support/models/Xenova/all- MiniLM-L6- v2/tokenizer_config.json".

时间: 2025-02-17 07:52:20 浏览: 121
### Xenova all-MiniLM-L6-v2 Tokenizer 配置文件的内容和用途 Tokenizer配置文件对于预处理输入文本至关重要,特别是在使用像 `all-MiniLM-L6-v2` 这样的模型时。此配置文件定义了分词器的行为参数,确保输入数据被正确编码成适合模型理解的形式。 #### 文件内容 通常情况下,`tokenizer_config.json` 文件会包含如下字段: - **model_type**: 表明所使用的具体模型架构,在这里是 `bert` 或类似的结构[^1]。 - **do_lower_case (布尔)**: 如果设置为 true,则在 tokenization 前将所有字符转换为小写形式;这对于不区分大小写的语言尤为重要[^2]. - **unk_token, sep_token, pad_token, cls_token, mask_token**: 定义特殊标记符,用于指示未知词汇、序列结束、填充位置以及分类任务中的起始点等特定含义的token. - **max_len**: 设定了单个输入的最大长度限制,超过这个长度的部分会被截断. 针对 `Xenova/all-MiniLM-L6-v2` 特定版本的具体配置可能略有不同,但大致遵循上述模式。以下是基于常规 BERT 类型模型的一个假设性的 `tokenizer_config.json` 示例: ```json { "model_type": "bert", "do_lower_case": false, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "max_len": 512 } ``` #### 文件用途 该配置文件主要用于指导如何准备送入 transformer 模型的数据集。通过指定这些选项,可以确保每次运行时都有一致性和可重复的结果。特别是当涉及到多语言支持或是自定义词汇表的情况下,合适的 tokenizer 设置变得尤为关键. 此外,它还帮助开发者快速调整超参数以适应不同的应用场景需求。
阅读全文

相关推荐

(base) HwHiAiUser@orangepiaipro-20t:~/chatglm/inference$ python3 main.py Traceback (most recent call last): File "/home/HwHiAiUser/chatglm/inference/main.py", line 7, in <module> infer_engine=LlamaInterface(cfg) File "/home/HwHiAiUser/chatglm/inference/inference.py", line 44, in __init__ self.tokenizer:AutoTokenizer=AutoTokenizer.from_pretrained(config.tokenizer,trust_remote_code=True) File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/transformers/models/auto/tokenization_auto.py", line 643, in from_pretrained tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs) File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/transformers/models/auto/tokenization_auto.py", line 487, in get_tokenizer_config resolved_config_file = cached_file( File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/transformers/utils/hub.py", line 417, in cached_file resolved_file = hf_hub_download( File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/huggingface_hub/utils/_deprecation.py", line 101, in inner_f return f(*args, **kwargs) File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn validate_repo_id(arg_value) File "/home/HwHiAiUser/.local/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id raise HFValidationError( huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': './tokenizer/'. Use repo_type argument if needed.

python web_demo.py Explicitly passing a revision is encouraged when loading a model with custom code to ensure no malicious code has been contributed in a newer revision. Traceback (most recent call last): File "/home/nano/THUDM/ChatGLM-6B/web_demo.py", line 5, in <module> tokenizer = AutoTokenizer.from_pretrained("/home/nano/THUDM/chatglm-6b", trust_remote_code=True) File "/home/nano/.local/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 679, in from_pretrained return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) File "/home/nano/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1804, in from_pretrained return cls._from_pretrained( File "/home/nano/.local/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 1958, in _from_pretrained tokenizer = cls(*init_inputs, **init_kwargs) File "/home/nano/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 221, in __init__ self.sp_tokenizer = SPTokenizer(vocab_file, num_image_tokens=num_image_tokens) File "/home/nano/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 64, in __init__ self.text_tokenizer = TextTokenizer(vocab_file) File "/home/nano/.cache/huggingface/modules/transformers_modules/chatglm-6b/tokenization_chatglm.py", line 22, in __init__ self.sp.Load(model_path) File "/home/nano/.local/lib/python3.10/site-packages/sentencepiece/__init__.py", line 905, in Load return self.LoadFromFile(model_file) File "/home/nano/.local/lib/python3.10/site-packages/sentencepiece/__init__.py", line 310, in LoadFromFile return _sentencepiece.SentencePieceProcessor_LoadFromFile(self, arg) RuntimeError: Internal: src/sentencepiece_processor.cc(1101) [model_proto->ParseFromArray(serialized.data(), serialized.size())]什么错误

