wehub-resource-sync
1422 lines
62 KiB
Python
1422 lines
62 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import annotations
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import warnings
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from typing import 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 import Tensor
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from paddle.nn import Layer
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try:
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from paddle.incubate.nn import FusedTransformerEncoderLayer
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except ImportError:
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FusedTransformerEncoderLayer = None
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from dataclasses import dataclass
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from paddlenlp.transformers.model_utils import PretrainedModel, register_base_model
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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 ..model_outputs import (
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BaseModelOutputWithPoolingAndCrossAttentions,
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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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)
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from .configuration import (
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BERT_PRETRAINED_INIT_CONFIGURATION,
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BERT_PRETRAINED_RESOURCE_FILES_MAP,
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BertConfig,
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)
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__all__ = [
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"BertModel",
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"BertPretrainedModel",
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"BertForPretraining",
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"BertPretrainingCriterion",
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"BertPretrainingHeads",
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"BertForSequenceClassification",
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"BertForTokenClassification",
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"BertForQuestionAnswering",
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"BertForMultipleChoice",
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"BertForMaskedLM",
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]
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class BertEmbeddings(Layer):
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"""
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Include embeddings from word, position and token_type embeddings
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"""
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def __init__(self, config: BertConfig):
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super(BertEmbeddings, self).__init__()
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self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size)
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self.position_embeddings = nn.Embedding(config.max_position_embeddings, config.hidden_size)
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self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
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self.layer_norm = nn.LayerNorm(config.hidden_size)
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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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input_ids: Tensor,
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token_type_ids: Optional[Tensor] = None,
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position_ids: Optional[Tensor] = None,
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past_key_values_length: Optional[int] = None,
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):
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if position_ids is None:
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ones = paddle.ones_like(input_ids, dtype="int64")
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seq_length = paddle.cumsum(ones, axis=-1)
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position_ids = seq_length - ones
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if past_key_values_length is not None:
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position_ids += past_key_values_length
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position_ids.stop_gradient = True
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if token_type_ids is None:
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token_type_ids = paddle.zeros_like(input_ids, dtype="int64")
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input_embedings = self.word_embeddings(input_ids)
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position_embeddings = self.position_embeddings(position_ids)
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token_type_embeddings = self.token_type_embeddings(token_type_ids)
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embeddings = input_embedings + position_embeddings + token_type_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 BertPooler(Layer):
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"""
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Pool the result of BertEncoder.
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"""
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def __init__(self, config: BertConfig):
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"""init the bert pooler with config & args/kwargs
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Args:
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config (BertConfig): BertConfig instance. Defaults to None.
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"""
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super(BertPooler, self).__init__()
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self.dense = nn.Linear(config.hidden_size, config.hidden_size)
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self.activation = nn.Tanh()
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self.pool_act = config.pool_act
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def forward(self, hidden_states):
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# We "pool" the model by simply taking the hidden state corresponding
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# to the first token.
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first_token_tensor = hidden_states[:, 0]
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pooled_output = self.dense(first_token_tensor)
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if self.pool_act == "tanh":
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pooled_output = self.activation(pooled_output)
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return pooled_output
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class BertPretrainedModel(PretrainedModel):
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"""
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An abstract class for pretrained BERT models. It provides BERT related
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`model_config_file`, `resource_files_names`, `pretrained_resource_files_map`,
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`pretrained_init_configuration`, `base_model_prefix` for downloading and
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loading pretrained models.
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See :class:`~paddlenlp.transformers.model_utils.PretrainedModel` for more details.
