modeling_pure_bert.py

import torch
import torch.nn as nn
import numpy as np
from torch.autograd import Function
from transformers import PreTrainedModel
from transformers.models.bert.modeling_bert import (
    BertEmbeddings, BertEncoder, BertPooler
)
from typing import Union, Tuple, Optional, List
from transformers.modeling_outputs import (
    SequenceClassifierOutput, 
    MultipleChoiceModelOutput, 
    QuestionAnsweringModelOutput, 
    BaseModelOutputWithPoolingAndCrossAttentions
)
from transformers.modeling_attn_mask_utils import (
    _prepare_4d_attention_mask_for_sdpa,
    _prepare_4d_causal_attention_mask_for_sdpa,
)
from transformers.utils import ModelOutput

from .configuration_pure_bert import PureBertConfig


class CovarianceFunction(Function):

    @staticmethod
    def forward(ctx, inputs):
        x = inputs
        b, c, h, w = x.data.shape
        m = h * w
        x = x.view(b, c, m)
        I_hat = (-1.0 / m / m) * torch.ones(m, m, device=x.device) + (
            1.0 / m
        ) * torch.eye(m, m, device=x.device)
        I_hat = I_hat.view(1, m, m).repeat(b, 1, 1).type(x.dtype)
        y = x @ I_hat @ x.transpose(-1, -2)
        ctx.save_for_backward(inputs, I_hat)
        return y

    @staticmethod
    def backward(ctx, grad_output):
        inputs, I_hat = ctx.saved_tensors
        x = inputs
        b, c, h, w = x.data.shape
        m = h * w
        x = x.view(b, c, m)
        grad_input = grad_output + grad_output.transpose(1, 2)
        grad_input = grad_input @ x @ I_hat
        grad_input = grad_input.reshape(b, c, h, w)
        return grad_input


class Covariance(nn.Module):

    def __init__(self):
        super(Covariance, self).__init__()
        
    def _covariance(self, x):
        return CovarianceFunction.apply(x)

    def forward(self, x):
        # x should be [batch_size, seq_len, embed_dim]
        if x.dim() == 2:
            x = x.transpose(-1, -2)
            C = self._covariance(x[None, :, :, None])
        C = C.squeeze(dim=0)
        return C


class PFSA(torch.nn.Module):
    """
    https://openreview.net/pdf?id=isodM5jTA7h
    """
    def __init__(self, input_dim, alpha=1):
        super(PFSA, self).__init__()
        self.input_dim = input_dim
        self.alpha = alpha

    def forward_one_sample(self, x):
        x = x.transpose(1, 2)[..., None]
        k = torch.mean(x, dim=[-1, -2], keepdim=True)
        kd = torch.sqrt((k - k.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, 1, 1]
        qd = torch.sqrt((x - x.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, T, 1]
        C_qk = (((x - x.mean(dim=1, keepdim=True)) * (k - k.mean(dim=1, keepdim=True))).sum(dim=1, keepdim=True)) / (qd * kd)
        A = (1 - torch.sigmoid(C_qk)) ** self.alpha
        out = x * A
        out = out.squeeze(dim=-1).transpose(1, 2)
        return out

    def forward(self, input_values, attention_mask=None):
        """
        x: [B, T, F]
        """
        out = []
        b, t, f = input_values.shape
        for x, mask in zip(input_values, attention_mask):
            x = x.view(1, t, f)
            # x_in = x[:, :sum(mask), :]
            x_in = x[:, :int(mask.sum().item()), :]
            x_out = self.forward_one_sample(x_in)
            x_expanded = torch.zeros_like(x, device=x.device)
            x_expanded[:, :x_out.shape[-2], :x_out.shape[-1]] = x_out
            out.append(x_expanded)
        out = torch.vstack(out)
        out = out.view(b, t, f)
        return out


class PURE(torch.nn.Module):

    def __init__(
        self, 
        in_dim, 
        svd_rank=16, 
        num_pc_to_remove=1, 
        center=False, 
        num_iters=2, 
        alpha=1, 
        disable_pcr=False, 
        disable_pfsa=False, 
        disable_covariance=True, 
        *args, **kwargs
    ):
        super().__init__()
        self.in_dim = in_dim
        self.svd_rank = svd_rank
        self.num_pc_to_remove = num_pc_to_remove
        self.center = center
        self.num_iters = num_iters
        self.do_pcr = not disable_pcr
        self.do_pfsa = not disable_pfsa
        self.do_covariance = not disable_covariance
        self.attention = PFSA(in_dim, alpha=alpha)
    
