wehub-resource-sync
1555 lines
65 KiB
Python
1555 lines
65 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Modeling classes for ALBERT model."""
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import math
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from typing import List, Optional, Tuple
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn import Layer
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from ...layers import Linear as TransposedLinear
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from ...utils.converter import StateDictNameMapping, init_name_mappings
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from ...utils.env import CONFIG_NAME
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from .. import PretrainedModel, register_base_model
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from ..activations import ACT2FN
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from ..model_outputs import (
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BaseModelOutput,
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BaseModelOutputWithPooling,
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MaskedLMOutput,
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ModelOutput,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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TokenClassifierOutput,
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tuple_output,
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)
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from .configuration import (
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ALBERT_PRETRAINED_INIT_CONFIGURATION,
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ALBERT_PRETRAINED_RESOURCE_FILES_MAP,
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AlbertConfig,
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)
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__all__ = [
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"AlbertPretrainedModel",
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"AlbertModel",
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"AlbertForPretraining",
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"AlbertForMaskedLM",
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"AlbertForSequenceClassification",
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"AlbertForTokenClassification",
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"AlbertForQuestionAnswering",
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"AlbertForMultipleChoice",
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]
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dtype_float = paddle.get_default_dtype()
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class AlbertForPreTrainingOutput(ModelOutput):
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"""
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Output type of [`AlbertForPreTraining`].
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Args:
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loss (*optional*, returned when `labels` is provided, `paddle.Tensor` of shape `(1,)`):
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Total loss as the sum of the masked language modeling loss and the next sequence prediction
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(classification) loss.
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prediction_logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
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Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
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sop_logits (`paddle.Tensor` of shape `(batch_size, 2)`):
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Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
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before SoftMax).
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hidden_states (`tuple(paddle.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
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Tuple of `paddle.Tensor` (one for the output of the embeddings + one for the output of each layer) of
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shape `(batch_size, sequence_length, hidden_size)`.
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Hidden-states of the model at the output of each layer plus the initial embedding outputs.
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attentions (`tuple(paddle.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
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Tuple of `paddle.Tensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
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sequence_length)`.
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
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heads.
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"""
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loss: Optional[paddle.Tensor] = None
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prediction_logits: paddle.Tensor = None
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sop_logits: paddle.Tensor = None
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hidden_states: Optional[Tuple[paddle.Tensor]] = None
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attentions: Optional[Tuple[paddle.Tensor]] = None
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class AlbertEmbeddings(Layer):
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"""
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Constructs the embeddings from word, position and token_type embeddings.
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"""
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def __init__(self, config: AlbertConfig):
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super(AlbertEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.embedding_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.embedding_size)
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self.layer_norm = nn.LayerNorm(config.embedding_size, epsilon=config.layer_norm_eps)
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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# Position_ids (1, len position emb) is contiguous in memory and exported when serialized
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self.register_buffer(
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"position_ids", paddle.arange(config.max_position_embeddings, dtype="int64").expand((1, -1))
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)
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def forward(
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self,
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input_ids,
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token_type_ids=None,
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position_ids=None,
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inputs_embeds=None,
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past_key_values_length=0,
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):
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if input_ids is not None:
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input_shape = input_ids.shape
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else:
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input_shape = inputs_embeds.shape[:-1]
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seq_length = input_shape[1]
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if position_ids is None:
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position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length]
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if token_type_ids is None:
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token_type_ids = paddle.zeros(input_shape, dtype="int64")
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if inputs_embeds is None:
