import os import tempfile from typing import TYPE_CHECKING, Any, Callable, Dict, Optional if TYPE_CHECKING: VLLM_HOST_IP: str = "" VLLM_PORT: Optional[int] = None VLLM_RPC_BASE_PATH: str = tempfile.gettempdir() VLLM_USE_MODELSCOPE: bool = False VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60 VLLM_INSTANCE_ID: Optional[str] = None VLLM_NCCL_SO_PATH: Optional[str] = None LD_LIBRARY_PATH: Optional[str] = None VLLM_USE_TRITON_FLASH_ATTN: bool = False LOCAL_RANK: int = 0 CUDA_VISIBLE_DEVICES: Optional[str] = None VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60 VLLM_API_KEY: Optional[str] = None S3_ACCESS_KEY_ID: Optional[str] = None S3_SECRET_ACCESS_KEY: Optional[str] = None S3_ENDPOINT_URL: Optional[str] = None VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm") VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm") VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai" VLLM_NO_USAGE_STATS: bool = False VLLM_DO_NOT_TRACK: bool = False VLLM_USAGE_SOURCE: str = "" VLLM_CONFIGURE_LOGGING: int = 1 VLLM_LOGGING_LEVEL: str = "INFO" VLLM_LOGGING_CONFIG_PATH: Optional[str] = None VLLM_TRACE_FUNCTION: int = 0 VLLM_ATTENTION_BACKEND: Optional[str] = None VLLM_PP_LAYER_PARTITION: Optional[str] = None VLLM_CPU_KVCACHE_SPACE: int = 0 VLLM_CPU_OMP_THREADS_BIND: str = "" VLLM_OPENVINO_KVCACHE_SPACE: int = 0 VLLM_OPENVINO_CPU_KV_CACHE_PRECISION: Optional[str] = None VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS: bool = False VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache") VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024 VLLM_USE_RAY_SPMD_WORKER: bool = False VLLM_USE_RAY_COMPILED_DAG: bool = False VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL: bool = True VLLM_WORKER_MULTIPROC_METHOD: str = "fork" VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets") VLLM_IMAGE_FETCH_TIMEOUT: int = 5 VLLM_TARGET_DEVICE: str = "cuda" MAX_JOBS: Optional[str] = None NVCC_THREADS: Optional[str] = None VLLM_USE_PRECOMPILED: bool = False VLLM_NO_DEPRECATION_WARNING: bool = False CMAKE_BUILD_TYPE: Optional[str] = None VERBOSE: bool = False VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False VLLM_TEST_FORCE_FP8_MARLIN: bool = False def get_default_cache_root(): return os.getenv( "XDG_CACHE_HOME", os.path.join(os.path.expanduser("~"), ".cache"), ) def get_default_config_root(): return os.getenv( "XDG_CONFIG_HOME", os.path.join(os.path.expanduser("~"), ".config"), ) # The begin-* and end* here are used by the documentation generator # to extract the used env vars. # begin-env-vars-definition environment_variables: Dict[str, Callable[[], Any]] = { # ================== Installation Time Env Vars ================== # Target device of vLLM, supporting [cuda (by default), # rocm, neuron, cpu, openvino] "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda"), # Maximum number of compilation jobs to run in parallel. # By default this is the number of CPUs "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None), # Number of threads to use for nvcc # By default this is 1. # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU. "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None), # If set, vllm will use precompiled binaries (*.so) "VLLM_USE_PRECOMPILED": lambda: bool(os.environ.get("VLLM_USE_PRECOMPILED")), # CMake build type # If not set, defaults to "Debug" or "RelWithDebInfo" # Available options: "Debug", "Release", "RelWithDebInfo" "CMAKE_BUILD_TYPE": lambda: os.getenv("CMAKE_BUILD_TYPE"), # If set, vllm will print verbose logs during installation "VERBOSE": lambda: bool(int(os.getenv('VERBOSE', '0'))), # Root directory for VLLM configuration files # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set # Note that this not only affects how vllm finds its configuration files # during runtime, but also affects how vllm installs its configuration # files during **installation**. "VLLM_CONFIG_ROOT": lambda: os.path.expanduser( os.getenv( "VLLM_CONFIG_ROOT", os.path.join(get_default_config_root(), "vllm"), )), # ================== Runtime Env Vars ================== # Root