modeling_pure_qwen2.py

import os
from typing import Union, Tuple, Optional, List

import torch
import torch.nn as nn
from torch.autograd import Function
from transformers import PreTrainedModel
from transformers.models.qwen2.modeling_qwen2 import (
    Qwen2DecoderLayer, Qwen2RMSNorm, Qwen2RotaryEmbedding
)
from transformers.utils import logging
from transformers.cache_utils import (
    Cache, DynamicCache, SlidingWindowCache, StaticCache
)
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import (
    SequenceClassifierOutput, 
    BaseModelOutputWithPast
)
from transformers.utils import ModelOutput

from .configuration_pure_qwen2 import PureQwen2Config

logger = logging.get_logger(__name__)


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 PureQwen2PreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = PureQwen2Config
    base_model_prefix = "model"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Qwen2DecoderLayer"]
    _skip_keys_device_placement = "past_key_values"
    _supports_flash_attn_2 = True
    _supports_sdpa = True
    _supports_cache_class = True
    _supports_quantized_cache = True
    _supports_static_cache = True

    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None:
                module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None:
                module.weight.data[module.padding_idx].zero_()


class PureQwen2Model(PureQwen2PreTrainedModel):
    """
    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]

    Args:
        config: Qwen2Config
    """

    def __init__(self, config: PureQwen2Config):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
        self.layers = nn.ModuleList(
            [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
        )
        self._attn_implementation = config._attn_implementation
        self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        self.rotary_emb = Qwen2RotaryEmbedding(config=config)

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

    def get_input_embeddings(self):
        return self.embed_tokens

    def set_input_embeddings(self, value):
        self.embed_tokens = value
        
    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        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
        )
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        if (input_ids is None) ^ (inputs_embeds is not None):
            raise ValueError("You must specify exactly one of input_ids or inputs_embeds")

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once(
                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
                )
                use_cache = False

        # kept for BC (non `Cache` `past_key_values` inputs)
        return_legacy_cache = False
        if use_cache and not isinstance(past_key_values, Cache):
            return_legacy_cache = True
            if past_key_values is None:
                past_key_values = DynamicCache()
            else:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
                logger.warning_once(
                    "We detected that you are passing `past_key_values` as a tuple of tuples. This is deprecated and "
                    "will be removed in v4.47. Please convert your cache or use an appropriate `Cache` class "
                    "(https://huggingface.co./docs/transformers/kv_cache#legacy-cache-format)"
                )

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(
                past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
            )
        if position_ids is None:
            position_ids = cache_position.unsqueeze(0)

        causal_mask = self._update_causal_mask(
            attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
        )

        hidden_states = inputs_embeds

        # create position embeddings to be shared across the decoder layers
        position_embeddings = self.rotary_emb(hidden_states, position_ids)

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = None

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    causal_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                    cache_position,
                    position_embeddings,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=causal_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                    cache_position=cache_position,
                    position_embeddings=position_embeddings,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache = layer_outputs[2 if output_attentions else 1]

            if output_attentions:
                all_self_attns += (layer_outputs[1],)

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        next_cache = next_decoder_cache if use_cache else None
        if return_legacy_cache:
            next_cache = next_cache.to_legacy_cache()

        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )
    
    # Copied from transformers.models.phi3.modeling_phi3.Phi3Model._update_causal_mask
    def _update_causal_mask(
        self,
        attention_mask: torch.Tensor,
        input_tensor: torch.Tensor,
        cache_position: torch.Tensor,
        past_key_values: Cache,
        output_attentions: bool,
    ):
        if self.config._attn_implementation == "flash_attention_2":
            if attention_mask is not None and 0.0 in attention_mask:
                return attention_mask
            return None

        # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in
        # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail
        # to infer the attention mask.
        past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
        using_static_cache = isinstance(past_key_values, StaticCache)
        using_sliding_window_cache = isinstance(past_key_values, SlidingWindowCache)

        # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward
        if (
            self.config._attn_implementation == "sdpa"
            and not (using_static_cache or using_sliding_window_cache)
            and not output_attentions
        ):
            if AttentionMaskConverter._ignore_causal_mask_sdpa(
                attention_mask,
                inputs_embeds=input_tensor,
                past_key_values_length=past_seen_tokens,
                sliding_window=self.config.sliding_window,
                is_training=self.training,
            ):
                return None

        dtype, device = input_tensor.dtype, input_tensor.device
        min_dtype = torch.finfo(dtype).min
        sequence_length = input_tensor.shape[1]
        # SlidingWindowCache or StaticCache
        if using_sliding_window_cache or using_static_cache:
            target_length = past_key_values.get_max_cache_shape()
        # DynamicCache or no cache
        else:
            target_length = (
                attention_mask.shape[-1]
                if isinstance(attention_mask, torch.Tensor)
                else past_seen_tokens + sequence_length + 1
            )

