envs.py 75 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
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_ROCM_SLEEP_MEM_CHUNK_SIZE: int = 256
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
Nick Hill's avatar
Nick Hill committed
45
46
    VLLM_LOGGING_COLOR: str = "auto"
    NO_COLOR: bool = False
47
    VLLM_LOG_STATS_INTERVAL: float = 10.0
48
    VLLM_TRACE_FUNCTION: int = 0
49
50
51
52
    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
53
    VLLM_CPU_OMP_THREADS_BIND: str = ""
54
    VLLM_CPU_NUM_OF_RESERVED_CPU: int | None = None
55
    VLLM_CPU_SGL_KERNEL: bool = False
56
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
57
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
58
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 16 * 1024
59
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
60
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl", "shm"] = "auto"
61
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
62
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
63
    VLLM_XLA_USE_SPMD: bool = False
64
    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "fork"
65
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
66
    VLLM_ASSETS_CACHE_MODEL_CLEAN: bool = False
67
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
68
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
69
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
70
    VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True
71
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
72
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
73
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
74
    VLLM_MEDIA_CONNECTOR: str = "http"
75
    VLLM_MM_INPUT_CACHE_GIB: int = 4
76
    VLLM_TARGET_DEVICE: str = "cuda"
77
    VLLM_MAIN_CUDA_VERSION: str = "12.8"
78
79
    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
80
    VLLM_USE_PRECOMPILED: bool = False
81
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
82
    VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
83
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
84
    CMAKE_BUILD_TYPE: Literal["Debug", "Release", "RelWithDebInfo"] | None = None
85
    VERBOSE: bool = False
86
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
87
    VLLM_RPC_TIMEOUT: int = 10000  # ms
88
    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
89
90
    VLLM_PLUGINS: list[str] | None = None
    VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
91
    VLLM_TORCH_CUDA_PROFILE: bool = False
92
    VLLM_TORCH_PROFILER_DIR: str | None = None
93
94
    VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
95
    VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: bool = False
96
    VLLM_USE_AOT_COMPILE: bool = False
97
    VLLM_USE_BYTECODE_HOOK: bool = False
98
    VLLM_FORCE_AOT_LOAD: bool = False
99
100
    VLLM_TORCH_PROFILER_WITH_STACK: bool = True
    VLLM_TORCH_PROFILER_WITH_FLOPS: bool = False
101
102
    VLLM_PROFILER_DELAY_ITERS: int = 0
    VLLM_PROFILER_MAX_ITERS: int = 0
103
    VLLM_USE_TRITON_AWQ: bool = False
104
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
105
    VLLM_SKIP_P2P_CHECK: bool = False
106
    VLLM_DISABLED_KERNELS: list[str] = []
107
    VLLM_DISABLE_PYNCCL: bool = False
108
    VLLM_ROCM_USE_AITER: bool = False
109
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
110
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
111
    VLLM_ROCM_USE_AITER_MOE: bool = True
112
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
113
    VLLM_ROCM_USE_AITER_MLA: bool = True
114
    VLLM_ROCM_USE_AITER_MHA: bool = True
115
    VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
116
    VLLM_ROCM_USE_AITER_TRITON_ROPE: bool = False
117
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
118
    VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
119
    VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = False
120
    VLLM_ROCM_USE_AITER_TRITON_GEMM: bool = True
121
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
122
    VLLM_ROCM_FP8_PADDING: bool = True
123
    VLLM_ROCM_MOE_PADDING: bool = True
124
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
125
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
126
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
127
    VLLM_DISABLE_COMPILE_CACHE: bool = False
128
    Q_SCALE_CONSTANT: int = 200
129
130
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
131
    VLLM_SERVER_DEV_MODE: bool = False
132
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
133
    VLLM_MLA_DISABLE: bool = False
134
    VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 32
135
136
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
137
    VLLM_CUDART_SO_PATH: str | None = None
138
    VLLM_DP_RANK: int = 0
139
    VLLM_DP_RANK_LOCAL: int = -1
140
    VLLM_DP_SIZE: int = 1
141
    VLLM_USE_STANDALONE_COMPILE: bool = True
142
143
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
144
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
145
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
146
    VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
147
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
148
    VLLM_MARLIN_INPUT_DTYPE: Literal["int8", "fp8"] | None = None
149
    VLLM_MXFP4_USE_MARLIN: bool | None = None
150
    VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
151
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
152
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
153
    VLLM_TPU_MOST_MODEL_LEN: int | None = None
154
    VLLM_TPU_USING_PATHWAYS: bool = False
155
    VLLM_USE_DEEP_GEMM: bool = True
156
    VLLM_MOE_USE_DEEP_GEMM: bool = True
157
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
158
159
160
161
162
    VLLM_DEEP_GEMM_WARMUP: Literal[
        "skip",
        "full",
        "relax",
    ] = "relax"
163
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
164
    VLLM_USE_FLASHINFER_MOE_FP16: bool = False
165
166
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: 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
Robert Shaw's avatar
Robert Shaw committed
174
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
175
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
176
177
178
179
180
181
182
183
    VLLM_ALL2ALL_BACKEND: Literal[
        "naive",
        "pplx",
        "deepep_high_throughput",
        "deepep_low_latency",
        "allgather_reducescatter",
        "flashinfer_all2allv",
    ] = "allgather_reducescatter"
184
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
185
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
186
    VLLM_SLEEP_WHEN_IDLE: bool = False
187
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
188
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
189
    VLLM_KV_CACHE_LAYOUT: Literal["NHD", "HND"] | None = None
190
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
191
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
192
193
194
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal[
        "FP", "INT8", "INT6", "INT4", "NONE"
    ] = "NONE"
195
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
196
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: int | None = None
197
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480
198
    VLLM_USE_CUDNN_PREFILL: bool = False
199
    VLLM_USE_TRTLLM_RAGGED_DEEPSEEK_PREFILL: bool = False
200
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
201
    VLLM_LOOPBACK_IP: str = ""
202
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = False
203
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
204
    VLLM_USE_TRTLLM_ATTENTION: str | None = None
205
    VLLM_NVFP4_GEMM_BACKEND: str | None = None
206
    VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION: bool = False
207
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
208
209
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
xiao-llm's avatar
xiao-llm committed
210
    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
211
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
212
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = True
213
    VLLM_TUNED_CONFIG_FOLDER: str | None = None
214
    VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS: set[str] = set()
215
    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
216
    VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
217
    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
218
    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
219
    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
220
    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
221
    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
222
223
    VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE: bool = False
    VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL: bool = False
224
    VLLM_DBO_COMM_SMS: int = 20
225
226
    VLLM_PATTERN_MATCH_DEBUG: str | None = None
    VLLM_DEBUG_DUMP_PATH: str | None = None
227
228
    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
229
    VLLM_USE_NCCL_SYMM_MEM: bool = False
230
    VLLM_NCCL_INCLUDE_PATH: str | None = None
231
    VLLM_USE_FBGEMM: bool = False
232
    VLLM_GC_DEBUG: str = ""
233
    VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
234
    VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD: int = 256
235
    VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
Woosuk Kwon's avatar
Woosuk Kwon committed
236
    VLLM_USE_V2_MODEL_RUNNER: bool = False
237

