Cogent-ai
/

cogent-csp-15m

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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.")