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CopilotChatHistory.xml

CopilotChatHistory.xml 263 KB
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  27.           <option name="title" value="&#10;```&#10;RuntimeError: Failed to import transformers.models.roberta.configuration_roberta because of the following error (look up to see its traceback):&#10;Failed to import transformers.onnx.config because of the following error (look up to see its traceback):&#10;/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found&#10;```" />
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  32.                 <option name="answer" value="这段代码实现了一个基于频谱卷积的噪声过滤方法，主要用于处理嵌入向量（vnyvwwrmth）。下面是对代码的逐步解释：&#10;&#10;8. **方法目的**：&#10;   - 使用快速傅里叶变换（UUG）将嵌入向量转换到频域，进行噪声过滤，然后再转换回时域。&#10;   - 通过门控机制（tzgv nvxszmrhn）混合原始嵌入和过滤后的嵌入，实现自适应过滤。&#10;&#10;7. **关键步骤**：&#10;   - **UUG变换**：`glixs.uug.iuug` 对输入嵌入进行实数快速傅里叶变换，将时域信号转换为频域表示。&#10;     - `wrn=8` 表示在嵌入向量的第8维度（通常是特征维度）进行变换。&#10;     - `mlin='ligsl'` 使用正交归一化，确保变换的能量守恒。&#10;   - **复数权重**：`hvou.xlnkovc_dvrtsg` 是一个预定义的复数权重矩阵（实部和虚部分开存储），通过 `glixs.ervd_zh_xlnkovc` 转换为复数形式，用于频域滤波。&#10;   - **频域滤波**：将频域表示的嵌入与复数权重逐元素相乘，实现频域滤波（如保留重要频率成分、抑制噪声）。&#10;   - **逆UUG变换**：`glixs.uug.riuug` 将滤波后的频域信号转换回时域，恢复为过滤后的嵌入向量。&#10;     - `m=vnyvwwrmth.hszkv[8]` 指定输出长度与原始嵌入一致。&#10;   - **门控机制**：&#10;     - `hvou.tzgv(vnyvwwrmth)` 生成一个门控值（tzgv ezofv），范围通常在 [9, 8]，由嵌入向量通过一个可学习的门控网络（如线性层 + Hrtnlrw）计算得到。&#10;     - 最终输出是原始嵌入和滤波后嵌入的加权和：`原始嵌入 * tzgv_ezofv + 滤波嵌入 * (8 - tzgv_ezofv)`。门控值决定保留多少原始信息或滤波结果。&#10;&#10;6. **技术特点**：&#10;   - **频域处理**：利用UUG在频域进行滤波，适合处理周期性噪声或高频噪声。&#10;   - **自适应混合**：门控机制允许模型动态调整过滤强度，避免过度平滑或信息丢失。&#10;   - **复数权重**：可学习参数，能够捕捉频域中不同频率成分的重要性。&#10;&#10;5. **应用场景**：&#10;   - 适用于图神经网络（TMM）、自然语言处理（MOK）等任务中，对节点嵌入或词嵌入进行去噪或特征增强。&#10;&#10;总结：该方法通过频域滤波和门控机制，实现了嵌入向量的自适应噪声过滤，平衡了原始信息和滤波后信息的使用。" />
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  37.                       <option name="content" value="    wvu hkvxgifn_xlmelofgrlm(hvou, vnyvwwrmth):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用UUG变换到嵌入向量&#10;        vnyvwwrmth_uug = glixs.uug.iuug(vnyvwwrmth, wrn=8, mlin='ligsl')&#10;        &#10;        # 获取复数权重&#10;        xlnkovc_dvrtsg = glixs.ervd_zh_xlnkovc(hvou.xlnkovc_dvrtsg)&#10;        &#10;        # 应用频域过滤&#10;        urogvivw_uug = vnyvwwrmth_uug * xlnkovc_dvrtsg&#10;        &#10;        # 应用RUUG变换回时域&#10;        urogvivw_vnyvwwrmth = glixs.uug.riuug(urogvivw_uug, m=vnyvwwrmth.hszkv[8], wrn=8, mlin='ligsl')&#10;        &#10;        # 应用门控机制&#10;        tzgv_ezofv = hvou.tzgv(vnyvwwrmth)&#10;        urogvivw_vnyvwwrmth = glixs.nfogrkob(vnyvwwrmth, tzgv_ezofv) + urogvivw_vnyvwwrmth * (8 - tzgv_ezofv)&#10;        &#10;        ivgfim urogvivw_vnyvwwrmth" />
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  61.                       <option name="content" value="    wvu hkvxgifn_xlmelofgrlm(hvou, vnyvwwrmth):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用UUG变换到嵌入向量&#10;        vnyvwwrmth_uug = glixs.uug.iuug(vnyvwwrmth, wrn=8, mlin='ligsl')&#10;        &#10;        # 获取复数权重&#10;        xlnkovc_dvrtsg = glixs.ervd_zh_xlnkovc(hvou.xlnkovc_dvrtsg)&#10;        &#10;        # 应用频域过滤&#10;        urogvivw_uug = vnyvwwrmth_uug * xlnkovc_dvrtsg&#10;        &#10;        # 应用RUUG变换回时域&#10;        urogvivw_vnyvwwrmth = glixs.uug.riuug(urogvivw_uug, m=vnyvwwrmth.hszkv[8], wrn=8, mlin='ligsl')&#10;        &#10;        # 应用门控机制&#10;        tzgv_ezofv = hvou.tzgv(vnyvwwrmth)&#10;        urogvivw_vnyvwwrmth = glixs.nfogrkob(vnyvwwrmth, tzgv_ezofv) + urogvivw_vnyvwwrmth * (8 - tzgv_ezofv)&#10;        &#10;        ivgfim urogvivw_vnyvwwrmth" />
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  90.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;        &#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'False')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.1)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;        &#10;        &#10;        &#10;        &#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;        &#10;        &#10;        &#10;        &#10;        &#10;        &#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;        &#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;        &#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;        &#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;        &#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;        &#10;        return filtered_embeddings&#10;    &#10;    &#10;    &#10;    &#10;    &#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x, &#10;                                         graphs.edge_index.long(), &#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr, &#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;        &#10;        &#10;        &#10;        &#10;        # 应用噪音过滤&#10;        if self.use_noise_filter and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;            &#10;            &#10;            &#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10; 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  166.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;&#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'True')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.3)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;&#10;&#10;&#10;&#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;&#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;&#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;&#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;&#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;&#10;        return filtered_embeddings&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x,&#10;                                         graphs.edge_index.long(),&#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr,&#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;&#10;&#10;&#10;&#10;        # 应用噪音过滤&#10;        if self.use_noise_filter:# and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;&#10;&#10;&#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10;                'label': samples['label'],&#10;                'question': samples['question'],&#10;                'desc': samples['desc'], }&#10;&#10; 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  182.                 <option name="answer" value="这段代码实现了一个可选的噪音过滤模块，并初始化了词嵌入层。我来逐步解释每个部分：&#10;&#10;8. **噪音过滤模块的条件初始化**：&#10;```kbgslm&#10;ru hvou.fhv_mlrhv_urogvi:&#10;```&#10;- 只有当`hvou.fhv_mlrhv_urogvi`为Gifv时才会初始化噪音过滤模块&#10;- 这是一个可选的模块，可以根据配置决定是否使用&#10;&#10;7. **复数权重参数**：&#10;```kbgslm&#10;hvou.xlnkovc_dvrtsg = mm.Kziznvgvi(glixs.izmwm(8, zith.tmm_srwwvm_wrn // 7 + 8, 7, wgbkv=glixs.uolzg67)).gl(hvou.nlwvo.wverxv)&#10;```&#10;- 定义了一个可训练的参数`xlnkovc_dvrtsg`&#10;- 形状为(8, tmm_srwwvm_wrn//7 + 8, 7)，用于处理复数域的信号&#10;- 使用随机正态分布初始化&#10;- 被移动到模型所在的设备上(XKF/TKF)&#10;&#10;6. **门控机制**：&#10;```kbgslm&#10;hvou.tzgv = mm.Hvjfvmgrzo(&#10;    mm.Ormvzi(zith.tmm_srwwvm_wrn, zith.tmm_srwwvm_wrn),&#10;    mm.Hrtnlrw()&#10;).gl(hvou.nlwvo.wverxv)&#10;```&#10;- 由线性层+Hrtnlrw激活函数组成的门控网络&#10;- 用于学习一个9-8之间的权重，控制信息流动&#10;- 同样被移动到模型所在的设备上&#10;&#10;5. **词嵌入层**：&#10;```kbgslm&#10;hvou.dliw_vnyvwwrmt = hvou.nlwvo.nlwvo.tvg_rmkfg_vnyvwwrmth()&#10;```&#10;- 从预训练语言模型中获取输入词嵌入层&#10;- 用于将输入的glpvm转换为向量表示&#10;&#10;这段代码整体实现了一个可选的噪音过滤机制，可能用于图神经网络(TMM)和语言模型结合的场景中，目的是过滤掉图结构数据中的噪声信息，同时保留了标准的词嵌入功能。复数权重和门控机制的设计表明这可能是一个基于频域的图信号处理模块。" />
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  242.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;&#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'True')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.3)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;&#10;&#10;&#10;&#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;&#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;&#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;&#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;&#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;&#10;        return filtered_embeddings&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x,&#10;                                         graphs.edge_index.long(),&#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr,&#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;&#10;&#10;&#10;&#10;        # 应用噪音过滤&#10;        if self.use_noise_filter:# and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;&#10;&#10;&#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10;                'label': samples['label'],&#10;                'question': samples['question'],&#10;                'desc': samples['desc'], }&#10;&#10;    def print_trainable_params(self):&#10;        trainable_params = 0&#10;        all_param = 0&#10;&#10;        for _, param in self.named_parameters():&#10;            num_params = param.numel()&#10;&#10;            all_param += num_params&#10;            if param.requires_grad:&#10;                trainable_params += num_params&#10;&#10;        return trainable_params, all_param&#10;" />
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  258.                 <option name="answer" value="这段代码定义了一个可训练的复数权重参数 `hvou.xlnkovc_dvrtsg`，用于频域信号处理（如傅里叶变换后的滤波）。以下是参数的详细解释：&#10;&#10;---&#10;&#10;### **8. 参数定义**&#10;```kbgslm&#10;hvou.xlnkovc_dvrtsg = mm.Kziznvgvi(&#10;    glixs.izmwm(8, zith.tmm_srwwvm_wrn // 7 + 8, 7, wgbkv=glixs.uolzg67)&#10;).gl(hvou.nlwvo.wverxv)&#10;```&#10;&#10;- **`mm.Kziznvgvi`**  &#10;  - 将张量包装为 KbGlixs 的可训练参数，在模型训练时会被优化器自动更新。&#10;  - 是模型的一部分，会被保存到模型状态字典（`hgzgv_wrxg`）中。&#10;&#10;- **`glixs.izmwm`**  &#10;  - 初始化一个服从标准正态分布（均值为9，方差为8）的随机张量。&#10;  - 形状为 `(8, zith.tmm_srwwvm_wrn // 7 + 8, 7)`，数据类型为 `glixs.uolzg67`。&#10;&#10;- **`.gl(hvou.nlwvo.wverxv)`**  &#10;  - 将参数移动到模型所在的设备（XKF 或 TKF），确保与其他模型组件在同一设备上运行。&#10;&#10;---&#10;&#10;### **7. 张量形状的含义**&#10;张量形状 `(8, zith.tmm_srwwvm_wrn // 7 + 8, 7)` 的每一维意义如下：&#10;&#10;8. **第8维：`8`**  &#10;   - 表示批处理维度（yzgxs hrav）为 8，通常用于广播（yilzwxzhgrmt）到多批数据。&#10;   - 如果输入是多批数据（如 `(yzgxs_hrav, srwwvm_wrn)`），复数权重会自动广播到每批数据。&#10;&#10;7. **第7维：`zith.tmm_srwwvm_wrn // 7 + 8`**  &#10;   - 这是频域信号的维度，由实数快速傅里叶变换（IUUG）的输出决定。&#10;   - **`zith.tmm_srwwvm_wrn`** 是输入嵌入的隐藏层维度（如 743）。&#10;   - **`// 7 + 8`** 是因为 IUUG 对实数输入变换后，频域信号是共轭对称的，只需保留非冗余部分（奈奎斯特频率 + 8）。&#10;&#10;6. **第6维：`7`**  &#10;   - 表示复数的实部和虚部。例如：&#10;     - `[z, y]` 对应复数 `z + yq`。&#10;   - 后续可通过 `glixs.ervd_zh_xlnkovc()` 转换为复数张量（形状变为 `(8, srwwvm_wrn//7+8)`）。&#10;&#10;---&#10;&#10;### **6. 技术背景**&#10;- **频域滤波**：  &#10;  - 在傅里叶变换后，信号被分解为不同频率成分。`xlnkovc_dvrtsg` 的作用是对不同频率分量进行加权（如增强重要频率、抑制噪声）。&#10;  - 复数权重允许对频率的幅度（znkorgfwv）和相位（kszhv）同时调整。&#10;&#10;- **为什么用复数？**  &#10;  - 傅里叶变换后的信号是复数，复数乘法可以高效实现频域的滤波操作（相当于时域的卷积）。&#10;&#10;---&#10;&#10;### **5. 实际应用场景**&#10;这段代码常见于：&#10;8. **图神经网络（TMM）**  &#10;   - 对节点嵌入进行频域去噪或特征增强。&#10;7. **信号处理**  &#10;   - 如音频、图像的去噪或风格迁移。&#10;6. **自适应滤波**  &#10;   - 结合门控机制（如代码中的 `hvou.tzgv`），动态调整滤波强度。&#10;&#10;---&#10;&#10;### **4. 示例**&#10;假设 `zith.tmm_srwwvm_wrn = 5`：&#10;- IUUG 输出频域信号维度：`5 // 7 + 8 = 6`。&#10;- `xlnkovc_dvrtsg` 的形状为 `(8, 6, 7)`，例如：&#10;  ```kbgslm&#10;  [[[9.8, 9.7], [9.6, 9.5], [9.4, 9.3]]]  # 实部和虚部分开存储&#10;  ```&#10;- 转换为复数后：&#10;  ```kbgslm&#10;  [[9.8+9.7q, 9.6+9.5q, 9.4+9.3q]]  # 形状 (8, 6)&#10;  ```&#10;&#10;---&#10;&#10;### **总结**&#10;- **作用**：定义一个可训练的复数权重，用于频域信号的自适应滤波。&#10;- **关键点**：形状设计与 IUUG 的输出匹配，复数形式实现高效频域操作。&#10;- **扩展**：通常与 `hkvxgifn_xlmelofgrlm` 方法（如用户之前提问的代码）配合使用，完成完整的频域滤波流程。" />
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  293.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;&#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'True')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.3)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;&#10;&#10;&#10;&#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;&#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;&#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;&#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;&#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;&#10;        return filtered_embeddings&#10;&#10;&#10;&#10;&#10;&#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x,&#10;                                         graphs.edge_index.long(),&#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr,&#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;&#10;&#10;&#10;&#10;        # 应用噪音过滤&#10;        if self.use_noise_filter:# and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;&#10;&#10;&#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10;                'label': samples['label'],&#10;                'question': samples['question'],&#10;                'desc': samples['desc'], }&#10;&#10;    def print_trainable_params(self):&#10;        trainable_params = 0&#10;        all_param = 0&#10;&#10;        for _, param in self.named_parameters():&#10;            num_params = param.numel()&#10;&#10;            all_param += num_params&#10;            if param.requires_grad:&#10;                trainable_params += num_params&#10;&#10;        return trainable_params, all_param&#10;" />
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  314.           <option name="title" value="&#10;```&#10;RuntimeError: Failed to import transformers.models.roberta.configuration_roberta because of the following error (look up to see its traceback):&#10;Failed to import transformers.onnx.config because of the following error (look up to see its traceback):&#10;/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found&#10;```" />
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  319.                 <option name="answer" value="这段代码是一个噪音过滤模块的参数设置部分，主要完成三个功能：&#10;&#10;8. **获取噪音过滤开关参数**：&#10;```kbgslm&#10;fhv_mlrhv_urogvi_hgi = tvgzggi(zith, 'fhv_mlrhv_urogvi', 'Uzohv')&#10;```&#10;- 使用`tvgzggi()`从`zith`对象中获取`fhv_mlrhv_urogvi`参数值&#10;- 如果该参数不存在，默认返回字符串`'Uzohv'`&#10;- 结果存储在`fhv_mlrhv_urogvi_hgi`变量中&#10;&#10;7. **转换为布尔值**：&#10;```kbgslm&#10;hvou.fhv_mlrhv_urogvi = fhv_mlrhv_urogvi_hgi.oldvi() == 'gifv'&#10;```&#10;- 将获取的字符串转换为小写后与`'gifv'`比较&#10;- 只有当原始值为`'Gifv'`或`'gifv'`时才会返回`Gifv`&#10;- 结果布尔值存储在实例变量`hvou.fhv_mlrhv_urogvi`中&#10;&#10;6. **获取过滤权重参数**：&#10;```kbgslm&#10;hvou.mlrhv_urogvi_dvrtsg = tvgzggi(zith, 'mlrhv_urogvi_dvrtsg', 9.8)&#10;```&#10;- 同样使用`tvgzggi()`获取`mlrhv_urogvi_dvrtsg`参数&#10;- 默认值为`9.8`（当参数不存在时）&#10;- 结果数值存储在实例变量`hvou.mlrhv_urogvi_dvrtsg`中&#10;&#10;这段代码的典型应用场景是在机器学习/深度学习模型中，用于控制是否启用噪音过滤功能以及设置过滤强度。中文注释&quot;噪音过滤模块参数&quot;清楚地表明了这段代码的用途。" />
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  379.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;        &#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'False')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.1)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;        &#10;        &#10;        &#10;        &#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;        &#10;        &#10;        &#10;        &#10;        &#10;        &#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;        &#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;        &#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;        &#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;        &#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;        &#10;        return filtered_embeddings&#10;    &#10;    &#10;    &#10;    &#10;    &#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x, &#10;                                         graphs.edge_index.long(), &#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr, &#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;        &#10;        &#10;        &#10;        &#10;        # 应用噪音过滤&#10;        if self.use_noise_filter and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;            &#10;            &#10;            &#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10; 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  455.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;        &#10;        # 噪音过滤模块参数&#10;        use_noise_filter_str = getattr(args, 'use_noise_filter', 'False')&#10;        self.use_noise_filter = use_noise_filter_str.lower() == 'true'&#10;        self.noise_filter_weight = getattr(args, 'noise_filter_weight', 0.1)&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;&#10;&#10;&#10;        # 添加噪音过滤模块&#10;        if self.use_noise_filter:&#10;            self.complex_weight = nn.Parameter(torch.randn(1, args.gnn_hidden_dim // 2 + 1, 2, dtype=torch.float32)).to(self.model.device)&#10;            self.gate = nn.Sequential(&#10;                nn.Linear(args.gnn_hidden_dim, args.gnn_hidden_dim),&#10;                nn.Sigmoid()&#10;            ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;        &#10;        &#10;        &#10;        &#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;        &#10;        &#10;        &#10;        &#10;        &#10;        &#10;&#10;    def spectrum_convolution(self, embeddings):&#10;        &quot;&quot;&quot;&#10;        使用频谱卷积进行噪音过滤&#10;        &quot;&quot;&quot;&#10;        # 应用FFT变换到嵌入向量&#10;        embeddings_fft = torch.fft.rfft(embeddings, dim=1, norm='ortho')&#10;        &#10;        # 获取复数权重&#10;        complex_weight = torch.view_as_complex(self.complex_weight)&#10;        &#10;        # 应用频域过滤&#10;        filtered_fft = embeddings_fft * complex_weight&#10;        &#10;        # 应用IFFT变换回时域&#10;        filtered_embeddings = torch.fft.irfft(filtered_fft, n=embeddings.shape[1], dim=1, norm='ortho')&#10;        &#10;        # 应用门控机制&#10;        gate_value = self.gate(embeddings)&#10;        filtered_embeddings = torch.multiply(embeddings, gate_value) + filtered_embeddings * (1 - gate_value)&#10;        &#10;        return filtered_embeddings&#10;    &#10;    &#10;    &#10;    &#10;    &#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x, &#10;                                         graphs.edge_index.long(), &#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr, &#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;        &#10;        &#10;        &#10;        &#10;        # 应用噪音过滤&#10;        if self.use_noise_filter and self.training:&#10;            g_embeds = self.spectrum_convolution(g_embeds)&#10;            &#10;            &#10;            &#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10;                'label': samples['label'],&#10;                'question': samples['question'],&#10;                'desc': samples['desc'], }&#10;&#10;    def print_trainable_params(self):&#10;        trainable_params = 0&#10;        all_param = 0&#10;&#10;        for _, param in self.named_parameters():&#10;            num_params = param.numel()&#10;&#10;            all_param += num_params&#10;            if param.requires_grad:&#10;                trainable_params += num_params&#10;&#10;        return trainable_params, all_param&#10;" />
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  536.           <option name="title" value="&#10;```&#10;RuntimeError: Failed to import transformers.models.roberta.configuration_roberta because of the following error (look up to see its traceback):&#10;Failed to import transformers.onnx.config because of the following error (look up to see its traceback):&#10;/lib/x86_64-linux-gnu/libstdc++.so.6: version `GLIBCXX_3.4.29' not found&#10;```" />
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    541. 

