Agent 边缘计算与端侧部署优化

import torch
import torch.quantization as quant
from torch.ao.quantization import QuantStub, DeQuantStub

class QuantizableAgentModel(torch.nn.Module):
    """可量化的 Agent 模型"""
    
    def __init__(self):
        super().__init__()
        self.quant = QuantStub()
        self.dequant = DeQuantStub()
        
        self.linear1 = torch.nn.Linear(768, 512)
        self.relu = torch.nn.ReLU()
        self.linear2 = torch.nn.Linear(512, 256)
        self.softmax = torch.nn.Softmax(dim=-1)
    
    def forward(self, x):
        x = self.quant(x)
        x = self.linear1(x)
        x = self.relu(x)
        x = self.linear2(x)
        x = self.softmax(x)
        x = self.dequant(x)
        return x

def post_training_quantization(model, calibration_loader):
    """
    训练后量化（PTQ）
    
    Args:
        model: 原始 FP32 模型
        calibration_loader: 校准数据集
    
    Returns:
        quantized_model: INT8 量化模型
    """
    # 1. 准备量化配置
    model.qconfig = quant.get_default_qconfig('fbgemm')
    
    # 2. 准备模型（插入观察器）
    model_prepared = quant.prepare(model)
    
    # 3. 校准（收集激活值统计信息）
    model_prepared.eval()
    with torch.no_grad():
        for data, _ in calibration_loader:
            model_prepared(data)
    
    # 4. 转换（将权重和激活值转换为 INT8）
    quantized_model = quant.convert(model_prepared)
    
    return quantized_model

# 使用示例
model = QuantizableAgentModel()
calibration_loader = get_calibration_data()  # 100-500 个样本

# 执行 PTQ
quantized_model = post_training_quantization(model, calibration_loader)

# 保存量化模型
torch.save(quantized_model.state_dict(), 'agent_int8.pt')

# 性能对比
print(f"原始模型大小：{get_model_size(model):.2f} MB")
print(f"量化模型大小：{get_model_size(quantized_model):.2f} MB")
print(f"压缩比：{get_model_size(model) / get_model_size(quantized_model):.1f}x")

# 推理速度对比
import time
input_tensor = torch.randn(1, 768)

# FP32 推理
model.eval()
start = time.time()
with torch.no_grad():
    for _ in range(100):
        model(input_tensor)
fp32_time = (time.time() - start) / 100

# INT8 推理
quantized_model.eval()
start = time.time()
with torch.no_grad():
    for _ in range(100):
        quantized_model(input_tensor)
int8_time = (time.time() - start) / 100

print(f"FP32 推理延迟：{fp32_time*1000:.2f} ms")
print(f"INT8 推理延迟：{int8_time*1000:.2f} ms")
print(f"加速比：{fp32_time / int8_time:.2f}x")

# 精度验证
accuracy = evaluate_accuracy(quantized_model, test_loader)
print(f"量化后精度：{accuracy:.2f}%")
print(f"精度损失：{fp32_accuracy - accuracy:.2f}%")

import asyncio
from typing import Dict, List, Optional, Tuple
from dataclasses import dataclass
from enum import Enum
import time

class DeviceType(Enum):
    DEVICE = "device"  # 终端设备
    EDGE = "edge"      # 边缘节点
    CLOUD = "cloud"    # 云端

@dataclass
class ModelPartition:
    """模型分割配置"""
    partition_id: str
    layers: List[str]
    target_device: DeviceType
    expected_latency_ms: float
    expected_memory_mb: float

@dataclass
class InferenceRequest:
    """推理请求"""
    request_id: str
    input_data: bytes
    priority: int  # 1-5, 5 最高
    latency_budget_ms: float
    privacy_level: int  # 1-5, 5 最敏感

class CollaborativeInferenceSystem:
    """云边端协同推理系统"""
    
    def __init__(self):
        self.device_executor = DeviceExecutor()
        self.edge_executor = EdgeExecutor()
        self.cloud_executor = CloudExecutor()
        self.partition_planner = PartitionPlanner()
        self.result_fusion = ResultFusion()
    
    async def infer(self, request: InferenceRequest) -> Dict:
        """
        执行协同推理
        
        Args:
            request: 推理请求
        
        Returns:
            推理结果
        """
        start_time = time.time()
        
        # 1. 分析请求特征
        request_profile = self._profile_request(request)
        
        # 2. 生成候选分割方案
        partitions = await self.partition_planner.generate_partitions(
            model=self.current_model,
            request_profile=request_profile,
            constraints={
                'max_latency': request.latency_budget_ms,
                'privacy_level': request.privacy_level,
                'device_status': await self._get_device_status()
            }
        )
        
