wehub-resource-sync
755 lines
33 KiB
Python
755 lines
33 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 Huawei Technologies Co., Ltd.
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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 typing import Optional, Tuple
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import paddle
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import paddle.nn as nn
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from paddle import Tensor
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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 ..bert.modeling import BertEmbeddings, BertPooler
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from ..model_outputs import (
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BaseModelOutputWithPoolingAndCrossAttentions,
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MultipleChoiceModelOutput,
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QuestionAnsweringModelOutput,
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SequenceClassifierOutput,
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tuple_output,
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)
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from .configuration import (
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TINYBERT_PRETRAINED_INIT_CONFIGURATION,
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TINYBERT_PRETRAINED_RESOURCE_FILES_MAP,
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TinyBertConfig,
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)
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__all__ = [
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"TinyBertModel",
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"TinyBertPretrainedModel",
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"TinyBertForPretraining",
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"TinyBertForSequenceClassification",
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"TinyBertForQuestionAnswering",
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"TinyBertForMultipleChoice",
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]
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class TinyBertPretrainedModel(PretrainedModel):
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"""
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An abstract class for pretrained TinyBERT models. It provides TinyBERT 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
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and 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 = TinyBertConfig
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resource_files_names = {"model_state": "model_state.pdparams"}
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pretrained_init_configuration = TINYBERT_PRETRAINED_INIT_CONFIGURATION
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pretrained_resource_files_map = TINYBERT_PRETRAINED_RESOURCE_FILES_MAP
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base_model_prefix = "tinybert"
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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 TinyBertModel(TinyBertPretrainedModel):
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"""
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The bare TinyBERT 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:`TinyBertConfig`):
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An instance of TinyBertConfig used to construct TinyBertModel.
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"""
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def __init__(self, config: TinyBertConfig):
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super(TinyBertModel, 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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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=0.0,
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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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# fit_dense(s) means a hidden states' transformation from student to teacher.
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# `fit_denses` is used in v2 model, and `fit_dense` is used in other pretraining models.
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self.fit_denses = nn.LayerList(
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[nn.Linear(config.hidden_size, config.fit_size) for i in range(config.num_hidden_layers + 1)]
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)
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self.fit_dense = nn.Linear(config.hidden_size, config.fit_size)
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def get_input_embeddings(self) -> nn.Embedding:
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"""get input embedding of TinyBert Pretrained Model
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Returns:
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nn.Embedding: the input embedding of tiny bert
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"""
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return self.embeddings.word_embeddings
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def set_input_embeddings(self, embedding: nn.Embedding) -> None:
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"""set the input embedding with the new embedding value
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Args:
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embedding (nn.Embedding): the new embedding value
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"""
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self.embeddings.word_embeddings = embedding
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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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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inputs_embeds: 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 TinyBertModel 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 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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For example, its shape can be [batch_size, sequence_length], [batch_size, sequence_length, sequence_length],
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[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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inputs_embeds (Tensor, optional):
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If you want to control how to convert `inputs_ids` indices into associated vectors, you can
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pass an embedded representation directly instead of passing `inputs_ids`.
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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 `False`.
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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 `False`.
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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 `False`.
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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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tuple: Returns tuple (`encoder_output`, `pooled_output`).
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With the fields:
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- `encoder_output` (Tensor):
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Sequence of hidden-states at the last layer of the model.
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It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
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- `pooled_output` (Tensor):
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The output of first token (`[CLS]`) in sequence.
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We "pool" the model by simply taking the hidden state corresponding to the first token.
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Its data type should be float32 and its shape is [batch_size, hidden_size].
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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 TinyBertModel, TinyBertTokenizer
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tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
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model = TinyBertModel.from_pretrained('tinybert-4l-312d')
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ")
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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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if input_ids is not None and inputs_embeds is not None:
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raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time.")
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# init the default bool value
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output_attentions = output_attentions if output_attentions is not None else False
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output_hidden_states = output_hidden_states if output_hidden_states is not None else False
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return_dict = return_dict if return_dict is not None else False
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use_cache = use_cache if use_cache is not None else False
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past_key_values_length = 0
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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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elif 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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# TODO(wj-Mcat): in current branch, not support `inputs_embeds`
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embedding_output = self.embeddings(
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input_ids, token_type_ids, position_ids, past_key_values_length=past_key_values_length
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)
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self.encoder._use_cache = use_cache # To be consistent with HF
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encoder_outputs = self.encoder(
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embedding_output,
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attention_mask,
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cache=past_key_values,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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if isinstance(encoder_outputs, type(embedding_output)):
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sequence_output = encoder_outputs
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pooled_output = self.pooler(sequence_output)
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return (sequence_output, pooled_output)
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sequence_output = encoder_outputs[0]
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pooled_output = self.pooler(sequence_output)
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if not return_dict:
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return (sequence_output, pooled_output) + encoder_outputs[1:]
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return BaseModelOutputWithPoolingAndCrossAttentions(
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last_hidden_state=sequence_output,
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pooler_output=pooled_output,
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past_key_values=encoder_outputs.past_key_values,
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hidden_states=encoder_outputs.hidden_states,
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attentions=encoder_outputs.attentions,
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)
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class TinyBertForPretraining(TinyBertPretrainedModel):
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"""
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TinyBert Model with pretraining tasks on top.