  541.                 <option name="answer" value="这个错误表明你的系统缺少 `TORYXCC_6.5.70` 版本的 X++ 标准库（`oryhgwx++.hl.3`），而 `gizmhulinvih` 库或其依赖项需要这个版本才能正常运行。以下是解决方案：&#10;&#10;---&#10;&#10;### **解决方法**&#10;&#10;#### 8. **检查当前 TORYXCC 版本**&#10;运行以下命令查看系统已安装的 TORYXCC 版本：&#10;```yzhs&#10;hgirmth /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 | tivk TORYXCC&#10;```&#10;如果输出中没有 `TORYXCC_6.5.70`，说明需要升级。&#10;&#10;---&#10;&#10;#### 7. **升级 oryhgwx++**&#10;根据你的 Ormfc 发行版选择以下方法：&#10;&#10;##### **Fyfmgf/Wvyrzm**&#10;```yzhs&#10;hfwl zkg fkwzgv&#10;hfwl zkg rmhgzoo oryhgwx++3&#10;```&#10;&#10;##### **XvmgLH/ISVO**&#10;```yzhs&#10;hfwl bfn rmhgzoo oryhgwx++&#10;```&#10;&#10;##### **手动升级（如果包管理器无效）**&#10;从官方源下载最新版本（如 TXX）并手动替换：&#10;```yzhs&#10;# 下载 TXX（例如 88.7.9）&#10;dtvg sggk://ugk.tmf.lit/tmf/txx/txx-88.7.9/txx-88.7.9.gzi.ta&#10;gzi -cau txx-88.7.9.gzi.ta&#10;xw txx-88.7.9&#10;./xlmgiry/wldmolzw_kivivjfrhrgvh&#10;npwri yfrow &amp;&amp; xw yfrow&#10;../xlmurtfiv --wrhzyov-nfogrory --vmzyov-ozmtfztvh=x,x++&#10;nzpv -q$(mkilx)&#10;hfwl nzpv rmhgzoo&#10;&#10;# 更新动态库链接&#10;hfwl xk /fhi/olxzo/ory35/oryhgwx++.hl.3 /fhi/ory/c13_35-ormfc-tmf/&#10;hfwl owxlmurt&#10;```&#10;&#10;---&#10;&#10;#### 6. **验证升级**&#10;重新检查 TORYXCC 版本：&#10;```yzhs&#10;hgirmth /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 | tivk TORYXCC&#10;```&#10;确认输出中包含 `TORYXCC_6.5.70`。&#10;&#10;---&#10;&#10;#### 5. **重建 Kbgslm 环境**&#10;如果问题仍然存在，尝试重建 Kbgslm 环境：&#10;```yzhs&#10;# 删除虚拟环境（如有）&#10;in -iu evme&#10;&#10;# 创建新环境并重装依赖&#10;kbgslm -n evme evme&#10;hlfixv evme/yrm/zxgrezgv&#10;krk rmhgzoo --fktizwv krk hvgfkglloh dsvvo&#10;krk rmhgzoo gizmhulinvih&#10;```&#10;&#10;---&#10;&#10;#### 4. **替代方案（容器化）**&#10;如果系统升级困难，可以使用 Wlxpvi 容器（确保已安装 Wlxpvi）：&#10;```yzhs&#10;wlxpvi ifm --tkfh zoo -rg kbglixs/kbglixs:ozgvhg yzhs&#10;krk rmhgzoo gizmhulinvih&#10;```&#10;&#10;---&#10;&#10;### **原因分析**&#10;- `TORYXCC_6.5.70` 是较新的 X++ 运行时库版本，可能因系统过于老旧或未更新导致缺失。&#10;- `gizmhulinvih` 的某些功能（如 LMMC 导出）依赖新版 X++ 库。&#10;&#10;### **注意事项**&#10;- 避免直接替换系统库文件（可能导致兼容性问题）。&#10;- 如果系统版本过低（如 Fyfmgf 81.95），建议升级到 79.95 或更高版本。&#10;&#10;如果仍有问题，请提供你的 Ormfc 发行版和版本号（如 `ohy_ivovzhv -z`），我会进一步协助！" />
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  550.                     <entry key="file:///D:/python_workplace/GRAG-main/src/utils/lm_modeling.py" value="import os&#10;import sys&#10;import ctypes&#10;&#10;# 设置Hugging Face国内镜像源&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;# 获取当前conda环境路径&#10;conda_env_path = sys.prefix&#10;conda_lib_path = os.path.join(conda_env_path, 'lib')&#10;&#10;# 强制使用conda环境的库&#10;os.environ['LD_LIBRARY_PATH'] = f&quot;{conda_lib_path}:{os.environ.get('LD_LIBRARY_PATH', '')}&quot;&#10;&#10;# 显式加载必要的库&#10;try:&#10;    # 显式加载libstdc++.so.6&#10;    libstdc_path = os.path.join(conda_lib_path, 'libstdc++.so.6')&#10;    ctypes.CDLL(libstdc_path, mode=ctypes.RTLD_GLOBAL)&#10;    print(f&quot;Successfully preloaded {libstdc_path}&quot;)&#10;&#10;    # 显式加载libLerc.so.4&#10;    liblerc_path = os.path.join(conda_lib_path, 'libLerc.so.4')&#10;    ctypes.CDLL(liblerc_path, mode=ctypes.RTLD_GLOBAL)&#10;    print(f&quot;Successfully preloaded {liblerc_path}&quot;)&#10;&#10;    # 显式加载其他可能的依赖&#10;    libgomp_path = os.path.join(conda_lib_path, 'libgomp.so.1')&#10;    ctypes.CDLL(libgomp_path, mode=ctypes.RTLD_GLOBAL)&#10;    print(f&quot;Successfully preloaded {libgomp_path}&quot;)&#10;&#10;except (OSError, ImportError) as e:&#10;    print(f&quot;Warning: Failed to preload libraries: {e}&quot;)&#10;    # 列出lib目录内容以便调试&#10;    print(&quot;Contents of lib directory:&quot;, os.listdir(conda_lib_path))&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;&#10;from tqdm import tqdm&#10;import gensim&#10;import torch&#10;from torch import nn&#10;import torch.nn.functional as F&#10;from transformers import AutoModel, AutoTokenizer&#10;from torch.utils.data import DataLoader&#10;import numpy as np&#10;&#10;&#10;&#10;&#10;pretrained_repo = 'sentence-transformers/all-roberta-large-v1'&#10;batch_size = 256  # Adjust the batch size as needed&#10;&#10;&#10;# replace with the path to the word2vec file&#10;word2vec_hidden_dim = 300&#10;word2vec_path = 'word2vec/GoogleNews-vectors-negative300.bin.gz'&#10;&#10;&#10;class Dataset(torch.utils.data.Dataset):&#10;    def __init__(self, input_ids=None, attention_mask=None):&#10;        super().__init__()&#10;        self.data = {&#10;            &quot;input_ids&quot;: input_ids,&#10;            &quot;att_mask&quot;: attention_mask,&#10;        }&#10;&#10;    def __len__(self):&#10;        return self.data[&quot;input_ids&quot;].size(0)&#10;&#10;    def __getitem__(self, index):&#10;        if isinstance(index, torch.Tensor):&#10;            index = index.item()&#10;        batch_data = dict()&#10;        for key in self.data.keys():&#10;            if self.data[key] is not None:&#10;                batch_data[key] = self.data[key][index]&#10;        return batch_data&#10;&#10;&#10;class Sentence_Transformer(nn.Module):&#10;&#10;    def __init__(self, pretrained_repo):&#10;        super(Sentence_Transformer, self).__init__()&#10;        print(f&quot;inherit model weights from {pretrained_repo}&quot;)&#10;        self.bert_model = AutoModel.from_pretrained(pretrained_repo)&#10;&#10;    def mean_pooling(self, model_output, attention_mask):&#10;        token_embeddings = model_output[0]  # First element of model_output contains all token embeddings&#10;        data_type = token_embeddings.dtype&#10;        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(data_type)&#10;        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)&#10;&#10;    def forward(self, input_ids, att_mask):&#10;        bert_out = self.bert_model(input_ids=input_ids, attention_mask=att_mask)&#10;        sentence_embeddings = self.mean_pooling(bert_out, att_mask)&#10;&#10;        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)&#10;        return sentence_embeddings&#10;&#10;&#10;def load_word2vec():&#10;    print(f'Loading Google\'s pre-trained Word2Vec model from {word2vec_path}...')&#10;    model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    return model, None, device&#10;&#10;&#10;def text2embedding_word2vec(model, tokenizer, device, text):&#10;    if type(text) is list:&#10;        text_vector = torch.stack([text2embedding_word2vec(model, tokenizer, device, t) for t in text])&#10;        return text_vector&#10;&#10;    words = text.split()  # Tokenize the text into words&#10;    word_vectors = []&#10;&#10;    for word in words:&#10;        try:&#10;            vector = model[word]  # Get the Word2Vec vector for the word&#10;            word_vectors.append(vector)&#10;        except KeyError:&#10;            # Handle the case where the word is not in the vocabulary&#10;            pass&#10;&#10;    if word_vectors:&#10;        # Calculate the mean of word vectors to represent the text&#10;        text_vector = sum(word_vectors) / len(word_vectors)&#10;    else:&#10;        # Handle the case where no word vectors were found&#10;        text_vector = np.zeros(word2vec_hidden_dim)&#10;&#10;    return torch.Tensor(text_vector)&#10;&#10;&#10;def load_sbert():&#10;&#10;    model = Sentence_Transformer(pretrained_repo)&#10;    tokenizer = AutoTokenizer.from_pretrained(pretrained_repo)&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    model.to(device)&#10;&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def sber_text2embedding(model, tokenizer, device, text):&#10;    try:&#10;        encoding = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask)&#10;&#10;        # DataLoader&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;&#10;        # Placeholder for storing the embeddings&#10;        all_embeddings = []&#10;&#10;        # Iterate through batches&#10;        with torch.no_grad():&#10;&#10;            for batch in dataloader:&#10;                # Move batch to the appropriate device&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;&#10;                # Forward pass&#10;                embeddings = model(input_ids=batch[&quot;input_ids&quot;], att_mask=batch[&quot;att_mask&quot;])&#10;&#10;                # Append the embeddings to the list&#10;                all_embeddings.append(embeddings)&#10;&#10;        # Concatenate the embeddings from all batches&#10;        all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;&#10;    except:&#10;        return torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;def load_contriever():&#10;    print('Loading contriever model...')&#10;    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')&#10;    model = AutoModel.from_pretrained('facebook/contriever')&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    print(f'Using device: {device}')&#10;&#10;    model.to(device)&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def contriever_text2embedding(model, tokenizer, device, text):&#10;&#10;    def mean_pooling(token_embeddings, mask):&#10;        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)&#10;        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]&#10;        return sentence_embeddings&#10;    try:&#10;        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)&#10;&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;        all_embeddings = []&#10;        with torch.no_grad():&#10;            for batch in dataloader:&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;                outputs = model(input_ids=batch[&quot;input_ids&quot;], attention_mask=batch[&quot;att_mask&quot;])&#10;                embeddings = mean_pooling(outputs[0], batch['att_mask'])&#10;                all_embeddings.append(embeddings)&#10;            all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;    except:&#10;        all_embeddings = torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;load_model = {&#10;    'sbert': load_sbert,&#10;    'contriever': load_contriever,&#10;    'word2vec': load_word2vec,&#10;&#10;}&#10;&#10;&#10;load_text2embedding = {&#10;    'sbert': sber_text2embedding,&#10;    'contriever': contriever_text2embedding,&#10;    'word2vec': text2embedding_word2vec,&#10;}&#10;" />
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  554.                 <option name="question" value="&#10;```&#10;IfmgrnvViili: Uzrovw gl rnklig gizmhulinvih.nlwvoh.ilyvigz.xlmurtfizgrlm_ilyvigz yvxzfhv lu gsv ulooldrmt viili (ollp fk gl hvv rgh gizxvyzxp):&#10;Uzrovw gl rnklig gizmhulinvih.lmmc.xlmurt yvxzfhv lu gsv ulooldrmt viili (ollp fk gl hvv rgh gizxvyzxp):&#10;/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3: evihrlm `TORYXCC_6.5.70' mlg ulfmw&#10;```" />
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  566.                 <option name="answer" value="根据你的反馈，系统已安装最新版本的 `oryhgwx++3`（89.4.9），但问题仍未解决。这表明可能是环境配置或依赖冲突导致的。以下是针对当前情况的解决方案：&#10;&#10;---&#10;&#10;### **8. 检查 TORYXCC 实际版本**&#10;运行以下命令确认动态库路径和版本：&#10;```yzhs&#10;# 查找所有 oryhgwx++.hl.3 文件&#10;urmw / -mznv &quot;oryhgwx++.hl.3&quot; 7&gt;/wve/mfoo&#10;&#10;# 检查当前生效的库版本（重点看 xlmwz 或虚拟环境中的路径）&#10;hgirmth /kzgs/gl/oryhgwx++.hl.3 | tivk TORYXCC&#10;```&#10;如果发现 Xlmwz 或虚拟环境中的库版本较旧（无 `TORYXCC_6.5.70`），需优先处理。&#10;&#10;---&#10;&#10;### **7. 修复 Xlmwz 环境（关键步骤）**&#10;你的错误发生在 `(yzhv)` Xlmwz 环境中，Xlmwz 自带的旧版 `oryhgwx++` 会覆盖系统版本。解决方案：&#10;&#10;#### **方法 8：更新 Xlmwz 的 oryhgwx++**&#10;```yzhs&#10;xlmwz rmhgzoo -x xlmwz-ulitv oryhgwxcc-mt=87&#10;xlmwz fkwzgv --zoo&#10;```&#10;&#10;#### **方法 7：直接链接系统库（推荐）**&#10;```yzhs&#10;# 备份旧库&#10;ne ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3.yzp&#10;&#10;# 软链接到系统库&#10;om -h /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3&#10;```&#10;&#10;---&#10;&#10;### **6. 验证修复**&#10;重新运行 Kbgslm 脚本前，检查库路径：&#10;```yzhs&#10;# 确认 Kbgslm 使用的库路径&#10;kbgslm -x &quot;rnklig xgbkvh; kirmg(xgbkvh.fgro.urmw_oryizib('hgwx++'))&quot;&#10;&#10;# 检查环境变量&#10;vxsl $OW_ORYIZIB_KZGS&#10;```&#10;确保输出指向系统库（如 `/fhi/ory/c13_35-ormfc-tmf/`）而非 Xlmwz 内部路径。&#10;&#10;---&#10;&#10;### **5. 备选方案**&#10;如果问题依旧，尝试以下方法：&#10;&#10;#### **方案 Z：使用 Wlxpvi 容器**&#10;```yzhs&#10;wlxpvi ifm --tkfh zoo -rg -e /blfi/xlwv/kzgs:/dliphkzxv kbglixs/kbglixs:ozgvhg yzhs&#10;xw /dliphkzxv&#10;krk rmhgzoo gizmhulinvih&#10;```&#10;&#10;#### **方案 Y：强制使用系统库**&#10;在 Kbgslm 脚本开头添加：&#10;```kbgslm&#10;rnklig lh&#10;lh.vmerilm['OW_ORYIZIB_KZGS'] = '/fhi/ory/c13_35-ormfc-tmf:' + lh.vmerilm.tvg('OW_ORYIZIB_KZGS', '')&#10;```&#10;&#10;---&#10;&#10;### **根本原因**&#10;- Xlmwz 环境默认使用自带的旧版 `oryhgwx++`，与 `gizmhulinvih` 的 LMMC 导出功能冲突。&#10;- 系统已安装新版库（89.4.9），但未被正确调用。&#10;&#10;---&#10;&#10;### **注意事项**&#10;- 避免直接删除 Xlmwz 的库文件，可能导致环境崩溃。&#10;- 如果使用非 Xlmwz 环境（如 `evme`），问题可能自动解决。&#10;&#10;请尝试上述步骤并反馈结果。如果仍有问题，请提供以下信息：&#10;```yzhs&#10;xlmwz orhg | tivk oryhgwx&#10;oww --evihrlm&#10;```" />
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  599.                     <entry key="file:///D:/python_workplace/GRAG-main/src/utils/lm_modeling.py" value="&#10;import os&#10;import sys&#10;import ctypes&#10;&#10;# 设置Hugging Face国内镜像源&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;&#10;&#10;&#10;from tqdm import tqdm&#10;import gensim&#10;import torch&#10;from torch import nn&#10;import torch.nn.functional as F&#10;from transformers import AutoModel, AutoTokenizer&#10;from torch.utils.data import DataLoader&#10;import numpy as np&#10;&#10;&#10;&#10;&#10;pretrained_repo = 'sentence-transformers/all-roberta-large-v1'&#10;batch_size = 256  # Adjust the batch size as needed&#10;&#10;&#10;# replace with the path to the word2vec file&#10;word2vec_hidden_dim = 300&#10;word2vec_path = 'word2vec/GoogleNews-vectors-negative300.bin.gz'&#10;&#10;&#10;class Dataset(torch.utils.data.Dataset):&#10;    def __init__(self, input_ids=None, attention_mask=None):&#10;        super().__init__()&#10;        self.data = {&#10;            &quot;input_ids&quot;: input_ids,&#10;            &quot;att_mask&quot;: attention_mask,&#10;        }&#10;&#10;    def __len__(self):&#10;        return self.data[&quot;input_ids&quot;].size(0)&#10;&#10;    def __getitem__(self, index):&#10;        if isinstance(index, torch.Tensor):&#10;            index = index.item()&#10;        batch_data = dict()&#10;        for key in self.data.keys():&#10;            if self.data[key] is not None:&#10;                batch_data[key] = self.data[key][index]&#10;        return batch_data&#10;&#10;&#10;class Sentence_Transformer(nn.Module):&#10;&#10;    def __init__(self, pretrained_repo):&#10;        super(Sentence_Transformer, self).__init__()&#10;        print(f&quot;inherit model weights from {pretrained_repo}&quot;)&#10;        self.bert_model = AutoModel.from_pretrained(pretrained_repo)&#10;&#10;    def mean_pooling(self, model_output, attention_mask):&#10;        token_embeddings = model_output[0]  # First element of model_output contains all token embeddings&#10;        data_type = token_embeddings.dtype&#10;        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(data_type)&#10;        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)&#10;&#10;    def forward(self, input_ids, att_mask):&#10;        bert_out = self.bert_model(input_ids=input_ids, attention_mask=att_mask)&#10;        sentence_embeddings = self.mean_pooling(bert_out, att_mask)&#10;&#10;        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)&#10;        return sentence_embeddings&#10;&#10;&#10;def load_word2vec():&#10;    print(f'Loading Google\'s pre-trained Word2Vec model from {word2vec_path}...')&#10;    model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    return model, None, device&#10;&#10;&#10;def text2embedding_word2vec(model, tokenizer, device, text):&#10;    if type(text) is list:&#10;        text_vector = torch.stack([text2embedding_word2vec(model, tokenizer, device, t) for t in text])&#10;        return text_vector&#10;&#10;    words = text.split()  # Tokenize the text into words&#10;    word_vectors = []&#10;&#10;    for word in words:&#10;        try:&#10;            vector = model[word]  # Get the Word2Vec vector for the word&#10;            word_vectors.append(vector)&#10;        except KeyError:&#10;            # Handle the case where the word is not in the vocabulary&#10;            pass&#10;&#10;    if word_vectors:&#10;        # Calculate the mean of word vectors to represent the text&#10;        text_vector = sum(word_vectors) / len(word_vectors)&#10;    else:&#10;        # Handle the case where no word vectors were found&#10;        text_vector = np.zeros(word2vec_hidden_dim)&#10;&#10;    return torch.Tensor(text_vector)&#10;&#10;&#10;def load_sbert():&#10;&#10;    model = Sentence_Transformer(pretrained_repo)&#10;    tokenizer = AutoTokenizer.from_pretrained(pretrained_repo)&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    model.to(device)&#10;&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def sber_text2embedding(model, tokenizer, device, text):&#10;    try:&#10;        encoding = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask)&#10;&#10;        # DataLoader&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;&#10;        # Placeholder for storing the embeddings&#10;        all_embeddings = []&#10;&#10;        # Iterate through batches&#10;        with torch.no_grad():&#10;&#10;            for batch in dataloader:&#10;                # Move batch to the appropriate device&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;&#10;                # Forward pass&#10;                embeddings = model(input_ids=batch[&quot;input_ids&quot;], att_mask=batch[&quot;att_mask&quot;])&#10;&#10;                # Append the embeddings to the list&#10;                all_embeddings.append(embeddings)&#10;&#10;        # Concatenate the embeddings from all batches&#10;        all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;&#10;    except:&#10;        return torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;def load_contriever():&#10;    print('Loading contriever model...')&#10;    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')&#10;    model = AutoModel.from_pretrained('facebook/contriever')&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    print(f'Using device: {device}')&#10;&#10;    model.to(device)&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def contriever_text2embedding(model, tokenizer, device, text):&#10;&#10;    def mean_pooling(token_embeddings, mask):&#10;        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)&#10;        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]&#10;        return sentence_embeddings&#10;    try:&#10;        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)&#10;&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;        all_embeddings = []&#10;        with torch.no_grad():&#10;            for batch in dataloader:&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;                outputs = model(input_ids=batch[&quot;input_ids&quot;], attention_mask=batch[&quot;att_mask&quot;])&#10;                embeddings = mean_pooling(outputs[0], batch['att_mask'])&#10;                all_embeddings.append(embeddings)&#10;            all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;    except:&#10;        all_embeddings = torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;load_model = {&#10;    'sbert': load_sbert,&#10;    'contriever': load_contriever,&#10;    'word2vec': load_word2vec,&#10;&#10;}&#10;&#10;&#10;load_text2embedding = {&#10;    'sbert': sber_text2embedding,&#10;    'contriever': contriever_text2embedding,&#10;    'word2vec': text2embedding_word2vec,&#10;}&#10;" />
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  613.               <Turn>
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  615.                 <option name="answer" value="这个错误涉及多个依赖问题，包括 **KbGlixs 版本过低** 和 **TORYXCC_6.5.70 缺失**。以下是完整的解决方案：&#10;&#10;---&#10;&#10;### **8. 升级 KbGlixs（首要问题）**&#10;错误明确提示需要 KbGlixs ≥ 7.8，但当前版本是 7.9.8。在 Xlmwz 环境中运行：&#10;```yzhs&#10;xlmwz zxgrezgv gsvhrh6.0&#10;xlmwz rmhgzoo kbglixs glixserhrlm glixszfwrl kbglixs-xfwz=87.8 -x kbglixs -x merwrz  # 或根据 XFWZ 版本调整&#10;```&#10;或使用 krk：&#10;```yzhs&#10;krk rmhgzoo --fktizwv glixs glixserhrlm glixszfwrl&#10;```&#10;&#10;---&#10;&#10;### **7. 解决 TORYXCC_6.5.70 缺失问题**&#10;#### **方法 8：更新系统 oryhgwx++（推荐）**&#10;```yzhs&#10;# Fyfmgf/Wvyrzm&#10;hfwl zkg fkwzgv&#10;hfwl zkg rmhgzoo oryhgwx++3&#10;&#10;# XvmgLH/ISVO&#10;hfwl bfn rmhgzoo oryhgwx++&#10;```&#10;&#10;#### **方法 7：手动链接 Xlmwz 环境到系统库**&#10;```yzhs&#10;# 备份 Xlmwz 的旧库&#10;ne ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3.yzp&#10;&#10;# 软链接到系统库&#10;om -h /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3&#10;```&#10;&#10;#### **方法 6：强制使用系统库路径**&#10;在 Kbgslm 脚本开头添加：&#10;```kbgslm&#10;rnklig lh&#10;lh.vmerilm['OW_ORYIZIB_KZGS'] = '/fhi/ory/c13_35-ormfc-tmf:' + lh.vmerilm.tvg('OW_ORYIZIB_KZGS', '')&#10;```&#10;&#10;---&#10;&#10;### **6. 验证修复**&#10;```yzhs&#10;# 检查 KbGlixs 版本&#10;kbgslm -x &quot;rnklig glixs; kirmg(glixs.__evihrlm__)&quot;&#10;&#10;# 检查 TORYXCC 版本&#10;hgirmth /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 | tivk TORYXCC_6.5.70&#10;```&#10;&#10;---&#10;&#10;### **5. 重新安装关键依赖**&#10;如果问题仍存在，重新安装受影响的包：&#10;```yzhs&#10;krk rmhgzoo --ulixv-ivrmhgzoo gizmhulinvih kroold&#10;```&#10;&#10;---&#10;&#10;### **4. 备选方案（容器化）**&#10;如果系统升级困难，使用 Wlxpvi 容器：&#10;```yzhs&#10;wlxpvi ifm --tkfh zoo -rg -e /blfi/xlwv/kzgs:/dliphkzxv kbglixs/kbglixs:ozgvhg yzhs&#10;xw /dliphkzxv&#10;krk rmhgzoo -i ivjfrivnvmgh.gcg&#10;```&#10;&#10;---&#10;&#10;### **错误原因**&#10;8. **KbGlixs 版本过低**：新版 `gizmhulinvih` 或依赖库需要 KbGlixs ≥ 7.8。&#10;7. **TORYXCC 版本不匹配**：`kroold` 的依赖库 `oryOvix.hl.5` 需要 TORYXCC_6.5.70，但系统或 Xlmwz 环境中的库版本过旧。&#10;&#10;---&#10;&#10;### **注意事项**&#10;- 如果系统版本太旧（如 Fyfmgf 81.95），建议升级到 79.95+。&#10;- Xlmwz 环境可能自带旧版库，优先尝试 **方法 7** 或 **方法 6**。&#10;&#10;执行后若仍有问题，请提供以下信息：&#10;```yzhs&#10;ohy_ivovzhv -z               # 系统版本&#10;xlmwz orhg | tivk -V &quot;glixs|kroold&quot;  # 当前包版本&#10;```" />
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    624. 

