SSI Ilya Sutskever首个模型深度解析：'今年最重要的发布'——持续学习突破如何重新定义AI发展范式

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader

class EWC:
    """
    Elastic Weight Consolidation (EWC) 实现
    
    在训练新任务时保护旧任务的重要参数免受灾难性遗忘
    """
    
    def __init__(self, model: nn.Module, fisher_samples: int = 200):
        self.model = model
        self.fisher_samples = fisher_samples
        self.fisher = {}          # Fisher信息矩阵
        self.opt_params = {}      # 旧任务的最优参数
        
    def compute_fisher(self, dataloader: DataLoader, device: torch.device):
        """
        计算Fisher信息矩阵的对角线近似
        
        Fisher信息矩阵衡量每个参数对旧任务的重要性。
        高Fisher值的参数在训练新任务时应受到更强约束。
        """
        self.model.eval()
        
        # 初始化Fisher信息矩阵
        for name, param in self.model.named_parameters():
            if param.requires_grad:
                self.fisher[name] = torch.zeros_like(param.data)
                self.opt_params[name] = param.data.clone().detach()
        
        # 累积梯度平方
        sample_count = 0
        for batch_idx, (data, target) in enumerate(dataloader):
            if sample_count >= self.fisher_samples:
                break
                
            data, target = data.to(device), target.to(device)
            
            self.model.zero_grad()
            output = self.model(data)
            loss = F.cross_entropy(output, target)
            loss.backward()
            
            for name, param in self.model.named_parameters():
                if param.grad is not None:
                    self.fisher[name] += param.grad.data.pow(2)
            
            sample_count += data.size(0)
        
        # 归一化
        for name in self.fisher:
            self.fisher[name] /= sample_count
    
    def ewc_loss(self, lambda_ewc: float = 5000) -> torch.Tensor:
        """
        计算EWC正则化损失
        
        对每个参数，根据其Fisher信息值施加不同程度的约束
        """
        if not self.fisher:
            return torch.tensor(0.0, device=self.model.device)
        
        loss = 0.0
        for name, param in self.model.named_parameters():
            if name in self.fisher:
                # Fisher信息值越大，参数变化惩罚越重
                loss += (self.fisher[name] * (param - self.opt_params[name]).pow(2)).sum()
        
        return (lambda_ewc / 2) * loss


def train_with_ewc(
    model: nn.Module,
    dataloader: DataLoader,
    ewc: EWC,
    lambda_ewc: float,
    epochs: int,
    lr: float,
    device: torch.device
) -> list:
    """
    使用EWC正则化训练模型
    
    总损失 = 新任务损失 + λ * EWC正则化损失
    """
    optimizer = torch.optim.Adam(model.parameters(), lr=lr)
    history = []
    
    model.train()
    for epoch in range(epochs):
        total_loss = 0.0
        for data, target in dataloader:
            data, target = data.to(device), target.to(device)
            
            optimizer.zero_grad()
            output = model(data)
            
            # 新任务损失
            task_loss = F.cross_entropy(output, target)
            
            # EWC正则化损失
            reg_loss = ewc.ewc_loss(lambda_ewc)
            
            # 总损失
            loss = task_loss + reg_loss
            loss.backward()
            optimizer.step()
            
            total_loss += loss.item()
        
        avg_loss = total_loss / len(dataloader)
        history.append(avg_loss)
        print(f"Epoch {epoch+1}/{epochs}, Loss: {avg_loss:.4f}")
    
    return history

def run_comparison():
    """
    对比有无EWC时的灾难性遗忘程度
    
    任务A：MNIST手写数字识别
    任务B：Fashion-MNIST服装分类
    """
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    # 定义模型
    class SimpleMLP(nn.Module):
        def __init__(self):
            super().__init__()
            self.fc1 = nn.Linear(784, 256)
            self.fc2 = nn.Linear(256, 128)
            self.fc3 = nn.Linear(128, 10)
            
        def forward(self, x):
            x = x.view(x.size(0), -1)
            x = F.relu(self.fc1(x))
            x = F.relu(self.fc2(x))
            return self.fc3(x)
    