[root@ac6b15bb1f77 evalscope-main]# python eval.py 2025-07-24 11:41:51,385 - evalscope - INFO - Args: Task config is provided with TaskConfig type. 2025-07-24 11:41:52,475 - evalscope - INFO - Loading model /models/z50051264/ascend-vllm-master/tools/AutoAWQ/examples/Qwen2.5-7B-awq ... Traceback (most recent call last): File "/models/z50051264/evalscope-main/eval.py", line 17, in <module> run_task(task_cfg=task_cfg) File "/models/z50051264/evalscope-main/evalscope/run.py", line 31, in run_task return run_single_task(task_cfg, run_time) File "/models/z50051264/evalscope-main/evalscope/run.py", line 44, in run_single_task result = evaluate_model(task_cfg, outputs) File "/models/z50051264/evalscope-main/evalscope/run.py", line 119, in evaluate_model base_model = get_local_model(task_cfg) File "/models/z50051264/evalscope-main/evalscope/models/local_model.py", line 121, in get_local_model base_model.load_model() File "/models/z50051264/evalscope-main/evalscope/models/local_model.py", line 59, in load_model self.tokenizer = AutoTokenizer.from_pretrained( File "/usr/local/python3.10.17/lib/python3.10/site-packages/modelscope/utils/hf_util/patcher.py", line 281, in from_pretrained module_obj = module_class.from_pretrained( File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 880, in from_pretrained return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs) File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2110, in from_pretrained return cls._from_pretrained( File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/tokenization_utils_base.py", line 2336, in _from_pretrained tokenizer = cls(*init_inputs, **init_kwargs) File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/models/qwen2/tokenization_qwen2_fast.py", line 120, in __init__ super().__init__( File "/usr/local/python3.10.17/lib/python3.10/site-packages/transformers/tokenization_utils_fast.py", line 114, in __init__ fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file) Exception: data did not match any variant of untagged enum ModelWrapper at line 757443 column 3 [ERROR] 2025-07-24-11:41:52 (PID:4414, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception 在对autoawq量化后的模型进行性能测试的时候,报错如上

/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:441: FutureWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. _torch_pytree._register_pytree_node( /home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:309: FutureWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. _torch_pytree._register_pytree_node( hornetq Traceback (most recent call last): File "/home/ls_lunwen/KEPT-main/Kept/trace/main/./train_trace_rapt.py", line 28, in <module> main() File "/home/ls_lunwen/KEPT-main/Kept/trace/main/./train_trace_rapt.py", line 19, in main preprocess_dataset(args.data_dir,args.project) File "/home/ls_lunwen/KEPT-main/Kept/trace/main/../../common/data_processing.py", line 314, in preprocess_dataset process_project(base_dir,project) File "/home/ls_lunwen/KEPT-main/Kept/trace/main/../../common/data_processing.py", line 301, in process_project bug_reports_pd, links_pd, file_diffs_pd = process_data(issue_path, commit_path,data_type) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/KEPT-main/Kept/trace/main/../../common/data_processing.py", line 205, in process_data issue_pd = pd.read_csv(issue_path) ^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/pandas/io/parsers/readers.py", line 912, in read_csv return _read(filepath_or_buffer, kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/pandas/io/parsers/readers.py", line 577, in _read parser = TextFileReader(filepath_or_buffer, **kwds) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/pandas/io/parsers/readers.py", line 1407, in __init__ self._engine = self._make_engine(f, self.engine) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/pandas/io/parsers/readers.py", line 1661, in _make_engine self.handles = get_handle( ^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/pandas/io/common.py", line 859, in get_handle handle = open( ^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/home/yueli/HuYworks1/kept/input_data/raw/issue/hornetq.csv' /home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:441: FutureWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. _torch_pytree._register_pytree_node( /home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/utils/generic.py:309: FutureWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead. _torch_pytree._register_pytree_node( eval parameters %s Namespace(project='hornetq', code_kg_mode='inner', data_dir='/home/yueli/HuYworks1/kept/input_data/', model_path='./output/hornetq', no_cuda=False, test_num=None, output_dir='./result/', overwrite=False, code_bert='../unixCoder', chunk_query_num=-1, per_gpu_eval_batch_size=32, code_kg_location='/home/yueli/HuYworks1/kept/input_data/hornetq/', text_kg_location='/home/yueli/HuYworks1/kept/input_data/hornetq/', length_limit=256, tqdm_interval=300.0, data_name='hornetq') Traceback (most recent call last): File "/home/ls_lunwen/KEPT-main/Kept/trace/main/eval_trace_rapt.py", line 25, in <module> model = Rapt(BertConfig(), args.code_bert) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/KEPT-main/Kept/trace/main/../../common/models.py", line 84, in __init__ self.ctokneizer = AutoTokenizer.from_pretrained(cbert_model, local_files_only=True) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 752, in from_pretrained config = AutoConfig.from_pretrained( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/models/auto/configuration_auto.py", line 1082, in from_pretrained config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/configuration_utils.py", line 644, in get_config_dict config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/configuration_utils.py", line 699, in _get_config_dict resolved_config_file = cached_file( ^^^^^^^^^^^^ File "/home/ls_lunwen/anaconda3/envs/llm/lib/python3.11/site-packages/transformers/utils/hub.py", line 360, in cached_file raise EnvironmentError( OSError: ../unixCoder does not appear to have a file named config.json. Checkout 'https://huggingface.co/../unixCoder/None' for available files.

