src/transformers/models/xlnet/modeling_xlnet.py/XLNetModel/forward
class XLNetModel:    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        mems: Optional[torch.Tensor] = None,
        perm_mask: Optional[torch.Tensor] = None,
        target_mapping: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        input_mask: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        use_mems: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,  # delete after depreciation warning is removed
    ) -> Union[Tuple, XLNetModelOutput]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if "use_cache" in kwargs:
            warnings.warn(
                "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems`"
                " instead.",
                FutureWarning,
            )
            use_mems = kwargs["use_cache"]

        if self.training:
            use_mems = use_mems if use_mems is not None else self.config.use_mems_train
        else:
            use_mems = use_mems if use_mems is not None else self.config.use_mems_eval

        # the original code for XLNet uses shapes [len, bsz] with the batch dimension at the end
        # but we want a unified interface in the library with the batch size on the first dimension
        # so we move here the first dimension (batch) to the end
        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")
        elif input_ids is not None:
            input_ids = input_ids.transpose(0, 1).contiguous()
            qlen, bsz = input_ids.shape[0], input_ids.shape[1]
        elif inputs_embeds is not None:
            inputs_embeds = inputs_embeds.transpose(0, 1).contiguous()
            qlen, bsz = inputs_embeds.shape[0], inputs_embeds.shape[1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        token_type_ids = token_type_ids.transpose(0, 1).contiguous() if token_type_ids is not None else None
        input_mask = input_mask.transpose(0, 1).contiguous() if input_mask is not None else None
        attention_mask = attention_mask.transpose(0, 1).contiguous() if attention_mask is not None else None
        perm_mask = perm_mask.permute(1, 2, 0).contiguous() if perm_mask is not None else None
        target_mapping = target_mapping.permute(1, 2, 0).contiguous() if target_mapping is not None else None

        mlen = mems[0].shape[0] if mems is not None and mems[0] is not None else 0
        klen = mlen + qlen

        dtype_float = self.dtype
        device = self.device

        # Attention mask
        # causal attention mask
        if self.attn_type == "uni":
            attn_mask = self.create_mask(qlen, mlen)
            attn_mask = attn_mask[:, :, None, None]
        elif self.attn_type == "bi":
            attn_mask = None
        else:
            raise ValueError(f"Unsupported attention type: {self.attn_type}")

        # data mask: input mask & perm mask
        assert input_mask is None or attention_mask is None, "You can only use one of input_mask (uses 1 for padding) "
        "or attention_mask (uses 0 for padding, added for compatibility with BERT). Please choose one."
        if input_mask is None and attention_mask is not None:
            input_mask = 1.0 - attention_mask
        if input_mask is not None and perm_mask is not None:
            data_mask = input_mask[None] + perm_mask
        elif input_mask is not None and perm_mask is None:
            data_mask = input_mask[None]
        elif input_mask is None and perm_mask is not None:
            data_mask = perm_mask
        else:
            data_mask = None

        if data_mask is not None:
            # all mems can be attended to
            if mlen > 0:
                mems_mask = torch.zeros([data_mask.shape[0], mlen, bsz]).to(data_mask)
                data_mask = torch.cat([mems_mask, data_mask], dim=1)
            if attn_mask is None:
                attn_mask = data_mask[:, :, :, None]
            else:
                attn_mask += data_mask[:, :, :, None]

        if attn_mask is not None:
            attn_mask = (attn_mask > 0).to(dtype_float)

        if attn_mask is not None:
            non_tgt_mask = -torch.eye(qlen).to(attn_mask)
            if mlen > 0:
                non_tgt_mask = torch.cat([torch.zeros([qlen, mlen]).to(attn_mask), non_tgt_mask], dim=-1)
            non_tgt_mask = ((attn_mask + non_tgt_mask[:, :, None, None]) > 0).to(attn_mask)
        else:
            non_tgt_mask = None

