envs.py 81 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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    VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
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    VLLM_FLASH_ATTN_VERSION: int | None = None
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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
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    VLLM_DISABLE_FLASHINFER_PREFILL: bool = False
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    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_ATTENTION_BACKEND: str | None = None
    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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    # Deprecated env variables for profiling, kept for backward compatibility
    # See also vllm/config/profiler.py and `--profiler-config` argument
    VLLM_TORCH_CUDA_PROFILE: str | None = None
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    VLLM_TORCH_PROFILER_DIR: str | None = None
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    VLLM_TORCH_PROFILER_RECORD_SHAPES: str | None = None
    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: str | None = None
    VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: str | None = None
    VLLM_TORCH_PROFILER_WITH_STACK: str | None = None
    VLLM_TORCH_PROFILER_WITH_FLOPS: str | None = None
    VLLM_TORCH_PROFILER_USE_GZIP: str | None = None
    VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: str | None = None
    VLLM_PROFILER_DELAY_ITERS: str | None = None
    VLLM_PROFILER_MAX_ITERS: str | None = None
    # End of deprecated env variables for profiling
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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_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_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 = False
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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_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 32
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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_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_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_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 = False
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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_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_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_ALL2ALL_BACKEND: Literal[
        "naive",
        "pplx",
        "deepep_high_throughput",
        "deepep_low_latency",
        "allgather_reducescatter",
        "flashinfer_all2allv",
    ] = "allgather_reducescatter"
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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_USE_CUDNN_PREFILL: bool = False
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    VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL: bool = False
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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_USE_TRTLLM_ATTENTION: str | None = None
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    VLLM_NVFP4_GEMM_BACKEND: str | None = None
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    VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION: bool = False
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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_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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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.platforms import current_platform
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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.dev")
        and not disable_compile_cache()
        # Disabling AOT_COMPILE for CPU
        # See: https://github.com/vllm-project/vllm/issues/32033
        and not current_platform.is_cpu()
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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:
        from urllib.parse import urlparse
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        parsed = urlparse(port)
        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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    # Use separate prefill and decode kernels for V1 attention instead of
    # the unified triton kernel.
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    "VLLM_V1_USE_PREFILL_DECODE_ATTENTION": lambda: (
        os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower()
        in ("true", "1")
    ),
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    # Force vllm to use a specific flash-attention version (2 or 3), only valid
    # when using the flash-attention backend.
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    "VLLM_FLASH_ATTN_VERSION": lambda: maybe_convert_int(
        os.environ.get("VLLM_FLASH_ATTN_VERSION", None)
    ),
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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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    # 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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    # 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_DISABLE_FLASHINFER_PREFILL": lambda: os.environ.get(
        "VLLM_DISABLE_FLASHINFER_PREFILL", "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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    # Backend for attention computation
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    # Example options:
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    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
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    # - "FLASHINFER": use flashinfer
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    # - "FLASHMLA": use FlashMLA
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    # - "FLASH_ATTN_MLA": use FlashAttention for MLA
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    # - "FLASHINFER_MLA": use FlashInfer for MLA
    # - "CUTLASS_MLA": use CUTLASS for MLA
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    # All possible options loaded dynamically from AttentionBackendEnum
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    "VLLM_ATTENTION_BACKEND": env_with_choices(
        "VLLM_ATTENTION_BACKEND",
        None,
        lambda: list(
            __import__(
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                "vllm.v1.attention.backends.registry", fromlist=["AttentionBackendEnum"]
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            ).AttentionBackendEnum.__members__.keys()
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        ),
    ),
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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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    # (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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    # 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.
