envs.py 85 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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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_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"] = "spawn"
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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_TARGET_DEVICE: str = "cuda"
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    VLLM_MAIN_CUDA_VERSION: str = "12.9"
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    VLLM_FLOAT32_MATMUL_PRECISION: Literal["ieee", "tf32"] = "ieee"
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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_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_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_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_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 = False
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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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    # add envs
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    VLLM_OPTEST_URLS_PORT: int | None = None
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    VLLM_OPTEST_MODELS_PATH: str = ""
    VLLM_USE_TRITON_PREFIX_FLASH_ATTN: bool = False
    VLLM_USE_FLASH_MLA: bool = False
    VLLM_USE_OPT_OP: bool = False
    VLLM_USE_TC_PAGED_ATTN: bool = False
    VLLM_USE_PA_PRINT_PARAM: bool = False 
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    VLLM_TREE_DECODING: bool = False
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    VLLM_SPEC_DECODE_EAGER: bool = False
    VLLM_PCIE_USE_CUSTOM_ALLREDUCE: bool = False
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    VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX: int = 16
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    VLLM_ENFORCE_EAGER_BS_THRESHOLD: int | None  = None
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    VLLM_HAS_CONTEXT_DEFAULT: bool = False
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    VLLM_USE_NN: bool = False
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    VLLM_ENABLE_TBO: bool = False
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    VLLM_ENABLE_MOE_FUSED_GATE: bool = False
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    VLLM_USE_FLASH_ATTN_PA: bool = False
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    VLLM_USE_APEX_RN: bool = False
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    VLLM_USE_GLOBAL_CACHE13: bool = False
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    VLLM_USE_LIGHTOP: bool = False
    VLLM_USE_OPT_CAT: bool = False
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    VLLM_USE_OPT_MOE_SUM: bool = False
    VLLM_USE_LIGHTOP_MOE_SUM: bool = False
    VLLM_USE_LIGHTOP_MOE_ALIGN: bool = False
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    VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
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    VLLM_USE_PD_SPLIT: bool = False
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    VLLM_USE_PP_SYNC: bool = False
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    VLLM_USE_PIECEWISE: bool = False
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    VLLM_USE_V32_ENCODE: bool = False
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    VLLM_USE_FUSE_SILU_AND_MUL: bool = False
    VLLM_USE_OPT_RESHAPE_AND_CACHE: bool = False
    VLLM_USE_TOPK_RENORM: bool = False
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    VLLM_USE_FUSED_RMS_ROPE: bool = False
    VLLM_USE_MARLIN_W16A16_MOE:bool = False
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def get_default_cache_root():
    return os.getenv(
        "XDG_CACHE_HOME",
        os.path.join(os.path.expanduser("~"), ".cache"),
    )


def get_default_config_root():
    return os.getenv(
        "XDG_CONFIG_HOME",
        os.path.join(os.path.expanduser("~"), ".config"),
    )