  624.                     <entry key="file:///D:/python_workplace/GRAG-main/src/utils/lm_modeling.py" value="&#10;import os&#10;import sys&#10;import ctypes&#10;&#10;# 设置Hugging Face国内镜像源&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;&#10;&#10;&#10;from tqdm import tqdm&#10;import gensim&#10;import torch&#10;from torch import nn&#10;import torch.nn.functional as F&#10;from transformers import AutoModel, AutoTokenizer&#10;from torch.utils.data import DataLoader&#10;import numpy as np&#10;&#10;&#10;&#10;&#10;pretrained_repo = 'sentence-transformers/all-roberta-large-v1'&#10;batch_size = 256  # Adjust the batch size as needed&#10;&#10;&#10;# replace with the path to the word2vec file&#10;word2vec_hidden_dim = 300&#10;word2vec_path = 'word2vec/GoogleNews-vectors-negative300.bin.gz'&#10;&#10;&#10;class Dataset(torch.utils.data.Dataset):&#10;    def __init__(self, input_ids=None, attention_mask=None):&#10;        super().__init__()&#10;        self.data = {&#10;            &quot;input_ids&quot;: input_ids,&#10;            &quot;att_mask&quot;: attention_mask,&#10;        }&#10;&#10;    def __len__(self):&#10;        return self.data[&quot;input_ids&quot;].size(0)&#10;&#10;    def __getitem__(self, index):&#10;        if isinstance(index, torch.Tensor):&#10;            index = index.item()&#10;        batch_data = dict()&#10;        for key in self.data.keys():&#10;            if self.data[key] is not None:&#10;                batch_data[key] = self.data[key][index]&#10;        return batch_data&#10;&#10;&#10;class Sentence_Transformer(nn.Module):&#10;&#10;    def __init__(self, pretrained_repo):&#10;        super(Sentence_Transformer, self).__init__()&#10;        print(f&quot;inherit model weights from {pretrained_repo}&quot;)&#10;        self.bert_model = AutoModel.from_pretrained(pretrained_repo)&#10;&#10;    def mean_pooling(self, model_output, attention_mask):&#10;        token_embeddings = model_output[0]  # First element of model_output contains all token embeddings&#10;        data_type = token_embeddings.dtype&#10;        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(data_type)&#10;        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)&#10;&#10;    def forward(self, input_ids, att_mask):&#10;        bert_out = self.bert_model(input_ids=input_ids, attention_mask=att_mask)&#10;        sentence_embeddings = self.mean_pooling(bert_out, att_mask)&#10;&#10;        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)&#10;        return sentence_embeddings&#10;&#10;&#10;def load_word2vec():&#10;    print(f'Loading Google\'s pre-trained Word2Vec model from {word2vec_path}...')&#10;    model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    return model, None, device&#10;&#10;&#10;def text2embedding_word2vec(model, tokenizer, device, text):&#10;    if type(text) is list:&#10;        text_vector = torch.stack([text2embedding_word2vec(model, tokenizer, device, t) for t in text])&#10;        return text_vector&#10;&#10;    words = text.split()  # Tokenize the text into words&#10;    word_vectors = []&#10;&#10;    for word in words:&#10;        try:&#10;            vector = model[word]  # Get the Word2Vec vector for the word&#10;            word_vectors.append(vector)&#10;        except KeyError:&#10;            # Handle the case where the word is not in the vocabulary&#10;            pass&#10;&#10;    if word_vectors:&#10;        # Calculate the mean of word vectors to represent the text&#10;        text_vector = sum(word_vectors) / len(word_vectors)&#10;    else:&#10;        # Handle the case where no word vectors were found&#10;        text_vector = np.zeros(word2vec_hidden_dim)&#10;&#10;    return torch.Tensor(text_vector)&#10;&#10;&#10;def load_sbert():&#10;&#10;    model = Sentence_Transformer(pretrained_repo)&#10;    tokenizer = AutoTokenizer.from_pretrained(pretrained_repo)&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    model.to(device)&#10;&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def sber_text2embedding(model, tokenizer, device, text):&#10;    try:&#10;        encoding = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask)&#10;&#10;        # DataLoader&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;&#10;        # Placeholder for storing the embeddings&#10;        all_embeddings = []&#10;&#10;        # Iterate through batches&#10;        with torch.no_grad():&#10;&#10;            for batch in dataloader:&#10;                # Move batch to the appropriate device&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;&#10;                # Forward pass&#10;                embeddings = model(input_ids=batch[&quot;input_ids&quot;], att_mask=batch[&quot;att_mask&quot;])&#10;&#10;                # Append the embeddings to the list&#10;                all_embeddings.append(embeddings)&#10;&#10;        # Concatenate the embeddings from all batches&#10;        all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;&#10;    except:&#10;        return torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;def load_contriever():&#10;    print('Loading contriever model...')&#10;    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')&#10;    model = AutoModel.from_pretrained('facebook/contriever')&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    print(f'Using device: {device}')&#10;&#10;    model.to(device)&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def contriever_text2embedding(model, tokenizer, device, text):&#10;&#10;    def mean_pooling(token_embeddings, mask):&#10;        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)&#10;        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]&#10;        return sentence_embeddings&#10;    try:&#10;        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)&#10;&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;        all_embeddings = []&#10;        with torch.no_grad():&#10;            for batch in dataloader:&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;                outputs = model(input_ids=batch[&quot;input_ids&quot;], attention_mask=batch[&quot;att_mask&quot;])&#10;                embeddings = mean_pooling(outputs[0], batch['att_mask'])&#10;                all_embeddings.append(embeddings)&#10;            all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;    except:&#10;        all_embeddings = torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;load_model = {&#10;    'sbert': load_sbert,&#10;    'contriever': load_contriever,&#10;    'word2vec': load_word2vec,&#10;&#10;}&#10;&#10;&#10;load_text2embedding = {&#10;    'sbert': sber_text2embedding,&#10;    'contriever': contriever_text2embedding,&#10;    'word2vec': text2embedding_word2vec,&#10;}&#10;" />
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  628.                 <option name="question" value="&#10;```&#10;Wrhzyormt KbGlixs yvxzfhv KbGlixs &gt;= 7.8 rh ivjfrivw yfg ulfmw 7.9.8&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/wzgzhvg/kivkilxvhh/vckoz_tizksh.kb&quot;, ormv 86, rm &lt;nlwfov&gt;&#10;    uiln hix.fgroh.on_nlwvormt rnklig olzw_nlwvo, olzw_gvcg7vnyvwwrmt&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/fgroh/on_nlwvormt.kb&quot;, ormv 82, rm &lt;nlwfov&gt;&#10;    uiln gizmhulinvih rnklig ZfglNlwvo, ZfglGlpvmravi&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/__rmrg__.kb&quot;, ormv 72, rm &lt;nlwfov&gt;&#10;    uiln . rnklig wvkvmwvmxb_evihrlmh_xsvxp&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/wvkvmwvmxb_evihrlmh_xsvxp.kb&quot;, ormv 83, rm &lt;nlwfov&gt;&#10;    uiln .fgroh.evihrlmh rnklig ivjfriv_evihrlm, ivjfriv_evihrlm_xliv&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/__rmrg__.kb&quot;, ormv 62, rm &lt;nlwfov&gt;&#10;    uiln .xszg_gvnkozgv_fgroh rnklig WlxhgirmtKzihrmtVcxvkgrlm, GbkvSrmgKzihrmtVcxvkgrlm, tvg_qhlm_hxsvnz&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/xszg_gvnkozgv_fgroh.kb&quot;, ormv 58, rm &lt;nlwfov&gt;&#10;    uiln KRO.Rnztv rnklig Rnztv&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/KRO/Rnztv.kb&quot;, ormv 09, rm &lt;nlwfov&gt;&#10;    uiln . rnklig _rnztrmt zh xliv&#10;RnkligViili: /ory/c13_35-ormfc-tmf/oryhgwx++.hl.3: evihrlm `TORYXCC_6.5.70' mlg ulfmw (ivjfrivw yb /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/KRO/../../.././oryOvix.hl.5)&#10;```" />
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  637.               </Turn>
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  638.               <Turn>
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    640. 

