input_mask = input_mask.transpose([1, 0]) if input_mask is not None else None
attention_mask = attention_mask.transpose([1, 0]) if attention_mask is not None else None
perm_mask = perm_mask.transpose([1, 2, 0]) if perm_mask is not None else None
target_mapping = target_mapping.transpose([1, 2, 0]) if target_mapping is not None else None
mlen = paddle.shape(mems[0])[0] if mems is not None and mems[0] is not None else 0
klen = mlen + qlen
# Attention mask
# Causal attention mask
if self.attn_type == "uni":
attn_mask = self.create_mask(qlen, mlen)
attn_mask = paddle.unsqueeze(attn_mask, axis=[2, 3])
elif self.attn_type == "bi":
attn_mask = None
else:
raise ValueError("Unsupported attention type: {}".format(self.attn_type))
# Data mask: input mask & perm mask
assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
"or attention_mask (uses 0 for padding, added for compatibility with BERT). Please choose one."
if input_mask is None and attention_mask is not None:
input_mask = 1.0 - attention_mask
if input_mask is not None and perm_mask is not None:
data_mask = paddle.unsqueeze(input_mask, axis=0) + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = paddle.unsqueeze(input_mask, axis=0)
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
if data_mask is not None:
# All mems can be attended to
if mlen > 0:
mems_mask = paddle.cast(paddle.zeros([paddle.shape(data_mask)[0], mlen, bsz]), dtype=dtype_float)
data_mask = paddle.concat([mems_mask, data_mask], axis=1)
if attn_mask is None:
attn_mask = paddle.unsqueeze(data_mask, axis=-1)
else:
attn_mask += paddle.unsqueeze(data_mask, axis=-1)
if attn_mask is not None:
attn_mask = paddle.cast((attn_mask > 0), dtype=dtype_float)
if attn_mask is not None:
fill_val = paddle.ones(qlen)
non_tgt_mask = paddle.cast(-paddle.diag(fill_val), dtype=dtype_float)
if mlen > 0:
non_tgt_mask = paddle.concat(
[paddle.cast(paddle.zeros([qlen, mlen]), dtype=dtype_float), non_tgt_mask], axis=-1
)
non_tgt_mask = paddle.cast(
((attn_mask + paddle.unsqueeze(non_tgt_mask, axis=[2, 3])) > 0), dtype=dtype_float
)
else:
non_tgt_mask = None
# Word embeddings and prepare h & g hidden states
if inputs_embeds is not None:
word_emb_k = inputs_embeds
else:
word_emb_k = self.word_embedding(input_ids)
output_h = self.dropout(word_emb_k)
if target_mapping is not None:
word_emb_q = self.mask_emb.expand([paddle.shape(target_mapping)[0], bsz, -1])
output_g = self.dropout(word_emb_q)
else:
output_g = None
# Segment embedding
if token_type_ids is not None:
# Convert `token_type_ids` to one-hot `seg_mat`
if mlen > 0:
mem_pad = paddle.zeros(shape=[mlen, bsz], dtype="int64")
cat_ids = paddle.concat(x=[mem_pad, token_type_ids], axis=0)
else:
cat_ids = token_type_ids
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = paddle.cast(
paddle.unsqueeze(token_type_ids, axis=1) != paddle.unsqueeze(cat_ids, axis=0), dtype="int64"
)
seg_mat = paddle.cast(F.one_hot(seg_mat, num_classes=2), dtype=dtype_float)
else:
seg_mat = None
# Positional encoding
pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
pos_emb = self.dropout(pos_emb)
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# Attention_probs has shape bsz x n_heads x N x N
# Input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
# And head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
if head_mask is not None:
if head_mask.dim() == 1:
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
head_mask = head_mask.expand([self.n_layer, -1, -1, -1, -1])
elif head_mask.dim() == 2:
head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
else:
head_mask = [None] * self.n_layer
new_mems = ()
if mems is None:
mems = [None] * len(self.layer)
attentions = [] if output_attentions else None
hidden_states = [] if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
if use_mems:
# Cache new mems
new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
if output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
outputs = layer_module(
output_h,
output_g,
attn_mask_h=non_tgt_mask,
attn_mask_g=attn_mask,
r=pos_emb,
seg_mat=seg_mat,
mems=mems[i],
target_mapping=target_mapping,
head_mask=head_mask[i],
output_attentions=output_attentions,
)
output_h, output_g = outputs[:2]
if output_attentions:
attentions.append(outputs[2])
# Add last hidden state
if output_hidden_states:
hidden_states.append((output_h, output_g) if output_g is not None else output_h)
output = self.dropout(output_g if output_g is not None else output_h)
# Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
output = paddle.transpose(output, perm=[1, 0, 2])
if not use_mems:
new_mems = None
if output_hidden_states:
if output_g is not None:
hidden_states = tuple(paddle.transpose(h, perm=[1, 0, 2]) for hs in hidden_states for h in hs)
else:
hidden_states = tuple(paddle.transpose(hs, perm=[1, 0, 2]) for hs in hidden_states)
if output_attentions:
if target_mapping is not None:
# When target_mapping is provided, there are 2-tuple of attentions
attentions = tuple(
tuple(paddle.transpose(att_stream, perm=[2, 3, 0, 1]) for att_stream in t) for t in attentions
)
else:
attentions = tuple(paddle.transpose(t, perm=[2, 3, 0, 1]) for t in attentions)
if not return_dict:
return tuple(v for v in [output, new_mems, hidden_states, attentions] if v is not None)
return XLNetModelOutput(
last_hidden_state=output, mems=new_mems, hidden_states=hidden_states, attentions=attentions
)
class XLNetClassificationHead(Layer):
"""Head for sentence-level classification tasks."""
