Source code for texar.torch.modules.encoders.roberta_encoder

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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 \

__all__ = [

[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