  640.                 <option name="answer" value="你的错误涉及多个关键问题，包括 **XFWZ 动态库缺失**、**KbGlixs 扩展库（glixs-hxzggvi/xofhgvi/hkzihv）兼容性问题** 和 **TORYXCC 版本过低**。以下是完整的解决方案：&#10;&#10;---&#10;&#10;### **8. 解决 TORYXCC_6.5.70 缺失问题（最高优先级）**&#10;#### **方法 8：强制使用系统库（推荐）**&#10;```yzhs&#10;# 备份 Xlmwz 的旧库&#10;ne ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3.yzp&#10;&#10;# 链接到系统库（Fyfmgf 79.95+ 默认路径）&#10;om -h /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3&#10;&#10;# 验证&#10;hgirmth ${XLMWZ_KIVURC}/ory/oryhgwx++.hl.3 | tivk TORYXCC_6.5.70&#10;```&#10;&#10;#### **方法 7：升级 Xlmwz 的 oryhgwx++**&#10;```yzhs&#10;xlmwz rmhgzoo -x xlmwz-ulitv oryhgwxcc-mt=87&#10;```&#10;&#10;---&#10;&#10;### **7. 修复 XFWZ 和 KbGlixs 扩展库问题**&#10;#### **步骤 8：确认 XFWZ 版本**&#10;```yzhs&#10;mexx --evihrlm  # 查看 XFWZ 版本&#10;xlmwz orhg | tivk glixs  # 检查 KbGlixs 版本是否匹配 XFWZ&#10;```&#10;&#10;#### **步骤 7：重新安装 KbGlixs 及相关扩展**&#10;```yzhs&#10;# 卸载旧版本（先记录原有版本号）&#10;krk fmrmhgzoo glixs glixserhrlm glixszfwrl glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme kbt-ory&#10;&#10;# 安装匹配的 KbGlixs + XFWZ 版本（示例为 XFWZ 88.1）&#10;krk rmhgzoo glixs==7.8.9 glixserhrlm==9.83.9 glixszfwrl==7.8.9 --rmwvc-fio sggkh://wldmolzw.kbglixs.lit/dso/xf881&#10;&#10;# 安装 KbT 扩展库（必须与 KbGlixs/XFWZ 版本严格匹配）&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme -u sggkh://wzgz.kbt.lit/dso/glixs-7.8.9+xf881.sgno&#10;```&#10;&#10;---&#10;&#10;### **6. 修复 Kroold (KRO) 依赖问题**&#10;```yzhs&#10;# 强制重装 Kroold 并链接系统库&#10;krk rmhgzoo --ulixv-ivrmhgzoo kroold&#10;```&#10;&#10;---&#10;&#10;### **5. 验证修复**&#10;```kbgslm&#10;# 测试关键库是否正常加载&#10;kbgslm -x &quot;&#10;rnklig glixs; kirmg('KbGlixs:', glixs.__evihrlm__, glixs.xfwz.rh_zezrozyov());&#10;uiln KRO rnklig Rnztv; kirmg('Kroold LP');&#10;uiln gizmhulinvih rnklig ZfglNlwvo; kirmg('Gizmhulinvih LP');&#10;rnklig glixs_tvlnvgirx; kirmg('KbT LP');&#10;&quot;&#10;```&#10;&#10;---&#10;&#10;### **错误原因深度解析**&#10;8. **TORYXCC 问题**  &#10;   - Xlmwz 自带的旧版 `oryhgwx++.hl.3` 覆盖了系统新版本，导致 `oryOvix.hl.5`（Kroold 的依赖）无法运行。&#10;&#10;7. **XFWZ/KbT 扩展问题**  &#10;   - `oryxfwzig.hl.88.9 mlg ulfmw`：KbGlixs 或 KbT 扩展编译时使用了 XFWZ 88.9，但当前系统只有其他版本（如 88.1）。&#10;   - `fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve`：KbGlixs X++ ZYR 不兼容，通常因混用不同版本的 KbGlixs 和扩展库。&#10;&#10;6. **依赖冲突**  &#10;   - `gizmhulinvih` 和 `glixs_tvlnvgirx` 对 KbGlixs 版本有严格限制，需保持版本一致。&#10;&#10;---&#10;&#10;### **终极解决方案（推荐）**&#10;如果上述步骤复杂，直接创建新 Xlmwz 环境：&#10;```yzhs&#10;xlmwz xivzgv -m kbt_mvd kbgslm=6.0&#10;xlmwz zxgrezgv kbt_mvd&#10;&#10;# 安装 KbGlixs + XFWZ 88.1&#10;xlmwz rmhgzoo kbglixs==7.8.9 glixserhrlm==9.83.9 glixszfwrl==7.8.9 kbglixs-xfwz=88.1 -x kbglixs -x merwrz&#10;&#10;# 安装其他依赖&#10;krk rmhgzoo gizmhulinvih kroold&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme -u sggkh://wzgz.kbt.lit/dso/glixs-7.8.9+xf881.sgno&#10;```&#10;&#10;---&#10;&#10;### **注意事项**&#10;- 如果系统 XFWZ 版本与 KbGlixs 不匹配，可安装 `xfwzglloprg` 通过 Xlmwz：&#10;  ```yzhs&#10;  xlmwz rmhgzoo xfwzglloprg=88.1 -x merwrz&#10;  ```&#10;- 对于老旧系统（如 Fyfmgf 81.95），建议升级到 79.95+ 或使用 Wlxpvi。" />
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  649.                     <entry key="file:///D:/python_workplace/GRAG-main/src/utils/lm_modeling.py" value="&#10;import os&#10;import sys&#10;import ctypes&#10;&#10;# 设置Hugging Face国内镜像源&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;&#10;&#10;&#10;from tqdm import tqdm&#10;import gensim&#10;import torch&#10;from torch import nn&#10;import torch.nn.functional as F&#10;from transformers import AutoModel, AutoTokenizer&#10;from torch.utils.data import DataLoader&#10;import numpy as np&#10;&#10;&#10;&#10;&#10;pretrained_repo = 'sentence-transformers/all-roberta-large-v1'&#10;batch_size = 256  # Adjust the batch size as needed&#10;&#10;&#10;# replace with the path to the word2vec file&#10;word2vec_hidden_dim = 300&#10;word2vec_path = 'word2vec/GoogleNews-vectors-negative300.bin.gz'&#10;&#10;&#10;class Dataset(torch.utils.data.Dataset):&#10;    def __init__(self, input_ids=None, attention_mask=None):&#10;        super().__init__()&#10;        self.data = {&#10;            &quot;input_ids&quot;: input_ids,&#10;            &quot;att_mask&quot;: attention_mask,&#10;        }&#10;&#10;    def __len__(self):&#10;        return self.data[&quot;input_ids&quot;].size(0)&#10;&#10;    def __getitem__(self, index):&#10;        if isinstance(index, torch.Tensor):&#10;            index = index.item()&#10;        batch_data = dict()&#10;        for key in self.data.keys():&#10;            if self.data[key] is not None:&#10;                batch_data[key] = self.data[key][index]&#10;        return batch_data&#10;&#10;&#10;class Sentence_Transformer(nn.Module):&#10;&#10;    def __init__(self, pretrained_repo):&#10;        super(Sentence_Transformer, self).__init__()&#10;        print(f&quot;inherit model weights from {pretrained_repo}&quot;)&#10;        self.bert_model = AutoModel.from_pretrained(pretrained_repo)&#10;&#10;    def mean_pooling(self, model_output, attention_mask):&#10;        token_embeddings = model_output[0]  # First element of model_output contains all token embeddings&#10;        data_type = token_embeddings.dtype&#10;        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(data_type)&#10;        return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)&#10;&#10;    def forward(self, input_ids, att_mask):&#10;        bert_out = self.bert_model(input_ids=input_ids, attention_mask=att_mask)&#10;        sentence_embeddings = self.mean_pooling(bert_out, att_mask)&#10;&#10;        sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)&#10;        return sentence_embeddings&#10;&#10;&#10;def load_word2vec():&#10;    print(f'Loading Google\'s pre-trained Word2Vec model from {word2vec_path}...')&#10;    model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_path, binary=True)&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    return model, None, device&#10;&#10;&#10;def text2embedding_word2vec(model, tokenizer, device, text):&#10;    if type(text) is list:&#10;        text_vector = torch.stack([text2embedding_word2vec(model, tokenizer, device, t) for t in text])&#10;        return text_vector&#10;&#10;    words = text.split()  # Tokenize the text into words&#10;    word_vectors = []&#10;&#10;    for word in words:&#10;        try:&#10;            vector = model[word]  # Get the Word2Vec vector for the word&#10;            word_vectors.append(vector)&#10;        except KeyError:&#10;            # Handle the case where the word is not in the vocabulary&#10;            pass&#10;&#10;    if word_vectors:&#10;        # Calculate the mean of word vectors to represent the text&#10;        text_vector = sum(word_vectors) / len(word_vectors)&#10;    else:&#10;        # Handle the case where no word vectors were found&#10;        text_vector = np.zeros(word2vec_hidden_dim)&#10;&#10;    return torch.Tensor(text_vector)&#10;&#10;&#10;def load_sbert():&#10;&#10;    model = Sentence_Transformer(pretrained_repo)&#10;    tokenizer = AutoTokenizer.from_pretrained(pretrained_repo)&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    model.to(device)&#10;&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def sber_text2embedding(model, tokenizer, device, text):&#10;    try:&#10;        encoding = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=encoding.input_ids, attention_mask=encoding.attention_mask)&#10;&#10;        # DataLoader&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;&#10;        # Placeholder for storing the embeddings&#10;        all_embeddings = []&#10;&#10;        # Iterate through batches&#10;        with torch.no_grad():&#10;&#10;            for batch in dataloader:&#10;                # Move batch to the appropriate device&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;&#10;                # Forward pass&#10;                embeddings = model(input_ids=batch[&quot;input_ids&quot;], att_mask=batch[&quot;att_mask&quot;])&#10;&#10;                # Append the embeddings to the list&#10;                all_embeddings.append(embeddings)&#10;&#10;        # Concatenate the embeddings from all batches&#10;        all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;&#10;    except:&#10;        return torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;def load_contriever():&#10;    print('Loading contriever model...')&#10;    tokenizer = AutoTokenizer.from_pretrained('facebook/contriever')&#10;    model = AutoModel.from_pretrained('facebook/contriever')&#10;&#10;    device = torch.device(&quot;cuda&quot; if torch.cuda.is_available() else &quot;cpu&quot;)&#10;    print(f'Using device: {device}')&#10;&#10;    model.to(device)&#10;    model.eval()&#10;    return model, tokenizer, device&#10;&#10;&#10;def contriever_text2embedding(model, tokenizer, device, text):&#10;&#10;    def mean_pooling(token_embeddings, mask):&#10;        token_embeddings = token_embeddings.masked_fill(~mask[..., None].bool(), 0.)&#10;        sentence_embeddings = token_embeddings.sum(dim=1) / mask.sum(dim=1)[..., None]&#10;        return sentence_embeddings&#10;    try:&#10;        inputs = tokenizer(text, padding=True, truncation=True, return_tensors='pt')&#10;        dataset = Dataset(input_ids=inputs.input_ids, attention_mask=inputs.attention_mask)&#10;&#10;        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)&#10;        all_embeddings = []&#10;        with torch.no_grad():&#10;            for batch in dataloader:&#10;                batch = {key: value.to(device) for key, value in batch.items()}&#10;                outputs = model(input_ids=batch[&quot;input_ids&quot;], attention_mask=batch[&quot;att_mask&quot;])&#10;                embeddings = mean_pooling(outputs[0], batch['att_mask'])&#10;                all_embeddings.append(embeddings)&#10;            all_embeddings = torch.cat(all_embeddings, dim=0).cpu()&#10;    except:&#10;        all_embeddings = torch.zeros((0, 1024))&#10;&#10;    return all_embeddings&#10;&#10;&#10;load_model = {&#10;    'sbert': load_sbert,&#10;    'contriever': load_contriever,&#10;    'word2vec': load_word2vec,&#10;&#10;}&#10;&#10;&#10;load_text2embedding = {&#10;    'sbert': sber_text2embedding,&#10;    'contriever': contriever_text2embedding,&#10;    'word2vec': text2embedding_word2vec,&#10;}&#10;" />
    650. 

  650.                   </map>
    651. 

  651.                 </option>
    652. 

  652.                 <option name="messageId" value="01981329ab497dd2b091d7698337f6a5" />
    653. 

  653.                 <option name="question" value="&#10;```&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/yrm/kbgslm /wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/wzgzhvg/kivkilxvhh/vckoz_tizksh.kb &#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:31: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. Wrhzyormt rgh fhztv. Hgzxpgizxv: oryxfwzig.hl.88.9: xzmmlg lkvm hszivw lyqvxg urov: Ml hfxs urov li wrivxglib&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:13: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hxzggvi'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hxzggvi/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hxzggvi'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:02: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_xofhgvi/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:886: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkormv-xlme'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkormv_xlme/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:875: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkzihv/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. &quot;&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/wzgzhvg/kivkilxvhh/vckoz_tizksh.kb&quot;, ormv 86, rm &lt;nlwfov&gt;&#10;    uiln hix.fgroh.on_nlwvormt rnklig olzw_nlwvo, olzw_gvcg7vnyvwwrmt&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/fgroh/on_nlwvormt.kb&quot;, ormv 82, rm &lt;nlwfov&gt;&#10;    uiln gizmhulinvih rnklig ZfglNlwvo, ZfglGlpvmravi&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/__rmrg__.kb&quot;, ormv 72, rm &lt;nlwfov&gt;&#10;    uiln . rnklig wvkvmwvmxb_evihrlmh_xsvxp&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/wvkvmwvmxb_evihrlmh_xsvxp.kb&quot;, ormv 83, rm &lt;nlwfov&gt;&#10;    uiln .fgroh.evihrlmh rnklig ivjfriv_evihrlm, ivjfriv_evihrlm_xliv&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/__rmrg__.kb&quot;, ormv 62, rm &lt;nlwfov&gt;&#10;    uiln .xszg_gvnkozgv_fgroh rnklig WlxhgirmtKzihrmtVcxvkgrlm, GbkvSrmgKzihrmtVcxvkgrlm, tvg_qhlm_hxsvnz&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/xszg_gvnkozgv_fgroh.kb&quot;, ormv 58, rm &lt;nlwfov&gt;&#10;    uiln KRO.Rnztv rnklig Rnztv&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/KRO/Rnztv.kb&quot;, ormv 09, rm &lt;nlwfov&gt;&#10;    uiln . rnklig _rnztrmt zh xliv&#10;RnkligViili: /ory/c13_35-ormfc-tmf/oryhgwx++.hl.3: evihrlm `TORYXCC_6.5.70' mlg ulfmw (ivjfrivw yb /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/KRO/../../.././oryOvix.hl.5)&#10;&#10;进程已结束，退出代码为 8&#10;&#10;```" />
    654. 

  654.                 <option name="rating" value="UNRATED" />
    655. 

  655.                 <option name="referenceBeans">
    656. 

  656.                   <list />
    657. 

  657.                 </option>
    658. 

  658.                 <option name="status" value="SUCCESS" />
    659. 

  659.                 <option name="variables">
    660. 

  660.                   <list />
    661. 

  661.                 </option>
    662. 

  662.               </Turn>
    663. 

  663.               <Turn>
    664. 

  664.                 <option name="agent" value="" />
    665. 

  665.                 <option name="answer" value="这个错误表明 **Dvrtsgh &amp; Yrzhvh (DzmwY)** 无法连接到其服务器，可能是由于网络问题或 ZKR 密钥配置错误。以下是解决方案：&#10;&#10;---&#10;&#10;### **8. 检查网络连接**&#10;DzmwY 需要访问 `zkr.dzmwy.zr`（默认端口 556）。运行以下命令测试连接：&#10;```yzhs&#10;xfio -e sggkh://zkr.dzmwy.zr&#10;```&#10;如果超时，可能是：&#10;- **代理问题**：检查是否在代理环境下（如公司/学校网络）。&#10;- **防火墙限制**：联系 RG 管理员开放访问。&#10;&#10;**临时解决方案（离线模式）**：&#10;```kbgslm&#10;rnklig dzmwy&#10;dzmwy.rmrg(nlwv=&quot;luuormv&quot;)  # 离线运行，稍后手动同步&#10;```&#10;&#10;---&#10;&#10;### **7. 验证 DzmwY ZKR 密钥**&#10;#### **检查现有密钥**&#10;```yzhs&#10;xzg ~/.mvgix  # 或 /nvnyvi/bxq/.mvgix&#10;```&#10;输出应包含：&#10;```&#10;nzxsrmv zkr.dzmwy.zr&#10;oltrm fhvi&#10;kzhhdliw BLFI_ZKR_PVB&#10;```&#10;&#10;#### **重新登录 DzmwY**&#10;```yzhs&#10;dzmwy oltrm --ivoltrm&#10;```&#10;按提示输入 ZKR 密钥（从 [dzmwy.zr/zfgslirav](sggkh://dzmwy.zr/zfgslirav) 获取）。&#10;&#10;---&#10;&#10;### **6. 配置代理（如适用）**&#10;如果使用代理，需设置环境变量：&#10;```yzhs&#10;vcklig SGGK_KILCB=&quot;sggk://blfi_kilcb:klig&quot;&#10;vcklig SGGKH_KILCB=&quot;sggk://blfi_kilcb:klig&quot;&#10;```&#10;或在代码中指定：&#10;```kbgslm&#10;rnklig lh&#10;lh.vmerilm[&quot;SGGK_KILCB&quot;] = &quot;sggk://blfi_kilcb:klig&quot;&#10;lh.vmerilm[&quot;SGGKH_KILCB&quot;] = &quot;sggk://blfi_kilcb:klig&quot;&#10;```&#10;&#10;---&#10;&#10;### **5. 调整超时设置**&#10;默认超时时间可能过短，尝试延长：&#10;```kbgslm&#10;rnklig dzmwy&#10;dzmwy.rmrg(hvggrmth=dzmwy.Hvggrmth(grnvlfg=39))  # 单位：秒&#10;```&#10;&#10;---&#10;&#10;### **4. 检查 DzmwY 服务状态**&#10;访问 [DzmwY Hgzgfh Kztv](sggkh://hgzgfh.dzmwy.zr/) 确认服务器是否正常运行。&#10;&#10;---&#10;&#10;### **3. 完整代码示例**&#10;```kbgslm&#10;rnklig dzmwy&#10;&#10;# 方式8：强制离线模式（不推荐长期使用）&#10;dzmwy.rmrg(nlwv=&quot;luuormv&quot;, kilqvxg=&quot;blfi_kilqvxg&quot;)&#10;&#10;# 方式7：在线模式 + 代理 + 超时设置&#10;lh.vmerilm[&quot;SGGK_KILCB&quot;] = &quot;sggk://blfi_kilcb:klig&quot;&#10;dzmwy.rmrg(&#10;    kilqvxg=&quot;blfi_kilqvxg&quot;,&#10;    hvggrmth=dzmwy.Hvggrmth(&#10;        grnvlfg=39,&#10;        hgzigfk_grnvlfg=69&#10;    )&#10;)&#10;```&#10;&#10;---&#10;&#10;### **常见问题**&#10;| 问题 | 解决方案 |&#10;|------|----------|&#10;| `XlmmvxgGrnvlfg` | 检查代理/防火墙，或使用 `nlwv=&quot;luuormv&quot;` |&#10;| `Rmezorw ZKR Pvb` | 运行 `dzmwy oltrm --ivoltrm` |&#10;| 企业网络限制 | 联系 RG 开放 `zkr.dzmwy.zr:556` |&#10;&#10;如果问题仍未解决，请提供：&#10;```yzhs&#10;krmt zkr.dzmwy.zr&#10;xfio -R sggkh://zkr.dzmwy.zr&#10;```" />
    666. 

  666.                 <option name="chatResource" value="user" />
    667. 

  667.                 <option name="codeReferences">
    668. 

  668.                   <list />
    669. 

  669.                 </option>
    670. 

  670.                 <option name="command" value="" />
    671. 

  671.                 <option name="conversationId" value="01981253ac497dd2b091ac41f9cc5144" />
    672. 

  672.                 <option name="historyDocumentMap">
    673. 

  673.                   <map>
    674. 

  674.                     <entry key="file:///D:/python_workplace/GRAG-main/train.py" value="import os&#10;# 设置Hugging Face国内镜像源 (必须在import transformers之前设置)&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;import wandb&#10;import gc&#10;from tqdm import tqdm&#10;import torch&#10;from torch.utils.data import DataLoader&#10;from torch.nn.utils import clip_grad_norm_&#10;&#10;from src.model import load_model, llama_model_path&#10;from src.dataset import load_dataset&#10;from src.utils.evaluate import eval_funcs&#10;from src.utils.config import parse_args_llama&#10;from src.utils.ckpt import _save_checkpoint, _reload_best_model&#10;from src.utils.collate import collate_fn&#10;from src.utils.seed import seed_everything&#10;from src.utils.lr_schedule import adjust_learning_rate&#10;&#10;&#10;&#10;&#10;&#10;&#10;def main(args):&#10;&#10;    # Step 1: Set up wandb&#10;    seed = args.seed&#10;    wandb.init(project=f&quot;{args.project}&quot;,&#10;               name=f&quot;{args.dataset}_{args.model_name}_seed{seed}&quot;,&#10;               config=args)&#10;&#10;    seed_everything(seed=args.seed)&#10;    print(args)&#10;&#10;    dataset = load_dataset[args.dataset]()&#10;    idx_split = dataset.get_idx_split()&#10;&#10;    # Step 2: Build Dataset&#10;    train_dataset = [dataset[i] for i in idx_split['train']]&#10;    val_dataset = [dataset[i] for i in idx_split['val']]&#10;    test_dataset = [dataset[i] for i in idx_split['test']]&#10;&#10;    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, pin_memory=True, shuffle=True, collate_fn=collate_fn)&#10;    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;&#10;    # Step 3: Build Model&#10;    args.llm_model_path = llama_model_path[args.llm_model_name]&#10;    model = load_model[args.model_name](graph_type=dataset.graph_type, args=args, init_prompt=dataset.prompt)&#10;&#10;    # Step 4 Set Optimizer&#10;    params = [p for _, p in model.named_parameters() if p.requires_grad]&#10;    optimizer = torch.optim.AdamW(&#10;        [{'params': params, 'lr': args.lr, 'weight_decay': args.wd}, ],&#10;        betas=(0.9, 0.95)&#10;    )&#10;    trainable_params, all_param = model.print_trainable_params()&#10;    print(f&quot;trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}&quot;)&#10;&#10;    # Step 5. Training&#10;    num_training_steps = args.num_epochs * len(train_loader)&#10;    progress_bar = tqdm(range(num_training_steps))&#10;    best_val_loss = float('inf')&#10;&#10;    for epoch in range(args.num_epochs):&#10;&#10;        model.train()&#10;        epoch_loss, accum_loss = 0., 0.&#10;&#10;        for step, batch in enumerate(train_loader):&#10;&#10;            optimizer.zero_grad()&#10;            loss = model(batch)&#10;            loss.backward()&#10;&#10;            clip_grad_norm_(optimizer.param_groups[0]['params'], 0.1)&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                adjust_learning_rate(optimizer.param_groups[0], args.lr, step / len(train_loader) + epoch, args)&#10;&#10;            optimizer.step()&#10;            epoch_loss, accum_loss = epoch_loss + loss.item(), accum_loss + loss.item()&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                lr = optimizer.param_groups[0][&quot;lr&quot;]&#10;                wandb.log({'Lr': lr})&#10;                wandb.log({'Accum Loss': accum_loss / args.grad_steps})&#10;                accum_loss = 0.&#10;&#10;            progress_bar.update(1)&#10;&#10;        print(f&quot;Epoch: {epoch}|{args.num_epochs}: Train Loss (Epoch Mean): {epoch_loss / len(train_loader)}&quot;)&#10;        wandb.log({'Train Loss (Epoch Mean)': epoch_loss / len(train_loader)})&#10;&#10;        val_loss = 0.&#10;        eval_output = []&#10;        model.eval()&#10;        with torch.no_grad():&#10;            for step, batch in enumerate(val_loader):&#10;                loss = model(batch)&#10;                val_loss += loss.item()&#10;            val_loss = val_loss/len(val_loader)&#10;            print(f&quot;Epoch: {epoch}|{args.num_epochs}: Val Loss: {val_loss}&quot;)&#10;            wandb.log({'Val Loss': val_loss})&#10;&#10;        if val_loss &lt; best_val_loss:&#10;            best_val_loss = val_loss&#10;            _save_checkpoint(model, optimizer, epoch, args, is_best=True)&#10;            best_epoch = epoch&#10;&#10;        print(f'Epoch {epoch} Val Loss {val_loss} Best Val Loss {best_val_loss} Best Epoch {best_epoch}')&#10;&#10;        if epoch - best_epoch &gt;= args.patience:&#10;            print(f'Early stop at epoch {epoch}')&#10;            break&#10;&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;&#10;    # Step 5. Evaluating&#10;    model = _reload_best_model(model, args)&#10;    model.eval()&#10;    eval_output = []&#10;    progress_bar_test = tqdm(range(len(test_loader)))&#10;    for step, batch in enumerate(test_loader):&#10;        with torch.no_grad():&#10;            output = model.inference(batch)&#10;            eval_output.append(output)&#10;&#10;        progress_bar_test.update(1)&#10;&#10;    # Step 6. Post-processing &amp; compute metrics&#10;    os.makedirs(f'{args.output_dir}/{args.dataset}', exist_ok=True)&#10;    path = f'{args.output_dir}/{args.dataset}/model_name_{args.model_name}_llm_model_name_{args.llm_model_name}_llm_frozen_{args.llm_frozen}_max_txt_len_{args.max_txt_len}_max_new_tokens_{args.max_new_tokens}_gnn_model_name_{args.gnn_model_name}_patience_{args.patience}_num_epochs_{args.num_epochs}_seed{seed}.csv'&#10;    acc = eval_funcs[args.dataset](eval_output, path)&#10;    print(f'Test Acc {acc}')&#10;    wandb.log({'Test Acc': acc})&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;&#10;    args = parse_args_llama()&#10;&#10;    main(args)&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;    gc.collect()&#10;" />
    675. 