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


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


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


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def use_aot_compile() -> bool:
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    from vllm.model_executor.layers.batch_invariant import (
        vllm_is_batch_invariant,
    )
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    from vllm.utils.torch_utils import is_torch_equal_or_newer
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    default_value = (
        "1"
        if is_torch_equal_or_newer("2.10.0.dev") and not disable_compile_cache()
        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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# 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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    # Accepted values:
    #   - "ieee" (default): force full IEEE FP32 matmul precision.
    #   - "tf32": enable TensorFloat32-based fast matmul.
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    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
        "VLLM_FLOAT32_MATMUL_PRECISION",
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        "ieee",
        ["ieee", "tf32"],
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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 if vllm should use triton flash attention
    "VLLM_USE_TRITON_FLASH_ATTN":
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    lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "False").lower() in
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             ("true", "1")),
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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(
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        int(os.environ.get("VLLM_USE_BYTECODE_HOOK", "0"))
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    ),
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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(
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        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "120")
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    ),
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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.attention.backends.registry", fromlist=["AttentionBackendEnum"]
            ).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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    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
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    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
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    "VLLM_CPU_NUM_OF_RESERVED_CPU": lambda: int(
        os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0")
    )
    if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ
    else None,
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    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
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    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
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    # If the env var is set, Ray Compiled Graph uses the specified
    # channel type to communicate between workers belonging to
    # different pipeline-parallel stages.
    # Available options:
    # - "auto": use the default channel type
    # - "nccl": use NCCL for communication
    # - "shm": use shared memory and gRPC for communication
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    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
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    # If the env var is set, it enables GPU communication overlap
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    # (experimental feature) in Ray's Compiled Graph.
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    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
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    # If the env var is set, it uses a Ray Communicator wrapping
    # vLLM's pipeline parallelism communicator to interact with Ray's
    # Compiled Graph. Otherwise, it uses Ray's NCCL communicator.
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    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
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    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
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    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
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        "VLLM_WORKER_MULTIPROC_METHOD", "spawn", ["spawn", "fork"]
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    ),
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    # Path to the cache for storing downloaded assets
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    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
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        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
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        )
    ),
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    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
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    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
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    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
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    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
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    # Timeout for fetching videos when serving multimodal models
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    # Default is 30 seconds
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    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
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    # Timeout for fetching audio when serving multimodal models
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    # Default is 10 seconds
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    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
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    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
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    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
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    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
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    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
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    # Maximum filesize in MB for a single audio file when processing
    # speech-to-text requests. Files larger than this will be rejected.
    # Default is 25 MB
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    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
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    # Backend for Video IO
    # - "opencv": Default backend that uses OpenCV stream buffered backend.
    #
    # 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"),
830
831
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
832
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
833
        os.getenv(
834
            "VLLM_XLA_CACHE_PATH",
835
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
836
837
        )
    ),
838
    # If set, assert on XLA recompilation after each execution step.
839
840
841
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
842
    # Enable SPMD mode for TPU backend.
843
844
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
845
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(16 * 1024))
846
    ),
847
848
849
    # 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.
850
851
852
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
853
854
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
855
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
856
        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
857
    ),
858
859
860
861
    # 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.
862
863
864
865
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
866
867
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
868
869
870
871
872
873
874
    "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"
    ),
875
876
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
877
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
878
    # Timeout in seconds for keeping HTTP connections alive in API server
879
880
881
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
882
883
884
    # 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
885
886
887
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
888
889
890
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
891
892
893
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
894
895
896
    # Enables torch CUDA profiling if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_CUDA_PROFILE": lambda: os.getenv("VLLM_TORCH_CUDA_PROFILE"),
897
    # Enables torch profiler if set.
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
    # 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")
924
925
926
    ),
    # Delay number of iterations before starting profiling when using
    # the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
927
928
    # Deprecated, see profiler_config.
    "VLLM_PROFILER_DELAY_ITERS": lambda: (os.getenv("VLLM_PROFILER_DELAY_ITERS")),
929
930
    # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
    # If set to 0, will not limit the number of iterations.
931
    "VLLM_PROFILER_MAX_ITERS": lambda: os.getenv("VLLM_PROFILER_MAX_ITERS"),
932
    # Control whether torch profiler gzip-compresses profiling files.
933
934
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: os.getenv("VLLM_TORCH_PROFILER_USE_GZIP"),
935
    # Control whether torch profiler dumps the self_cuda_time_total table.
936
937
938
939
    # 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")
940
    ),
941
    # If set, vLLM will use Triton implementations of AWQ.
942
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
943
    # If set, allow loading or unloading lora adapters in runtime,
944
945
946
947
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
948
949
950
951
952
953
    # 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
954
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
955
956
957
958
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
959
960
961
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
962
    # Disable pynccl (using torch.distributed instead)
963
964
965
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
966
967
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
968
969
970
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
971
972
    # Whether to use aiter paged attention.
    # By default is disabled.
973
974
975
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
976
977
978
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
979
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
980
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "False").lower() in ("true", "1")
981
    ),
982
983
    # Whether to use aiter moe ops.
    # By default is enabled.
984
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
985
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "False").lower() in ("true", "1")
986
    ),
987
    # use aiter rms norm op if aiter ops are enabled.
988
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
989
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "False").lower() in ("true", "1")
990
    ),
991
992
    # Whether to use aiter mla ops.
    # By default is enabled.
993
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
994
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "False").lower() in ("true", "1")
995
    ),
996
997
    # Whether to use aiter mha ops.
    # By default is enabled.
998
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
999
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "False").lower() in ("true", "1")
1000
    ),
1001
1002
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
1003
1004
1005
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
1006
1007
    # Whether to use aiter rope.
    # By default is disabled.
1008
1009
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "False").lower() in ("true", "1")
1010
    ),
1011
1012
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
1013
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
1014
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "False").lower() in ("true", "1")
1015
    ),
1016
1017
1018
1019
1020
    # 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")
    ),
1021
    # Whether to use aiter fusion shared experts ops.
1022
    # By default is disabled.
1023
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
1024
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
1025
1026
        in ("true", "1")
    ),
1027
1028
1029
    # Whether to use aiter triton kernels for gemm ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_TRITON_GEMM": lambda: (
1030
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_GEMM", "False").lower() in ("true", "1")
1031
    ),
1032
    # use rocm skinny gemms
1033
1034
1035
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
1036
    # Pad the fp8 weights to 256 bytes for ROCm
1037
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
1038
    # Pad the weights for the moe kernel
1039
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "0"))),
1040
    # custom paged attention kernel for MI3* cards
1041
1042
1043
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
1044
1045
1046
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
1047
1048
1049
1050
1051
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
1052
1053
1054
1055
    # 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
1056
1057
1058
1059
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1060
1061
1062
1063
1064
1065
    # 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.
1066
1067
1068
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1069
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1070
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1071
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1072
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1073
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1074
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1075
    # If set, enable multiprocessing in LLM for the V1 code path.
1076
1077
1078
1079
1080
1081
    "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")
    ),
1082
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1083
1084
1085
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1086
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1087
1088
1089
1090
1091
1092
1093
    # 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.
1094
1095
1096
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1097
    # If set, vLLM will disable the MLA attention optimizations.
1098
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1099
1100
    # If set, vLLM will pick up the provided Flash Attention MLA
    # max number splits for cuda graph decode
1101
1102
1103
    "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": lambda: int(
        os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH", "32")
    ),
1104
1105
1106
    # 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.
1107
1108
1109
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1110
1111
1112
    # 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"
1113
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1114
1115
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1116
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1117
    # Rank of the process in the data parallel setting
1118
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1119
1120
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1121
1122
1123
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1124
    # World size of the data parallel setting
1125
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1126
    # IP address of the master node in the data parallel setting
1127
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1128
    # Port of the master node in the data parallel setting
1129
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1130
1131
1132
1133
1134
    # 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.
1135
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1136
1137
1138
    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
    ),
1139
    # Randomize inputs during dummy runs when using Data Parallel
1140
1141
1142
1143
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1144
1145
1146
1147
1148
1149
1150
    # 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;
1151
1152
1153
    # - "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;
1154
1155
1156
1157
    # 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"
    ),
1158
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1159
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1160
    # Use model_redirect to redirect the model name to a local folder.
1161
1162
1163
1164
1165
    # `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
1166
1167
1168
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1169
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1170
1171
1172
1173
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1174
    # Whether to use marlin kernel in mxfp4 quantization method
1175
1176
1177
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1178
1179
1180
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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 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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    # all2all backend for vllm's expert parallel communication
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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",
        "allgather_reducescatter",
        [
            "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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    # 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
    # - <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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    ),
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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(
        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "0"))
    ),
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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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    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": lambda: os.getenv(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME", "VLLM_OBJECT_STORAGE_SHM_BUFFER"
    ),
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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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    # add envs
    