        # In case the provided `attention` mask is 2D, we generate a causal mask here (4D).
        causal_mask = self._prepare_4d_causal_attention_mask_with_cache_position(
            attention_mask,
            sequence_length=sequence_length,
            target_length=target_length,
            dtype=dtype,
            device=device,
            cache_position=cache_position,
            batch_size=input_tensor.shape[0],
            config=self.config,
            past_key_values=past_key_values,
        )

        if (
            self.config._attn_implementation == "sdpa"
            and attention_mask is not None
            and attention_mask.device.type == "cuda"
            and not output_attentions
        ):
            # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when
            # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
            # Details: https://github.com/pytorch/pytorch/issues/110213
            causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype)

        return causal_mask

    @staticmethod
    # Copied from transformers.models.mistral.modeling_mistral.MistralModel._prepare_4d_causal_attention_mask_with_cache_position with Mistral->Qwen2
    def _prepare_4d_causal_attention_mask_with_cache_position(
        attention_mask: torch.Tensor,
        sequence_length: int,
        target_length: int,
        dtype: torch.dtype,
        device: torch.device,
        cache_position: torch.Tensor,
        batch_size: int,
        config: PureQwen2Config,
        past_key_values: Cache,
    ):
        """
        Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
        `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing.

        Args:
            attention_mask (`torch.Tensor`):
                A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`.
            sequence_length (`int`):
                The sequence length being processed.
            target_length (`int`):
                The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet.
            dtype (`torch.dtype`):
                The dtype to use for the 4D attention mask.
            device (`torch.device`):
                The device to plcae the 4D attention mask on.
            cache_position (`torch.Tensor`):
                Indices depicting the position of the input sequence tokens in the sequence.
            batch_size (`torch.Tensor`):
                Batch size.
            config (`Qwen2Config`):
                The model's configuration class
            past_key_values (`Cache`):
                The cache class that is being used currently to generate
        """
        if attention_mask is not None and attention_mask.dim() == 4:
            # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing.
            causal_mask = attention_mask
        else:
            min_dtype = torch.finfo(dtype).min
            causal_mask = torch.full(
                (sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device
            )
            diagonal_attend_mask = torch.arange(target_length, device=device) > cache_position.reshape(-1, 1)
            if config.sliding_window is not None:
                # if we have sliding window, we should not attend to tokens beyond sliding window length, so we mask them out also
                # the check is needed to verify is current checkpoint was trained with sliding window or not
                if not isinstance(past_key_values, SlidingWindowCache) or sequence_length > target_length:
                    sliding_attend_mask = torch.arange(target_length, device=device) <= (
                        cache_position.reshape(-1, 1) - config.sliding_window
                    )
                    diagonal_attend_mask.bitwise_or_(sliding_attend_mask)
            causal_mask *= diagonal_attend_mask
            causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1)
            if attention_mask is not None:
                causal_mask = causal_mask.clone()  # copy to contiguous memory for in-place edit
                if attention_mask.shape[-1] > target_length:
                    attention_mask = attention_mask[:, :target_length]
                mask_length = attention_mask.shape[-1]
                padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :]
                padding_mask = padding_mask == 0
                causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill(
                    padding_mask, min_dtype
                )
        return causal_mask


class PureQwen2ForSequenceClassification(PureQwen2PreTrainedModel):

    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.model = PureQwen2Model(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)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

    def forward_pure_embeddings(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, ModelOutput]:
        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

        transformer_outputs = self.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        token_embeddings = transformer_outputs[0]
        
        token_embeddings = self.pure(token_embeddings, attention_mask)

        return ModelOutput(
            last_hidden_state=token_embeddings,
        )

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        # position_ids: Optional[torch.LongTensor] = 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.model(
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        token_embeddings = outputs[0]
        
        token_embeddings = self.pure(token_embeddings, attention_mask)
        pooled_output = self.mean(token_embeddings).squeeze(1)
        logits = self.score(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,
        )