3.2.3 工具链组件实现

3.2.3 工具链组件实现

AWQ算法的实现依赖于完整的工具链支持，包括校准工具、验证工具和优化工具。

校准工具

class CalibrationTool:
    """校准工具"""
    
    def __init__(self, config: AWQConfig, scaling_manager: ScalingFactorManager):
        self.config = config
        self.scaling_manager = scaling_manager
        self.calibration_data = None
        self.calibration_results = {}
        
    def load_calibration_dataset(self, dataset_path: str, 
                                sample_size: int = 1000) -> torch.Tensor:
        """
        加载校准数据集
        
        Args:
            dataset_path: 数据集路径
            sample_size: 采样数量
            
        Returns:
            校准数据张量
        """
        print(f"加载校准数据集: {dataset_path}")
        
        # 这里应该实现具体的数据加载逻辑
        # 简化处理，生成随机数据
        self.calibration_data = torch.randn(sample_size, 512)  # 假设输入维度为512
        
        print(f"校准数据加载完成，样本数量: {self.calibration_data.size(0)}")
        return self.calibration_data
    
    def run_calibration(self, model: nn.Module) -> Dict:
        """
        运行校准过程
        
        Args:
            model: 模型
            
        Returns:
            校准结果字典
        """
        print("开始运行校准...")
        
        if self.calibration_data is None:
            raise ValueError("校准数据未加载")
            
        calibration_results = {}
        
        # 收集各层的激活值统计
        layer_stats = {}
        for name, module in model.named_modules():
            if isinstance(module, (AWQLinearQuantizationWrapper, AWQConvQuantizationWrapper)):
                # 收集激活值统计
                activations = self._collect_layer_activations(model, name)
                layer_stats[name] = {
                    'activations': activations,
                    'mean': torch.mean(activations).item(),
                    'std': torch.std(activations).item(),
                    'max': torch.max(activations).item(),
                    'min': torch.min(activations).item()
                }
                
        # 更新缩放因子
        for name, stats in layer_stats.items():
            layer_module = dict(model.named_modules())[name]
            
            if isinstance(layer_module, AWQLinearQuantizationWrapper):
                # 更新线性层缩放因子
                new_scale = self.scaling_manager.calculate_adaptive_scaling(
                    stats['activations'], f"{name}_activation"
                )
                layer_module.update_scaling_factors(new_scale)
                
            elif isinstance(layer_module, AWQConvQuantizationWrapper):
                # 更新卷积层缩放因子
                for i in range(layer_module.out_channels):
                    channel_activations = stats['activations'][i]
                    new_scale = self.scaling_manager.calculate_adaptive_scaling(
                        channel_activations, f"{name}_channel_{i}_activation"
                    )
                    layer_module.weight_scales.data[i] = new_scale
        
        calibration_results['layer_stats'] = layer_stats
        calibration_results['scaling_factors_updated'] = len(layer_stats)
        
        print("校准完成")
        return calibration_results
    
    def _collect_layer_activations(self, model: nn.Module, 
                                 layer_name: str) -> torch.Tensor:
        """收集层激活值"""
        # 简化处理，实际需要更复杂的激活值收集逻辑
        with torch.no_grad():
            # 假设我们想要收集最后一个隐藏层的激活值
            activations = model(self.calibration_data)
            
        return activations
​

验证工具

class ValidationTool:
    """验证工具"""
    
    def __init__(self, config: AWQConfig, original_model: nn.Module):
        self.config = config
        self.original_model = original_model
        self.validation_history = []
        
    def validate_quantization(self, quantized_model: nn.Module, 
                            test_data: torch.Tensor) -> Dict:
        """
        验证量化效果
        
        Args:
            quantized_model: 量化后的模型
            test_data: 测试数据
            
        Returns:
            验证结果字典
        """
        print("开始验证量化效果...")
        
        # 精度验证
        accuracy_results = self._validate_accuracy(quantized_model, test_data)
        
        # 性能验证
        performance_results = self._validate_performance(quantized_model, test_data)
        
        # 内存验证
        memory_results = self._validate_memory_usage(quantized_model)
        
        # 综合结果
        validation_results = {
            'accuracy': accuracy_results,
            'performance': performance_results,
            'memory': memory_results,
            'overall_score': self._calculate_overall_score(
                accuracy_results, performance_results, memory_results
            )
        }
        
        self.validation_history.append(validation_results)
        
        print("验证完成")
        return validation_results
    
    def _validate_accuracy(self, quantized_model: nn.Module, 
                         test_data: torch.Tensor) -> Dict:
        """验证精度"""
        with torch.no_grad():
            original_output = self.original_model(test_data)
            quantized_output = quantized_model(test_data)
            
        # 计算MSE误差
        mse_error = torch.nn.functional.mse_loss(
            original_output, quantized_output
        ).item()
        
