import torch import torch.nn as nn import torch.nn.functional as F from safetensors.torch import save_file as safetensors_save_file from torchvision import models import safetensors import os # --- 1. 原始模型架構 (從 reconstruct_original_model.py 複製) --- class OriginalMoETransformerBlock(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim, num_experts): super().__init__() self.num_experts = num_experts self.input_dim = input_dim self.hidden_dim = hidden_dim self.output_dim = output_dim self.experts_w1 = nn.ModuleList([nn.Linear(input_dim, hidden_dim, bias=False) for _ in range(num_experts)]) self.experts_w2 = nn.ModuleList([nn.Linear(hidden_dim, output_dim, bias=False) for _ in range(num_experts)]) self.gate = nn.Linear(input_dim, num_experts, bias=False) def forward(self, x): gate_logits = self.gate(x) weights = torch.softmax(gate_logits, dim=-1) expert_outputs = torch.empty(x.shape[0], self.num_experts, self.output_dim, device=x.device) for i in range(self.num_experts): expert_outputs[:, i, :] = self.experts_w2[i](self.experts_w1[i](x)) output = torch.sum(expert_outputs * weights.unsqueeze(-1), dim=1) return output class OriginalModelReconstructed(nn.Module): def __init__(self, vocab_size, embedding_dim, moe_hidden_dim, num_experts): super().__init__() self.embeddings = nn.Embedding(vocab_size, embedding_dim) self.transformer_block_0 = OriginalMoETransformerBlock(embedding_dim, moe_hidden_dim, embedding_dim, num_experts) self.norm = nn.LayerNorm(embedding_dim) self.output_layer = nn.Linear(embedding_dim, vocab_size) # 新增 Action Prediction Head for Comet-like capability # 輸出結構化行動 (例如: [action_type, x_coord, y_coord, element_id_token]) # 簡化為一個線性層，輸出一個固定長度的向量 self.action_prediction_head = nn.Sequential( nn.Linear(embedding_dim, 256), nn.ReLU(), nn.Linear(256, 4) # 假設輸出 4 個值: action_type, x, y, element_id_token ) def forward(self, x): x = self.embeddings(x).squeeze(1) x = self.transformer_block_0(x) x = self.norm(x) x = self.output_layer(x) return x # --- 2. 記憶共生引擎模組 (Person X Memory Symbiosis Engine) --- class MemorySymbiosisEngine(nn.Module): def __init__(self, embedding_dim, memory_slots=10, memory_dim=256): super().__init__() self.memory_slots = memory_slots self.memory_dim = memory_dim self.memory_keys = nn.Parameter(torch.randn(memory_slots, memory_dim)) self.memory_values = nn.Parameter(torch.randn(memory_slots, embedding_dim)) self.query_projection = nn.Linear(embedding_dim, memory_dim) self.memory_read_fusion = nn.Linear(embedding_dim + embedding_dim, embedding_dim) def forward(self, current_features, user_profile_embedding=None): query = self.query_projection(current_features) attention_scores = torch.matmul(query, self.memory_keys.T) attention_weights = torch.softmax(attention_scores, dim=-1) read_memory = torch.matmul(attention_weights, self.memory_values) fused_with_memory = torch.cat((current_features, read_memory), dim=-1) output_features = self.memory_read_fusion(fused_with_memory) return output_features # --- 3. 真實錨定 (Reality Anchor, RA) 技術模組 --- class RealityAnchor(nn.Module): def __init__(self, embedding_dim, fact_memory_size=1000, fact_dim=256, threshold=0.7): super().__init__() self.fact_memory_size = fact_memory_size self.fact_dim = fact_dim self.threshold = threshold # 內置的事實記憶庫：這裡用可學習的張量模擬 # 實際中，這會是一個預訓練的、經過壓縮的知識表示 self.fact_memory_bank = nn.Parameter(torch.randn(fact_memory_size, fact_dim)) # 事實注意力機制 (Fact-Attention) 的查詢投影 self.fact_query_projection = nn.Linear(embedding_dim, fact_dim) # 調整信號生成模塊 (輕量級神經網絡) self.adjustment_module = nn.Sequential( nn.Linear(embedding_dim + fact_dim, embedding_dim // 2), nn.ReLU(), nn.Linear(embedding_dim // 2, embedding_dim) # 輸出與 embedding_dim 相同維度，用於調整 ) # 事實一致性評分器 (這裡簡化為一個線性層) self.consistency_scorer = nn.Linear(fact_dim, 1) def forward(self, current_token_embedding, generated_context_embedding): # current_token_embedding: 當前生成詞元的嵌入 (batch_size, embedding_dim) # generated_context_embedding: 當前生成上下文的嵌入 (batch_size, embedding_dim) # 1. 