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Args:
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config (:class:`TinyBertConfig`):
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An instance of TinyBertConfig used to construct TinyBertForPretraining.
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"""
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def __init__(self, config: TinyBertConfig):
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super(TinyBertForPretraining, self).__init__(config)
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self.tinybert = TinyBertModel(config)
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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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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inputs_embeds: Optional[Tensor] = 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 TinyBertForPretraining forward method, overrides the __call__() special method.
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Args:
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input_ids (Tensor):
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See :class:`TinyBertModel`.
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token_type_ids (Tensor, optional):
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See :class:`TinyBertModel`.
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position_ids (Tensor, optional):
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See :class:`TinyBertModel`.
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attention_mask (Tensor, optional):
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See :class:`TinyBertModel`.
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Returns:
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Tensor: Returns tensor `sequence_output`, sequence of hidden-states at the last layer of the model.
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It's data type should be float32 and its shape is [batch_size, sequence_length, hidden_size].
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers.tinybert.modeling import TinyBertForPretraining
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from paddlenlp.transformers.tinybert.tokenizer import TinyBertTokenizer
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tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
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model = TinyBertForPretraining.from_pretrained('tinybert-4l-312d')
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ")
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inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
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outputs = model(**inputs)
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logits = outputs[0]
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"""
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outputs = self.tinybert(
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input_ids,
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token_type_ids,
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position_ids,
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attention_mask,
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inputs_embeds=inputs_embeds,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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# return the sequence presentation
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if not return_dict:
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return outputs[0]
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return outputs
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class TinyBertForSequenceClassification(TinyBertPretrainedModel):
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"""
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TinyBert Model with a linear layer on top of the output layer,
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designed for sequence classification/regression tasks like GLUE tasks.
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Args:
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config (:class:`TinyBertConfig`):
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An instance of TinyBertConfig used to construct TinyBertForSequenceClassification.
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"""
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def __init__(self, config: TinyBertConfig):
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super(TinyBertForSequenceClassification, self).__init__(config)
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self.tinybert = TinyBertModel(config)
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self.num_labels = config.num_labels
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self.dropout = nn.Dropout(
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config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
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)
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self.classifier = nn.Linear(config.hidden_size, config.num_labels)
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self.activation = nn.ReLU()
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def forward(
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self,
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input_ids: Optional[Tensor] = None,
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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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labels: Optional[Tensor] = None,
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inputs_embeds: Optional[Tensor] = 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 TinyBertForSequenceClassification forward method, overrides the __call__() special method.
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Args:
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input_ids (Tensor):
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See :class:`TinyBertModel`.
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token_type_ids (Tensor, optional):
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See :class:`TinyBertModel`.
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position_ids (Tensor, optional):
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See :class:`TinyBertModel`.
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attention_mask_list (list, optional):
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See :class:`TinyBertModel`.
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labels (Tensor of shape `(batch_size,)`, optional):
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Labels for computing the sequence classification/regression loss.
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Indices should be in `[0, ..., num_labels - 1]`. If `num_labels == 1`
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a regression loss is computed (Mean-Square loss), If `num_labels > 1`
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a classification loss is computed (Cross-Entropy).
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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 `False`.
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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 `False`.
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return_dict (bool, optional):
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Whether to return a :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` object. If
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`False`, the output will be a tuple of tensors. Defaults to `False`.
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Returns:
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An instance of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput` if `return_dict=True`.
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Otherwise it returns a tuple of tensors corresponding to ordered and
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not None (depending on the input arguments) fields of :class:`~paddlenlp.transformers.model_outputs.SequenceClassifierOutput`.
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Example:
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.. code-block::
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import paddle
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from paddlenlp.transformers.tinybert.modeling import TinyBertForSequenceClassification
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from paddlenlp.transformers.tinybert.tokenizer import TinyBertTokenizer
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tokenizer = TinyBertTokenizer.from_pretrained('tinybert-4l-312d')
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model = TinyBertForSequenceClassification.from_pretrained('tinybert-4l-312d')
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inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP! ")
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inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
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outputs = model(**inputs)
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logits = outputs[0]
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"""
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|
outputs = self.tinybert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
logits = self.classifier(self.activation(outputs[1]))
|
|
|
|
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 tuple_output(output, loss)
|
|
|
|
return SequenceClassifierOutput(
|
|
loss=loss,
|
|
logits=logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class TinyBertForQuestionAnswering(TinyBertPretrainedModel):
|
|
"""
|
|
TinyBert 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:
|
|
Args:
|
|
config (:class:`TinyBertConfig`):
|
|
An instance of TinyBertConfig used to construct TinyBertForQuestionAnswering.
|
|
"""
|
|
|
|
def __init__(self, config: TinyBertConfig):
|
|
super(TinyBertForQuestionAnswering, self).__init__(config)
|
|
self.tinybert = TinyBertModel(config)
|
|
self.classifier = nn.Linear(config.hidden_size, 2)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: 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"""
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`TinyBertModel`.
