envs.py 78.6 KB
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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import functools
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import json
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import logging
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import os
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import sys
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import tempfile
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import uuid
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from collections.abc import Callable
from typing import TYPE_CHECKING, Any, Literal
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if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
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    VLLM_PORT: int | None = None
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    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
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    VLLM_USE_MODELSCOPE: bool = False
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    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
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    VLLM_NCCL_SO_PATH: str | None = None
    LD_LIBRARY_PATH: str | None = None
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    VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE: int = 256
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    LOCAL_RANK: int = 0
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    CUDA_VISIBLE_DEVICES: str | None = None
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    VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
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    VLLM_ENGINE_READY_TIMEOUT_S: int = 600
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    VLLM_API_KEY: str | None = None
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    VLLM_DEBUG_LOG_API_SERVER_RESPONSE: bool = False
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    S3_ACCESS_KEY_ID: str | None = None
    S3_SECRET_ACCESS_KEY: str | None = None
    S3_ENDPOINT_URL: str | None = None
    VLLM_MODEL_REDIRECT_PATH: str | None = None
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    VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm")
    VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm")
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    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 = ""
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    VLLM_CONFIGURE_LOGGING: bool = True
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    VLLM_LOGGING_LEVEL: str = "INFO"
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    VLLM_LOGGING_PREFIX: str = ""
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    VLLM_LOGGING_STREAM: str = "ext://sys.stdout"
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    VLLM_LOGGING_CONFIG_PATH: str | None = None
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    VLLM_LOGGING_COLOR: str = "auto"
    NO_COLOR: bool = False
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    VLLM_LOG_STATS_INTERVAL: float = 10.0
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    VLLM_TRACE_FUNCTION: int = 0
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    VLLM_USE_FLASHINFER_SAMPLER: bool | None = None
    VLLM_PP_LAYER_PARTITION: str | None = None
    VLLM_CPU_KVCACHE_SPACE: int | None = 0
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    VLLM_CPU_OMP_THREADS_BIND: str = ""
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    VLLM_CPU_NUM_OF_RESERVED_CPU: int | None = None
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    VLLM_CPU_SGL_KERNEL: bool = False
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    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
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    VLLM_XLA_CHECK_RECOMPILATION: bool = False
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    VLLM_FUSED_MOE_CHUNK_SIZE: int = 16 * 1024
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    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
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    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl", "shm"] = "auto"
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    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
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    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
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    VLLM_XLA_USE_SPMD: bool = False
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    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "fork"
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    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
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    VLLM_ASSETS_CACHE_MODEL_CLEAN: bool = False
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    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
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    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
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    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
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    VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True
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    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
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    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
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    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
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    VLLM_MEDIA_CONNECTOR: str = "http"
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    VLLM_MM_HASHER_ALGORITHM: str = "blake3"
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    VLLM_TARGET_DEVICE: str = "cuda"
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    VLLM_MAIN_CUDA_VERSION: str = "12.9"
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    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
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    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
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    VLLM_USE_PRECOMPILED: bool = False
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    VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX: bool = False
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    VLLM_DOCKER_BUILD_CONTEXT: bool = False
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    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
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    CMAKE_BUILD_TYPE: Literal["Debug", "Release", "RelWithDebInfo"] | None = None
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    VERBOSE: bool = False
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    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
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    VLLM_RPC_TIMEOUT: int = 10000  # ms
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    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
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    VLLM_PLUGINS: list[str] | None = None
    VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
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    VLLM_LORA_RESOLVER_HF_REPO_LIST: str | None = None
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    VLLM_USE_AOT_COMPILE: bool = False
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    VLLM_USE_BYTECODE_HOOK: bool = False
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    VLLM_FORCE_AOT_LOAD: bool = False
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    VLLM_USE_MEGA_AOT_ARTIFACT: bool = False
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    VLLM_USE_TRITON_AWQ: bool = False
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    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
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    VLLM_SKIP_P2P_CHECK: bool = False
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    VLLM_DISABLED_KERNELS: list[str] = []
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    VLLM_DISABLE_PYNCCL: bool = False
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    VLLM_USE_OINK_OPS: bool = False
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    VLLM_ROCM_USE_AITER: bool = False
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    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
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    VLLM_ROCM_USE_AITER_LINEAR: bool = True
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    VLLM_ROCM_USE_AITER_MOE: bool = True
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    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
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    VLLM_ROCM_USE_AITER_MLA: bool = True
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    VLLM_ROCM_USE_AITER_MHA: bool = True
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    VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
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    VLLM_ROCM_USE_AITER_TRITON_ROPE: bool = True
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    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
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    VLLM_ROCM_USE_AITER_FP4BMM: bool = True
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    VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
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    VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = False
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    VLLM_ROCM_USE_AITER_TRITON_GEMM: bool = True
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    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
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    VLLM_ROCM_FP8_PADDING: bool = True
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    VLLM_ROCM_MOE_PADDING: bool = True
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    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
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    VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT: bool = False
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    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
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    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
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    VLLM_DISABLE_COMPILE_CACHE: bool = False
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    Q_SCALE_CONSTANT: int = 200
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    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
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    VLLM_SERVER_DEV_MODE: bool = False
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    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
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    VLLM_MLA_DISABLE: bool = False
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    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
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    VLLM_CUDART_SO_PATH: str | None = None
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    VLLM_DP_RANK: int = 0
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    VLLM_DP_RANK_LOCAL: int = -1
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    VLLM_DP_SIZE: int = 1
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    VLLM_USE_STANDALONE_COMPILE: bool = True
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    VLLM_ENABLE_PREGRAD_PASSES: bool = False
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    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
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    VLLM_MOE_DP_CHUNK_SIZE: int = 256
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    VLLM_ENABLE_MOE_DP_CHUNK: bool = True
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    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
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    VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
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    VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY: str = ""
    VLLM_RAY_EXTRA_ENV_VARS_TO_COPY: str = ""
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    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
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    VLLM_MARLIN_INPUT_DTYPE: Literal["int8", "fp8"] | None = None
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    VLLM_MXFP4_USE_MARLIN: bool | None = None
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    VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
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    VLLM_V1_USE_OUTLINES_CACHE: bool = False
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    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
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    VLLM_TPU_MOST_MODEL_LEN: int | None = None
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    VLLM_TPU_USING_PATHWAYS: bool = False
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    VLLM_USE_DEEP_GEMM: bool = True
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    VLLM_MOE_USE_DEEP_GEMM: bool = True
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    VLLM_USE_DEEP_GEMM_E8M0: bool = True
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    VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES: bool = True
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    VLLM_DEEP_GEMM_WARMUP: Literal[
        "skip",
        "full",
        "relax",
    ] = "relax"
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    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
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    VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER: bool = True
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    VLLM_USE_FLASHINFER_MOE_FP16: bool = False
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    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
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    VLLM_USE_FLASHINFER_MOE_INT4: bool = False
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    VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency", "masked_gemm"] = (
        "latency"
    )
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    VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE: int = 394 * 1024 * 1024
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    VLLM_XGRAMMAR_CACHE_MB: int = 0
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    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
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    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
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    VLLM_DISABLE_REQUEST_ID_RANDOMIZATION: bool = False
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    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
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    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
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    VLLM_MOONCAKE_BOOTSTRAP_PORT: int = 8998
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    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
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    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
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    VLLM_SLEEP_WHEN_IDLE: bool = False
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    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
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    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
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    VLLM_KV_CACHE_LAYOUT: Literal["NHD", "HND"] | None = None
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    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
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    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
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    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal[
        "FP", "INT8", "INT6", "INT4", "NONE"
    ] = "NONE"
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    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
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    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: int | None = None
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    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480
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    VLLM_MORIIO_CONNECTOR_READ_MODE: bool = False
    VLLM_MORIIO_QP_PER_TRANSFER: int = 1
    VLLM_MORIIO_POST_BATCH_SIZE: int = -1
    VLLM_MORIIO_NUM_WORKERS: int = 1
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    VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT: int = 480
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    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
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    VLLM_LOOPBACK_IP: str = ""
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    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = True
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    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
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    VLLM_NVFP4_GEMM_BACKEND: str | None = None
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    VLLM_HAS_FLASHINFER_CUBIN: bool = False
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    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
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    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
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    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
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    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = True
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    VLLM_TUNED_CONFIG_FOLDER: str | None = None
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    VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS: set[str] = set()
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    VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT: bool = False
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    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
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    VLLM_SYSTEM_START_DATE: str | None = None
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    VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
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    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
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    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
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    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
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    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
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    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
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    VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE: bool = False
    VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL: bool = False
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    VLLM_DBO_COMM_SMS: int = 20
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    VLLM_PATTERN_MATCH_DEBUG: str | None = None
    VLLM_DEBUG_DUMP_PATH: str | None = None
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    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
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    VLLM_USE_NCCL_SYMM_MEM: bool = False
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    VLLM_NCCL_INCLUDE_PATH: str | None = None
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    VLLM_USE_FBGEMM: bool = False
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    VLLM_GC_DEBUG: str = ""
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    VLLM_DEBUG_WORKSPACE: bool = False
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    VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
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    VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD: int = 256
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    VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
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    VLLM_USE_V2_MODEL_RUNNER: bool = False
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    VLLM_LOG_MODEL_INSPECTION: bool = False
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    VLLM_DEBUG_MFU_METRICS: bool = False
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    VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY: bool = False
    VLLM_WEIGHT_OFFLOADING_DISABLE_UVA: bool = False
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    VLLM_DISABLE_LOG_LOGO: bool = False
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    VLLM_LORA_DISABLE_PDL: bool = False
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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"),
    )


