import torch import torch.nn as nn from safetensors.torch import save_file as safetensors_save_file from torchvision import models, transforms from PIL import Image 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) 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) --- # 這個模組將負責存儲、檢索和整合用戶的歷史記憶。 # 這裡我們使用一個簡化的記憶網絡作為示例，實際實現會更複雜。 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 # 記憶存儲：簡單的鍵值對記憶，這裡用可學習的張量模擬 self.memory_keys = nn.Parameter(torch.randn(memory_slots, memory_dim)) self.memory_values = nn.Parameter(torch.randn(memory_slots, embedding_dim)) # 存儲與 embedding_dim 兼容的記憶 # 記憶讀取機制：使用注意力機制從記憶中檢索相關信息 self.query_projection = nn.Linear(embedding_dim, memory_dim) self.memory_read_fusion = nn.Linear(embedding_dim + embedding_dim, embedding_dim) # 融合查詢和讀取到的記憶 def forward(self, current_features, user_profile_embedding=None): # current_features: 當前輸入的融合特徵 (batch_size, embedding_dim) # 生成查詢向量 query = self.query_projection(current_features) # (batch_size, memory_dim) # 計算查詢與記憶鍵的相似度 (點積注意力) attention_scores = torch.matmul(query, self.memory_keys.T) # (batch_size, memory_slots) attention_weights = torch.softmax(attention_scores, dim=-1) # (batch_size, memory_slots) # 根據權重讀取記憶值 read_memory = torch.matmul(attention_weights, self.memory_values) # (batch_size, embedding_dim) # 將讀取到的記憶與當前特徵融合 fused_with_memory = torch.cat((current_features, read_memory), dim=-1) output_features = self.memory_read_fusion(fused_with_memory) # 記憶更新機制 (簡化：這裡不實作複雜的記憶寫入，假定記憶是預訓練或緩慢更新的) return output_features # --- 3. 智能體生態框架接口 (Agent Matrix Intelligent Agent Ecosystem Framework) --- # 這個模組將作為模型與外部 Agent Matrix 框架交互的接口。 # 模型本身作為一個智能體，接收指令，輸出結果。 # 這裡我們用一個簡單的接口類來模擬，實際的框架交互會通過 RPC 或消息隊列實現。 class AgentMatrixInterface(nn.Module): def __init__(self, model_core): super().__init__() self.model_core = model_core # 核心模型，負責處理多模態輸入和記憶 # 假設 Agent Matrix 框架會通過一個 "command" 來指示模型執行什麼任務 # 這裡我們模擬一個簡單的任務映射 self.task_mapping = { "analyze_image_text": self._analyze_image_text, "retrieve_memory": self._retrieve_memory, "generate_response": self._generate_response } def _analyze_image_text(self, text_input, image_input): # 這裡調用核心模型的前向傳播，獲取輸出 return self.model_core(text_input, image_input, return_fused_features=True) # 返回融合後的特徵 def _retrieve_memory(self, query_text_input, query_image_input=None): # 模擬記憶檢索，可能需要一個單獨的記憶查詢模組 # 暫時直接調用核心模型，讓記憶模組自行處理 # 這裡我們假設模型能夠在 forward 內部處理記憶檢索 dummy_image = torch.randn(1, 3, 224, 224) # 假設一個 dummy image dummy_text = torch.tensor([0]) # 假設一個 dummy text token # 為了演示，直接從記憶引擎讀取 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 # 如果沒有圖像輸入，則只使用文本特徵 return self.model_core.memory_engine(current_features) def _generate_response(self, text_input, image_input): return self.model_core(text_input, image_input) def forward(self, command, **kwargs): if command in self.task_mapping: return self.task_mapping[command](**kwargs) else: raise ValueError(f"Unknown command: {command}") # --- 4. 整合後的完整模型架構 --- # 整合 On-Device Compute 的考慮： # 這裡的架構設計本身就是輕量化的，embedding_dim 較小。 # 為了實現 On-Device Compute，我們在訓練後會對模型進行量化、剪枝等優化。 # 這部分不會直接體現在 PyTorch 模型定義中，而是在部署時進行。 class FullyIntegratedModel(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): super().__init__() state_dict = {} if 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) # 原始模型的核心部分，詞彙量和嵌入維度保持與原始模型兼容 # 這裡我們使用原始的 vocab_size 和 embedding_dim 來載入原始模型的 MoE 權重 # 之後我們會替換 embeddings 和 output_layer 以支持新的 vocab_size 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 self.original_model_core = OriginalModelReconstructed(original_vocab_size, original_embedding_dim, moe_hidden_dim, num_experts) # 載入原始權重到核心模型 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 # 凍結原始模型的 Transformer Block 和 LayerNorm 權重 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 # 重新定義 embeddings 和 output_layer 以支持新的 vocab_size self.embeddings = nn.Embedding(vocab_size, embedding_dim) self.output_layer = nn.Linear(embedding_dim, vocab_size) # 視覺前端：使用預訓練的 ResNet18 作為圖像編碼器 print("Initializing vision encoder (ResNet18). This may take some time to download weights...") self.vision_encoder = models.resnet18(weights=models.ResNet18_Weights.IMAGENET1K_V1) self.vision_encoder = nn.Sequential(*list(self.vision_encoder.children())[:-1]) print("Vision encoder initialized.") self.vision_projection = nn.Linear(512, visual_feature_dim) # 凍結視覺編碼器權重 for param in self.vision_encoder.parameters(): param.requires_grad = False # 特徵融合層：將視覺特徵與文本特徵融合 # 這裡的 embedding_dim 是新的 embedding_dim self.initial_fusion_layer = nn.Linear(visual_feature_dim + embedding_dim, embedding_dim) # 記憶共生引擎 self.memory_engine = MemorySymbiosisEngine(embedding_dim, memory_slots, memory_dim) # 最終融合層：融合記憶引擎的輸出 self.final_fusion_layer = nn.Linear(embedding_dim, embedding_dim) # 記憶引擎輸出也是 embedding_dim def forward(self, text_input, image_input, user_profile_embedding=None, return_fused_features=False): # 1. 