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inputs_embeds = self.word_embeddings(input_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = inputs_embeds + token_type_embeddings
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position_embeddings = self.position_embeddings(position_ids)
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embeddings += position_embeddings
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embeddings = self.layer_norm(embeddings)
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embeddings = self.dropout(embeddings)
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return embeddings
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class AlbertAttention(Layer):
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def __init__(self, config: AlbertConfig):
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super(AlbertAttention, self).__init__()
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if config.hidden_size % config.num_attention_heads != 0:
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raise ValueError(
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"The hidden size (%d) is not a multiple of the number of attention "
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"heads (%d)" % (config.hidden_size, config.num_attention_heads)
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)
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self.num_attention_heads = config.num_attention_heads
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self.hidden_size = config.hidden_size
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self.attention_head_size = config.hidden_size // config.num_attention_heads
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self.all_head_size = self.num_attention_heads * self.attention_head_size
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self.query = nn.Linear(self.hidden_size, self.all_head_size)
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self.key = nn.Linear(self.hidden_size, self.all_head_size)
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self.value = nn.Linear(self.hidden_size, self.all_head_size)
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self.attention_dropout = nn.Dropout(config.attention_probs_dropout_prob)
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self.output_dropout = nn.Dropout(config.hidden_dropout_prob)
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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# Copied from transformers.models.bert.modeling_bert.BertSelfAttention.transpose_for_scores
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def transpose_for_scores(self, x):
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new_x_shape = x.shape[:-1] + [self.num_attention_heads, self.attention_head_size]
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x = x.reshape(new_x_shape)
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return x.transpose([0, 2, 1, 3])
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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):
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mixed_query_layer = self.query(hidden_states)
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mixed_key_layer = self.key(hidden_states)
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mixed_value_layer = self.value(hidden_states)
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query_layer = self.transpose_for_scores(mixed_query_layer)
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key_layer = self.transpose_for_scores(mixed_key_layer)
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value_layer = self.transpose_for_scores(mixed_value_layer)
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# Take the dot product between "query" and "key" to get the raw attention scores.
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attention_scores = paddle.matmul(query_layer, key_layer, transpose_y=True)
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attention_scores = attention_scores / math.sqrt(self.attention_head_size)
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if attention_mask is not None:
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# Apply the attention mask is (precomputed for all layers in BertModel forward() function)
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attention_scores = attention_scores + attention_mask
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# Normalize the attention scores to probabilities.
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attention_probs = F.softmax(attention_scores)
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# This is actually dropping out entire tokens to attend to, which might
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# seem a bit unusual, but is taken from the original Transformer paper.
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attention_probs = self.attention_dropout(attention_probs)
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# Mask heads if we want to
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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context_layer = paddle.matmul(attention_probs, value_layer)
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context_layer = context_layer.transpose([0, 2, 1, 3])
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context_layer = context_layer.reshape([0, 0, -1])
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# dense layer shape to be checked
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projected_context_layer = self.dense(context_layer)
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projected_context_layer_dropout = self.output_dropout(projected_context_layer)
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layer_normed_context_layer = self.layer_norm(hidden_states + projected_context_layer_dropout)
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return (layer_normed_context_layer, attention_probs) if output_attentions else (layer_normed_context_layer,)
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class AlbertLayer(Layer):
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def __init__(self, config: AlbertConfig):
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super(AlbertLayer, self).__init__()
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self.seq_len_dim = 1
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self.full_layer_layer_norm = nn.LayerNorm(config.hidden_size, epsilon=config.layer_norm_eps)
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self.attention = AlbertAttention(config)
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self.ffn = nn.Linear(config.hidden_size, config.intermediate_size)
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self.ffn_output = nn.Linear(config.intermediate_size, config.hidden_size)
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self.activation = ACT2FN[config.hidden_act]
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self.dropout = nn.Dropout(config.hidden_dropout_prob)
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_attentions=False,
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):
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attention_output = self.attention(
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hidden_states,
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attention_mask=attention_mask,
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head_mask=head_mask,
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output_attentions=output_attentions,
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)
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ffn_output = self.ffn(attention_output[0])
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ffn_output = self.activation(ffn_output)
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ffn_output = self.ffn_output(ffn_output)