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def maybe_convert_int(value: str | None) -> int | None:
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    if value is None:
        return None
    return int(value)


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def maybe_convert_bool(value: str | None) -> bool | None:
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    if value is None:
        return None
    return bool(int(value))


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def disable_compile_cache() -> bool:
    return bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0")))


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def use_aot_compile() -> bool:
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    from vllm.model_executor.layers.batch_invariant import (
        vllm_is_batch_invariant,
    )
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    from vllm.utils.torch_utils import is_torch_equal_or_newer
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    default_value = (
        "1"
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        if is_torch_equal_or_newer("2.10.0") and not disable_compile_cache()
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        else "0"
    )

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    return (
        not vllm_is_batch_invariant()
        and os.environ.get("VLLM_USE_AOT_COMPILE", default_value) == "1"
    )
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def env_with_choices(
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    env_name: str,
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    default: str | None,
    choices: list[str] | Callable[[], list[str]],
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    case_sensitive: bool = True,
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) -> Callable[[], str | None]:
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    """
    Create a lambda that validates environment variable against allowed choices
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    Args:
        env_name: Name of the environment variable
        default: Default value if not set (can be None)
        choices: List of valid string options or callable that returns list
        case_sensitive: Whether validation should be case sensitive
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    Returns:
        Lambda function for environment_variables dict
    """

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    def _get_validated_env() -> str | None:
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        value = os.getenv(env_name)
        if value is None:
            return default

        # Resolve choices if it's a callable (for lazy loading)
        actual_choices = choices() if callable(choices) else choices

        if not case_sensitive:
            check_value = value.lower()
            check_choices = [choice.lower() for choice in actual_choices]
        else:
            check_value = value
            check_choices = actual_choices

        if check_value not in check_choices:
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            raise ValueError(
                f"Invalid value '{value}' for {env_name}. "
                f"Valid options: {actual_choices}."
            )
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        return value

    return _get_validated_env


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def env_list_with_choices(
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    env_name: str,
    default: list[str],
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    choices: list[str] | Callable[[], list[str]],
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    case_sensitive: bool = True,
) -> Callable[[], list[str]]:
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    """
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    Create a lambda that validates environment variable
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    containing comma-separated values against allowed choices
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    Args:
        env_name: Name of the environment variable
        default: Default list of values if not set
        choices: List of valid string options or callable that returns list
        case_sensitive: Whether validation should be case sensitive
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    Returns:
        Lambda function for environment_variables
        dict that returns list of strings
    """

    def _get_validated_env_list() -> list[str]:
        value = os.getenv(env_name)
        if value is None:
            return default

        # Split comma-separated values and strip whitespace
        values = [v.strip() for v in value.split(",") if v.strip()]

        if not values:
            return default

        # Resolve choices if it's a callable (for lazy loading)
        actual_choices = choices() if callable(choices) else choices

        # Validate each value
        for val in values:
            if not case_sensitive:
                check_value = val.lower()
                check_choices = [choice.lower() for choice in actual_choices]
            else:
                check_value = val
                check_choices = actual_choices

            if check_value not in check_choices:
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                raise ValueError(
                    f"Invalid value '{val}' in {env_name}. "
                    f"Valid options: {actual_choices}."
                )
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        return values

    return _get_validated_env_list


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def env_set_with_choices(
    env_name: str,
    default: list[str],
    choices: list[str] | Callable[[], list[str]],
    case_sensitive: bool = True,
) -> Callable[[], set[str]]:
    """
    Creates a lambda which that validates environment variable
    containing comma-separated values against allowed choices which
    returns choices as a set.
    """

    def _get_validated_env_set() -> set[str]:
        return set(env_list_with_choices(env_name, default, choices, case_sensitive)())

    return _get_validated_env_set


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def get_vllm_port() -> int | None:
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    """Get the port from VLLM_PORT environment variable.
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    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
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    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
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    if "VLLM_PORT" not in os.environ:
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        return None

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    port = os.getenv("VLLM_PORT", "0")
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    try:
        return int(port)
    except ValueError as err:
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        from urllib3.util import parse_url
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        parsed = parse_url(port)
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        if parsed.scheme:
            raise ValueError(
                f"VLLM_PORT '{port}' appears to be a URI. "
                "This may be caused by a Kubernetes service discovery issue,"
                "check the warning in: https://docs.vllm.ai/en/stable/serving/env_vars.html"
            ) from None
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        raise ValueError(f"VLLM_PORT '{port}' must be a valid integer") from err
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def get_env_or_set_default(
    env_name: str,
    default_factory: Callable[[], str],
) -> Callable[[], str]:
    """
    Create a lambda that returns an environment variable value if set,
    or generates and sets a default value using the provided factory function.
    """

    def _get_or_set_default() -> str:
        value = os.getenv(env_name)
        if value is not None:
            return value

        default_value = default_factory()
        os.environ[env_name] = default_value
        return default_value

    return _get_or_set_default


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# The start-* and end* here are used by the documentation generator
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# to extract the used env vars.