(pointllm) qiheng@qiheng-System-Product-Name:~/PointLLM$ python pointllm/eval/eval_objaverse.py --model_name /home/qiheng/PointLLM_7B_v1.2 --task_type captioning --prompt_index 2 Loading validation datasets. Loading anno file from data/anno_data/PointLLM_brief_description_val_200_GT.json. Using conversation_type: ('simple_description',) Before filtering, the dataset size is: 200. After filtering, the dataset size is: 200. Number of simple_description: 200 Done! [INFO] Loading model from local directory: /home/qiheng/PointLLM_7B_v1.2 [ERROR] Failed to load model from local path: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/qiheng/PointLLM_7B_v1.2'. Use repo_type argument if needed. [ERROR] This indicates a critical issue with your local model files. [ERROR] Please ensure all model files are present in the directory. Traceback (most recent call last): File "/home/qiheng/PointLLM/pointllm/eval/eval_objaverse.py", line 283, in <module> main(args) File "/home/qiheng/PointLLM/pointllm/eval/eval_objaverse.py", line 222, in main model, tokenizer, conv = init_model(args) File "/home/qiheng/PointLLM/pointllm/eval/eval_objaverse.py", line 63, in init_model tokenizer = AutoTokenizer.from_pretrained( File "/home/qiheng/anaconda3/envs/pointllm/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 622, in from_pretrained tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs) File "/home/qiheng/anaconda3/envs/pointllm/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 466, in get_tokenizer_config resolved_config_file = cached_file( File "/home/qiheng/anaconda3/envs/pointllm/lib/python3.10/site-packages/transformers/utils/hub.py", line 409, in cached_file resolved_file = hf_hub_download( File "/home/qiheng/anaconda3/envs/pointllm/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn validate_repo_id(arg_value) File "/home/qiheng/anaconda3/envs/pointllm/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id raise HFValidationError( huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/qiheng/PointLLM_7B_v1.2'. Use repo_type argument if needed. (pointllm) qiheng@qiheng-System-Product-Name:~/PointLLM$

2025-06-13 18:54:07,509 [INFO] 使用本地模型路径: ./models 2025-06-13 18:54:07,511 [INFO] 模型目录内容: ['pytorch_model.bin', 'config.json', 'tokenizer_config.json', 'vocab.txt'] 2025-06-13 18:54:07,516 [INFO] 读取训练数据... 2025-06-13 18:54:07,774 [INFO] 训练数据样本数: 55 2025-06-13 18:54:07,780 [INFO] 训练集大小: 44, 验证集大小: 11 2025-06-13 18:54:07,782 [INFO] 创建多任务模型... 2025-06-13 18:54:07,784 [INFO] 正在加载基础模型... --------------------------------------------------------------------------- ValueError Traceback (most recent call last) File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:708, in getattribute_from_module(module, attr) 707 try: --> 708 return getattribute_from_module(transformers_module, attr) 709 except ValueError: File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:712, in getattribute_from_module(module, attr) 711 else: --> 712 raise ValueError(f"Could not find {attr} in {transformers_module}!") ValueError: Could not find BertModel in <module 'transformers' from '/opt/conda/lib/python3.11/site-packages/transformers/__init__.py'>! During handling of the above exception, another exception occurred: ValueError Traceback (most recent call last) Cell In[10], line 275 273 # 创建模型 274 logging.info("创建多任务模型...") --> 275 model = MultiTaskModel(num_labels_dict) 277 # 6. 自定义训练循环(简化版) 278 device = torch.device("cuda" if torch.cuda.is_available() else "cpu") Cell In[10], line 107, in MultiTaskModel.__init__(self, num_labels_dict) 105 # 直接从本地加载模型 106 logging.info("正在加载基础模型...") --> 107 self.base_model = AutoModel.from_pretrained(MODEL_PATH, local_files_only=True) 108 logging.info("基础模型加载成功") 110 # 为每个特征构建分类头 File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:568, in _BaseAutoModelClass.from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs) 564 return model_class.from_pretrained( 565 pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs 566 ) 567 elif type(config) in cls._model_mapping.keys(): --> 568 model_class = _get_model_class(config, cls._model_mapping) 569 if model_class.config_class == config.sub_configs.get("text_config", None): 570 config = config.get_text_config() File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:388, in _get_model_class(config, model_mapping) 387 def _get_model_class(config, model_mapping): --> 388 supported_models = model_mapping[type(config)] 389 if not isinstance(supported_models, (list, tuple)): 390 return supported_models File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:774, in _LazyAutoMapping.__getitem__(self, key) 772 if model_type in self._model_mapping: 773 model_name = self._model_mapping[model_type] --> 774 return self._load_attr_from_module(model_type, model_name) 776 # Maybe there was several model types associated with this config. 777 model_types = [k for k, v in self._config_mapping.items() if v == key.__name__] File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:788, in _LazyAutoMapping._load_attr_from_module(self, model_type, attr) 786 if module_name not in self._modules: 787 self._modules[module_name] = importlib.import_module(f".{module_name}", "transformers.models") --> 788 return getattribute_from_module(self._modules[module_name], attr) File /opt/conda/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py:710, in getattribute_from_module(module, attr) 708 return getattribute_from_module(transformers_module, attr) 709 except ValueError: --> 710 raise ValueError(f"Could not find {attr} neither in {module} nor in {transformers_module}!") 711 else: 712 raise ValueError(f"Could not find {attr} in {transformers_module}!") ValueError: Could not find BertModel neither in <module 'transformers.models.bert' from '/opt/conda/lib/python3.11/site-packages/transformers/models/bert/__init__.py'> nor in <module 'transformers' from '/opt/conda/lib/python3.11/site-packages/transformers/__init__.py'>!