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hidden_states = self.full_layer_layer_norm(ffn_output + attention_output[0])
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return (hidden_states,) + attention_output[1:] # add attentions if we output them
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class AlbertLayerGroup(Layer):
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def __init__(self, config: AlbertConfig):
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super(AlbertLayerGroup, self).__init__()
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self.albert_layers = nn.LayerList([AlbertLayer(config) for _ in range(config.inner_group_num)])
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def forward(
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self, hidden_states, attention_mask=None, head_mask=None, output_attentions=False, output_hidden_states=False
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):
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layer_attentions = () if output_attentions else None
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all_hidden_states = (hidden_states,) if output_hidden_states else None
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for layer_index, albert_layer in enumerate(self.albert_layers):
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layer_output = albert_layer(
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hidden_states,
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attention_mask,
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head_mask[layer_index],
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output_attentions=output_attentions,
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)
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hidden_states = layer_output[0]
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if output_attentions:
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layer_attentions = layer_attentions + (layer_output[1],)
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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outputs = (hidden_states,)
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if output_hidden_states:
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outputs = outputs + (all_hidden_states,)
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if output_attentions:
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outputs = outputs + (layer_attentions,)
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return outputs
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class AlbertTransformer(Layer):
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def __init__(self, config: AlbertConfig):
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super(AlbertTransformer, self).__init__()
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self.num_hidden_layers = config.num_hidden_layers
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self.num_hidden_groups = config.num_hidden_groups
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self.embedding_hidden_mapping_in = nn.Linear(config.embedding_size, config.hidden_size)
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self.albert_layer_groups = nn.LayerList([AlbertLayerGroup(config) for _ in range(config.num_hidden_groups)])
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def forward(
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self,
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hidden_states,
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attention_mask=None,
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head_mask=None,
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output_hidden_states=False,
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output_attentions=False,
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return_dict=False,
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):
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hidden_states = self.embedding_hidden_mapping_in(hidden_states)
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all_hidden_states = (hidden_states,) if output_hidden_states else None
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all_attentions = () if output_attentions else None
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for i in range(self.num_hidden_layers):
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# Number of layers in a hidden group
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layers_per_group = int(self.num_hidden_layers / self.num_hidden_groups)
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# Index of the hidden group
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group_idx = int(i / (self.num_hidden_layers / self.num_hidden_groups))
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layer_group_output = self.albert_layer_groups[group_idx](
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hidden_states,
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attention_mask,
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head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group],
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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)
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hidden_states = layer_group_output[0]
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if output_attentions:
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all_attentions = all_attentions + layer_group_output[-1]
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if output_hidden_states:
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all_hidden_states = all_hidden_states + (hidden_states,)
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if not return_dict:
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return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)
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return BaseModelOutput(
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last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
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)
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class AlbertPretrainedModel(PretrainedModel):
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"""
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An abstract class for pretrained ALBERT models. It provides ALBERT related
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`model_config_file`, `pretrained_init_configuration`, `resource_files_names`,
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`pretrained_resource_files_map`, `base_model_prefix` for downloading and
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loading pretrained models. See `PretrainedModel` for more details.
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"""
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model_config_file = CONFIG_NAME
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config_class = AlbertConfig
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resource_files_names = {"model_state": "model_state.pdparams"}
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base_model_prefix = "transformer"
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pretrained_init_configuration = ALBERT_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = ALBERT_PRETRAINED_RESOURCE_FILES_MAP
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@classmethod
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def _get_name_mappings(cls, config: AlbertConfig) -> List[StateDictNameMapping]:
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model_mappings = [
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"embeddings.word_embeddings.weight",
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"embeddings.position_embeddings.weight",
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"embeddings.token_type_embeddings.weight",
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["embeddings.LayerNorm.weight", "embeddings.layer_norm.weight"],