動態事實檢索與融合 (這裡簡化為基於上下文的檢索) # 假設 generated_context_embedding 已經包含了足夠的信息來查詢事實 fact_query = self.fact_query_projection(generated_context_embedding) # (batch_size, fact_dim) # 近似最近鄰搜索 (這裡簡化為點積相似度) similarity_scores = torch.matmul(fact_query, self.fact_memory_bank.T) # (batch_size, fact_memory_size) # 獲取最相關的事實 (這裡取 top-1) top_fact_scores, top_fact_indices = torch.topk(similarity_scores, 1, dim=-1) retrieved_fact = self.fact_memory_bank[top_fact_indices.squeeze(-1)] # (batch_size, fact_dim) # 2. 事實一致性評分 (Fact Consistency Scoring) # 衡量當前詞元嵌入與檢索到的事實的一致性 # 這裡簡化為直接評分檢索到的事實，實際可能需要更複雜的交互 fact_consistency_score = torch.sigmoid(self.consistency_scorer(retrieved_fact)) # (batch_size, 1) # 3. 即時調整生成 (Real-Time Generation Adjustment) adjustment_signal = torch.zeros_like(current_token_embedding) # 初始化調整信號 # 如果事實一致性分數低於閾值，則觸發調整 # 注意：這裡的閾值判斷是簡化的，實際應用中可能需要更精細的控制 needs_adjustment = (fact_consistency_score < self.threshold).squeeze(-1) # (batch_size,) if needs_adjustment.any(): # 組合當前詞元嵌入和檢索到的事實來生成調整信號 combined_for_adjustment = torch.cat((current_token_embedding[needs_adjustment], retrieved_fact[needs_adjustment]), dim=-1) adjustment_signal[needs_adjustment] = self.adjustment_module(combined_for_adjustment) # 將調整信號應用於當前詞元嵌入 adjusted_token_embedding = current_token_embedding + adjustment_signal return adjusted_token_embedding, fact_consistency_score # --- 4. 多維度生成協調器 (Multidimensional Generation Orchestrator, MGO) 技術模組 --- class MultidimensionalGenerationOrchestrator(nn.Module): def __init__(self, embedding_dim, vocab_size, max_output_length=50, num_intent_types=5): super().__init__() self.embedding_dim = embedding_dim self.vocab_size = vocab_size self.max_output_length = max_output_length self.num_intent_types = num_intent_types # 1. 生成意圖解析與分解 (Generation Intent Parsing and Decomposition) # 這裡簡化為一個線性層來預測意圖類型 self.intent_parser = nn.Linear(embedding_dim, num_intent_types) self.intent_embedding = nn.Embedding(num_intent_types, embedding_dim) # 意圖嵌入 # 2. 多維度生成規劃 (Multidimensional Generation Planning) # 這裡簡化為根據意圖調整生成參數 self.planning_layer = nn.Linear(embedding_dim, 4) # 輸出控制：豐富度、連貫性、長度、風格 # 3. 自適應生成引導 (Adaptive Generation Guidance) self.guidance_controller = nn.Linear(embedding_dim * 2 + 4, embedding_dim) # 融合上下文、意圖、規劃參數 self.length_controller = nn.Linear(embedding_dim, 1) # 預測生成終止概率 def forward(self, context_embedding, current_output_length=0, target_intent_idx=None): # context_embedding: 融合了所有特徵的上下文嵌入 (batch_size, embedding_dim) # 1. 生成意圖解析 (簡化：如果未指定，則從上下文預測) if target_intent_idx is None: intent_logits = self.intent_parser(context_embedding) target_intent_idx = torch.argmax(intent_logits, dim=-1) # (batch_size,) intent_embedding = self.intent_embedding(target_intent_idx) # (batch_size, embedding_dim) # 2. 多維度生成規劃 planning_params = torch.sigmoid(self.planning_layer(intent_embedding)) # (batch_size, 4) # planning_params: [content_richness, semantic_coherence, length_target_ratio, style_adaptability] # 3. 自適應生成引導 # 組合上下文、意圖嵌入和規劃參數 combined_guidance_input = torch.cat([ context_embedding, intent_embedding, planning_params ], dim=-1) guidance_signal = self.guidance_controller(combined_guidance_input) # (batch_size, embedding_dim) # 長度控制 length_control_signal = torch.sigmoid(self.length_controller(context_embedding)) # (batch_size, 1) # 這個信號可以被用來調整生成終止的概率 # 這裡返回引導信號和規劃參數，供外部模型使用來調整生成過程 return guidance_signal, planning_params, length_control_signal # --- 5. 智能體生態框架接口 (Agent Matrix Intelligent Agent Ecosystem Framework) --- class AgentMatrixInterface(nn.Module): def __init__(self, model_core): super().