directory for VLLM cache files # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set "VLLM_CACHE_ROOT": lambda: os.path.expanduser( os.getenv( "VLLM_CACHE_ROOT", os.path.join(get_default_cache_root(), "vllm"), )), # used in distributed environment to determine the master address 'VLLM_HOST_IP': lambda: os.getenv('VLLM_HOST_IP', "") or os.getenv("HOST_IP", ""), # used in distributed environment to manually set the communication port # Note: if VLLM_PORT is set, and some code asks for multiple ports, the # VLLM_PORT will be used as the first port, and the rest will be generated # by incrementing the VLLM_PORT value. # '0' is used to make mypy happy 'VLLM_PORT': lambda: int(os.getenv('VLLM_PORT', '0')) if 'VLLM_PORT' in os.environ else None, # path used for ipc when the frontend api server is running in # multi-processing mode to communicate with the backend engine process. 'VLLM_RPC_BASE_PATH': lambda: os.getenv('VLLM_RPC_BASE_PATH', tempfile.gettempdir()), # If true, will load models from ModelScope instead of Hugging Face Hub. # note that the value is true or false, not numbers "VLLM_USE_MODELSCOPE": lambda: os.environ.get("VLLM_USE_MODELSCOPE", "False").lower() == "true", # Instance id represents an instance of the VLLM. All processes in the same # instance should have the same instance id. "VLLM_INSTANCE_ID": lambda: os.environ.get("VLLM_INSTANCE_ID", None), # Interval in seconds to log a warning message when the ring buffer is full "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")), # path to cudatoolkit home directory, under which should be bin, include, # and lib directories. "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None), # Path to the NCCL library file. It is needed because nccl>=2.19 brought # by PyTorch contains a bug: https://github.com/NVIDIA/nccl/issues/1234 "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None), # when `VLLM_NCCL_SO_PATH` is not set, vllm will try to find the nccl # library file in the locations specified by `LD_LIBRARY_PATH` "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None), # flag to control if vllm should use triton flash attention "VLLM_USE_TRITON_FLASH_ATTN": lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in ("true", "1")), # Internal flag to enable Dynamo graph capture "VLLM_TEST_DYNAMO_GRAPH_CAPTURE": lambda: int(os.environ.get("VLLM_TEST_DYNAMO_GRAPH_CAPTURE", "0")), # local rank of the process in the distributed setting, used to determine # the GPU device id "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")), # used to control the visible devices in the distributed setting "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None), # timeout for each iteration in the engine "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")), # API key for VLLM API server "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None), # S3 access information, used for tensorizer to load model from S3 "S3_ACCESS_KEY_ID": lambda: os.environ.get("S3_ACCESS_KEY_ID", None), "S3_SECRET_ACCESS_KEY": lambda: os.environ.get("S3_SECRET_ACCESS_KEY", None), "S3_ENDPOINT_URL": lambda: os.environ.get("S3_ENDPOINT_URL", None), # Usage stats collection "VLLM_USAGE_STATS_SERVER": lambda: os.environ.get("VLLM_USAGE_STATS_SERVER", "https://stats.vllm.ai"), "VLLM_NO_USAGE_STATS": lambda: os.environ.get("VLLM_NO_USAGE_STATS", "0") == "1", "VLLM_DO_NOT_TRACK": lambda: (os.environ.get("VLLM_DO_NOT_TRACK", None) or os.environ.get( "DO_NOT_TRACK", None) or "0") == "1", "VLLM_USAGE_SOURCE": lambda: os.environ.get("VLLM_USAGE_SOURCE", "production"), # Logging configuration # If set to 0, vllm will not configure logging # If set to 1, vllm will configure logging using the default configuration # or the configuration file specified by VLLM_LOGGING_CONFIG_PATH "VLLM_CONFIGURE_LOGGING": lambda: int(os.getenv("VLLM_CONFIGURE_LOGGING", "1")), "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"), # this is used for configuring the default logging level "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO"), # Trace function calls # If set to 1, vllm