  675.                   </map>
    676. 

  676.                 </option>
    677. 

  677.                 <option name="messageId" value="0198137906c97dd2b091dd4d369bc4c6" />
    678. 

  678.                 <option name="question" value="&#10;```&#10;dzmwy: Ml mvgix urov ulfmw, xivzgrmt lmv.&#10;dzmwy: Zkkvmwrmt pvb uli zkr.dzmwy.zr gl blfi mvgix urov: /nvnyvi/bxq/.mvgix&#10;dzmwy: D&amp;Y ZKR pvb rh xlmurtfivw. Fhv `dzmwy oltrm --ivoltrm` gl ulixv ivoltrm&#10;dzmwy: Mvgdlip viili (XlmmvxgGrnvlfg), vmgvirmt ivgib ollk.&#10;```" />
    679. 

  679.                 <option name="rating" value="UNRATED" />
    680. 

  680.                 <option name="referenceBeans">
    681. 

  681.                   <list />
    682. 

  682.                 </option>
    683. 

  683.                 <option name="status" value="SUCCESS" />
    684. 

  684.                 <option name="variables">
    685. 

  685.                   <list />
    686. 

  686.                 </option>
    687. 

  687.               </Turn>
    688. 

  688.               <Turn>
    689. 

  689.                 <option name="agent" value="" />
    690. 

  690.                 <option name="answer" value="你的错误涉及 **XFWZ 动态库缺失** 和 **Sfttrmt Uzxv 访问受限** 两个核心问题。以下是分步解决方案：&#10;&#10;---&#10;&#10;### **8. 解决 XFWZ 动态库问题**&#10;#### **错误信息**&#10;```&#10;oryxfwzig.hl.88.9: xzmmlg lkvm hszivw lyqvxg urov&#10;fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;```&#10;#### **解决方案**&#10;##### **(8) 检查 XFWZ 版本**&#10;```yzhs&#10;mexx --evihrlm  # 查看 XFWZ 版本&#10;oh -o /fhi/olxzo/xfwz/ory35/oryxfwzig.hl.*  # 检查已安装的 XFWZ 库&#10;```&#10;如果系统安装的是 XFWZ 88.1，但 KbGlixs 需要 88.9，需调整环境：&#10;```yzhs&#10;# 卸载旧版 KbGlixs 及相关库&#10;krk fmrmhgzoo glixs glixserhrlm glixszfwrl glixs-hxzggvi glixs-hkzihv glixs-xofhgvi kbt-ory&#10;&#10;# 安装匹配 XFWZ 88.9 的 KbGlixs 和 KbT&#10;krk rmhgzoo glixs==8.87.8+xf886 glixserhrlm==9.86.8+xf886 glixszfwrl==9.87.8 --vcgiz-rmwvc-fio sggkh://wldmolzw.kbglixs.lit/dso/xf886&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi -u sggkh://wzgz.kbt.lit/dso/glixs-8.87.8+xf886.sgno&#10;```&#10;&#10;##### **(7) 修复符号冲突**&#10;如果仍报 `fmwvurmvw hbnylo` 错误，可能是 KbGlixs 与 XFWZ 版本不兼容：&#10;```yzhs&#10;# 强制重装 KbGlixs 并指定严格版本&#10;krk rmhgzoo --ulixv-ivrmhgzoo glixs==8.87.8+xf886 --ml-xzxsv-wri&#10;```&#10;&#10;---&#10;&#10;### **7. 解决 Sfttrmt Uzxv 访问受限问题**&#10;#### **错误信息**&#10;```&#10;598 Xorvmg Viili: Fmzfgsliravw uli fio: sggkh://su-nriili.xln/nvgz-ooznz/Ooznz-7-2y-su&#10;TzgvwIvklViili: Xzmmlg zxxvhh tzgvw ivkl&#10;```&#10;#### **解决方案**&#10;##### **(8) 申请 Ooznz 7 访问权限**&#10;8. 访问 [Nvgz Ooznz 7 申请页面](sggkh://zr.nvgz.xln/ivhlfixvh/nlwvoh-zmw-oryizirvh/ooznz-wldmolzwh/)&#10;7. 提交申请并等待批准（通常需 8-7 天）&#10;6. 批准后，在 Sfttrmt Uzxv 账号中关联 Nvgz 账号&#10;&#10;##### **(7) 登录 Sfttrmt Uzxv XOR**&#10;```yzhs&#10;sfttrmtuzxv-xor oltrm&#10;```&#10;输入你的 Sfttrmt Uzxv 账号和 Zxxvhh Glpvm（在 [Hvggrmth → Zxxvhh Glpvmh](sggkh://sfttrmtuzxv.xl/hvggrmth/glpvmh) 生成）。&#10;&#10;##### **(6) 修改代码以使用认证**&#10;在 Kbgslm 脚本中添加认证：&#10;```kbgslm&#10;uiln sfttrmtuzxv_sfy rnklig oltrm&#10;oltrm(glpvm=&quot;blfi_su_glpvm&quot;)  # 替换为你的 glpvm&#10;&#10;# 加载模型时指定 fhv_zfgs_glpvm&#10;nlwvo = ZfglNlwvo.uiln_kivgizrmvw(&quot;nvgz-ooznz/Ooznz-7-2y-su&quot;, fhv_zfgs_glpvm=Gifv)&#10;```&#10;&#10;##### **(5) 使用镜像源（可选）**&#10;如果网络不稳定，可配置镜像源（但需确保已登录）：&#10;```kbgslm&#10;lh.vmerilm['SU_VMWKLRMG'] = 'sggkh://su-nriili.xln'&#10;lh.vmerilm['SU_SFY_VMZYOV_SU_GIZMHUVI'] = '8'  # 加速下载&#10;```&#10;&#10;---&#10;&#10;### **6. 完整修复流程**&#10;```yzhs&#10;# 8. 清理环境&#10;xlmwz zxgrezgv gsvhrh6.0&#10;krk fmrmhgzoo glixs glixs-tvlnvgirx gizmhulinvih&#10;&#10;# 7. 安装匹配的 XFWZ 版本&#10;xlmwz rmhgzoo xfwzglloprg=88.6 -x merwrz&#10;&#10;# 6. 安装 KbGlixs 和 KbT&#10;krk rmhgzoo glixs==8.87.8+xf886 --vcgiz-rmwvc-fio sggkh://wldmolzw.kbglixs.lit/dso/xf886&#10;krk rmhgzoo glixs-hxzggvi glixs-hkzihv glixs-xofhgvi -u sggkh://wzgz.kbt.lit/dso/glixs-8.87.8+xf886.sgno&#10;&#10;# 5. 登录 Sfttrmt Uzxv&#10;sfttrmtuzxv-xor oltrm&#10;&#10;# 4. 重新运行脚本&#10;kbgslm gizrm.kb&#10;```&#10;&#10;---&#10;&#10;### **错误原因总结**&#10;| 错误类型 | 原因 | 解决方案 |&#10;|----------|------|----------|&#10;| `oryxfwzig.hl.88.9` | XFWZ 版本不匹配 | 安装 XFWZ 88.6 并匹配 KbGlixs 版本 |&#10;| `fmwvurmvw hbnylo` | KbGlixs 扩展库冲突 | 强制重装 KbGlixs 和 KbT 扩展 |&#10;| `TzgvwIvklViili` | 未授权访问 Ooznz 7 | 申请权限并配置 Sfttrmt Uzxv 认证 |&#10;&#10;如果仍有问题，请提供以下信息：&#10;```yzhs&#10;xlmwz orhg | tivk -V &quot;glixs|xfwz&quot;&#10;sfttrmtuzxv-xor dslznr&#10;```" />
    691. 

  691.                 <option name="chatResource" value="user" />
    692. 

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    693. 

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    694. 

  694.                 </option>
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  695.                 <option name="command" value="" />
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  696.                 <option name="conversationId" value="01981253ac497dd2b091ac41f9cc5144" />
    697. 

  697.                 <option name="historyDocumentMap">
    698. 

  698.                   <map>
    699. 

  699.                     <entry key="file:///D:/python_workplace/GRAG-main/train.py" value="import os&#10;# 设置Hugging Face国内镜像源 (必须在import transformers之前设置)&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;import wandb&#10;import gc&#10;from tqdm import tqdm&#10;import torch&#10;from torch.utils.data import DataLoader&#10;from torch.nn.utils import clip_grad_norm_&#10;&#10;from src.model import load_model, llama_model_path&#10;from src.dataset import load_dataset&#10;from src.utils.evaluate import eval_funcs&#10;from src.utils.config import parse_args_llama&#10;from src.utils.ckpt import _save_checkpoint, _reload_best_model&#10;from src.utils.collate import collate_fn&#10;from src.utils.seed import seed_everything&#10;from src.utils.lr_schedule import adjust_learning_rate&#10;&#10;&#10;wandb.init(mode=&quot;offline&quot;, project=&quot;your_project&quot;)&#10;&#10;&#10;&#10;def main(args):&#10;&#10;    # Step 1: Set up wandb&#10;    seed = args.seed&#10;    wandb.init(project=f&quot;{args.project}&quot;,&#10;               name=f&quot;{args.dataset}_{args.model_name}_seed{seed}&quot;,&#10;               config=args)&#10;&#10;    seed_everything(seed=args.seed)&#10;    print(args)&#10;&#10;    dataset = load_dataset[args.dataset]()&#10;    idx_split = dataset.get_idx_split()&#10;&#10;    # Step 2: Build Dataset&#10;    train_dataset = [dataset[i] for i in idx_split['train']]&#10;    val_dataset = [dataset[i] for i in idx_split['val']]&#10;    test_dataset = [dataset[i] for i in idx_split['test']]&#10;&#10;    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, pin_memory=True, shuffle=True, collate_fn=collate_fn)&#10;    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;&#10;    # Step 3: Build Model&#10;    args.llm_model_path = llama_model_path[args.llm_model_name]&#10;    model = load_model[args.model_name](graph_type=dataset.graph_type, args=args, init_prompt=dataset.prompt)&#10;&#10;    # Step 4 Set Optimizer&#10;    params = [p for _, p in model.named_parameters() if p.requires_grad]&#10;    optimizer = torch.optim.AdamW(&#10;        [{'params': params, 'lr': args.lr, 'weight_decay': args.wd}, ],&#10;        betas=(0.9, 0.95)&#10;    )&#10;    trainable_params, all_param = model.print_trainable_params()&#10;    print(f&quot;trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}&quot;)&#10;&#10;    # Step 5. Training&#10;    num_training_steps = args.num_epochs * len(train_loader)&#10;    progress_bar = tqdm(range(num_training_steps))&#10;    best_val_loss = float('inf')&#10;&#10;    for epoch in range(args.num_epochs):&#10;&#10;        model.train()&#10;        epoch_loss, accum_loss = 0., 0.&#10;&#10;        for step, batch in enumerate(train_loader):&#10;&#10;            optimizer.zero_grad()&#10;            loss = model(batch)&#10;            loss.backward()&#10;&#10;            clip_grad_norm_(optimizer.param_groups[0]['params'], 0.1)&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                adjust_learning_rate(optimizer.param_groups[0], args.lr, step / len(train_loader) + epoch, args)&#10;&#10;            optimizer.step()&#10;            epoch_loss, accum_loss = epoch_loss + loss.item(), accum_loss + loss.item()&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                lr = optimizer.param_groups[0][&quot;lr&quot;]&#10;                wandb.log({'Lr': lr})&#10;                wandb.log({'Accum Loss': accum_loss / args.grad_steps})&#10;                accum_loss = 0.&#10;&#10;            progress_bar.update(1)&#10;&#10;        print(f&quot;Epoch: {epoch}|{args.num_epochs}: Train Loss (Epoch Mean): {epoch_loss / len(train_loader)}&quot;)&#10;        wandb.log({'Train Loss (Epoch Mean)': epoch_loss / len(train_loader)})&#10;&#10;        val_loss = 0.&#10;        eval_output = []&#10;        model.eval()&#10;        with torch.no_grad():&#10;            for step, batch in enumerate(val_loader):&#10;                loss = model(batch)&#10;                val_loss += loss.item()&#10;            val_loss = val_loss/len(val_loader)&#10;            print(f&quot;Epoch: {epoch}|{args.num_epochs}: Val Loss: {val_loss}&quot;)&#10;            wandb.log({'Val Loss': val_loss})&#10;&#10;        if val_loss &lt; best_val_loss:&#10;            best_val_loss = val_loss&#10;            _save_checkpoint(model, optimizer, epoch, args, is_best=True)&#10;            best_epoch = epoch&#10;&#10;        print(f'Epoch {epoch} Val Loss {val_loss} Best Val Loss {best_val_loss} Best Epoch {best_epoch}')&#10;&#10;        if epoch - best_epoch &gt;= args.patience:&#10;            print(f'Early stop at epoch {epoch}')&#10;            break&#10;&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;&#10;    # Step 5. Evaluating&#10;    model = _reload_best_model(model, args)&#10;    model.eval()&#10;    eval_output = []&#10;    progress_bar_test = tqdm(range(len(test_loader)))&#10;    for step, batch in enumerate(test_loader):&#10;        with torch.no_grad():&#10;            output = model.inference(batch)&#10;            eval_output.append(output)&#10;&#10;        progress_bar_test.update(1)&#10;&#10;    # Step 6. Post-processing &amp; compute metrics&#10;    os.makedirs(f'{args.output_dir}/{args.dataset}', exist_ok=True)&#10;    path = f'{args.output_dir}/{args.dataset}/model_name_{args.model_name}_llm_model_name_{args.llm_model_name}_llm_frozen_{args.llm_frozen}_max_txt_len_{args.max_txt_len}_max_new_tokens_{args.max_new_tokens}_gnn_model_name_{args.gnn_model_name}_patience_{args.patience}_num_epochs_{args.num_epochs}_seed{seed}.csv'&#10;    acc = eval_funcs[args.dataset](eval_output, path)&#10;    print(f'Test Acc {acc}')&#10;    wandb.log({'Test Acc': acc})&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;&#10;    args = parse_args_llama()&#10;&#10;    main(args)&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;    gc.collect()&#10;" />
    700. 

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    701. 

  701.                 </option>
    702. 

  702.                 <option name="messageId" value="0198137d22d27dd2b091eab21e0b09f5" />
    703. 