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# --8<-- [start:env-vars-definition]
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logger = logging.getLogger(__name__)

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environment_variables: dict[str, Callable[[], Any]] = {
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    # ================== Installation Time Env Vars ==================
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    # Target device of vLLM, supporting [cuda (by default),
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    # rocm, cpu]
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    "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
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    # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
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    "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
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    or "12.9",
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    # Controls PyTorch float32 matmul precision mode within vLLM workers.
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    # Valid options mirror torch.set_float32_matmul_precision
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    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
        "VLLM_FLOAT32_MATMUL_PRECISION",
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        "highest",
        ["highest", "high", "medium"],
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        case_sensitive=False,
    ),
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    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
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    "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
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    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
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    "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
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    # If set, vllm will use precompiled binaries (*.so)
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    "VLLM_USE_PRECOMPILED": lambda: os.environ.get("VLLM_USE_PRECOMPILED", "")
    .strip()
    .lower()
    in ("1", "true")
    or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
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    # If set, skip adding +precompiled suffix to version string
    "VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX": lambda: bool(
        int(os.environ.get("VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX", "0"))
    ),
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    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
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    "VLLM_DOCKER_BUILD_CONTEXT": lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "")
    .strip()
    .lower()
    in ("1", "true"),
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    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
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    "CMAKE_BUILD_TYPE": env_with_choices(
        "CMAKE_BUILD_TYPE", None, ["Debug", "Release", "RelWithDebInfo"]
    ),
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    # If set, vllm will print verbose logs during installation
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    "VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
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    # Root directory for vLLM configuration files
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    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
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    # 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**.
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    "VLLM_CONFIG_ROOT": lambda: os.path.expanduser(
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        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
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        )
    ),
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    # ================== Runtime Env Vars ==================
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    # Root directory for vLLM cache files
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    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
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    "VLLM_CACHE_ROOT": lambda: os.path.expanduser(
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        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
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        )
    ),
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    # used in distributed environment to determine the ip address
    # of the current node, when the node has multiple network interfaces.
    # If you are using multi-node inference, you should set this differently
    # on each node.
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    "VLLM_HOST_IP": lambda: os.getenv("VLLM_HOST_IP", ""),
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    # used in distributed environment to manually set the communication port
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    # 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.
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    "VLLM_PORT": get_vllm_port,
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    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
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    "VLLM_RPC_BASE_PATH": lambda: os.getenv(
        "VLLM_RPC_BASE_PATH", tempfile.gettempdir()
    ),
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    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
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    "VLLM_USE_MODELSCOPE": lambda: os.environ.get(
        "VLLM_USE_MODELSCOPE", "False"
    ).lower()
    == "true",
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    # Interval in seconds to log a warning message when the ring buffer is full
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    "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
        os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
    ),
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    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
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    "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None),
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    # 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
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    "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),
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    # 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`
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    "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
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    # flag to control the chunk size (in MB) for sleeping memory allocations under ROCm
    "VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE": lambda: int(
        os.environ.get("VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE", "256")
    ),
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    # Feature flag to enable/disable Inductor standalone compile.
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    # In torch <= 2.7 we ignore this flag; in torch >= 2.9 this is
    # enabled by default.
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    "VLLM_USE_STANDALONE_COMPILE": lambda: os.environ.get(
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        "VLLM_USE_STANDALONE_COMPILE", "1"
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    )
    == "1",
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    # Inductor's pre-grad passes don't do anything for vLLM.
    # The pre-grad passes get run even on cache-hit and negatively impact
    # vllm cold compile times by O(1s)
    # Can remove this after the following issue gets fixed
    # https://github.com/pytorch/pytorch/issues/174502
    "VLLM_ENABLE_PREGRAD_PASSES": lambda: os.environ.get(
        "VLLM_ENABLE_PREGRAD_PASSES", "0"
    )
    == "1",
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    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
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    "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get(
        "VLLM_PATTERN_MATCH_DEBUG", None
    ),
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    # Dump fx graphs to the given directory.
    # It will override CompilationConfig.debug_dump_path if set.
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    "VLLM_DEBUG_DUMP_PATH": lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None),
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    # Feature flag to enable/disable AOT compilation. This will ensure
    # compilation is done in warmup phase and the compilation will be
    # reused in subsequent calls.
    "VLLM_USE_AOT_COMPILE": use_aot_compile,
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    # Feature flag to enable/disable bytecode in
    # TorchCompileWithNoGuardsWrapper.
    "VLLM_USE_BYTECODE_HOOK": lambda: bool(
        int(os.environ.get("VLLM_USE_BYTECODE_HOOK", "1"))
    ),
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    # Force vllm to always load AOT compiled models from disk. Failure
    # to load will result in a hard error when this is enabled.
    # Will be ignored when VLLM_USE_AOT_COMPILE is disabled.
    "VLLM_FORCE_AOT_LOAD": lambda: os.environ.get("VLLM_FORCE_AOT_LOAD", "0") == "1",
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    # Enable loading compiled models directly from cached standalone compile artifacts
    # without re-splitting graph modules. This reduces overhead during model
    # loading by using reconstruct_serializable_fn_from_mega_artifact.
    "VLLM_USE_MEGA_AOT_ARTIFACT": lambda: os.environ.get(
        "VLLM_USE_MEGA_AOT_ARTIFACT", "0"
    )
    == "1",
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    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
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    "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")),
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    # used to control the visible devices in the distributed setting
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    "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
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    # timeout for each iteration in the engine
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    "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")
    ),
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    # Timeout in seconds for waiting for engine cores to become ready
    # during startup. Default is 600 seconds (10 minutes).
    "VLLM_ENGINE_READY_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_READY_TIMEOUT_S", "600")
    ),
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    # API key for vLLM API server
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    "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None),
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    # Whether to log responses from API Server for debugging
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    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: os.environ.get(
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
    ).lower()
    == "true",
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    # S3 access information, used for tensorizer to load model from S3
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    "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),
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    # Usage stats collection
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    "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"),
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    # 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
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    "VLLM_CONFIGURE_LOGGING": lambda: bool(
        int(os.getenv("VLLM_CONFIGURE_LOGGING", "1"))
    ),
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    "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),
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    # this is used for configuring the default logging level
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    "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
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    # this is used for configuring the default logging stream
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    "VLLM_LOGGING_STREAM": lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),
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    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
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    "VLLM_LOGGING_PREFIX": lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