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"""
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model_config_file = CONFIG_NAME
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config_class = BertConfig
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resource_files_names = {"model_state": "model_state.pdparams"}
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base_model_prefix = "bert"
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pretrained_init_configuration = BERT_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = BERT_PRETRAINED_RESOURCE_FILES_MAP
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@classmethod
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def _get_name_mappings(cls, config: BertConfig) -> list[StateDictNameMapping]:
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mappings: 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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["pooler.dense.weight", None, "transpose"],
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"pooler.dense.bias",
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# for TokenClassification
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]
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for layer_index in range(config.num_hidden_layers):
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layer_mappings = [
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[
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f"encoder.layer.{layer_index}.attention.self.query.weight",
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f"encoder.layers.{layer_index}.self_attn.q_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.query.bias",
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f"encoder.layers.{layer_index}.self_attn.q_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.key.weight",
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f"encoder.layers.{layer_index}.self_attn.k_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.key.bias",
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f"encoder.layers.{layer_index}.self_attn.k_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.value.weight",
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f"encoder.layers.{layer_index}.self_attn.v_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.self.value.bias",
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f"encoder.layers.{layer_index}.self_attn.v_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.dense.weight",
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f"encoder.layers.{layer_index}.self_attn.out_proj.weight",
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"transpose",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.dense.bias",
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f"encoder.layers.{layer_index}.self_attn.out_proj.bias",
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],
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[
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f"encoder.layer.{layer_index}.intermediate.dense.weight",
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f"encoder.layers.{layer_index}.linear1.weight",
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"transpose",
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],
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[f"encoder.layer.{layer_index}.intermediate.dense.bias", f"encoder.layers.{layer_index}.linear1.bias"],
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[
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f"encoder.layer.{layer_index}.attention.output.LayerNorm.weight",
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f"encoder.layers.{layer_index}.norm1.weight",
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],
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[
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f"encoder.layer.{layer_index}.attention.output.LayerNorm.bias",
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f"encoder.layers.{layer_index}.norm1.bias",
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],
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[
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f"encoder.layer.{layer_index}.output.dense.weight",
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f"encoder.layers.{layer_index}.linear2.weight",
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"transpose",
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],
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[f"encoder.layer.{layer_index}.output.dense.bias", f"encoder.layers.{layer_index}.linear2.bias"],
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[f"encoder.layer.{layer_index}.output.LayerNorm.weight", f"encoder.layers.{layer_index}.norm2.weight"],
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[f"encoder.layer.{layer_index}.output.LayerNorm.bias", f"encoder.layers.{layer_index}.norm2.bias"],
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]
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model_mappings.extend(layer_mappings)
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init_name_mappings(model_mappings)
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# base-model prefix "BertModel"
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if "BertModel" not in config.architectures:
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for mapping in model_mappings:
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mapping[0] = "bert." + mapping[0]
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mapping[1] = "bert." + mapping[1]
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# downstream mappings
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if "BertForQuestionAnswering" in config.architectures:
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model_mappings.extend(
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[["qa_outputs.weight", "classifier.weight", "transpose"], ["qa_outputs.bias", "classifier.bias"]]
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)
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if (
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"BertForMultipleChoice" in config.architectures
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or "BertForSequenceClassification" in config.architectures
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or "BertForTokenClassification" in config.architectures
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):
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model_mappings.extend([["classifier.weight", "classifier.weight", "transpose"]])
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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):
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"""Initialization hook"""
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if isinstance(layer, (nn.Linear, nn.Embedding)):
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# In the dygraph mode, use the `set_value` to reset the parameter directly,
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# and reset the `state_dict` to update parameter in static mode.
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if isinstance(layer.weight, paddle.Tensor):
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layer.weight.set_value(
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paddle.tensor.normal(
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mean=0.0,
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std=self.config.initializer_range,
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shape=layer.weight.shape,
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)
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)
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elif isinstance(layer, nn.LayerNorm):
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layer._epsilon = self.config.layer_norm_eps
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@register_base_model
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class BertModel(BertPretrainedModel):
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"""
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The bare BERT Model transformer outputting raw hidden-states.
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This model inherits from :class:`~paddlenlp.transformers.model_utils.PretrainedModel`.
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Refer to the superclass documentation for the generic methods.
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This model is also a Paddle `paddle.nn.Layer <https://www.paddlepaddle.org.cn/documentation
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/docs/zh/api/paddle/nn/Layer_cn.html>`__ subclass. Use it as a regular Paddle Layer
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and refer to the Paddle documentation for all matter related to general usage and behavior.
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Args:
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config (:class:`BertConfig`):
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An instance of BertConfig used to construct BertModel.
