envs.py 77.9 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 json
6
import logging
7
import os
8
import sys
9
import tempfile
10
import uuid
11
12
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, Literal
13
14
15

if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
16
    VLLM_PORT: int | None = None
17
    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
18
    VLLM_USE_MODELSCOPE: bool = False
19
    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
20
21
    VLLM_NCCL_SO_PATH: str | None = None
    LD_LIBRARY_PATH: str | None = None
22
    VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE: int = 256
23
    LOCAL_RANK: int = 0
24
    CUDA_VISIBLE_DEVICES: str | None = None
25
    VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
26
    VLLM_ENGINE_READY_TIMEOUT_S: int = 600
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
37
38
    VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai"
    VLLM_NO_USAGE_STATS: bool = False
    VLLM_DO_NOT_TRACK: bool = False
    VLLM_USAGE_SOURCE: str = ""
39
    VLLM_CONFIGURE_LOGGING: bool = True
40
    VLLM_LOGGING_LEVEL: str = "INFO"
41
    VLLM_LOGGING_PREFIX: str = ""
42
    VLLM_LOGGING_STREAM: str = "ext://sys.stdout"
43
    VLLM_LOGGING_CONFIG_PATH: str | None = None
Nick Hill's avatar
Nick Hill committed
44
45
    VLLM_LOGGING_COLOR: str = "auto"
    NO_COLOR: bool = False
46
    VLLM_LOG_STATS_INTERVAL: float = 10.0
47
    VLLM_TRACE_FUNCTION: int = 0
48
49
50
    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_SGL_KERNEL: bool = False
54
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
55
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
56
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 16 * 1024
57
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
58
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl", "shm"] = "auto"
59
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
60
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
61
    VLLM_XLA_USE_SPMD: bool = False
62
    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "fork"
63
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
64
    VLLM_ASSETS_CACHE_MODEL_CLEAN: bool = False
65
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
66
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
67
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
68
    VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True
69
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
70
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
71
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
72
    VLLM_MEDIA_CONNECTOR: str = "http"
73
    VLLM_MM_HASHER_ALGORITHM: str = "blake3"
74
    VLLM_TARGET_DEVICE: str = "cuda"
75
    VLLM_MAIN_CUDA_VERSION: str = "12.9"
76
    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
77
78
    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
79
    VLLM_USE_PRECOMPILED: bool = False
80
    VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX: bool = False
81
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
82
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
83
    CMAKE_BUILD_TYPE: Literal["Debug", "Release", "RelWithDebInfo"] | None = None
84
    VERBOSE: bool = False
85
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
86
    VLLM_RPC_TIMEOUT: int = 10000  # ms
87
    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
88
89
    VLLM_PLUGINS: list[str] | None = None
    VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
90
    VLLM_LORA_RESOLVER_HF_REPO_LIST: str | None = None
91
    VLLM_USE_AOT_COMPILE: bool = False
92
    VLLM_USE_BYTECODE_HOOK: bool = False
93
    VLLM_FORCE_AOT_LOAD: bool = False
94
    VLLM_USE_MEGA_AOT_ARTIFACT: bool = False
95
    VLLM_USE_TRITON_AWQ: bool = False
96
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
97
    VLLM_SKIP_P2P_CHECK: bool = False
98
    VLLM_DISABLED_KERNELS: list[str] = []
99
    VLLM_DISABLE_PYNCCL: bool = False
100
    VLLM_ROCM_USE_AITER: bool = False
101
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
102
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
103
    VLLM_ROCM_USE_AITER_MOE: bool = True
104
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
105
    VLLM_ROCM_USE_AITER_MLA: bool = True
106
    VLLM_ROCM_USE_AITER_MHA: bool = True
107
    VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
108
    VLLM_ROCM_USE_AITER_TRITON_ROPE: bool = False
109
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
110
    VLLM_ROCM_USE_AITER_FP4BMM: bool = True
111
    VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
112
    VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = False
113
    VLLM_ROCM_USE_AITER_TRITON_GEMM: 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_ROCM_SHUFFLE_KV_CACHE_LAYOUT: bool = False
119
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
120
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
121
    VLLM_DISABLE_COMPILE_CACHE: bool = False
122
    Q_SCALE_CONSTANT: int = 200
123
124
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
125
    VLLM_SERVER_DEV_MODE: bool = False
126
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
127
    VLLM_MLA_DISABLE: bool = False
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
    VLLM_ENABLE_PREGRAD_PASSES: bool = False
136
137
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
138
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
139
    VLLM_ENABLE_MOE_DP_CHUNK: bool = True
140
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
141
    VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
142
143
    VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY: str = ""
    VLLM_RAY_EXTRA_ENV_VARS_TO_COPY: str = ""
144
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
145
    VLLM_MARLIN_INPUT_DTYPE: Literal["int8", "fp8"] | None = None
146
    VLLM_MXFP4_USE_MARLIN: bool | None = None
147
    VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
148
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
149
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
150
    VLLM_TPU_MOST_MODEL_LEN: int | None = None
151
    VLLM_TPU_USING_PATHWAYS: bool = False
152
    VLLM_USE_DEEP_GEMM: bool = True
153
    VLLM_MOE_USE_DEEP_GEMM: bool = True
154
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
155
    VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES: bool = True
156
157
158
159
160
    VLLM_DEEP_GEMM_WARMUP: Literal[
        "skip",
        "full",
        "relax",
    ] = "relax"
161
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
162
    VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER: bool = False
163
    VLLM_USE_FLASHINFER_MOE_FP16: bool = False
164
165
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
166
    VLLM_USE_FLASHINFER_MOE_INT4: bool = False
167
168
169
    VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency", "masked_gemm"] = (
        "latency"
    )
170
    VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE: int = 394 * 1024 * 1024
171
    VLLM_XGRAMMAR_CACHE_MB: int = 0
172
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
173
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
174
    VLLM_DISABLE_REQUEST_ID_RANDOMIZATION: bool = False
Robert Shaw's avatar
Robert Shaw committed
175
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
176
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
177
    VLLM_MOONCAKE_BOOTSTRAP_PORT: int = 8998
178
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
179
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
180
    VLLM_SLEEP_WHEN_IDLE: bool = False
181
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
182
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
183
    VLLM_KV_CACHE_LAYOUT: Literal["NHD", "HND"] | None = None
184
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
185
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
186
187
188
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal[
        "FP", "INT8", "INT6", "INT4", "NONE"
    ] = "NONE"
189
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
190
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: int | None = None
191
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480
192
193
194
195
    VLLM_MORIIO_CONNECTOR_READ_MODE: bool = False
    VLLM_MORIIO_QP_PER_TRANSFER: int = 1
    VLLM_MORIIO_POST_BATCH_SIZE: int = -1
    VLLM_MORIIO_NUM_WORKERS: int = 1
196
    VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT: int = 480
197
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
198
    VLLM_LOOPBACK_IP: str = ""
199
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = True
200
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
201
    VLLM_NVFP4_GEMM_BACKEND: str | None = None
202
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
203
204
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
xiao-llm's avatar
xiao-llm committed
205
    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
206
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
207
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = True
208
    VLLM_TUNED_CONFIG_FOLDER: str | None = None
209
    VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS: set[str] = set()
210
    VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT: bool = False
211
    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
212
    VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
213
    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
214
    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
215
    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
216
    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
217
    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
218
219
    VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE: bool = False
    VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL: bool = False
220
    VLLM_DBO_COMM_SMS: int = 20
221
222
    VLLM_PATTERN_MATCH_DEBUG: str | None = None
    VLLM_DEBUG_DUMP_PATH: str | None = None
223
224
    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
225
    VLLM_USE_NCCL_SYMM_MEM: bool = False
226
    VLLM_NCCL_INCLUDE_PATH: str | None = None
227
    VLLM_USE_FBGEMM: bool = False
228
    VLLM_GC_DEBUG: str = ""
229
    VLLM_DEBUG_WORKSPACE: bool = False
230
    VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
231
    VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD: int = 256
232
    VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
Woosuk Kwon's avatar
Woosuk Kwon committed
233
    VLLM_USE_V2_MODEL_RUNNER: bool = False
234
    VLLM_LOG_MODEL_INSPECTION: bool = False
235
    VLLM_DEBUG_MFU_METRICS: bool = False
236
237
    VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY: bool = False
    VLLM_WEIGHT_OFFLOADING_DISABLE_UVA: bool = False
238
    VLLM_DISABLE_LOG_LOGO: bool = False
239
    VLLM_LORA_DISABLE_PDL: bool = False
240