__init__() self.model_core = model_core self.task_mapping = { "predict_action": self._predict_action, # 新增 Comet-like 行動判斷 "analyze_image_text": self._analyze_image_text, "analyze_image_text": self._analyze_image_text, "retrieve_memory": self._retrieve_memory, "generate_response": self._generate_response, "generate_anchored_response": self._generate_anchored_response # 新增 RA 命令 } def _analyze_image_text(self, text_input, image_input): return self.model_core(text_input, image_input, return_fused_features=True) def _retrieve_memory(self, query_text_input, query_image_input=None): text_features = self.model_core.embeddings(query_text_input) if text_features.dim() == 3: text_features = text_features.squeeze(1) if query_image_input is not None: image_features = self.model_core.vision_encoder(query_image_input) image_features = image_features.view(image_features.size(0), -1) image_features = self.model_core.vision_projection(image_features) current_features = self.model_core.initial_fusion_layer(torch.cat((text_features, image_features), dim=1)) else: current_features = text_features return self.model_core.memory_engine(current_features) def _generate_response(self, text_input, image_input): # 這裡調用原始的 forward 方法，不啟用 RA return self.model_core(text_input, image_input, use_ra=False) def _predict_action(self, text_input, image_input): # 視覺感知和行動判斷 # 這裡調用啟用 action_prediction 的 forward 方法 return self.model_core(text_input, image_input, return_action_prediction=True) def _generate_anchored_response(self, text_input, image_input): # 這裡調用啟用 RA 的 forward 方法 return self.model_core(text_input, image_input, use_ra=True) def forward(self, command, **kwargs): if command in self.task_mapping: if command.startswith("generate"): # 生成相關的命令現在返回多個值 if command == "generate_anchored_response": logits, consistency_score, planning_params, length_control_signal = self.task_mapping[command](**kwargs) return logits, consistency_score, planning_params, length_control_signal else: logits, planning_params, length_control_signal = self.task_mapping[command](**kwargs) return logits, planning_params, length_control_signal elif command == "predict_action": return self.task_mapping[command](**kwargs) else: # 生成相關的命令現在返回多個值 if command == "generate_anchored_response": logits, consistency_score, planning_params, length_control_signal = self.task_mapping[command](**kwargs) return logits, consistency_score, planning_params, length_control_signal else: logits, planning_params, length_control_signal = self.task_mapping[command](**kwargs) return logits, planning_params, length_control_signal else: return self.task_mapping[command](**kwargs) else: raise ValueError(f"Unknown command: {command}") # --- 5. 整合後的完整模型架構 (包含 RA) --- class FullyIntegratedModelWithRA(nn.Module): def __init__(self, original_model_path, vocab_size, embedding_dim, moe_hidden_dim, num_experts, visual_feature_dim=256, memory_slots=10, memory_dim=256, fact_memory_size=1000, fact_dim=256, ra_threshold=0.7): super().__init__() state_dict = {} if original_model_path and os.path.exists(original_model_path): with safetensors.safe_open(original_model_path, framework="pt", device="cpu") as f: for key in f.keys(): state_dict[key] = f.get_tensor(key) original_vocab_size = 5000 # 假設原始模型的詞彙量 original_embedding_dim = 64 # 假設原始模型的 embedding_dim # 判斷是否需要重新初始化 OriginalMoETransformerBlock 和 LayerNorm # 如果 embedding_dim 或 moe_hidden_dim 改變，則不載入原始權重，讓其重新初始化 if embedding_dim != original_embedding_dim or moe_hidden_dim != 1024: # 假設原始 moe_hidden_dim 是 1024 print("Embedding_dim or moe_hidden_dim changed. OriginalMoETransformerBlock and LayerNorm will be reinitialized.") self.original_model_core = OriginalModelReconstructed(original_vocab_size, original_embedding_dim, 1024, 16) # 參數擴展: moe_hidden_dim=1024, num_experts=16ts=16 # 這些層將是可訓練的 else: self.original_model_core = OriginalModelReconstructed(vocab_size, embedding_dim, 1024, 16) # 參數擴展: moe_hidden_dim=1024, num_experts=16im, num_experts) # 載入原始權重到核心模型 (如果存在) if state_dict: print("Loading original model weights...") self.original_model_core.norm.weight.data = state_dict["gamma"] self.original_model_core.norm.bias.data = state_dict["beta"] for i in range(num_experts): self.original_model_core.transformer_block_0.experts_w1[i].weight.data = state_dict[f"transformer_block.0.moe.experts.w1.weight"][i].T self.original_model_core.transformer_block_0.experts_w2[i].weight.data = state_dict[f"transformer_block.0.moe.experts.w2.weight"][i].T self.original_model_core.transformer_block_0.gate.weight.data = state_dict["transformer_block.0.moe.gate.weight"].T print("Original model weights loaded.") for param in self.original_model_core.transformer_block_0.parameters(): param.requires_grad = False for param in self.original_model_core.norm.parameters(): param.requires_grad = False self.embeddings = nn.Embedding(vocab_size, embedding_dim) self.output_layer = nn.Linear(embedding_dim, vocab_size) # 新增 Action Prediction Head for Comet-like capability # 輸出結構化行動 (例如: [action_type, x_coord, y_coord, element_id_token]) # 簡化為一個線性層，輸出一個固定長度的向量 self.action_prediction_head = nn.Sequential( nn.Linear(embedding_dim, 256), nn.ReLU(), nn.Linear(256, 4) # 假設輸出 4 個值: action_type, x, y, element_id_token ) self.vision_encoder = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1) self.vision_encoder = nn.Sequential(*list(self.vision_encoder.children())[:-1]) self.vision_projection = nn.Linear(512, visual_feature_dim) for param in self.vision_encoder.parameters(): param.requires_grad = False self.initial_fusion_layer = nn.Linear(visual_feature_dim + embedding_dim, embedding_dim) self.memory_engine = MemorySymbiosisEngine(embedding_dim, memory_slots, memory_dim) # 引入 Reality Anchor 模組 self.reality_anchor = RealityAnchor(embedding_dim, fact_memory_size, fact_dim, ra_threshold) # 引入 Multidimensional Generation Orchestrator (MGO) 模組 self.mgo = MultidimensionalGenerationOrchestrator(embedding_dim, vocab_size) def forward(self, text_input, image_input, user_profile_embedding=None, use_ra=False, return_fused_features=False, return_action_prediction=False): text_features = self.embeddings(text_input) if text_features.dim() == 3: text_features = text_features.squeeze(1) image_features = self.vision_encoder(image_input) image_features = image_features.view(image_features.size(0), -1) image_features = self.vision_projection(image_features) fused_initial_features = self.initial_fusion_layer(torch.cat((text_features, image_features), dim=1)) if return_fused_features: return fused_initial_features memory_enhanced_features = self.memory_engine(fused_initial_features, user_profile_embedding) # 應用 Reality Anchor (RA) 技術 if use_ra: # 在這裡，我們假設 memory_enhanced_features 作為生成上下文的嵌入 # 並將其作為 current_token_embedding 傳遞給 RA 進行調 # 實際應用中, RA 可能會更細粒度地作用於每個生成的 token adjusted_features, consistency_score = self.reality_anchor(memory_enhanced_features, memory_enhanced_features) # 簡化: 上下文和當前 token 相同 x = self.original_model_core.transformer_block_0(adjusted_features) else: x = self.original_model_core.transformer_block_0(memory_enhanced_features) x = self.original_model_core.norm(x) final_features = x # 將 x 賦值給 final_features, 以便 MGO 處理 # 應用 Multidimensional Generation Orchestrator (MGO) 技術 guidance_signal, planning_params, length_control_signal = self.mgo(final_features, current_output_length=0, target_intent_idx=None) final_features = final_features + guidance_signal # 疊加引導信號 output = self.output_layer(final_features) if return_action_prediction: action_prediction = self.action_prediction_head(final_features) return action_prediction if use_ra: return output, consistency_score, planning_params, length_control_signal else: return output, planning_params, length_control_signal if __name__ == "__main__": original_model_path = "/home/ubuntu/upload/moe_model.safetensors" vocab_size = 10000 # 詞彙量保持不變 embedding_dim = 128 # 增加嵌入維度以增加參數 moe_hidden_dim = 384 # 增加 MoE 隱藏維度以增加參數 num_experts = 16 visual_feature_dim = 256 memory_slots = 10 memory_dim = 256 fact_memory_size = 1000 fact_dim = 256 ra_threshold = 0.7 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 初始化整合模型 (包含 RA) integrated_model_ra = FullyIntegratedModelWithRA( original_model_path=original_model_path, vocab_size=vocab_size, embedding_dim=embedding_dim, moe_hidden_dim=moe_hidden_dim, num_experts=num_experts, visual_feature_dim=visual_feature_dim, memory_slots=memory_slots, memory_dim=memory_dim, fact_memory_size=fact_memory_size, fact_dim=fact_dim, ra_threshold=ra_threshold ).to(device) integrated_model_ra.eval() # 設置為評估模式 print("Fully Integrated Model with RA initialized successfully.") # 計算總參數數量 total_trainable_params = sum(p.numel() for p in integrated_model_ra.parameters() if p.requires_grad) print(f"Total trainable parameters: {total_trainable_params / 1_000_000:.2f}M") total_all_params = sum(p.numel() for p in integrated_model_ra.parameters()) print(f"Total all parameters (including frozen): {total_all_params / 1_000_000:.2f}M") # --- 模擬 Agent Matrix 框架交互 (包含 RA 命令) --- agent_interface_ra = AgentMatrixInterface(integrated_model_ra) print("Agent Matrix Interface with RA initialized.") # 模擬輸入 dummy_text_input = torch.tensor([[100]], dtype=torch.long).to(device) # Batch size 1, 1 token dummy_image_input = torch.randn(1, 3, 224, 224).to(device) # Batch size 1, 3 channels, 224x224 print("\n--- Simulating Agent Matrix Commands (with RA) ---") # 模擬 'analyze_image_text' 命令 try: print("Executing command: analyze_image_text") fused_features = agent_interface_ra(command="analyze_image_text", text_input=dummy_text_input, image_input=dummy_image_input) print(f"Analyzed features shape: {fused_features.shape}") except Exception as e: print(f"Error executing analyze_image_text: {e}") # 模擬 'generate_response' 命令 (不啟用 RA) try: print("Executing command: generate_response (without RA)") output_logits, planning_params, length_control_signal = agent_interface_ra(command="generate_response", text_input=dummy_text_input, image_input=dummy_image_input) print(f"Generated response logits shape: {output_logits.shape}") print(f"MGO Planning Params: {planning_params.tolist()}") print(f"MGO Length Control Signal: {length_control_signal.item():.4f}") except Exception as e: print(f"Error executing generate_response (without RA): {e}") # 模擬 'generate_anchored_response' 命令 (啟用 RA) try: print("Executing command: generate_anchored_response (with RA)") output_logits_ra, consistency_score, planning_params_ra, length_control_signal_ra = agent_interface_ra(command="generate_anchored_response", text_input=dummy_text_input, image_input=dummy_image_input) print(f"Generated anchored response logits shape: {output_logits_ra.shape}") print(f"Fact consistency score: {consistency_score.item():.4f}") print(f"MGO Planning Params (RA): {planning_params_ra.tolist()}") print(f"MGO Length Control Signal (RA): {length_control_signal_ra.item():.4f}") except Exception as e: print(f"Error executing generate_anchored_response (with RA): {e}") # 模擬 'retrieve_memory' 命令 try: print("Executing command: retrieve_memory") retrieved_memory = agent_interface_ra(command="retrieve_memory", query_text_input=dummy_text_input, query_image_input=dummy_image_input) print(f"Retrieved memory shape: {retrieved_memory.shape}") except Exception as e: print(f"Error executing retrieve_memory: {e}") # 保存整合後的模型 (包含 RA) state_dict_to_save_ra = integrated_model_ra.state_dict() keys_to_remove_ra = [key for key in state_dict_to_save_ra.keys() if 'vision_encoder' in key] for key in keys_to_remove_ra: del state_dict_to_save_ra[key] for key in state_dict_to_save_ra: if isinstance(state_dict_to_save_ra[key], torch.Tensor): state_dict_to_save_ra[key] = state_dict_to_save_ra[key].contiguous() safetensors_save_file(state_dict_to_save_ra, "fully_integrated_model_with_ra_mgo.safetensors") print("Fully integrated model with RA and MGO saved to fully_integrated_model_with_ra_mgo.safetensors")