r""" Args: input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using :class:`~modelscope.models.nlp.ponet.PoNetTokenizer`. See
(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
segment_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
)
| 696 | self.encoder.layer[layer].attention.prune_heads(heads) |
| 697 | |
| 698 | def forward( |
| 699 | self, |
| 700 | input_ids=None, |
| 701 | attention_mask=None, |
| 702 | token_type_ids=None, |
| 703 | segment_ids=None, |
| 704 | position_ids=None, |
| 705 | head_mask=None, |
| 706 | inputs_embeds=None, |
| 707 | encoder_hidden_states=None, |
| 708 | encoder_attention_mask=None, |
| 709 | past_key_values=None, |
| 710 | use_cache=None, |
| 711 | output_attentions=None, |
| 712 | output_hidden_states=None, |
| 713 | return_dict=None, |
| 714 | ): |
| 715 | r""" |
| 716 | Args: |
| 717 | input_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`): |
| 718 | Indices of input sequence tokens in the vocabulary. |
| 719 | |
| 720 | Indices can be obtained using :class:`~modelscope.models.nlp.ponet.PoNetTokenizer`. See |
| 721 | :meth:`transformers.PreTrainedTokenizer.encode` and :meth:`transformers.PreTrainedTokenizer.__call__` |
| 722 | for details. |
| 723 | |
| 724 | attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
| 725 | Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``: |
| 726 | |
| 727 | - 1 for tokens that are **not masked**, |
| 728 | - 0 for tokens that are **masked**. |
| 729 | |
| 730 | token_type_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
| 731 | Segment token indices to indicate first and second portions of the inputs. Indices are selected in |
| 732 | ``[0,1]``: |
| 733 | |
| 734 | - 0 corresponds to a `sentence A` token, |
| 735 | - 1 corresponds to a `sentence B` token. |
| 736 | |
| 737 | position_ids (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`): |
| 738 | Indices of positions of each input sequence tokens in the position embeddings. Selected in the range |
| 739 | ``[0,config.max_position_embeddings - 1]``. |
| 740 | |
| 741 | head_mask (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, |
| 742 | `optional`): |
| 743 | Mask to nullify selected heads of the self-attention modules. Mask values selected in ``[0, 1]``: |
| 744 | |
| 745 | - 1 indicates the head is **not masked**, |
| 746 | - 0 indicates the head is **masked**. |
| 747 | |
| 748 | inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, |
| 749 | `optional`): |
| 750 | Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded |
| 751 | representation. This is useful if you want more control over how to convert :obj:`input_ids` |
| 752 | indices into associated vectors than the model's internal embedding lookup matrix. |
| 753 | output_attentions (:obj:`bool`, `optional`): |
| 754 | Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under |
| 755 | returned tensors for more detail. |
nothing calls this directly
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