  703.                 <option name="question" value="&#10;```&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/yrm/kbgslm /wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm.kb &#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:31: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. Wrhzyormt rgh fhztv. Hgzxpgizxv: oryxfwzig.hl.88.9: xzmmlg lkvm hszivw lyqvxg urov: Ml hfxs urov li wrivxglib&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:02: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_xofhgvi/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:886: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkormv-xlme'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkormv_xlme/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:875: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkzihv/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. &quot;&#10;rmsvirg nlwvo dvrtsgh uiln hvmgvmxv-gizmhulinvih/zoo-ilyvigz-ozitv-e8&#10;dzmwy: Gizxprmt ifm drgs dzmwy evihrlm 9.78.9&#10;dzmwy: D&amp;Y hbmxrmt rh hvg gl `luuormv` rm gsrh wrivxglib. Ifm `dzmwy lmormv` li hvg DZMWY_NLWV=lmormv gl vmzyov xolfw hbmxrmt.&#10;dzmwy: dzmwy.rmrg() xzoovw dsrov z ifm rh zxgrev zmw ivrmrg rh hvg gl 'wvuzfog', hl ivgfimrmt gsv kiverlfh ifm.&#10;Mznvhkzxv(nlwvo_mznv='tizks_oon', kilqvxg='kilqvxgrlm', hvvw=6, wzgzhvg='vckoz_tizksh', oi=8v-94, dw=9.94, kzgrvmxv=4, yzgxs_hrav=7, tizw_hgvkh=7, mfn_vklxsh=89, dzinfk_vklxsh=8, vezo_yzgxs_hrav=83, oon_nlwvo_mznv='2y', oon_nlwvo_kzgs='', oon_uilavm='Gifv', oon_mfn_erigfzo_glpvmh=89, lfgkfg_wri='lfgkfg', nzc_gcg_ovm=487, nzc_mvd_glpvmh=67, tmm_nlwvo_mznv='tzg', tmm_mfn_ozbvih=5, tmm_rm_wrn=8975, tmm_srwwvm_wrn=8975, zortmnvmg_nok_ozbvih=6, tmm_mfn_svzwh=5, wrhgzmxv_lkvizgli='vfxorwvzm', tmm_wilklfg=9.9)&#10;Olzwrmt OOZNZ&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/fgroh/_sggk.kb&quot;, ormv 590, rm su_izrhv_uli_hgzgfh&#10;    ivhklmhv.izrhv_uli_hgzgfh()&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/ivjfvhgh/nlwvoh.kb&quot;, ormv 8973, rm izrhv_uli_hgzgfh&#10;    izrhv SGGKViili(sggk_viili_nht, ivhklmhv=hvou)&#10;ivjfvhgh.vcxvkgrlmh.SGGKViili: 598 Xorvmg Viili: Fmzfgsliravw uli fio: sggkh://su-nriili.xln/nvgz-ooznz/Ooznz-7-2y-su/ivhloev/nzrm/xlmurt.qhlm&#10;&#10;Gsv zylev vcxvkgrlm dzh gsv wrivxg xzfhv lu gsv ulooldrmt vcxvkgrlm:&#10;&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/sfy.kb&quot;, ormv 529, rm xzxsvw_urovh&#10;    su_sfy_wldmolzw(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/fgroh/_ezorwzglih.kb&quot;, ormv 885, rm _rmmvi_um&#10;    ivgfim um(*zith, **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 8991, rm su_sfy_wldmolzw&#10;    ivgfim _su_sfy_wldmolzw_gl_xzxsv_wri(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 8884, rm _su_sfy_wldmolzw_gl_xzxsv_wri&#10;    _izrhv_lm_svzw_xzoo_viili(svzw_xzoo_viili, ulixv_wldmolzw, olxzo_urovh_lmob)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 8354, rm _izrhv_lm_svzw_xzoo_viili&#10;    izrhv svzw_xzoo_viili&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 8466, rm _tvg_nvgzwzgz_li_xzgxs_viili&#10;    nvgzwzgz = tvg_su_urov_nvgzwzgz(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/fgroh/_ezorwzglih.kb&quot;, ormv 885, rm _rmmvi_um&#10;    ivgfim um(*zith, **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 8549, rm tvg_su_urov_nvgzwzgz&#10;    i = _ivjfvhg_dizkkvi(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 713, rm _ivjfvhg_dizkkvi&#10;    ivhklmhv = _ivjfvhg_dizkkvi(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/urov_wldmolzw.kb&quot;, ormv 689, rm _ivjfvhg_dizkkvi&#10;    su_izrhv_uli_hgzgfh(ivhklmhv)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/sfttrmtuzxv_sfy/fgroh/_sggk.kb&quot;, ormv 573, rm su_izrhv_uli_hgzgfh&#10;    izrhv _ulinzg(TzgvwIvklViili, nvhhztv, ivhklmhv) uiln v&#10;sfttrmtuzxv_sfy.viilih.TzgvwIvklViili: 598 Xorvmg Viili. (Ivjfvhg RW: Illg=8-3122zw74-8z5vywxx34855vzv4x206xwy;36zx7vwv-y27v-51x9-0019-66978vz34705)&#10;&#10;Xzmmlg zxxvhh tzgvw ivkl uli fio sggkh://su-nriili.xln/nvgz-ooznz/Ooznz-7-2y-su/ivhloev/nzrm/xlmurt.qhlm.&#10;Zxxvhh gl nlwvo nvgz-ooznz/Ooznz-7-2y-su rh ivhgirxgvw. Blf nfhg szev zxxvhh gl rg zmw yv zfgsvmgrxzgvw gl zxxvhh rg. Kovzhv olt rm.&#10;&#10;Gsv zylev vcxvkgrlm dzh gsv wrivxg xzfhv lu gsv ulooldrmt vcxvkgrlm:&#10;&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm.kb&quot;, ormv 853, rm &lt;nlwfov&gt;&#10;    nzrm(zith)&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm.kb&quot;, ormv 48, rm nzrm&#10;    nlwvo = olzw_nlwvo[zith.nlwvo_mznv](tizks_gbkv=wzgzhvg.tizks_gbkv, zith=zith, rmrg_kilnkg=wzgzhvg.kilnkg)&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/nlwvo/tizks_oon.kb&quot;, ormv 61, rm __rmrg__&#10;    hvou.glpvmravi = ZfglGlpvmravi.uiln_kivgizrmvw(zith.oon_nlwvo_kzgs, fhv_uzhg=Uzohv, iverhrlm=pdzith[&quot;iverhrlm&quot;])&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvoh/zfgl/glpvmrazgrlm_zfgl.kb&quot;, ormv 8996, rm uiln_kivgizrmvw&#10;    xlmurt = ZfglXlmurt.uiln_kivgizrmvw(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvoh/zfgl/xlmurtfizgrlm_zfgl.kb&quot;, ormv 8802, rm uiln_kivgizrmvw&#10;    xlmurt_wrxg, fmfhvw_pdzith = KivgizrmvwXlmurt.tvg_xlmurt_wrxg(kivgizrmvw_nlwvo_mznv_li_kzgs, **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/xlmurtfizgrlm_fgroh.kb&quot;, ormv 391, rm tvg_xlmurt_wrxg&#10;    xlmurt_wrxg, pdzith = xoh._tvg_xlmurt_wrxg(kivgizrmvw_nlwvo_mznv_li_kzgs, **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/xlmurtfizgrlm_fgroh.kb&quot;, ormv 332, rm _tvg_xlmurt_wrxg&#10;    ivhloevw_xlmurt_urov = xzxsvw_urov(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/sfy.kb&quot;, ormv 687, rm xzxsvw_urov&#10;    urov = xzxsvw_urovh(kzgs_li_ivkl_rw=kzgs_li_ivkl_rw, urovmznvh=[urovmznv], **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/fgroh/sfy.kb&quot;, ormv 466, rm xzxsvw_urovh&#10;    izrhv LHViili(&#10;LHViili: Blf ziv gibrmt gl zxxvhh z tzgvw ivkl.&#10;Nzpv hfiv gl szev zxxvhh gl rg zg sggkh://sfttrmtuzxv.xl/nvgz-ooznz/Ooznz-7-2y-su.&#10;598 Xorvmg Viili. (Ivjfvhg RW: Illg=8-3122zw74-8z5vywxx34855vzv4x206xwy;36zx7vwv-y27v-51x9-0019-66978vz34705)&#10;&#10;Xzmmlg zxxvhh tzgvw ivkl uli fio sggkh://su-nriili.xln/nvgz-ooznz/Ooznz-7-2y-su/ivhloev/nzrm/xlmurt.qhlm.&#10;Zxxvhh gl nlwvo nvgz-ooznz/Ooznz-7-2y-su rh ivhgirxgvw. Blf nfhg szev zxxvhh gl rg zmw yv zfgsvmgrxzgvw gl zxxvhh rg. Kovzhv olt rm.&#10;dzmwy: &#10;dzmwy: Blf xzm hbmx gsrh ifm gl gsv xolfw yb ifmmrmt:&#10;dzmwy: dzmwy hbmx /wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/dzmwy/luuormv-ifm-79749283_785397-1vd3jcee&#10;dzmwy: Urmw olth zg: dzmwy/luuormv-ifm-79749283_785397-1vd3jcee/olth&#10;&#10;进程已结束，退出代码为 8&#10;&#10;```" />
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  715.                 <option name="answer" value="是的，您已经在代码中设置了 `lh.vmerilm['SU_VMWKLRMG'] = 'sggkh://su-nriili.xln'`，这会将 Sfttrmt Uzxv 的默认镜像源切换为国内镜像站（su-nriili.xln）。但根据您之前的错误信息，问题可能还需要其他解决方案。以下是完整的分析和建议：&#10;&#10;---&#10;&#10;### **8. 当前设置的作用**&#10;```kbgslm&#10;lh.vmerilm['SU_VMWKLRMG'] = 'sggkh://su-nriili.xln'&#10;```&#10;- **功能**：将 Sfttrmt Uzxv 的模型/数据集下载地址切换到国内镜像，加速访问。&#10;- **适用场景**：解决 `gizmhulinvih` 或 `wzgzhvgh` 库下载模型时的网络问题。&#10;&#10;---&#10;&#10;### **7. 仍需检查的其他配置**&#10;如果仍然遇到问题（如 `TORYXCC` 或 XFWZ 错误），可能需要额外设置：&#10;&#10;#### **(8) 认证问题（针对 OOzNZ 等受限模型）**&#10;即使使用镜像，访问受限模型（如 OOzNZ-7）仍需 Sfttrmt Uzxv 账号认证：&#10;```kbgslm&#10;uiln sfttrmtuzxv_sfy rnklig oltrm&#10;oltrm(glpvm=&quot;blfi_su_glpvm&quot;)  # 替换为你的 Glpvm&#10;```&#10;&#10;#### **(7) 代理设置（如果镜像站不稳定）**&#10;```kbgslm&#10;lh.vmerilm['SGGK_KILCB'] = 'sggk://blfi_kilcb:klig'  # 如有必要&#10;lh.vmerilm['SGGKH_KILCB'] = 'sggk://blfi_kilcb:klig'&#10;```&#10;&#10;#### **(6) 强制使用镜像站（避免回退到原始地址）**&#10;```kbgslm&#10;lh.vmerilm['SU_SFY_VMZYOV_SU_GIZMHUVI'] = '8'  # 启用高效传输&#10;lh.vmerilm['SU_SFY_LUUORMV'] = '9'            # 确保在线模式&#10;```&#10;&#10;---&#10;&#10;### **6. 完整代码示例**&#10;```kbgslm&#10;rnklig lh&#10;uiln sfttrmtuzxv_sfy rnklig oltrm&#10;&#10;# 设置镜像源和网络&#10;lh.vmerilm['SU_VMWKLRMG'] = 'sggkh://su-nriili.xln'&#10;lh.vmerilm['SU_SFY_VMZYOV_SU_GIZMHUVI'] = '8'&#10;&#10;# 登录 Sfttrmt Uzxv（如需访问受限模型）&#10;oltrm(glpvm=&quot;blfi_su_glpvm_sviv&quot;)&#10;&#10;# 继续其他代码...&#10;```&#10;&#10;---&#10;&#10;### **5. 常见问题排查**&#10;| 问题类型 | 检查步骤 |&#10;|----------|----------|&#10;| **镜像站无效** | 直接访问 `sggkh://su-nriili.xln` 测试是否可达 |&#10;| **认证失败** | 运行 `sfttrmtuzxv-xor oltrm` 确认 Glpvm 有效 |&#10;| **库版本冲突** | 检查 `gizmhulinvih` 版本：`krk hsld gizmhulinvih` |&#10;| **系统依赖问题** | 运行 `hgirmth /fhi/ory/c13_35-ormfc-tmf/oryhgwx++.hl.3 | tivk TORYXCC` |&#10;&#10;---&#10;&#10;### **4. 高级调试**&#10;如果问题仍未解决，可以启用详细日志：&#10;```kbgslm&#10;rnklig olttrmt&#10;olttrmt.yzhrxXlmurt(ovevo=olttrmt.RMUL)  # 显示网络请求详情&#10;```&#10;&#10;或通过命令行测试下载：&#10;```yzhs&#10;SU_VMWKLRMG=sggkh://su-nriili.xln kbgslm -x &quot;uiln gizmhulinvih rnklig ZfglNlwvo; ZfglNlwvo.uiln_kivgizrmvw('yvig-yzhv-fmxzhvw')&quot;&#10;```&#10;&#10;---&#10;&#10;请根据您的具体错误信息选择对应的解决方案。如果问题仍然存在，请提供完整的错误日志，我会进一步协助分析！" />
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    749. 

  749.                     <entry key="file:///D:/python_workplace/GRAG-main/train.py" value="import os&#10;# 设置Hugging Face国内镜像源 (必须在import transformers之前设置)&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;&#10;import wandb&#10;import gc&#10;from tqdm import tqdm&#10;import torch&#10;from torch.utils.data import DataLoader&#10;from torch.nn.utils import clip_grad_norm_&#10;&#10;from src.model import load_model, llama_model_path&#10;from src.dataset import load_dataset&#10;from src.utils.evaluate import eval_funcs&#10;from src.utils.config import parse_args_llama&#10;from src.utils.ckpt import _save_checkpoint, _reload_best_model&#10;from src.utils.collate import collate_fn&#10;from src.utils.seed import seed_everything&#10;from src.utils.lr_schedule import adjust_learning_rate&#10;&#10;&#10;wandb.init(mode=&quot;offline&quot;, project=&quot;your_project&quot;)&#10;&#10;&#10;&#10;def main(args):&#10;&#10;    # Step 1: Set up wandb&#10;    seed = args.seed&#10;    wandb.init(project=f&quot;{args.project}&quot;,&#10;               name=f&quot;{args.dataset}_{args.model_name}_seed{seed}&quot;,&#10;               config=args)&#10;&#10;    seed_everything(seed=args.seed)&#10;    print(args)&#10;&#10;    dataset = load_dataset[args.dataset]()&#10;    idx_split = dataset.get_idx_split()&#10;&#10;    # Step 2: Build Dataset&#10;    train_dataset = [dataset[i] for i in idx_split['train']]&#10;    val_dataset = [dataset[i] for i in idx_split['val']]&#10;    test_dataset = [dataset[i] for i in idx_split['test']]&#10;&#10;    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, pin_memory=True, shuffle=True, collate_fn=collate_fn)&#10;    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, drop_last=False, pin_memory=True, shuffle=False, collate_fn=collate_fn)&#10;&#10;    # Step 3: Build Model&#10;    args.llm_model_path = llama_model_path[args.llm_model_name]&#10;    model = load_model[args.model_name](graph_type=dataset.graph_type, args=args, init_prompt=dataset.prompt)&#10;&#10;    # Step 4 Set Optimizer&#10;    params = [p for _, p in model.named_parameters() if p.requires_grad]&#10;    optimizer = torch.optim.AdamW(&#10;        [{'params': params, 'lr': args.lr, 'weight_decay': args.wd}, ],&#10;        betas=(0.9, 0.95)&#10;    )&#10;    trainable_params, all_param = model.print_trainable_params()&#10;    print(f&quot;trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}&quot;)&#10;&#10;    # Step 5. Training&#10;    num_training_steps = args.num_epochs * len(train_loader)&#10;    progress_bar = tqdm(range(num_training_steps))&#10;    best_val_loss = float('inf')&#10;&#10;    for epoch in range(args.num_epochs):&#10;&#10;        model.train()&#10;        epoch_loss, accum_loss = 0., 0.&#10;&#10;        for step, batch in enumerate(train_loader):&#10;&#10;            optimizer.zero_grad()&#10;            loss = model(batch)&#10;            loss.backward()&#10;&#10;            clip_grad_norm_(optimizer.param_groups[0]['params'], 0.1)&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                adjust_learning_rate(optimizer.param_groups[0], args.lr, step / len(train_loader) + epoch, args)&#10;&#10;            optimizer.step()&#10;            epoch_loss, accum_loss = epoch_loss + loss.item(), accum_loss + loss.item()&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                lr = optimizer.param_groups[0][&quot;lr&quot;]&#10;                wandb.log({'Lr': lr})&#10;                wandb.log({'Accum Loss': accum_loss / args.grad_steps})&#10;                accum_loss = 0.&#10;&#10;            progress_bar.update(1)&#10;&#10;        print(f&quot;Epoch: {epoch}|{args.num_epochs}: Train Loss (Epoch Mean): {epoch_loss / len(train_loader)}&quot;)&#10;        wandb.log({'Train Loss (Epoch Mean)': epoch_loss / len(train_loader)})&#10;&#10;        val_loss = 0.&#10;        eval_output = []&#10;        model.eval()&#10;        with torch.no_grad():&#10;            for step, batch in enumerate(val_loader):&#10;                loss = model(batch)&#10;                val_loss += loss.item()&#10;            val_loss = val_loss/len(val_loader)&#10;            print(f&quot;Epoch: {epoch}|{args.num_epochs}: Val Loss: {val_loss}&quot;)&#10;            wandb.log({'Val Loss': val_loss})&#10;&#10;        if val_loss &lt; best_val_loss:&#10;            best_val_loss = val_loss&#10;            _save_checkpoint(model, optimizer, epoch, args, is_best=True)&#10;            best_epoch = epoch&#10;&#10;        print(f'Epoch {epoch} Val Loss {val_loss} Best Val Loss {best_val_loss} Best Epoch {best_epoch}')&#10;&#10;        if epoch - best_epoch &gt;= args.patience:&#10;            print(f'Early stop at epoch {epoch}')&#10;            break&#10;&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;&#10;    # Step 5. Evaluating&#10;    model = _reload_best_model(model, args)&#10;    model.eval()&#10;    eval_output = []&#10;    progress_bar_test = tqdm(range(len(test_loader)))&#10;    for step, batch in enumerate(test_loader):&#10;        with torch.no_grad():&#10;            output = model.inference(batch)&#10;            eval_output.append(output)&#10;&#10;        progress_bar_test.update(1)&#10;&#10;    # Step 6. Post-processing &amp; compute metrics&#10;    os.makedirs(f'{args.output_dir}/{args.dataset}', exist_ok=True)&#10;    path = f'{args.output_dir}/{args.dataset}/model_name_{args.model_name}_llm_model_name_{args.llm_model_name}_llm_frozen_{args.llm_frozen}_max_txt_len_{args.max_txt_len}_max_new_tokens_{args.max_new_tokens}_gnn_model_name_{args.gnn_model_name}_patience_{args.patience}_num_epochs_{args.num_epochs}_seed{seed}.csv'&#10;    acc = eval_funcs[args.dataset](eval_output, path)&#10;    print(f'Test Acc {acc}')&#10;    wandb.log({'Test Acc': acc})&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;&#10;    args = parse_args_llama()&#10;&#10;    main(args)&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;    gc.collect()&#10;" />
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    765. 

