""" 模型可解释性工具：LIME 和 SHAP（简化版） LIME: Local Interpretable Model-agnostic Explanations SHAP: SHapley Additive exPlanations 注意：这是简化版实现，用于演示目的 实际使用时建议安装 lime 和 shap 包：pip install lime shap """ import sys import math import hashlib import torch import numpy as np from pathlib import Path from typing import List, Optional, Dict from collections import defaultdict sys.path.insert(0, '.') class SimplifiedLIME: """ 简化版 LIME 解释器 通过遮蔽输入 token 来衡量每个 token 对输出的贡献 """ def __init__(self, model, tokenizer=None, num_samples: int = 100): self.model = model self.tokenizer = tokenizer self.num_samples = num_samples def explain(self, text: str, top_k: int = 10) -> Dict: """ 解释单个文本的预测 Args: text: 输入文本 top_k: 返回最重要的 top_k token Returns: dict: {token_importances: [{token, importance}], prediction, confidence} """ self.model.eval() # Tokenize if self.tokenizer: tokens = text.split() # 简化分词 else: tokens = text.split() if not tokens: return {'token_importances': [], 'num_tokens': 0, 'original_score': 0.0} # 获取原始预测 original_score = self._predict_score(tokens) # 遮蔽每个 token 并计算重要性 importances = [] for i in range(len(tokens)): masked_tokens = tokens[:i] + ['[MASK]'] + tokens[i+1:] masked_score = self._predict_score(masked_tokens) # 重要性 = 原始分数 - 遮蔽后分数 importance = original_score - masked_score importances.append({ 'token': tokens[i], 'index': i, 'importance': importance, }) # 排序 importances.sort(key=lambda x: abs(x['importance']), reverse=True) return { 'token_importances': importances[:top_k], 'num_tokens': len(tokens), 'original_score': original_score, } def _predict_score(self, tokens): """使用模型预测分数（简化版）""" # 将 token 转为 input_ids vocab_size = getattr(self.model.config, 'vocab_size', 100) if hasattr(self.model, 'config') else 100 input_ids = [] for t in tokens: # 简单 hash 到 vocab 范围 token_id = int(hashlib.md5(t.encode()).hexdigest(), 16) % vocab_size input_ids.append(token_id) input_ids = torch.tensor([input_ids]) attention_mask = torch.ones_like(input_ids) with torch.no_grad(): try: outputs = self.model(input_ids=input_ids, attention_mask=attention_mask) if isinstance(outputs, dict): logits = outputs.get('logits', outputs.get('output', torch.tensor([[0.0]]))) else: logits = outputs[0] if isinstance(outputs, (list, tuple)) else outputs # 返回最大 logit 作为分数 score = logits[0, -1].max().item() except: score = 0.0 return score class SimplifiedSHAP: """ 简化版 SHAP 解释器 使用 Shapley 值的近似计算来衡量每个 token 的贡献 """ def __init__(self, model, tokenizer=None, max_coalitions: int = 50): self.model = model self.tokenizer = tokenizer self.max_coalitions = max_coalitions def explain(self, text: str, top_k: int = 10) -> Dict: """ 使用 Shapley 值解释单个文本 Args: text: 输入文本 top_k: 返回最重要的 top_k token Returns: dict: {shap_values: [{token, shap_value}], base_value, prediction} """ self.model.eval() tokens = text.split() n = len(tokens) if n == 0: return {'shap_values': [], 'base_value': 0.0} # 计算 base value（空输入的预测） base_value = self._predict_score([]) # 计算 Shapley 值 shap_values = [] for i in range(n): # 简化 Shapley 值计算：随机采样联盟 marginal_contributions = [] for _ in range(min(self.max_coalitions, 2 ** n)): # 随机生成联盟（不包含 token i） coalition = [j for j in range(n) if j != i and np.random.random() > 0.5] # 有 token i 的联盟 coalition_with_i = coalition + [i] # 计算边际贡献 score_without = self._predict_score([tokens[j] for j in coalition]) score_with = self._predict_score([tokens[j] for j in coalition_with_i]) marginal_contributions.append(score_with - score_without) # Shapley 值 = 边际贡献的平均 shap_value = np.mean(marginal_contributions) if marginal_contributions else 0.0 shap_values.append({ 'token': tokens[i], 'index': i, 'shap_value': shap_value, }) # 排序 shap_values.sort(key=lambda x: abs(x['shap_value']), reverse=True) return { 'shap_values': shap_values[:top_k], 'base_value': base_value, 'num_tokens': n, } def _predict_score(self, tokens): """使用模型预测分数""" vocab_size = getattr(self.model.config, 'vocab_size', 100) if hasattr(self.model, 'config') else 100 if not tokens: return 0.0 input_ids = [] for t in tokens: token_id = int(hashlib.md5(t.encode()).hexdigest(), 16) % vocab_size input_ids.append(token_id) input_ids = torch.tensor([input_ids]) attention_mask = torch.ones_like(input_ids) with torch.no_grad(): try: outputs = self.model(input_ids=input_ids, attention_mask=attention_mask) if isinstance(outputs, dict): logits = outputs.get('logits', outputs.get('output', torch.tensor([[0.0]]))) else: logits = outputs[0] if isinstance(outputs, (list, tuple)) else outputs score = logits[0, -1].max().item() except: score = 0.0 return score class AttentionVisualizer: """ 基于注意力权重的解释工具 """ def __init__(self, model): self.model = model def get_attention_weights(self, input_ids, attention_mask=None): """ 获取注意力权重（简化版） 注意：需要模型支持输出注意力权重 """ self.model.eval() with torch.no_grad(): try: outputs = self.model(input_ids=input_ids, attention_mask=attention_mask, output_attentions=True) if isinstance(outputs, dict) and 'attentions' in outputs: return outputs['attentions'] except: pass return None if __name__ == "__main__": print("=" * 60) print("Fusion-LLM 模型可解释性工具测试") print("=" * 60) print() # 创建模型 print("[1] 创建模型...") from models.fusion_mini import FusionMini, FusionMiniConfig config = FusionMiniConfig(vocab_size=100, hidden_size=32, num_hidden_layers=1) model = FusionMini(config) print(" 模型已创建") print() # 测试 LIME print("[2] 测试 LIME 解释器...") lime = SimplifiedLIME(model, num_samples=10) explanation = lime.explain("the cat sat on the mat", top_k=5) print(f" Token 数量: {explanation['num_tokens']}") print(f" 原始分数: {explanation['original_score']:.4f}") print(f" Top-5 重要 token:") for item in explanation['token_importances']: print(f" '{item['token']}' (index={item['index']}): {item['importance']:.4f}") print(" LIME 测试通过") print() # 测试 SHAP print("[3] 测试 SHAP 解释器...") np.random.seed(42) shap = SimplifiedSHAP(model, max_coalitions=10) shap_result = shap.explain("the cat sat on the mat", top_k=5) print(f" Base value: {shap_result['base_value']:.4f}") print(f" Top-5 SHAP token:") for item in shap_result['shap_values']: print(f" '{item['token']}' (index={item['index']}): {item['shap_value']:.4f}") print(" SHAP 测试通过") print() # 边缘情况 print("[4] 边缘情况测试...") empty_explain = lime.explain("", top_k=5) assert empty_explain['num_tokens'] == 0 print(" 空输入测试通过") single_explain = lime.explain("hello", top_k=5) assert single_explain['num_tokens'] == 1 print(" 单 token 测试通过") print() print("[PASS] 模型可解释性工具测试全部通过") sys.exit(0)