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    # Controls colored logging output. Options: "auto" (default, colors when terminal),
    # "1" (always use colors), "0" (never use colors)
    "VLLM_LOGGING_COLOR": lambda: os.getenv("VLLM_LOGGING_COLOR", "auto"),
    # Standard unix flag for disabling ANSI color codes
    "NO_COLOR": lambda: os.getenv("NO_COLOR", "0") != "0",
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    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
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    "VLLM_LOG_STATS_INTERVAL": lambda: val
    if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10."))) > 0.0
    else 10.0,
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    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
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    "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
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    # If set, vllm will use flashinfer sampler
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    "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
        int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"])
    )
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
    else None,
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    # Pipeline stage partition strategy
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    "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
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    # (CPU backend only) CPU key-value cache space.
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    # default is None and will be set as 4 GB
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    "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ
    else None,
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    # (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 '|'.
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    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
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    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
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    "VLLM_CPU_NUM_OF_RESERVED_CPU": lambda: int(
        os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0")
    )
    if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ
    else None,
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    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
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    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
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    # If the env var is set, Ray Compiled Graph uses the specified
    # channel type to communicate between workers belonging to
    # different pipeline-parallel stages.
    # Available options:
    # - "auto": use the default channel type
    # - "nccl": use NCCL for communication
    # - "shm": use shared memory and gRPC for communication
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    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
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    # If the env var is set, it enables GPU communication overlap
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    # (experimental feature) in Ray's Compiled Graph.
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    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
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    # If the env var is set, it uses a Ray Communicator wrapping
    # vLLM's pipeline parallelism communicator to interact with Ray's
    # Compiled Graph. Otherwise, it uses Ray's NCCL communicator.
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    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
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    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
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    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
        "VLLM_WORKER_MULTIPROC_METHOD", "fork", ["spawn", "fork"]
    ),
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    # Path to the cache for storing downloaded assets
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    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
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        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
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        )
    ),
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    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
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    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
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    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
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    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
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    # Timeout for fetching videos when serving multimodal models
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    # Default is 30 seconds
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    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
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    # Timeout for fetching audio when serving multimodal models
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    # Default is 10 seconds
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    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
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    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
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    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
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    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
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    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
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    # Maximum filesize in MB for a single audio file when processing
    # speech-to-text requests. Files larger than this will be rejected.
    # Default is 25 MB
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    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
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    # Backend for Video IO
    # - "opencv": Default backend that uses OpenCV stream buffered backend.
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    # - "identity": Returns raw video bytes for model processor to handle.
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    #
    # Custom backend implementations can be registered
    # via `@VIDEO_LOADER_REGISTRY.register("my_custom_video_loader")` and
    # imported at runtime.
    # If a non-existing backend is used, an AssertionError will be thrown.
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    "VLLM_VIDEO_LOADER_BACKEND": lambda: os.getenv(
        "VLLM_VIDEO_LOADER_BACKEND", "opencv"
    ),
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    # Media connector implementation.
    # - "http": Default connector that supports fetching media via HTTP.
    #
    # Custom implementations can be registered
    # via `@MEDIA_CONNECTOR_REGISTRY.register("my_custom_media_connector")` and
    # imported at runtime.
    # If a non-existing backend is used, an AssertionError will be thrown.
    "VLLM_MEDIA_CONNECTOR": lambda: os.getenv("VLLM_MEDIA_CONNECTOR", "http"),
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    # Hash algorithm for multimodal content hashing.
    # - "blake3": Default, fast cryptographic hash (not FIPS 140-3 compliant)
    # - "sha256": FIPS 140-3 compliant, widely supported
    # - "sha512": FIPS 140-3 compliant, faster on 64-bit systems
    # Use sha256 or sha512 for FIPS compliance in government/enterprise deployments
    "VLLM_MM_HASHER_ALGORITHM": env_with_choices(
        "VLLM_MM_HASHER_ALGORITHM",
        "blake3",
        ["blake3", "sha256", "sha512"],
        case_sensitive=False,
    ),
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    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
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    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
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        os.getenv(
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            "VLLM_XLA_CACHE_PATH",
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            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
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        )
    ),
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    # If set, assert on XLA recompilation after each execution step.
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    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
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    # Enable SPMD mode for TPU backend.
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    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
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    ),
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    # Control whether to use fused MoE activation chunking. Current chunking
    # logic is incompatible with torch.compile and causes IMA. See issue
    # https://github.com/vllm-project/vllm/issues/19631.
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    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
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    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
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    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
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        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
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    ),
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    # 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.
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    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
840
841
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
842
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844
845
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848
    "VLLM_TEST_FORCE_FP8_MARLIN": lambda: (
        os.environ.get("VLLM_TEST_FORCE_FP8_MARLIN", "0").strip().lower()
        in ("1", "true")
    ),
    "VLLM_TEST_FORCE_LOAD_FORMAT": lambda: os.getenv(
        "VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"
    ),
849
850
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
851
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
852
    # Timeout in seconds for keeping HTTP connections alive in API server
853
854
855
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
856
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858
    # a list of plugin names to load, separated by commas.
    # if this is not set, it means all plugins will be loaded
    # if this is set to an empty string, no plugins will be loaded
859
860
861
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
862
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864
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
865
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867
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
868
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874
    # A remote HF repo(s) containing one or more LoRA adapters, which
    # may be downloaded and leveraged as needed. Only works if plugins
    # are enabled and VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
    # Values should be comma separated.
    "VLLM_LORA_RESOLVER_HF_REPO_LIST": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_HF_REPO_LIST", None
    ),
875
    # If set, vLLM will use Triton implementations of AWQ.
876
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
877
    # If set, allow loading or unloading lora adapters in runtime,
878
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881
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
882
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886
887
    # We assume drivers can report p2p status correctly.
    # If the program hangs when using custom allreduce,
    # potantially caused by a bug in the driver (535 series),
    # if might be helpful to set VLLM_SKIP_P2P_CHECK=0
    # so that vLLM can verify if p2p is actually working.
    # See https://github.com/vllm-project/vllm/blob/a9b15c606fea67a072416ea0ea115261a2756058/vllm/distributed/device_communicators/custom_all_reduce_utils.py#L101-L108 for details. # noqa
888
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
889
890
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892
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
893
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895
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
896
    # Disable pynccl (using torch.distributed instead)
897
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899