运行CUDA_VISIBLE_DEVICES=0,2 dbgpt start webserver --config /app/configs/dbgpt-local-vllm.toml报错如下: =========================== VLLMDeployModelParameters =========================== name: DeepSeek-R1-Distill-Qwen-32B provider: vllm verbose: False concurrency: 100 backend: None prompt_template: None context_length: None reasoning_model: None path: models/DeepSeek-R1-Distill-Qwen-32B device: auto trust_remote_code: True download_dir: None load_format: auto config_format: auto dtype: auto kv_cache_dtype: auto seed: 0 max_model_len: None distributed_executor_backend: None pipeline_parallel_size: 1 tensor_parallel_size: 1 max_parallel_loading_workers: None block_size: None enable_prefix_caching: None swap_space: 4.0 cpu_offload_gb: 0.0 gpu_memory_utilization: 0.9 max_num_batched_tokens: None max_num_seqs: 2 max_logprobs: 20 revision: None code_revision: None tokenizer_revision: None tokenizer_mode: auto quantization: fp8 max_seq_len_to_capture: 8192 worker_cls: auto extras: None ====================================================================== 2025-08-05 07:43:32 1249bbe41ac7 dbgpt.util.code.server[4248] INFO Code server is ready INFO 08-05 07:43:36 config.py:520] This model supports multiple tasks: {'score', 'classify', 'generate', 'reward', 'embed'}. Defaulting to 'generate'. WARNING 08-05 07:43:36 arg_utils.py:1107] Chunked prefill is enabled by default for models with max_model_len > 32K. Currently, chunked prefill might not work with some features or models. If you encounter any issues, please disable chunked prefill by setting --enable-chunked-prefill=False. INFO 08-05 07:43:36 config.py:1483] Chunked prefill is enabled with max_num_batched_tokens=2048. INFO 08-05 07:43:37 llm_engine.py:232] Initializing an LLM engine (v0.7.0) with config: model='/app/models/DeepSeek-R1-Distill-Qwen-32B', speculative_config=None, tokenizer='/app/models/DeepSeek-R1-Distill-Qwen-32B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, override_neuron_config=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=LoadFormat.AUTO, tensor_parallel_size=1, pipeline_parallel_size=1, disable_custom_all_reduce=False, quantization=fp8, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='xgrammar'), observability_config=ObservabilityConfig(otlp_traces_endpoint=None, collect_model_forward_time=False, collect_model_execute_time=False), seed=0, served_model_name=/app/models/DeepSeek-R1-Distill-Qwen-32B, num_scheduler_steps=1, multi_step_stream_outputs=True, enable_prefix_caching=False, chunked_prefill_enabled=True, use_async_output_proc=True, disable_mm_preprocessor_cache=False, mm_processor_kwargs=None, pooler_config=None, compilation_config={"splitting_ops":[],"compile_sizes":[],"cudagraph_capture_sizes":[2,1],"max_capture_size":2}, use_cached_outputs=False, INFO 08-05 07:43:37 cuda.py:225] Using Flash Attention backend. INFO 08-05 07:43:37 model_runner.py:1110] Starting to load model /app/models/DeepSeek-R1-Distill-Qwen-32B... /app/packages/dbgpt-core/src/dbgpt/util/model_utils.py:27: UserWarning: 'has_mps' is deprecated, please use 'torch.backends.mps.is_built()' if (hasattr(backends, "mps") and backends.mps.is_built()) or torch.has_mps: 2025-08-05 07:43:38 1249bbe41ac7 dbgpt.util.model_utils[4248] INFO Clear torch cache of device: cuda:0 2025-08-05 07:43:38 1249bbe41ac7 dbgpt.util.model_utils[4248] INFO Clear torch cache of device: cuda:1 2025-08-05 07:43:38 1249bbe41ac7 dbgpt.model.cluster.worker.embedding_worker[4248] INFO Load embeddings model: bge-large-zh-v1.5 2025-08-05 07:43:38 1249bbe41ac7 datasets[4248] INFO PyTorch version 2.5.1+cu121 available. 2025-08-05 07:43:38 1249bbe41ac7 datasets[4248] INFO Duckdb version 1.2.0 available. 2025-08-05 07:43:38 1249bbe41ac7 sentence_transformers.SentenceTransformer[4248] INFO Use pytorch device_name: cuda 2025-08-05 07:43:38 1249bbe41ac7 sentence_transformers.SentenceTransformer[4248] INFO Load pretrained SentenceTransformer: /app/models/bge-large-zh-v1.5 INFO: 127.0.0.1:42180 - "POST /api/controller/models HTTP/1.1" 200 OK 2025-08-05 07:43:39 1249bbe41ac7 dbgpt.model.cluster.worker.manager[4248] ERROR Error starting worker manager: model DeepSeek-R1-Distill-Qwen-32B@vllm(172.17.0.2:8002) start failed, Traceback (most recent call last): File "/app/packages/dbgpt-core/src/dbgpt/model/cluster/worker/manager.py", line 631, in _start_worker await self.run_blocking_func( File "/app/packages/dbgpt-core/src/dbgpt/model/cluster/worker/manager.py", line 146, in run_blocking_func return await loop.run_in_executor(self.executor, func, *args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/lib/python3.11/concurrent/futures/thread.py", line 58, in run result = self.fn(*self.args, **self.kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/packages/dbgpt-core/src/dbgpt/model/cluster/worker/default_worker.py", line 122, in start self.model, self.tokenizer = self.ml.loader_with_params( ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/packages/dbgpt-core/src/dbgpt/model/adapter/loader.py", line 70, in loader_with_params return llm_adapter.load_from_params(model_params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/app/packages/dbgpt-core/src/dbgpt/model/adapter/vllm_adapter.py", line 488, in load_from_params engine = AsyncLLMEngine.from_engine_args(engine_args) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 642, in from_engine_args engine = cls( ^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 592, in __init__ self.engine = self._engine_class(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/engine/async_llm_engine.py", line 265, in __init__ super().__init__(*args, **kwargs) File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/engine/llm_engine.py", line 271, in __init__ self.model_executor = executor_class(vllm_config=vllm_config, ) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/executor/executor_base.py", line 49, in __init__ self._init_executor() File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/executor/uniproc_executor.py", line 40, in _init_executor self.collective_rpc("load_model") File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/executor/uniproc_executor.py", line 49, in collective_rpc answer = run_method(self.driver_worker, method, args, kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/utils.py", line 2208, in run_method return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/worker/worker.py", line 182, in load_model self.model_runner.load_model() File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/worker/model_runner.py", line 1112, in load_model self.model = get_model(vllm_config=self.vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/model_loader/__init__.py", line 12, in get_model return loader.load_model(vllm_config=vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/model_loader/loader.py", line 376, in load_model model = _initialize_model(vllm_config=vllm_config) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/model_loader/loader.py", line 118, in _initialize_model return model_class(vllm_config=vllm_config, prefix=prefix) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/qwen2.py", line 451, in __init__ self.model = Qwen2Model(vllm_config=vllm_config, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/compilation/decorators.py", line 149, in __init__ old_init(self, vllm_config=vllm_config, prefix=prefix, **kwargs) File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/qwen2.py", line 305, in __init__ self.start_layer, self.end_layer, self.layers = make_layers( ^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 555, in make_layers [PPMissingLayer() for _ in range(start_layer)] + [ ^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/utils.py", line 556, in maybe_offload_to_cpu(layer_fn(prefix=f"{prefix}.{idx}")) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/qwen2.py", line 307, in <lambda> lambda prefix: Qwen2DecoderLayer(config=config, ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/qwen2.py", line 206, in __init__ self.self_attn = Qwen2Attention( ^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/models/qwen2.py", line 134, in __init__ self.qkv_proj = QKVParallelLinear( ^^^^^^^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/layers/linear.py", line 728, in __init__ super().__init__(input_size=input_size, File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/layers/linear.py", line 311, in __init__ self.quant_method.create_weights( File "//opt/.uv.venv/lib/python3.11/site-packages/vllm/model_executor/layers/quantization/fp8.py", line 199, in create_weights weight = ModelWeightParameter(data=torch.empty( ^^^^^^^^^^^^ File "//opt/.uv.venv/lib/python3.11/site-packages/torch/utils/_device.py", line 106, in __torch_function__ return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 70.00 MiB. GPU 0 has a total capacity of 44.53 GiB of which 15.94 MiB is free. Process 1334940 has 44.51 GiB memory in use. Of the allocated memory 44.17 GiB is allocated by PyTorch, and 14.30 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables) ;model bge-large-zh-v1.5@hf(172.17.0.2:8002) start successfully INFO: Shutting down