will trace function calls # Useful for debugging "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")), # Backend for attention computation # Available options: # - "TORCH_SDPA": use torch.nn.MultiheadAttention # - "FLASH_ATTN": use FlashAttention # - "XFORMERS": use XFormers # - "ROCM_FLASH": use ROCmFlashAttention # - "FLASHINFER": use flashinfer "VLLM_ATTENTION_BACKEND": lambda: os.getenv("VLLM_ATTENTION_BACKEND", None), # Pipeline stage partition strategy "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None), # (CPU backend only) CPU key-value cache space. # default is 4GB "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0")), # (CPU backend only) CPU core ids bound by OpenMP threads, e.g., "0-31", # "0,1,2", "0-31,33". CPU cores of different ranks are separated by '|'. "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "all"), # OpenVINO key-value cache space # default is 4GB "VLLM_OPENVINO_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_OPENVINO_KVCACHE_SPACE", "0")), # OpenVINO KV cache precision # default is bf16 if natively supported by platform, otherwise f16 # To enable KV cache compression, please, explicitly specify u8 "VLLM_OPENVINO_CPU_KV_CACHE_PRECISION": lambda: os.getenv("VLLM_OPENVINO_CPU_KV_CACHE_PRECISION", None), # Enables weights compression during model export via HF Optimum # default is False "VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS": lambda: bool(os.getenv("VLLM_OPENVINO_ENABLE_QUANTIZED_WEIGHTS", False)), # If the env var is set, then all workers will execute as separate # processes from the engine, and we use the same mechanism to trigger # execution on all workers. # Run vLLM with VLLM_USE_RAY_SPMD_WORKER=1 to enable it. "VLLM_USE_RAY_SPMD_WORKER": lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))), # If the env var is set, it uses the Ray's compiled DAG API # which optimizes the control plane overhead. # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it. "VLLM_USE_RAY_COMPILED_DAG": lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))), # If the env var is set, it uses NCCL for communication in # Ray's compiled DAG. This flag is ignored if # VLLM_USE_RAY_COMPILED_DAG is not set. "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL", "1")) ), # Use dedicated multiprocess context for workers. # Both spawn and fork work "VLLM_WORKER_MULTIPROC_METHOD": lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "fork"), # Path to the cache for storing downloaded assets "VLLM_ASSETS_CACHE": lambda: os.path.expanduser( os.getenv( "VLLM_ASSETS_CACHE", os.path.join(get_default_cache_root(), "vllm", "assets"), )), # Timeout for fetching images when serving multimodal models # Default is 5 seconds "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")), # Path to the XLA persistent cache directory. # Only used for XLA devices such as TPUs. "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser( os.getenv( "VLLM_ASSETS_CACHE", os.path.join(get_default_cache_root(), "vllm", "xla_cache"), )), "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "65536")), # If set, vllm will skip the deprecation warnings. "VLLM_NO_DEPRECATION_WARNING": lambda: bool(int(os.getenv("VLLM_NO_DEPRECATION_WARNING", "0"))), # If the env var VLLM_ALLOW_LONG_MAX_MODEL_LEN is set, it allows # the user to specify a max sequence length greater than # the max length derived from the model's config.json. # To enable this, set VLLM_ALLOW_LONG_MAX_MODEL_LEN=1. "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower() in ("1", "true")), # If set, forces FP8 Marlin to be used for FP8 quantization regardless # of the hardware support for FP8 compute. "VLLM_TEST_FORCE_FP8_MARLIN": lambda: (os.environ.get("VLLM_TEST_FORCE_FP8_MARLIN", "0").strip().lower() in ("1", "true")), } # end-env-vars-definition def __getattr__(name: str): # lazy evaluation of environment variables if name in environment_variables: return environment_variables[name]() raise AttributeError(f"module {__name__!r} has no attribute {name!r}") def __dir__(): return list(environment_variables.keys())