        # 计算相对误差
        relative_error = torch.mean(
            torch.abs(original_output - quantized_output) / 
            (torch.abs(original_output) + 1e-8)
        ).item()
        
        # 计算输出分布差异
        output_diff = torch.abs(
            torch.mean(original_output) - torch.mean(quantized_output)
        ).item()
        
        return {
            'mse_error': mse_error,
            'relative_error': relative_error,
            'output_diff': output_diff,
            'accuracy_score': 1.0 / (1.0 + mse_error)  # 简化的精度评分
        }
    
    def _validate_performance(self, quantized_model: nn.Module, 
                            test_data: torch.Tensor) -> Dict:
        """验证性能"""
        import time
        
        # 推理时间测试
        start_time = time.time()
        with torch.no_grad():
            for _ in range(10):  # 运行10次取平均
                _ = quantized_model(test_data)
        avg_inference_time = (time.time() - start_time) / 10
        
        # 内存使用测试
        memory_usage = self._measure_memory_usage(quantized_model)
        
        return {
            'avg_inference_time': avg_inference_time,
            'memory_usage': memory_usage,
            'performance_score': 1.0 / (1.0 + avg_inference_time)  # 简化的性能评分
        }
    
    def _validate_memory_usage(self, quantized_model: nn.Module) -> Dict:
        """验证内存使用"""
        # 参数数量
        param_count = sum(p.numel() for p in quantized_model.parameters())
        
        # 模型大小
        model_size = sum(p.numel() * p.element_size() for p in quantized_model.parameters())
        
        return {
            'param_count': param_count,
            'model_size_mb': model_size / (1024 * 1024),
            'memory_efficiency_score': 1.0 / (1.0 + model_size / 1024 / 1024)  # 简化的内存评分
        }
    
    def _measure_memory_usage(self, model: nn.Module) -> float:
        """测量内存使用"""
        import psutil
        import os
        
        process = psutil.Process(os.getpid())
        memory_info = process.memory_info()
        
        return memory_info.rss / (1024 * 1024)  # MB
    
    def _calculate_overall_score(self, accuracy: Dict, performance: Dict, 
                              memory: Dict) -> float:
        """计算综合评分"""
        # 权重：精度0.5，性能0.3，内存0.2
        overall_score = (
            0.5 * accuracy['accuracy_score'] +
            0.3 * performance['performance_score'] +
            0.2 * memory['memory_efficiency_score']
        )
        
        return overall_score
​

优化工具

class OptimizationTool:
    """优化工具"""
    
    def __init__(self, config: AWQConfig, optimization_engine: OptimizationEngine):
        self.config = config
        self.optimization_engine = optimization_engine
        self.optimization_results = []
        
    def optimize_model_quantization(self, model: nn.Module, 
                                 calibration_data: torch.Tensor) -> Dict:
        """
        优化模型量化
        
        Args:
            model: 模型
            calibration_data: 校准数据
            
        Returns:
            优化结果字典
        """
        print("开始优化模型量化...")
        
        # 运行优化
        optimization_results = self.optimization_engine.optimize_quantization(
            model, calibration_data
        )
        
        self.optimization_results.append(optimization_results)
        
        print(f"优化完成，最佳损失: {optimization_results['best_loss']:.6f}")
        return optimization_results
    
    def analyze_optimization_results(self) -> Dict:
        """分析优化结果"""
        if not self.optimization_results:
            return {'error': '无优化结果'}
            
        analysis = {
            'total_iterations': len(self.optimization_results),
            'best_loss': min(r['best_loss'] for r in self.optimization_results),
            'convergence_rate': self._calculate_convergence_rate(),
            'improvement_trend': self._calculate_improvement_trend()
        }
        
        return analysis
    
    def _calculate_convergence_rate(self) -> float:
        """计算收敛速度"""
        if len(self.optimization_results) < 2:
            return 0.0
            
        loss_values = [r['best_loss'] for r in self.optimization_results]
        improvements = []
        
        for i in range(1, len(loss_values)):
            if loss_values[i-1] > 0:
                improvement = (loss_values[i-1] - loss_values[i]) / loss_values[i-1]
                improvements.append(improvement)
        
        if improvements:
            return sum(improvements) / len(improvements)
        else:
            return 0.0
    
    def _calculate_improvement_trend(self) -> str:
        """计算改进趋势"""
        if len(self.optimization_results) < 2:
            return "insufficient_data"
            
        loss_values = [r['best_loss'] for r in self.optimization_results]
        
        # 计算最近3次迭代的改进趋势
        recent_losses = loss_values[-3:]
        if len(recent_losses) < 3:
            return "insufficient_data"
            
        if recent_losses[-1] < recent_losses[0]:
            return "improving"
        elif recent_losses[-1] > recent_losses[0]:
            return "deteriorating"
        else:
            return "stable"
​