envs.py 62.4 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import hashlib
5
import json
6
import os
7
import sys
8
import tempfile
9
from typing import TYPE_CHECKING, Any, Callable, Literal, Optional, Union
10
11
12

if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
13
    VLLM_PORT: Optional[int] = None
14
    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
15
    VLLM_USE_MODELSCOPE: bool = False
16
    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
17
18
    VLLM_NCCL_SO_PATH: Optional[str] = None
    LD_LIBRARY_PATH: Optional[str] = None
19
    VLLM_USE_TRITON_FLASH_ATTN: bool = True
20
    VLLM_V1_USE_PREFILL_DECODE_ATTENTION: bool = False
21
    VLLM_USE_AITER_UNIFIED_ATTENTION: bool = False
22
    VLLM_FLASH_ATTN_VERSION: Optional[int] = None
23
24
25
26
27
28
29
    LOCAL_RANK: int = 0
    CUDA_VISIBLE_DEVICES: Optional[str] = None
    VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
    VLLM_API_KEY: Optional[str] = None
    S3_ACCESS_KEY_ID: Optional[str] = None
    S3_SECRET_ACCESS_KEY: Optional[str] = None
    S3_ENDPOINT_URL: Optional[str] = None
30
    VLLM_MODEL_REDIRECT_PATH: Optional[str] = None
31
32
    VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm")
    VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm")
33
34
    VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai"
    VLLM_NO_USAGE_STATS: bool = False
35
    VLLM_DISABLE_FLASHINFER_PREFILL: bool = False
36
37
38
    VLLM_DO_NOT_TRACK: bool = False
    VLLM_USAGE_SOURCE: str = ""
    VLLM_CONFIGURE_LOGGING: int = 1
39
    VLLM_LOGGING_LEVEL: str = "INFO"
40
    VLLM_LOGGING_PREFIX: str = ""
41
    VLLM_LOGGING_STREAM: str = "ext://sys.stdout"
42
    VLLM_LOGGING_CONFIG_PATH: Optional[str] = None
43
    VLLM_LOGITS_PROCESSOR_THREADS: Optional[int] = None
44
    VLLM_LOG_STATS_INTERVAL: float = 10.
45
46
    VLLM_TRACE_FUNCTION: int = 0
    VLLM_ATTENTION_BACKEND: Optional[str] = None
47
    VLLM_USE_FLASHINFER_SAMPLER: Optional[bool] = None
48
    VLLM_PP_LAYER_PARTITION: Optional[str] = None
49
    VLLM_CPU_KVCACHE_SPACE: Optional[int] = 0
50
    VLLM_CPU_OMP_THREADS_BIND: str = ""
51
    VLLM_CPU_NUM_OF_RESERVED_CPU: Optional[int] = None
52
    VLLM_CPU_MOE_PREPACK: bool = True
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 = 64 * 1024
57
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
58
    VLLM_USE_RAY_SPMD_WORKER: bool = False
59
    VLLM_USE_RAY_COMPILED_DAG: bool = False
60
61
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl",
                                                    "shm"] = "auto"
62
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
63
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
64
    VLLM_XLA_USE_SPMD: bool = False
65
    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "fork"
66
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
67
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
68
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
69
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
70
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
71
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
72
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
73
    VLLM_MM_INPUT_CACHE_GIB: int = 4
74
    VLLM_TARGET_DEVICE: str = "cuda"
75
    VLLM_MAIN_CUDA_VERSION: str = "12.8"
76
77
78
    MAX_JOBS: Optional[str] = None
    NVCC_THREADS: Optional[str] = None
    VLLM_USE_PRECOMPILED: bool = False
79
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
80
    VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
81
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
82
83
    CMAKE_BUILD_TYPE: Optional[Literal["Debug", "Release",
                                       "RelWithDebInfo"]] = 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
    VLLM_PLUGINS: Optional[list[str]] = None
89
    VLLM_LORA_RESOLVER_CACHE_DIR: Optional[str] = None
90
    VLLM_TORCH_PROFILER_DIR: Optional[str] = None
91
92
93
94
    VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
    VLLM_TORCH_PROFILER_WITH_STACK: bool = True
    VLLM_TORCH_PROFILER_WITH_FLOPS: 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_NCCL_FOR_DP_SYNCHRONIZATION: bool = False
100
    VLLM_USE_V1: bool = True
101
    VLLM_ROCM_USE_AITER: bool = False
102
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
103
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
104
    VLLM_ROCM_USE_AITER_MOE: bool = True
105
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
106
    VLLM_ROCM_USE_AITER_MLA: bool = True
107
    VLLM_ROCM_USE_AITER_MHA: bool = True
108
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
109