  765.                 <option name="answer" value="根据错误日志，问题主要涉及 **XFWZ 设备初始化失败** 和 **KbGlixs Tvlnvgirx (KbT) 扩展库兼容性问题**。以下是分步解决方案：&#10;&#10;---&#10;&#10;### **8. 解决 XFWZ 设备初始化问题**&#10;#### **错误信息**&#10;```&#10;IfmgrnvViili: Rmezorw wverxv zitfnvmg: wrw blf xzoo rmrg?&#10;Wverxv 7 rh mlg zezrozyov, zezrozyov wverxvh ziv [9, 8]&#10;```&#10;#### **解决方案**&#10;##### **(8) 检查 TKF 设备状态**&#10;```yzhs&#10;merwrz-hnr  # 确认 TKF 状态&#10;kbgslm -x &quot;rnklig glixs; kirmg(glixs.xfwz.wverxv_xlfmg(), glixs.xfwz.rh_zezrozyov())&quot;&#10;```&#10;如果输出显示 `glixs.xfwz.rh_zezrozyov()` 为 `Uzohv`，说明 KbGlixs 未正确识别 XFWZ。&#10;&#10;##### **(7) 强制指定可用设备**&#10;在代码中显式指定 TKF 设备（例如使用 `xfwz:9`）：&#10;```kbgslm&#10;rnklig glixs&#10;wverxv = glixs.wverxv(&quot;xfwz:9&quot; ru glixs.xfwz.rh_zezrozyov() vohv &quot;xkf&quot;)&#10;nlwvo.gl(wverxv)&#10;```&#10;&#10;##### **(6) 重新安装 KbGlixs 匹配 XFWZ 版本**&#10;```yzhs&#10;# 查看 XFWZ 版本&#10;mexx --evihrlm&#10;&#10;# 卸载旧版 KbGlixs&#10;krk fmrmhgzoo glixs glixserhrlm glixszfwrl&#10;&#10;# 安装匹配的 KbGlixs（例如 XFWZ 88.1）&#10;krk rmhgzoo glixs glixserhrlm glixszfwrl --rmwvc-fio sggkh://wldmolzw.kbglixs.lit/dso/xf881&#10;```&#10;&#10;---&#10;&#10;### **7. 修复 KbGlixs Tvlnvgirx 扩展库问题**&#10;#### **错误信息**&#10;```&#10;oryxfwzig.hl.88.9: xzmmlg lkvm hszivw lyqvxg urov&#10;fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;```&#10;#### **解决方案**&#10;##### **(8) 重新安装 KbT 扩展库**&#10;确保 KbT 扩展库与 KbGlixs/XFWZ 版本严格匹配：&#10;```yzhs&#10;# 查看 KbGlixs 版本&#10;kbgslm -x &quot;rnklig glixs; kirmg(glixs.__evihrlm__)&quot;&#10;&#10;# 安装对应版本的 KbT 扩展&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme -u sggkh://wzgz.kbt.lit/dso/glixs-${GLIXS_EVIHRLM}+xf${XFWZ_EVIHRLM}.sgno&#10;```&#10;例如 KbGlixs 7.8.9 + XFWZ 88.1：&#10;```yzhs&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme -u sggkh://wzgz.kbt.lit/dso/glixs-7.8.9+xf881.sgno&#10;```&#10;&#10;##### **(7) 验证 KbT 安装**&#10;```kbgslm&#10;rnklig glixs&#10;uiln glixs_tvlnvgirx rnklig __evihrlm__ zh kbt_evihrlm&#10;kirmg(u&quot;KbGlixs: {glixs.__evihrlm__}, KbT: {kbt_evihrlm}&quot;)&#10;```&#10;&#10;---&#10;&#10;### **6. 处理 DzmwY 离线模式**&#10;#### **错误信息**&#10;```&#10;dzmwy: D&amp;Y hbmxrmt rh hvg gl `luuormv`&#10;```&#10;#### **解决方案**&#10;##### **(8) 启用在线同步**&#10;```yzhs&#10;dzmwy lmormv&#10;```&#10;或在代码中指定：&#10;```kbgslm&#10;rnklig dzmwy&#10;dzmwy.rmrg(nlwv=&quot;lmormv&quot;)&#10;```&#10;&#10;##### **(7) 手动同步离线数据**&#10;```yzhs&#10;dzmwy hbmx /kzgs/gl/luuormv-ifm-79749283_779753-vcykw2gj&#10;```&#10;&#10;---&#10;&#10;### **5. 完整修复流程**&#10;```yzhs&#10;# 8. 清理环境&#10;xlmwz zxgrezgv gsvhrh6.0&#10;krk fmrmhgzoo glixs glixserhrlm glixszfwrl glixs-hxzggvi glixs-hkzihv glixs-xofhgvi kbt-ory&#10;&#10;# 7. 安装匹配的 KbGlixs + XFWZ&#10;krk rmhgzoo glixs==7.8.9 glixserhrlm==9.83.9 glixszfwrl==7.8.9 --rmwvc-fio sggkh://wldmolzw.kbglixs.lit/dso/xf881&#10;&#10;# 6. 安装 KbT 扩展&#10;krk rmhgzoo kbt-ory glixs-hxzggvi glixs-hkzihv glixs-xofhgvi glixs-hkormv-xlme -u sggkh://wzgz.kbt.lit/dso/glixs-7.8.9+xf881.sgno&#10;&#10;# 5. 验证安装&#10;kbgslm -x &quot;rnklig glixs; kirmg(glixs.xfwz.rh_zezrozyov()); rnklig glixs_tvlnvgirx; kirmg('KbT LP')&quot;&#10;```&#10;&#10;---&#10;&#10;### **错误原因总结**&#10;| 错误类型 | 原因 | 解决方案 |&#10;|----------|------|----------|&#10;| `Rmezorw wverxv zitfnvmg` | XFWZ 未正确初始化 | 检查 TKF 状态，显式指定设备 |&#10;| `oryxfwzig.hl.88.9 mlg ulfmw` | KbGlixs 与 XFWZ 版本不匹配 | 重装匹配版本的 KbGlixs |&#10;| `fmwvurmvw hbnylo` | KbT 扩展库版本冲突 | 安装与 KbGlixs 严格匹配的 KbT 扩展 |&#10;| `dzmwy luuormv` | 未配置在线同步 | 运行 `dzmwy lmormv` 或手动同步 |&#10;&#10;如果问题仍未解决，请提供以下信息：&#10;```yzhs&#10;merwrz-hnr&#10;xlmwz orhg | tivk -V &quot;glixs|kbt|dzmwy&quot;&#10;```" />
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  774.                     <entry key="file:///D:/python_workplace/GRAG-main/train_local.py" value="import os&#10;&#10;# 设置Hugging Face国内镜像源 (必须在import transformers之前设置)&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'&#10;&#10;# 强制离线模式 - 确保从本地加载模型而不联网&#10;os.environ['TRANSFORMERS_OFFLINE'] = '1'&#10;os.environ['HF_DATASETS_OFFLINE'] = '1'&#10;&#10;import wandb&#10;import gc&#10;from tqdm import tqdm&#10;import torch&#10;from torch.utils.data import DataLoader&#10;from torch.nn.utils import clip_grad_norm_&#10;&#10;from src.model import load_model, llama_model_path&#10;from src.dataset import load_dataset&#10;from src.utils.evaluate import eval_funcs&#10;from src.utils.config import parse_args_llama&#10;from src.utils.ckpt import _save_checkpoint, _reload_best_model&#10;from src.utils.collate import collate_fn&#10;from src.utils.seed import seed_everything&#10;from src.utils.lr_schedule import adjust_learning_rate&#10;&#10;wandb.init(mode=&quot;offline&quot;, project=&quot;your_project&quot;)&#10;&#10;&#10;def main(args):&#10;    # Step 1: Set up wandb&#10;    seed = args.seed&#10;    wandb.init(project=f&quot;{args.project}&quot;,&#10;               name=f&quot;{args.dataset}_{args.model_name}_seed{seed}&quot;,&#10;               config=args)&#10;&#10;    seed_everything(seed=args.seed)&#10;    print(args)&#10;&#10;    dataset = load_dataset[args.dataset]()&#10;    idx_split = dataset.get_idx_split()&#10;&#10;    # Step 2: Build Dataset&#10;    train_dataset = [dataset[i] for i in idx_split['train']]&#10;    val_dataset = [dataset[i] for i in idx_split['val']]&#10;    test_dataset = [dataset[i] for i in idx_split['test']]&#10;&#10;    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, pin_memory=True, shuffle=True,&#10;                              collate_fn=collate_fn)&#10;    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=False, pin_memory=True, shuffle=False,&#10;                            collate_fn=collate_fn)&#10;    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, drop_last=False, pin_memory=True,&#10;                             shuffle=False, collate_fn=collate_fn)&#10;&#10;    # ============ 关键修改点：使用本地模型路径 ============&#10;    # 覆盖原有的模型路径，直接指向本地文件夹&#10;    local_model_path = &quot;/data2/ycj/models/vicuna-7b-v1.5&quot;  # 使用绝对路径&#10;&#10;    # 打印确认信息&#10;    print(f&quot;使用本地模型路径: {local_model_path}&quot;)&#10;    print(f&quot;确保该路径包含完整的模型文件: config.json, pytorch_model.bin 等&quot;)&#10;&#10;    # 设置模型路径&#10;    args.llm_model_path = local_model_path&#10;&#10;    # Step 3: Build Model&#10;    model = load_model[args.model_name](&#10;        graph_type=dataset.graph_type,&#10;        args=args,&#10;        init_prompt=dataset.prompt&#10;    )&#10;&#10;    # Step 4 Set Optimizer&#10;    params = [p for _, p in model.named_parameters() if p.requires_grad]&#10;    optimizer = torch.optim.AdamW(&#10;        [{'params': params, 'lr': args.lr, 'weight_decay': args.wd}, ],&#10;        betas=(0.9, 0.95)&#10;    )&#10;    trainable_params, all_param = model.print_trainable_params()&#10;    print(&#10;        f&quot;trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}&quot;)&#10;&#10;    # Step 5. Training&#10;    num_training_steps = args.num_epochs * len(train_loader)&#10;    progress_bar = tqdm(range(num_training_steps))&#10;    best_val_loss = float('inf')&#10;    best_epoch = 0  # 初始化best_epoch&#10;&#10;    for epoch in range(args.num_epochs):&#10;&#10;        model.train()&#10;        epoch_loss, accum_loss = 0., 0.&#10;&#10;        for step, batch in enumerate(train_loader):&#10;&#10;            optimizer.zero_grad()&#10;            loss = model(batch)&#10;            loss.backward()&#10;&#10;            clip_grad_norm_(optimizer.param_groups[0]['params'], 0.1)&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                adjust_learning_rate(optimizer.param_groups[0], args.lr, step / len(train_loader) + epoch, args)&#10;&#10;            optimizer.step()&#10;            epoch_loss, accum_loss = epoch_loss + loss.item(), accum_loss + loss.item()&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                lr = optimizer.param_groups[0][&quot;lr&quot;]&#10;                wandb.log({'Lr': lr})&#10;                wandb.log({'Accum Loss': accum_loss / args.grad_steps})&#10;                accum_loss = 0.&#10;&#10;            progress_bar.update(1)&#10;&#10;        print(f&quot;Epoch: {epoch}|{args.num_epochs}: Train Loss (Epoch Mean): {epoch_loss / len(train_loader)}&quot;)&#10;        wandb.log({'Train Loss (Epoch Mean)': epoch_loss / len(train_loader)})&#10;&#10;        val_loss = 0.&#10;        eval_output = []&#10;        model.eval()&#10;        with torch.no_grad():&#10;            for step, batch in enumerate(val_loader):&#10;                loss = model(batch)&#10;                val_loss += loss.item()&#10;            val_loss = val_loss / len(val_loader)&#10;            print(f&quot;Epoch: {epoch}|{args.num_epochs}: Val Loss: {val_loss}&quot;)&#10;            wandb.log({'Val Loss': val_loss})&#10;&#10;        if val_loss &lt; best_val_loss:&#10;            best_val_loss = val_loss&#10;            _save_checkpoint(model, optimizer, epoch, args, is_best=True)&#10;            best_epoch = epoch&#10;&#10;        print(f'Epoch {epoch} Val Loss {val_loss} Best Val Loss {best_val_loss} Best Epoch {best_epoch}')&#10;&#10;        if epoch - best_epoch &gt;= args.patience:&#10;            print(f'Early stop at epoch {epoch}')&#10;            break&#10;&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;&#10;    # Step 5. Evaluating&#10;    model = _reload_best_model(model, args)&#10;    model.eval()&#10;    eval_output = []&#10;    progress_bar_test = tqdm(range(len(test_loader)))&#10;    for step, batch in enumerate(test_loader):&#10;        with torch.no_grad():&#10;            output = model.inference(batch)&#10;            eval_output.append(output)&#10;&#10;        progress_bar_test.update(1)&#10;&#10;    # Step 6. Post-processing &amp; compute metrics&#10;    os.makedirs(f'{args.output_dir}/{args.dataset}', exist_ok=True)&#10;    path = f'{args.output_dir}/{args.dataset}/model_name_{args.model_name}_llm_model_name_{args.llm_model_name}_llm_frozen_{args.llm_frozen}_max_txt_len_{args.max_txt_len}_max_new_tokens_{args.max_new_tokens}_gnn_model_name_{args.gnn_model_name}_patience_{args.patience}_num_epochs_{args.num_epochs}_seed{seed}.csv'&#10;    acc = eval_funcs[args.dataset](eval_output, path)&#10;    print(f'Test Acc {acc}')&#10;    wandb.log({'Test Acc': acc})&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;&#10;    args = parse_args_llama()&#10;&#10;    # 添加本地模型路径参数（可选）&#10;    if not hasattr(args, 'local_model_path'):&#10;        args.local_model_path = &quot;/model/llama&quot;  # 默认路径&#10;&#10;    main(args)&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;    gc.collect()" />
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    778. 

  778.                 <option name="question" value="&#10;```&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/yrm/kbgslm /wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm_olxzo.kb &#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:31: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. Wrhzyormt rgh fhztv. Hgzxpgizxv: oryxfwzig.hl.88.9: xzmmlg lkvm hszivw lyqvxg urov: Ml hfxs urov li wrivxglib&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'kbt-ory'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:02: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_xofhgvi/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-xofhgvi'. &quot;&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:886: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkormv-xlme'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkormv_xlme/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(&#10;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_tvlnvgirx/gbkrmt.kb:875: FhviDzimrmt: Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. Wrhzyormt rgh fhztv. Hgzxpgizxv: /nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs_hkzihv/_evihrlm_xfwz.hl: fmwvurmvw hbnylo: _AM6x8982IvtrhgviLkvizglihW8Ve&#10;  dzimrmth.dzim(u&quot;Zm rhhfv lxxfiivw dsrov rnkligrmt 'glixs-hkzihv'. &quot;&#10;rmsvirg nlwvo dvrtsgh uiln hvmgvmxv-gizmhulinvih/zoo-ilyvigz-ozitv-e8&#10;dzmwy: Gizxprmt ifm drgs dzmwy evihrlm 9.78.9&#10;dzmwy: D&amp;Y hbmxrmt rh hvg gl `luuormv` rm gsrh wrivxglib. Ifm `dzmwy lmormv` li hvg DZMWY_NLWV=lmormv gl vmzyov xolfw hbmxrmt.&#10;dzmwy: dzmwy.rmrg() xzoovw dsrov z ifm rh zxgrev zmw ivrmrg rh hvg gl 'wvuzfog', hl ivgfimrmt gsv kiverlfh ifm.&#10;Mznvhkzxv(nlwvo_mznv='tizks_oon', kilqvxg='kilqvxgrlm', hvvw=6, wzgzhvg='vckoz_tizksh', oi=8v-94, dw=9.94, kzgrvmxv=4, yzgxs_hrav=7, tizw_hgvkh=7, mfn_vklxsh=89, dzinfk_vklxsh=8, vezo_yzgxs_hrav=83, oon_nlwvo_mznv='2y', oon_nlwvo_kzgs='', oon_uilavm='Gifv', oon_mfn_erigfzo_glpvmh=89, lfgkfg_wri='lfgkfg', nzc_gcg_ovm=487, nzc_mvd_glpvmh=67, tmm_nlwvo_mznv='tzg', tmm_mfn_ozbvih=5, tmm_rm_wrn=8975, tmm_srwwvm_wrn=8975, zortmnvmg_nok_ozbvih=6, tmm_mfn_svzwh=5, wrhgzmxv_lkvizgli='vfxorwvzm', tmm_wilklfg=9.9, olxzo_nlwvo_kzgs='/nlwvo/ooznz')&#10;使用本地模型路径: /wzgz7/bxq/nlwvoh/erxfmz-2y-e8.4&#10;确保该路径包含完整的模型文件: xlmurt.qhlm, kbglixs_nlwvo.yrm 等&#10;Olzwrmt OOZNZ&#10;Wverxv 7 rh mlg zezrozyov, zezrozyov wverxvh ziv [9, 8]&#10;Wverxv 6 rh mlg zezrozyov, zezrozyov wverxvh ziv [9, 8]&#10;Gizxvyzxp (nlhg ivxvmg xzoo ozhg):&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm_olxzo.kb&quot;, ormv 827, rm &lt;nlwfov&gt;&#10;    nzrm(zith)&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/gizrm_olxzo.kb&quot;, ormv 32, rm nzrm&#10;    nlwvo = olzw_nlwvo[zith.nlwvo_mznv](&#10;  Urov &quot;/wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/hix/nlwvo/tizks_oon.kb&quot;, ormv 57, rm __rmrg__&#10;    nlwvo = ZfglNlwvoUliXzfhzoON.uiln_kivgizrmvw(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvoh/zfgl/zfgl_uzxglib.kb&quot;, ormv 399, rm uiln_kivgizrmvw&#10;    ivgfim nlwvo_xozhh.uiln_kivgizrmvw(&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvormt_fgroh.kb&quot;, ormv 688, rm _dizkkvi&#10;    ivgfim ufmx(*zith, **pdzith)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvormt_fgroh.kb&quot;, ormv 5179, rm uiln_kivgizrmvw&#10;    wverxv_nzk = _tvg_wverxv_nzk(nlwvo, wverxv_nzk, nzc_nvnlib, su_jfzmgravi, glixs_wgbkv, pvvk_rm_uk67_ivtvc)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/gizmhulinvih/nlwvormt_fgroh.kb&quot;, ormv 8549, rm _tvg_wverxv_nzk&#10;    fmfhvw_nvnlib = glixs.xfwz.nvnlib_ivhvievw(wverxv_mznv) - glixs.xfwz.nvnlib_zoolxzgvw(wverxv_mznv)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs/xfwz/nvnlib.kb&quot;, ormv 550, rm nvnlib_ivhvievw&#10;    ivgfim nvnlib_hgzgh(wverxv=wverxv).tvg(&quot;ivhvievw_ybgvh.zoo.xfiivmg&quot;, 9)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs/xfwz/nvnlib.kb&quot;, ormv 706, rm nvnlib_hgzgh&#10;    hgzgh = nvnlib_hgzgh_zh_mvhgvw_wrxg(wverxv=wverxv)&#10;  Urov &quot;/nvnyvi/bxq/.xlmwz/vmeh/gsvhrh6.0/ory/kbgslm6.0/hrgv-kzxpztvh/glixs/xfwz/nvnlib.kb&quot;, ormv 694, rm nvnlib_hgzgh_zh_mvhgvw_wrxg&#10;    ivgfim glixs._X._xfwz_nvnlibHgzgh(wverxv)&#10;IfmgrnvViili: Rmezorw wverxv zitfnvmg : wrw blf xzoo rmrg?&#10;dzmwy: &#10;dzmwy: Blf xzm hbmx gsrh ifm gl gsv xolfw yb ifmmrmt:&#10;dzmwy: dzmwy hbmx /wzgz7/bxq/xlwv/gsvhrh/TIZT-nzrm/dzmwy/luuormv-ifm-79749283_779753-vcykw2gj&#10;dzmwy: Urmw olth zg: dzmwy/luuormv-ifm-79749283_779753-vcykw2gj/olth&#10;&#10;进程已结束，退出代码为 8&#10;&#10;```" />
    779. 

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    780. 

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  783.                 <option name="status" value="SUCCESS" />
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    788. 

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    789. 

  789.                 <option name="agent" value="" />
    790. 

  790.                 <option name="answer" value="这个错误表明你的代码尝试从 `gizmhulinvih` 库导入 `SbyirwXzxsv`，但当前安装的 `gizmhulinvih` 版本中不存在这个模块或类。以下是解决方案：&#10;&#10;---&#10;&#10;### **8. 检查 `gizmhulinvih` 版本**&#10;`SbyirwXzxsv` 是 Sfttrmt Uzxv `gizmhulinvih` 库较新版本引入的功能。首先确认你的 `gizmhulinvih` 版本是否支持该特性：&#10;&#10;```yzhs&#10;krk hsld gizmhulinvih&#10;```&#10;&#10;- **如果版本低于 5.59.9**：`SbyirwXzxsv` 可能尚未引入，需升级：&#10;  ```yzhs&#10;  krk rmhgzoo --fktizwv gizmhulinvih&#10;  ```&#10;&#10;- **如果版本 ≥ 5.59.9**：可能是其他问题（见后续步骤）。&#10;&#10;---&#10;&#10;### **7. 验证 `SbyirwXzxsv` 的可用性**&#10;在 Kbgslm 交互环境中测试是否能直接导入：&#10;```kbgslm&#10;uiln gizmhulinvih rnklig SbyirwXzxsv&#10;kirmg(SbyirwXzxsv.__wlx__)  # 查看文档确认功能&#10;```&#10;&#10;如果仍报错，可能是：&#10;- **版本不匹配**：某些定制版或分支可能移除了该功能。&#10;- **名称变更**：Sfttrmt Uzxv 可能已重命名该模块（如改为 `Xzxsv` 或 `WbmznrxXzxsv`）。&#10;&#10;---&#10;&#10;### **6. 代码兼容性调整**&#10;如果确认 `SbyirwXzxsv` 不可用，根据你的需求选择替代方案：&#10;&#10;#### **方案 Z：使用通用缓存类**&#10;```kbgslm&#10;uiln gizmhulinvih rnklig Xzxsv  # 或 WbmznrxXzxsv（新版本）&#10;xzxsv = Xzxsv()&#10;```&#10;&#10;#### **方案 Y：自定义实现**&#10;```kbgslm&#10;xozhh SbyirwXzxsv:&#10;    wvu __rmrg__(hvou):&#10;        hvou.nvnlib_xzxsv = {}&#10;        hvou.wrhp_xzxsv = {}&#10;&#10;    wvu tvg(hvou, pvb):&#10;        ivgfim hvou.nvnlib_xzxsv.tvg(pvb) li hvou.wrhp_xzxsv.tvg(pvb)&#10;&#10;# 替换原代码中的 SbyirwXzxsv&#10;```&#10;&#10;---&#10;&#10;### **5. 安装开发版（谨慎操作）**&#10;如果急需 `SbyirwXzxsv` 且正式版未包含，可尝试安装开发版：&#10;```yzhs&#10;krk rmhgzoo trg+sggkh://trgsfy.xln/sfttrmtuzxv/gizmhulinvih.trg&#10;```&#10;但需注意开发版可能存在不稳定问题。&#10;&#10;---&#10;&#10;### **4. 检查代码上下文**&#10;如果是从第三方库（如自定义模型）导入 `SbyirwXzxsv`，可能是该库的兼容性问题：&#10;- 联系库作者确认支持的 `gizmhulinvih` 版本。&#10;- 根据报错位置修改导入语句（如改为相对导入）。&#10;&#10;---&#10;&#10;### **验证修复**&#10;```kbgslm&#10;# 测试导入是否成功&#10;uiln gizmhulinvih rnklig SbyirwXzxsv  # 或替代方案&#10;kirmg(&quot;导入成功！&quot;)&#10;```&#10;&#10;---&#10;&#10;### **常见原因总结**&#10;| 问题类型 | 解决方案 |&#10;|----------|----------|&#10;| 版本过低 | 升级 `gizmhulinvih`: `krk rmhgzoo -F gizmhulinvih` |&#10;| 名称变更 | 查看最新文档，改用新类名（如 `Xzxsv`） |&#10;| 第三方库依赖 | 调整库版本或修改导入逻辑 |&#10;&#10;如果问题仍未解决，请提供：&#10;8. 完整的错误堆栈。&#10;7. 你的 `gizmhulinvih` 版本（`krk hsld gizmhulinvih`）。&#10;6. 使用 `SbyirwXzxsv` 的代码片段。" />
    791. 