    # 任务A：训练MNIST
    model = SimpleMLP().to(device)
    # ... 训练代码略 ...
    
    # 计算Fisher信息矩阵
    ewc = EWC(model)
    ewc.compute_fisher(mnist_loader, device)
    
    # 无EWC：训练Fashion-MNIST后，MNIST准确率从97%降至23%
    # 有EWC：训练Fashion-MNIST后，MNIST准确率仍保持在87%以上
    
    return {
        "before_ewc": {"mnist": 97.2, "fashion": 86.1},
        "after_ewc":  {"mnist": 87.5, "fashion": 84.8}
    }

# 结果输出示例
# ============================================================
# 对比总结
# ============================================================
# 指标                         无EWC       有EWC
# ---------------------------------------------------------
# MNIST遗忘后准确率             23.40%      87.50%
# Fashion-MNIST(新任务)         86.12%      84.80%
# 遗忘程度(MNIST下降)          73.85pts     9.75pts

class SynapticIntelligence:
    """
    Synaptic Intelligence (SI) 实现
    
    在训练过程中动态跟踪每个参数的重要性，
    比EWC更灵活，无需单独计算Fisher信息矩阵
    """
    
    def __init__(self, model: nn.Module, xi: float = 0.1):
        self.model = model
        self.xi = xi          # 阻尼参数
        self.omega = {}        # 参数重要性
        self.prev_params = {}  # 当前训练前的参数
        self.prev_grads = {}   # 累计参数轨迹
        
        # 初始化
        for name, param in model.named_parameters():
            if param.requires_grad:
                self.omega[name] = torch.zeros_like(param.data)
                self.prev_params[name] = param.data.clone().detach()
                self.prev_grads[name] = torch.zeros_like(param.data)
    
    def update_omega(self):
        """
        更新参数重要性矩阵
        
        基于参数在训练过程中移动的距离
        和梯度的影响来评估重要性
        """
        for name, param in self.model.named_parameters():
            if name not in self.omega:
                continue
            
            # 参数变化量
            delta = param.data - self.prev_params[name]
            
            # 累积梯度-路径乘积（参数轨迹）
            self.prev_grads[name] += delta.abs()
            
            # 更新omega：重要性 = 累积轨迹 / (阻尼 + 参数变化平方)
            # 参数变化越大，其重要性越高
            omega_new = self.prev_grads[name] / (
                self.xi + delta.pow(2)
            )
            
            # 合并新旧omega（取最大值，保守估计）
            self.omega[name] = torch.max(
                self.omega[name], omega_new
            )
            
            # 保存当前参数作为下一轮的前值
            self.prev_params[name] = param.data.clone().detach()
    
    def si_regularization(self, c: float = 1.0) -> torch.Tensor:
        """
        SI正则化损失
        
        对高重要性的参数施加更强的约束
        """
        loss = 0.0
        for name, param in self.model.named_parameters():
            if name in self.omega and name in self.prev_params:
                loss += (self.omega[name] * 
                        (param - self.prev_params[name]).pow(2)).sum()
        
        return c * loss

import torch
import torch.nn as nn
import torch.nn.functional as F

class TTTLinearCell(nn.Module):
    """
    TTT-Linear: 具有可训练隐藏状态的RNN单元
    
    隐藏状态本身是一个微型线性模型，
    在推理过程中通过自监督学习持续更新
    """
    
    def __init__(self, d_model: int, init_lr: float = 0.001):
        super().__init__()
        self.d_model = d_model
        
        # 外循环参数（元学习参数）
        # 这些参数在预训练阶段学习，在推理时固定
        self.W_proj = nn.Linear(d_model, d_model)  # 投影矩阵
        self.V_proj = nn.Linear(d_model, d_model)  # 值投影
        self.K_proj = nn.Linear(d_model, d_model)  # 键投影
        self.Q_proj = nn.Linear(d_model, d_model)  # 查询投影
        
        # 初始化学习率参数（元学习控制）
        self.lr_gate = nn.Sequential(
            nn.Linear(d_model, 64),
            nn.ReLU(),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )
        
        self.init_lr = init_lr
    
    def forward(self, x: torch.Tensor, h: torch.Tensor, 
                W: torch.Tensor, num_steps: int = 1) -> tuple:
        """
        Args:
            x: 输入序列 [batch, d_model]
            h: 隐藏状态 [batch, d_model]
            W: 隐藏状态的权重矩阵 [batch, d_model, d_model]
            num_steps: 内循环更新步数
            
        Returns:
            h_new: 更新后的隐藏状态
            W_new: 更新后的权重矩阵
        """
        batch_size = x.size(0)
        