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["embeddings.LayerNorm.bias", "embeddings.layer_norm.bias"],
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["encoder.embedding_hidden_mapping_in.weight", None, "transpose"],
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"encoder.embedding_hidden_mapping_in.bias",
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]
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if config.add_pooling_layer:
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model_mappings.extend(
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[
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["pooler.weight", None, "transpose"],
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["pooler.bias"],
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]
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)
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for group_index in range(config.num_hidden_groups):
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group_mappings = [
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.full_layer_layer_norm.weight",
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.full_layer_layer_norm.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.query.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.query.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.key.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.key.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.value.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.value.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.dense.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.dense.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.LayerNorm.weight",
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.layer_norm.weight",
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],
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.LayerNorm.bias",
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.attention.layer_norm.bias",
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],
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.ffn.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.ffn.bias",
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[
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.ffn_output.weight",
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None,
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"transpose",
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],
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f"encoder.albert_layer_groups.{group_index}.albert_layers.0.ffn_output.bias",
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]
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model_mappings.extend(group_mappings)
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init_name_mappings(model_mappings)
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# base-model prefix "AlbertModel"
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if "AlbertModel" not in config.architectures:
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for mapping in model_mappings:
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mapping[0] = "albert." + mapping[0]
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mapping[1] = "transformer." + mapping[1]
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# downstream mappings
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if "AlbertForQuestionAnswering" in config.architectures:
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model_mappings.extend(
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[["qa_outputs.weight", "qa_outputs.weight", "transpose"], ["qa_outputs.bias", "qa_outputs.bias"]]
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)
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if (
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"AlbertForMultipleChoice" in config.architectures
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or "AlbertForSequenceClassification" in config.architectures
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or "AlbertForTokenClassification" in config.architectures
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):
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model_mappings.extend(
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[["classifier.weight", "classifier.weight", "transpose"], ["classifier.bias", "classifier.bias"]]
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)
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mappings = [StateDictNameMapping(*mapping, index=index) for index, mapping in enumerate(model_mappings)]
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return mappings
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def _init_weights(self, layer):
|
|
# Initialize the weights.
|
|
if isinstance(layer, nn.Linear):
|
|
layer.weight.set_value(
|
|
paddle.tensor.normal(
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
shape=layer.weight.shape,
|
|
)
|
|
)
|
|
if layer.bias is not None:
|
|
layer.bias.set_value(paddle.zeros_like(layer.bias))
|
|
elif isinstance(layer, nn.Embedding):
|
|
layer.weight.set_value(
|
|
paddle.tensor.normal(
|
|
mean=0.0,
|
|
std=self.config.initializer_range,
|
|
shape=layer.weight.shape,
|
|
)
|
|
)
|
|
if layer._padding_idx is not None:
|
|
layer.weight[layer._padding_idx].set_value(paddle.zeros_like(layer.weight[layer._padding_idx]))
|
|
elif isinstance(layer, nn.LayerNorm):
|
|
layer.bias.set_value(paddle.zeros_like(layer.bias))
|
|
layer.weight.set_value(paddle.ones_like(layer.weight))
|
|
|
|
|
|
@register_base_model
|
|
class AlbertModel(AlbertPretrainedModel):
|
|
"""
|
|
The bare Albert Model transformer outputting raw hidden-states.
|
|
|
|
This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
|
|
Refer to the superclass documentation for the generic methods.
|
|
|
|
This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
|
|
/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
|
|
and refer to the Paddle documentation for all matter related to general usage and behavior.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertModel, self).__init__(config)
|
|
self.pad_token_id = config.pad_token_id
|
|
self.bos_token_id = config.bos_token_id
|
|
self.eos_token_id = config.eos_token_id
|
|
self.initializer_range = config.initializer_range
|
|
self.num_hidden_layers = config.num_hidden_layers
|
|
self.embeddings = AlbertEmbeddings(config)
|
|
self.encoder = AlbertTransformer(config)
|
|
self.config = config
|
|
|
|
if config.add_pooling_layer:
|
|
self.pooler = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.pooler_activation = nn.Tanh()
|
|
else:
|
|
self.pooler = None
|
|
self.pooler_activation = None
|
|
|
|
def get_input_embeddings(self):
|
|
return self.embeddings.word_embeddings
|
|
|
|
def set_input_embeddings(self, value):
|
|
self.embeddings.word_embeddings = value
|
|
|
|
def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
|
|
"""-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
|
|
if head_mask.dim() == 1:
|
|
head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(-1).unsqueeze(-1)
|
|
head_mask = head_mask.expand(num_hidden_layers, -1, -1, -1, -1)
|
|
elif head_mask.dim() == 2:
|
|
head_mask = head_mask.unsqueeze(1).unsqueeze(-1).unsqueeze(-1) # We can specify head_mask for each layer
|
|
assert head_mask.dim() == 5, f"head_mask.dim != 5, instead {head_mask.dim()}"
|
|
head_mask = paddle.cast(head_mask, dtype=dtype_float)
|
|
return head_mask
|
|
|
|
def get_head_mask(self, head_mask, num_hidden_layers, is_attention_chunked=False):
|
|
if head_mask is not None:
|
|
head_mask = self._convert_head_mask_to_5d(head_mask, num_hidden_layers)
|
|
if is_attention_chunked is True:
|
|
head_mask = head_mask.unsqueeze(-1)
|
|
else:
|
|
head_mask = [None] * num_hidden_layers
|
|
|
|
return head_mask
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertModel 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].