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"""
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def __init__(self, config: BertConfig):
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super(BertModel, self).__init__(config)
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self.pad_token_id = config.pad_token_id
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self.initializer_range = config.initializer_range
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self.embeddings = BertEmbeddings(config)
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if config.fuse and FusedTransformerEncoderLayer is None:
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warnings.warn(
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"FusedTransformerEncoderLayer is not supported by the running Paddle. "
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"The flag fuse_transformer will be ignored. Try Paddle >= 2.3.0"
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)
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self.fuse = config.fuse and FusedTransformerEncoderLayer is not None
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if self.fuse:
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self.encoder = nn.LayerList(
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[
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FusedTransformerEncoderLayer(
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config.hidden_size,
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config.num_attention_heads,
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config.intermediate_size,
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dropout_rate=config.hidden_dropout_prob,
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activation=config.hidden_act,
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attn_dropout_rate=config.attention_probs_dropout_prob,
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act_dropout_rate=0.0,
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)
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for _ in range(config.num_hidden_layers)
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]
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)
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else:
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encoder_layer = nn.TransformerEncoderLayer(
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config.hidden_size,
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config.num_attention_heads,
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config.intermediate_size,
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dropout=config.hidden_dropout_prob,
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activation=config.hidden_act,
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attn_dropout=config.attention_probs_dropout_prob,
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act_dropout=0,
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)
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self.encoder = nn.TransformerEncoder(encoder_layer, config.num_hidden_layers)
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self.pooler = BertPooler(config)
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def get_input_embeddings(self):
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, value):
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self.embeddings.word_embeddings = value
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def forward(
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self,
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input_ids: Tensor,
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token_type_ids: Optional[Tensor] = None,
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position_ids: Optional[Tensor] = None,
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attention_mask: Optional[Tensor] = None,
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past_key_values: Optional[Tuple[Tuple[Tensor]]] = None,
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use_cache: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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):
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r"""
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The BertModel forward method, overrides the `__call__()` special method.
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Args:
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input_ids (Tensor):
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Indices of input sequence tokens in the vocabulary. They are
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numerical representations of tokens that build the input sequence.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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token_type_ids (Tensor, optional):
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Segment token indices to indicate different portions of the inputs.
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Selected in the range ``[0, type_vocab_size - 1]``.
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If `type_vocab_size` is 2, which means the inputs have two portions.
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Indices can either be 0 or 1:
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- 0 corresponds to a *sentence A* token,
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- 1 corresponds to a *sentence B* token.
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Its data type should be `int64` and it has a shape of [batch_size, sequence_length].
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Defaults to `None`, which means we don't add segment embeddings.
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position_ids(Tensor, optional):
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Indices of positions of each input sequence tokens in the position embeddings. Selected in the range ``[0,
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max_position_embeddings - 1]``.
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Shape as `(batch_size, num_tokens)` and dtype as int64. Defaults to `None`.
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attention_mask (Tensor, optional):
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Mask used in multi-head attention to avoid performing attention on to some unwanted positions,
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usually the paddings or the subsequent positions.
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Its data type can be int, float and bool.
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When the data type is bool, the `masked` tokens have `False` values and the others have `True` values.
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When the data type is int, the `masked` tokens have `0` values and the others have `1` values.
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When the data type is float, the `masked` tokens have `-INF` values and the others have `0` values.
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It is a tensor with shape broadcasted to `[batch_size, num_attention_heads, sequence_length, sequence_length]`.
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Defaults to `None`, which means nothing needed to be prevented attention to.
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past_key_values (tuple(tuple(Tensor)), optional):
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The length of tuple equals to the number of layers, and each inner
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tuple haves 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`)
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which contains precomputed key and value hidden states of the attention blocks.
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If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
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don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
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`input_ids` of shape `(batch_size, sequence_length)`.
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use_cache (`bool`, optional):
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If set to `True`, `past_key_values` key value states are returned.
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Defaults to `None`.
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output_hidden_states (bool, optional):
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Whether to return the hidden states of all layers.
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Defaults to `None`.
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output_attentions (bool, optional):
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Whether to return the attentions tensors of all attention layers.
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Defaults to `None`.
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return_dict (bool, optional):
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Whether to return a :class:`~paddlenlp.transformers.model_outputs.ModelOutput` object. If `False`, the output
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will be a tuple of tensors. Defaults to `None`.
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Returns:
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An instance of :class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions` if
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`return_dict=True`. Otherwise it returns a tuple of tensors corresponding
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to ordered and not None (depending on the input arguments) fields of
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:class:`~paddlenlp.transformers.model_outputs.BaseModelOutputWithPoolingAndCrossAttentions`.
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers import BertModel, BertTokenizer
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tokenizer = BertTokenizer.from_pretrained('bert-wwm-chinese')
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model = BertModel.from_pretrained('bert-wwm-chinese')
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inputs = tokenizer("欢迎使用百度飞桨!")