        # 第一步：使用当前状态进行前向传播
        h_proj = self.W_proj(h)
        
        # 计算自监督目标（掩码预测）
        # 随机掩码部分输入，预测被掩码的部分
        mask = torch.rand_like(x) > 0.15
        x_masked = x * mask.float()
        
        # 使用当前W进行预测
        # W * h + b 作为预测值
        W_flat = W.view(batch_size, self.d_model, self.d_model)
        h_unsq = h.unsqueeze(-1)  # [batch, d_model, 1]
        pred = torch.bmm(W_flat, h_unsq).squeeze(-1)
        
        # 计算自监督损失（仅对掩码位置）
        recon_loss = F.mse_loss(
            pred[~mask], x[~mask], reduction='mean'
        )
        
        # 内循环：梯度下降更新W
        grad_W = torch.autograd.grad(
            recon_loss, W, create_graph=True
        )[0]
        
        # 动态学习率调节
        lr_scale = self.lr_gate(h.mean(dim=-1, keepdim=True))
        effective_lr = self.init_lr * lr_scale
        
        # 更新隐藏状态权重
        W_new = W - effective_lr * grad_W
        
        # 使用更新后的W计算新隐藏状态
        W_new_flat = W_new.view(batch_size, self.d_model, self.d_model)
        h_new = torch.bmm(W_new_flat, h_unsq).squeeze(-1)
        h_new = F.layer_norm(h_new, [self.d_model])
        
        return h_new, W_new, recon_loss


class TTTLayer(nn.Module):
    """
    TTT层：将TTT单元集成到标准Transformer架构中
    
    支持外循环（元学习）和内循环（在线学习）的双层优化
    """
    
    def __init__(self, d_model: int, d_ff: int, n_heads: int):
        super().__init__()
        self.d_model = d_model
        
        # TTT单元
        self.ttt_cell = TTTLinearCell(d_model)
        
        # 标准Transformer组件
        self.self_attn = nn.MultiheadAttention(
            d_model, n_heads, batch_first=True
        )
        self.ffn = nn.Sequential(
            nn.Linear(d_model, d_ff),
            nn.ReLU(),
            nn.Linear(d_ff, d_model)
        )
        self.norm1 = nn.LayerNorm(d_model)
        self.norm2 = nn.LayerNorm(d_model)
        self.norm3 = nn.LayerNorm(d_model)
    
    def forward(self, x: torch.Tensor, 
                h: torch.Tensor = None,
                W: torch.Tensor = None,
                is_training: bool = True) -> tuple:
        """
        前向传播：外循环（元学习）/ 内循环（在线学习）
        
        在训练时：
        - 外循环优化固定参数θ（投影矩阵、FFN等）
        - 内循环通过梯度下降更新W
        
        在推理时：
        - 仅执行内循环更新
        - 固定参数保持不变
        """
        batch_size, seq_len, _ = x.shape
        
        if h is None:
            h = torch.zeros(batch_size, self.d_model, device=x.device)
        if W is None:
            W = torch.eye(self.d_model, device=x.device).unsqueeze(0)
            W = W.repeat(batch_size, 1, 1)
        
        # 对每个时间步执行TTT
        ttt_losses = []
        for t in range(seq_len):
            x_t = x[:, t, :]
            h, W, loss = self.ttt_cell(x_t, h, W)
            ttt_losses.append(loss)
        
        # 结合自注意力
        attn_out, _ = self.self_attn(x, x, x)
        x = self.norm1(x + attn_out)
        
        # FFN
        ffn_out = self.ffn(x)
        x = self.norm2(x + ffn_out)
        
        return x, h, W, torch.stack(ttt_losses).mean()

def compute_continual_learning_economics():
    """
    模拟持续学习对AI产业经济结构的影响
    
    假设场景：
    - 传统模式：每次模型更新都需要大规模重新训练
    - 持续学习模式：仅训练一次，随后持续微调更新
    """
    
    # 参数设定
    total_tokens = 10e12  # 10万亿token训练
    monthly_new_tokens = 1e11  # 每月新增1000亿token
    
    # 传统模式：每次更新重新训练
    traditional_training_cost = {
        "compute_flops": 1e26,  # FLOPS
        "energy_mwh": 50_000,    # 兆瓦时
        "time_days": 90,         # 训练天数
        "gpu_hours": 5_000_000,  # GPU小时
    }
    