    #
    # 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.
845
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
846
        os.getenv(
847
            "VLLM_XLA_CACHE_PATH",
848
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
849
850
        )
    ),
851
    # If set, assert on XLA recompilation after each execution step.
852
853
854
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
855
    # Enable SPMD mode for TPU backend.
856
857
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
858
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(16 * 1024))
859
    ),
860
861
862
    # 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.
863
864
865
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
866
867
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
868
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
869
        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
870
    ),
871
872
873
874
    # 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.
875
876
877
878
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
879
880
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
881
882
883
884
885
886
887
    "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"
    ),
888
889
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
890
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
891
    # Timeout in seconds for keeping HTTP connections alive in API server
892
893
894
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
895
896
897
    # 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
898
899
900
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
901
902
903
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
904
905
906
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
907
908
909
    # Enables torch CUDA profiling if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_CUDA_PROFILE": lambda: os.getenv("VLLM_TORCH_CUDA_PROFILE"),
910
    # Enables torch profiler if set.
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DIR": lambda: os.getenv("VLLM_TORCH_PROFILER_DIR"),
    # Enable torch profiler to record shapes if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES")
    ),
    # Enable torch profiler to profile memory if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY")
    ),
    # Enable torch profiler to profile stack if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK")
    ),
    # Enable torch profiler to profile flops if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS")
    ),
    # Disable torch profiling of the AsyncLLMEngine process if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM")
937
938
939
    ),
    # Delay number of iterations before starting profiling when using
    # the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
940
941
    # Deprecated, see profiler_config.
    "VLLM_PROFILER_DELAY_ITERS": lambda: (os.getenv("VLLM_PROFILER_DELAY_ITERS")),
942
943
    # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
    # If set to 0, will not limit the number of iterations.
944
    "VLLM_PROFILER_MAX_ITERS": lambda: os.getenv("VLLM_PROFILER_MAX_ITERS"),
945
    # Control whether torch profiler gzip-compresses profiling files.
946
947
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: os.getenv("VLLM_TORCH_PROFILER_USE_GZIP"),
948
    # Control whether torch profiler dumps the self_cuda_time_total table.
949
950
951
952
    # Set to 0 to disable dumping the table.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL")
953
    ),
954
    # If set, vLLM will use Triton implementations of AWQ.
955
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
956
    # If set, allow loading or unloading lora adapters in runtime,
957
958
959
960
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
961
962
963
964
965
966
    # 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
967
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
968
969
970
971
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
972
973
974
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
975
    # Disable pynccl (using torch.distributed instead)
976
977
978
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
979
980
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
981
982
983
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
984
985
    # Whether to use aiter paged attention.
    # By default is disabled.
986
987
988
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
989
990
991
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
992
993
994
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
    ),
995
996
    # Whether to use aiter moe ops.
    # By default is enabled.
997
998
999
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in ("true", "1")
    ),
1000
    # use aiter rms norm op if aiter ops are enabled.
1001
1002
1003
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in ("true", "1")
    ),
1004
1005
    # Whether to use aiter mla ops.
    # By default is enabled.
1006
1007
1008
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in ("true", "1")
    ),
1009
1010
    # Whether to use aiter mha ops.
    # By default is enabled.
1011
1012
1013
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in ("true", "1")
    ),
1014
1015
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
1016
1017
1018
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
1019
1020
    # Whether to use aiter rope.
    # By default is disabled.
1021
1022
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "False").lower() in ("true", "1")
1023
    ),
1024
1025
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
1026
1027
1028
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
    ),
1029
1030
1031
1032
1033
    # 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")
    ),
1034
1035
1036
1037
1038
    # 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")
    ),
1039
    # Whether to use aiter fusion shared experts ops.
1040
    # By default is disabled.
1041
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
1042
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
1043
1044
        in ("true", "1")