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inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
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output = model(**inputs)
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"""
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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past_key_values_length = None
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if past_key_values is not None:
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past_key_values_length = past_key_values[0][0].shape[2]
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if attention_mask is None:
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attention_mask = paddle.unsqueeze(
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(input_ids == self.pad_token_id).astype(self.pooler.dense.weight.dtype) * -1e4, axis=[1, 2]
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)
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if past_key_values is not None:
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batch_size = past_key_values[0][0].shape[0]
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past_mask = paddle.zeros([batch_size, 1, 1, past_key_values_length], dtype=attention_mask.dtype)
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attention_mask = paddle.concat([past_mask, attention_mask], axis=-1)
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else:
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if attention_mask.ndim == 2:
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# attention_mask [batch_size, sequence_length] -> [batch_size, 1, 1, sequence_length]
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attention_mask = attention_mask.unsqueeze(axis=[1, 2]).astype(paddle.get_default_dtype())
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attention_mask = (1.0 - attention_mask) * -1e4
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embedding_output = self.embeddings(
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input_ids=input_ids,
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position_ids=position_ids,
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token_type_ids=token_type_ids,
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past_key_values_length=past_key_values_length,
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)
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if self.fuse:
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assert not output_attentions, "Not support attentions output currently."
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assert past_key_values is None, "Not support past_key_values currently."
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hidden_states = embedding_output
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all_hidden_states = [] if output_hidden_states else None
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for layer in self.encoder:
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hidden_states = layer(hidden_states, attention_mask)
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if output_hidden_states:
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all_hidden_states.append(hidden_states)
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pooled_output = self.pooler(hidden_states)
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if return_dict:
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|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=hidden_states, pooler_output=pooled_output, hidden_states=all_hidden_states
|
|
)
|
|
else:
|
|
return (
|
|
(hidden_states, pooled_output, all_hidden_states)
|
|
if output_hidden_states
|
|
else (hidden_states, pooled_output)
|
|
)
|
|
else:
|
|
self.encoder._use_cache = use_cache # To be consistent with HF
|
|
encoder_outputs = self.encoder(
|
|
embedding_output,
|
|
src_mask=attention_mask,
|
|
cache=past_key_values,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
if isinstance(encoder_outputs, type(embedding_output)):
|
|
sequence_output = encoder_outputs
|
|
pooled_output = self.pooler(sequence_output)
|
|
return (sequence_output, pooled_output)
|
|
else:
|
|
sequence_output = encoder_outputs[0]
|
|
pooled_output = self.pooler(sequence_output)
|
|
if not return_dict:
|
|
return (sequence_output, pooled_output) + encoder_outputs[1:]
|
|
return BaseModelOutputWithPoolingAndCrossAttentions(
|
|
last_hidden_state=sequence_output,
|
|
pooler_output=pooled_output,
|
|
past_key_values=encoder_outputs.past_key_values,
|
|
hidden_states=encoder_outputs.hidden_states,
|
|
attentions=encoder_outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertForQuestionAnswering(BertPretrainedModel):
|
|
"""
|
|
Bert 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:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForQuestionAnswering.
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertForQuestionAnswering, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
start_positions: Optional[Tensor] = None,
|
|
end_positions: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The BertForQuestionAnswering forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`BertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
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 `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
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 `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.QuestionAnsweringModelOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers.bert.modeling import BertForQuestionAnswering
|
|
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
|
model = BertForQuestionAnswering.from_pretrained('bert-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]
|
|
"""
|
|
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
logits = self.classifier(sequence_output)
|
|
logits = paddle.transpose(logits, perm=[2, 0, 1])
|
|
start_logits, end_logits = paddle.unstack(x=logits, axis=0)
|
|
|
|
total_loss = None
|
|
if start_positions is not None and end_positions is not None:
|
|
# If we are on multi-GPU, split add a dimension
|
|
if start_positions.ndim > 1:
|
|
start_positions = start_positions.squeeze(-1)
|
|
if start_positions.ndim > 1:
|
|
end_positions = end_positions.squeeze(-1)
|
|
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
|
ignored_index = 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) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertForSequenceClassification(BertPretrainedModel):
|
|
"""
|
|
Bert Model with a linear layer on top of the output layer,
|
|
designed for sequence classification/regression tasks like GLUE tasks.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForSequenceClassification.