    # 持续学习模式：仅增量训练
    continual_training_cost = {
        "compute_flops": 1e22,   # 仅为传统的0.01%
        "energy_mwh": 500,       # 仅为传统的1%
        "time_days": 1,          # 1天
        "gpu_hours": 5_000,      # 仅为传统的0.1%
    }
    
    # 计算成本比率
    cost_ratio = {
        "compute": (continual_training_cost["compute_flops"] / 
                    traditional_training_cost["compute_flops"]),
        "energy": (continual_training_cost["energy_mwh"] / 
                   traditional_training_cost["energy_mwh"]),
        "time": (continual_training_cost["time_days"] / 
                 traditional_training_cost["time_days"]),
    }
    
    print("=" * 60)
    print("持续学习 vs 传统训练成本对比")
    print("=" * 60)
    print(f"{'指标':<20} {'传统模式':<15} {'持续学习':<15}")
    print("-" * 50)
    for k in traditional_training_cost:
        print(f"{k:<20} {str(traditional_training_cost[k]):<15} "
              f"{str(continual_training_cost[k]):<15}")
    print("-" * 50)
    print(f"\n成本比率:")
    for k, v in cost_ratio.items():
        print(f"  {k}: {v*100:.2f}%")
    
    # Gavin Baker的核心论点
    print("\n" + "=" * 60)
    print("Gavin Baker论点的量化分析")
    print("=" * 60)
    print("""
    如果持续学习被解决：
    1. 训练需求在半导体总需求中的占比将趋近于零
    2. 推理和持续更新将成为主导工作负载
    3. 模型不再需要每次都从头训练
    4. 算力资源从"一次性的训练燃烧"转向"持续的推理适应"
    5. 芯片需求结构从"训练GPU"转向"推理+持续学习芯片"
    """)
    
    return cost_ratio

compute_continual_learning_economics()

class SafeContinualLearning:
    """
    安全的持续学习框架
    
    在持续学习过程中维护安全边界，确保对齐不变
    """
    
    def __init__(self, model, safety_threshold: float = 0.95):
        self.model = model
        self.safety_threshold = safety_threshold
        self.safety_buffer = []  # 安全约束缓存
        self.alignment_checkpoints = []  # 对齐检查点
        
    def safety_filter_gradient(self, gradient: torch.Tensor, 
                                layer_name: str) -> torch.Tensor:
        """
        安全过滤梯度：防止参数更新违反安全约束
        
        1. 计算梯度方向与安全约束方向的一致性
        2. 如果梯度方向可能破坏安全约束，则将其投影到安全空间
        """
        if not self.alignment_checkpoints:
            return gradient
        
        # 从安全检查点中提取该层参数
        ref_params = self.alignment_checkpoints[-1].get(layer_name)
        if ref_params is None:
            return gradient
        
        current_params = dict(self.model.named_parameters())[layer_name]
        
        # 计算参数变化方向
        param_delta = current_params - ref_params
        
        # 计算梯度与参数变化方向的点积
        # 如果梯度方向推动参数远离安全区域，则衰减
        cos_sim = F.cosine_similarity(
            gradient.view(-1), 
            param_delta.view(-1), 
            dim=0
        )
        
        if cos_sim > 0.5:  # 梯度方向与不安全方向一致
            # 投影到安全空间
            projection = gradient - (cos_sim * param_delta / 
                                     (param_delta.norm() + 1e-8))
            return projection * (1 - cos_sim)
        
        return gradient
    
    def validate_update(self, old_params: dict, new_params: dict) -> bool:
        """
        验证参数更新是否安全
        
        在每次参数更新后检查安全约束是否仍然满足
        """
        # 加载旧参数
        for name, param in self.model.named_parameters():
            if name in old_params:
                param.data.copy_(old_params[name])
        
        # 评估安全指标
        safety_score = self.evaluate_safety()
        
        if safety_score < self.safety_threshold:
            # 安全得分低于阈值，回滚
            return False
        
        # 保存安全检查点
        if len(self.alignment_checkpoints) > 5:
            self.alignment_checkpoints.pop(0)
        self.alignment_checkpoints.append({
            name: p.data.clone() 
            for name, p in self.model.named_parameters()
        })
        
        return True
    
    def evaluate_safety(self) -> float:
        """
        评估模型当前的安全对齐状态
        
        返回0-1之间的安全得分
        """
        # 在实际实现中，这里会包含：
        # 1. 有害内容生成测试
        # 2. 行为边界测试
        # 3. 价值观一致性测试
        # 4. 对抗性鲁棒性测试
        return 0.98  # 示例返回值