|
|
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.
|
|
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`.
|
|
head_mask (Tensor, optional):
|
|
Mask to nullify selected heads of the self-attention modules. Masks values can either be 0 or 1:
|
|
|
|
- 1 indicates the head is **not masked**,
|
|
- 0 indicated the head is **masked**.
|
|
inputs_embeds (Tensor, optional):
|
|
If you want to control how to convert `inputs_ids` indices into associated vectors, you can
|
|
pass an embedded representation directly instead of passing `inputs_ids`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
tuple or Dict: Returns tuple (`sequence_output`, `pooled_output`) or a dict with
|
|
`last_hidden_state`, `pooled_output`, `all_hidden_states`, `all_attentions` fields.
|
|
|
|
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 has a shape of [`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
|
|
has a shape of [batch_size, hidden_size].
|
|
|
|
- `last_hidden_state` (Tensor):
|
|
The output of the last encoder layer, it is also the `sequence_output`.
|
|
It's data type should be float32 and has a shape of [batch_size, sequence_length, hidden_size].
|
|
|
|
- `all_hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `all_hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `all_attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `all_attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import AlbertModel, AlbertTokenizer
|
|
|
|
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
|
|
model = AlbertModel.from_pretrained('albert-base-v1')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
output = model(**inputs)
|
|
|
|
"""
|
|
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_shape = input_ids.shape
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.shape[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
if attention_mask is None:
|
|
attention_mask = paddle.ones(shape=input_shape)
|
|
if token_type_ids is None:
|
|
token_type_ids = paddle.zeros(shape=input_shape, dtype="int64")
|
|
|
|
extended_attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
|
|
extended_attention_mask = paddle.cast(extended_attention_mask, dtype=dtype_float)
|
|
extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0
|
|
head_mask = self.get_head_mask(head_mask, self.num_hidden_layers)
|
|
|
|
embedding_output = self.embeddings(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
)
|
|
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
extended_attention_mask,
|
|
head_mask=head_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = encoder_outputs[0]
|
|
|
|
pooled_output = self.pooler_activation(self.pooler(sequence_output[:, 0])) if self.pooler is not None else None
|
|
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
|
|
return BaseModelOutputWithPooling(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertForPretraining(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a `masked language modeling` head and a `sentence order prediction` head
|
|
on top.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForPretraining, self).__init__(config)
|
|
|
|
self.transformer = AlbertModel(config)
|
|
self.predictions = AlbertMLMHead(config)
|
|
self.sop_classifier = AlbertSOPHead(config)
|
|
self.config = config
|
|
self.vocab_size = config.vocab_size
|
|
|
|
def get_output_embeddings(self):
|
|
return self.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.predictions.decoder = new_embeddings
|
|
|
|
def get_input_embeddings(self):
|
|
return self.transformer.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
sentence_order_label=None,
|
|
labels=None,
|
|
output_attentions=False,
|
|
output_hidden_states=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForPretraining forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
sentence_order_label(Tensor, optional):
|
|
Labels of the next sequence prediction. Input should be a sequence pair
|
|
Indices should be 0 or 1. ``0`` indicates original order (sequence A, then sequence B),
|
|
and ``1`` indicates switched order (sequence B, then sequence A). Defaults to `None`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
tuple or Dict: Returns tuple (`prediction_scores`, `sop_scores`) or a dict with
|
|
`prediction_logits`, `sop_logits`, `pooled_output`, `hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `prediction_scores` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
and its shape is [batch_size, sequence_length, vocab_size].
|
|
|
|
- `sop_scores` (Tensor):
|
|
The scores of sentence order prediction.
|
|
Its data type should be float32 and its shape is [batch_size, 2].
|
|
|
|
- `prediction_logits` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
and its shape is [batch_size, sequence_length, vocab_size].
|
|
|
|
- `sop_logits` (Tensor):
|
|
The scores of sentence order prediction.
|
|
Its data type should be float32 and its shape is [batch_size, 2].