241
242
243
244
245
246
247
248
249
250
251
252
253
254
255

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"),
    )


256
def maybe_convert_int(value: str | None) -> int | None:
257
258
259
260
261
    if value is None:
        return None
    return int(value)


262
def maybe_convert_bool(value: str | None) -> bool | None:
263
264
265
266
267
    if value is None:
        return None
    return bool(int(value))


268
269
270
271
def disable_compile_cache() -> bool:
    return bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0")))


272
def use_aot_compile() -> bool:
273
274
275
    from vllm.model_executor.layers.batch_invariant import (
        vllm_is_batch_invariant,
    )
276
    from vllm.utils.torch_utils import is_torch_equal_or_newer
277

278
279
    default_value = (
        "1"
280
        if is_torch_equal_or_newer("2.10.0") and not disable_compile_cache()
281
282
283
        else "0"
    )

284
285
286
287
    return (
        not vllm_is_batch_invariant()
        and os.environ.get("VLLM_USE_AOT_COMPILE", default_value) == "1"
    )
288
289


290
def env_with_choices(
291
    env_name: str,
292
293
    default: str | None,
    choices: list[str] | Callable[[], list[str]],
294
    case_sensitive: bool = True,
295
) -> Callable[[], str | None]:
296
297
    """
    Create a lambda that validates environment variable against allowed choices
298

299
300
301
302
303
    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
304

305
306
307
308
    Returns:
        Lambda function for environment_variables dict
    """

309
    def _get_validated_env() -> str | None:
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
        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:
325
326
327
328
            raise ValueError(
                f"Invalid value '{value}' for {env_name}. "
                f"Valid options: {actual_choices}."
            )
329
330
331
332
333
334

        return value

    return _get_validated_env


335
def env_list_with_choices(
336
337
    env_name: str,
    default: list[str],
338
    choices: list[str] | Callable[[], list[str]],
339
340
    case_sensitive: bool = True,
) -> Callable[[], list[str]]:
341
    """
342
    Create a lambda that validates environment variable
343
    containing comma-separated values against allowed choices
344

345
346
347
348
349
    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
350