|
|
token_type_ids (Tensor, optional):
|
|
See :class:`TinyBertModel`.
|
|
position_ids (Tensor, optional):
|
|
See :class:`TinyBertModel`.
|
|
attention_mask (Tensor, optional):
|
|
See :class:`TinyBertModel`.
|
|
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: Returns tuple (`start_logits`, `end_logits`).
|
|
|
|
With the fields:
|
|
|
|
- `start_logits` (Tensor):
|
|
A tensor of the input token classification logits, indicates the start position of the labelled span.
|
|
Its data type should be float32 and its shape is [batch_size, sequence_length].
|
|
|
|
- `end_logits` (Tensor):
|
|
A tensor of the input token classification logits, indicates the end position of the labelled span.
|
|
Its data type should be float32 and its shape is [batch_size, sequence_length].
|
|
|
|
Example:
|
|
.. code-block::
|
|
|
|
import paddle
|
|
from paddlenlp.transformers import TinyBertForQuestionAnswering, TinyBertTokenizer
|
|
|
|
tokenizer = TinyBertTokenizer.from_pretrained('tinybert-6l-768d-zh')
|
|
model = TinyBertForQuestionAnswering.from_pretrained('tinybert-6l-768d-zh')
|
|
|
|
inputs = tokenizer("Welcome to use PaddlePaddle and PaddleNLP!")
|
|
inputs = {k:paddle.to_tensor([v]) for (k, v) in inputs.items()}
|
|
logits = model(**inputs)
|
|
"""
|
|
|
|
outputs = self.tinybert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
attention_mask=attention_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
logits = self.classifier(outputs[0])
|
|
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 tuple_output(output, total_loss)
|
|
|
|
return QuestionAnsweringModelOutput(
|
|
loss=total_loss,
|
|
start_logits=start_logits,
|
|
end_logits=end_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
|
|
class TinyBertForMultipleChoice(TinyBertPretrainedModel):
|
|
"""
|
|
TinyBERT Model with a linear layer on top of the hidden-states output layer,
|
|
designed for multiple choice tasks like RocStories/SWAG tasks.
|
|
|
|
Args:
|
|
Args:
|
|
config (:class:`TinyBertConfig`):
|
|
An instance of TinyBertConfig used to construct TinyBertForMultipleChoice.
|
|
"""
|
|
|
|
def __init__(self, config: TinyBertConfig):
|
|
super(TinyBertForMultipleChoice, self).__init__(config)
|
|
self.num_choices = config.num_choices
|
|
self.tinybert = TinyBertModel(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, 1)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[Tensor] = None,
|
|
token_type_ids: Optional[Tensor] = None,
|
|
position_ids: Optional[Tensor] = None,
|
|
attention_mask: Optional[Tensor] = None,
|
|
inputs_embeds: 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 TinyBertForMultipleChoice forward method, overrides the __call__() special method.
|
|
|
|
Args:
|
|
input_ids (Tensor):
|
|
See :class:`TinyBertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
token_type_ids(Tensor, optional):
|
|
See :class:`TinyBertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
position_ids(Tensor, optional):
|
|
See :class:`TinyBertModel` and shape as [batch_size, num_choice, sequence_length].
|
|
attention_mask (list, optional):
|
|
See :class:`TinyBertModel` 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 `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: Returns tensor `reshaped_logits`, a tensor of the multiple choice classification logits.
|
|
Shape as `[batch_size, num_choice]` and dtype as `float32`.
|
|
|
|
"""
|
|
# input_ids: [bs, num_choice, seq_l]
|
|
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.")
|
|
|
|
if input_ids is None and inputs_embeds is None:
|
|
raise ValueError("input_ids and inputs_embeds should not be None at the same time.")
|
|
if inputs_embeds is not None:
|
|
inputs_embeds = inputs_embeds.reshape([-1, inputs_embeds.shape[-2], inputs_embeds.shape[-1]])
|
|
else:
|
|
input_ids = input_ids.reshape(shape=(-1, input_ids.shape[-1])) # flat_input_ids: [bs*num_choice,seq_l]
|
|
|
|
if token_type_ids is not None:
|
|
token_type_ids = token_type_ids.reshape(shape=(-1, token_type_ids.shape[-1]))
|
|
|
|
if position_ids is not None:
|
|
position_ids = position_ids.reshape(shape=(-1, position_ids.shape[-1]))
|
|
|
|
if attention_mask is not None:
|
|
attention_mask = attention_mask.reshape(shape=(-1, attention_mask.shape[-1]))
|
|
|
|
outputs = self.tinybert(
|
|
input_ids,
|
|
token_type_ids=token_type_ids,
|
|
position_ids=position_ids,
|
|
inputs_embeds=inputs_embeds,
|
|
attention_mask=attention_mask,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
pooled_output = self.dropout(outputs[1])
|
|
|
|
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 tuple_output(output, loss)
|
|
|
|
return MultipleChoiceModelOutput(
|
|
loss=loss,
|
|
logits=reshaped_logits,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|