238
239
240
241
242
243
244
245
246
247
248
249
250
251
252

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"),
    )


253
def maybe_convert_int(value: str | None) -> int | None:
254
255
256
257
258
    if value is None:
        return None
    return int(value)


259
def maybe_convert_bool(value: str | None) -> bool | None:
260
261
262
263
264
    if value is None:
        return None
    return bool(int(value))


265
266
267
268
def disable_compile_cache() -> bool:
    return bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0")))


269
def use_aot_compile() -> bool:
270
271
272
    from vllm.model_executor.layers.batch_invariant import (
        vllm_is_batch_invariant,
    )
273
    from vllm.utils.torch_utils import is_torch_equal_or_newer
274

275
276
277
278
279
280
    default_value = (
        "1"
        if is_torch_equal_or_newer("2.10.0.dev") and not disable_compile_cache()
        else "0"
    )

281
282
283
284
    return (
        not vllm_is_batch_invariant()
        and os.environ.get("VLLM_USE_AOT_COMPILE", default_value) == "1"
    )
285
286


287
def env_with_choices(
288
    env_name: str,
289
290
    default: str | None,
    choices: list[str] | Callable[[], list[str]],
291
    case_sensitive: bool = True,
292
) -> Callable[[], str | None]:
293
294
    """
    Create a lambda that validates environment variable against allowed choices
295

296
297
298
299
300
    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
301

302
303
304
305
    Returns:
        Lambda function for environment_variables dict
    """

306
    def _get_validated_env() -> str | None:
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
        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:
322
323
324
325
            raise ValueError(
                f"Invalid value '{value}' for {env_name}. "
                f"Valid options: {actual_choices}."
            )
326
327
328
329
330
331

        return value

    return _get_validated_env


332
def env_list_with_choices(
333
334
    env_name: str,
    default: list[str],
335
    choices: list[str] | Callable[[], list[str]],
336
337
    case_sensitive: bool = True,
) -> Callable[[], list[str]]:
338
    """
339
    Create a lambda that validates environment variable
340
    containing comma-separated values against allowed choices
341

342
343
344
345
346
    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
347