        # 3. 选择最优分割方案
        best_partition = self._select_best_partition(partitions)
        
        # 4. 执行分布式推理
        intermediate_results = await self._execute_partitioned_inference(
            request,
            best_partition
        )
        
        # 5. 融合结果
        final_result = await self.result_fusion.fuse(intermediate_results)
        
        # 6. 记录指标
        total_latency = (time.time() - start_time) * 1000
        await self._log_metrics(request, final_result, total_latency)
        
        return {
            'result': final_result,
            'latency_ms': total_latency,
            'partition_strategy': best_partition,
            'device_breakdown': self._get_device_breakdown(intermediate_results)
        }
    
    def _profile_request(self, request: InferenceRequest) -> Dict:
        """分析请求特征"""
        return {
            'input_size': len(request.input_data),
            'priority': request.priority,
            'latency_budget': request.latency_budget_ms,
            'privacy_level': request.privacy_level,
            'estimated_complexity': self._estimate_complexity(request.input_data)
        }
    
    async def _execute_partitioned_inference(
        self,
        request: InferenceRequest,
        partitions: List[ModelPartition]
    ) -> List[Dict]:
        """执行分割推理"""
        intermediate_data = request.input_data
        results = []
        
        for partition in partitions:
            # 根据目标设备选择执行器
            if partition.target_device == DeviceType.DEVICE:
                executor = self.device_executor
            elif partition.target_device == DeviceType.EDGE:
                # 传输到边缘
                intermediate_data = await self._transmit_to_edge(intermediate_data)
                executor = self.edge_executor
            else:  # CLOUD
                # 传输到云端
                intermediate_data = await self._transmit_to_cloud(intermediate_data)
                executor = self.cloud_executor
            
            # 执行该部分推理
            result = await executor.execute(
                partition_id=partition.partition_id,
                input_data=intermediate_data,
                layers=partition.layers
            )
            
            results.append({
                'partition_id': partition.partition_id,
                'device': partition.target_device.value,
                'latency_ms': result['latency_ms'],
                'output': result['output']
            })
            
            # 更新中间数据
            intermediate_data = result['output']
        
        return results
    
    async def _transmit_to_edge(self, data: bytes) -> bytes:
        """传输到边缘节点"""
        # 实现与边缘节点的通信
        # 包括数据压缩、加密、传输
        compressed_data = await self._compress_data(data)
        edge_response = await self.edge_channel.send(compressed_data)
        return await self._decompress_data(edge_response)
    
    async def _transmit_to_cloud(self, data: bytes) -> bytes:
        """传输到云端"""
        # 实现与云端的通信
        compressed_data = await self._compress_data(data)
        cloud_response = await self.cloud_channel.send(compressed_data)
        return await self._decompress_data(cloud_response)
    
    def _select_best_partition(
        self,
        partitions: List[List[ModelPartition]]
    ) -> List[ModelPartition]:
        """选择最优分割方案"""
        best_score = float('inf')
        best_partition = None
        
        for candidate in partitions:
            # 计算综合得分（延迟、能耗、精度）
            score = self._calculate_partition_score(candidate)
            
            if score < best_score:
                best_score = score
                best_partition = candidate
        
        return best_partition
    
    def _calculate_partition_score(
        self,
        partitions: List[ModelPartition]
    ) -> float:
        """计算分割方案得分"""
        total_latency = sum(p.expected_latency_ms for p in partitions)
        total_memory = sum(p.expected_memory_mb for p in partitions)
        
        # 加权得分（可根据场景调整权重）
        latency_weight = 0.5
        memory_weight = 0.3
        communication_weight = 0.2
        
        communication_cost = len(partitions) - 1  # 分割点数量
        
        score = (
            latency_weight * total_latency +
            memory_weight * total_memory +
            communication_weight * communication_cost * 50  # 每次通信折算 50ms
        )
        
        return score


# 使用示例
async def collaborative_inference_example():
    """协同推理示例"""
    
    system = CollaborativeInferenceSystem()
    
    # 创建推理请求
    request = InferenceRequest(
        request_id="req_001",
        input_data=b"audio_waveform_data...",
        priority=4,
        latency_budget_ms=100.0,  # 100ms 延迟预算
        privacy_level=3  # 中等隐私
    )
    
    # 执行协同推理
    result = await system.infer(request)
    
    print(f"推理完成:")
    print(f"  总延迟：{result['latency_ms']:.2f} ms")
    print(f"  分割策略：{len(result['partition_strategy'])} 部分")
    print(f"  设备分布：{result['device_breakdown']}")
    
    # 输出示例:
    # 推理完成:
    #   总延迟：78.34 ms
    #   分割策略：3 部分
    #   设备分布：{'device': 1, 'edge': 1, 'cloud': 1}