components/multi_lingual_model.py

#the model
from typing import List, Optional, Tuple

import torch
from torch import Tensor
from torch import nn
from transformers import RobertaModel

from faknow.model.layers.layer import TextCNNLayer
from faknow.model.model import AbstractModel
from faknow.data.process.text_process import TokenizerFromPreTrained
import pandas as pd
import gdown
import os

class _MLP(nn.Module):
    def __init__(self,
                 input_dim: int,
                 embed_dims: List[int],
                 dropout_rate: float,
                 output_layer=True):
        super().__init__()
        layers = list()
        for embed_dim in embed_dims:
            layers.append(nn.Linear(input_dim, embed_dim))
            layers.append(nn.BatchNorm1d(embed_dim))
            layers.append(nn.ReLU())
            layers.append(nn.Dropout(p=dropout_rate))
            input_dim = embed_dim
        if output_layer:
            layers.append(torch.nn.Linear(input_dim, 1))
        self.mlp = torch.nn.Sequential(*layers)

    def forward(self, x: Tensor) -> Tensor:
        """

        Args:
            x (Tensor): shared feature from domain and text, shape=(batch_size, embed_dim)

        """
        return self.mlp(x)


class _MaskAttentionLayer(torch.nn.Module):
    """
    Compute attention layer
    """
    def __init__(self, input_size: int):
        super(_MaskAttentionLayer, self).__init__()
        self.attention_layer = torch.nn.Linear(input_size, 1)

    def forward(self,
                inputs: Tensor,
                mask: Optional[Tensor] = None) -> Tuple[Tensor, Tensor]:
        weights = self.attention_layer(inputs).view(-1, inputs.size(1))
        if mask is not None:
            weights = weights.masked_fill(mask == 0, float("-inf"))
        weights = torch.softmax(weights, dim=-1).unsqueeze(1)
        outputs = torch.matmul(weights, inputs).squeeze(1)
        return outputs, weights


class MDFEND(AbstractModel):
    r"""
    MDFEND: Multi-domain Fake News Detection, CIKM 2021
    paper: https://dl.acm.org/doi/10.1145/3459637.3482139
    code: https://github.com/kennqiang/MDFEND-Weibo21
    """
    def __init__(self,
                 pre_trained_bert_name: str,
                 domain_num: int,
                 mlp_dims: Optional[List[int]] = None,
                 dropout_rate=0.2,
                 expert_num=5):
        """

        Args:
            pre_trained_bert_name (str): the name or local path of pre-trained bert model
            domain_num (int): total number of all domains
            mlp_dims (List[int]): a list of the dimensions in MLP layer, if None, [384] will be taken as default, default=384
            dropout_rate (float): rate of Dropout layer, default=0.2
            expert_num (int): number of experts also called TextCNNLayer, default=5
        """
        super(MDFEND, self).__init__()
        self.domain_num = domain_num
        self.expert_num = expert_num
        self.bert = RobertaModel.from_pretrained(
            pre_trained_bert_name).requires_grad_(False)
        self.embedding_size = self.bert.config.hidden_size
        self.loss_func = nn.BCELoss()
        if mlp_dims is None:
            mlp_dims = [384]

        filter_num = 64
        filter_sizes = [1, 2, 3, 5, 10]
        experts = [
            TextCNNLayer(self.embedding_size, filter_num, filter_sizes)
            for _ in range(self.expert_num)
        ]
        self.experts = nn.ModuleList(experts)

        self.gate = nn.Sequential(
            nn.Linear(self.embedding_size * 2, mlp_dims[-1]), nn.ReLU(),
            nn.Linear(mlp_dims[-1], self.expert_num), nn.Softmax(dim=1))

        self.attention = _MaskAttentionLayer(self.embedding_size)

        self.domain_embedder = nn.Embedding(num_embeddings=self.domain_num,
                                            embedding_dim=self.embedding_size)
        self.classifier = _MLP(320, mlp_dims, dropout_rate)

    def forward(self, token_id: Tensor, mask: Tensor,
                domain: Tensor) -> Tensor:
        """

        Args:
            token_id (Tensor): token ids from bert tokenizer, shape=(batch_size, max_len)
            mask (Tensor): mask from bert tokenizer, shape=(batch_size, max_len)
            domain (Tensor): domain id, shape=(batch_size,)

        Returns:
            FloatTensor: the prediction of being fake, shape=(batch_size,)
        """
        text_embedding = self.bert(token_id,
                                   attention_mask=mask).last_hidden_state
        attention_feature, _ = self.attention(text_embedding, mask)

        domain_embedding = self.domain_embedder(domain.view(-1, 1)).squeeze(1)

        gate_input = torch.cat([domain_embedding, attention_feature], dim=-1)
        gate_output = self.gate(gate_input)

        shared_feature = 0
        for i in range(self.expert_num):
            expert_feature = self.experts[i](text_embedding)
            shared_feature += (expert_feature * gate_output[:, i].unsqueeze(1))

        label_pred = self.classifier(shared_feature)

        return torch.sigmoid(label_pred.squeeze(1))

    def calculate_loss(self, data) -> Tensor:
        """
        calculate loss via BCELoss

        Args:
            data (dict): batch data dict

        Returns:
            loss (Tensor): loss value
        """

        token_ids = data['text']['token_id']
        masks = data['text']['mask']
        domains = data['domain']
        labels = data['label']
        output = self.forward(token_ids, masks, domains)
        return self.loss_func(output, labels.float())

    def predict(self, data_without_label) -> Tensor:
        """
        predict the probability of being fake news

        Args:
            data_without_label (Dict[str, Any]): batch data dict

        Returns:
            Tensor: one-hot probability, shape=(batch_size, 2)
        """

        token_ids = data_without_label['text']['token_id']
        masks = data_without_label['text']['mask']
        domains = data_without_label['domain']

        # shape=(n,), data = 1 or 0
        round_pred = torch.round(self.forward(token_ids, masks,
                                              domains)).long()
        # after one hot: shape=(n,2), data = [0,1] or [1,0]
        one_hot_pred = torch.nn.functional.one_hot(round_pred, num_classes=2)
        return one_hot_pred


def download_from_gdrive(file_id, output_path):
        output = os.path.join(output_path)
        
        # Check if the file already exists
        if not os.path.exists(output):
            gdown.download(id=file_id, output=output, quiet=False)

        
        return output



def loading_model_and_tokenizer():
    max_len, bert = 160, 'FacebookAI/xlm-roberta-base'
    #https://drive.google.com/file/d/1--6GB3Ff81sILwtuvVTuAW3shGW_5VWC/view

    file_id = "1--6GB3Ff81sILwtuvVTuAW3shGW_5VWC"

    model_path = '/content/drive/MyDrive/models/last-epoch-model-2024-03-17-01_00_32_1.pth'

    MODEL_SAVE_PATH = download_from_gdrive(file_id, model_path)
    domain_num = 4



    tokenizer = TokenizerFromPreTrained(max_len, bert)

    model = MDFEND(bert, domain_num , expert_num=12 , mlp_dims = [3010, 2024 ,1012 ,606 , 400])

    model.load_state_dict(torch.load(f=MODEL_SAVE_PATH , map_location=torch.device('cpu')))

    model.requires_grad_(False)

    return tokenizer , model