Upload integrated_model_design.py

integrated_model_design.py

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+
+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��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.")
+