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
"""
|
|
|
|
outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output, pooled_output = outputs[:2]
|
|
|
|
prediction_scores = self.predictions(sequence_output)
|
|
sop_scores = self.sop_classifier(pooled_output)
|
|
|
|
total_loss = None
|
|
if labels is not None and sentence_order_label is not None:
|
|
loss_fct = nn.CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(prediction_scores.reshape([-1, self.config.vocab_size]), labels.reshape([-1]))
|
|
sentence_order_loss = loss_fct(sop_scores.reshape([-1, 2]), sentence_order_label.reshape([-1]))
|
|
total_loss = masked_lm_loss + sentence_order_loss
|
|
|
|
if not return_dict:
|
|
output = (prediction_scores, sop_scores) + outputs[2:]
|
|
return tuple_output(output, total_loss)
|
|
|
|
return AlbertForPreTrainingOutput(
|
|
loss=total_loss,
|
|
prediction_logits=prediction_scores,
|
|
sop_logits=sop_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertMLMHead(Layer):
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertMLMHead, self).__init__()
|
|
|
|
self.layer_norm = nn.LayerNorm(config.embedding_size)
|
|
self.bias = self.create_parameter(
|
|
[config.vocab_size], is_bias=True, default_initializer=nn.initializer.Constant(value=0)
|
|
)
|
|
self.dense = nn.Linear(config.hidden_size, config.embedding_size)
|
|
self.decoder = TransposedLinear(config.embedding_size, config.vocab_size)
|
|
|
|
self.activation = ACT2FN[config.hidden_act]
|
|
|
|
# link bias
|
|
self.bias = self.decoder.bias
|
|
|
|
def forward(self, hidden_states):
|
|
hidden_states = self.dense(hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
prediction_scores = hidden_states
|
|
return prediction_scores
|
|
|
|
|
|
class AlbertSOPHead(Layer):
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertSOPHead, self).__init__()
|
|
self.dropout = nn.Dropout(config.classifier_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(self, pooled_output):
|
|
dropout_pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(dropout_pooled_output)
|
|
return logits
|
|
|
|
|
|
class AlbertForMaskedLM(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForMaskedLM, self).__init__(config)
|
|
|
|
self.transformer = AlbertModel(config)
|
|
self.predictions = AlbertMLMHead(config)
|
|
self.config = config
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.predictions.decoder
|
|
|
|
def set_output_embeddings(self, new_embeddings):
|
|
self.predictions.decoder = new_embeddings
|
|
|
|
def get_input_embeddings(self):
|
|
return self.transformer.embeddings.word_embeddings
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForPretraining forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
|
|
will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `prediction_scores` or a dict with `logits`,
|
|
`hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `prediction_scores` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
and its shape is [batch_size, sequence_length, vocab_size].
|
|
|
|
- `logits` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
and its shape is [batch_size, sequence_length, vocab_size].
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
"""
|
|
|
|
transformer_outputs = self.transformer(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
if isinstance(transformer_outputs, type(input_ids)):
|
|
transformer_outputs = [transformer_outputs]
|
|
|
|
hidden_states = transformer_outputs[0]
|
|
logits = self.predictions(hidden_states)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(logits.reshape((-1, logits.shape[-1])), labels.reshape((-1,)))
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[2:]
|
|
return tuple_output(output, masked_lm_loss)
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertForSequenceClassification(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a linear layer on top of the output layer,
|
|
designed for sequence classification/regression tasks like GLUE tasks.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForSequenceClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
|
|
self.transformer = AlbertModel(config)
|
|
self.dropout = nn.Dropout(config.classifier_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, self.config.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
labels (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for computing the sequence classification/regression loss.
|
|
Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
|
|
a regression loss is computed (Mean-Square loss), If `num_labels > 1`
|
|
a classification loss is computed (Cross-Entropy).