    def _compute_pc(self, X, attention_mask):
        """
        x: (B, T, F)
        """
        pcs = []
        bs, seqlen, dim = X.shape
        for x, mask in zip(X, attention_mask):
            rank = int(mask.sum().item())
            x = x[:rank, :]
            if self.do_covariance:
                x = Covariance()(x)
                q = self.svd_rank
            else:
                q = min(self.svd_rank, rank)
            _, _, V = torch.pca_lowrank(x, q=q, center=self.center, niter=self.num_iters)
            # _, _, Vh = torch.linalg.svd(x_, full_matrices=False)
            # V = Vh.mH
            pc = V.transpose(0, 1)[:self.num_pc_to_remove, :] # pc: [K, F]
            pcs.append(pc)
        # pcs = torch.vstack(pcs)
        # pcs = pcs.view(bs, self.num_pc_to_remove, dim)
        return pcs

    def _remove_pc(self, X, pcs):
        """
        [B, T, F], [B, ..., F]
        """
        b, t, f = X.shape
        out = []
        for i, (x, pc) in enumerate(zip(X, pcs)):
            # v = []
            # for j, t in enumerate(x):
            #     t_ = t
            #     for c_ in c:
            #         t_ = t_.view(f, 1) - c_.view(f, 1) @ c_.view(1, f) @ t.view(f, 1)
            #     v.append(t_.transpose(-1, -2))
            # v = torch.vstack(v)
            v = x - x @ pc.transpose(0, 1) @ pc
            out.append(v[None, ...])
        out = torch.vstack(out)
        return out

    def forward(self, input_values, attention_mask=None, *args, **kwargs):
        """
        PCR -> Attention
        x: (B, T, F)
        """
        x = input_values
        if self.do_pcr:
            pc = self._compute_pc(x, attention_mask) # pc: [B, K, F]
            xx = self._remove_pc(x, pc)
            # xx = xt - xt @ pc.transpose(1, 2) @ pc # [B, T, F] * [B, F, K] * [B, K, F] = [B, T, F]
        else:
            xx = x
        if self.do_pfsa:
            xx = self.attention(xx, attention_mask)
        return xx


class StatisticsPooling(torch.nn.Module):

    def __init__(self, return_mean=True, return_std=True):
        super().__init__()

        # Small value for GaussNoise
        self.eps = 1e-5
        self.return_mean = return_mean
        self.return_std = return_std
        if not (self.return_mean or self.return_std):
            raise ValueError(
                "both of statistics are equal to False \n"
                "consider enabling mean and/or std statistic pooling"
            )

    def forward(self, input_values, attention_mask=None):
        """Calculates mean and std for a batch (input tensor).

        Arguments
        ---------
        x : torch.Tensor
            It represents a tensor for a mini-batch.
        """
        x = input_values
        if attention_mask is None:
            if self.return_mean:
                mean = x.mean(dim=1)
            if self.return_std:
                std = x.std(dim=1)
        else:
            mean = []
            std = []
            for snt_id in range(x.shape[0]):
                # Avoiding padded time steps
                lengths = torch.sum(attention_mask, dim=1)
                relative_lengths = lengths / torch.max(lengths)
                actual_size = torch.round(relative_lengths[snt_id] * x.shape[1]).int()
                # actual_size = int(torch.round(lengths[snt_id] * x.shape[1]))

                # computing statistics
                if self.return_mean:
                    mean.append(
                        torch.mean(x[snt_id, 0:actual_size, ...], dim=0)
                    )
                if self.return_std:
                    std.append(torch.std(x[snt_id, 0:actual_size, ...], dim=0))
            if self.return_mean:
                mean = torch.stack(mean)
            if self.return_std:
                std = torch.stack(std)

        if self.return_mean:
            gnoise = self._get_gauss_noise(mean.size(), device=mean.device)
            gnoise = gnoise
            mean += gnoise
        if self.return_std:
            std = std + self.eps

        # Append mean and std of the batch
        if self.return_mean and self.return_std:
            pooled_stats = torch.cat((mean, std), dim=1)
            pooled_stats = pooled_stats.unsqueeze(1)
        elif self.return_mean:
            pooled_stats = mean.unsqueeze(1)
        elif self.return_std:
            pooled_stats = std.unsqueeze(1)

        return pooled_stats

    def _get_gauss_noise(self, shape_of_tensor, device="cpu"):
        """Returns a tensor of epsilon Gaussian noise.