        # Word embeddings and prepare h & g hidden states
        if inputs_embeds is not None:
            word_emb_k = inputs_embeds
        else:
            word_emb_k = self.word_embedding(input_ids)
        output_h = self.dropout(word_emb_k)
        if target_mapping is not None:
            word_emb_q = self.mask_emb.expand(target_mapping.shape[0], bsz, -1)
            # else:  # We removed the inp_q input which was same as target mapping
            #     inp_q_ext = inp_q[:, :, None]
            #     word_emb_q = inp_q_ext * self.mask_emb + (1 - inp_q_ext) * word_emb_k
            output_g = self.dropout(word_emb_q)
        else:
            output_g = None

        # Segment embedding
        if token_type_ids is not None:
            # Convert `token_type_ids` to one-hot `seg_mat`
            if mlen > 0:
                mem_pad = torch.zeros([mlen, bsz], dtype=torch.long, device=device)
                cat_ids = torch.cat([mem_pad, token_type_ids], dim=0)
            else:
                cat_ids = token_type_ids

            # `1` indicates not in the same segment [qlen x klen x bsz]
            seg_mat = (token_type_ids[:, None] != cat_ids[None, :]).long()
            seg_mat = nn.functional.one_hot(seg_mat, num_classes=2).to(dtype_float)
        else:
            seg_mat = None

        # Positional encoding
        pos_emb = self.relative_positional_encoding(qlen, klen, bsz=bsz)
        pos_emb = pos_emb.to(output_h.device)
        pos_emb = self.dropout(pos_emb)

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] (a head_mask for each layer)
        # and head_mask is converted to shape [num_hidden_layers x qlen x klen x bsz x n_head]
        if head_mask is not None:
            if head_mask.dim() == 1:
                head_mask = head_mask.unsqueeze(0).unsqueeze(0).unsqueeze(0).unsqueeze(0)
                head_mask = head_mask.expand(self.n_layer, -1, -1, -1, -1)
            elif head_mask.dim() == 2:
                head_mask = head_mask.unsqueeze(1).unsqueeze(1).unsqueeze(1)
            head_mask = head_mask.to(
                dtype=next(self.parameters()).dtype
            )  # switch to float if need + fp16 compatibility
        else:
            head_mask = [None] * self.n_layer

        new_mems = ()
        if mems is None:
            mems = [None] * len(self.layer)

        attentions = [] if output_attentions else None
        hidden_states = [] if output_hidden_states else None
        for i, layer_module in enumerate(self.layer):
            if use_mems:
                # cache new mems
                new_mems = new_mems + (self.cache_mem(output_h, mems[i]),)
            if output_hidden_states:
                hidden_states.append((output_h, output_g) if output_g is not None else output_h)

            outputs = layer_module(
                output_h,
                output_g,
                attn_mask_h=non_tgt_mask,
                attn_mask_g=attn_mask,
                r=pos_emb,
                seg_mat=seg_mat,
                mems=mems[i],
                target_mapping=target_mapping,
                head_mask=head_mask[i],
                output_attentions=output_attentions,
            )
            output_h, output_g = outputs[:2]
            if output_attentions:
                attentions.append(outputs[2])

        # Add last hidden state
        if output_hidden_states:
            hidden_states.append((output_h, output_g) if output_g is not None else output_h)

        output = self.dropout(output_g if output_g is not None else output_h)

        # Prepare outputs, we transpose back here to shape [bsz, len, hidden_dim] (cf. beginning of forward() method)
        output = output.permute(1, 0, 2).contiguous()

        if not use_mems:
            new_mems = None

        if output_hidden_states:
            if output_g is not None:
                hidden_states = tuple(h.permute(1, 0, 2).contiguous() for hs in hidden_states for h in hs)
            else:
                hidden_states = tuple(hs.permute(1, 0, 2).contiguous() for hs in hidden_states)

        if output_attentions:
            if target_mapping is not None:
                # when target_mapping is provided, there are 2-tuple of attentions
                attentions = tuple(
                    tuple(att_stream.permute(2, 3, 0, 1).contiguous() for att_stream in t) for t in attentions
                )
            else:
                attentions = tuple(t.permute(2, 3, 0, 1).contiguous() for t in attentions)

        if not return_dict:
            return tuple(v for v in [output, new_mems, hidden_states, attentions] if v is not None)

        return XLNetModelOutput(
            last_hidden_state=output, mems=new_mems, hidden_states=hidden_states, attentions=attentions
        )