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
110
    VLLM_ROCM_FP8_PADDING: bool = True
111
    VLLM_ROCM_MOE_PADDING: bool = True
112
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
113
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
114
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
115
    VLLM_DISABLE_COMPILE_CACHE: bool = False
116
    Q_SCALE_CONSTANT: int = 200
117
118
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
119
    VLLM_SERVER_DEV_MODE: bool = False
120
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
121
    VLLM_MLA_DISABLE: bool = False
122
    VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH: int = 16
123
124
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
125
    VLLM_CUDART_SO_PATH: Optional[str] = None
126
    VLLM_DP_RANK: int = 0
127
    VLLM_DP_RANK_LOCAL: int = -1
128
    VLLM_DP_SIZE: int = 1
129
    VLLM_USE_STANDALONE_COMPILE: bool = False
130
131
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
132
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
133
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
134
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
135
    VLLM_MXFP4_USE_MARLIN: Optional[bool] = None
136
    VLLM_V0_USE_OUTLINES_CACHE: bool = False
137
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
138
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
139
    VLLM_TPU_MOST_MODEL_LEN: Optional[int] = None
140
    VLLM_TPU_USING_PATHWAYS: bool = False
141
    VLLM_USE_DEEP_GEMM: bool = True
142
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
143
    VLLM_USE_DEEP_GEMM_E8M0_HOPPER: bool = False
144
    VLLM_SKIP_DEEP_GEMM_WARMUP: bool = False
145
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
146
147
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
148
149
    VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput",
                                         "latency"] = "throughput"
150
    VLLM_XGRAMMAR_CACHE_MB: int = 0
151
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
152
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
Robert Shaw's avatar
Robert Shaw committed
153
154
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5557
155
156
157
158
159
    VLLM_ALL2ALL_BACKEND: Literal["naive", "pplx",
                                  "deepep_high_throughput",
                                  "deepep_low_latency",
                                  "allgather_reducescatter"] = \
                                  "allgather_reducescatter"
160
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
161
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
162
    VLLM_SLEEP_WHEN_IDLE: bool = False
163
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
164
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
165
    VLLM_KV_CACHE_LAYOUT: Optional[Literal["NHD", "HND"]] = None
166
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
167
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
168
169
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal["FP", "INT8", "INT6", "INT4",
                                                 "NONE"] = "NONE"
170
171
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: Optional[int] = None
172
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 120
173
    VLLM_USE_CUDNN_PREFILL: bool = False
174
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
175
    VLLM_LOOPBACK_IP: str = ""
176
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = False
177
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
178
    VLLM_USE_TRTLLM_ATTENTION: Optional[str] = None
179
    VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION: bool = False
180
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
181
182
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
xiao-llm's avatar
xiao-llm committed
183
    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
184
185
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = False
186
    VLLM_TUNED_CONFIG_FOLDER: Optional[str] = None
187
    VLLM_DISABLE_PAD_FOR_CUDAGRAPH: bool = False
188
    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
189
    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
190
    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
191
    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
192
193
    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
    VLLM_DBO_COMM_SMS: int = 20
194
    GPT_OSS_SYSTEM_TOOL_MCP_LABELS: list[str] = []
195
    VLLM_PATTERN_MATCH_DEBUG: Optional[str] = None
196

197
198
199
200
201
202
203
204
205
206
207
208
209
210
211

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"),
    )