[root@ac6b15bb1f77 evalscope-main]# python eval.py 2025-07-24 11:48:06,102 - evalscope - INFO - Args: Task config is provided with TaskConfig type. 2025-07-24 11:48:07,163 - evalscope - INFO - Loading model /models/z50051264/ascend-vllm-master/tools/AutoAWQ/examples/Qwen2.5-7B-awq ... *****tokenizer加载失败! Traceback (most recent call last): File "/models/z50051264/evalscope-main/eval.py", line 17, in <module> run_task(task_cfg=task_cfg) File "/models/z50051264/evalscope-main/evalscope/run.py", line 31, in run_task return run_single_task(task_cfg, run_time) File "/models/z50051264/evalscope-main/evalscope/run.py", line 44, in run_single_task result = evaluate_model(task_cfg, outputs) File "/models/z50051264/evalscope-main/evalscope/run.py", line 119, in evaluate_model base_model = get_local_model(task_cfg) File "/models/z50051264/evalscope-main/evalscope/models/local_model.py", line 125, in get_local_model base_model.load_model() File "/models/z50051264/evalscope-main/evalscope/models/local_model.py", line 71, in load_model if self.tokenizer.pad_token is None: AttributeError: 'NoneType' object has no attribute 'pad_token' [ERROR] 2025-07-24-11:48:07 (PID:4554, Device:-1, RankID:-1) ERR99999 UNKNOWN applicaiton exception try: self.tokenizer = AutoTokenizer.from_pretrained( self.model_id, revision=self.model_revision, trust_remote_code=True, cache_dir=self.cache_dir, ) print("*****tokenizer加载成功!") except: print("*****tokenizer加载失败!") 为什么???