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertForSequenceClassification, self).__init__(config)
|
|
|
|
self.bert = BertModel(config)
|
|
self.num_labels = config.num_labels
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The BertForSequenceClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`BertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
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 `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
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 `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers.bert.modeling import BertForSequenceClassification
|
|
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
|
model = BertForSequenceClassification.from_pretrained('bert-base-cased', num_labels=2)
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
|
|
logits = model(**inputs)
|
|
print(logits.shape)
|
|
# [1, 2]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = 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,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertForTokenClassification(BertPretrainedModel):
|
|
"""
|
|
Bert Model with a linear layer on top of the hidden-states output layer,
|
|
designed for token classification tasks like NER tasks.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForTokenClassification.
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super().__init__(config)
|
|
|
|
self.bert = BertModel(config)
|
|
self.num_labels = config.num_labels
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The BertForTokenClassification forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
position_ids(Tensor, optional):
|
|
See :class:`BertModel`.
|
|
attention_mask (list, optional):
|
|
See :class:`BertModel`.
|
|
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 `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
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 `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.TokenClassifierOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers.bert.modeling import BertForTokenClassification
|
|
from paddlenlp.transformers.bert.tokenizer import BertTokenizer
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
|
|
model = BertForTokenClassification.from_pretrained('bert-base-cased', num_labels=2)
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
|
|
logits = model(**inputs)
|
|
print(logits.shape)
|
|
# [1, 13, 2]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
sequence_output = outputs[0]
|
|
|
|
sequence_output = self.dropout(sequence_output)
|
|
logits = self.classifier(sequence_output)
|
|
|
|
loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
loss = loss_fct(logits.reshape((-1, self.num_labels)), labels.reshape((-1,)))
|
|
if not return_dict:
|
|
output = (logits,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return TokenClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertLMPredictionHead(Layer):
|
|
"""
|
|
Bert Model with a `language modeling` head on top for CLM fine-tuning.
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertLMPredictionHead, self).__init__()
|
|
|
|
self.transform = nn.Linear(config.hidden_size, config.hidden_size)
|
|
self.activation = getattr(nn.functional, config.hidden_act)
|
|
self.layer_norm = nn.LayerNorm(config.hidden_size)
|
|
self.decoder = TransposedLinear(config.hidden_size, config.vocab_size)
|
|
# link bias to load pretrained weights
|
|
self.decoder_bias = self.decoder.bias
|
|
|
|
def forward(self, hidden_states, masked_positions=None):
|
|
if masked_positions is not None:
|
|
hidden_states = paddle.reshape(hidden_states, [-1, hidden_states.shape[-1]])
|
|
hidden_states = paddle.tensor.gather(hidden_states, masked_positions)
|
|
# gather masked tokens might be more quick
|
|
hidden_states = self.transform(hidden_states)
|
|
hidden_states = self.activation(hidden_states)
|
|
hidden_states = self.layer_norm(hidden_states)
|
|
hidden_states = self.decoder(hidden_states)
|
|
return hidden_states
|
|
|
|
|
|
class BertPretrainingHeads(Layer):
|
|
"""
|
|
Perform language modeling task and next sentence classification task.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForPretraining.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertPretrainingHeads, self).__init__()
|
|
self.predictions = BertLMPredictionHead(config)
|
|
self.seq_relationship = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(self, sequence_output, pooled_output, masked_positions=None):
|
|
"""
|
|
Args:
|
|
sequence_output(Tensor):
|
|
Sequence of hidden-states at the last layer of the model.
|
|
It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
|
|
pooled_output(Tensor):
|
|
The output of first token (`[CLS]`) in sequence.
|
|
We "pool" the model by simply taking the hidden state corresponding to the first token.
|
|
Its data type should be float32 and its shape is [batch_size, hidden_size].
|
|
masked_positions(Tensor, optional):
|
|
A tensor indicates positions to be masked in the position embedding.
|
|
Its data type should be int64 and its shape is [batch_size, mask_token_num].
|
|
`mask_token_num` is the number of masked tokens. It should be no bigger than `sequence_length`.
|
|
Defaults to `None`, which means we output hidden-states of all tokens in masked token prediction.
|
|
|
|
Returns:
|
|
tuple: Returns tuple (``prediction_scores``, ``seq_relationship_score``).