348
349
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
    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:
377
378
379
380
                raise ValueError(
                    f"Invalid value '{val}' in {env_name}. "
                    f"Valid options: {actual_choices}."
                )
381
382
383
384
385
386

        return values

    return _get_validated_env_list


387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
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


405
def get_vllm_port() -> int | None:
406
    """Get the port from VLLM_PORT environment variable.
407

408
409
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
410

411
412
413
    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
414
    if "VLLM_PORT" not in os.environ:
415
416
        return None

417
    port = os.getenv("VLLM_PORT", "0")
418
419
420
421
422

    try:
        return int(port)
    except ValueError as err:
        from urllib.parse import urlparse
423

424
425
426
427
428
429
430
        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
431
        raise ValueError(f"VLLM_PORT '{port}' must be a valid integer") from err
432
433


Ning Xie's avatar
Ning Xie committed
434
# The start-* and end* here are used by the documentation generator
435
436
# to extract the used env vars.

437
# --8<-- [start:env-vars-definition]
438

439
440
logger = logging.getLogger(__name__)

441
environment_variables: dict[str, Callable[[], Any]] = {
442
    # ================== Installation Time Env Vars ==================
443
    # Target device of vLLM, supporting [cuda (by default),
444
    # rocm, cpu]
445
    "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
446
447
    # Main CUDA version of vLLM, supporting [12.6, 12.8, 12.9],
    # 12.8 is the default. This follows PyTorch but can be overridden.
448
449
    "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
    or "12.8",
450
451
    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
452
    "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
453
454
455
    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
456
    "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
457
    # If set, vllm will use precompiled binaries (*.so)
458
459
460
461
462
    "VLLM_USE_PRECOMPILED": lambda: os.environ.get("VLLM_USE_PRECOMPILED", "")
    .strip()
    .lower()
    in ("1", "true")
    or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
463
464
    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
465
466
467
468
    "VLLM_DOCKER_BUILD_CONTEXT": lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "")
    .strip()
    .lower()
    in ("1", "true"),
469
470
    # Whether to force using nightly wheel in python build.
    # This is used for testing the nightly wheel in python build.
471
472
473
    "VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL": lambda: bool(
        int(os.getenv("VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL", "0"))
    ),
474
475
476
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
477
478
479
    "CMAKE_BUILD_TYPE": env_with_choices(
        "CMAKE_BUILD_TYPE", None, ["Debug", "Release", "RelWithDebInfo"]
    ),
480
    # If set, vllm will print verbose logs during installation
481
    "VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
482
    # Root directory for vLLM configuration files
483
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
484
485
486
    # 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**.
487
    "VLLM_CONFIG_ROOT": lambda: os.path.expanduser(
488
489
490
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
491
492
        )
    ),
493
    # ================== Runtime Env Vars ==================
494
    # Root directory for vLLM cache files
495
    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
496
    "VLLM_CACHE_ROOT": lambda: os.path.expanduser(
497
498
499
        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
500
501
        )
    ),
502
503
504
505
    # 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.
506
    "VLLM_HOST_IP": lambda: os.getenv("VLLM_HOST_IP", ""),
507
    # used in distributed environment to manually set the communication port
508
509
510
    # 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.
511
    "VLLM_PORT": get_vllm_port,
512
513
    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
514
515
516
    "VLLM_RPC_BASE_PATH": lambda: os.getenv(
        "VLLM_RPC_BASE_PATH", tempfile.gettempdir()
    ),
517
518
    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
519
520
521
522
    "VLLM_USE_MODELSCOPE": lambda: os.environ.get(
        "VLLM_USE_MODELSCOPE", "False"
    ).lower()
    == "true",
523
    # Interval in seconds to log a warning message when the ring buffer is full
524
525
526
    "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
        os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
    ),
527
528
    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
529
    "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None),
530
531
    # 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
532
    "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),
533
534
    # 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`
535
    "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
536
537
538
539
    # 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")
    ),
540
541
    # Use separate prefill and decode kernels for V1 attention instead of
    # the unified triton kernel.
542
543
544
545
    "VLLM_V1_USE_PREFILL_DECODE_ATTENTION": lambda: (
        os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower()
        in ("true", "1")
    ),
546
547
    # Force vllm to use a specific flash-attention version (2 or 3), only valid
    # when using the flash-attention backend.
548
549
550
    "VLLM_FLASH_ATTN_VERSION": lambda: maybe_convert_int(
        os.environ.get("VLLM_FLASH_ATTN_VERSION", None)
    ),
551
    # Feature flag to enable/disable Inductor standalone compile.
552
553
    # In torch <= 2.7 we ignore this flag; in torch >= 2.9 this is
    # enabled by default.
554
    "VLLM_USE_STANDALONE_COMPILE": lambda: os.environ.get(
555
        "VLLM_USE_STANDALONE_COMPILE", "1"
556
557
    )
    == "1",
558
559
    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
560
561
562
    "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get(
        "VLLM_PATTERN_MATCH_DEBUG", None
    ),
563
564
    # Dump fx graphs to the given directory.
    # It will override CompilationConfig.debug_dump_path if set.
565
    "VLLM_DEBUG_DUMP_PATH": lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None),
566
567
568
569
    # 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,
570
571
572
573
574
    # Feature flag to enable/disable bytecode in
    # TorchCompileWithNoGuardsWrapper.
    "VLLM_USE_BYTECODE_HOOK": lambda: bool(
        int(os.environ.get("VLLM_USE_BYTECODE_HOOK", "1"))
    ),
575
576
577
578
    # 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",
579
580
    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
581
    "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")),
582
    # used to control the visible devices in the distributed setting
583
    "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
584
    # timeout for each iteration in the engine
585
586
587
    "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")
    ),
588
    # API key for vLLM API server
589