    # used in optest environment to manually set the https port
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    'VLLM_OPTEST_URLS_PORT':
    lambda: int(os.getenv('VLLM_OPTEST_URLS_PORT', '8000'))
    if 'VLLM_OPTEST_URLS_PORT' in os.environ else None,
    
    # Path to the optest models.
    # If set, will load models from local path instead of Hugging Face Hub.
    'VLLM_OPTEST_MODELS_PATH':
    lambda: os.getenv('VLLM_OPTEST_MODELS_PATH', "") or os.getenv("OPTEST_MODELS_PATH", ""),
    
    # flag to control if vllm should use triton prefix flash attention
    "VLLM_USE_TRITON_PREFIX_FLASH_ATTN":
    lambda: (os.environ.get("VLLM_USE_TRITON_PREFIX_FLASH_ATTN", "False").lower() in
             ("true", "1")),
    
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    # If set, vLLM will use FLASH MLA attention optimizations.
    "VLLM_USE_FLASH_MLA":
    lambda: bool(int(os.getenv("VLLM_USE_FLASH_MLA", "1"))),
    
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    # flag to control vllm to use optimized kernels
    "VLLM_USE_OPT_OP":
    lambda: (os.environ.get("VLLM_USE_OPT_OP", "True").lower() in
             ("true", "1")),
    
    # flag to control vllm to use optimized tc paged attn kernels
    "VLLM_USE_TC_PAGED_ATTN":
    lambda: (os.environ.get("VLLM_USE_TC_PAGED_ATTN", "True").lower() in
             ("true", "1")),
    
    # flag to control if vllm print pa parameters
    "VLLM_USE_PA_PRINT_PARAM":
    lambda: (os.environ.get("VLLM_USE_PA_PRINT_PARAM", "False").lower() in
             ("true", "1")),
    
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    # If set, vLLM will use tree-style speculative decoding.
    "VLLM_TREE_DECODING":
    lambda: 
    (os.environ.get("VLLM_TREE_DECODING", "0").strip().lower() in
     ("1", "true")),
    
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    # If set, vLLM will disable the draft model in cudagraph mode.
    "VLLM_SPEC_DECODE_EAGER":
    lambda: bool(int(os.getenv("VLLM_SPEC_DECODE_EAGER", "0"))),
    
    # flag to control vllm to use optimized kernels
    "VLLM_PCIE_USE_CUSTOM_ALLREDUCE":
    lambda: bool(int(os.environ.get("VLLM_PCIE_USE_CUSTOM_ALLREDUCE", "0"))),
    
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    # flag to control vllm to use optimized kernels
    "VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX":
    lambda: int(os.getenv("VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX", "16")),
    
