unlearning_algorithm.py

import torch
import torch.nn as nn
import torch.optim as optim
from transformers import AutoModelForSequenceClassification, AutoTokenizer

class LLMUnlearning:
    def __init__(self, model_name):
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.criterion = nn.CrossEntropyLoss()

    def fine_tune(self, train_dataloader, epochs=3, learning_rate=1e-5):
        optimizer = optim.AdamW(self.model.parameters(), lr=learning_rate)
        self.model.train()
        for epoch in range(epochs):
            for batch in train_dataloader:
                inputs = self.tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)
                labels = batch['labels']
                outputs = self.model(**inputs)
                loss = self.criterion(outputs.logits, labels)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

    def gradient_descent_unlearning(self, data_to_unlearn, learning_rate=1e-5):
        optimizer = optim.AdamW(self.model.parameters(), lr=learning_rate)
        self.model.train()
        for data in data_to_unlearn:
            inputs = self.tokenizer(data['text'], return_tensors='pt', padding=True, truncation=True)
            labels = data['labels']
            outputs = self.model(**inputs)
            loss = self.criterion(outputs.logits, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

    def data_augmentation(self, augmented_data):
        augmented_dataloader = torch.utils.data.DataLoader(augmented_data, batch_size=32, shuffle=True)
        self.fine_tune(augmented_dataloader)

    def regularization(self, train_dataloader, epochs=3, learning_rate=1e-5, weight_decay=0.01):
        optimizer = optim.AdamW(self.model.parameters(), lr=learning_rate, weight_decay=weight_decay)
        self.model.train()
        for epoch in range(epochs):
            for batch in train_dataloader:
                inputs = self.tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)
                labels = batch['labels']
                outputs = self.model(**inputs)
                loss = self.criterion(outputs.logits, labels)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

    def knowledge_distillation(self, teacher_model, student_model, train_dataloader, epochs=3, learning_rate=1e-5):
        teacher_model.eval()
        student_model.train()
        optimizer = optim.AdamW(student_model.parameters(), lr=learning_rate)
        for epoch in range(epochs):
            for batch in train_dataloader:
                inputs = self.tokenizer(batch['text'], return_tensors='pt', padding=True, truncation=True)
                with torch.no_grad():
                    teacher_outputs = teacher_model(**inputs)
                student_outputs = student_model(**inputs)
                loss = self.criterion(student_outputs.logits, teacher_outputs.logits)
                optimizer.zero_grad()
                loss.backward()
                optimizer.step()

    def selective_unlearning(self, data_to_unlearn, learning_rate=1e-5):
        optimizer = optim.AdamW(self.model.parameters(), lr=learning_rate)
        self.model.train()
        for data in data_to_unlearn:
            inputs = self.tokenizer(data['text'], return_tensors='pt', padding=True, truncation=True)
            labels = data['labels']
            outputs = self.model(**inputs)
            loss = self.criterion(outputs.logits, labels)
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()