最新推荐

recommend-type

2014年直流电压电流采样仪生产方案:电路板、BOM单、STM单片机程序及应用 核心版

2014年设计的一款直流电压电流采样仪的整套产品生产方案。该产品已量产1000余套,适用于电力、电子、通信等领域。文中涵盖了硬件和软件两大部分的内容。硬件方面,包括电路板设计、BOM单、外围器件清单以及外壳设计;软件方面,则涉及STM单片机程序和配套的上位机电脑软件。该采样仪的最大测量范围为1000V/100A,具备高精度、高稳定性的特点,能记录并存储8组电压电流数据,并带有触发模式用于实时监测和故障诊断。 适合人群:从事电力、电子、通信领域的工程师和技术人员,尤其是对直流电压电流采样仪有需求的研发人员。 使用场景及目标:①帮助工程师和技术人员了解直流电压电流采样仪的整体设计方案;②提供详细的硬件和软件资料,便于实际生产和应用;③适用于需要高精度、高稳定性的电压电流测量场合。 其他说明:该产品已经成功量产并获得市场好评,文中提供的方案对于相关领域的项目开发具有重要参考价值。
recommend-type

Python程序TXLWizard生成TXL文件及转换工具介绍

### 知识点详细说明: #### 1. 图形旋转与TXL向导 图形旋转是图形学领域的一个基本操作,用于改变图形的方向。在本上下文中,TXL向导(TXLWizard)是由Esteban Marin编写的Python程序,它实现了特定的图形旋转功能,主要用于电子束光刻掩模的生成。光刻掩模是半导体制造过程中非常关键的一个环节,它确定了在硅片上沉积材料的精确位置。TXL向导通过生成特定格式的TXL文件来辅助这一过程。 #### 2. TXL文件格式与用途 TXL文件格式是一种基于文本的文件格式,它设计得易于使用,并且可以通过各种脚本语言如Python和Matlab生成。这种格式通常用于电子束光刻中,因为它的文本形式使得它可以通过编程快速创建复杂的掩模设计。TXL文件格式支持引用对象和复制对象数组(如SREF和AREF),这些特性可以用于优化电子束光刻设备的性能。 #### 3. TXLWizard的特性与优势 - **结构化的Python脚本:** TXLWizard 使用结构良好的脚本来创建遮罩,这有助于开发者创建清晰、易于维护的代码。 - **灵活的Python脚本:** 作为Python程序,TXLWizard 可以利用Python语言的灵活性和强大的库集合来编写复杂的掩模生成逻辑。 - **可读性和可重用性:** 生成的掩码代码易于阅读,开发者可以轻松地重用和修改以适应不同的需求。 - **自动标签生成:** TXLWizard 还包括自动为图形对象生成标签的功能,这在管理复杂图形时非常有用。 #### 4. TXL转换器的功能 - **查看.TXL文件:** TXL转换器(TXLConverter)允许用户将TXL文件转换成HTML或SVG格式,这样用户就可以使用任何现代浏览器或矢量图形应用程序来查看文件。 - **缩放和平移:** 转换后的文件支持缩放和平移功能,这使得用户在图形界面中更容易查看细节和整体结构。 - **快速转换:** TXL转换器还提供快速的文件转换功能,以实现有效的蒙版开发工作流程。 #### 5. 应用场景与技术参考 TXLWizard的应用场景主要集中在电子束光刻技术中,特别是用于设计和制作半导体器件时所需的掩模。TXLWizard作为一个向导,不仅提供了生成TXL文件的基础框架,还提供了一种方式来优化掩模设计,提高光刻过程的效率和精度。对于需要进行光刻掩模设计的工程师和研究人员来说,TXLWizard提供了一种有效的方法来实现他们的设计目标。 #### 6. 系统开源特性 标签“系统开源”表明TXLWizard遵循开放源代码的原则,这意味着源代码对所有人开放,允许用户自由地查看、修改和分发软件。开源项目通常拥有活跃的社区,社区成员可以合作改进软件,添加新功能,或帮助解决遇到的问题。这种开放性促进了技术创新,并允许用户根据自己的需求定制软件。 #### 7. 压缩包子文件的文件名称列表 文件名称列表中的“txlwizard-master”可能指的是TXLWizard项目的主版本库或主分支。这个名称表明了这是项目源代码的中心点,其他开发者会从这个主分支拉取代码进行合作开发或部署。以“-master”结尾通常是版本控制系统中表示主要开发线路的常见约定,例如Git中的master(现在更常被称为main)分支。 通过这些知识点的详细解释,我们可以看到TXLWizard不仅是一个用于生成TXL文件的工具,它还整合了一系列的功能,使得电子束光刻掩模的设计工作更为高效和直观。同时,作为一个开源项目,它能够借助社区的力量不断进步,为用户带来更多的便利和创新。
recommend-type

【创新图生成:扣子平台的技术前沿与创新思维】:引领图像生成技术的新潮流

# 1. 扣子平台的图像生成技术概述 在当今数字化时代,图像生成技术正成为推动技术创新和社会变革的关键力量。扣子平台正是这一技术革新的代表。本章将对扣子平台的图像生成技术进行一个全面概述,探究它如何将理论技术转化为实际应用,并带来显著的社会影响。 扣子平台运用了先进的图像生成技术,将现实世界和数字空间无缝融合,打造了沉浸式的视觉体验。其技术的核心是通过高度复杂的算法模型,快速生成高质
recommend-type

