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from dataclasses import dataclass |
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from typing import Optional, Tuple |
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import torch |
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from torch import nn |
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from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel |
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from transformers.utils import ModelOutput |
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@dataclass |
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class TransformationModelOutput(ModelOutput): |
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""" |
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Base class for text model's outputs that also contains a pooling of the last hidden states. |
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Args: |
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text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): |
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The text embeddings obtained by applying the projection layer to the pooler_output. |
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last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): |
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Sequence of hidden-states at the output of the last layer of the model. |
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hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): |
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Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + |
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one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. |
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Hidden-states of the model at the output of each layer plus the optional initial embedding outputs. |
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attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): |
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Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, |
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sequence_length)`. |
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Attentions weights after the attention softmax, used to compute the weighted average in the self-attention |
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heads. |
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""" |
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projection_state: Optional[torch.FloatTensor] = None |
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last_hidden_state: torch.FloatTensor = None |
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hidden_states: Optional[Tuple[torch.FloatTensor]] = None |
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attentions: Optional[Tuple[torch.FloatTensor]] = None |
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class RobertaSeriesConfig(XLMRobertaConfig): |
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def __init__( |
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self, |
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pad_token_id=1, |
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bos_token_id=0, |
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eos_token_id=2, |
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project_dim=512, |
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pooler_fn="cls", |
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learn_encoder=False, |
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use_attention_mask=True, |
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**kwargs, |
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): |
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super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) |
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self.project_dim = project_dim |
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self.pooler_fn = pooler_fn |
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self.learn_encoder = learn_encoder |
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self.use_attention_mask = use_attention_mask |
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class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel): |
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_keys_to_ignore_on_load_unexpected = [r"pooler"] |
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_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] |
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base_model_prefix = "roberta" |
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config_class = RobertaSeriesConfig |
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def __init__(self, config): |
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super().__init__(config) |
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self.roberta = XLMRobertaModel(config) |
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self.transformation = nn.Linear(config.hidden_size, config.project_dim) |
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self.post_init() |
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def forward( |
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self, |
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input_ids: Optional[torch.Tensor] = None, |
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attention_mask: Optional[torch.Tensor] = None, |
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token_type_ids: Optional[torch.Tensor] = None, |
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position_ids: Optional[torch.Tensor] = None, |
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head_mask: Optional[torch.Tensor] = None, |
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inputs_embeds: Optional[torch.Tensor] = None, |
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encoder_hidden_states: Optional[torch.Tensor] = None, |
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encoder_attention_mask: Optional[torch.Tensor] = None, |
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output_attentions: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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): |
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r""" """ |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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outputs = self.base_model( |
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input_ids=input_ids, |
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attention_mask=attention_mask, |
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token_type_ids=token_type_ids, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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encoder_hidden_states=encoder_hidden_states, |
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encoder_attention_mask=encoder_attention_mask, |
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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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projection_state = self.transformation(outputs.last_hidden_state) |
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return TransformationModelOutput( |
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projection_state=projection_state, |
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last_hidden_state=outputs.last_hidden_state, |
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hidden_states=outputs.hidden_states, |
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attentions=outputs.attentions, |
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) |
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