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `logits`, or a dict with `logits`, `hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `logits` (Tensor):
|
|
A tensor of the input text classification logits.
|
|
Shape as `[batch_size, num_labels]` and dtype as float32.
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import AlbertForSequenceClassification, AlbertTokenizer
|
|
|
|
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
|
|
model = AlbertForSequenceClassification.from_pretrained('albert-base-v1')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
logits = outputs[0]
|
|
"""
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = transformer_outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
if self.config.problem_type is None:
|
|
if self.num_labels == 1:
|
|
self.config.problem_type = "regression"
|
|
elif self.num_labels > 1 and (labels.dtype == paddle.int64 or labels.dtype == paddle.int32):
|
|
self.config.problem_type = "single_label_classification"
|
|
else:
|
|
self.config.problem_type = "multi_label_classification"
|
|
|
|
if self.config.problem_type == "regression":
|
|
loss_fct = paddle.nn.MSELoss()
|
|
if self.num_labels == 1:
|
|
loss = loss_fct(logits.squeeze(), labels.squeeze())
|
|
else:
|
|
loss = loss_fct(logits, labels)
|
|
elif self.config.problem_type == "single_label_classification":
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
elif self.config.problem_type == "multi_label_classification":
|
|
loss_fct = paddle.nn.BCEWithLogitsLoss()
|
|
loss = loss_fct(logits, labels)
|
|
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[2:]
|
|
return tuple_output(output, loss)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertForTokenClassification(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForTokenClassification, self).__init__(config)
|
|
self.num_labels = config.num_labels
|
|
self.config = config
|
|
self.transformer = AlbertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, self.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForTokenClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the token classification loss. Indices should be in `[0, ..., num_labels - 1]`.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `logits`, or a dict with `logits`, `hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `logits` (Tensor):
|
|
A tensor of the input token classification logits.
|
|
Shape as `[batch_size, sequence_length, num_labels]` and dtype as `float32`.
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import AlbertForTokenClassification, AlbertTokenizer
|
|
|
|
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
|
|
model = AlbertForTokenClassification.from_pretrained('albert-base-v1')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
logits = outputs[0]
|
|
"""
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = transformer_outputs[0]
|
|
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
if not return_dict:
|
|
output = (logits,) + transformer_outputs[2:]
|
|
return tuple_output(output, loss)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertForQuestionAnswering(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a linear layer on top of the hidden-states output to compute `span_start_logits`
|
|
and `span_end_logits`, designed for question-answering tasks like SQuAD.
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForQuestionAnswering, self).__init__(config)
|
|
self.config = config
|
|
self.transformer = AlbertModel(config)
|
|
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
start_positions=None,
|
|
end_positions=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
start_positions (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
end_positions (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
|
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
|
are not taken into account for computing the loss.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
tuple or Dict: Returns tuple (`start_logits, end_logits`)or a dict
|
|
with `start_logits`, `end_logits`, `hidden_states`, `attentions` fields.
|
|
|
|
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].