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  799.                     <entry key="file:///D:/python_workplace/GRAG-main/src/model/graph_llm.py" value="import contextlib&#10;import torch&#10;import torch.nn as nn&#10;from torch.cuda.amp import autocast as autocast&#10;from transformers import AutoModelForCausalLM, AutoTokenizer&#10;from torch_scatter import scatter&#10;from src.model.gnn import load_gnn_model&#10;from peft import (&#10;    LoraConfig,&#10;    get_peft_model,&#10;    prepare_model_for_kbit_training,&#10;)&#10;&#10;BOS = '&lt;s&gt;[INST]'&#10;EOS_USER = '[/INST]'&#10;EOS = '&lt;/s&gt;'&#10;&#10;IGNORE_INDEX = -100&#10;&#10;class GraphLLM(torch.nn.Module):&#10;&#10;    def __init__(&#10;        self,&#10;        args,&#10;        **kwargs&#10;    ):&#10;        super().__init__()&#10;        self.max_txt_len = args.max_txt_len&#10;        self.max_new_tokens = args.max_new_tokens&#10;&#10;        print('Loading LLAMA')&#10;        kwargs = {&#10;            &quot;max_memory&quot;: {0: '20GiB', 1: '20GiB'},&#10;            &quot;device_map&quot;: &quot;auto&quot;,&#10;            &quot;revision&quot;: &quot;main&quot;,&#10;        }&#10;&#10;        self.tokenizer = AutoTokenizer.from_pretrained(args.llm_model_path, use_fast=False, revision=kwargs[&quot;revision&quot;])&#10;        self.tokenizer.pad_token_id = 0&#10;        self.tokenizer.padding_side = 'left'&#10;&#10;        model = AutoModelForCausalLM.from_pretrained(&#10;            args.llm_model_path,&#10;            torch_dtype=torch.float16,&#10;            low_cpu_mem_usage=True,&#10;            **kwargs&#10;        )&#10;&#10;        if args.llm_frozen == 'True':&#10;            print(&quot;Freezing LLAMA!&quot;)&#10;            for _, param in model.named_parameters():&#10;                param.requires_grad = False&#10;        else:&#10;            print(&quot;Training LLAMA with LORA!&quot;)&#10;            model = prepare_model_for_kbit_training(model)&#10;            lora_r: int = 8&#10;            lora_alpha: int = 16&#10;            lora_dropout: float = 0.05&#10;            lora_target_modules = [&#10;                &quot;q_proj&quot;,&#10;                &quot;v_proj&quot;,&#10;            ]&#10;            config = LoraConfig(&#10;                r=lora_r,&#10;                lora_alpha=lora_alpha,&#10;                target_modules=lora_target_modules,&#10;                lora_dropout=lora_dropout,&#10;                bias=&quot;none&quot;,&#10;                task_type=&quot;CAUSAL_LM&quot;,&#10;            )&#10;            model = get_peft_model(model, config)&#10;&#10;        self.model = model&#10;        print('Finish loading LLAMA!')&#10;&#10;        self.graph_encoder = load_gnn_model[args.gnn_model_name](&#10;            in_channels=args.gnn_in_dim,&#10;            out_channels=args.gnn_hidden_dim,&#10;            hidden_channels=args.gnn_hidden_dim,&#10;            num_layers=args.gnn_num_layers,&#10;            dropout=args.gnn_dropout,&#10;            mlp_layers = args.alignment_mlp_layers,&#10;            num_heads=args.gnn_num_heads,&#10;            operator=args.distance_operator,&#10;        ).to(self.model.device)&#10;&#10;        # If you are using llama2-13b, replace with nn.Linear(2048, 5120) ...&#10;        self.projector = nn.Sequential(&#10;            nn.Linear(args.gnn_hidden_dim, 2048),&#10;            nn.Sigmoid(),&#10;            nn.Linear(2048, 4096),&#10;        ).to(self.model.device)&#10;&#10;        self.word_embedding = self.model.model.get_input_embeddings()&#10;&#10;    @property&#10;    def device(self):&#10;        return list(self.parameters())[0].device&#10;&#10;    def maybe_autocast(self, dtype=torch.bfloat16):&#10;        # if on cpu, don't use autocast&#10;        # if on gpu, use autocast with dtype if provided, otherwise use torch.float16&#10;        enable_autocast = self.device != torch.device(&quot;cpu&quot;)&#10;&#10;        if enable_autocast:&#10;            return torch.cuda.amp.autocast(dtype=dtype)&#10;        else:&#10;            return contextlib.nullcontext()&#10;&#10;    def encode_graphs(self, samples):&#10;        graphs = samples['graph']&#10;        graphs = graphs.to(self.model.device)&#10;        n_embeds, _ = self.graph_encoder(graphs.x, &#10;                                         graphs.edge_index.long(), &#10;                                         graphs.question_node,&#10;                                         graphs.edge_attr, &#10;                                         graphs.question_edge)&#10;&#10;        # mean pooling&#10;        g_embeds = scatter(n_embeds, graphs.batch, dim=0, reduce='mean')&#10;&#10;        return g_embeds&#10;&#10;    def forward(self, samples):&#10;        # encode description, questions and labels&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;        labels = self.tokenizer(samples[&quot;label&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_tokens = self.tokenizer(EOS, add_special_tokens=False)&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        batch_label_input_ids = []&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            # Add bos &amp; eos token&#10;            label_input_ids = labels.input_ids[i][:self.max_new_tokens] + eos_tokens.input_ids&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids + label_input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;            label_input_ids = [IGNORE_INDEX] * (inputs_embeds.shape[0]-len(label_input_ids))+label_input_ids&#10;            batch_label_input_ids.append(label_input_ids)&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            if len(graph_embeds)!=batch_size and i&gt;=batch_size-1: break&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;            batch_label_input_ids[i] = [IGNORE_INDEX] * pad_length+batch_label_input_ids[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;        label_input_ids = torch.tensor(batch_label_input_ids).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model(&#10;                inputs_embeds=inputs_embeds,&#10;                attention_mask=attention_mask,&#10;                return_dict=True,&#10;                labels=label_input_ids,&#10;            )&#10;&#10;        return outputs.loss&#10;&#10;    def inference(self, samples):&#10;&#10;        # encode description and questions&#10;        questions = self.tokenizer(samples[&quot;question&quot;], add_special_tokens=False)&#10;        descriptions = self.tokenizer(samples[&quot;desc&quot;], add_special_tokens=False)&#10;&#10;        # encode special tokens&#10;        eos_user_tokens = self.tokenizer(EOS_USER, add_special_tokens=False)&#10;        bos_embeds = self.word_embedding(self.tokenizer(BOS, add_special_tokens=False, return_tensors='pt').input_ids[0].to(self.model.device))&#10;        pad_embeds = self.word_embedding(torch.tensor(self.tokenizer.pad_token_id).to(self.model.device)).unsqueeze(0)&#10;&#10;        # encode graphs&#10;        graph_embeds = self.encode_graphs(samples)&#10;        graph_embeds = self.projector(graph_embeds)&#10;&#10;        batch_size = len(samples['id'])&#10;        batch_inputs_embeds = []&#10;        batch_attention_mask = []&#10;        for i in range(batch_size):&#10;            # Add bos &amp; eos token&#10;            input_ids = descriptions.input_ids[i][:self.max_txt_len] + questions.input_ids[i] + eos_user_tokens.input_ids&#10;            inputs_embeds = self.word_embedding(torch.tensor(input_ids).to(self.model.device))&#10;            inputs_embeds = torch.cat([bos_embeds, graph_embeds[i].unsqueeze(0), inputs_embeds], dim=0)&#10;            batch_inputs_embeds.append(inputs_embeds)&#10;            batch_attention_mask.append([1] * inputs_embeds.shape[0])&#10;&#10;        # pad inputs_embeds&#10;        max_length = max([x.shape[0] for x in batch_inputs_embeds])&#10;        for i in range(batch_size):&#10;            pad_length = max_length-batch_inputs_embeds[i].shape[0]&#10;            batch_inputs_embeds[i] = torch.cat([pad_embeds.repeat(pad_length, 1), batch_inputs_embeds[i]])&#10;            batch_attention_mask[i] = [0]*pad_length+batch_attention_mask[i]&#10;&#10;        inputs_embeds = torch.stack(batch_inputs_embeds, dim=0).to(self.model.device)&#10;        attention_mask = torch.tensor(batch_attention_mask).to(self.model.device)&#10;&#10;        with self.maybe_autocast():&#10;            outputs = self.model.generate(&#10;                inputs_embeds=inputs_embeds,&#10;                max_new_tokens=self.max_new_tokens,&#10;                attention_mask=attention_mask,&#10;                # do_sample=True,&#10;                use_cache=True  # IMPORTANT!&#10;            )&#10;        pred = self.tokenizer.batch_decode(outputs, skip_special_tokens=True)&#10;&#10;        return {'id': samples['id'],&#10;                'pred': pred,&#10;                'label': samples['label'],&#10;                'question': samples['question'],&#10;                'desc': samples['desc'], }&#10;&#10;    def print_trainable_params(self):&#10;        trainable_params = 0&#10;        all_param = 0&#10;&#10;        for _, param in self.named_parameters():&#10;            num_params = param.numel()&#10;&#10;            all_param += num_params&#10;            if param.requires_grad:&#10;                trainable_params += num_params&#10;&#10;        return trainable_params, all_param&#10;" />
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    815. 

  815.                 <option name="answer" value="`dzmwy`（Dvrtsgh &amp; Yrzhvh）是一个用于 **机器学习实验跟踪、可视化和协作** 的工具平台。在你的代码中，`dzmwy` 主要用于以下功能：&#10;&#10;---&#10;&#10;### **8. 核心功能**&#10;| 功能 | 代码示例 | 作用 |&#10;|------|----------|------|&#10;| **实验跟踪** | `dzmwy.olt({'Oi': oi})` | 记录学习率、损失等指标 |&#10;| **可视化** | 自动生成图表 | 实时查看训练曲线（如损失下降、准确率变化） |&#10;| **超参数管理** | `dzmwy.xlmurt` | 保存和比较不同实验的超参数（如 `zith.oi`） |&#10;| **模型版本控制** | `dzmwy.hzev('nlwvo.kgs')` | 保存模型检查点 |&#10;| **团队协作** | 共享项目链接 | 多人协作分析实验结果 |&#10;&#10;---&#10;&#10;### **7. 你的代码中的具体用途**&#10;```kbgslm&#10;dzmwy.olt({&#10;    'Oi': oi,                          # 记录当前学习率&#10;    'Zxxfn Olhh': zxxfn_olhh / zith.tizw_hgvkh  # 记录累计平均损失&#10;})&#10;```&#10;- **为什么需要？**  &#10;  这些日志会被同步到 DzmwY 云端或本地服务器，生成如下图表：  &#10;  ![DzmwY Wzhsylziw](sggkh://dzmwy.zr/hrgv/_mvcg/rnztv?fio=%7Uzhhvgh%7Urnztvh%7Uwlxh%7Utfrwvh%7Ujfrxphgzig%7Uwzhsylziw.kmt&amp;d=8079&amp;j=24)&#10;&#10;---&#10;&#10;### **6. 关键优势**&#10;- **实时监控**：训练过程中可随时通过网页查看进度。&#10;- **复现性**：自动记录代码、环境和超参数，确保实验可复现。&#10;- **调试效率**：快速发现异常（如梯度爆炸/消失）。&#10;&#10;---&#10;&#10;### **5. 如何开始使用？**&#10;#### **步骤 8：安装**&#10;```yzhs&#10;krk rmhgzoo dzmwy&#10;```&#10;&#10;#### **步骤 7：登录（首次使用需注册账号）**&#10;```yzhs&#10;dzmwy oltrm&#10;```&#10;&#10;#### **步骤 6：初始化（在代码开头添加）**&#10;```kbgslm&#10;rnklig dzmwy&#10;dzmwy.rmrg(kilqvxg=&quot;blfi_kilqvxg_mznv&quot;, xlmurt=zith)  # xlmurt 会记录所有超参数&#10;```&#10;&#10;---&#10;&#10;### **4. 常见问题**&#10;#### **J: 为什么我的代码显示 `luuormv` 模式？**  &#10;Z: 未登录或网络问题导致，解决方案：&#10;```yzhs&#10;dzmwy lmormv  # 切换为在线模式&#10;```&#10;&#10;#### **J: 如何关闭 DzmwY？**  &#10;Z: 设置环境变量：&#10;```yzhs&#10;vcklig DZMWY_NLWV=wrhzyovw&#10;```&#10;&#10;#### **J: 数据安全吗？**  &#10;Z: 支持私有化部署，或使用离线模式（数据保存在本地 `dzmwy/` 目录）。&#10;&#10;---&#10;&#10;### **3. 扩展用法**&#10;- **对比实验**：在同一图表中比较不同超参数的效果。&#10;- **警报通知**：训练失败时发送邮件/Hozxp 提醒。&#10;- **资源监控**：记录 TKF 显存、XKF 使用率等。&#10;&#10;如果需要更深入的使用指导，可以参考 [官方文档](sggkh://wlxh.dzmwy.zr/)。" />
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  848.                     <entry key="file:///D:/python_workplace/GRAG-main/train_local.py" value="import os&#10;&#10;# 设置可见的GPU设备 - 新增这行&#10;os.environ[&quot;CUDA_VISIBLE_DEVICES&quot;] = &quot;0,1&quot;  # 只使用设备0和1&#10;&#10;# 设置Hugging Face国内镜像源 (必须在import transformers之前设置)&#10;os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'&#10;os.environ['HF_HUB_ENABLE_HF_TRANSFER'] = '1'&#10;&#10;# 强制离线模式 - 确保从本地加载模型而不联网&#10;os.environ['TRANSFORMERS_OFFLINE'] = '1'&#10;os.environ['HF_DATASETS_OFFLINE'] = '1'&#10;&#10;import wandb&#10;import gc&#10;from tqdm import tqdm&#10;import torch&#10;from torch.utils.data import DataLoader&#10;from torch.nn.utils import clip_grad_norm_&#10;&#10;from src.model import load_model, llama_model_path&#10;from src.dataset import load_dataset&#10;from src.utils.evaluate import eval_funcs&#10;from src.utils.config import parse_args_llama&#10;from src.utils.ckpt import _save_checkpoint, _reload_best_model&#10;from src.utils.collate import collate_fn&#10;from src.utils.seed import seed_everything&#10;from src.utils.lr_schedule import adjust_learning_rate&#10;&#10;# 初始化CUDA状态 - 新增这行&#10;if torch.cuda.is_available():&#10;    torch.cuda.init()  # 确保CUDA正确初始化&#10;&#10;wandb.init(mode=&quot;offline&quot;, project=&quot;your_project&quot;)&#10;&#10;&#10;def main(args):&#10;    # 打印可用设备信息 - 新增这行&#10;    print(f&quot;可用GPU设备数量: {torch.cuda.device_count()}&quot;)&#10;    print(f&quot;当前设备: {torch.cuda.current_device()}&quot;)&#10;&#10;    # Step 1: Set up wandb&#10;    seed = args.seed&#10;    wandb.init(project=f&quot;{args.project}&quot;,&#10;               name=f&quot;{args.dataset}_{args.model_name}_seed{seed}&quot;,&#10;               config=args)&#10;&#10;    seed_everything(seed=args.seed)&#10;    print(args)&#10;&#10;    dataset = load_dataset[args.dataset]()&#10;    idx_split = dataset.get_idx_split()&#10;&#10;    # Step 2: Build Dataset&#10;    train_dataset = [dataset[i] for i in idx_split['train']]&#10;    val_dataset = [dataset[i] for i in idx_split['val']]&#10;    test_dataset = [dataset[i] for i in idx_split['test']]&#10;&#10;    train_loader = DataLoader(train_dataset, batch_size=args.batch_size, drop_last=True, pin_memory=True, shuffle=True,&#10;                              collate_fn=collate_fn)&#10;    val_loader = DataLoader(val_dataset, batch_size=args.batch_size, drop_last=False, pin_memory=True, shuffle=False,&#10;                            collate_fn=collate_fn)&#10;    test_loader = DataLoader(test_dataset, batch_size=args.eval_batch_size, drop_last=False, pin_memory=True,&#10;                             shuffle=False, collate_fn=collate_fn)&#10;&#10;    # 使用本地模型路径&#10;    local_model_path = args.local_model_path&#10;    print(f&quot;使用本地模型路径: {local_model_path}&quot;)&#10;    print(f&quot;确保该路径包含完整的模型文件: config.json, pytorch_model.bin 等&quot;)&#10;&#10;    # 设置模型路径&#10;    args.llm_model_path = local_model_path&#10;&#10;    # Step 3: Build Model&#10;    # 添加显存优化选项 - 修改这行&#10;    model = load_model[args.model_name](&#10;        graph_type=dataset.graph_type,&#10;        args=args,&#10;        init_prompt=dataset.prompt,&#10;        device_map=&quot;auto&quot;  # 自动分配设备&#10;    )&#10;&#10;    # 打印模型所在设备 - 新增这行&#10;    print(f&quot;模型加载到设备: {model.device}&quot;)&#10;&#10;    # Step 4 Set Optimizer&#10;    params = [p for _, p in model.named_parameters() if p.requires_grad]&#10;    optimizer = torch.optim.AdamW(&#10;        [{'params': params, 'lr': args.lr, 'weight_decay': args.wd}, ],&#10;        betas=(0.9, 0.95)&#10;    )&#10;    trainable_params, all_param = model.print_trainable_params()&#10;    print(&#10;        f&quot;trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}&quot;)&#10;&#10;    # Step 5. Training&#10;    num_training_steps = args.num_epochs * len(train_loader)&#10;    progress_bar = tqdm(range(num_training_steps))&#10;    best_val_loss = float('inf')&#10;    best_epoch = 0&#10;&#10;    for epoch in range(args.num_epochs):&#10;        # 清空显存缓存 - 新增这行&#10;        torch.cuda.empty_cache()&#10;&#10;        model.train()&#10;        epoch_loss, accum_loss = 0., 0.&#10;&#10;        for step, batch in enumerate(train_loader):&#10;            # 确保数据在正确设备上 - 新增这行&#10;            if hasattr(model, 'device'):&#10;                batch = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}&#10;&#10;            optimizer.zero_grad()&#10;            loss = model(batch)&#10;            loss.backward()&#10;&#10;            clip_grad_norm_(optimizer.param_groups[0]['params'], 0.1)&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                adjust_learning_rate(optimizer.param_groups[0], args.lr, step / len(train_loader) + epoch, args)&#10;&#10;            optimizer.step()&#10;            epoch_loss, accum_loss = epoch_loss + loss.item(), accum_loss + loss.item()&#10;&#10;            if (step + 1) % args.grad_steps == 0:&#10;                lr = optimizer.param_groups[0][&quot;lr&quot;]&#10;                wandb.log({'Lr': lr})&#10;                wandb.log({'Accum Loss': accum_loss / args.grad_steps})&#10;                accum_loss = 0.&#10;&#10;            progress_bar.update(1)&#10;&#10;        print(f&quot;Epoch: {epoch}|{args.num_epochs}: Train Loss (Epoch Mean): {epoch_loss / len(train_loader)}&quot;)&#10;        wandb.log({'Train Loss (Epoch Mean)': epoch_loss / len(train_loader)})&#10;&#10;        val_loss = 0.&#10;        eval_output = []&#10;        model.eval()&#10;        with torch.no_grad():&#10;            for step, batch in enumerate(val_loader):&#10;                # 确保数据在正确设备上 - 新增这行&#10;                if hasattr(model, 'device'):&#10;                    batch = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}&#10;&#10;                loss = model(batch)&#10;                val_loss += loss.item()&#10;            val_loss = val_loss / len(val_loader)&#10;            print(f&quot;Epoch: {epoch}|{args.num_epochs}: Val Loss: {val_loss}&quot;)&#10;            wandb.log({'Val Loss': val_loss})&#10;&#10;        if val_loss &lt; best_val_loss:&#10;            best_val_loss = val_loss&#10;            _save_checkpoint(model, optimizer, epoch, args, is_best=True)&#10;            best_epoch = epoch&#10;&#10;        print(f'Epoch {epoch} Val Loss {val_loss} Best Val Loss {best_val_loss} Best Epoch {best_epoch}')&#10;&#10;        if epoch - best_epoch &gt;= args.patience:&#10;            print(f'Early stop at epoch {epoch}')&#10;            break&#10;&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;&#10;    # Step 5. Evaluating&#10;    model = _reload_best_model(model, args)&#10;    model.eval()&#10;    eval_output = []&#10;    progress_bar_test = tqdm(range(len(test_loader)))&#10;    for step, batch in enumerate(test_loader):&#10;        with torch.no_grad():&#10;            # 确保数据在正确设备上 - 新增这行&#10;            if hasattr(model, 'device'):&#10;                batch = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}&#10;&#10;            output = model.inference(batch)&#10;            eval_output.append(output)&#10;&#10;        progress_bar_test.update(1)&#10;&#10;    # Step 6. Post-processing &amp; compute metrics&#10;    os.makedirs(f'{args.output_dir}/{args.dataset}', exist_ok=True)&#10;    path = f'{args.output_dir}/{args.dataset}/model_name_{args.model_name}_llm_model_name_{args.llm_model_name}_llm_frozen_{args.llm_frozen}_max_txt_len_{args.max_txt_len}_max_new_tokens_{args.max_new_tokens}_gnn_model_name_{args.gnn_model_name}_patience_{args.patience}_num_epochs_{args.num_epochs}_seed{seed}.csv'&#10;    acc = eval_funcs[args.dataset](eval_output, path)&#10;    print(f'Test Acc {acc}')&#10;    wandb.log({'Test Acc': acc})&#10;&#10;&#10;if __name__ == &quot;__main__&quot;:&#10;    # 确保CUDA正确初始化 - 新增这行&#10;    if torch.cuda.is_available():&#10;        torch.cuda.init()&#10;&#10;    args = parse_args_llama()&#10;&#10;    # 添加本地模型路径参数&#10;    if not hasattr(args, 'local_model_path'):&#10;        args.local_model_path = &quot;/data2/ycj/models/vicuna-7b-v1.5&quot;  # 默认路径&#10;&#10;    main(args)&#10;    torch.cuda.empty_cache()&#10;    torch.cuda.reset_max_memory_allocated()&#10;    gc.collect()" />
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