    ),
1045
1046
1047
1048
1049
    # 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")
    ),
1050
    # use rocm skinny gemms
1051
1052
1053
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
1054
    # Pad the fp8 weights to 256 bytes for ROCm
1055
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
1056
    # Pad the weights for the moe kernel
1057
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
1058
    # custom paged attention kernel for MI3* cards
1059
1060
1061
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
1062
1063
1064
1065
    # 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")
    ),
1066
1067
1068
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
1069
1070
1071
1072
1073
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
1074
1075
1076
1077
    # 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
1078
1079
1080
1081
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1082
1083
1084
1085
1086
1087
    # 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.
1088
1089
1090
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1091
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1092
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1093
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1094
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1095
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1096
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1097
    # If set, enable multiprocessing in LLM for the V1 code path.
1098
1099
1100
1101
1102
1103
    "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")
    ),
1104
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1105
1106
1107
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1108
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1109
1110
1111
1112
1113
1114
1115
    # 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.
1116
1117
1118
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1119
    # If set, vLLM will disable the MLA attention optimizations.
1120
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1121
1122
    # If set, vLLM will pick up the provided Flash Attention MLA
    # max number splits for cuda graph decode
1123
1124
1125
    "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": lambda: int(
        os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH", "32")
    ),
1126
1127
1128
    # 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.
1129
1130
1131
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1132
1133
1134
    # 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"
1135
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1136
1137
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1138
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1139
    # Rank of the process in the data parallel setting
1140
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1141
1142
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1143
1144
1145
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1146
    # World size of the data parallel setting
1147
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1148
    # IP address of the master node in the data parallel setting
1149
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1150
    # Port of the master node in the data parallel setting
1151
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1152
1153
1154
1155
1156
    # 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.
1157
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1158
1159
1160
    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
    ),
1161
    # Randomize inputs during dummy runs when using Data Parallel
1162
1163
1164
1165
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1166
1167
1168
1169
1170
1171
1172
    # 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;
1173
1174
1175
    # - "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;
1176
1177
1178
1179
    # 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"
    ),
1180
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1181
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1182
    # Use model_redirect to redirect the model name to a local folder.
1183
1184
1185
1186
1187
    # `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
1188
1189
1190
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1191
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1192
1193
1194
1195
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
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    # Whether to use marlin kernel in mxfp4 quantization method
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    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
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    # The activation dtype for marlin kernel
    "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
        "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
    ),
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    # 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"))
    ),
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    # 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.
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    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
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    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
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    "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)
    ),
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    # Whether using Pathways
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    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
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    # Allow use of DeepGemm kernels for fused moe ops.
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    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
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    # 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"))
    ),
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    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
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    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
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    # 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.
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    # 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(
        int(os.getenv("VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER", "0"))
    ),
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    # Allow use of FlashInfer 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 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 CUTLASS 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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    # 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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    # 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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    # [DEPRECATED - will be removed in v0.15.0] all2all backend for vllm's
    # expert parallel communication. Use --all2all-backend CLI argument instead.
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    # Available options:
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    # - "naive": naive all2all implementation using broadcasts
    # - "allgather_reducescatter": all2all implementation based on allgather and
    #  reducescatter
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    # - "pplx": use pplx kernels
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    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
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    # - "flashinfer_all2allv", use flashinfer alltoallv kernels for mnnvl
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    "VLLM_ALL2ALL_BACKEND": env_with_choices(
        "VLLM_ALL2ALL_BACKEND",
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        None,
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        [
            "naive",
            "pplx",
            "deepep_high_throughput",
            "deepep_low_latency",
            "allgather_reducescatter",
            "flashinfer_all2allv",
        ],
    ),
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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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    # Controls whether or not to use cudnn prefill
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    "VLLM_USE_CUDNN_PREFILL": lambda: bool(
        int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))
    ),
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    # Controls whether to use TRT-LLM ragged DeepSeek prefill
    "VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL": lambda: bool(
        int(os.getenv("VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL", "0"))
    ),
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    # If set to 1/True, use the TRTLLM attention backend in flashinfer.
    # If set to 0/False, use the default attention backend in flashinfer.
    # If not set, auto-detect the attention backend in flashinfer.
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    "VLLM_USE_TRTLLM_ATTENTION": lambda: (
        None
        if "VLLM_USE_TRTLLM_ATTENTION" not in os.environ
        else os.environ["VLLM_USE_TRTLLM_ATTENTION"].lower() in ("1", "true")
    ),
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    # If set to 1, when we use fp8 kv, we do not quantize Q to fp8
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    "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION": lambda: bool(
        int(os.getenv("VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION", "0"))
    ),
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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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    # 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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    ),
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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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}

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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():
        __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 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",
        "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