        Arguments
        ---------
        shape_of_tensor : tensor
            It represents the size of tensor for generating Gaussian noise.
        """
        gnoise = torch.randn(shape_of_tensor, device=device)
        gnoise -= torch.min(gnoise)
        gnoise /= torch.max(gnoise)
        gnoise = self.eps * ((1 - 9) * gnoise + 9)

        return gnoise


class PureBertPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = PureBertConfig
    base_model_prefix = "bert"
    supports_gradient_checkpointing = True
    _supports_sdpa = True

    def _init_weights(self, module):
        """Initialize the weights"""
        if isinstance(module, nn.Linear):
            # Slightly different from the TF version which uses truncated_normal for initialization
            # cf https://github.com/pytorch/pytorch/pull/5617
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)


class BertModel(PureBertPreTrainedModel):
    """

    The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of
    cross-attention is added between the self-attention layers, following the architecture described in [Attention is
    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit,
    Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set
    to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and
    `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass.
    """

    _no_split_modules = ["BertEmbeddings", "BertLayer"]

    def __init__(self, config, add_pooling_layer=True):
        super().__init__(config)
        self.config = config

        self.embeddings = BertEmbeddings(config)
        self.encoder = BertEncoder(config)

        self.pooler = BertPooler(config) if add_pooling_layer else None

        self.attn_implementation = config._attn_implementation
        self.position_embedding_type = config.position_embedding_type

        # Initialize weights and apply final processing
        self.post_init()

    def get_input_embeddings(self):
        return self.embeddings.word_embeddings

    def set_input_embeddings(self, value):
        self.embeddings.word_embeddings = value

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        for layer, heads in heads_to_prune.items():
            self.encoder.layer[layer].attention.prune_heads(heads)

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        encoder_attention_mask: Optional[torch.Tensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]:
        r"""
        encoder_hidden_states  (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
            the model is configured as a decoder.
        encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)` or `(batch_size, sequence_length, target_length)`, *optional*):
            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
            the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.

            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        """
        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 self.config.is_decoder:
            use_cache = use_cache if use_cache is not None else self.config.use_cache
        else:
            use_cache = False

        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:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None:
            input_shape = inputs_embeds.size()[:-1]
        else:
            raise ValueError("You have to specify either input_ids or inputs_embeds")

        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device

        # past_key_values_length
        past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0

        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else:
                token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)

        embedding_output = self.embeddings(
            input_ids=input_ids,
            position_ids=position_ids,
            token_type_ids=token_type_ids,
            inputs_embeds=inputs_embeds,
            past_key_values_length=past_key_values_length,
        )

        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device)

        use_sdpa_attention_masks = (
            self.attn_implementation == "sdpa"
            and self.position_embedding_type == "absolute"
            and head_mask is None
            and not output_attentions
        )

        # Expand the attention mask
        if use_sdpa_attention_masks and attention_mask.dim() == 2:
            # Expand the attention mask for SDPA.
            # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
            if self.config.is_decoder:
                extended_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                    attention_mask,
                    input_shape,
                    embedding_output,
                    past_key_values_length,
                )
            else:
                extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
                    attention_mask, embedding_output.dtype, tgt_len=seq_length
                )
        else:
            # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
            # ourselves in which case we just need to make it broadcastable to all heads.
            extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)

        # If a 2D or 3D attention mask is provided for the cross-attention
        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
        if self.config.is_decoder and encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)

            if use_sdpa_attention_masks and encoder_attention_mask.dim() == 2:
                # Expand the attention mask for SDPA.
                # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len]
                encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa(
                    encoder_attention_mask, embedding_output.dtype, tgt_len=seq_length
                )
            else:
                encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else:
            encoder_extended_attention_mask = None

        # 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]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)

        encoder_outputs = self.encoder(
            embedding_output,
            attention_mask=extended_attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_extended_attention_mask,
            past_key_values=past_key_values,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        sequence_output = encoder_outputs[0]
        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None

        if not return_dict:
            return (sequence_output, pooled_output) + encoder_outputs[1:]

        return BaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            past_key_values=encoder_outputs.past_key_values,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
            cross_attentions=encoder_outputs.cross_attentions,
        )


class PureBertForSequenceClassification(PureBertPreTrainedModel):

    def __init__(
        self, 
        config, 
        label_smoothing=0.0, 
    ):
        super().__init__(config)
        self.label_smoothing = label_smoothing
        self.num_labels = config.num_labels
        self.config = config

        self.bert = BertModel(config, add_pooling_layer=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.pure = PURE(
            in_dim=config.hidden_size, 
            svd_rank=config.svd_rank, 
            num_pc_to_remove=config.num_pc_to_remove, 
            center=config.center, 
            num_iters=config.num_iters, 
            alpha=config.alpha, 
            disable_pcr=config.disable_pcr, 
            disable_pfsa=config.disable_pfsa, 
            disable_covariance=config.disable_covariance
        )
        self.mean = StatisticsPooling(return_mean=True, return_std=False)
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def forward_pure_embeddings(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        token_embeddings = outputs.last_hidden_state
        token_embeddings = self.pure(token_embeddings, attention_mask)

        return ModelOutput(
            last_hidden_state=token_embeddings,
        )

