模型量化技术详解

import torch
import torch.quantization as quant

# 动态量化 - 只量化权重，激活值运行时量化
model_int8 = torch.quantization.quantize_dynamic(
    model,  # 原始FP32模型
    {torch.nn.Linear, torch.nn.Conv2d},  # 要量化的层类型
    dtype=torch.qint8
)

# 静态量化 - 权重和激活值都预先量化
model.qconfig = torch.quantization.get_default_qconfig('fbgemm')
torch.quantization.prepare(model, inplace=True)

# 校准：使用代表性数据集
with torch.no_grad():
    for data in calibration_loader:
        model(data)

# 转换为量化模型
quantized_model = torch.quantization.convert(model, inplace=True)
                    

# QAT 训练流程
import torch.quantization as quant

# 设置QAT配置
model.qconfig = quant.get_default_qat_qconfig('fbgemm')

# 准备QAT
model_qat = quant.prepare_qat(model, inplace=True)

# QAT训练循环
for epoch in range(num_epochs):
    for batch in train_loader:
        optimizer.zero_grad()
        
        # 前向传播（包含伪量化）
        output = model_qat(batch)
        loss = criterion(output, target)
        
        # 反向传播
        loss.backward()
        optimizer.step()

# 转换为真正的量化模型
model_qat.eval()
quantized_model = quant.convert(model_qat, inplace=True)
                    

# 渐进式量化：逐层量化以最小化精度损失
def progressive_quantization(model, layers_to_quantize):
    quantized_layers = []
    
    for layer_name in layers_to_quantize:
        # 量化单个层
        layer = getattr(model, layer_name)
        quantized_layer = quantize_layer(layer)
        setattr(model, layer_name, quantized_layer)
        
        # 评估精度损失
        accuracy = evaluate_model(model)
        
        if accuracy < threshold:
            # 如枟精度损失太大，回滚
            setattr(model, layer_name, layer)
        else:
            quantized_layers.append(layer_name)
    
    return quantized_layers
                    

# Transformer专用量化配置
def get_transformer_qconfig():
    qconfig_dict = {
        # 注意力层保持FP16
        'attention': torch.quantization.float16_dynamic_qconfig,
        
        # 前馈网络使用INT8
        'feed_forward': torch.quantization.default_dynamic_qconfig,
        
        # 嵌入层特殊处理
        'embedding': torch.quantization.default_qconfig
    }
    return qconfig_dict
                    

def analyze_quantization_quality(original_model, quantized_model, test_data):
    """分析量化质量"""
    
    # 1. 精度对比
    orig_acc = evaluate_accuracy(original_model, test_data)
    quant_acc = evaluate_accuracy(quantized_model, test_data)
    accuracy_drop = orig_acc - quant_acc
    
    # 2. 激活值分布分析
    orig_activations = get_activations(original_model, test_data)
    quant_activations = get_activations(quantized_model, test_data)
    
    # 3. 量化误差统计
    for layer_name in orig_activations:
        orig_act = orig_activations[layer_name]
        quant_act = quant_activations[layer_name]
        
        mse = torch.mean((orig_act - quant_act) ** 2)
        snr = torch.mean(orig_act ** 2) / mse
        
        print(f"{layer_name}: MSE={mse:.6f}, SNR={snr:.2f}dB")
    
    return {
        'accuracy_drop': accuracy_drop,
        'model_size_ratio': get_model_size(quantized_model) / get_model_size(original_model),
        'inference_speedup': measure_inference_speed(quantized_model) / measure_inference_speed(original_model)
    }
                    

# 生产环境量化检查清单
def production_quantization_checklist():
    """生产环境量化部署检查清单"""
    
    checklist = {
        "数据准备": [
            "校准数据集代表性检查",
            "数据预处理流程一致性",
            "异常值检测和处理"
        ],
        "模型验证": [
            "端到端精度测试",
            "关键指标回归测试", 
            "边界情况处理"
        ],
        "性能测试": [
            "推理速度基准测试",
            "内存使用量测试",
            "并发负载测试"
        ],
        "部署监控": [
            "实时精度监控",
            "性能指标追踪",
            "异常检测机制"
        ]
    }
    
    return checklist