|
|
|
|
With the fields:
|
|
|
|
- `prediction_scores` (Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
|
|
Otherwise, its shape is [batch_size, mask_token_num, vocab_size].
|
|
|
|
- `seq_relationship_score` (Tensor):
|
|
The scores of next sentence prediction.
|
|
Its data type should be float32 and its shape is [batch_size, 2].
|
|
|
|
"""
|
|
prediction_scores = self.predictions(sequence_output, masked_positions)
|
|
seq_relationship_score = self.seq_relationship(pooled_output)
|
|
return prediction_scores, seq_relationship_score
|
|
|
|
|
|
@dataclass
|
|
class BertForPreTrainingOutput(ModelOutput):
|
|
"""
|
|
Output type of [`BertForPreTraining`].
|
|
|
|
Args:
|
|
loss (*optional*, returned when `labels` is provided, `paddle.Tensor` of shape `(1,)`):
|
|
Total loss as the sum of the masked language modeling loss and the next sequence prediction
|
|
(classification) loss.
|
|
prediction_logits (`paddle.Tensor` of shape `(batch_size, sequence_length, config.vocab_size)`):
|
|
Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
|
|
seq_relationship_logits (`paddle.Tensor` of shape `(batch_size, 2)`):
|
|
Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
|
|
before SoftMax).
|
|
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
|
|
prediction_logits: paddle.Tensor = None
|
|
seq_relationship_logits: paddle.Tensor = None
|
|
hidden_states: Optional[Tuple[paddle.Tensor]] = None
|
|
attentions: Optional[Tuple[paddle.Tensor]] = None
|
|
|
|
|
|
class BertForPretraining(BertPretrainedModel):
|
|
"""
|
|
Bert Model with pretraining tasks on top.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForPretraining.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertForPretraining, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
self.cls = BertPretrainingHeads(config)
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
masked_positions: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
next_sentence_label: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
masked_positions(Tensor, optional):
|
|
See :class:`BertPretrainingHeads`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked),
|
|
the loss is only computed for the tokens with labels in `[0, ..., vocab_size]`.
|
|
next_sentence_label (Tensor of shape `(batch_size,)`, optional):
|
|
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence
|
|
pair (see `input_ids` docstring) Indices should be in `[0, 1]`:
|
|
|
|
- 0 indicates sequence B is a continuation of sequence A,
|
|
- 1 indicates sequence B is a random sequence.
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.bert.BertForPreTrainingOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.bert.BertForPreTrainingOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.bert.BertForPreTrainingOutput`.
|
|
|
|
"""
|
|
with paddle.static.amp.fp16_guard():
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output, pooled_output = outputs[:2]
|
|
prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output, masked_positions)
|
|
|
|
total_loss = None
|
|
if labels is not None and next_sentence_label is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss()
|
|
masked_lm_loss = loss_fct(
|
|
prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1,))
|
|
)
|
|
next_sentence_loss = loss_fct(
|
|
seq_relationship_score.reshape((-1, 2)), next_sentence_label.reshape((-1,))
|
|
)
|
|
total_loss = masked_lm_loss + next_sentence_loss
|
|
if not return_dict:
|
|
output = (prediction_scores, seq_relationship_score) + outputs[2:]
|
|
return ((total_loss,) + output) if total_loss is not None else output
|
|
|
|
return BertForPreTrainingOutput(
|
|
loss=total_loss,
|
|
prediction_logits=prediction_scores,
|
|
seq_relationship_logits=seq_relationship_score,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertPretrainingCriterion(paddle.nn.Layer):
|
|
"""
|
|
|
|
Args:
|
|
vocab_size(int):
|
|
Vocabulary size of `inputs_ids` in `BertModel`. Defines the number of different tokens that can
|
|
be represented by the `inputs_ids` passed when calling `BertModel`.
|
|
|
|
"""
|
|
|
|
def __init__(self, vocab_size):
|
|
super(BertPretrainingCriterion, self).__init__()
|
|
# CrossEntropyLoss is expensive since the inner reshape (copy)
|
|
self.loss_fn = paddle.nn.loss.CrossEntropyLoss(ignore_index=-1)
|
|
self.vocab_size = vocab_size
|
|
|
|
def forward(
|
|
self, prediction_scores, seq_relationship_score, masked_lm_labels, next_sentence_labels, masked_lm_scale
|
|
):
|
|
"""
|
|
Args:
|
|
prediction_scores(Tensor):
|
|
The scores of masked token prediction. Its data type should be float32.