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    # If set, vLLM will disable the draft model in cudagraph mode.
    "VLLM_ENFORCE_EAGER_BS_THRESHOLD":
    lambda: int(os.environ.get("VLLM_ENFORCE_EAGER_BS_THRESHOLD", "-1")),
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    # 'has_comtext' is a variable in common.py, which is calculated
    # by metadata by default. However, it may introduce synchronization 
    # and affect performance, so it is directly assigned as False. 
    # If there are any problems during use, use environment variables 
    # to restore the default usage.
    "VLLM_HAS_CONTEXT_DEFAULT":
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    lambda: bool(int(os.getenv("VLLM_HAS_CONTEXT_DEFAULT", "1"))),
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    # If set, vLLM will transpose weight to use nn layout
    "VLLM_USE_NN":
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    lambda: (os.environ.get("VLLM_USE_NN", "True").lower() in
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             ("true", "1")),
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    # Enable two batch overlap.
    "VLLM_ENABLE_TBO":
    lambda: bool(int(os.getenv("VLLM_ENABLE_TBO", "0"))),
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    # If set, vLLM will enable the moe_fused_gate kernel.
    "VLLM_ENABLE_MOE_FUSED_GATE":
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    lambda: bool(int(os.getenv("VLLM_ENABLE_MOE_FUSED_GATE", "1"))),
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    # vLLM will use FlashAttention Backend for page attention computation on rocm
    "VLLM_USE_FLASH_ATTN_PA":
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    lambda: (os.environ.get("VLLM_USE_FLASH_ATTN_PA", "True").lower() in
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             ("true", "1")),
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    # vLLM will use apex for rmsnorm
    "VLLM_USE_APEX_RN":
    lambda: (os.environ.get("VLLM_USE_APEX_RN", "False").lower() in
             ("true", "1")),
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    # vLLM will use global cache for moe
    "VLLM_USE_GLOBAL_CACHE13":
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        lambda: (os.environ.get("VLLM_USE_GLOBAL_CACHE13", "False").lower() in
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                 ("true", "1")),
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    # vLLM will use lightop for deepseek-v3
    "VLLM_USE_LIGHTOP":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP", "False").lower() in
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                 ("true", "1")),
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    # vLLM will use opt cat for deepseek-v3
    "VLLM_USE_OPT_CAT":
        lambda: (os.environ.get("VLLM_USE_OPT_CAT", "True").lower() in
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                 ("true", "1")),  
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    # vLLM will use triton moe_sum 
    "VLLM_USE_OPT_MOE_SUM":
        lambda: (os.environ.get("VLLM_USE_OPT_MOE_SUM", "False").lower() in
                 ("true", "1")),  
    # vLLM will use lightop moe_sum 
    "VLLM_USE_LIGHTOP_MOE_SUM":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM", "False").lower() in
                 ("true", "1")),  
    # vLLM will use lightop moe_align_block_size 
    "VLLM_USE_LIGHTOP_MOE_ALIGN":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_ALIGN", "False").lower() in
                 ("true", "1")),     
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    # vLLM will use opt merge_aatn_states,not triton
    "VLLM_USE_MERGE_ATTN_STATES_OPT":
        lambda: (os.environ.get("VLLM_USE_MERGE_ATTN_STATES_OPT", "True").lower() in
                 ("true", "1")),  
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    # vLLM will split prefill and decode, not mix up
    "VLLM_USE_PD_SPLIT":
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        lambda: (os.environ.get("VLLM_USE_PD_SPLIT", "False").lower() in
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                 ("true", "1")), 
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    # vLLM will sync to avoid pp vmfault
    "VLLM_USE_PP_SYNC":
        lambda: (os.environ.get("VLLM_USE_PP_SYNC", "False").lower() in
                 ("true", "1")), 
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    # vLLM will use piecewise
    "VLLM_USE_PIECEWISE":
        lambda: (os.environ.get("VLLM_USE_PIECEWISE", "True").lower() in
                 ("true", "1")), 
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    # vllm will use encoding_dsv32.py for dpsk-v32
    "VLLM_USE_V32_ENCODE":
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        lambda: (os.environ.get('VLLM_USE_V32_ENCODE', 'False').lower() in
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                 ("true", "1")),  
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    # vLLM will use fused silu+mul kernel (fp16 + qwen3-30b)
    "VLLM_USE_FUSE_SILU_AND_MUL":
        lambda: (os.environ.get("VLLM_USE_FUSE_SILU_AND_MUL", "False").lower() in
                 ("true", "1")),
    # vLLM will use optimized reshape_and_cache kernel when enabled (fp16 + qwen3-30b)
    "VLLM_USE_OPT_RESHAPE_AND_CACHE":
        lambda:
        (os.environ.get("VLLM_USE_OPT_RESHAPE_AND_CACHE", "False").lower() in
                ("true", "1")),
    # vLLM will use optimized topk_softmax + renormalize
    "VLLM_USE_TOPK_RENORM":
        lambda:
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        (os.environ.get("VLLM_USE_TOPK_RENORM", "False").lower() in
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                ("true", "1")),
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    # vLLM will use fused RMS + RoPE kernel
    "VLLM_USE_FUSED_RMS_ROPE":
        lambda: (os.environ.get("VLLM_USE_FUSED_RMS_ROPE", "False").lower() in
                 ("true", "1")),
    # vLLM will use Marlin W16A16 kernel for MoE experts
    "VLLM_USE_MARLIN_W16A16_MOE":
        lambda: (os.environ.get("VLLM_USE_MARLIN_W16A16_MOE", "False").lower() in
                 ("true", "1")),
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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",
        "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",
1936
    ]
1937

1938
1939
    for var in ray_noset_env_vars:
        factors[var] = normalize_value(os.getenv(var))
1940

1941
    return factors