海康威视机器视觉工程师考核

<think>好的,我现在需要帮助用户了解海康威视机器视觉工程师的考核内容和要求。根据用户提供的引用资料,特别是引用[1]和[2],里面提到了考核素材包分为初级和中级,涵盖理论、算法、应用案例等。首先,我要整理这些信息,确保结构清晰,符合用户要求的格式。 接下来,我需要确认素材包的具体内容,比如初级和中级的不同点。引用[2]提到初级包含基础理论、算法实现和实际案例,中级则增加复杂算法和项目分析。这部分需要分点说明,方便用户理解层次。 另外,用户可能想知道如何准备考核,比如下载素材、学习顺序、模拟考核等,引用[2]中有使用说明和注意事项,这部分也要涵盖进去。同时要注意提醒用户考核窗口已关闭,
recommend-type

Linux环境下Docker Hub公共容器映像检测工具集

在给出的知识点中,我们需要详细解释有关Docker Hub、公共容器映像、容器编排器以及如何与这些工具交互的详细信息。同时,我们会涵盖Linux系统下的相关操作和工具使用,以及如何在ECS和Kubernetes等容器编排工具中运用这些检测工具。 ### Docker Hub 和公共容器映像 Docker Hub是Docker公司提供的一项服务,它允许用户存储、管理以及分享Docker镜像。Docker镜像可以视为应用程序或服务的“快照”,包含了运行特定软件所需的所有必要文件和配置。公共容器映像指的是那些被标记为公开可见的Docker镜像,任何用户都可以拉取并使用这些镜像。 ### 静态和动态标识工具 静态和动态标识工具在Docker Hub上用于识别和分析公共容器映像。静态标识通常指的是在不运行镜像的情况下分析镜像的元数据和内容,例如检查Dockerfile中的指令、环境变量、端口映射等。动态标识则需要在容器运行时对容器的行为和性能进行监控和分析,如资源使用率、网络通信等。 ### 容器编排器与Docker映像 容器编排器是用于自动化容器部署、管理和扩展的工具。在Docker环境中,容器编排器能够自动化地启动、停止以及管理容器的生命周期。常见的容器编排器包括ECS和Kubernetes。 - **ECS (Elastic Container Service)**:是由亚马逊提供的容器编排服务,支持Docker容器,并提供了一种简单的方式来运行、停止以及管理容器化应用程序。 - **Kubernetes**:是一个开源平台,用于自动化容器化应用程序的部署、扩展和操作。它已经成为容器编排领域的事实标准。 ### 如何使用静态和动态标识工具 要使用这些静态和动态标识工具,首先需要获取并安装它们。从给定信息中了解到,可以通过克隆仓库或下载压缩包并解压到本地系统中。之后,根据需要针对不同的容器编排环境(如Dockerfile、ECS、Kubernetes)编写配置,以集成和使用这些检测工具。 ### Dockerfile中的工具使用 在Dockerfile中使用工具意味着将检测工具的指令嵌入到构建过程中。这可能包括安装检测工具的命令、运行容器扫描的步骤,以及将扫描结果集成到镜像构建流程中,确保只有通过安全和合规检查的容器镜像才能被构建和部署。 ### ECS与Kubernetes中的工具集成 在ECS或Kubernetes环境中,工具的集成可能涉及到创建特定的配置文件、定义服务和部署策略,以及编写脚本或控制器来自动执行检测任务。这样可以在容器编排的过程中实现实时监控,确保容器编排器只使用符合预期的、安全的容器镜像。 ### Linux系统下的操作 在Linux系统下操作这些工具,用户可能需要具备一定的系统管理和配置能力。这包括使用Linux命令行工具、管理文件系统权限、配置网络以及安装和配置软件包等。 ### 总结 综上所述,Docker Hub上的静态和动态标识工具提供了一种方法来检测和分析公共容器映像,确保这些镜像的安全性和可靠性。这些工具在Linux开发环境中尤为重要,因为它们帮助开发人员和运维人员确保他们的容器映像满足安全要求。通过在Dockerfile、ECS和Kubernetes中正确使用这些工具,可以提高应用程序的安全性,减少由于使用不安全的容器镜像带来的风险。此外,掌握Linux系统下的操作技能,可以更好地管理和维护这些工具,确保它们能够有效地发挥作用。
recommend-type

【扣子平台图像艺术探究:理论与实践的完美结合】:深入学习图像生成的艺术

# 1. 图像艺术的理论基础 艺术领域的每一个流派和技巧都有其理论基础。在图像艺术中,理论基础不仅是对艺术表现形式的认知,也是掌握艺术创作内在逻辑的关键。深入理解图像艺术的理论基础,能够帮助艺术家们在创作过程中更加明确地表达自己的艺术意图,以及更好地与观众沟通。 图像艺术的理论
recommend-type