|
|
If `masked_positions` is None, its shape is [batch_size, sequence_length, vocab_size].
|
|
Otherwise, its shape is [batch_size, mask_token_num, vocab_size]
|
|
seq_relationship_score(Tensor):
|
|
The scores of next sentence prediction. Its data type should be float32 and
|
|
its shape is [batch_size, 2]
|
|
masked_lm_labels(Tensor):
|
|
The labels of the masked language modeling, its dimensionality is equal to `prediction_scores`.
|
|
Its data type should be int64. If `masked_positions` is None, its shape is [batch_size, sequence_length, 1].
|
|
Otherwise, its shape is [batch_size, mask_token_num, 1]
|
|
next_sentence_labels(Tensor):
|
|
The labels of the next sentence prediction task, the dimensionality of `next_sentence_labels`
|
|
is equal to `seq_relation_labels`. Its data type should be int64 and
|
|
its shape is [batch_size, 1]
|
|
masked_lm_scale(Tensor or int):
|
|
The scale of masked tokens. Used for the normalization of masked language modeling loss.
|
|
If it is a `Tensor`, its data type should be int64 and its shape is equal to `prediction_scores`.
|
|
|
|
Returns:
|
|
Tensor: The pretraining loss, equals to the sum of `masked_lm_loss` plus the mean of `next_sentence_loss`.
|
|
Its data type should be float32 and its shape is [1].
|
|
|
|
|
|
"""
|
|
with paddle.static.amp.fp16_guard():
|
|
masked_lm_loss = F.cross_entropy(prediction_scores, masked_lm_labels, reduction="none", ignore_index=-1)
|
|
masked_lm_loss = masked_lm_loss / masked_lm_scale
|
|
next_sentence_loss = F.cross_entropy(seq_relationship_score, next_sentence_labels, reduction="none")
|
|
return paddle.sum(masked_lm_loss) + paddle.mean(next_sentence_loss)
|
|
|
|
|
|
class BertForMultipleChoice(BertPretrainedModel):
|
|
"""
|
|
Bert Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForMultipleChoice.
|
|
|
|
Examples:
|
|
>>> model = BertForMultipleChoice(config, dropout=0.1)
|
|
>>> # or
|
|
>>> config.hidden_dropout_prob = 0.1
|
|
>>> model = BertForMultipleChoice(config)
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertForMultipleChoice, self).__init__(config)
|
|
|
|
self.bert = BertModel(config)
|
|
self.num_choices = config.num_choices
|
|
self.dropout = nn.Dropout(
|
|
config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
|
|
)
|
|
self.classifier = nn.Linear(config.hidden_size, 1)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
The BertForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
token_type_ids(Tensor, optional):
|
|
See :class:`BertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
position_ids(Tensor, optional):
|
|
See :class:`BertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (list, optional):
|
|
See :class:`BertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
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 `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
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 `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MultipleChoiceModelOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import BertForMultipleChoice, BertTokenizer
|
|
from paddlenlp.data import Pad, Dict
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = BertForMultipleChoice.from_pretrained('bert-base-uncased', num_choices=2)
|
|
|
|
data = [
|
|
{
|
|
"question": "how do you turn on an ipad screen?",
|
|
"answer1": "press the volume button.",
|
|
"answer2": "press the lock button.",
|
|
"label": 1,
|
|
},
|
|
{
|
|
"question": "how do you indent something?",
|
|
"answer1": "leave a space before starting the writing",
|
|
"answer2": "press the spacebar",
|
|
"label": 0,
|
|
},
|
|
]
|
|
|
|
text = []
|
|
text_pair = []
|
|
for d in data:
|
|
text.append(d["question"])
|
|
text_pair.append(d["answer1"])
|
|
text.append(d["question"])
|
|
text_pair.append(d["answer2"])
|
|
|
|
inputs = tokenizer(text, text_pair)
|
|
batchify_fn = lambda samples, fn=Dict(
|
|
{
|
|
"input_ids": Pad(axis=0, pad_val=tokenizer.pad_token_id), # input_ids
|
|
"token_type_ids": Pad(
|
|
axis=0, pad_val=tokenizer.pad_token_type_id
|
|
), # token_type_ids
|
|
}
|
|
): fn(samples)
|
|
inputs = batchify_fn(inputs)
|
|
|
|
reshaped_logits = model(
|
|
input_ids=paddle.to_tensor(inputs[0], dtype="int64"),
|
|
token_type_ids=paddle.to_tensor(inputs[1], dtype="int64"),
|
|
)
|
|
print(reshaped_logits.shape)
|
|