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
900
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903
904
    # Optional: enable external Oink custom ops (e.g., Blackwell RMSNorm).
    # Disabled by default.
    "VLLM_USE_OINK_OPS": lambda: (
        os.getenv("VLLM_USE_OINK_OPS", "False").lower() in ("true", "1")
    ),
905
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    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
907
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909
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
910
911
    # Whether to use aiter paged attention.
    # By default is disabled.
912
913
914
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
915
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917
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
918
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920
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
    ),
921
922
    # Whether to use aiter moe ops.
    # By default is enabled.
923
924
925
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in ("true", "1")
    ),
926
    # use aiter rms norm op if aiter ops are enabled.
927
928
929
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in ("true", "1")
    ),
930
931
    # Whether to use aiter mla ops.
    # By default is enabled.
932
933
934
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in ("true", "1")
    ),
935
936
    # Whether to use aiter mha ops.
    # By default is enabled.
937
938
939
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in ("true", "1")
    ),
940
941
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
942
943
944
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
945
    # Whether to use aiter rope.
946
    # By default is enabled.
947
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
948
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "True").lower() in ("true", "1")
949
    ),
950
951
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
952
953
954
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
    ),
955
956
957
958
959
    # Whether to use aiter triton fp4 bmm kernel
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_FP4BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4BMM", "True").lower() in ("true", "1")
    ),
960
961
962
963
964
    # Use AITER triton unified attention for V1 attention
    "VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION", "False").lower()
        in ("true", "1")
    ),
965
    # Whether to use aiter fusion shared experts ops.
966
    # By default is disabled.
967
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
968
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
969
970
        in ("true", "1")
    ),
971
972
973
974
975
    # Whether to use aiter triton kernels for gemm ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_TRITON_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_GEMM", "True").lower() in ("true", "1")
    ),
976
    # use rocm skinny gemms
977
978
979
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
980
    # Pad the fp8 weights to 256 bytes for ROCm
981
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
982
    # Pad the weights for the moe kernel
983
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
984
    # custom paged attention kernel for MI3* cards
985
986
987
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
988
989
990
991
    # Whether to use the shuffled kv cache layout
    "VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT": lambda: (
        os.getenv("VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT", "False").lower() in ("true", "1")
    ),
992
993
994
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
995
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997
998
999
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
1000
1001
1002
1003
    # Custom quick allreduce kernel for MI3* cards
    # Due to the lack of the bfloat16 asm instruction, bfloat16
    # kernels are slower than fp16,
    # If environment variable is set to 1, the input is converted to fp16
1004
1005
1006
1007
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1008
1009
1010
1011
1012
1013
    # Custom quick allreduce kernel for MI3* cards.
    # Controls the maximum allowed number of data bytes(MB) for custom quick
    # allreduce communication.
    # Default: 2048 MB.
    # Data exceeding this size will use either custom allreduce or RCCL
    # communication.
1014
1015
1016
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1017
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1018
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1019
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1020
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1021
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1022
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1023
    # If set, enable multiprocessing in LLM for the V1 code path.
1024
1025
1026
1027
1028
1029
    "VLLM_ENABLE_V1_MULTIPROCESSING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))
    ),
    "VLLM_LOG_BATCHSIZE_INTERVAL": lambda: float(
        os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")
    ),
1030
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1031
1032
1033
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1034
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1035
1036
1037
1038
1039
1040
1041
    # Controls the maximum number of requests to handle in a
    # single asyncio task when processing per-token outputs in the
    # V1 AsyncLLM interface. It is applicable when handling a high
    # concurrency of streaming requests.
    # Setting this too high can result in a higher variance of
    # inter-message latencies. Setting it too low can negatively impact
    # TTFT and overall throughput.
1042
1043
1044
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1045
    # If set, vLLM will disable the MLA attention optimizations.
1046
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1047
    # If set, vLLM will pick up the provided Flash Attention MLA
1048
1049
1050
    # Number of GPUs per worker in Ray, if it is set to be a fraction,
    # it allows ray to schedule multiple actors on a single GPU,
    # so that users can colocate other actors on the same GPUs as vLLM.
1051
1052
1053
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1054
1055
1056
    # Bundle indices for Ray, if it is set, it can control precisely
    # which indices are used for the Ray bundle, for every worker.
    # Format: comma-separated list of integers, e.g. "0,1,2,3"
1057
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1058
1059
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1060
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1061
    # Rank of the process in the data parallel setting
1062
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1063
1064
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1065
1066
1067
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1068
    # World size of the data parallel setting
1069
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1070
    # IP address of the master node in the data parallel setting
1071
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1072
    # Port of the master node in the data parallel setting
1073
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1074
1075
1076
1077
1078
    # In the context of executing MoE models with Data-Parallel, Expert-Parallel
    # and Batched All-to-All dispatch/combine kernels, VLLM_MOE_DP_CHUNK_SIZE
    # dictates the quantum of tokens that can be dispatched from a DP
    # rank. All DP ranks process the activations in VLLM_MOE_DP_CHUNK_SIZE
    # units.
1079
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1080
1081
1082
    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
    ),
1083
    # Randomize inputs during dummy runs when using Data Parallel
1084
1085
1086
1087
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1088
1089
1090
1091
1092
1093
1094
    # Strategy to pack the data parallel ranks for Ray.
    # Available options:
    # - "fill":
    #   for DP master node, allocate exactly data-parallel-size-local DP ranks,
    #   for non-master nodes, allocate as many DP ranks as can fit;
    # - "strict":
    #   allocate exactly data-parallel-size-local DP ranks to each picked node;
1095
1096
1097
    # - "span":
    #   Should be used only when a single DP rank requires multiple nodes.
    #   allocate one DP rank over as many nodes as required for set world_size;
1098
1099
1100
1101
    # This environment variable is ignored if data-parallel-backend is not Ray.
    "VLLM_RAY_DP_PACK_STRATEGY": lambda: os.getenv(
        "VLLM_RAY_DP_PACK_STRATEGY", "strict"
    ),
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
    # Comma-separated *additional* prefixes of env vars to copy from the
    # driver to Ray workers.  These are merged with the built-in defaults
    # defined in ``vllm.ray.ray_env`` (VLLM_, etc.).  Example: "MYLIB_,OTHER_"
    "VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY": lambda: os.getenv(
        "VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY", ""
    ),
    # Comma-separated *additional* individual env var names to copy from
    # the driver to Ray workers.  Merged with the built-in defaults
    # defined in ``vllm.ray.ray_env`` (PYTHONHASHSEED).
    # Example: "MY_SECRET,MY_FLAG"
    "VLLM_RAY_EXTRA_ENV_VARS_TO_COPY": lambda: os.getenv(
        "VLLM_RAY_EXTRA_ENV_VARS_TO_COPY", ""
    ),
1115
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1116
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1117
    # Use model_redirect to redirect the model name to a local folder.
1118
1119
1120
1121
1122
    # `model_redirect` can be a json file mapping the model between
    # repo_id and local folder:
    # {"meta-llama/Llama-3.2-1B": "/tmp/Llama-3.2-1B"}
    # or a space separated values table file:
    # meta-llama/Llama-3.2-1B   /tmp/Llama-3.2-1B
1123
1124
1125
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1126
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1127
1128
1129
1130
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1131
    # Whether to use marlin kernel in mxfp4 quantization method
1132
1133
1134
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1135
1136
1137
1138
    # The activation dtype for marlin kernel
    "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
        "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
    ),
1139
1140
1141
1142
1143
1144
    # Whether to use DeepEPLL kernels for NVFP4 quantization and dispatch method
    # only supported on Blackwell GPUs and with
    # https://github.com/deepseek-ai/DeepEP/pull/341
    "VLLM_DEEPEPLL_NVFP4_DISPATCH": lambda: bool(
        int(os.getenv("VLLM_DEEPEPLL_NVFP4_DISPATCH", "0"))
    ),
1145
1146
1147
    # Whether to turn on the outlines cache for V1
    # This cache is unbounded and on disk, so it's not safe to use in
    # an environment with potentially malicious users.
1148
1149
1150
1151
    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
1152
1153
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
1154
1155
1156
1157
1158
1159
1160
1161
    "VLLM_TPU_BUCKET_PADDING_GAP": lambda: int(
        os.environ["VLLM_TPU_BUCKET_PADDING_GAP"]
    )
    if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ
    else 0,
    "VLLM_TPU_MOST_MODEL_LEN": lambda: maybe_convert_int(
        os.environ.get("VLLM_TPU_MOST_MODEL_LEN", None)
    ),
1162
    # Whether using Pathways
1163
1164
1165
    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
1166
    # Allow use of DeepGemm kernels for fused moe ops.
1167
    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1168
1169
1170
1171
    # Allow use of DeepGemm specifically for MoE fused ops (overrides only MoE).
    "VLLM_MOE_USE_DEEP_GEMM": lambda: bool(
        int(os.getenv("VLLM_MOE_USE_DEEP_GEMM", "1"))
    ),
1172
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
1173
1174
1175
    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
1176
1177
1178
1179
    # Whether to create TMA-aligned scale tensor when DeepGEMM is used.
    "VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES", "1"))
    ),
1180
1181
1182
1183
    # DeepGemm JITs the kernels on-demand. The warmup attempts to make DeepGemm
    # JIT all the required kernels before model execution so there is no
    # JIT'ing in the hot-path. However, this warmup increases the engine
    # startup time by a couple of minutes.
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
    # Available options:
    #  - "skip"  : Skip warmup.
    #  - "full"  : Warmup deepgemm by running all possible gemm shapes the
    #   engine could encounter.
    #  - "relax" : Select gemm shapes to run based on some heuristics. The
    #   heuristic aims to have the same effect as running all possible gemm
    #   shapes, but provides no guarantees.
    "VLLM_DEEP_GEMM_WARMUP": env_with_choices(
        "VLLM_DEEP_GEMM_WARMUP",
        "relax",
        [
            "skip",
            "full",
            "relax",
        ],
