[AI学习笔记]深度动态路由演进：DeepSeek门控网络设计全解析

# 传统路由算法性能模拟
import numpy as np

class LegacyRouter:
    def __init__(self, node_count):
        self.adjacency_matrix = np.random.rand(node_count, node_count)
    
    def find_path(self, source, target):
        # 基于Dijkstra算法的静态路径计算
        paths = self._dijkstra(source)
        return paths[target]
        
    def _dijkstra(self, start):
        # 经典最短路径算法实现
        pass  # 实现细节省略

# 测试10节点网络拓扑
router = LegacyRouter(10)
print(router.find_path(0, 9))  # 输出固定路径

import torch
import torch.nn as nn

class DynamicGating(nn.Module):
    def __init__(self, input_dim, output_dim, iterations=3):
        super().__init__()
        self.iterations = iterations
        self.transform = nn.Linear(input_dim, output_dim, bias=False)
        
    def forward(self, inputs):
        batch_size, num_caps, _ = inputs.shape
        priors = torch.zeros(batch_size, num_caps, num_caps).to(inputs.device)
        
        for _ in range(self.iterations):
            # 动态路由计算
            gates = torch.softmax(priors, dim=-1)
            outputs = torch.einsum('bij,bjk->bik', gates, self.transform(inputs))
            
            if _ != self.iterations - 1:
                # 更新先验权重
                priors += torch.einsum('bik,bjk->bij', outputs, self.transform(inputs))
                
        return outputs

# 分布式训练启动脚本
#!/bin/bash
export CUDA_VISIBLE_DEVICES=0,1,2,3
python -m torch.distributed.launch \
    --nproc_per_node=4 \
    --nnodes=8 \
    --node_rank=${SLURM_NODEID} \
    --master_addr="master.example.com" \
    --master_port=12345 \
    train.py \
    --batch_size 1024 \
    --use_fp16 \
    --gradient_checkpointing

# 导入必要的库
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np

# 定义DeepSeek门控网络的结构
class DeepSeekGate(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(DeepSeekGate, self).__init__()
        self.hidden_layer = nn.Linear(input_size, hidden_size)
        self.gate_layer = nn.Linear(input_size, hidden_size)
        self.output_layer = nn.Linear(hidden_size, output_size)
        self.activation = nn.ReLU()
        self.gate_activation = nn.Sigmoid()
        self.softmax = nn.Softmax(dim=1)
        
    def forward(self, x):
        # 隐藏层处理
        hidden_output = self.activation(self.hidden_layer(x))
        # 门控层处理
        gate_output = self.gate_activation(self.gate_layer(x))
        # 门控机制控制信息流动
        gated_hidden = torch.mul(hidden_output, gate_output)
        # 输出层生成路由选择概率分布
        output = self.softmax(self.output_layer(gated_hidden))
        return output

# 准备训练数据
# 假设我们已经收集了网络状态数据和对应的路由选择标签
# network_states: 形状为[num_samples, input_size]的二维数组
# route_labels: 形状为[num_samples, output_size]的二维数组
# 这里使用随机数据作为示例，实际应用中需要替换为真实数据
np.random.seed(42)
input_size = 10  # 网络状态信息的维度
output_size = 5  # 可能的路由路径数量
num_samples = 1000  # 训练样本数量
network_states = np.random.rand(num_samples, input_size)
route_labels = np.random.rand(num_samples, output_size)
route_labels = route_labels / route_labels.sum(axis=1, keepdims=True)  # 归一化为概率分布

# 转换为PyTorch张量
network_states_tensor = torch.tensor(network_states, dtype=torch.float32)
route_labels_tensor = torch.tensor(route_labels, dtype=torch.float32)

# 创建数据加载器
dataset = TensorDataset(network_states_tensor, route_labels_tensor)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# 初始化模型、损失函数和优化器
hidden_size = 64  # 隐藏层神经元数量
model = DeepSeekGate(input_size, hidden_size, output_size)
criterion = nn.MSELoss()  # 使用均方误差损失
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 100
for epoch in range(num_epochs):
    model.train()
    for batch_idx, (data, target) in enumerate(dataloader):
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
    # 每个epoch打印一次损失
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

# 保存训练好的模型
torch.save(model.state_dict(), 'deepseek_gate_model.pth')
print("模型保存完成！")