# [2, 2]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
# input_ids: [bs, num_choice, seq_l]
|
|
input_ids = input_ids.reshape(shape=(-1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l]
|
|
|
|
if position_ids is not None:
|
|
position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1]))
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.reshape(shape=(-1, token_type_ids.shape[-1]))
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.reshape(shape=(-1, attention_mask.shape[-1]))
|
|
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = outputs[1]
|
|
pooled_output = self.dropout(pooled_output)
|
|
|
|
logits = self.classifier(pooled_output) # logits: (bs*num_choice,1)
|
|
reshaped_logits = logits.reshape(shape=(-1, self.num_choices)) # logits: (bs, num_choice)
|
|
|
|
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,) + outputs[2:]
|
|
return ((loss,) + output) if loss is not None else (output[0] if len(output) == 1 else output)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class BertOnlyMLMHead(nn.Layer):
|
|
def __init__(self, config: BertConfig):
|
|
super().__init__()
|
|
self.predictions = BertLMPredictionHead(config=config)
|
|
|
|
def forward(self, sequence_output, masked_positions=None):
|
|
prediction_scores = self.predictions(sequence_output, masked_positions)
|
|
return prediction_scores
|
|
|
|
|
|
class BertForMaskedLM(BertPretrainedModel):
|
|
"""
|
|
Bert Model with a `masked language modeling` head on top.
|
|
|
|
Args:
|
|
config (:class:`BertConfig`):
|
|
An instance of BertConfig used to construct BertForMaskedLM.
|
|
|
|
"""
|
|
|
|
def __init__(self, config: BertConfig):
|
|
super(BertForMaskedLM, self).__init__(config)
|
|
self.bert = BertModel(config)
|
|
|
|
self.cls = BertOnlyMLMHead(config=config)
|
|
self.tie_weights()
|
|
|
|
def get_output_embeddings(self):
|
|
return self.cls.predictions.decoder
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Tensor,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
masked_positions: Optional[Tensor] = None,
|
|
labels: Optional[Tensor] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
):
|
|
r"""
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`BertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`BertModel`.
|
|
labels (Tensor of shape `(batch_size, sequence_length)`, optional):
|
|
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
|
|
vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
|
|
loss is only computed for the tokens with labels in `[0, ..., vocab_size]`
|
|
output_hidden_states (bool, optional):
|
|
Whether to return the hidden states of all layers.
|
|
Defaults to `None`.
|
|
output_attentions (bool, optional):
|
|
Whether to return the attentions tensors of all attention layers.
|
|
Defaults to `None`.
|
|
return_dict (bool, optional):
|
|
Whether to return a :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` object. If
|
|
`False`, the output will be a tuple of tensors. Defaults to `None`.
|
|
|
|
Returns:
|
|
An instance of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput` if `return_dict=True`.
|
|
Otherwise it returns a tuple of tensors corresponding to ordered and
|
|
not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.MaskedLMOutput`.
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import BertForMaskedLM, BertTokenizer
|
|
|
|
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
|
|
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
|
|
logits = model(**inputs)
|
|
print(logits.shape)
|
|
# [1, 13, 30522]
|
|
|
|
"""
|
|
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
|
outputs = self.bert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
sequence_output = outputs[0]
|
|
prediction_scores = self.cls(sequence_output, masked_positions=masked_positions)
|
|
|
|
masked_lm_loss = None
|
|
if labels is not None:
|
|
loss_fct = paddle.nn.CrossEntropyLoss() # -100 index = padding token
|
|
masked_lm_loss = loss_fct(
|
|
prediction_scores.reshape((-1, prediction_scores.shape[-1])), labels.reshape((-1,))
|
|
)
|
|
if not return_dict:
|
|
output = (prediction_scores,) + outputs[2:]
|
|
return (
|
|
((masked_lm_loss,) + output)
|
|
if masked_lm_loss is not None
|
|
else (output[0] if len(output) == 1 else output)
|
|
)
|
|
|
|
return MaskedLMOutput(
|
|
loss=masked_lm_loss,
|
|
logits=prediction_scores,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|