import json import numpy as np import os from zscore import kurtosis_outlier_layers def mean_and_var(arr): """ 输入: arr (np.ndarray 或能转成 np.array 的对象) 输出: (mean, variance) """ arr = np.asarray(arr, dtype=float) mean = np.mean(arr) var = np.var(arr) # 默认是总体方差，如果要无偏估计可用 ddof=1 return mean, var def select_layers_shaped(kurtosis, alpha, k=5, return_scores=False, pos_gaussians=None, neg_gaussians=None): """ 强塑形联合指标: score_i = (alpha_i / kurtosis_i) * ( 1 + sum_j b_j * exp(-((kurtosis_i - c_j)^2) / s_j^2) - sum_t d_t * exp(-((kurtosis_i - u_t)^2) / r_t^2) ) 参数 ---- kurtosis : list/ndarray κ alpha : list/ndarray α k : 取前 k 个下标 pos_gaussians : [(c, s, b), ...] 正向高斯(中心c, 带宽s>0, 系数b>0) neg_gaussians : [(u, r, d), ...] 负向高斯(中心u, 带宽r>0, 系数d>0) 返回 ---- idx_topk : list[int] (可选) scores : ndarray """ krt = np.asarray(kurtosis, dtype=float) alp = np.asarray(alpha, dtype=float) if krt.shape != alp.shape: raise ValueError("kurtosis 与 alpha 形状不一致") base = alp - krt c, sigma = mean_and_var(krt) beta = 1.5 weight = 1.0 + beta * np.exp(-((krt - c) ** 2) / (sigma ** 2)) scores = base * weight return scores def joint_score(kurtosis, alpha, alpha0=4.20, s_alpha=0.090, s_k=1.254, w_alpha=9.48, w_k=1.02): """ Kurtosis-Alpha 联合指标 (Taguchi 损失型) ------------------------- 参数 kurtosis : list/ndarray 每层的 kurtosis 值 alpha : list/ndarray 每层的 alpha 值 alpha0 : float alpha 的目标值（名义最佳点） s_alpha : float alpha 的尺度因子 s_k : float kurtosis 的尺度因子 w_alpha, w_k : float alpha 和 kurtosis 的权重 返回 scores : ndarray 每层的综合得分（越大越好） """ k = np.array(kurtosis) a = np.array(alpha) loss_k = (k / s_k) ** 2 loss_a = ((a - alpha0) / s_alpha) ** 2 scores = -(w_k * loss_k + w_alpha * loss_a) return scores def simple_joint_score(kurtosis, alpha, stable_rank): """ 极简联合指标: S_l = -k_l * (alpha_l - Q80(alpha))^2 参数 ---- kurtosis : list/ndarray 每层的 kurtosis 值 alpha : list/ndarray 每层的 alpha 值 返回 ---- scores : ndarray 每层的综合得分（越大越好） """ k = np.array(kurtosis) a = np.array(alpha) s = np.array(stable_rank) print('alpha_hat_datas') # h = np.array(alpha_hat_datas) k = (k - k.min()) / (k.max() - k.min()) # k_exp = np.exp(k) # 防止溢出 # k = k_exp / np.sum(k_exp) a = 1 / a a = (a - a.min()) / (a.max() - a.min()) # a_exp = np.exp(a) # 防止溢出 # a = a_exp / np.sum(a_exp) s = 1 / s s = (s - s.min()) / (s.max() - s.min()) # s_exp = np.exp(s) # s = s_exp / np.sum(s_exp) # h = 1 / h # # h= (h - h.min()) / (h.max() - h.min()) # h_exp = np.exp(h) # 防止溢出 # h = h_exp / np.sum(h_exp) # alpha_target = np.percentile(a, 80) # α 的 80 分位数 # # alpha_target = 0 # scores = -k * (a - alpha_target) ** 2 # k = k * 0.5 # h = h * 0.5 # print(k) # print(a) # print(h) # a = a + 0.3 * h # a = a +1 # k = k +1 # h = h +1 print('a', {i:aa for i, aa in enumerate(a)}) print() print('k', {i:aa for i, aa in enumerate(k)}) print() print('s', {i:aa for i, aa in enumerate(s)}) # k = 100 * k # scores = (k / a) * (k - a) k = 0.2 * k s = 1.2 * s scores = k + a + s # scores = (k * h) * (k + h) # print() # print('scores', {i:aa for i, aa in enumerate(scores)}) return scores model_names = ['Llama-2-7b-hf',] # 'Llama-2-13b-hf', 'Qwen3-8B', 'Qwen3-4B', 'Mistral-7B-Instruct-v0.3','Llama-3.2-3B-Instruct'] for model in model_names: # model = "Llama-2-7b-hf" print(model) alpha_path = f'/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/alpha_values/alpha_values_{model}.json' kurtosis_path = f'/mnt/bn/life-mllm/users/cxr/quantization/lm-quant-toolkit/kurtosis_means/kurtosis_means-{model}.json' alpha_hat_path = f'/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/alpha_hat/alpha_values_{model}.json' stable_rank_path = f'/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/stable_rank/stable_rank_{model}.json' zd_path = f'/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/ZD/ZD_{model}.json' bi_path = f'/mnt/bn/life-mllm/users/cxr/quantization/quantization_metric/BI/BI_{model}.json' with open(stable_rank_path, 'r', encoding='utf-8') as f: stable_rank_datas = json.load(f) with open(alpha_path, 'r', encoding='utf-8') as f: alpha_datas = json.load(f) with open(kurtosis_path, 'r', encoding='utf-8') as f: kurtosis_datas = json.load(f) with open(alpha_hat_path, 'r', encoding='utf-8') as f: alpha_hat_datas = json.load(f) with open(zd_path, 'r', encoding='utf-8') as f: zd_datas = json.load(f) with open(bi_path, 'r', encoding='utf-8') as f: bi_datas = json.load(f) print(len(kurtosis_datas)) print('kurtosis_datas', np.argsort(kurtosis_datas)) print('stable_rank_datas', np.argsort([-a for a in stable_rank_datas])) print('alpha_datas', np.argsort([-a for a in alpha_datas])) print('alpha_hat_datas', np.argsort([-a for a in alpha_hat_datas])) print('zd_datas', np.argsort([-a for a in zd_datas])) print('bi_datas', np.argsort([a for a in bi_datas])) scores = simple_joint_score(kurtosis_datas, alpha_datas, stable_rank_datas) idx_sorted = np.argsort(scores) # print('scores:', scores) print('idx_sorted:',idx_sorted) print() kurtosis_idx = np.argsort(kurtosis_datas).tolist() stable_rank_idx = np.argsort([-a for a in stable_rank_datas]).tolist() alpha_idx = np.argsort([-a for a in alpha_datas]).tolist() bi_idx = np.argsort([a for a in bi_datas]).tolist() z_idx = kurtosis_outlier_layers(kurtosis_datas) zd_idx = np.argsort([-a for a in zd_datas]).tolist() layrs = [5, 10] for layr in layrs: # bits = [4 for _ in range(len(alpha_datas))] # print(kurtosis_idx[:layr]) # for i in kurtosis_idx[:layr]: # bits[i] = 2 # with open(os.path.join('/mnt/bn/life-mllm/users/cxr/quantization/baselines', f'{model}_kurtosis_idx_{layr}.json'), "w", encoding="utf-8") as f: # json.dump(bits, f, ensure_ascii=False, indent=4) # bits = [4 for _ in range(len(alpha_datas))] # for i in alpha_idx[:layr]: # bits[i] = 2 # with open(os.path.join('/mnt/bn/life-mllm/users/cxr/quantization/baselines', f'{model}_alpha_idx_{layr}.json'), "w", encoding="utf-8") as f: # json.dump(bits, f, ensure_ascii=False, indent=4) # bits = [4 for _ in range(len(alpha_datas))] # for i in z_idx[:layr]: # bits[i] = 2 # with open(os.path.join('/mnt/bn/life-mllm/users/cxr/quantization/baselines', f'{model}_z_idx_{layr}.json'), "w", encoding="utf-8") as f: # json.dump(bits, f, ensure_ascii=False, indent=4) # bits = [4 for _ in range(len(alpha_datas))] # print(bi_idx[:layr]) # for i in bi_idx[:layr]: # bits[i] = 2 # with open(os.path.join('/mnt/bn/life-mllm/users/cxr/quantization/baselines_1', f'{model}_bi_idx_{layr}.json'), "w", encoding="utf-8") as f: # json.dump(bits, f, ensure_ascii=False, indent=4) kurtosis_datas, stable_rank_datas bits = [4 for _ in range(len(alpha_datas))] print(idx_sorted[:layr]) for i in idx_sorted[:layr]: bits[i] = 2 with open(os.path.join('/mnt/bn/life-mllm/users/cxr/quantization/ours2', f'{model}_aks_plus_idx_sorted_{layr}.json'), "w", encoding="utf-8") as f: json.dump(bits, f, ensure_ascii=False, indent=4)