    "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None),
590
    # Whether to log responses from API Server for debugging
591
592
593
594
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: os.environ.get(
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
    ).lower()
    == "true",
595
    # S3 access information, used for tensorizer to load model from S3
596
597
598
    "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),
599
    # Usage stats collection
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
    "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"),
615
616
617
618
    # 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
619
620
    "VLLM_CONFIGURE_LOGGING": lambda: int(os.getenv("VLLM_CONFIGURE_LOGGING", "1")),
    "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),
621
    # this is used for configuring the default logging level
622
    "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
623
    # this is used for configuring the default logging stream
624
    "VLLM_LOGGING_STREAM": lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),
625
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
626
    "VLLM_LOGGING_PREFIX": lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
Nick Hill's avatar
Nick Hill committed
627
628
629
630
631
    # 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",
632
633
    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
634
635
636
    "VLLM_LOG_STATS_INTERVAL": lambda: val
    if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10."))) > 0.0
    else 10.0,
637
638
639
    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
640
    "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
641
    # Backend for attention computation
642
    # Example options:
643
644
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
645
    # - "FLASHINFER": use flashinfer
646
    # - "FLASHMLA": use FlashMLA
647
    # - "FLASH_ATTN_MLA": use FlashAttention for MLA
648
649
    # - "FLASHINFER_MLA": use FlashInfer for MLA
    # - "CUTLASS_MLA": use CUTLASS for MLA
650
    # All possible options loaded dynamically from AttentionBackendEnum
651
652
653
654
655
    "VLLM_ATTENTION_BACKEND": env_with_choices(
        "VLLM_ATTENTION_BACKEND",
        None,
        lambda: list(
            __import__(
656
657
                "vllm.attention.backends.registry", fromlist=["AttentionBackendEnum"]
            ).AttentionBackendEnum.__members__.keys()
658
659
        ),
    ),
660
    # If set, vllm will use flashinfer sampler
661
662
663
664
665
    "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
        int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"])
    )
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
    else None,
666
    # Pipeline stage partition strategy
667
    "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
668
    # (CPU backend only) CPU key-value cache space.
669
    # default is None and will be set as 4 GB
670
671
672
    "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ
    else None,
673
674
    # (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 '|'.
675
    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
676
677
    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
678
679
680
681
682
    "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,
683
    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
684
    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
685
686
687
688
689
690
691
    # 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
692
693
694
    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
695
    # If the env var is set, it enables GPU communication overlap
696
    # (experimental feature) in Ray's Compiled Graph.
697
698
699
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
700
701
702
    # 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.
703
704
705
    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
706
707
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
708
709
710
    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
        "VLLM_WORKER_MULTIPROC_METHOD", "fork", ["spawn", "fork"]
    ),
711
    # Path to the cache for storing downloaded assets
712
    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
713
714
715
        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
716
717
        )
    ),
718
719
    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
720
721
722
    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
723
724
    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
725
    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
726
    # Timeout for fetching videos when serving multimodal models
727
    # Default is 30 seconds
728
729
730
    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
731
    # Timeout for fetching audio when serving multimodal models
732
    # Default is 10 seconds
733
734
735
    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
736
737
    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
738
739
740
    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
741
742
743
    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
744
745
746
    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
747
748
749
    # 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
750
751
752
    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
753
754
755
756
757
758
759
    # 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.
760
761
762
    "VLLM_VIDEO_LOADER_BACKEND": lambda: os.getenv(
        "VLLM_VIDEO_LOADER_BACKEND", "opencv"
    ),
763
764
765
766
767
768
769
770
    # 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"),
771
    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
772
    # Default is 4 GiB per API process + 4 GiB per engine core process
773
    "VLLM_MM_INPUT_CACHE_GIB": lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
774
775
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
776
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
777
        os.getenv(
778
            "VLLM_XLA_CACHE_PATH",
779
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
780
781
        )
    ),
782
    # If set, assert on XLA recompilation after each execution step.
783
784
785
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
786
    # Enable SPMD mode for TPU backend.
787
788
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
789
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(16 * 1024))
790
    ),
791
792
793
    # 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.
794
795
796
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
797
798
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
799
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
800
        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
801
    ),
802
803
804
805
    # 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.
806
807
808
809
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
810
811
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
812
813
814
815
816
817
818
    "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"
    ),
819
820
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
821
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
822
    # Timeout in seconds for keeping HTTP connections alive in API server
823
824
825
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
826
827
828
    # 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
829
830
831