351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
    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:
380
381
382
383
                raise ValueError(
                    f"Invalid value '{val}' in {env_name}. "
                    f"Valid options: {actual_choices}."
                )
384
385
386
387
388
389

        return values

    return _get_validated_env_list


390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
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


408
def get_vllm_port() -> int | None:
409
    """Get the port from VLLM_PORT environment variable.
410

411
412
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
413

414
415
416
    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
417
    if "VLLM_PORT" not in os.environ:
418
419
        return None

420
    port = os.getenv("VLLM_PORT", "0")
421
422
423
424

    try:
        return int(port)
    except ValueError as err:
425
        from urllib3.util import parse_url
426

427
        parsed = parse_url(port)
428
429
430
431
432
433
        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
434
        raise ValueError(f"VLLM_PORT '{port}' must be a valid integer") from err
435
436


437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
def get_env_or_set_default(
    env_name: str,
    default_factory: Callable[[], str],
) -> Callable[[], str]:
    """
    Create a lambda that returns an environment variable value if set,
    or generates and sets a default value using the provided factory function.
    """

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

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

    return _get_or_set_default


Ning Xie's avatar
Ning Xie committed
458
# The start-* and end* here are used by the documentation generator
459
460
# to extract the used env vars.

461
# --8<-- [start:env-vars-definition]
462

463
464
logger = logging.getLogger(__name__)

465
environment_variables: dict[str, Callable[[], Any]] = {
466
    # ================== Installation Time Env Vars ==================
467
    # Target device of vLLM, supporting [cuda (by default),
468
    # rocm, cpu]
469
    "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
470
    # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
471
    "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
472
    or "12.9",
473
    # Controls PyTorch float32 matmul precision mode within vLLM workers.
474
    # Valid options mirror torch.set_float32_matmul_precision
475
476
    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
        "VLLM_FLOAT32_MATMUL_PRECISION",
477
478
        "highest",
        ["highest", "high", "medium"],
479
480
        case_sensitive=False,
    ),
481
482
    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
483
    "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
484
485
486
    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
487
    "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
488
    # If set, vllm will use precompiled binaries (*.so)
489
490
491
492
493
    "VLLM_USE_PRECOMPILED": lambda: os.environ.get("VLLM_USE_PRECOMPILED", "")
    .strip()
    .lower()
    in ("1", "true")
    or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
494
495
496
497
    # 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"))
    ),
498
499
    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
500
501
502
503
    "VLLM_DOCKER_BUILD_CONTEXT": lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "")
    .strip()
    .lower()
    in ("1", "true"),
504
505
506
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
507
508
509
    "CMAKE_BUILD_TYPE": env_with_choices(
        "CMAKE_BUILD_TYPE", None, ["Debug", "Release", "RelWithDebInfo"]
    ),
510
    # If set, vllm will print verbose logs during installation
511
    "VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
512
    # Root directory for vLLM configuration files
513
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
514
515
516
    # 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**.
517
    "VLLM_CONFIG_ROOT": lambda: os.path.expanduser(
518
519
520
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
521
522
        )
    ),
523
    # ================== Runtime Env Vars ==================
524
    # Root directory for vLLM cache files
525
    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
526
    "VLLM_CACHE_ROOT": lambda: os.path.expanduser(
527
528
529
        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
530
531
        )
    ),
532
533
534
535
    # 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.
536
    "VLLM_HOST_IP": lambda: os.getenv("VLLM_HOST_IP", ""),
537
    # used in distributed environment to manually set the communication port
538
539
540
    # 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.
541
    "VLLM_PORT": get_vllm_port,
542
543
    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
544
545
546
    "VLLM_RPC_BASE_PATH": lambda: os.getenv(
        "VLLM_RPC_BASE_PATH", tempfile.gettempdir()
    ),
547
548
    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
549
550
551
552
    "VLLM_USE_MODELSCOPE": lambda: os.environ.get(
        "VLLM_USE_MODELSCOPE", "False"
    ).lower()
    == "true",
553
    # Interval in seconds to log a warning message when the ring buffer is full
554
555
556
    "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
        os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
    ),
557
558
    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
559
    "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None),
560
561
    # 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
562
    "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),
563
564
    # 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`
565
    "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
566
567
568
569
    # 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")
    ),
570
    # Feature flag to enable/disable Inductor standalone compile.
571
572
    # In torch <= 2.7 we ignore this flag; in torch >= 2.9 this is
    # enabled by default.
573
    "VLLM_USE_STANDALONE_COMPILE": lambda: os.environ.get(
574
        "VLLM_USE_STANDALONE_COMPILE", "1"
575
576
    )
    == "1",
577
578
579
580
581
582
583
584
585
    # Inductor's pre-grad passes don't do anything for vLLM.
    # The pre-grad passes get run even on cache-hit and negatively impact
    # vllm cold compile times by O(1s)
    # Can remove this after the following issue gets fixed
    # https://github.com/pytorch/pytorch/issues/174502
    "VLLM_ENABLE_PREGRAD_PASSES": lambda: os.environ.get(
        "VLLM_ENABLE_PREGRAD_PASSES", "0"
    )
    == "1",
586
587
    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
588
589
590
    "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get(
        "VLLM_PATTERN_MATCH_DEBUG", None
    ),
591
592
    # Dump fx graphs to the given directory.
    # It will override CompilationConfig.debug_dump_path if set.
593
    "VLLM_DEBUG_DUMP_PATH": lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None),
594
595
596
597
    # 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,
598
599
600
601
602
    # Feature flag to enable/disable bytecode in
    # TorchCompileWithNoGuardsWrapper.
    "VLLM_USE_BYTECODE_HOOK": lambda: bool(
        int(os.environ.get("VLLM_USE_BYTECODE_HOOK", "1"))
    ),
603
604
605
606
    # 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",
607
608
609
610
611
612
613
    # Enable loading compiled models directly from cached standalone compile artifacts
    # without re-splitting graph modules. This reduces overhead during model
    # loading by using reconstruct_serializable_fn_from_mega_artifact.
    "VLLM_USE_MEGA_AOT_ARTIFACT": lambda: os.environ.get(
        "VLLM_USE_MEGA_AOT_ARTIFACT", "0"
    )
    == "1",
614
615
    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
616
    "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")),
617
    # used to control the visible devices in the distributed setting
618
    "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
619
    # timeout for each iteration in the engine
620
621
622
    "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")
    ),
623
624
625
626
627
    # Timeout in seconds for waiting for engine cores to become ready
    # during startup. Default is 600 seconds (10 minutes).
    "VLLM_ENGINE_READY_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_READY_TIMEOUT_S", "600")
    ),
628
    # API key for vLLM API server
629
    "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None),
630
    # Whether to log responses from API Server for debugging
631
632
633
634
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: os.environ.get(
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
    ).lower()
    == "true",
635
    # S3 access information, used for tensorizer to load model from S3
636
637
638
    "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),
639
    # Usage stats collection
640
641
642
643
644
645
646
647
648
649
650
    "VLLM_USAGE_STATS_SERVER": lambda: os.environ.get(
        "VLLM_USAGE_STATS_SERVER", "https://stats.vllm.ai"
    ),
    "VLLM_NO_USAGE_STATS": lambda: os.environ.get("VLLM_NO_USAGE_STATS", "0") == "1",
    "VLLM_DO_NOT_TRACK": lambda: (
        os.environ.get("VLLM_DO_NOT_TRACK", None)
        or os.environ.get("DO_NOT_TRACK", None)
        or "0"
    )
    == "1",
    "VLLM_USAGE_SOURCE": lambda: os.environ.get("VLLM_USAGE_SOURCE", "production"),
651
652
653
654
    # 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
655
656
657
    "VLLM_CONFIGURE_LOGGING": lambda: bool(
        int(os.getenv("VLLM_CONFIGURE_LOGGING", "1"))
    ),
658
    "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),
659
    # this is used for configuring the default logging level
660
    "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
661
    # this is used for configuring the default logging stream
662
    "VLLM_LOGGING_STREAM": lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),
663
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
664
    "VLLM_LOGGING_PREFIX": lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
Nick Hill's avatar
Nick Hill committed
665
666
667
668
669
    # 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",
670
671
    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
672
673
674
    "VLLM_LOG_STATS_INTERVAL": lambda: val
    if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10."))) > 0.0
    else 10.0,
675
676
677
    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
678
    "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
679
    # If set, vllm will use flashinfer sampler
680
681
682
683
684
    "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
        int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"])
    )
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
    else None,
685
    # Pipeline stage partition strategy
686
    "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
687
    # (CPU backend only) CPU key-value cache space.
688
    # default is None and will be set as 4 GB
689
690
691
    "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ
    else None,
692
693
    # (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 '|'.
694
    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
695
696
    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
697
698
699
700
701
    "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,
702
    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
703
    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
704
705
706
707
708
709
710
    # 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
711
712
713
    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
714
    # If the env var is set, it enables GPU communication overlap
715
    # (experimental feature) in Ray's Compiled Graph.
716
717
718
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
719
720
721
    # 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.
722
723
724
    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
725
726
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
727
728
729
    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
        "VLLM_WORKER_MULTIPROC_METHOD", "fork", ["spawn", "fork"]
    ),
730
    # Path to the cache for storing downloaded assets
731
    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
732
733
734
        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
735
736
        )
    ),
737
738
    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
739
740
741
    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
742
743
    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
744
    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
745
    # Timeout for fetching videos when serving multimodal models
746
    # Default is 30 seconds
747
748
749
    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
750
    # Timeout for fetching audio when serving multimodal models
751
    # Default is 10 seconds
752
753
754
    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
755
756
    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
757
758
759
    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
760
761
762
    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
763
764
765
    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
766
767
768
    # 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
769
770
771
    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
772
773
    # Backend for Video IO
    # - "opencv": Default backend that uses OpenCV stream buffered backend.
Roger Wang's avatar
Roger Wang committed
774
    # - "identity": Returns raw video bytes for model processor to handle.
775
776
777
778
779
    #
    # 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.
780
781
782
    "VLLM_VIDEO_LOADER_BACKEND": lambda: os.getenv(
        "VLLM_VIDEO_LOADER_BACKEND", "opencv"
    ),
783
784
785
786
787
788
789
790
    # 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"),
791
792
793
794
795
796
797
798
799
800
801
    # Hash algorithm for multimodal content hashing.
    # - "blake3": Default, fast cryptographic hash (not FIPS 140-3 compliant)
    # - "sha256": FIPS 140-3 compliant, widely supported
    # - "sha512": FIPS 140-3 compliant, faster on 64-bit systems
    # Use sha256 or sha512 for FIPS compliance in government/enterprise deployments
    "VLLM_MM_HASHER_ALGORITHM": env_with_choices(
        "VLLM_MM_HASHER_ALGORITHM",
        "blake3",
        ["blake3", "sha256", "sha512"],
        case_sensitive=False,
    ),
802
803
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
804
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
805
        os.getenv(
806
            "VLLM_XLA_CACHE_PATH",
807
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
808
809
        )
    ),
810
    # If set, assert on XLA recompilation after each execution step.
811
812
813
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
814
    # Enable SPMD mode for TPU backend.
815
816
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
817
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(16 * 1024))
818
    ),
819
820
821
    # 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.
822
823
824
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
825
826
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
827
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
828
        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
829
    ),
830
831
832
833
    # 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.
834
835
836
837
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
838
839
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
840
841
842
843
844
845
846
    "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"
    ),
847
848
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
849
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
850
    # Timeout in seconds for keeping HTTP connections alive in API server
851
852
853
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
854
855
856
    # 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
857
858
859
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
860
861
862
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
863
864
865
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
866
867
868
869
870
871
872
    # A remote HF repo(s) containing one or more LoRA adapters, which
    # may be downloaded and leveraged as needed. Only works if plugins
    # are enabled and VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
    # Values should be comma separated.
    "VLLM_LORA_RESOLVER_HF_REPO_LIST": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_HF_REPO_LIST", None
    ),
873
    # If set, vLLM will use Triton implementations of AWQ.
874
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
875
    # If set, allow loading or unloading lora adapters in runtime,
876
877
878
879
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
880
881
882
883
884
885
    # 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
886
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
887
888
889
890
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
891
892
893
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
894
    # Disable pynccl (using torch.distributed instead)
895
896
897
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
898
899
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
900
901
902
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
903
904
    # Whether to use aiter paged attention.
    # By default is disabled.
905
906
907
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
908
909
910
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
911
912
913
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
    ),
914
915
    # Whether to use aiter moe ops.
    # By default is enabled.
916
917
918
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in ("true", "1")
    ),
919
    # use aiter rms norm op if aiter ops are enabled.
920
921
922
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in ("true", "1")
    ),
923
924
    # Whether to use aiter mla ops.
    # By default is enabled.
925
926
927
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in ("true", "1")
    ),
928
929
    # Whether to use aiter mha ops.
    # By default is enabled.
930
931
932
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in ("true", "1")
    ),
933
934
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
935
936
937
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
938
939
    # Whether to use aiter rope.
    # By default is disabled.
940
941
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "False").lower() in ("true", "1")
942
    ),
943
944
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
945
946
947
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
    ),
948
949
950
951
952
    # Whether to use aiter triton fp4 bmm kernel
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_FP4BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4BMM", "True").lower() in ("true", "1")
    ),
953
954
955
956
957
    # 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")
    ),
958
    # Whether to use aiter fusion shared experts ops.
959
    # By default is disabled.
960
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
961
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
962
963
        in ("true", "1")
    ),
964
965
966
967
968
    # Whether to use aiter triton kernels for gemm ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_TRITON_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_GEMM", "True").lower() in ("true", "1")
    ),
969
    # use rocm skinny gemms
970
971
972
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
973
    # Pad the fp8 weights to 256 bytes for ROCm
974
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
975
    # Pad the weights for the moe kernel
976
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
977
    # custom paged attention kernel for MI3* cards
978
979
980
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
981
982
983
984
    # Whether to use the shuffled kv cache layout
    "VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT": lambda: (
        os.getenv("VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT", "False").lower() in ("true", "1")
    ),
985
986
987
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
988
989
990
991
992
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
993
994
995
996
    # 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
997
998
999
1000
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1001
1002
1003
1004
1005
1006
    # 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.
1007
1008
1009
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1010
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1011
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1012
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1013
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1014
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1015
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1016
    # If set, enable multiprocessing in LLM for the V1 code path.
1017
1018
1019
1020
1021
1022
    "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")
    ),
1023
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1024
1025
1026
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1027
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1028
1029
1030
1031
1032
1033
1034
    # 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.
1035
1036
1037
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1038
    # If set, vLLM will disable the MLA attention optimizations.
1039
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1040
    # If set, vLLM will pick up the provided Flash Attention MLA
1041
1042
1043
    # 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.
1044
1045
1046
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1047
1048
1049
    # 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"
1050
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1051
1052
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1053
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1054
    # Rank of the process in the data parallel setting
1055
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1056
1057
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1058
1059
1060
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1061
    # World size of the data parallel setting
1062
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1063
    # IP address of the master node in the data parallel setting
1064
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1065
    # Port of the master node in the data parallel setting
1066
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1067
1068
1069
1070
1071
    # 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.
1072
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1073
1074
1075
    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
    ),
1076
    # Randomize inputs during dummy runs when using Data Parallel
1077
1078
1079
1080
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1081
1082
1083
1084
1085
1086
1087
    # 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;
1088
1089
1090
    # - "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;
1091
1092
1093
1094
    # 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"
    ),
1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
    # Comma-separated *additional* prefixes of env vars to copy from the
    # driver to Ray workers.  These are merged with the built-in defaults
    # defined in ``vllm.ray.ray_env`` (VLLM_, etc.).  Example: "MYLIB_,OTHER_"
    "VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY": lambda: os.getenv(
        "VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY", ""
    ),
    # Comma-separated *additional* individual env var names to copy from
    # the driver to Ray workers.  Merged with the built-in defaults
    # defined in ``vllm.ray.ray_env`` (PYTHONHASHSEED).
    # Example: "MY_SECRET,MY_FLAG"
    "VLLM_RAY_EXTRA_ENV_VARS_TO_COPY": lambda: os.getenv(
        "VLLM_RAY_EXTRA_ENV_VARS_TO_COPY", ""
    ),
1108
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1109
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1110
    # Use model_redirect to redirect the model name to a local folder.
1111
1112
1113
1114
1115
    # `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
1116
1117
1118
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1119
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1120
1121
1122
1123
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1124
    # Whether to use marlin kernel in mxfp4 quantization method
1125
1126
1127
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1128
1129
1130
1131
    # The activation dtype for marlin kernel
    "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
        "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
    ),
1132
1133
1134
1135
1136
1137
    # 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"))
    ),
1138
1139
1140
    # 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.
1141
1142
1143
1144
    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
1145
1146
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
1147
1148
1149
1150
1151
1152
1153
1154
    "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)
    ),
1155
    # Whether using Pathways
1156
1157
1158
    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
1159
    # Allow use of DeepGemm kernels for fused moe ops.
1160
    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1161
1162
1163
1164
    # 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"))
    ),
1165
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
1166
1167
1168
    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
1169
1170
1171
1172
    # Whether to create TMA-aligned scale tensor when DeepGEMM is used.
    "VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES", "1"))
    ),
1173
1174
1175
1176
    # 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.
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
    # 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",
        ],
1192
    ),
1193
    # Whether to use fused grouped_topk used for MoE expert selection.
1194
1195
1196
    "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
        int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))
    ),
1197
1198
1199
1200
1201
    # Allow use of FlashInfer FP8 block-scale GEMM for linear layers.
    # This uses TensorRT-LLM kernels and requires SM90+ (Hopper).
    "VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER": lambda: bool(
        int(os.getenv("VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER", "0"))
    ),
1202
    # Allow use of FlashInfer BF16 MoE kernels for fused moe ops.
1203
1204
1205
    "VLLM_USE_FLASHINFER_MOE_FP16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP16", "0"))
    ),
1206
    # Allow use of FlashInfer FP8 MoE kernels for fused moe ops.
1207
1208
1209
    "VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
    ),
1210
    # Allow use of FlashInfer NVFP4 MoE kernels for fused moe ops.
1211
1212
1213
    "VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
    ),
1214
1215
1216
1217
    # Allow use of FlashInfer MxInt4 MoE kernels for fused moe ops.
    "VLLM_USE_FLASHINFER_MOE_INT4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_INT4", "0"))
    ),
1218
1219
    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
1220
1221
1222
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))
    ),
1223
1224
1225
1226
    # 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.
1227
1228
1229
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0"))
    ),
1230
1231
    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
1232
1233
1234
    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
    ),
1235
1236
1237
    # 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.
1238
    "VLLM_XGRAMMAR_CACHE_MB": lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
1239
1240
1241
1242
1243
1244
1245
    # 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.
1246
1247
1248
    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
        os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
    ),
1249
1250
1251
    # 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.
1252
1253
1254
    "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
        int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
    ),
1255
1256
1257
1258
1259
    # Temporary: skip adding random suffix to internal request IDs. May be
    # needed for KV connectors that match request IDs across instances.
    "VLLM_DISABLE_REQUEST_ID_RANDOMIZATION": lambda: bool(
        int(os.getenv("VLLM_DISABLE_REQUEST_ID_RANDOMIZATION", "0"))
    ),
Robert Shaw's avatar
Robert Shaw committed
1260
    # IP address used for NIXL handshake between remote agents.
1261
1262
1263
    "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
        "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
    ),
Robert Shaw's avatar
Robert Shaw committed
1264
    # Port used for NIXL handshake between remote agents.
1265
1266
1267
    "VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
        os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")
    ),
1268
1269
1270
1271
    # Port used for Mooncake handshake between remote agents.
    "VLLM_MOONCAKE_BOOTSTRAP_PORT": lambda: int(
        os.getenv("VLLM_MOONCAKE_BOOTSTRAP_PORT", "8998")
    ),
1272
1273
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
1274
1275
1276
1277
1278
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
1279
    "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
1280
1281
1282
        "VLLM_FLASHINFER_MOE_BACKEND",
        "latency",
        ["throughput", "latency", "masked_gemm"],
1283
    ),
1284
1285
1286
1287
    # 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))
    ),
1288
1289
1290
1291
    # 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.
1292
1293
1294
    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
        os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")
    ),
1295
1296
1297
1298
    # 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> }
1299
    # Unspecified world sizes will fall back to
1300
    #     { 2: 64, 4: 1, <everything else>: 0.5 }
1301
1302
1303
    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB": lambda: json.loads(
        os.getenv("VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")
    ),
1304
1305
1306
    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
1307
    # Custom routing strategies can be registered by
1308
1309
    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
1310
1311
1312
    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: os.environ.get(
        "VLLM_MOE_ROUTING_SIMULATION_STRATEGY", ""
    ).lower(),
1313
    # Regex timeout for use by the vLLM tool parsing plugins.
1314
1315
1316
    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
    ),
1317
1318
    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
1319
    "VLLM_SLEEP_WHEN_IDLE": lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
1320
1321
1322
    # 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.
1323
1324
1325
    "VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
        os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
    ),
1326
1327
    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
1328
1329
1330
    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
    ),
1331
1332
1333
1334
1335
1336
1337
    # 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.
1338
1339
1340
    "VLLM_KV_CACHE_LAYOUT": env_with_choices(
        "VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]
    ),
1341
1342
1343
    # 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.
1344
1345
1346
    "VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
        int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
    ),
1347
1348
1349
    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
1350
1351
1352
    "VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
        int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
    ),
1353
1354
1355
1356
    # 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.
1357
1358
1359
    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")
    ),
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
    # Controls the read mode for the Mori-IO connector
    "VLLM_MORIIO_CONNECTOR_READ_MODE": lambda: (
        os.getenv("VLLM_MORIIO_CONNECTOR_READ_MODE", "False").lower() in ("true", "1")
    ),
    # Controls the QP (Queue Pair) per transfer configuration for the Mori-IO connector
    "VLLM_MORIIO_QP_PER_TRANSFER": lambda: int(
        os.getenv("VLLM_MORIIO_QP_PER_TRANSFER", "1")
    ),
    # Controls the post-processing batch size for the Mori-IO connector
    "VLLM_MORIIO_POST_BATCH_SIZE": lambda: int(
        os.getenv("VLLM_MORIIO_POST_BATCH_SIZE", "-1")
    ),
    # Controls the number of workers for Mori operations for the Mori-IO connector
    "VLLM_MORIIO_NUM_WORKERS": lambda: int(os.getenv("VLLM_MORIIO_NUM_WORKERS", "1")),
1374
1375
1376
1377
    # Timeout (in seconds) for MooncakeConnector in PD disaggregated setup.
    "VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT", "480")
    ),
1378
1379
    # If set, it means we pre-downloaded cubin files and flashinfer will
    # read the cubin files directly.
1380
1381
1382
    "VLLM_HAS_FLASHINFER_CUBIN": lambda: bool(
        int(os.getenv("VLLM_HAS_FLASHINFER_CUBIN", "0"))
    ),
1383
1384
1385
1386
    # Supported options:
    # - "flashinfer-cudnn": use flashinfer cudnn GEMM backend
    # - "flashinfer-trtllm": use flashinfer trtllm GEMM backend
    # - "flashinfer-cutlass": use flashinfer cutlass GEMM backend
1387
    # - "marlin": use marlin GEMM backend (for GPUs without native FP4 support)
1388
1389
1390
1391
    # - <none>: automatically pick an available backend
    "VLLM_NVFP4_GEMM_BACKEND": env_with_choices(
        "VLLM_NVFP4_GEMM_BACKEND",
        None,
1392
1393
1394
1395
1396
        [
            "flashinfer-cudnn",
            "flashinfer-trtllm",
            "flashinfer-cutlass",
            "cutlass",
1397
            "marlin",
1398
        ],
1399
    ),
1400
1401
1402
    # 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.
1403
1404
1405
    "VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
        int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
    ),
1406
    # Used to force set up loopback IP
1407
    "VLLM_LOOPBACK_IP": lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1408
1409
1410
    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
1411
    "VLLM_PROCESS_NAME_PREFIX": lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
1412
1413
1414
1415
1416
1417
1418
    # 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.
1419
    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
1420
        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "1"))
1421
    ),
1422
1423
    # 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
1424
1425
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1426
1427
1428
1429
1430
    # 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.
1431
1432
1433
    "VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
    ),
xiao-llm's avatar
xiao-llm committed
1434
    # If set, use the fp8 mfma in rocm paged attention.
1435
1436
1437
    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN": lambda: bool(
        int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))
    ),
1438
    # Whether to use pytorch symmetric memory for allreduce
1439
    "VLLM_ALLREDUCE_USE_SYMM_MEM": lambda: bool(
1440
        int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1"))
1441
    ),
1442
1443
1444
1445
    # 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"))
    ),
1446
    # Allows vllm to find tuned config under customized folder
1447
    "VLLM_TUNED_CONFIG_FOLDER": lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
1448
1449
1450
1451
1452
1453
1454
1455
1456
    # 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"],
    ),
1457
    # Allows harmony instructions to be injected on system messages
1458
1459
1460
    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS": lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))
    ),
1461
1462
1463
1464
1465
1466
    # 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"))
    ),
1467
    # Add optional custom scopes for profiling, disable to avoid overheads
1468
1469
1470
    "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
    ),
1471
    # Add optional nvtx scopes for profiling, disable to avoid overheads
1472
1473
1474
    "VLLM_NVTX_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))
    ),
1475
1476
    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
1477
1478
1479
    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES": lambda: bool(
        int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))
    ),
1480
1481
    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
1482
1483
1484
1485
1486
    # Automatically generates a unique UUID-based name per process tree
    # if not explicitly set.
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": get_env_or_set_default(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
        lambda: f"VLLM_OBJECT_STORAGE_SHM_BUFFER_{uuid.uuid4().hex}",
1487
    ),
1488
    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
1489
1490
1491
    "VLLM_DEEPEP_BUFFER_SIZE_MB": lambda: int(
        os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")
    ),
1492
1493
1494
1495
1496
1497
1498
1499
1500
1501
1502
    # 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"))
    ),
1503
1504
    # 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
1505
    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
1506
1507
1508
    # 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)
1509
1510
1511
    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))
    ),
1512
1513
    # If set to 1, enable coordinate_descent_tuning;
    # By default, this is enabled (1)
1514
1515
1516
    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING", "1"))
    ),
1517
    # Flag to enable NCCL symmetric memory allocation and registration
1518
1519
1520
    "VLLM_USE_NCCL_SYMM_MEM": lambda: bool(
        int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))
    ),
1521
    # NCCL header path
1522
    "VLLM_NCCL_INCLUDE_PATH": lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None),
1523
1524
    # Flag to enable FBGemm kernels on model execution
    "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))),
1525
1526
1527
1528
1529
1530
    # 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", ""),
1531
1532
1533
    # Debug workspace allocations.
    # logging of workspace resize operations.
    "VLLM_DEBUG_WORKSPACE": lambda: bool(int(os.getenv("VLLM_DEBUG_WORKSPACE", "0"))),
1534
    # Disables parallel execution of shared_experts via separate cuda stream
1535
1536
    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: bool(
        int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "0"))
1537
    ),
1538
1539
1540
1541
1542
1543
1544
    # 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))
    ),
1545
1546
1547
1548
1549
1550
1551
1552
1553
    # 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"]
    ),
Woosuk Kwon's avatar
Woosuk Kwon committed
1554
1555
1556
1557
    # Flag to enable v2 model runner.
    "VLLM_USE_V2_MODEL_RUNNER": lambda: bool(
        int(os.getenv("VLLM_USE_V2_MODEL_RUNNER", "0"))
    ),
1558
1559
1560
1561
1562
1563
    # Log model inspection after loading.
    # If enabled, logs a transformers-style hierarchical view of the model
    # with quantization methods and attention backends.
    "VLLM_LOG_MODEL_INSPECTION": lambda: bool(
        int(os.getenv("VLLM_LOG_MODEL_INSPECTION", "0"))
    ),
1564
1565
1566
1567
    # Debug logging for --enable-mfu-metrics
    "VLLM_DEBUG_MFU_METRICS": lambda: bool(
        int(os.getenv("VLLM_DEBUG_MFU_METRICS", "0"))
    ),
1568
1569
1570
1571
1572
1573
1574
1575
    # Disable using pytorch's pin memory for CPU offloading.
    "VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY": lambda: bool(
        int(os.getenv("VLLM_WEIGHT_OFFLOADING_DISABLE_PIN_MEMORY", "0"))
    ),
    # Disable using UVA (Unified Virtual Addressing) for CPU offloading.
    "VLLM_WEIGHT_OFFLOADING_DISABLE_UVA": lambda: bool(
        int(os.getenv("VLLM_WEIGHT_OFFLOADING_DISABLE_UVA", "0"))
    ),
1576
1577
    # Disable logging of vLLM logo at server startup time.
    "VLLM_DISABLE_LOG_LOGO": lambda: bool(int(os.getenv("VLLM_DISABLE_LOG_LOGO", "0"))),
1578
1579
1580
    # Disable PDL for LoRA, as enabling PDL with LoRA on SM100 causes
    # Triton compilation to fail.
    "VLLM_LORA_DISABLE_PDL": lambda: bool(int(os.getenv("VLLM_LORA_DISABLE_PDL", "0"))),
1581
1582
}

1583

1584
# --8<-- [end:env-vars-definition]
1585

1586

1587
def __getattr__(name: str):
1588
1589
1590
1591
1592
1593
    """
    Gets environment variables lazily.

    NOTE: After enable_envs_cache() invocation (which triggered after service
    initialization), all environment variables will be cached.
    """
1594
1595
1596
1597
1598
    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


1599
1600
1601
1602
1603
1604
def _is_envs_cache_enabled() -> bool:
    """Checked if __getattr__ is wrapped with functools.cache"""
    global __getattr__
    return hasattr(__getattr__, "cache_clear")


1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
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.
    """
1615
1616
1617
    if _is_envs_cache_enabled():
        # Avoid wrapping functools.cache multiple times
        return
1618
1619
1620
1621
1622
1623
1624
1625
1626
    # Tag __getattr__ with functools.cache
    global __getattr__
    __getattr__ = functools.cache(__getattr__)

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


1627
1628
1629
1630
1631
1632
1633
1634
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():
1635
        assert hasattr(__getattr__, "__wrapped__")
1636
1637
1638
        __getattr__ = __getattr__.__wrapped__


1639
1640
def __dir__():
    return list(environment_variables.keys())
1641
1642
1643
1644
1645
1646
1647
1648
1649


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}")


1650
1651
1652
1653
1654
1655
1656
1657
1658
def validate_environ(hard_fail: bool) -> None:
    for env in os.environ:
        if env.startswith("VLLM_") and env not in environment_variables:
            if hard_fail:
                raise ValueError(f"Unknown vLLM environment variable detected: {env}")
            else:
                logger.warning("Unknown vLLM environment variable detected: %s", env)


1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
1670
1671
1672
1673
1674
1675
1676
1677
1678
1679
1680
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",
1681
        "VLLM_FORCE_AOT_LOAD",
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
        "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",
Nick Hill's avatar
Nick Hill committed
1692
        "VLLM_LOGGING_COLOR",
1693
1694
1695
1696
1697
1698
1699
1700
1701
1702
1703
1704
1705
1706
1707
1708
        "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",
1709
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
1710
1711
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
        "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",
Nick Hill's avatar
Nick Hill committed
1723
        "NO_COLOR",
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
    }

    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
1742

1743
        factors[factor] = normalize_value(raw)
1744

1745
1746
1747
1748
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
1766
    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",
    ]

1767
1768
    for var in ray_noset_env_vars:
        factors[var] = normalize_value(os.getenv(var))
1769

1770
    return factors