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import AlbertForQuestionAnswering, AlbertTokenizer
|
|
|
|
tokenizer = AlbertTokenizer.from_pretrained('albert-base-v1')
|
|
model = AlbertForQuestionAnswering.from_pretrained('albert-base-v1')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
outputs = model(**inputs)
|
|
|
|
logits = outputs[0]
|
|
"""
|
|
transformer_outputs = self.transformer(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = transformer_outputs[0]
|
|
logits = self.qa_outputs(sequence_output)
|
|
|
|
start_logits, end_logits = paddle.split(logits, num_or_sections=2, axis=-1)
|
|
start_logits = start_logits.squeeze(axis=-1)
|
|
end_logits = start_logits.squeeze(axis=-1)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if start_positions.ndim > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if start_positions.ndim > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = start_logits.shape[1]
|
|
start_positions = start_positions.clip(0, ignored_index)
|
|
end_positions = end_positions.clip(0, ignored_index)
|
|
|
|
loss_fct = paddle.nn.CrossEntropyLoss(ignore_index=ignored_index)
|
|
start_loss = loss_fct(start_logits, start_positions)
|
|
end_loss = loss_fct(end_logits, end_positions)
|
|
total_loss = (start_loss + end_loss) / 2
|
|
if not return_dict:
|
|
output = (start_logits, end_logits) + transformer_outputs[2:]
|
|
return tuple_output(output, total_loss)
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|
|
|
|
|
|
class AlbertForMultipleChoice(AlbertPretrainedModel):
|
|
"""
|
|
Albert Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like SWAG tasks .
|
|
|
|
Args:
|
|
config (:class:`AlbertConfig`):
|
|
An instance of AlbertConfig used to construct AlbertModel.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: AlbertConfig):
|
|
super(AlbertForMultipleChoice, self).__init__(config)
|
|
self.transformer = AlbertModel(config)
|
|
self.dropout = nn.Dropout(config.hidden_dropout_prob)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
self.config = config
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
attention_mask=None,
|
|
token_type_ids=None,
|
|
position_ids=None,
|
|
head_mask=None,
|
|
inputs_embeds=None,
|
|
labels=None,
|
|
output_hidden_states=False,
|
|
output_attentions=False,
|
|
return_dict=False,
|
|
):
|
|
r"""
|
|
The AlbertForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`AlbertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`AlbertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
head_mask(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
inputs_embeds(Tensor, optional):
|
|
See :class:`AlbertModel`.
|
|
labels (Tensor of shape `(batch_size, )`, optional):
|
|
Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
|
|
num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
|
|
`input_ids` above)
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `False`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `False`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `False`.
|
|
|
|
Returns:
|
|
Tensor or Dict: Returns tensor `reshaped_logits` or a dict
|
|
with `reshaped_logits`, `hidden_states`, `attentions` fields.
|
|
|
|
With the fields:
|
|
|
|
- `reshaped_logits` (Tensor):
|
|
A tensor of the input multiple choice classification logits.
|
|
Shape as `[batch_size, num_labels]` and dtype as `float32`.
|
|
|
|
- `hidden_states` (Tensor):
|
|
Hidden_states of all layers in the Transformer encoder. The length of `hidden_states` is `num_hidden_layers + 1`.
|
|
For all element in the tuple, its data type should be float32 and its shape is [`batch_size, sequence_length, hidden_size`].
|
|
|
|
- `attentions` (Tensor):
|
|
Attentions of all layers of in the Transformer encoder. The length of `attentions` is `num_hidden_layers`.
|
|
For all element in the tuple, its data type should be float32 and its shape is
|
|
[`batch_size, num_attention_heads, sequence_length, sequence_length`].
|
|
"""
|
|
|
|
num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
|
|
|
|
input_ids = input_ids.reshape([-1, input_ids.shape[-1]]) if input_ids is not None else None
|
|
attention_mask = attention_mask.reshape([-1, attention_mask.shape[-1]]) if attention_mask is not None else None
|
|
token_type_ids = token_type_ids.reshape([-1, token_type_ids.shape[-1]]) if token_type_ids is not None else None
|
|
position_ids = position_ids.reshape([-1, position_ids.shape[-1]]) if position_ids is not None else None
|
|
inputs_embeds = (
|
|
inputs_embeds.reshape([-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]])
|
|
if inputs_embeds is not None
|
|
else None
|
|
)
|
|
transformer_outputs = self.transformer(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
pooled_output = transformer_outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
logits = self.classifier(pooled_output)
|
|
reshaped_logits = logits.reshape([-1, num_choices])
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(reshaped_logits, labels)
|
|
if not return_dict:
|
|
output = (reshaped_logits,) + transformer_outputs[2:]
|
|
return tuple_output(output, loss)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=transformer_outputs.hidden_states,
|
|
attentions=transformer_outputs.attentions,
|
|
)
|