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
832
833
834
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
835
836
837
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
838
839
840
841
842
    # Enables torch CUDA profiling if set.
    # On NVIDIA GPUs, this will start/stop cudaProfilerApi when triggered.
    "VLLM_TORCH_CUDA_PROFILE": lambda: bool(
        os.getenv("VLLM_TORCH_CUDA_PROFILE", "0") != "0"
    ),
843
844
845
846
    # 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.
847
848
    "VLLM_TORCH_PROFILER_DIR": lambda: (
        None
849
850
851
852
853
        if (val := os.getenv("VLLM_TORCH_PROFILER_DIR")) is None
        else (
            val
            if val.startswith("gs://") and val[5:] and val[5] != "/"
            else os.path.abspath(os.path.expanduser(val))
854
855
        )
    ),
856
857
858
    # Enable torch profiler to record shapes if set
    # VLLM_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
    # not record shapes.
859
860
861
    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"
    ),
862
863
864
    # Enable torch profiler to profile memory if set
    # VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
    # will not profile memory.
865
866
867
    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"
    ),
868
869
870
    # Enable torch profiler to profile stack if set
    # VLLM_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
    # profile stack by default.
871
872
873
    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"
    ),
874
875
876
    # Enable torch profiler to profile flops if set
    # VLLM_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
    # not profile flops.
877
878
879
    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"
    ),
880
881
882
883
884
885
886
887
888
889
890
891
892
    # Disable torch profiling of the AsyncLLMEngine process.
    # If set to 1, will not profile the engine process.
    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM", "0") != "0"
    ),
    # Delay number of iterations before starting profiling when using
    # the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
    "VLLM_PROFILER_DELAY_ITERS": lambda: int(
        os.getenv("VLLM_PROFILER_DELAY_ITERS", "0")
    ),
    # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
    # If set to 0, will not limit the number of iterations.
    "VLLM_PROFILER_MAX_ITERS": lambda: int(os.getenv("VLLM_PROFILER_MAX_ITERS", "0")),
893
    # If set, vLLM will use Triton implementations of AWQ.
894
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
895
    # If set, allow loading or unloading lora adapters in runtime,
896
897
898
899
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
900
901
902
903
904
905
    # 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
906
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
907
908
909
910
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
911
912
913
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
914
    # Disable pynccl (using torch.distributed instead)
915
916
917
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
918
919
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
920
921
922
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
923
924
    # Whether to use aiter paged attention.
    # By default is disabled.
925
926
927
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
928
929
930
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
931
932
933
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in ("true", "1")
    ),
934
935
    # Whether to use aiter moe ops.
    # By default is enabled.
936
937
938
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in ("true", "1")
    ),
939
    # use aiter rms norm op if aiter ops are enabled.
940
941
942
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in ("true", "1")
    ),
943
944
    # Whether to use aiter mla ops.
    # By default is enabled.
945
946
947
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in ("true", "1")
    ),
948
949
    # Whether to use aiter mha ops.
    # By default is enabled.
950
951
952
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in ("true", "1")
    ),
953
954
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
955
956
957
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
958
959
    # Whether to use aiter rope.
    # By default is disabled.
960
961
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "False").lower() in ("true", "1")
962
    ),
963
964
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
965
966
967
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
    ),
968
969
970
971
972
    # 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")
    ),
973
    # Whether to use aiter fusion shared experts ops.
974
    # By default is disabled.
975
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
976
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
977
978
        in ("true", "1")
    ),
979
980
981
982
983
    # 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")
    ),
984
    # use rocm skinny gemms
985
986
987
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
988
    # Pad the fp8 weights to 256 bytes for ROCm
989
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
990
    # Pad the weights for the moe kernel
991
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),
992
    # custom paged attention kernel for MI3* cards
993
994
995
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
996
997
998
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
999
1000
1001
1002
1003
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
1004
1005
1006
1007
    # 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
1008
1009
1010
1011
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1012
1013
1014
1015
1016
1017
    # 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.
1018
1019
1020
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1021
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1022
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1023
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1024
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1025
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1026
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1027
    # If set, enable multiprocessing in LLM for the V1 code path.
1028
1029
1030
1031
1032
1033
    "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")
    ),
1034
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1035
1036
1037
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1038
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1039
1040
1041
1042
1043
1044
1045
    # 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.
1046
1047
1048
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1049
    # If set, vLLM will disable the MLA attention optimizations.
1050
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1051
1052
    # If set, vLLM will pick up the provided Flash Attention MLA
    # max number splits for cuda graph decode
1053
1054
1055
    "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": lambda: int(
        os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH", "32")
    ),
1056
1057
1058
    # 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.
1059
1060
1061
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1062
1063
1064
    # 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"
1065
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1066
1067
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1068
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1069
    # Rank of the process in the data parallel setting
1070
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1071
1072
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1073
1074
1075
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1076
    # World size of the data parallel setting
1077
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1078
    # IP address of the master node in the data parallel setting
1079
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1080
    # Port of the master node in the data parallel setting
1081
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1082
1083
1084
1085
1086
    # 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.
1087
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1088
    # Randomize inputs during dummy runs when using Data Parallel
1089
1090
1091
1092
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1093
1094
1095
1096
1097
1098
1099
    # 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;
1100
1101
1102
    # - "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;
1103
1104
1105
1106
    # 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"
    ),
1107
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1108
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1109
    # Use model_redirect to redirect the model name to a local folder.
1110
1111
1112
1113
1114
    # `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
1115
1116
1117
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1118
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1119
1120
1121
1122
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1123
    # Whether to use marlin kernel in mxfp4 quantization method
1124
1125
1126
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1127
1128
1129
1130
    # The activation dtype for marlin kernel
    "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
        "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
    ),
1131
1132
1133
1134
1135
1136
    # 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"))
    ),
1137
1138
1139
    # 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.
1140
1141
1142
1143
    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
1144
1145
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
1146
1147
1148
1149
1150
1151
1152
1153
    "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)
    ),
1154
    # Whether using Pathways
1155
1156
1157
    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
1158
    # Allow use of DeepGemm kernels for fused moe ops.
1159
    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1160
1161
1162
1163
    # 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"))
    ),
1164
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
1165
1166
1167
    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
1168
1169
1170
1171
    # 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.
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
    # 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",
        ],
1187
    ),
1188
    # Whether to use fused grouped_topk used for MoE expert selection.
1189
1190
1191
    "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
        int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))
    ),
1192
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1193
1194
1195
    "VLLM_USE_FLASHINFER_MOE_FP16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP16", "0"))
    ),
1196
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1197
1198
1199
    "VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
    ),
1200
    # Allow use of FlashInfer CUTLASS kernels for fused moe ops.
1201
1202
1203
    "VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
    ),
1204
1205
    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
1206
1207
1208
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))
    ),
1209
1210
1211
1212
    # 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.
1213
1214
1215
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0"))
    ),
1216
1217
    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
1218
1219
1220
    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
    ),
1221
1222
1223
    # 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.
1224
    "VLLM_XGRAMMAR_CACHE_MB": lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
1225
1226
1227
1228
1229
1230
1231
    # 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.
1232
1233
1234
    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
        os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
    ),
1235
1236
1237
    # 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.
1238
1239
1240
    "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
        int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
    ),
Robert Shaw's avatar
Robert Shaw committed
1241
    # IP address used for NIXL handshake between remote agents.
1242
1243
1244
    "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
        "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
    ),
Robert Shaw's avatar
Robert Shaw committed
1245
    # Port used for NIXL handshake between remote agents.
1246
1247
1248
    "VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
        os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")
    ),
1249
    # all2all backend for vllm's expert parallel communication
1250
    # Available options:
1251
1252
1253
    # - "naive": naive all2all implementation using broadcasts
    # - "allgather_reducescatter": all2all implementation based on allgather and
    #  reducescatter
1254
    # - "pplx": use pplx kernels
1255
1256
    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
1257
    # - "flashinfer_all2allv", use flashinfer alltoallv kernels for mnnvl
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
    "VLLM_ALL2ALL_BACKEND": env_with_choices(
        "VLLM_ALL2ALL_BACKEND",
        "allgather_reducescatter",
        [
            "naive",
            "pplx",
            "deepep_high_throughput",
            "deepep_low_latency",
            "allgather_reducescatter",
            "flashinfer_all2allv",
        ],
    ),
1270
1271
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
1272
1273
1274
1275
1276
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
1277
    "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
1278
1279
1280
        "VLLM_FLASHINFER_MOE_BACKEND",
        "latency",
        ["throughput", "latency", "masked_gemm"],
1281
    ),
1282
1283
1284
1285
    # 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))
    ),
1286
1287
1288
1289
    # 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.
1290
1291
1292
    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
        os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")
    ),
1293
1294
1295
1296
    # 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> }
1297
    # Unspecified world sizes will fall back to
1298
    #     { 2: 64, 4: 1, <everything else>: 0.5 }
1299
1300
1301
    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB": lambda: json.loads(
        os.getenv("VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")
    ),
1302
1303
1304
    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
1305
    # Custom routing strategies can be registered by
1306
1307
    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
1308
1309
1310
    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: os.environ.get(
        "VLLM_MOE_ROUTING_SIMULATION_STRATEGY", ""
    ).lower(),
1311
    # Regex timeout for use by the vLLM tool parsing plugins.
1312
1313
1314
    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
    ),
1315
1316
    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
1317
    "VLLM_SLEEP_WHEN_IDLE": lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
1318
1319
1320
    # 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.
1321
1322
1323
    "VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
        os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
    ),
1324
1325
    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
1326
1327
1328
    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
    ),
1329
1330
1331
1332
1333
1334
1335
    # 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.
1336
1337
1338
    "VLLM_KV_CACHE_LAYOUT": env_with_choices(
        "VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]
    ),
1339
1340
1341
    # 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.
1342
1343
1344
    "VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
        int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
    ),
1345
1346
1347
    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
1348
1349
1350
    "VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
        int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
    ),
1351
1352
1353
1354
    # 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.
1355
1356
1357
    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")
    ),
1358
    # Controls whether or not to use cudnn prefill
1359
1360
1361
    "VLLM_USE_CUDNN_PREFILL": lambda: bool(
        int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))
    ),
1362
1363
1364
1365
    # 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"))
    ),
1366
1367
1368
    # 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.
1369
1370
1371
1372
1373
    "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")
    ),
1374
    # If set to 1, when we use fp8 kv, we do not quantize Q to fp8
1375
1376
1377
    "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION": lambda: bool(
        int(os.getenv("VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION", "0"))
    ),
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
1387
1388
1389
1390
    # 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,
1391
        ["flashinfer-cudnn", "flashinfer-trtllm", "flashinfer-cutlass", "cutlass"],
1392
    ),
1393
1394
1395
    # 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.
1396
1397
1398
    "VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
        int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
    ),
1399
    # Used to force set up loopback IP
1400
    "VLLM_LOOPBACK_IP": lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1401
1402
1403
    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
1404
    "VLLM_PROCESS_NAME_PREFIX": lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
1405
1406
1407
1408
1409
1410
1411
    # 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.
1412
1413
1414
    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "0"))
    ),
1415
1416
    # 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
1417
1418
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1419
1420
1421
1422
1423
    # 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.
1424
1425
1426
    "VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
    ),
xiao-llm's avatar
xiao-llm committed
1427
    # If set, use the fp8 mfma in rocm paged attention.
1428
1429
1430
    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN": lambda: bool(
        int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))
    ),
1431
    # Whether to use pytorch symmetric memory for allreduce
1432
    "VLLM_ALLREDUCE_USE_SYMM_MEM": lambda: bool(
1433
        int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1"))
1434
    ),
1435
    # Allows vllm to find tuned config under customized folder
1436
    "VLLM_TUNED_CONFIG_FOLDER": lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
1437
1438
1439
1440
1441
1442
1443
1444
1445
    # 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"],
    ),
1446
    # Allows harmony instructions to be injected on system messages
1447
1448
1449
    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS": lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))
    ),
1450
1451
1452
1453
1454
1455
    # 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"))
    ),
1456
    # Add optional custom scopes for profiling, disable to avoid overheads
1457
1458
1459
    "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
    ),
1460
    # Add optional nvtx scopes for profiling, disable to avoid overheads
1461
1462
1463
    "VLLM_NVTX_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))
    ),
1464
1465
    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
1466
1467
1468
    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES": lambda: bool(
        int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))
    ),
1469
1470
    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
1471
1472
1473
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": lambda: os.getenv(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME", "VLLM_OBJECT_STORAGE_SHM_BUFFER"
    ),
1474
    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
1475
1476
1477
    "VLLM_DEEPEP_BUFFER_SIZE_MB": lambda: int(
        os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")
    ),
1478
1479
1480
1481
1482
1483
1484
1485
1486
1487
1488
    # 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"))
    ),
1489
1490
    # 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
1491
    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
1492
1493
1494
    # 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)
1495
1496
1497
    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))
    ),
1498
1499
    # If set to 1, enable coordinate_descent_tuning;
    # By default, this is enabled (1)
1500
1501
1502
    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING", "1"))
    ),
1503
    # Flag to enable NCCL symmetric memory allocation and registration
1504
1505
1506
    "VLLM_USE_NCCL_SYMM_MEM": lambda: bool(
        int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))
    ),
1507
    # NCCL header path
1508
    "VLLM_NCCL_INCLUDE_PATH": lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None),
1509
1510
    # Flag to enable FBGemm kernels on model execution
    "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))),
1511
1512
1513
1514
1515
1516
    # 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", ""),
1517
    # Disables parallel execution of shared_experts via separate cuda stream
1518
1519
    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: bool(
        int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "0"))
1520
    ),
1521
1522
1523
1524
1525
1526
1527
    # 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))
    ),
1528
1529
1530
1531
1532
1533
1534
1535
1536
    # 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
1537
1538
1539
1540
    # Flag to enable v2 model runner.
    "VLLM_USE_V2_MODEL_RUNNER": lambda: bool(
        int(os.getenv("VLLM_USE_V2_MODEL_RUNNER", "0"))
    ),
1541
1542
}

1543
# --8<-- [end:env-vars-definition]
1544

1545

1546
def __getattr__(name: str):
1547
1548
1549
1550
1551
1552
    """
    Gets environment variables lazily.

    NOTE: After enable_envs_cache() invocation (which triggered after service
    initialization), all environment variables will be cached.
    """
1553
1554
1555
1556
1557
    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
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)


1577
1578
def __dir__():
    return list(environment_variables.keys())
1579
1580
1581
1582
1583
1584
1585
1586
1587


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}")


1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
def compile_factors() -> dict[str, object]:
    """Return env vars used for torch.compile cache keys.

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

    ignored_factors: set[str] = {
        "MAX_JOBS",
        "VLLM_RPC_BASE_PATH",
        "VLLM_USE_MODELSCOPE",
        "VLLM_RINGBUFFER_WARNING_INTERVAL",
        "VLLM_DEBUG_DUMP_PATH",
        "VLLM_PORT",
        "VLLM_CACHE_ROOT",
        "LD_LIBRARY_PATH",
        "VLLM_SERVER_DEV_MODE",
        "VLLM_DP_MASTER_IP",
        "VLLM_DP_MASTER_PORT",
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS",
        "VLLM_CI_USE_S3",
        "VLLM_MODEL_REDIRECT_PATH",
        "VLLM_HOST_IP",
        "S3_ACCESS_KEY_ID",
        "S3_SECRET_ACCESS_KEY",
        "S3_ENDPOINT_URL",
        "VLLM_USAGE_STATS_SERVER",
        "VLLM_NO_USAGE_STATS",
        "VLLM_DO_NOT_TRACK",
        "VLLM_LOGGING_LEVEL",
        "VLLM_LOGGING_PREFIX",
        "VLLM_LOGGING_STREAM",
        "VLLM_LOGGING_CONFIG_PATH",
Nick Hill's avatar
Nick Hill committed
1620
        "VLLM_LOGGING_COLOR",
1621
1622
1623
1624
1625
1626
1627
1628
1629
1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
1641
1642
1643
1644
1645
1646
1647
1648
1649
1650
        "VLLM_LOG_STATS_INTERVAL",
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE",
        "VLLM_TUNED_CONFIG_FOLDER",
        "VLLM_ENGINE_ITERATION_TIMEOUT_S",
        "VLLM_HTTP_TIMEOUT_KEEP_ALIVE",
        "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS",
        "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH",
        "VLLM_SLEEP_WHEN_IDLE",
        "VLLM_IMAGE_FETCH_TIMEOUT",
        "VLLM_VIDEO_FETCH_TIMEOUT",
        "VLLM_AUDIO_FETCH_TIMEOUT",
        "VLLM_MEDIA_URL_ALLOW_REDIRECTS",
        "VLLM_MEDIA_LOADING_THREAD_COUNT",
        "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB",
        "VLLM_VIDEO_LOADER_BACKEND",
        "VLLM_MEDIA_CONNECTOR",
        "VLLM_ASSETS_CACHE",
        "VLLM_ASSETS_CACHE_MODEL_CLEAN",
        "VLLM_MM_INPUT_CACHE_GIB",
        "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
1651
        "NO_COLOR",
1652
1653
1654
1655
1656
1657
1658
1659
1660
1661
1662
1663
1664
1665
1666
1667
1668
1669
    }

    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
1670

1671
        factors[factor] = normalize_value(raw)
1672

1673
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683
1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
    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",
    ]

1695
1696
    for var in ray_noset_env_vars:
        factors[var] = normalize_value(os.getenv(var))
1697

1698
    return factors