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        token_embeddings = outputs.last_hidden_state
        token_embeddings = self.pure(token_embeddings, attention_mask)
        pooled_output = self.mean(token_embeddings).squeeze(1)
        pooled_output = self.dropout(pooled_output)
        logits = self.classifier(pooled_output)

        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1:
                    self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                    self.config.problem_type = "single_label_classification"
                else:
                    self.config.problem_type = "multi_label_classification"

            if self.config.problem_type == "regression":
                loss_fct = nn.MSELoss()
                if self.num_labels == 1:
                    loss = loss_fct(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = nn.CrossEntropyLoss(label_smoothing=self.label_smoothing)
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = nn.BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return SequenceClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class PureBertForMultipleChoice(PureBertPreTrainedModel):

    def __init__(
        self, 
        config, 
        label_smoothing=0.0, 
    ):
        super().__init__(config)
        self.label_smoothing = label_smoothing

        self.bert = BertModel(config)
        self.pure = PURE(
            in_dim=config.hidden_size, 
            svd_rank=config.svd_rank, 
            num_pc_to_remove=config.num_pc_to_remove, 
            center=config.center, 
            num_iters=config.num_iters, 
            alpha=config.alpha, 
            disable_pcr=config.disable_pcr, 
            disable_pfsa=config.disable_pfsa, 
            disable_covariance=config.disable_covariance
        )
        self.mean = StatisticsPooling(return_mean=True, return_std=False)
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.classifier = nn.Linear(config.hidden_size, 1)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        labels: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the multiple choice classification loss. Indices should be in `[0, ...,
            num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See
            `input_ids` above)
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]

        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (
            inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1))
            if inputs_embeds is not None
            else None
        )

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        token_embeddings = outputs.last_hidden_state
        token_embeddings = self.pure(token_embeddings, attention_mask)
        pooled_output = self.mean(token_embeddings).squeeze(1)
        pooled_output = self.dropout(pooled_output)

        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)

        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss(label_smoothing=self.label_smoothing)
            loss = loss_fct(reshaped_logits, labels)

        if not return_dict:
            output = (reshaped_logits,) + outputs[2:]
            return ((loss,) + output) if loss is not None else output

        return MultipleChoiceModelOutput(
            loss=loss,
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


class PureBertForQuestionAnswering(PureBertPreTrainedModel):

    def __init__(
        self, 
        config, 
        label_smoothing=0.0, 
    ):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.label_smoothing = label_smoothing

        self.bert = BertModel(config, add_pooling_layer=False)
        self.pure = PURE(
            in_dim=config.hidden_size, 
            svd_rank=config.svd_rank, 
            num_pc_to_remove=config.num_pc_to_remove, 
            center=config.center, 
            num_iters=config.num_iters, 
            alpha=config.alpha, 
            disable_pcr=config.disable_pcr, 
            disable_pfsa=config.disable_pfsa, 
            disable_covariance=config.disable_covariance
        )
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)

        # Initialize weights and apply final processing
        self.post_init()

    def forward(
        self,
        input_ids: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
        token_type_ids: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.Tensor] = None,
        head_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
        start_positions: Optional[torch.Tensor] = None,
        end_positions: Optional[torch.Tensor] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        r"""
        start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the start of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for position (index) of the end of the labelled span for computing the token classification loss.
            Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
            are not taken into account for computing the loss.
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.bert(
            input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            inputs_embeds=inputs_embeds,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        token_embeddings = outputs.last_hidden_state
        sequence_output = self.pure(token_embeddings, attention_mask)

        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1).contiguous()
        end_logits = end_logits.squeeze(-1).contiguous()

        total_loss = None
        if start_positions is not None and end_positions is not None:
            # If we are on multi-GPU, split add a dimension
            if len(start_positions.size()) > 1:
                start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1:
                end_positions = end_positions.squeeze(-1)
            # sometimes the start/end positions are outside our model inputs, we ignore these terms
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)

            loss_fct = nn.CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2

        if not return_dict:
            output = (start_logits, end_logits) + outputs[2:]
            return ((total_loss,) + output) if total_loss is not None else output

        return QuestionAnsweringModelOutput(
            loss=total_loss,
            start_logits=start_logits,
            end_logits=end_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )