envs.py 70.6 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import functools
5
import hashlib
6
import json
7
import os
8
import sys
9
import tempfile
10
11
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, Literal
12
13
14

if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
15
    VLLM_PORT: int | None = None
16
    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
17
    VLLM_USE_MODELSCOPE: bool = False
18
    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
19
20
    VLLM_NCCL_SO_PATH: str | None = None
    LD_LIBRARY_PATH: str | None = None
21
    VLLM_USE_TRITON_FLASH_ATTN: bool = True
22
    VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
23
    VLLM_FLASH_ATTN_VERSION: int | None = None
24
    LOCAL_RANK: int = 0
25
    CUDA_VISIBLE_DEVICES: str | None = None
26
    VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
27
    VLLM_API_KEY: str | None = None
28
    VLLM_DEBUG_LOG_API_SERVER_RESPONSE: bool = False
29
30
31
32
    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
33
34
    VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm")
    VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm")
35
36
    VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai"
    VLLM_NO_USAGE_STATS: bool = False
37
    VLLM_DISABLE_FLASHINFER_PREFILL: bool = False
38
39
40
    VLLM_DO_NOT_TRACK: bool = False
    VLLM_USAGE_SOURCE: str = ""
    VLLM_CONFIGURE_LOGGING: int = 1
41
    VLLM_LOGGING_LEVEL: str = "INFO"
42
    VLLM_LOGGING_PREFIX: str = ""
43
    VLLM_LOGGING_STREAM: str = "ext://sys.stdout"
44
    VLLM_LOGGING_CONFIG_PATH: str | None = None
45
    VLLM_LOG_STATS_INTERVAL: float = 10.0
46
    VLLM_TRACE_FUNCTION: int = 0
47
48
49
50
    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
51
    VLLM_CPU_OMP_THREADS_BIND: str = ""
52
    VLLM_CPU_NUM_OF_RESERVED_CPU: int | None = None
53
    VLLM_CPU_MOE_PREPACK: bool = True
54
    VLLM_CPU_SGL_KERNEL: bool = False
55
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
56
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
57
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
58
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
59
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl", "shm"] = "auto"
60
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
61
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
62
    VLLM_XLA_USE_SPMD: bool = False
63
    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "fork"
64
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
65
    VLLM_ASSETS_CACHE_MODEL_CLEAN: bool = False
66
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
67
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
68
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
69
    VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True
70
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
71
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
72
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
73
    VLLM_MM_INPUT_CACHE_GIB: int = 4
74
    VLLM_TARGET_DEVICE: str = "cuda"
75
    VLLM_MAIN_CUDA_VERSION: str = "12.8"
76
77
    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
78
    VLLM_USE_PRECOMPILED: bool = False
79
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
80
    VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
81
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
82
    CMAKE_BUILD_TYPE: Literal["Debug", "Release", "RelWithDebInfo"] | None = None
83
    VERBOSE: bool = False
84
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
85
    VLLM_RPC_TIMEOUT: int = 10000  # ms
86
    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
87
88
89
    VLLM_PLUGINS: list[str] | None = None
    VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
    VLLM_TORCH_PROFILER_DIR: str | None = None
90
91
    VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
92
93
    VLLM_USE_AOT_COMPILE: bool = False
    VLLM_FORCE_AOT_LOAD: bool = False
94
95
    VLLM_TORCH_PROFILER_WITH_STACK: bool = True
    VLLM_TORCH_PROFILER_WITH_FLOPS: bool = False
96
    VLLM_USE_TRITON_AWQ: bool = False
97
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
98
    VLLM_SKIP_P2P_CHECK: bool = False
99
    VLLM_DISABLED_KERNELS: list[str] = []
100
    VLLM_DISABLE_PYNCCL: bool = False
101
    VLLM_USE_V1: bool = True
102
    VLLM_ROCM_USE_AITER: bool = False
103
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
104
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
105
    VLLM_ROCM_USE_AITER_MOE: bool = True
106
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
107
    VLLM_ROCM_USE_AITER_MLA: bool = True
108
    VLLM_ROCM_USE_AITER_MHA: bool = True
109
110
    VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
    VLLM_ROCM_USE_TRITON_ROPE: bool = False
111
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
112
    VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
113
    VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = True
114
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
115
    VLLM_ROCM_FP8_PADDING: bool = True
116
    VLLM_ROCM_MOE_PADDING: bool = True
117
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
118
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
119
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
120
    VLLM_DISABLE_COMPILE_CACHE: bool = False
121
    Q_SCALE_CONSTANT: int = 200
122
123
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
124
    VLLM_SERVER_DEV_MODE: bool = False
125
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
126
    VLLM_MLA_DISABLE: bool = False
127
    VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 32
128
129
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
130
    VLLM_CUDART_SO_PATH: str | None = None
131
    VLLM_DP_RANK: int = 0
132
    VLLM_DP_RANK_LOCAL: int = -1
133
    VLLM_DP_SIZE: int = 1
134
    VLLM_USE_STANDALONE_COMPILE: bool = True
135
136
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
137
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
138
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
139
    VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
140
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
141
    VLLM_MXFP4_USE_MARLIN: bool | None = None
142
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
143
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
144
    VLLM_TPU_MOST_MODEL_LEN: int | None = None
145
    VLLM_TPU_USING_PATHWAYS: bool = False
146
    VLLM_USE_DEEP_GEMM: bool = True
147
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
148
149
150
151
152
    VLLM_DEEP_GEMM_WARMUP: Literal[
        "skip",
        "full",
        "relax",
    ] = "relax"
153
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
154
    VLLM_USE_FLASHINFER_MOE_FP16: bool = False
155
156
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
157
    VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency"] = "throughput"
158
    VLLM_XGRAMMAR_CACHE_MB: int = 0
159
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
160
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
Robert Shaw's avatar
Robert Shaw committed
161
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
162
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
163
164
165
166
167
168
169
170
    VLLM_ALL2ALL_BACKEND: Literal[
        "naive",
        "pplx",
        "deepep_high_throughput",
        "deepep_low_latency",
        "allgather_reducescatter",
        "flashinfer_all2allv",
    ] = "allgather_reducescatter"
171
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
172
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
173
    VLLM_SLEEP_WHEN_IDLE: bool = False
174
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
175
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
176
    VLLM_KV_CACHE_LAYOUT: Literal["NHD", "HND"] | None = None
177
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
178
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
179
180
181
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal[
        "FP", "INT8", "INT6", "INT4", "NONE"
    ] = "NONE"
182
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
183
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: int | None = None
184
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480
185
    VLLM_USE_CUDNN_PREFILL: bool = False
186
    VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL: bool = False
187
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
188
    VLLM_LOOPBACK_IP: str = ""
189
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = False
190
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
191
    VLLM_USE_TRTLLM_ATTENTION: str | None = None
192
    VLLM_NVFP4_GEMM_BACKEND: str | None = None
193
    VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION: bool = False
194
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
195
196
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
xiao-llm's avatar
xiao-llm committed
197
    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
198
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
199
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = False
200
    VLLM_TUNED_CONFIG_FOLDER: str | None = None
201
    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
202
    VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
203
    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
204
    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
205
    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
206
    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
207
    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
208
    VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE: bool = False
209
    VLLM_DEEPEP_LOW_LATENCY_ALLOW_NVLINK: bool = True
210
    VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL: bool = False
211
    VLLM_DBO_COMM_SMS: int = 20
212
    GPT_OSS_SYSTEM_TOOL_MCP_LABELS: list[str] = []
213
214
    VLLM_PATTERN_MATCH_DEBUG: str | None = None
    VLLM_DEBUG_DUMP_PATH: str | None = None
215
216
    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
217
    VLLM_USE_NCCL_SYMM_MEM: bool = False
218
    VLLM_NCCL_INCLUDE_PATH: str | None = None
219
    VLLM_USE_FBGEMM: bool = False
220
    VLLM_GC_DEBUG: str = ""
221
    VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
222

223
224
225
226
227
228
229
230
231
232
233
234
235
236
237

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"),
    )


238
def maybe_convert_int(value: str | None) -> int | None:
239
240
241
242
243
    if value is None:
        return None
    return int(value)


244
def maybe_convert_bool(value: str | None) -> bool | None:
245
246
247
248
249
    if value is None:
        return None
    return bool(int(value))


250
def use_aot_compile() -> bool:
251
    from vllm.utils.torch_utils import is_torch_equal_or_newer
252
253
254
255
256

    default_value = "1" if is_torch_equal_or_newer("2.10.0.dev") else "0"
    return os.environ.get("VLLM_USE_AOT_COMPILE", default_value) == "1"


257
def env_with_choices(
258
    env_name: str,
259
260
    default: str | None,
    choices: list[str] | Callable[[], list[str]],
261
    case_sensitive: bool = True,
262
) -> Callable[[], str | None]:
263
264
    """
    Create a lambda that validates environment variable against allowed choices
265

266
267
268
269
270
    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
271

272
273
274
275
    Returns:
        Lambda function for environment_variables dict
    """

276
    def _get_validated_env() -> str | None:
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
        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:
292
293
294
295
            raise ValueError(
                f"Invalid value '{value}' for {env_name}. "
                f"Valid options: {actual_choices}."
            )
296
297
298
299
300
301

        return value

    return _get_validated_env


302
def env_list_with_choices(
303
304
    env_name: str,
    default: list[str],
305
    choices: list[str] | Callable[[], list[str]],
306
307
    case_sensitive: bool = True,
) -> Callable[[], list[str]]:
308
    """
309
    Create a lambda that validates environment variable
310
    containing comma-separated values against allowed choices
311

312
313
314
315
316
    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
317

318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
    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:
347
348
349
350
                raise ValueError(
                    f"Invalid value '{val}' in {env_name}. "
                    f"Valid options: {actual_choices}."
                )
351
352
353
354
355
356

        return values

    return _get_validated_env_list


357
def get_vllm_port() -> int | None:
358
    """Get the port from VLLM_PORT environment variable.
359

360
361
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
362

363
364
365
    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
366
    if "VLLM_PORT" not in os.environ:
367
368
        return None

369
    port = os.getenv("VLLM_PORT", "0")
370
371
372
373
374

    try:
        return int(port)
    except ValueError as err:
        from urllib.parse import urlparse
375

376
377
378
379
380
381
382
        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
383
        raise ValueError(f"VLLM_PORT '{port}' must be a valid integer") from err
384
385


386
387
388
# The begin-* and end* here are used by the documentation generator
# to extract the used env vars.

389
# --8<-- [start:env-vars-definition]
390

391
environment_variables: dict[str, Callable[[], Any]] = {
392
    # ================== Installation Time Env Vars ==================
393
    # Target device of vLLM, supporting [cuda (by default),
394
    # rocm, cpu]
395
    "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
396
397
    # Main CUDA version of vLLM, supporting [12.6, 12.8, 12.9],
    # 12.8 is the default. This follows PyTorch but can be overridden.
398
399
    "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
    or "12.8",
400
401
    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
402
    "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
403
404
405
    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
406
    "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
407
    # If set, vllm will use precompiled binaries (*.so)
408
409
410
411
412
    "VLLM_USE_PRECOMPILED": lambda: os.environ.get("VLLM_USE_PRECOMPILED", "")
    .strip()
    .lower()
    in ("1", "true")
    or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
413
414
    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
415
416
417
418
    "VLLM_DOCKER_BUILD_CONTEXT": lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "")
    .strip()
    .lower()
    in ("1", "true"),
419
420
    # Whether to force using nightly wheel in python build.
    # This is used for testing the nightly wheel in python build.
421
422
423
    "VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL": lambda: bool(
        int(os.getenv("VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL", "0"))
    ),
424
425
426
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
427
428
429
    "CMAKE_BUILD_TYPE": env_with_choices(
        "CMAKE_BUILD_TYPE", None, ["Debug", "Release", "RelWithDebInfo"]
    ),
430
    # If set, vllm will print verbose logs during installation
431
    "VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
432
    # Root directory for vLLM configuration files
433
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
434
435
436
    # 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**.
437
    "VLLM_CONFIG_ROOT": lambda: os.path.expanduser(
438
439
440
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
441
442
        )
    ),
443
    # ================== Runtime Env Vars ==================
444
    # Root directory for vLLM cache files
445
    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
446
    "VLLM_CACHE_ROOT": lambda: os.path.expanduser(
447
448
449
        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
450
451
        )
    ),
452
453
454
455
    # 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.
456
    "VLLM_HOST_IP": lambda: os.getenv("VLLM_HOST_IP", ""),
457
    # used in distributed environment to manually set the communication port
458
459
460
    # 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.
461
    "VLLM_PORT": get_vllm_port,
462
463
    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
464
465
466
    "VLLM_RPC_BASE_PATH": lambda: os.getenv(
        "VLLM_RPC_BASE_PATH", tempfile.gettempdir()
    ),
467
468
    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
469
470
471
472
    "VLLM_USE_MODELSCOPE": lambda: os.environ.get(
        "VLLM_USE_MODELSCOPE", "False"
    ).lower()
    == "true",
473
    # Interval in seconds to log a warning message when the ring buffer is full
474
475
476
    "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
        os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
    ),
477
478
    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
479
    "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None),
480
481
    # 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
482
    "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),
483
484
    # 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`
485
    "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
486
    # flag to control if vllm should use triton flash attention
487
488
489
    "VLLM_USE_TRITON_FLASH_ATTN": lambda: (
        os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in ("true", "1")
    ),
490
491
    # Use separate prefill and decode kernels for V1 attention instead of
    # the unified triton kernel.
492
493
494
495
    "VLLM_V1_USE_PREFILL_DECODE_ATTENTION": lambda: (
        os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower()
        in ("true", "1")
    ),
496
497
    # Force vllm to use a specific flash-attention version (2 or 3), only valid
    # when using the flash-attention backend.
498
499
500
    "VLLM_FLASH_ATTN_VERSION": lambda: maybe_convert_int(
        os.environ.get("VLLM_FLASH_ATTN_VERSION", None)
    ),
501
    # Feature flag to enable/disable Inductor standalone compile.
502
503
    # In torch <= 2.7 we ignore this flag; in torch >= 2.9 this is
    # enabled by default.
504
    "VLLM_USE_STANDALONE_COMPILE": lambda: os.environ.get(
505
        "VLLM_USE_STANDALONE_COMPILE", "1"
506
507
    )
    == "1",
508
509
    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
510
511
512
    "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get(
        "VLLM_PATTERN_MATCH_DEBUG", None
    ),
513
514
    # Dump fx graphs to the given directory.
    # It will override CompilationConfig.debug_dump_path if set.
515
    "VLLM_DEBUG_DUMP_PATH": lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None),
516
517
518
519
520
521
522
523
    # 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,
    # 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",
524
525
    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
526
    "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")),
527
    # used to control the visible devices in the distributed setting
528
    "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
529
    # timeout for each iteration in the engine
530
531
532
    "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")
    ),
533
    # API key for vLLM API server
534
    "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None),
535
    # Whether to log responses from API Server for debugging
536
537
538
539
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: os.environ.get(
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
    ).lower()
    == "true",
540
    # S3 access information, used for tensorizer to load model from S3
541
542
543
    "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),
544
    # Usage stats collection
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
    "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"),
560
561
562
563
    # 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
564
565
    "VLLM_CONFIGURE_LOGGING": lambda: int(os.getenv("VLLM_CONFIGURE_LOGGING", "1")),
    "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),
566
    # this is used for configuring the default logging level
567
    "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
568
    # this is used for configuring the default logging stream
569
    "VLLM_LOGGING_STREAM": lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),
570
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
571
    "VLLM_LOGGING_PREFIX": lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
572
573
    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
574
575
576
    "VLLM_LOG_STATS_INTERVAL": lambda: val
    if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10."))) > 0.0
    else 10.0,
577
578
579
    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
580
    "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
581
    # Backend for attention computation
582
    # Example options:
583
584
585
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
    # - "XFORMERS": use XFormers
586
    # - "FLASHINFER": use flashinfer
587
    # - "FLASHMLA": use FlashMLA
588
    # - "FLASH_ATTN_MLA": use FlashAttention for MLA
589
590
    # - "FLASHINFER_MLA": use FlashInfer for MLA
    # - "CUTLASS_MLA": use CUTLASS for MLA
591
    # All possible options loaded dynamically from _Backend enum
592
593
594
595
596
597
598
599
600
    "VLLM_ATTENTION_BACKEND": env_with_choices(
        "VLLM_ATTENTION_BACKEND",
        None,
        lambda: list(
            __import__(
                "vllm.attention.backends.registry", fromlist=["_Backend"]
            )._Backend.__members__.keys()
        ),
    ),
601
    # If set, vllm will use flashinfer sampler
602
603
604
605
606
    "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
        int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"])
    )
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
    else None,
607
    # Pipeline stage partition strategy
608
    "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
609
    # (CPU backend only) CPU key-value cache space.
610
    # default is None and will be set as 4 GB
611
612
613
    "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ
    else None,
614
615
    # (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 '|'.
616
    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
617
618
    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
619
620
621
622
623
    "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,
624
625
626
    # (CPU backend only) whether to use prepack for MoE layer. This will be
    # passed to ipex.llm.modules.GatedMLPMOE. On unsupported CPUs, you might
    # need to set this to "0" (False).
627
    "VLLM_CPU_MOE_PREPACK": lambda: bool(int(os.getenv("VLLM_CPU_MOE_PREPACK", "1"))),
628
    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
629
    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
630
631
632
633
634
635
636
    # 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
637
638
639
    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
640
    # If the env var is set, it enables GPU communication overlap
641
    # (experimental feature) in Ray's Compiled Graph.
642
643
644
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
645
646
647
    # 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.
648
649
650
    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
651
652
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
653
654
655
    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
        "VLLM_WORKER_MULTIPROC_METHOD", "fork", ["spawn", "fork"]
    ),
656
    # Path to the cache for storing downloaded assets
657
    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
658
659
660
        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
661
662
        )
    ),
663
664
    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
665
666
667
    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
668
669
    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
670
    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
671
    # Timeout for fetching videos when serving multimodal models
672
    # Default is 30 seconds
673
674
675
    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
676
    # Timeout for fetching audio when serving multimodal models
677
    # Default is 10 seconds
678
679
680
    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
681
682
    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
683
684
685
    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
686
687
688
    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
689
690
691
    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
692
693
694
    # 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
695
696
697
    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
698
699
700
701
702
703
704
    # 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.
705
706
707
    "VLLM_VIDEO_LOADER_BACKEND": lambda: os.getenv(
        "VLLM_VIDEO_LOADER_BACKEND", "opencv"
    ),
708
    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
709
    # Default is 4 GiB per API process + 4 GiB per engine core process
710
    "VLLM_MM_INPUT_CACHE_GIB": lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
711
712
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
713
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
714
        os.getenv(
715
            "VLLM_XLA_CACHE_PATH",
716
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
717
718
        )
    ),
719
    # If set, assert on XLA recompilation after each execution step.
720
721
722
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
723
    # Enable SPMD mode for TPU backend.
724
725
726
727
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")
    ),
728
729
730
    # 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.
731
732
733
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
734
735
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
736
737
738
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
        os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", 0)
    ),
739
740
741
742
    # 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.
743
744
745
746
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
747
748
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
749
750
751
752
753
754
755
    "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"
    ),
756
757
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
758
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
759
    # Timeout in seconds for keeping HTTP connections alive in API server
760
761
762
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
763
764
765
    # 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
766
767
768
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
769
770
771
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
772
773
774
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
775
776
777
778
    # Enables torch profiler if set.
    # Both AsyncLLM's CPU traces as well as workers'
    # traces (CPU & GPU) will be saved under this directory.
    # Note that it must be an absolute path.
779
780
781
782
783
784
785
    "VLLM_TORCH_PROFILER_DIR": lambda: (
        None
        if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None
        else os.path.abspath(
            os.path.expanduser(os.getenv("VLLM_TORCH_PROFILER_DIR", "."))
        )
    ),
786
787
788
    # Enable torch profiler to record shapes if set
    # VLLM_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
    # not record shapes.
789
790
791
    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"
    ),
792
793
794
    # Enable torch profiler to profile memory if set
    # VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
    # will not profile memory.
795
796
797
    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"
    ),
798
799
800
    # Enable torch profiler to profile stack if set
    # VLLM_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
    # profile stack by default.
801
802
803
    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"
    ),
804
805
806
    # Enable torch profiler to profile flops if set
    # VLLM_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
    # not profile flops.
807
808
809
    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"
    ),
810
    # If set, vLLM will use Triton implementations of AWQ.
811
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
812
    # If set, allow loading or unloading lora adapters in runtime,
813
814
815
816
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
817
818
819
820
821
822
    # 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
823
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
824
825
826
827
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
828
829
830
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
831
    # Disable pynccl (using torch.distributed instead)
832
833
834
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
835
    # If set, use the V1 code path.
836
    "VLLM_USE_V1": lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),
837
838
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
839
840
841
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
842
843
    # Whether to use aiter paged attention.
    # By default is disabled.
844
845
846
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
847
848
849
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
850
851
852
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
    ),
853
854
    # Whether to use aiter moe ops.
    # By default is enabled.
855
856
857
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in ("true", "1")
    ),
858
    # use aiter rms norm op if aiter ops are enabled.
859
860
861
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in ("true", "1")
    ),
862
863
    # Whether to use aiter mla ops.
    # By default is enabled.
864
865
866
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in ("true", "1")
    ),
867
868
    # Whether to use aiter mha ops.
    # By default is enabled.
869
870
871
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in ("true", "1")
    ),
872
873
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
874
875
876
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
877
878
    # Whether to use aiter rope.
    # By default is disabled.
879
880
881
    "VLLM_ROCM_USE_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_TRITON_ROPE", "False").lower() in ("true", "1")
    ),
882
883
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
884
885
886
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
    ),
887
888
889
890
891
    # 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")
    ),
892
893
894
895
896
897
    # Whether to use aiter fusion shared experts ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "True").lower()
        in ("true", "1")
    ),
898
    # use rocm skinny gemms
899
900
901
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
902
    # Pad the fp8 weights to 256 bytes for ROCm
903
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
904
    # Pad the weights for the moe kernel
905
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
906
    # custom paged attention kernel for MI3* cards
907
908
909
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
910
911
912
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
913
914
915
916
917
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
918
919
920
921
    # 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
922
923
924
925
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
926
927
928
929
930
931
    # 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.
932
933
934
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
935
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
936
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
937
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
938
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
939
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
940
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
941
    # If set, enable multiprocessing in LLM for the V1 code path.
942
943
944
945
946
947
948
949
950
    "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")
    ),
    "VLLM_DISABLE_COMPILE_CACHE": lambda: bool(
        int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))
    ),
951
952
953
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
954
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
955
956
957
958
959
960
961
    # 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.
962
963
964
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
965
    # If set, vLLM will disable the MLA attention optimizations.
966
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
967
968
    # If set, vLLM will pick up the provided Flash Attention MLA
    # max number splits for cuda graph decode
969
970
971
    "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": lambda: int(
        os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH", "32")
    ),
972
973
974
    # 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.
975
976
977
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
978
979
980
    # 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"
981
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
982
983
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
984
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
985
    # Rank of the process in the data parallel setting
986
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
987
988
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
989
990
991
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
992
    # World size of the data parallel setting
993
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
994
    # IP address of the master node in the data parallel setting
995
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
996
    # Port of the master node in the data parallel setting
997
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
998
999
1000
1001
1002
    # 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.
1003
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1004
    # Randomize inputs during dummy runs when using Data Parallel
1005
1006
1007
1008
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1009
1010
1011
1012
1013
1014
1015
    # 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;
1016
1017
1018
    # - "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;
1019
1020
1021
1022
    # 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"
    ),
1023
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1024
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1025
    # Use model_redirect to redirect the model name to a local folder.
1026
1027
1028
1029
1030
    # `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
1031
1032
1033
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1034
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1035
1036
1037
1038
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1039
    # Whether to use marlin kernel in mxfp4 quantization method
1040
1041
1042
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1043
1044
1045
    # 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.
1046
1047
1048
1049
    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
1050
1051
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
1052
1053
1054
1055
1056
1057
1058
1059
    "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)
    ),
1060
    # Whether using Pathways
1061
1062
1063
    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
1064
    # Allow use of DeepGemm kernels for fused moe ops.
1065
    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1066
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
1067
1068
1069
    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
1070
1071
1072
1073
    # 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.
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
    # 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",
        ],
1089
    ),
1090
    # Whether to use fused grouped_topk used for MoE expert selection.
1091
1092
1093
    "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
        int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))
    ),
1094
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1095
1096
1097
    "VLLM_USE_FLASHINFER_MOE_FP16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP16", "0"))
    ),
1098
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1099
1100
1101
    "VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
    ),
1102
    # Allow use of FlashInfer CUTLASS kernels for fused moe ops.
1103
1104
1105
    "VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
    ),
1106
1107
    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
1108
1109
1110
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))
    ),
1111
1112
1113
1114
    # 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.
1115
1116
1117
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0"))
    ),
1118
1119
    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
1120
1121
1122
    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
    ),
1123
1124
1125
    # 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.
1126
    "VLLM_XGRAMMAR_CACHE_MB": lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
1127
1128
1129
1130
1131
1132
1133
    # 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.
1134
1135
1136
    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
        os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
    ),
1137
1138
1139
    # 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.
1140
1141
1142
    "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
        int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
    ),
Robert Shaw's avatar
Robert Shaw committed
1143
    # IP address used for NIXL handshake between remote agents.
1144
1145
1146
    "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
        "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
    ),
Robert Shaw's avatar
Robert Shaw committed
1147
    # Port used for NIXL handshake between remote agents.
1148
1149
1150
    "VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
        os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")
    ),
1151
    # all2all backend for vllm's expert parallel communication
1152
    # Available options:
1153
1154
1155
    # - "naive": naive all2all implementation using broadcasts
    # - "allgather_reducescatter": all2all implementation based on allgather and
    #  reducescatter
1156
    # - "pplx": use pplx kernels
1157
1158
    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
1159
    # - "flashinfer_all2allv", use flashinfer alltoallv kernels for mnnvl
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
    "VLLM_ALL2ALL_BACKEND": env_with_choices(
        "VLLM_ALL2ALL_BACKEND",
        "allgather_reducescatter",
        [
            "naive",
            "pplx",
            "deepep_high_throughput",
            "deepep_low_latency",
            "allgather_reducescatter",
            "flashinfer_all2allv",
        ],
    ),
1172
1173
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
1174
1175
1176
1177
1178
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
1179
1180
1181
    "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
        "VLLM_FLASHINFER_MOE_BACKEND", "throughput", ["throughput", "latency"]
    ),
1182
1183
1184
1185
    # 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.
1186
1187
1188
    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
        os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")
    ),
1189
1190
1191
1192
    # 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> }
1193
    # Unspecified world sizes will fall back to
1194
    #     { 2: 64, 4: 1, <everything else>: 0.5 }
1195
1196
1197
    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB": lambda: json.loads(
        os.getenv("VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")
    ),
1198
1199
1200
1201
1202
1203
    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
    # Cutstom routing strategies can be registered by
    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
1204
1205
1206
    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: os.environ.get(
        "VLLM_MOE_ROUTING_SIMULATION_STRATEGY", ""
    ).lower(),
1207
    # Regex timeout for use by the vLLM tool parsing plugins.
1208
1209
1210
    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
    ),
1211
1212
    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
1213
    "VLLM_SLEEP_WHEN_IDLE": lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
1214
1215
1216
    # 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.
1217
1218
1219
    "VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
        os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
    ),
1220
1221
    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
1222
1223
1224
    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
    ),
1225
1226
1227
1228
1229
1230
1231
    # 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.
1232
1233
1234
    "VLLM_KV_CACHE_LAYOUT": env_with_choices(
        "VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]
    ),
1235
1236
1237
    # 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.
1238
1239
1240
    "VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
        int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
    ),
1241
1242
1243
    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
1244
1245
1246
    "VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
        int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
    ),
1247
1248
1249
1250
    # 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.
1251
1252
1253
    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")
    ),
1254
    # Controls whether or not to use cudnn prefill
1255
1256
1257
    "VLLM_USE_CUDNN_PREFILL": lambda: bool(
        int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))
    ),
1258
1259
1260
1261
    # 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"))
    ),
1262
1263
1264
    # 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.
1265
1266
1267
1268
1269
    "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")
    ),
1270
    # If set to 1, when we use fp8 kv, we do not quantize Q to fp8
1271
1272
1273
    "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION": lambda: bool(
        int(os.getenv("VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION", "0"))
    ),
1274
1275
    # If set, it means we pre-downloaded cubin files and flashinfer will
    # read the cubin files directly.
1276
    "VLLM_HAS_FLASHINFER_CUBIN": lambda: os.getenv("VLLM_HAS_FLASHINFER_CUBIN", False),
1277
1278
1279
1280
1281
1282
1283
1284
1285
    # 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,
        ["flashinfer-cudnn", "flashinfer-trtllm", "flashinfer-cutlass"],
1286
    ),
1287
1288
1289
    # 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.
1290
1291
1292
    "VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
        int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
    ),
1293
    # Used to force set up loopback IP
1294
    "VLLM_LOOPBACK_IP": lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1295
1296
1297
    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
1298
    "VLLM_PROCESS_NAME_PREFIX": lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
1299
1300
1301
1302
1303
1304
1305
    # 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.
1306
1307
1308
    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "0"))
    ),
1309
1310
    # 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
1311
1312
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1313
1314
1315
1316
1317
    # 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.
1318
1319
1320
    "VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
    ),
xiao-llm's avatar
xiao-llm committed
1321
    # If set, use the fp8 mfma in rocm paged attention.
1322
1323
1324
    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN": lambda: bool(
        int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))
    ),
1325
    # Whether to use pytorch symmetric memory for allreduce
1326
    "VLLM_ALLREDUCE_USE_SYMM_MEM": lambda: bool(
1327
        int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "0"))
1328
    ),
1329
    # Allows vllm to find tuned config under customized folder
1330
    "VLLM_TUNED_CONFIG_FOLDER": lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
1331
    # Allows harmony instructions to be injected on system messages
1332
1333
1334
    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS": lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))
    ),
1335
1336
1337
1338
1339
1340
    # 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"))
    ),
1341
    # Add optional custom scopes for profiling, disable to avoid overheads
1342
1343
1344
    "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
    ),
1345
    # Add optional nvtx scopes for profiling, disable to avoid overheads
1346
1347
1348
    "VLLM_NVTX_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))
    ),
1349
1350
    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
1351
1352
1353
    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES": lambda: bool(
        int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))
    ),
1354
1355
    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
1356
1357
1358
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": lambda: os.getenv(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME", "VLLM_OBJECT_STORAGE_SHM_BUFFER"
    ),
1359
    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
1360
1361
1362
    "VLLM_DEEPEP_BUFFER_SIZE_MB": lambda: int(
        os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")
    ),
1363
1364
1365
1366
1367
1368
1369
1370
1371
    # 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 nvlink for internode_ll kernel, turn this on for
    # better latency on GB200 like system
    "VLLM_DEEPEP_LOW_LATENCY_ALLOW_NVLINK": lambda: bool(
1372
        int(os.getenv("VLLM_DEEPEP_LOW_LATENCY_ALLOW_NVLINK", "1"))
1373
1374
1375
1376
1377
1378
    ),
    # 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"))
    ),
1379
1380
    # 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
1381
    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
1382
1383
    # Valid values are container,code_interpreter,web_search_preview
    # ex GPT_OSS_SYSTEM_TOOL_MCP_LABELS=container,code_interpreter
1384
1385
1386
1387
1388
    "GPT_OSS_SYSTEM_TOOL_MCP_LABELS": env_list_with_choices(
        "GPT_OSS_SYSTEM_TOOL_MCP_LABELS",
        [],
        ["container", "code_interpreter", "web_search_preview"],
    ),
1389
1390
1391
    # 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)
1392
1393
1394
    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))
    ),
1395
1396
    # If set to 1, enable coordinate_descent_tuning;
    # By default, this is enabled (1)
1397
1398
1399
    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING", "1"))
    ),
1400
    # Flag to enable NCCL symmetric memory allocation and registration
1401
1402
1403
    "VLLM_USE_NCCL_SYMM_MEM": lambda: bool(
        int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))
    ),
1404
    # NCCL header path
1405
    "VLLM_NCCL_INCLUDE_PATH": lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None),
1406
1407
    # Flag to enable FBGemm kernels on model execution
    "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))),
1408
1409
1410
1411
1412
1413
    # 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", ""),
1414
1415
1416
1417
    # Disables parallel execution of shared_experts via separate cuda stream
    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: os.getenv(
        "VLLM_DISABLE_SHARED_EXPERTS_STREAM", False
    ),
1418
1419
}

1420
# --8<-- [end:env-vars-definition]
1421

1422

1423
def __getattr__(name: str):
1424
1425
1426
1427
1428
1429
    """
    Gets environment variables lazily.

    NOTE: After enable_envs_cache() invocation (which triggered after service
    initialization), all environment variables will be cached.
    """
1430
1431
1432
1433
1434
    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


1435
1436
1437
1438
1439
1440
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
1451
1452
1453
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.
    """
    # Tag __getattr__ with functools.cache
    global __getattr__
    __getattr__ = functools.cache(__getattr__)

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


1454
1455
def __dir__():
    return list(environment_variables.keys())
1456
1457
1458
1459
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469


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}")


def set_vllm_use_v1(use_v1: bool):
    if is_set("VLLM_USE_V1"):
        raise ValueError(
            "Should not call set_vllm_use_v1() if VLLM_USE_V1 is set "
            "explicitly by the user. Please raise this as a Github "
1470
1471
            "Issue and explicitly set VLLM_USE_V1=0 or 1."
        )
1472
    os.environ["VLLM_USE_V1"] = "1" if use_v1 else "0"
1473
1474
1475
1476
1477
1478
1479
1480


def compute_hash() -> str:
    """
    WARNING: Whenever a new key is added to this environment
    variables, ensure that it is included in the factors list if
    it affects the computation graph. For example, different values
    of VLLM_PP_LAYER_PARTITION will generate different computation
1481
    graphs, so it is included in the factors list. The env vars that
1482
1483
1484
1485
1486
1487
1488
1489
1490
1491
1492
    affect the choice of different kernels or attention backends should
    also be included in the factors list.
    """

    # The values of envs may affects the computation graph.
    # TODO(DefTruth): hash all environment variables?
    # for key in environment_variables:
    #     factorize(key)
    environment_variables_to_hash = [
        "VLLM_PP_LAYER_PARTITION",
        "VLLM_MLA_DISABLE",
1493
        "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH",
1494
1495
1496
1497
        "VLLM_USE_TRITON_FLASH_ATTN",
        "VLLM_USE_TRITON_AWQ",
        "VLLM_DP_RANK",
        "VLLM_DP_SIZE",
1498
        "VLLM_USE_STANDALONE_COMPILE",
1499
        "VLLM_FUSED_MOE_CHUNK_SIZE",
1500
1501
1502
1503
1504
1505
        "VLLM_FLASHINFER_MOE_BACKEND",
        "VLLM_V1_USE_PREFILL_DECODE_ATTENTION",
        "VLLM_ATTENTION_BACKEND",
        "VLLM_USE_FLASHINFER_SAMPLER",
        "VLLM_DISABLED_KERNELS",
        "VLLM_USE_DEEP_GEMM",
1506
        "VLLM_USE_DEEP_GEMM_E8M0",
1507
        "VLLM_USE_FUSED_MOE_GROUPED_TOPK",
1508
        "VLLM_USE_FLASHINFER_MOE_FP16",
1509
1510
1511
        "VLLM_USE_FLASHINFER_MOE_FP8",
        "VLLM_USE_FLASHINFER_MOE_FP4",
        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8",
1512
        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS",
1513
1514
        "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16",
        "VLLM_USE_CUDNN_PREFILL",
1515
        "VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL",
1516
        "VLLM_USE_TRTLLM_ATTENTION",
1517
        "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION",
1518
1519
1520
1521
1522
1523
1524
        "VLLM_ROCM_USE_AITER",
        "VLLM_ROCM_USE_AITER_PAGED_ATTN",
        "VLLM_ROCM_USE_AITER_LINEAR",
        "VLLM_ROCM_USE_AITER_MOE",
        "VLLM_ROCM_USE_AITER_RMSNORM",
        "VLLM_ROCM_USE_AITER_MLA",
        "VLLM_ROCM_USE_AITER_MHA",
1525
1526
        "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM",
        "VLLM_ROCM_USE_TRITON_ROPE",
1527
        "VLLM_ROCM_USE_AITER_FP8BMM",
1528
        "VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION",
1529
1530
1531
1532
1533
1534
1535
        "VLLM_ROCM_USE_SKINNY_GEMM",
        "VLLM_ROCM_FP8_PADDING",
        "VLLM_ROCM_MOE_PADDING",
        "VLLM_ROCM_CUSTOM_PAGED_ATTN",
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16",
        "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB",
xiao-llm's avatar
xiao-llm committed
1536
        "VLLM_ROCM_FP8_MFMA_PAGE_ATTN",
1537
1538
        "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE",
        "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING",
1539
        "VLLM_NVFP4_GEMM_BACKEND",
1540
        "VLLM_USE_FBGEMM",
1541
1542
1543
        "VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE",
        "VLLM_DEEPEP_LOW_LATENCY_ALLOW_NVLINK",
        "VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL",
1544
1545
    ]
    for key in environment_variables_to_hash:
1546
1547
        # if this goes out of sync with environment_variables,
        # it's not a user error, it's a bug
1548
        assert key in environment_variables, (
1549
            "Please update environment_variables_to_hash in envs.py"
1550
        )
1551

1552
    factors = [environment_variables[key]() for key in environment_variables_to_hash]
1553

1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
    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",
    ]
    factors.extend([os.getenv(var) for var in ray_noset_env_vars])

1577
    hash_str = hashlib.md5(str(factors).encode(), usedforsecurity=False).hexdigest()
1578
1579

    return hash_str