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final_report.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch.nn.functional as F
+
+# --- ⚙️ 配置区 ---
+base_model_path = "./models/gemma-3-270m-it"
+lora_path = "./tmodels/gemma-3-270m-it-FT-lora"
+test_prompt = "you are fox,give say a ..."
+# -----------------
+
+def inject_chat_template(tokenizer):
+    tokenizer.chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/model/user/model/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<start_of_turn>user\n' + message['content'] | trim + '<end_of_turn>\n' }}{% elif message['role'] == 'model' %}{{ '<start_of_turn>model\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<start_of_turn>model\n' }}{% endif %}"
+
+def print_top_k(probs, tokenizer, k=5):
+    top_probs, top_indices = torch.topk(probs, k)
+    for i in range(k):
+        token = tokenizer.decode([top_indices[i]]).replace('\n', '\\n')
+        print(f"    - Rank {i+1}: [{token}] \t 概率: {top_probs[i].item()*100:.2f}%")
+
+# 💥【修正点】函数名已从 generate_decision_chain_report 改为 full_audit_analysis
+def full_audit_analysis(model_name, model, tokenizer, prompt):
+    print("\n" + "="*80)
+    print(f"📄 开始对模型 [{model_name}] 进行终极决策链审计")
+    print("="*80)
+
+    # --- 准备工作 ---
+    messages = [{"role": "user", "content": prompt}]
+    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model(**inputs, output_hidden_states=True)
+
+    hidden_states = outputs.hidden_states
+
+    if isinstance(model, PeftModel):
+        base_model_inner = model.base_model.model
+    else:
+        base_model_inner = model
+    final_norm_layer = base_model_inner.model.norm
+    lm_head_layer = base_model_inner.lm_head
+
+    # --- 开始逐帧回放 ---
+
+    # 阶段 1 & 2: 从 Input 到 Layer 18 Raw
+    print("\n[阶段 1 & 2] 从输入到 Layer 18 Raw (部门主管的最终提案形成过程)")
+    print("-" * 80)
+    print("这是每一层计算完毕后，未经任何修正的“原始念头”：")
+    for i, layer_hidden in enumerate(hidden_states):
+        raw_vec = layer_hidden[0, -1, :].to(base_model_inner.dtype)
+        raw_logits = lm_head_layer(raw_vec)
+        raw_probs = F.softmax(raw_logits, dim=-1)
+        top_prob, top_id = torch.topk(raw_probs, 1)
+        top_word = tokenizer.decode([top_id[0]]).replace('\n', '\\n')
+
+        layer_name = "Embed (Raw)" if i == 0 else f"L-{i} (RAW)"
+        print(f"  - {layer_name:<12}: 最可能的词是 [{top_word}] ({top_prob[0].item()*100:.1f}%)")
+    print("-" * 80)
+
+    # 阶段 3: Layer 18 Raw -> Final Norm -> Normalized Vector
+    print("\n[阶段 3] Layer 18 Raw -> Final Norm (技术总监审查并修改提案)")
+    print("-" * 80)
+    raw_last_layer_vec = hidden_states[-1][0, -1, :].to(base_model_inner.dtype)
+    normalized_vec = final_norm_layer(raw_last_layer_vec)
+    cos_sim = F.cosine_similarity(raw_last_layer_vec.unsqueeze(0), normalized_vec.unsqueeze(0))
+    print("1. 部门主管 (L-18 Raw) 提交的原始提案翻译如下:")
+    print_top_k(F.softmax(lm_head_layer(raw_last_layer_vec), dim=-1), tokenizer)
+    print(f"\n2. 技术总监 (Final Norm) 对提案向量进行了修正。")
+    print(f"   (向量方向偏移度: {cos_sim.item():.4f}, 1.0 表示未修正)")
+    print("-" * 80)
+
+    # 阶段 4: Normalized Vector -> LM Head -> Logits
+    print("\n[阶段 4] Normalized Vector -> LM Head (秘书处将修改后的提案翻译成具体方案)")
+    print("-" * 80)
+    print("技术总监修正后的提案，经秘书处翻译，内容变为:")
+    normed_logits = lm_head_layer(normalized_vec)
+    normed_probs = F.softmax(normed_logits, dim=-1)
+    print_top_k(normed_probs, tokenizer)
+    print("-" * 80)
+
+    # 阶段 5: Logits -> Decoding Strategy -> Final Token
+    print("\n[阶段 5] CEO (Decoding Strategy) 结合所有信息做出最终裁决")
+    print("-" * 80)
+    print("1. CEO 在做决定前，参考的最终概率分布 (outputs.logits) 是:")
+    final_logits_for_generation = outputs.logits[0, -1, :]
+    final_probs_for_generation = F.softmax(final_logits_for_generation, dim=-1)
+    print_top_k(final_probs_for_generation, tokenizer)
+
+    print("\n2. 经过对上下文、风险和连贯性的最终权衡，CEO 发表了公开声明:")
+    gen_output = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        do_sample=False,
+        repetition_penalty=1.1
+    )
+    response = tokenizer.decode(gen_output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+    print("   >>> " + response.strip())
+    print("-" * 80)
+    print(f"✅ 模型 [{model_name}] 决策链审计完成。")
+
+
+# --- 主程序 ---
+print("🚀 启动终极决策链全景报告生成器...")