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    ),
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    # Whether to use fused grouped_topk used for MoE expert selection.
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    "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
        int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))
    ),
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    # Allow use of FlashInfer FP8 block-scale GEMM for linear layers.
    # This uses TensorRT-LLM kernels and requires SM90+ (Hopper).
    "VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER": lambda: bool(
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        int(os.getenv("VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER", "1"))
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    ),
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    # Allow use of FlashInfer BF16 MoE kernels for fused moe ops.
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    "VLLM_USE_FLASHINFER_MOE_FP16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP16", "0"))
    ),
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    # Allow use of FlashInfer FP8 MoE kernels for fused moe ops.
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    "VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
    ),
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    # Allow use of FlashInfer NVFP4 MoE kernels for fused moe ops.
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    "VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
    ),
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    # Allow use of FlashInfer MxInt4 MoE kernels for fused moe ops.
    "VLLM_USE_FLASHINFER_MOE_INT4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_INT4", "0"))
    ),
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    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
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    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))
    ),
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    # If set to 1, use the FlashInfer CUTLASS backend for
    # MXFP8 (activation) x MXFP4 (weight) MoE.
    # This is separate from the TRTLLMGEN path controlled by
    # VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8.
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    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0"))
    ),
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    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
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    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
    ),
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    # Control the cache sized used by the xgrammar compiler. The default
    # of 512 MB should be enough for roughly 1000 JSON schemas.
    # It can be changed with this variable if needed for some reason.
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    "VLLM_XGRAMMAR_CACHE_MB": lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
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    # Control the threshold for msgspec to use 'zero copy' for
    # serialization/deserialization of tensors. Tensors below
    # this limit will be encoded into the msgpack buffer, and
    # tensors above will instead be sent via a separate message.
    # While the sending side still actually copies the tensor
    # in all cases, on the receiving side, tensors above this
    # limit will actually be zero-copy decoded.
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    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
        os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
    ),
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    # If set, allow insecure serialization using pickle.
    # This is useful for environments where it is deemed safe to use the
    # insecure method and it is needed for some reason.
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    "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
        int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
    ),
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    # Temporary: skip adding random suffix to internal request IDs. May be
    # needed for KV connectors that match request IDs across instances.
    "VLLM_DISABLE_REQUEST_ID_RANDOMIZATION": lambda: bool(
        int(os.getenv("VLLM_DISABLE_REQUEST_ID_RANDOMIZATION", "0"))
    ),
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    # IP address used for NIXL handshake between remote agents.
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    "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
        "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
    ),
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    # Port used for NIXL handshake between remote agents.
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    "VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
        os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")
    ),
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    # Port used for Mooncake handshake between remote agents.
    "VLLM_MOONCAKE_BOOTSTRAP_PORT": lambda: int(
        os.getenv("VLLM_MOONCAKE_BOOTSTRAP_PORT", "8998")
    ),
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    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
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    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
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    "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
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        "VLLM_FLASHINFER_MOE_BACKEND",
        "latency",
        ["throughput", "latency", "masked_gemm"],
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    ),
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    # Control the workspace buffer size for the FlashInfer backend.
    "VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE": lambda: int(
        os.getenv("VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE", str(394 * 1024 * 1024))
    ),
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    # Control the maximum number of tokens per expert supported by the
    # NVFP4 MoE CUTLASS Kernel. This value is used to create a buffer for
    # the blockscale tensor of activations NVFP4 Quantization.
    # This is used to prevent the kernel from running out of memory.
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    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
        os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")
    ),
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    # Specifies the thresholds of the communicated tensor sizes under which
    # vllm should use flashinfer fused allreduce. The variable should be a
    # JSON with the following format:
    #     { <world size>: <max size in mb> }
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    # Unspecified world sizes will fall back to
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    #     { 2: 64, 4: 1, <everything else>: 0.5 }
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    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB": lambda: json.loads(
        os.getenv("VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")
    ),
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    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
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    # Custom routing strategies can be registered by
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    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
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    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: os.environ.get(
        "VLLM_MOE_ROUTING_SIMULATION_STRATEGY", ""
    ).lower(),
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    # Regex timeout for use by the vLLM tool parsing plugins.
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    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
    ),
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    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
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    "VLLM_SLEEP_WHEN_IDLE": lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
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    # Control the max chunk bytes (in MB) for the rpc message queue.
    # Object larger than this threshold will be broadcast to worker
    # processes via zmq.
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    "VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
        os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
    ),
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    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
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    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
    ),
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    # KV Cache layout used throughout vllm.
    # Some common values are:
    # - NHD
    # - HND
    # Where N=num_blocks, H=num_heads and D=head_size. The default value will
    # leave the layout choice to the backend. Mind that backends may only
    # implement and support a subset of all possible layouts.
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    "VLLM_KV_CACHE_LAYOUT": env_with_choices(
        "VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]
    ),
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    # Enable checking whether the generated logits contain NaNs,
    # indicating corrupted output. Useful for debugging low level bugs
    # or bad hardware but it may add compute overhead.
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    "VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
        int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
    ),
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    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
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    "VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
        int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
    ),
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    # Time (in seconds) after which the KV cache on the producer side is
    # automatically cleared if no READ notification is received from the
    # consumer. This is only applicable when using NixlConnector in a
    # disaggregated decode-prefill setup.
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    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")
    ),
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    # Controls the read mode for the Mori-IO connector
    "VLLM_MORIIO_CONNECTOR_READ_MODE": lambda: (
        os.getenv("VLLM_MORIIO_CONNECTOR_READ_MODE", "False").lower() in ("true", "1")
    ),
    # Controls the QP (Queue Pair) per transfer configuration for the Mori-IO connector
    "VLLM_MORIIO_QP_PER_TRANSFER": lambda: int(
        os.getenv("VLLM_MORIIO_QP_PER_TRANSFER", "1")
    ),
    # Controls the post-processing batch size for the Mori-IO connector
    "VLLM_MORIIO_POST_BATCH_SIZE": lambda: int(
        os.getenv("VLLM_MORIIO_POST_BATCH_SIZE", "-1")
    ),
    # Controls the number of workers for Mori operations for the Mori-IO connector
    "VLLM_MORIIO_NUM_WORKERS": lambda: int(os.getenv("VLLM_MORIIO_NUM_WORKERS", "1")),
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    # Timeout (in seconds) for MooncakeConnector in PD disaggregated setup.
    "VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT", "480")
    ),
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    # If set, it means we pre-downloaded cubin files and flashinfer will
    # read the cubin files directly.
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    "VLLM_HAS_FLASHINFER_CUBIN": lambda: bool(
        int(os.getenv("VLLM_HAS_FLASHINFER_CUBIN", "0"))
    ),
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    # Supported options:
    # - "flashinfer-cudnn": use flashinfer cudnn GEMM backend
    # - "flashinfer-trtllm": use flashinfer trtllm GEMM backend
    # - "flashinfer-cutlass": use flashinfer cutlass GEMM backend
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    # - "marlin": use marlin GEMM backend (for GPUs without native FP4 support)
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    # - <none>: automatically pick an available backend
    "VLLM_NVFP4_GEMM_BACKEND": env_with_choices(
        "VLLM_NVFP4_GEMM_BACKEND",
        None,
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        [
            "flashinfer-cudnn",
            "flashinfer-trtllm",
            "flashinfer-cutlass",
            "cutlass",
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            "marlin",
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        ],
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    ),
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    # Controls garbage collection during CUDA graph capture.
    # If set to 0 (default), enables GC freezing to speed up capture time.
