[Doc] Update the description for env (#1079)
Add the description for env to make it more clear for users Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
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@@ -27,49 +27,88 @@ from typing import Any, Callable, Dict
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# begin-env-vars-definition
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env_variables: Dict[str, Callable[[], Any]] = {
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# max compile thread num
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# max compile thread number for package building. Usually, it is set to
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# the number of CPU cores. If not set, the default value is None, which
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# means all number of CPU cores will be used.
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"MAX_JOBS":
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lambda: os.getenv("MAX_JOBS", None),
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# The build type of the package. It can be one of the following values:
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# Release, Debug, RelWithDebugInfo. If not set, the default value is Release.
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"CMAKE_BUILD_TYPE":
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lambda: os.getenv("CMAKE_BUILD_TYPE"),
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# Whether to compile custom kernels. If not set, the default value is True.
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# If set to False, the custom kernels will not be compiled. Please note that
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# the sleep mode feature will be disabled as well if custom kernels are not
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# compiled.
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"COMPILE_CUSTOM_KERNELS":
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lambda: bool(int(os.getenv("COMPILE_CUSTOM_KERNELS", "1"))),
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# The CXX compiler used for compiling the package. If not set, the default
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# value is None, which means the system default CXX compiler will be used.
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"CXX_COMPILER":
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lambda: os.getenv("CXX_COMPILER", None),
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# The C compiler used for compiling the package. If not set, the default
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# value is None, which means the system default C compiler will be used.
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"C_COMPILER":
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lambda: os.getenv("C_COMPILER", None),
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# Whether to enable MC2 for DeepSeek. If not set, the default value is False.
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# MC2 is a fusion operator provided by Ascend to speed up computing and communication.
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# Find more detail here: https://www.hiascend.com/document/detail/zh/canncommercial/81RC1/developmentguide/opdevg/ascendcbestP/atlas_ascendc_best_practices_10_0043.html
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"VLLM_ENABLE_MC2":
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lambda: bool(int(os.getenv("VLLM_ENABLE_MC2", '0'))),
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# Whether to enable the topk optimization. It's disabled by default for experimental support
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# We'll make it enabled by default in the future.
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"VLLM_ASCEND_ENABLE_TOPK_OPTIMZE":
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lambda: bool(int(os.getenv("VLLM_ASCEND_ENABLE_TOPK_OPTIMZE", '0'))),
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# Whether to use LCCL communication. If not set, the default value is False.
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"USING_LCCL_COM":
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lambda: bool(int(os.getenv("USING_LCCL_COM", '0'))),
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# The version of the Ascend chip. If not set, the default value is
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# ASCEND910B1. It's used for package building. Please make sure that the
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# version is correct.
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"SOC_VERSION":
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lambda: os.getenv("SOC_VERSION", "ASCEND910B1"),
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# If set, vllm-ascend will print verbose logs during compilation
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"VERBOSE":
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lambda: bool(int(os.getenv('VERBOSE', '0'))),
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# The home path for CANN toolkit. If not set, the default value is
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# /usr/local/Ascend/ascend-toolkit/latest
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"ASCEND_HOME_PATH":
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lambda: os.getenv("ASCEND_HOME_PATH", None),
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"LD_LIBRARY_PATH":
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lambda: os.getenv("LD_LIBRARY_PATH", None),
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# Used for disaggregated prefilling
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# The path for HCCN Tool, the tool will be called by disaggregated prefilling
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# case.
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"HCCN_PATH":
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lambda: os.getenv("HCCN_PATH", "/usr/local/Ascend/driver/tools/hccn_tool"),
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# The path for HCCL library, it's used by pyhccl communicator backend. If
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# not set, the default value is libhccl.so。
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"HCCL_SO_PATH":
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# The prefill device id for disaggregated prefilling case.
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lambda: os.environ.get("HCCL_SO_PATH", None),
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"PROMPT_DEVICE_ID":
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lambda: os.getenv("PROMPT_DEVICE_ID", None),
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# The decode device id for disaggregated prefilling case.
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"DECODE_DEVICE_ID":
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lambda: os.getenv("DECODE_DEVICE_ID", None),
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# The port number for llmdatadist communication. If not set, the default
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# value is 26000.
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"LLMDATADIST_COMM_PORT":
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lambda: os.getenv("LLMDATADIST_COMM_PORT", "26000"),
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# The wait time for llmdatadist sync cache. If not set, the default value is
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# 5000ms.
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"LLMDATADIST_SYNC_CACHE_WAIT_TIME":
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lambda: os.getenv("LLMDATADIST_SYNC_CACHE_WAIT_TIME", "5000"),
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"CXX_COMPILER":
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lambda: os.getenv("CXX_COMPILER", None),
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"C_COMPILER":
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lambda: os.getenv("C_COMPILER", None),
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# The version of vllm is installed. This value is used for developers who
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# installed vllm from source locally. In this case, the version of vllm is
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# usually changed. For example, if the version of vllm is "0.9.0", but when
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# it's installed from source, the version of vllm is usually set to "0.9.1".
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# In this case, developers need to set this value to "0.9.0" to make sure
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# that the correct package is installed.
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"VLLM_VERSION":
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lambda: os.getenv("VLLM_VERSION", None),
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# Whether to enable the trace recompiles from pytorch.
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"VLLM_ASCEND_TRACE_RECOMPILES":
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lambda: bool(int(os.getenv("VLLM_ASCEND_TRACE_RECOMPILES", '0'))),
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# Whether to enable the model execute time observe profile. Disable it when
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# running vllm ascend in production environment.
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"VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE":
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lambda: bool(int(os.getenv("VLLM_ASCEND_MODEL_EXECUTE_TIME_OBSERVE", '0'))
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),
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