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"""
RoBERTa encoder.
"""
from typing import Optional, Union
import torch
from texar.torch.modules.encoders.bert_encoder import BERTEncoder
from texar.torch.modules.pretrained.roberta import \
PretrainedRoBERTaMixin
__all__ = [
"RoBERTaEncoder",
]
[docs]class RoBERTaEncoder(PretrainedRoBERTaMixin, BERTEncoder):
r"""RoBERTa Transformer for encoding sequences. Please see
:class:`~texar.torch.modules.PretrainedRoBERTaMixin` for a brief description
of RoBERTa.
This module basically stacks
:class:`~texar.torch.modules.WordEmbedder`,
:class:`~texar.torch.modules.PositionEmbedder`,
:class:`~texar.torch.modules.TransformerEncoder` and a dense
pooler.
Args:
pretrained_model_name (optional): a `str`, the name
of pre-trained model (e.g., ``roberta-base``). Please refer to
:class:`~texar.torch.modules.PretrainedRoBERTaMixin` for
all supported models.
If `None`, the model name in :attr:`hparams` is used.
cache_dir (optional): the path to a folder in which the
pre-trained models will be cached. If `None` (default),
a default directory (``texar_data`` folder under user's home
directory) will be used.
hparams (dict or HParams, optional): Hyperparameters. Missing
hyperparameter will be set to default values. See
:meth:`default_hparams` for the hyperparameter structure
and default values.
"""
[docs] @staticmethod
def default_hparams():
r"""Returns a dictionary of hyperparameters with default values.
* The encoder arch is determined by the constructor argument
:attr:`pretrained_model_name` if it's specified. In this case,
`hparams` are ignored.
* Otherwise, the encoder arch is determined by
`hparams['pretrained_model_name']` if it's specified. All other
configurations in `hparams` are ignored.
* If the above two are `None`, the encoder arch is defined by the
configurations in `hparams` and weights are randomly initialized.
.. code-block:: python
{
"pretrained_model_name": "roberta-base",
"embed": {
"dim": 768,
"name": "word_embeddings"
},
"vocab_size": 50265,
"position_embed": {
"dim": 768,
"name": "position_embeddings"
},
"position_size": 514,
"encoder": {
"dim": 768,
"embedding_dropout": 0.1,
"multihead_attention": {
"dropout_rate": 0.1,
"name": "self",
"num_heads": 12,
"num_units": 768,
"output_dim": 768,
"use_bias": True
},
"name": "encoder",
"num_blocks": 12,
"eps": 1e-12,
"poswise_feedforward": {
"layers": [
{
"kwargs": {
"in_features": 768,
"out_features": 3072,
"bias": True
},
"type": "Linear"
},
{"type": "BertGELU"},
{
"kwargs": {
"in_features": 3072,
"out_features": 768,
"bias": True
},
"type": "Linear"
}
]
},
"residual_dropout": 0.1,
"use_bert_config": True
},
"hidden_size": 768,
"initializer": None,
"name": "roberta_encoder",
}
Here:
The default parameters are values for RoBERTa-Base model.
`"pretrained_model_name"`: str or None
The name of the pre-trained RoBERTa model. If None, the model
will be randomly initialized.
`"embed"`: dict
Hyperparameters for word embedding layer.
`"vocab_size"`: int
The vocabulary size of `inputs` in RoBERTa model.
`"position_embed"`: dict
Hyperparameters for position embedding layer.
`"position_size"`: int
The maximum sequence length that this model might ever be used with.
`"encoder"`: dict
Hyperparameters for the TransformerEncoder.
See :func:`~texar.torch.modules.TransformerEncoder.default_hparams`
for details.
`"hidden_size"`: int
Size of the pooler dense layer.
`"eps"`: float
Epsilon values for layer norm layers.
`"initializer"`: dict, optional
Hyperparameters of the default initializer that initializes
variables created in this module.
See :func:`~texar.torch.core.get_initializer` for details.
`"name"`: str
Name of the module.
"""
return {
'pretrained_model_name': 'roberta-base',
'embed': {
'dim': 768,
'name': 'word_embeddings'
},
'vocab_size': 50265,
'position_embed': {
'dim': 768,
'name': 'position_embeddings'
},
'position_size': 514,
'encoder': {
'dim': 768,
'embedding_dropout': 0.1,
'multihead_attention': {
'dropout_rate': 0.1,
'name': 'self',
'num_heads': 12,
'num_units': 768,
'output_dim': 768,
'use_bias': True
},
'name': 'encoder',
'num_blocks': 12,
'eps': 1e-12,
'poswise_feedforward': {
'layers': [
{
'kwargs': {
'in_features': 768,
'out_features': 3072,
'bias': True
},
'type': 'Linear'
},
{"type": "BertGELU"},
{
'kwargs': {
'in_features': 3072,
'out_features': 768,
'bias': True
},
'type': 'Linear'
}
]
},
'residual_dropout': 0.1,
'use_bert_config': True
},
'hidden_size': 768,
'initializer': None,
'name': 'roberta_encoder',
'@no_typecheck': ['pretrained_model_name']
}
[docs] def forward(self, # type: ignore
inputs: Union[torch.Tensor, torch.LongTensor],
sequence_length: Optional[torch.LongTensor] = None,
segment_ids: Optional[torch.LongTensor] = None):
r"""Encodes the inputs. Differing from the standard BERT, the RoBERTa
model does not use segmentation embedding. As a result, RoBERTa does not
require `segment_ids` as an input.
Args:
inputs: Either a **2D Tensor** of shape `[batch_size, max_time]`,
containing the ids of tokens in input sequences, or
a **3D Tensor** of shape `[batch_size, max_time, vocab_size]`,
containing soft token ids (i.e., weights or probabilities)
used to mix the embedding vectors.
sequence_length (optional): A 1D Tensor of shape `[batch_size]`.
Input tokens beyond respective sequence lengths are masked
out automatically.
Returns:
A pair :attr:`(outputs, pooled_output)`
- :attr:`outputs`: A Tensor of shape
`[batch_size, max_time, dim]` containing the encoded vectors.
- :attr:`pooled_output`: A Tensor of size
`[batch_size, hidden_size]` which is the output of a pooler
pre-trained on top of the hidden state associated to the first
character of the input (`CLS`), see RoBERTa's paper.
"""
if segment_ids is not None:
raise ValueError("segment_ids should be None in RoBERTaEncoder.")
output, pooled_output = super().forward(inputs=inputs,
sequence_length=sequence_length,
segment_ids=None)
return output, pooled_output