    # If set to 1, allows GC to run during capture.
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    "VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
        int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
    ),
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    # Used to force set up loopback IP
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    "VLLM_LOOPBACK_IP": lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
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    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
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    "VLLM_PROCESS_NAME_PREFIX": lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
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    # Allow chunked local attention with hybrid kv cache manager.
    # Currently using the Hybrid KV cache manager with chunked local attention
    # in the Llama4 models (the only models currently using chunked local attn)
    # causes a latency regression. For this reason, we disable it by default.
    # This flag is used to allow users to enable it if they want to (to save on
    # kv-cache memory usage and enable longer contexts)
    # TODO(lucas): Remove this flag once latency regression is resolved.
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    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
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        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "1"))
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    ),
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    # Enables support for the "store" option in the OpenAI Responses API.
    # When set to 1, vLLM's OpenAI server will retain the input and output
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    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
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    # NOTE/WARNING:
    # 1. Messages are kept in memory only (not persisted to disk) and will be
    #    lost when the vLLM server shuts down.
    # 2. Enabling this option will cause a memory leak, as stored messages are
    #    never removed from memory until the server terminates.
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    "VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
    ),
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    # If set, use the fp8 mfma in rocm paged attention.
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    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN": lambda: bool(
        int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))
    ),
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    # Whether to use pytorch symmetric memory for allreduce
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    "VLLM_ALLREDUCE_USE_SYMM_MEM": lambda: bool(
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        int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1"))
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    ),
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    # Experimental: use this to enable MCP tool calling for non harmony models
    "VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT": lambda: bool(
        int(os.getenv("VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT", "0"))
    ),
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    # Allows vllm to find tuned config under customized folder
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    "VLLM_TUNED_CONFIG_FOLDER": lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
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    # Valid values are container,code_interpreter,web_search_preview
    # ex VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS=container,code_interpreter
    # If the server_label of your mcp tool is not in this list it will
    # be completely ignored.
    "VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS": env_set_with_choices(
        "VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS",
        default=[],
        choices=["container", "code_interpreter", "web_search_preview"],
    ),
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    # Allows harmony instructions to be injected on system messages
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    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS": lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))
    ),
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    # Pin the conversation start date injected into the Harmony system
    # message. When unset the current date is used, which introduces
    # non-determinism (different tokens -> different model behaviour at
    # temperature=0). Set to an ISO date string, e.g. "2023-09-12",
    # for reproducible inference or testing.
    "VLLM_SYSTEM_START_DATE": lambda: os.getenv("VLLM_SYSTEM_START_DATE", None),
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    # Enable automatic retry when tool call JSON parsing fails
    # If enabled, returns an error message to the model to retry
    # If disabled (default), raises an exception and fails the request
    "VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY": lambda: bool(
        int(os.getenv("VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY", "0"))
    ),
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    # Add optional custom scopes for profiling, disable to avoid overheads
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    "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
    ),
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    # Add optional nvtx scopes for profiling, disable to avoid overheads
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    "VLLM_NVTX_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))
    ),
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    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
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    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES": lambda: bool(
        int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))
    ),
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    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
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    # Automatically generates a unique UUID-based name per process tree
    # if not explicitly set.
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": get_env_or_set_default(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
        lambda: f"VLLM_OBJECT_STORAGE_SHM_BUFFER_{uuid.uuid4().hex}",
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    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
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    "VLLM_DEEPEP_BUFFER_SIZE_MB": lambda: int(
        os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")
    ),
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    # Force DeepEP to use intranode kernel for inter-node communication in
    # high throughput mode. This is useful archive higher prefill throuhgput
    # on system supports multi-node nvlink (e.g GB200).
    "VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE": lambda: bool(
        int(os.getenv("VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE", "0"))
    ),
    # Allow DeepEP to use MNNVL (multi-node nvlink) for internode_ll kernel,
    # turn this for better latency on GB200 like system
    "VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL": lambda: bool(
        int(os.getenv("VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL", "0"))
    ),
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    # The number of SMs to allocate for communication kernels when running DBO
    # the rest of the SMs on the device will be allocated to compute
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    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
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    # Enable max_autotune & coordinate_descent_tuning in inductor_config
    # to compile static shapes passed from compile_sizes in compilation_config
    # If set to 1, enable max_autotune; By default, this is enabled (1)
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    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))
    ),
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    # If set to 1, enable coordinate_descent_tuning;
    # By default, this is enabled (1)
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    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING", "1"))
    ),
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    # Flag to enable NCCL symmetric memory allocation and registration
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    "VLLM_USE_NCCL_SYMM_MEM": lambda: bool(
        int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))
    ),
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    # NCCL header path
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    "VLLM_NCCL_INCLUDE_PATH": lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None),
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    # Flag to enable FBGemm kernels on model execution
    "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))),
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    # GC debug config
    # - VLLM_GC_DEBUG=0: disable GC debugger
    # - VLLM_GC_DEBUG=1: enable GC debugger with gc.collect elpased times
    # - VLLM_GC_DEBUG='{"top_objects":5}': enable GC debugger with
    #                                      top 5 collected objects
    "VLLM_GC_DEBUG": lambda: os.getenv("VLLM_GC_DEBUG", ""),
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    # Debug workspace allocations.
    # logging of workspace resize operations.
    "VLLM_DEBUG_WORKSPACE": lambda: bool(int(os.getenv("VLLM_DEBUG_WORKSPACE", "0"))),
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    # Disables parallel execution of shared_experts via separate cuda stream
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    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: bool(
        int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "0"))
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    ),
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    # Limits when we run shared_experts in a separate stream.
    # We found out that for large batch sizes, the separate stream
    # execution is not beneficial (most likely because of the input clone)
    # TODO(alexm-redhat): Tune to be more dynamic based on GPU type
    "VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD": lambda: int(
        int(os.getenv("VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD", 256))
    ),
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    # Format for saving torch.compile cache artifacts
    # - "binary": saves as binary file
    #     Safe for multiple vllm serve processes accessing the same torch compile cache.
    # - "unpacked": saves as directory structure (for inspection/debugging)
    #     NOT multiprocess safe - race conditions may occur with multiple processes.
    #     Allows viewing and setting breakpoints in Inductor's code output files.
    "VLLM_COMPILE_CACHE_SAVE_FORMAT": env_with_choices(
        "VLLM_COMPILE_CACHE_SAVE_FORMAT", "binary", ["binary", "unpacked"]
    ),
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    # Flag to enable v2 model runner.
    "VLLM_USE_V2_MODEL_RUNNER": lambda: bool(
        int(os.getenv("VLLM_USE_V2_MODEL_RUNNER", "0"))
    ),
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    # Log model inspection after loading.
    # If enabled, logs a transformers-style hierarchical view of the model
    # with quantization methods and attention backends.
    "VLLM_LOG_MODEL_INSPECTION": lambda: bool(
        int(os.getenv("VLLM_LOG_MODEL_INSPECTION", "0"))
    ),
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    # Debug logging for --enable-mfu-metrics
    "VLLM_DEBUG_MFU_METRICS": lambda: bool(
        int(os.getenv("VLLM_DEBUG_MFU_METRICS", "0"))
    ),
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    # Disable using pytorch's pin memory for CPU offloading.
    "VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY": lambda: bool(
        int(os.getenv("VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY", "0"))
    ),
    # Disable using UVA (Unified Virtual Addressing) for CPU offloading.
    "VLLM_WEIGHT_OFFLOADING_DISABLE_UVA": lambda: bool(
        int(os.getenv("VLLM_WEIGHT_OFFLOADING_DISABLE_UVA", "0"))
    ),
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    # Disable logging of vLLM logo at server startup time.
    "VLLM_DISABLE_LOG_LOGO": lambda: bool(int(os.getenv("VLLM_DISABLE_LOG_LOGO", "0"))),
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    # Disable PDL for LoRA, as enabling PDL with LoRA on SM100 causes
    # Triton compilation to fail.
    "VLLM_LORA_DISABLE_PDL": lambda: bool(int(os.getenv("VLLM_LORA_DISABLE_PDL", "0"))),
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}

