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* [Feature] add a new reasoning parser (#4571) * add new reasoning_parser initial commit * add parser file content * add register * ernie_test_reasoning_parser * support <tool_call> token and add tool_parser * add and fix unit tests * modify reasoning_parser * modify reasoning parser and tool parser * modify unit tests * modify reasoning_parser and tool_parser * modify unit tests * fix tool_parser * modify the logic of reasoning_parser and tool_parser * add and modify unit tests * standardize code style * simplify reasoning_parser and tool_parser * modify unit test * [BugFix] Fix finish reason in _create_chat_completion_choice (#4582) * fix n_param _create_chat_completion_choicel * fix unit test * fix final_res * modify unit tests * [BugFix] fix offline llm chat "enable_thinking" is always "False" (#4686) * fix enable_thinking * recover ernie4_5_vl_processor * [Feature] Unify the registration name recognition for tool_parser and reasoning_parser to “-” (#4668) * parser register name unify * change ernie_x1 to ernie-x1 * change ernie4_5_vl to ernie-45-vl * fix unit test
92 lines
3.3 KiB
Markdown
92 lines
3.3 KiB
Markdown
[English](../../usage/environment_variables.md)
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# FastDeploy 环境变量说明
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FastDeploy 的环境变量保存在了代码库根目录下 fastdeploy/envs.py 文件中，以下是其对应的中文版说明：
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```python
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environment_variables: dict[str, Callable[[], Any]] = {
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# 构建 FastDeploy 时使用的 CUDA 架构版本，这是一个字符串列表，例如[80,90]
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"FD_BUILDING_ARCS":
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lambda: os.getenv("FD_BUILDING_ARCS", "[]"),
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# 日志目录
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"FD_LOG_DIR":
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lambda: os.getenv("FD_LOG_DIR", "log"),
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# 是否启用调试模式，可设置为 0 或 1
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"FD_DEBUG":
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lambda: int(os.getenv("FD_DEBUG", "0")),
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# FastDeploy 日志保留天数
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"FD_LOG_BACKUP_COUNT":
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lambda: os.getenv("FD_LOG_BACKUP_COUNT", "7"),
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# 模型下载缓存目录
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"FD_MODEL_CACHE":
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lambda: os.getenv("FD_MODEL_CACHE", None),
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# 停止序列的最大数量
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"FD_MAX_STOP_SEQS_NUM":
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lambda: os.getenv("FD_MAX_STOP_SEQS_NUM", "5"),
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# 停止序列的最大长度
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"FD_STOP_SEQS_MAX_LEN":
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lambda: os.getenv("FD_STOP_SEQS_MAX_LEN", "8"),
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# 将要使用的GPU设备，这是一个用逗号分隔的字符串，例如 0,1,2
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"CUDA_VISIBLE_DEVICES":
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lambda: os.getenv("CUDA_VISIBLE_DEVICES", None),
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# 是否使用 HuggingFace 分词器
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"FD_USE_HF_TOKENIZER":
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lambda: bool(int(os.getenv("FD_USE_HF_TOKENIZER", 0))),
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# 设置 ZMQ 初始化期间接收数据的高水位标记（HWM）
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"FD_ZMQ_SNDHWM":
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lambda: os.getenv("FD_ZMQ_SNDHWM", 10000),
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# 缓存 KV 量化参数的目录
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"FD_CACHE_PARAMS":
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lambda: os.getenv("FD_CACHE_PARAMS", "none"),
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# 设置注意力机制后端，当前可设置为 "NATIVE_ATTN"、"APPEND_ATTN" 或 "MLA_ATTN"
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"FD_ATTENTION_BACKEND":
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lambda: os.getenv("FD_ATTENTION_BACKEND", "APPEND_ATTN"),
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# 设置采样类别，当前可设置为 "base"、"base_non_truncated"、"air" 或 "rejection"
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"FD_SAMPLING_CLASS":
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lambda: os.getenv("FD_SAMPLING_CLASS", "base"),
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# 设置MoE后端，当前可设置为 "cutlass"、"marlin" 或 "triton"
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"FD_MOE_BACKEND":
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lambda: os.getenv("FD_MOE_BACKEND", "cutlass"),
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# 设置 Triton 内核 JIT 编译目录
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"FD_TRITON_KERNEL_CACHE_DIR":
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lambda: os.getenv("FD_TRITON_KERNEL_CACHE_DIR", None),
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# 是否从单机 PD 分离转换为集中式推理
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"FD_PD_CHANGEABLE":
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lambda: os.getenv("FD_PD_CHANGEABLE", "1"),
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# 是否使用DeepGemm后端的FP8 blockwise MoE.
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"FD_USE_DEEP_GEMM":
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lambda: bool(int(os.getenv("FD_USE_DEEP_GEMM", "0"))),
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# 是否启用模型权重缓存功能
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"FD_ENABLE_MODEL_LOAD_CACHE": lambda: bool(int(os.getenv("FD_ENABLE_MODEL_LOAD_CACHE", "0"))),
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# 是否使用 Machete 后端的 wint4 GEMM.
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"FD_USE_MACHETE": lambda: os.getenv("FD_USE_MACHETE", "1"),
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# Used to truncate the string inserted during thinking when reasoning in a model. (</think> for ernie-45-vl, \n</think>\n\n for ernie-x1)
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"FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR": lambda: os.getenv("FD_LIMIT_THINKING_CONTENT_TRUNCATE_STR", "</think>"),
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# cache_transfer_manager 进程残留时退出等待超时时间
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"FD_CACHE_PROC_EXIT_TIMEOUT": lambda: int(os.getenv("FD_CACHE_PROC_EXIT_TIMEOUT", "600")),
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# cache_transfer_manager 进程残留时连续错误阈值
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"FD_CACHE_PROC_ERROR_COUNT": lambda: int(os.getenv("FD_CACHE_PROC_ERROR_COUNT", "10")),}
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```
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