增广路定理的证明

<think>嗯,用户想查找增广路定理的证明,关于图论和网络流算法的。我得先理解增广路定理是什么,然后找到相关的证明方法。根据之前的引用,尤其是引用5提到最大流最小割定理,里面有三个等价条件,其中第二个是残余网络中没有增广路径时,流就是最大流。这可能和增广路定理相关,也就是当残余网络中没有增广路时,当前流就是最大流,这可能就是增广路定理的内容。 首先,我需要明确增广路定理的陈述。根据引用5,增广路定理可能指的是:一个流是最大流当且仅当残余网络中不存在增广路径。这个定理的证明需要用到最大流最小割定理,也就是第三个条件,即最大流的流量等于最小割的容量。 证明的步骤可能需要分为两个方向:必要性(
recommend-type

Pulse:基于SwiftUI的Apple平台高效日志记录与网络监控

从给定文件信息中,我们可以提取出以下IT知识点进行详细阐述: **Pulse概览:** Pulse是一个专门针对Apple平台(如iOS、iPadOS、macOS等)的功能强大的日志记录系统。其设计目的是为了简化开发者在这些平台上调试网络请求和应用日志的过程。Pulse的核心特色是它使用SwiftUI来构建,这有助于开发者利用现代Swift语言的声明式UI优势来快速开发和维护。 **SwiftUI框架:** SwiftUI是一种声明式框架,由苹果公司推出,用于构建用户界面。与传统的UIKit相比,SwiftUI使用更加简洁的代码来描述界面和界面元素,它允许开发者以声明的方式定义视图和界面布局。SwiftUI支持跨平台,这意味着同一套代码可以在不同的Apple设备上运行,大大提高了开发效率和复用性。Pulse选择使用SwiftUI构建,显示了其对现代化、高效率开发的支持。 **Network Inspector功能:** Pulse具备Network Inspector功能,这个功能使得开发者能够在开发iOS应用时,直接从应用内记录和检查网络请求和日志。这种内嵌式的网络诊断能力非常有助于快速定位网络请求中的问题,如不正确的URL、不返回预期响应等。与传统的需要外部工具来抓包和分析的方式相比,这样的内嵌式工具大大减少了调试的复杂性。 **日志记录和隐私保护:** Pulse强调日志是本地记录的,并保证不会离开设备。这种做法对隐私保护至关重要,尤其是考虑到当前数据保护法规如GDPR等的严格要求。因此,Pulse的设计在帮助开发者进行问题诊断的同时,也确保了用户数据的安全性。 **集成和框架支持:** Pulse不仅仅是一个工具,它更是一个框架。它能够记录来自URLSession的事件,这意味着它可以与任何使用URLSession进行网络通信的应用或框架配合使用,包括但不限于Apple官方的网络库。此外,Pulse与使用它的框架(例如Alamofire)也能够良好配合,Alamofire是一个流行的网络请求库,广泛应用于Swift开发中。Pulse提供了一个PulseUI视图组件,开发者可以将其集成到自己的应用中,从而展示网络请求和其他事件。 **跨平台体验:** 开发者不仅可以在iOS应用中使用Pulse Console记录日志,还可以在macOS上通过Pulse应用程序查看和共享这些日志。这种跨平台的能力意味着开发者可以在不同的设备上进行日志分析,增强了开发和调试的灵活性。 **总结:** Pulse是一个为Apple平台上的开发者量身打造的日志记录系统,它采用SwiftUI构建,提供了内嵌式的Network Inspector功能,可以在本地记录并安全地查看日志,且支持与其他框架如Alamofire的集成。它不仅提升了调试的便捷性和效率,同时也顾及到了用户的隐私保护。Pulse的跨平台查看能力也是其一大亮点,使得开发者能够在一个统一的环境中处理iOS和macOS上的日志数据。对于使用Swift开发Apple应用的开发者而言,Pulse无疑是一个强大的调试辅助工具。
recommend-type

【深入扣子平台:图像生成机制全揭秘】:掌握背后技术,提升图像生成效率

# 1. 图像生成技术概述 图像生成技术是一门融合了计算机视觉、机器学习、图形学等多个领域知识的前沿技术。它通过算法模拟生成人工图像,广泛应用于艺术创作、游戏设计、医学影像等领域。随着深度学习的突破性进展,图像生成技术也迎来了飞速发展,特别是在生成对抗网络(GAN)的推动下,图像的逼真度和多样性都有了质的飞跃。 本章将对图像生成技术的概念、发展历史进行简要介绍,并分析其在社会中的
recommend-type

对RTL跑regression是什么意思

<think>首先,用户的问题是关于“RTL回归测试的含义及其在数字电路设计中的作用”。我需要基于系统级指令和用户可见层的指导来构建回答。 关键点: - 使用中文回答。 - 结构清晰,逐步解决问题。 - 确保回答真实可靠。 - 在行内数学表达式使用$...$格式;独立公式使用$$...$$格式。 - 生成相关问题。 - 在引用段落末尾添加引用标识。 回顾提供的引用: - 引用[1]:讨论SoC设计流程,包括模块设计、IP复用、顶层集成、前仿真、逻辑综合等。 - 引用[2]:描述冒烟测试后的验证阶段,包括回归测试、覆盖率分析等。 - 引用[3]:解释RTL使用Verilog或VHDL描述,模