📦 模型压缩（Compression）

# AWQ 核心思想：保护重要权重的精度
# 通过激活值感知，识别对输出影响大的权重

import awq

quant_config = {
    "zero_point": True,      # 使用 zero_point
    "q_group_size": 128,     # 量化组大小
    "w_bit": 4,              # 权重位宽
    "version": "GEMM",       # 使用 GEMM kernel
}

model = AutoAWQForCausalLM.from_pretrained(
    "model_path",
    quant_config=quant_config
)
model.quantize(tokenizer, quant_config=quant_config)

# 基于通道范数的结构化剪枝示例
import torch

def prune_channels(model, ratio=0.2):
    """
    基于权重范数剪枝模型中的冗余通道
    """
    for name, module in model.named_modules():
        if isinstance(module, (torch.nn.Linear,)):
            # 计算每个输出的权重 L2 范数
            weight = module.weight.data
            channel_norm = weight.norm(dim=1)
            
            # 找到需要剪枝的通道（范数最小的）
            num_prune = int(ratio * len(channel_norm))
            prune_indices = torch.topk(channel_norm, num_prune, largest=False).indices
            
            # 掩码化
            mask = torch.ones_like(channel_norm)
            mask[prune_indices] = 0
            module.weight.data *= mask[:, None]

from transformers import DistillationTrainer, AutoModelForCausalLM

# 教师模型（冻结）
teacher = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-70b-hf")
teacher.eval()
teacher.requires_grad_(False)

# 学生模型
student = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# 蒸馏训练配置
distill_config = {
    "alpha": 0.5,    # hard loss 权重
    "beta": 0.5,     # soft loss 权重
    "temperature": 4.0,  # soft logit 温度
}

trainer = DistillationTrainer(
    model=student,
    teacher=teacher,
    args=training_args,
    distillation_config=distill_config,
    ...
)

# 完整量化流程
from optimum.bettertransformer import BetterTransformer
from auto_gptq import AutoGPTQForCausalLM

# 1. 加载模型
model = AutoModelForCausalLM.from_pretrained("model_path")

# 2. 准备校准数据（256-512 条真实 query）
calibration_data = load_calibration_dataset("samples", n_samples=512)

# 3. 执行量化
quantized_model = AutoGPTQForCausalLM.from_pretrained(
    model,
    quantize_config={
        "bits": 4,
        "group_size": 128,
        "damp_percent": 0.01,
        "desc_act": False,
    }
)
quantized_model.quantize(calibration_data)

# 4. 评估效果
eval_results = evaluate(quantized_model, test_dataset)
print(f"MMLU: {eval_results['mmlu']:.1f}%")  # 检查精度损失

# 5. 保存
quantized_model.save_quantized("quantized_model/")