def __init__(self, config: XLNetConfig):
super(XLNetClassificationHead, self).__init__()
self.dense = nn.Linear(config.d_model, config.d_model)
self.dropout = nn.Dropout(config.classfier_dropout)
self.out_proj = nn.Linear(config.d_model, config.num_labels)
def forward(self, features, **kwargs):
x = features[:, -1, :] # Take <cls> token
x = self.dropout(x)
x = self.dense(x)
x = get_activation("tanh")(x)
x = self.dropout(x)
x = self.out_proj(x)
return x
[文档]
class XLNetForSequenceClassification(XLNetPretrainedModel):
"""
XLNet Model with a linear layer on top of the output layer,
designed for sequence classification/regression tasks like GLUE tasks.
Args:
config (:class:`XLNetConfig`):
An instance of :class:`XLNetConfig`.
"""
def __init__(self, config: XLNetConfig):
super(XLNetForSequenceClassification, self).__init__(config)
self.num_classes = config.num_classes
self.transformer = XLNetModel(config)
self.classifier = XLNetClassificationHead(config)
[文档]
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems_train=False,
use_mems_eval=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
problem_type: str = "single_label_classification",
):
r"""
The XLNetForSequenceClassification forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
See :class:`XLNetModel`.
token_type_ids (Tensor, optional):
See :class:`XLNetModel`.
attention_mask (Tensor, optional):
See :class:`XLNetModel`.
mems (Tensor, optional):
See :class:`XLNetModel`.
perm_mask (Tensor, optional):
See :class:`XLNetModel`.
target_mapping (Tensor, optional):
See :class:`XLNetModel`.
input_mask (Tensor, optional):
See :class:`XLNetModel`.
head_mask (Tensor, optional):
See :class:`XLNetModel`.
inputs_embeds (Tensor, optional):
See :class:`XLNetModel`.
use_mems_train (bool, optional):
See :class:`XLNetModel`.
use_mems_eval (bool, optional):
See :class:`XLNetModel`.
return_dict (bool, optional):
See :class:`XLNetModel`.
Returns:
Tensor or dict: Returns tensor `logits` or a dict with key-value pairs:
{"logits": `logits`, "mems": `mems`,
"hidden_states": `hidden_states`, "attentions": `attentions`}.
With the corresponding fields:
- `logits` (Tensor):
Classification scores before SoftMax (also called logits). It's data type should be `float32`
and has a shape of [batch_size, num_classes].
- `mems` (List[Tensor]):
See :class:`XLNetModel`.
- `hidden_states` (List[Tensor], optional):
See :class:`XLNetModel`.
- `attentions` (List[Tensor], optional):
See :class:`XLNetModel`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.xlnet.modeling import XLNetForSequenceClassification
from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetForSequenceClassification.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hey, Paddle-paddle is awesome !")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
logits = outputs[0]
"""
transformer_outputs = self.transformer(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_mems_train=use_mems_train,
use_mems_eval=use_mems_eval,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
output = transformer_outputs[0]
logits = self.classifier(output)
loss = None
if labels is not None:
if problem_type == "regression":
loss_fct = MSELoss()
if self.num_classes == 1:
loss = loss_fct(logits.squeeze(), labels.squeeze())
else:
loss = loss_fct(logits, labels)
elif problem_type == "single_label_classification":
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(shape=[-1, self.num_classes]), labels.reshape(shape=[-1]))
elif problem_type == "multi_label_classification":
loss_fct = BCEWithLogitsLoss()
loss = loss_fct(logits, labels)
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return tuple_output(output, loss)
return XLNetForSequenceClassificationOutput(
loss=loss,
logits=logits,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
[文档]
class XLNetForTokenClassification(XLNetPretrainedModel):
"""
XLNet Model with a linear layer on top of the hidden-states output layer,
designed for token classification tasks like NER tasks.