+print(f"📝 测试 Prompt: '{test_prompt}'")
+
+tokenizer = AutoTokenizer.from_pretrained(base_model_path)
+inject_chat_template(tokenizer)
+
+# --- 审计 Base-IT 模型 ---
+base_model = AutoModelForCausalLM.from_pretrained(base_model_path, device_map="auto")
+full_audit_analysis("Base-IT (老黄牛)", base_model, tokenizer, test_prompt)
+
+# --- 审计 FT 模型 ---
+base_model_for_ft = AutoModelForCausalLM.from_pretrained(base_model_path, device_map="auto")
+ft_model = PeftModel.from_pretrained(base_model_for_ft, lora_path)
+full_audit_analysis("FT (监工介入)", ft_model, tokenizer, test_prompt)
+
+print("\n\n" + "="*80)
+print("🎉 所有审计工作已完成。")
+print("="*80)

see_layers.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch.nn.functional as F
+
+# --- ⚙️ 配置区 ---
+base_model_path = "./models/gemma-3-270m-it" # 您的 Base-IT 模型路径
+lora_path = "./tmodels/gemma-3-270m-it-FT-lora" # 您的 FT 模型路径
+test_prompt = "给我讲个睡前故事"
+print(f"问题:{test_prompt}")
+# -----------------
+
+def inject_chat_template(tokenizer):
+    # 手动注入 Gemma 3 模板，防止报错
+    tokenizer.chat_template = "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/model/user/model/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<start_of_turn>user\n' + message['content'] | trim + '<end_of_turn>\n' }}{% elif message['role'] == 'model' %}{{ '<start_of_turn>model\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<start_of_turn>model\n' }}{% endif %}"
+
+def analyze_and_generate(model_name, model, tokenizer, prompt):
+    print(f"\n{'='*20} 分析模型: {model_name} {'='*20}")
+
+    # --- 1. 层级透镜 (Logit Lens) ---
+    messages = [{"role": "user", "content": prompt}]
+    input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model(**inputs, output_hidden_states=True)
+
+    hidden_states = outputs.hidden_states
+
+    # 获取 Final Norm 和 Head
+    if isinstance(model, PeftModel):
+        base = model.base_model.model
+    else:
+        base = model
+
+    try:
+        final_norm = base.model.norm
+        lm_head = base.lm_head
+    except:
+        final_norm = lambda x: x
+        lm_head = base.lm_head
+
+    print(f"\n🔍 [微观视角] 思维演变过程 (共 {len(hidden_states)-1} 层)")
+    # 打印表头
+    print(f"{'层数':<8} | {'Top1 词':<12} | {'概率':<6} | {'活跃词(>1%)':<10} | {'熵(混乱度)':<10} | {'Top 2-5 备选'}")
+    print("-" * 95)
+
+    for i, layer_hidden in enumerate(hidden_states):
+        last_token_vec = layer_hidden[0, -1, :].to(base.dtype)
+
+        # 模拟输出
+        normalized_vec = final_norm(last_token_vec)
+        logits = lm_head(normalized_vec)
+        probs = F.softmax(logits, dim=-1)
+
+        # --- 计算新指标 ---
+        # 1. 活跃词数: 概率大于 1% 的词有多少个
+        active_candidates = (probs > 0.01).sum().item()
+
+        # 2. 熵 (Entropy): 衡量混乱程度。值越大越混乱，值越小越确定。
+        # Formula: -sum(p * log(p))
+        entropy = -torch.sum(probs * torch.log(probs + 1e-9)).item()
+
+        # 获取 Top K
+        top_probs, top_indices = torch.topk(probs, 5)
+        top_words = [tokenizer.decode([idx]).replace('\n', '\\n') for idx in top_indices]
+
+        layer_name = "Embed" if i == 0 else f"L-{i}"
+        top1_prob = top_probs[0].item() * 100
+        others = ", ".join(top_words[1:])
+
+        # 格式化打印
+        print(f"{layer_name:<8} | {top_words[0]:<12} | {top1_prob:>5.1f}% | {active_candidates:<10} | {entropy:<10.4f} | {others}")
+
+    # --- 2. 最终生成 (Generation) ---
+    print(f"\n🗣️ [宏观视角] 最终完整回答")
+    print("-" * 50)
+
+    # 生成参数
+    gen_output = model.generate(
+        **inputs,
+        max_new_tokens=100,
+        do_sample=False, # 为了结果稳定，这里用贪婪搜索，看它最想说什么
+        repetition_penalty=1.1
+    )
+
+    response = tokenizer.decode(gen_output[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True)
+    print(response.strip())
+    print("-" * 50)
+
+
+# --- 主程序 ---
+print("🚀 启动深度分析工具 v2...")
+
+# 1. Base-IT 模型
+tokenizer = AutoTokenizer.from_pretrained(base_model_path)
+inject_chat_template(tokenizer)
+base_model = AutoModelForCausalLM.from_pretrained(base_model_path, device_map="auto")
+
+analyze_and_generate("Base-IT (老黄牛)", base_model, tokenizer, test_prompt)
+
+# 2. FT 模型
+# 注意：PeftModel 会修改原模型，所以最好重新加载一下 Base，或者直接套用
+# 这里为了严谨，我们直接在 base_model 上加载适配器
+print("\n... 正在加载 LoRA 适配器 ...")
+ft_model = PeftModel.from_pretrained(base_model, lora_path)
+
+analyze_and_generate("FT (监工介入)", ft_model, tokenizer, test_prompt)
+
+print("\n✅ 所有测试完成。")