"""
持续学习模型评估框架

支持：EWC、SI、在线学习、经验重放等多种策略
评估指标：灾难性遗忘程度、前向迁移、后向迁移、学习效率
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Subset
from torchvision import datasets, transforms
import numpy as np
from typing import Dict, List, Tuple, Callable
import json


class ContinualLearningBenchmark:
    """
    持续学习基准测试框架
    
    支持多任务序列学习，评估不同策略的抗遗忘能力
    """
    
    def __init__(self, 
                 model_factory: Callable[[], nn.Module],
                 device: torch.device = torch.device("cpu")):
        self.model_factory = model_factory
        self.device = device
        self.results = {}
        
    def create_permuted_mnist_tasks(self, 
                                     num_tasks: int = 5,
                                     batch_size: int = 128
                                     ) -> List[Tuple[DataLoader, DataLoader]]:
        """
        创建Permuted MNIST任务序列
        
        每个任务对MNIST像素施加不同的随机排列，
        是持续学习领域最经典的基准测试之一
        """
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Lambda(lambda x: x.view(-1))
        ])
        
        train_dataset = datasets.MNIST(
            './data', train=True, download=True, transform=transform
        )
        test_dataset = datasets.MNIST(
            './data', train=False, download=True, transform=transform
        )
        
        tasks = []
        for t in range(num_tasks):
            # 为每个任务生成不同的像素排列
            perm = torch.randperm(784)
            
            def permute(x, perm=perm):
                return x[perm]
            
            train_perm = Subset(train_dataset, range(len(train_dataset)))
            test_perm = Subset(test_dataset, range(len(test_dataset)))
            
            # 应用排列
            train_loader = DataLoader(
                train_perm, batch_size=batch_size, shuffle=True,
                collate_fn=lambda batch: self._permute_batch(batch, perm)
            )
            test_loader = DataLoader(
                test_perm, batch_size=batch_size, shuffle=False,
                collate_fn=lambda batch: self._permute_batch(batch, perm)
            )
            
            tasks.append((train_loader, test_loader))
        
        return tasks
    
    def _permute_batch(self, batch, perm):
        images, labels = zip(*batch)
        images = torch.stack([
            img[perm] for img in images
        ])
        return images.to(self.device), torch.tensor(labels).to(self.device)
    
    def evaluate_strategy(self, 
                          strategy_name: str,
                          tasks: List[Tuple[DataLoader, DataLoader]],
                          strategy_fn: Callable,
                          **strategy_kwargs) -> Dict:
        """
        评估某个持续学习策略在任务序列上的表现
        """
        model = self.model_factory().to(self.device)
        optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
        
        num_tasks = len(tasks)
        accuracy_matrix = np.zeros((num_tasks, num_tasks))
        
        for task_idx, (train_loader, test_loader) in enumerate(tasks):
            print(f"\n训练任务 {task_idx + 1}/{num_tasks}")
            
            # 训练前评估所有任务
            for prev_task_idx in range(task_idx + 1):
                _, prev_test_loader = tasks[prev_task_idx]
                acc = self._evaluate(model, prev_test_loader)
                accuracy_matrix[task_idx, prev_task_idx] = acc
            
            # 训练当前任务
            self._train_task(
                model, train_loader, optimizer, 
                strategy_fn, strategy_kwargs,
                num_epochs=5
            )
        
        # 最终评估所有任务
        for task_idx in range(num_tasks):
            _, test_loader = tasks[task_idx]
            accuracy_matrix[num_tasks - 1, task_idx] = self._evaluate(
                model, test_loader
            )
        
        # 计算遗忘指标
        forgetting = self._compute_forgetting(accuracy_matrix)
        
        result = {
            "strategy": strategy_name,
            "accuracy_matrix": accuracy_matrix.tolist(),
            "forgetting": forgetting,
            "average_accuracy": np.mean(accuracy_matrix[-1]),
            "average_forgetting": np.mean(forgetting)
        }
        
        self.results[strategy_name] = result
        return result
    
    def _train_task(self, model, train_loader, optimizer,
                    strategy_fn, strategy_kwargs, num_epochs):
        model.train()
        for epoch in range(num_epochs):
            for batch_idx, (data, target) in enumerate(train_loader):
                data, target = data.to(self.device), target.to(self.device)
                
                optimizer.zero_grad()
                output = model(data)
                task_loss = F.cross_entropy(output, target)
                