Args:
config (:class:`XLNetConfig`):
An instance of :class:`XLNetConfig`.
"""
def __init__(self, config: XLNetConfig):
super(XLNetForTokenClassification, self).__init__(config)
self.num_classes = config.num_classes
self.transformer = XLNetModel(config)
self.classifier = nn.Linear(self.transformer.d_model, config.num_classes)
[文档]
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems_train=False,
use_mems_eval=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The XLNetForTokenClassification forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
See :class:`XLNetModel`.
token_type_ids (Tensor, optional):
See :class:`XLNetModel`.
attention_mask (Tensor, optional):
See :class:`XLNetModel`.
mems (Tensor, optional):
See :class:`XLNetModel`.
perm_mask (Tensor, optional):
See :class:`XLNetModel`.
target_mapping (Tensor, optional):
See :class:`XLNetModel`.
input_mask (Tensor, optional):
See :class:`XLNetModel`.
head_mask (Tensor, optional):
See :class:`XLNetModel`.
inputs_embeds (Tensor, optional):
See :class:`XLNetModel`.
use_mems_train (bool, optional):
See :class:`XLNetModel`.
use_mems_eval (bool, optional):
See :class:`XLNetModel`.
return_dict (bool, optional):
See :class:`XLNetModel`.
Returns:
Tensor or dict: Returns tensor `logits` or a dict with key-value pairs:
{"logits": `logits`, "mems": `mems`,
"hidden_states": `hidden_states`, "attentions": `attentions`}.
With the corresponding fields:
- `logits` (Tensor):
Classification scores before SoftMax (also called logits). It's data type should be `float32`
and has a shape of [batch_size, sequence_length, num_classes].
- `mems` (List[Tensor]):
See :class:`XLNetModel`.
- `hidden_states` (List[Tensor], optional):
See :class:`XLNetModel`.
- `attentions` (List[Tensor], optional):
See :class:`XLNetModel`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.xlnet.modeling import XLNetForTokenClassification
from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetForTokenClassification.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hey, Paddle-paddle is awesome !")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
logits = outputs[0]
"""
outputs = self.transformer(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_mems_train=use_mems_train,
use_mems_eval=use_mems_eval,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
logits = self.classifier(sequence_output)
loss = None
if labels is not None:
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(shape=[-1, self.num_classes]), labels.reshape(shape=[-1]))
if not return_dict:
output = (logits,) + outputs[1:]
return tuple_output(output, loss)
return XLNetForTokenClassificationOutput(
loss=loss,
logits=logits,
mems=outputs.mems,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
[文档]
class XLNetLMHeadModel(XLNetPretrainedModel):
"""
XLNet Model with a language modeling head on top (linear layer with weights tied to the input embeddings).
Args:
config (:class:`XLNetConfig`):
An instance of :class:`XLNetConfig`.
"""
def __init__(self, config: XLNetConfig):
super(XLNetLMHeadModel, self).__init__(config)
self.transformer = XLNetModel(config)
self.decoder_weight = self.transformer.word_embedding.weight
self.decoder_bias = self.create_parameter(
shape=[config.vocab_size], dtype=self.decoder_weight.dtype, is_bias=True
)
[文档]
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
labels=None,
use_mems_train=False,
use_mems_eval=False,
output_attentions=False,
output_hidden_states=False,
return_dict=False,
):
r"""
The XLNetLMHeadModel forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
See :class:`XLNetModel`.
token_type_ids (Tensor, optional):
See :class:`XLNetModel`.
attention_mask (Tensor, optional):
See :class:`XLNetModel`.
mems (Tensor, optional):
See :class:`XLNetModel`.
perm_mask (Tensor, optional):
See :class:`XLNetModel`.
target_mapping (Tensor, optional):
See :class:`XLNetModel`.
input_mask (Tensor, optional):
See :class:`XLNetModel`.
head_mask (Tensor, optional):
See :class:`XLNetModel`.
inputs_embeds (Tensor, optional):
See :class:`XLNetModel`.
use_mems_train (bool, optional):
See :class:`XLNetModel`.
use_mems_eval (bool, optional):
See :class:`XLNetModel`.
return_dict (bool, optional):
See :class:`XLNetModel`.
Returns:
Tensor or dict: Returns tensor `logits` or a dict with key-value pairs:
{"logits": `logits`, "mems": `mems`,
"hidden_states": `hidden_states`, "attentions": `attentions`}.