處理文本輸入 text_features = self.embeddings(text_input) if text_features.dim() == 3: text_features = text_features.squeeze(1) # 2. 處理圖像輸入 image_features = self.vision_encoder(image_input) image_features = image_features.view(image_features.size(0), -1) image_features = self.vision_projection(image_features) # 3. 初始特徵融合 (文本 + 視覺) fused_initial_features = torch.cat((text_features, image_features), dim=1) fused_initial_features = self.initial_fusion_layer(fused_initial_features) if return_fused_features: return fused_initial_features # 4. 記憶共生引擎處理 # 將融合後的特徵傳遞給記憶引擎，獲取記憶增強後的特徵 memory_enhanced_features = self.memory_engine(fused_initial_features, user_profile_embedding) # 5. 最終融合層 (可選，這裡直接使用記憶引擎的輸出) # memory_enhanced_features = self.final_fusion_layer(memory_enhanced_features) # 6. 傳遞給原始模型的 MoE Transformer Block # 這裡使用 self.original_model_core 的 transformer_block_0 和 norm x = self.original_model_core.transformer_block_0(memory_enhanced_features) x = self.original_model_core.norm(x) # 7. 輸出層 output = self.output_layer(x) return output # 範例使用 if __name__ == "__main__": original_model_path = "/home/ubuntu/upload/moe_model.safetensors" # 模型參數 # 這裡的 vocab_size 和 embedding_dim 應該與您在 integrate_vision_retained.py 中使用的兼容 # 為了演示，我們使用一個較小的 vocab_size 和 embedding_dim vocab_size = 10000 # 示例詞彙量 embedding_dim = 64 # 嵌入維度，與原始 MoE Block 兼容 moe_hidden_dim = 192 num_experts = 16 visual_feature_dim = 256 memory_slots = 10 memory_dim = 256 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # 初始化整合模型 integrated_model = FullyIntegratedModel( 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 ).to(device) integrated_model.eval() # 設置為評估模式 print("Fully Integrated Model initialized successfully.") # 檢查 ResNet18 是否已下載 resnet_cache_dir = torch.hub.get_dir() print(f"PyTorch Hub cache directory: {resnet_cache_dir}") resnet_weights_path = os.path.join(resnet_cache_dir, "checkpoints", "resnet18-f37072fd.pth") if os.path.exists(resnet_weights_path): print(f"ResNet18 weights found at: {resnet_weights_path}") else: print("ResNet18 weights not found in cache. They might be downloaded during initialization.") # 計算總參數數量 total_params = sum(p.numel() for p in integrated_model.parameters() if p.requires_grad) print(f"Total trainable parameters: {total_params / 1_000_000:.2f}M") total_all_params = sum(p.numel() for p in integrated_model.parameters()) print(f"Total all parameters (including frozen): {total_all_params / 1_000_000:.2f}M") # --- 模擬 Agent Matrix 框架交互 --- agent_interface = AgentMatrixInterface(integrated_model) print("Agent Matrix Interface 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 ---") # 模擬 'analyze_image_text' 命令 try: print("Executing command: analyze_image_text") fused_features = agent_interface(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 try: print("Executing command: generate_response") output_logits = agent_interface(command="generate_response", text_input=dummy_text_input, image_input=dummy_image_input) print(f"Generated response logits shape: {output_logits.shape}") except Exception as e: print(f"Error executing generate_response: {e}") # 模擬 'retrieve_memory' 命令 try: print("Executing command: retrieve_memory") retrieved_memory = agent_interface(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 (只保存可訓練的參數) state_dict_to_save = integrated_model.state_dict() # 移除凍結的 vision_encoder 權重，因為它們是預訓練的，不需要保存到 safetensors keys_to_remove = [key for key in state_dict_to_save.keys() if 'vision_encoder' in key] for key in keys_to_remove: del state_dict_to_save[key] # 確保所有張量都是連續的 for key in state_dict_to_save: if isinstance(state_dict_to_save[key], torch.Tensor): state_dict_to_save[key] = state_dict_to_save[key].contiguous() safetensors_save_file(state_dict_to_save, "fully_integrated_model.safetensors") print("Fully integrated model saved to fully_integrated_model.safetensors") # --- On-Device Compute 部署考慮 (此處僅為說明，不實作) --- print("\n--- On-Device Compute Considerations ---") print("To enable On-Device Compute, this model would typically undergo further optimization steps after training:") print("1. Quantization: Convert model weights and activations to lower precision (e.g., INT8) to reduce size and speed up inference.") print("2. Pruning: Remove redundant connections/neurons to make the model sparse.") print("3. Export to optimized formats: Convert to formats like ONNX, TensorFlow Lite, or Core ML for efficient deployment on edge devices.") print("4. Hardware-specific optimization: Utilize dedicated AI accelerators (NPUs) on target devices.") print("These steps are part of the deployment pipeline and are not directly implemented in the PyTorch model definition.")