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# --8<-- [end:env-vars-definition]
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def __getattr__(name: str):
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    """
    Gets environment variables lazily.

    NOTE: After enable_envs_cache() invocation (which triggered after service
    initialization), all environment variables will be cached.
    """
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    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


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def _is_envs_cache_enabled() -> bool:
    """Checked if __getattr__ is wrapped with functools.cache"""
    global __getattr__
    return hasattr(__getattr__, "cache_clear")


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def enable_envs_cache() -> None:
    """
    Enables caching of environment variables. This is useful for performance
    reasons, as it avoids the need to re-evaluate environment variables on
    every call.

    NOTE: Currently, it's invoked after service initialization to reduce
    runtime overhead. This also means that environment variables should NOT
    be updated after the service is initialized.
    """
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    if _is_envs_cache_enabled():
        # Avoid wrapping functools.cache multiple times
        return
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    # Tag __getattr__ with functools.cache
    global __getattr__
    __getattr__ = functools.cache(__getattr__)

    # Cache all environment variables
    for key in environment_variables:
        __getattr__(key)


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def disable_envs_cache() -> None:
    """
    Resets the environment variables cache. It could be used to isolate environments
    between unit tests.
    """
    global __getattr__
    # If __getattr__ is wrapped by functions.cache, unwrap the caching layer.
    if _is_envs_cache_enabled():
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        assert hasattr(__getattr__, "__wrapped__")
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        __getattr__ = __getattr__.__wrapped__


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def __dir__():
    return list(environment_variables.keys())
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def is_set(name: str):
    """Check if an environment variable is explicitly set."""
    if name in environment_variables:
        return name in os.environ
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


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def validate_environ(hard_fail: bool) -> None:
    for env in os.environ:
        if env.startswith("VLLM_") and env not in environment_variables:
            if hard_fail:
                raise ValueError(f"Unknown vLLM environment variable detected: {env}")
            else:
                logger.warning("Unknown vLLM environment variable detected: %s", env)


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def compile_factors() -> dict[str, object]:
    """Return env vars used for torch.compile cache keys.

    Start with every known vLLM env var; drop entries in `ignored_factors`;
    hash everything else. This keeps the cache key aligned across workers."""

    ignored_factors: set[str] = {
        "MAX_JOBS",
        "VLLM_RPC_BASE_PATH",
        "VLLM_USE_MODELSCOPE",
        "VLLM_RINGBUFFER_WARNING_INTERVAL",
        "VLLM_DEBUG_DUMP_PATH",
        "VLLM_PORT",
        "VLLM_CACHE_ROOT",
        "LD_LIBRARY_PATH",
        "VLLM_SERVER_DEV_MODE",
        "VLLM_DP_MASTER_IP",
        "VLLM_DP_MASTER_PORT",
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS",
        "VLLM_CI_USE_S3",
        "VLLM_MODEL_REDIRECT_PATH",
        "VLLM_HOST_IP",
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        "VLLM_FORCE_AOT_LOAD",
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        "S3_ACCESS_KEY_ID",
        "S3_SECRET_ACCESS_KEY",
        "S3_ENDPOINT_URL",
        "VLLM_USAGE_STATS_SERVER",
        "VLLM_NO_USAGE_STATS",
        "VLLM_DO_NOT_TRACK",
        "VLLM_LOGGING_LEVEL",
        "VLLM_LOGGING_PREFIX",
        "VLLM_LOGGING_STREAM",
        "VLLM_LOGGING_CONFIG_PATH",
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        "VLLM_LOGGING_COLOR",
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        "VLLM_LOG_STATS_INTERVAL",
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE",
        "VLLM_TUNED_CONFIG_FOLDER",
        "VLLM_ENGINE_ITERATION_TIMEOUT_S",
        "VLLM_HTTP_TIMEOUT_KEEP_ALIVE",
        "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS",
        "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH",
        "VLLM_SLEEP_WHEN_IDLE",
        "VLLM_IMAGE_FETCH_TIMEOUT",
        "VLLM_VIDEO_FETCH_TIMEOUT",
        "VLLM_AUDIO_FETCH_TIMEOUT",
        "VLLM_MEDIA_URL_ALLOW_REDIRECTS",
        "VLLM_MEDIA_LOADING_THREAD_COUNT",
        "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB",
        "VLLM_VIDEO_LOADER_BACKEND",
        "VLLM_MEDIA_CONNECTOR",
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        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
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        "VLLM_ASSETS_CACHE",
        "VLLM_ASSETS_CACHE_MODEL_CLEAN",
        "VLLM_WORKER_MULTIPROC_METHOD",
        "VLLM_ENABLE_V1_MULTIPROCESSING",
        "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE",
        "VLLM_CPU_KVCACHE_SPACE",
        "VLLM_CPU_OMP_THREADS_BIND",
        "VLLM_CPU_NUM_OF_RESERVED_CPU",
        "VLLM_CPU_MOE_PREPACK",
        "VLLM_CPU_SGL_KERNEL",
        "VLLM_TEST_FORCE_LOAD_FORMAT",
        "LOCAL_RANK",
        "CUDA_VISIBLE_DEVICES",
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        "NO_COLOR",
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    }

    from vllm.config.utils import normalize_value

    factors: dict[str, object] = {}
    for factor, getter in environment_variables.items():
        if factor in ignored_factors:
            continue

        try:
            raw = getter()
        except Exception as exc:  # pragma: no cover - defensive logging
            logger.warning(
                "Skipping environment variable %s while hashing compile factors: %s",
                factor,
                exc,
            )
            continue
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        factors[factor] = normalize_value(raw)
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    ray_noset_env_vars = [
        # Refer to
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/nvidia_gpu.py#L11
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/amd_gpu.py#L11
        # https://github.com/ray-project/ray/blob/b97d21dab233c2bd8ed7db749a82a1e594222b5c/python/ray/_private/accelerators/amd_gpu.py#L10
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/npu.py#L12
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/hpu.py#L12
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/neuron.py#L14
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/tpu.py#L38
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/intel_gpu.py#L10
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/rbln.py#L10
        "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HABANA_VISIBLE_MODULES",
        "RAY_EXPERIMENTAL_NOSET_NEURON_RT_VISIBLE_CORES",
        "RAY_EXPERIMENTAL_NOSET_TPU_VISIBLE_CHIPS",
        "RAY_EXPERIMENTAL_NOSET_ONEAPI_DEVICE_SELECTOR",
        "RAY_EXPERIMENTAL_NOSET_RBLN_RT_VISIBLE_DEVICES",
    ]

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    for var in ray_noset_env_vars:
        factors[var] = normalize_value(os.getenv(var))
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    return factors