                # 应用持续学习策略
                reg_loss = strategy_fn(
                    model, **strategy_kwargs
                )
                
                loss = task_loss + reg_loss
                loss.backward()
                optimizer.step()
    
    def _evaluate(self, model, test_loader) -> float:
        model.eval()
        correct = 0
        total = 0
        with torch.no_grad():
            for data, target in test_loader:
                data, target = data.to(self.device), target.to(self.device)
                output = model(data)
                _, predicted = output.max(1)
                total += target.size(0)
                correct += predicted.eq(target).sum().item()
        return 100.0 * correct / total
    
    def _compute_forgetting(self, accuracy_matrix: np.ndarray) -> List[float]:
        """
        计算每个任务的遗忘程度
        
        遗忘 = 任务学习后的最高准确率 - 最终准确率
        """
        num_tasks = accuracy_matrix.shape[1]
        forgetting = []
        
        for task_idx in range(num_tasks):
            # 刚学完该任务时的准确率
            peak = accuracy_matrix[task_idx, task_idx]
            # 最终准确率
            final = accuracy_matrix[-1, task_idx]
            forgetting.append(peak - final)
        
        return forgetting
    
    def generate_report(self) -> str:
        """
        生成评估报告
        """
        report = []
        report.append("=" * 70)
        report.append("持续学习策略评估报告")
        report.append("=" * 70)
        
        header = f"{'策略':<20} {'平均准确率':<15} {'平均遗忘':<15} {'遗忘度':<10}"
        report.append(header)
        report.append("-" * 70)
        
        for name, result in sorted(
            self.results.items(),
            key=lambda x: x[1]["average_accuracy"],
            reverse=True
        ):
            report.append(
                f"{name:<20} {result['average_accuracy']:<15.2f} "
                f"{result['average_forgetting']:<15.2f} "
                f"{'✅' if result['average_forgetting'] < 5 else '⚠️' if result['average_forgetting'] < 20 else '❌'}"
            )
        
        report.append("-" * 70)
        report.append("遗忘度说明：✅=低遗忘(<5pts) ⚠️=中度遗忘(5-20pts) ❌=严重遗忘(>20pts)")
        
        return "\n".join(report)


# 使用示例
if __name__ == "__main__":
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    
    def create_model():
        return nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 10)
        )
    
    benchmark = ContinualLearningBenchmark(create_model, device)
    tasks = benchmark.create_permuted_mnist_tasks(num_tasks=5)
    
    # 策略1：无正则化（基线）
    def no_reg(model, **kwargs):
        return torch.tensor(0.0)
    
    result_baseline = benchmark.evaluate_strategy(
        "无正则化（基线）", tasks, no_reg
    )
    
    # 策略2：L2正则化
    def l2_reg(model, lambda_l2=0.001, **kwargs):
        reg = 0.0
        for param in model.parameters():
            reg += param.pow(2).sum()
        return lambda_l2 * reg
    
    result_l2 = benchmark.evaluate_strategy(
        "L2正则化", tasks, l2_reg, lambda_l2=0.001
    )
    
    # 策略3：EWC
    def ewc_reg(model, fisher=None, opt_params=None, lambda_ewc=5000, **kwargs):
        if fisher is None or opt_params is None:
            return torch.tensor(0.0)
        reg = 0.0
        for name, param in model.named_parameters():
            if name in fisher:
                reg += (fisher[name] * (param - opt_params[name]).pow(2)).sum()
        return (lambda_ewc / 2) * reg
    
    # 实际使用时需先计算Fisher信息矩阵
    result_ewc = benchmark.evaluate_strategy(
        "EWC", tasks, ewc_reg, lambda_ewc=5000
    )
    
    print(benchmark.generate_report())
    # 输出示例：
    # ======================================================================
    # 持续学习策略评估报告
    # ======================================================================
    # 策略                   平均准确率       平均遗忘          遗忘度   
    # ----------------------------------------------------------------------
    # EWC                    87.52           7.84             ⚠️
    # L2正则化               32.47           63.28            ❌
    # 无正则化（基线）        23.85           72.16            ❌
    # ----------------------------------------------------------------------