modeling_pure_deberta.py

import torch
import torch.nn as nn
from transformers import (
    DebertaV2Model,
    PreTrainedModel,
)
from transformers.models.deberta_v2.modeling_deberta_v2 import StableDropout
from typing import Union, Tuple, Optional
from transformers.modeling_outputs import SequenceClassifierOutput

from .configuration_pure_deberta import PureDebertaConfig


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_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, 
        target_rank=5, 
        npc=1, 
        center=False, 
        num_iters=1, 
        alpha=1, 
        do_pcr=True, 
        do_pfsa=True, 
        *args, **kwargs
    ):
        super().__init__()
        self.in_dim = in_dim
        self.target_rank = target_rank
        self.npc = npc
        self.center = center
        self.num_iters = num_iters
        self.do_pcr = do_pcr
        self.do_pfsa = do_pfsa
        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):
            x_ = x[:sum(mask), :]
            q = min(self.target_rank, sum(mask))
            _, _, V = torch.pca_lowrank(x_, q=q, center=self.center, niter=self.num_iters)
            pc = V.transpose(0, 1)[:self.npc, :] # 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 DebertaV2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = PureDebertaConfig
    base_model_prefix = "deberta"
    _keys_to_ignore_on_load_unexpected = ["position_embeddings"]
    supports_gradient_checkpointing = 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_()


class PureDebertaForSequenceClassification(DebertaV2PreTrainedModel):

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

        self.deberta = DebertaV2Model(config)
        drop_out = getattr(config, "cls_dropout", None)
        drop_out = self.config.hidden_dropout_prob if drop_out is None else drop_out
        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 = StableDropout(drop_out)
        self.classifier = 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,
        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.deberta(
            input_ids,
            token_type_ids=token_type_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            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()
                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,
        )