With the corresponding fields:
- `logits` (Tensor):
Classification scores before SoftMax (also called logits). It's data type should be `float32`
and has a shape of [batch_size, sequence_length, num_classes].
- `mems` (List[Tensor]):
See :class:`XLNetModel`.
- `hidden_states` (List[Tensor], optional):
See :class:`XLNetModel`.
- `attentions` (List[Tensor], optional):
See :class:`XLNetModel`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.xlnet.modeling import XLNetLMHeadModel
from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetLMHeadModel.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hey, Paddle-paddle is awesome !")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
logits = outputs
"""
transformer_outputs = self.transformer(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_mems_train=use_mems_train,
use_mems_eval=use_mems_eval,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
logits = paddle.matmul(transformer_outputs[0], self.decoder_weight, transpose_y=True) + self.decoder_bias
loss = None
if labels is not None:
# Flatten the tokens
loss_fct = CrossEntropyLoss()
loss = loss_fct(logits.reshape(shape=[-1, logits.shape[-1]]), labels.reshape(shape=[-1]))
if not return_dict:
output = (logits,) + transformer_outputs[1:]
return tuple_output(output, loss)
return XLNetLMHeadModelOutput(
loss=loss,
logits=logits,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
[文档]
class XLNetForQuestionAnswering(XLNetPretrainedModel):
"""
XLNet Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
Args:
config (:class:`XLNetConfig`):
An instance of :class:`XLNetConfig`.
"""
def __init__(self, config: XLNetConfig):
super(XLNetForQuestionAnswering, self).__init__(config)
self.transformer = XLNetModel(config)
self.qa_outputs = nn.Linear(config.d_model, 2)
[文档]
def forward(
self,
input_ids,
token_type_ids=None,
attention_mask=None,
mems=None,
perm_mask=None,
target_mapping=None,
start_positions=None,
end_positions=None,
input_mask=None,
head_mask=None,
inputs_embeds=None,
use_mems_train=False,
use_mems_eval=False,
return_dict=False,
):
r"""
The XLNetForQuestionAnswering forward method, overrides the `__call__()` special method.
Args:
input_ids (Tensor):
See :class:`XLNetModel`.
token_type_ids (Tensor, optional):
See :class:`XLNetModel`.
attention_mask (Tensor, optional):
See :class:`XLNetModel`.
mems (Tensor, optional):
See :class:`XLNetModel`.
perm_mask (Tensor, optional):
See :class:`XLNetModel`.
target_mapping (Tensor, optional):
See :class:`XLNetModel`.
input_mask (Tensor, optional):
See :class:`XLNetModel`.
head_mask (Tensor, optional):
See :class:`XLNetModel`.
inputs_embeds (Tensor, optional):
See :class:`XLNetModel`.
use_mems_train (bool, optional):
See :class:`XLNetModel`.
use_mems_eval (bool, optional):
See :class:`XLNetModel`.
return_dict (bool, optional):
See :class:`XLNetModel`.
Returns:
tuple or dict: Returns tensor (`start_logits`, `end_logits`) or a dict with key-value pairs:
{"start_logits": `start_logits`, "end_logits": `end_logits`, "mems": `mems`,
"hidden_states": `hidden_states`, "attentions": `attentions`}
With the corresponding 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].
- `mems` (List[Tensor]):
See :class:`XLNetModel`.
- `hidden_states` (List[Tensor], optional):
See :class:`XLNetModel`.
- `attentions` (List[Tensor], optional):
See :class:`XLNetModel`.
Example:
.. code-block::
import paddle
from paddlenlp.transformers.xlnet.modeling import XLNetForQuestionAnswering
from paddlenlp.transformers.xlnet.tokenizer import XLNetTokenizer
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetForQuestionAnswering.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
outputs = model(**inputs)
start_logits = outputs[0]
end_logits = outputs[1]
"""
transformer_outputs = self.transformer(
input_ids,
token_type_ids=token_type_ids,
attention_mask=attention_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
input_mask=input_mask,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
use_mems_train=use_mems_train,
use_mems_eval=use_mems_eval,
return_dict=return_dict,
)
output = transformer_outputs[0]
logits = self.qa_outputs(output)
logits = paddle.transpose(logits, perm=[2, 0, 1])
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
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)
loss = (start_loss + end_loss) / 2
if not return_dict:
output = (start_logits, end_logits) + transformer_outputs[1:]
# the length of output must be larger than 1
return tuple_output(output, loss)
return XLNetForQuestionAnsweringSimpleOutput(
loss=loss,
start_logits=start_logits,
end_logits=end_logits,
mems=transformer_outputs.mems,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
XLNetForCausalLM = XLNetLMHeadModel