212
213
214
215
216
217
def maybe_convert_int(value: Optional[str]) -> Optional[int]:
    if value is None:
        return None
    return int(value)


218
219
220
221
222
223
def maybe_convert_bool(value: Optional[str]) -> Optional[bool]:
    if value is None:
        return None
    return bool(int(value))


224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
def env_with_choices(
        env_name: str,
        default: Optional[str],
        choices: Union[list[str], Callable[[], list[str]]],
        case_sensitive: bool = True) -> Callable[[], Optional[str]]:
    """
    Create a lambda that validates environment variable against allowed choices
    
    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
        
    Returns:
        Lambda function for environment_variables dict
    """

    def _get_validated_env() -> Optional[str]:
        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:
            raise ValueError(f"Invalid value '{value}' for {env_name}. "
                             f"Valid options: {actual_choices}.")

        return value

    return _get_validated_env


266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
def env_list_with_choices(
        env_name: str,
        default: list[str],
        choices: Union[list[str], Callable[[], list[str]]],
        case_sensitive: bool = True) -> Callable[[], list[str]]:
    """
    Create a lambda that validates environment variable 
    containing comma-separated values against allowed choices
    
    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
        
    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:
                raise ValueError(f"Invalid value '{val}' in {env_name}. "
                                 f"Valid options: {actual_choices}.")

        return values

    return _get_validated_env_list


318
319
def get_vllm_port() -> Optional[int]:
    """Get the port from VLLM_PORT environment variable.
320

321
322
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
323

324
325
326
327
328
329
330
331
332
333
334
335
    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
    if 'VLLM_PORT' not in os.environ:
        return None

    port = os.getenv('VLLM_PORT', '0')

    try:
        return int(port)
    except ValueError as err:
        from urllib.parse import urlparse
336
337
338
339
340
341
342
        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
343
344
345
346
        raise ValueError(
            f"VLLM_PORT '{port}' must be a valid integer") from err


347
348
349
# The begin-* and end* here are used by the documentation generator
# to extract the used env vars.

350
# --8<-- [start:env-vars-definition]
351

352
environment_variables: dict[str, Callable[[], Any]] = {
353
354
355

    # ================== Installation Time Env Vars ==================

356
    # Target device of vLLM, supporting [cuda (by default),
357
    # rocm, cpu]
358
    "VLLM_TARGET_DEVICE":
359
    lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
360

361
362
363
364
365
    # Main CUDA version of vLLM, supporting [12.6, 12.8, 12.9],
    # 12.8 is the default. This follows PyTorch but can be overridden.
    "VLLM_MAIN_CUDA_VERSION":
    lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower() or "12.8",

366
367
368
369
370
371
372
373
374
375
376
377
378
    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
    "MAX_JOBS":
    lambda: os.getenv("MAX_JOBS", None),

    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
    "NVCC_THREADS":
    lambda: os.getenv("NVCC_THREADS", None),

    # If set, vllm will use precompiled binaries (*.so)
    "VLLM_USE_PRECOMPILED":
379
380
381
382
383
384
385
386
    lambda: os.environ.get("VLLM_USE_PRECOMPILED", "").strip().lower() in
    ("1", "true") or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),

    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
    "VLLM_DOCKER_BUILD_CONTEXT":
    lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "").strip().lower() in
    ("1", "true"),
387

388
389
390
391
392
393
    # Whether to force using nightly wheel in python build.
    # This is used for testing the nightly wheel in python build.
    "VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL":
    lambda: bool(int(os.getenv("VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL", "0"))
                 ),

394
395
396
397
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
    "CMAKE_BUILD_TYPE":
398
399
    env_with_choices("CMAKE_BUILD_TYPE", None,
        ["Debug", "Release", "RelWithDebInfo"]),
400
401
402
403
404

    # If set, vllm will print verbose logs during installation
    "VERBOSE":
    lambda: bool(int(os.getenv('VERBOSE', '0'))),

405
    # Root directory for vLLM configuration files
406
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
407
408
409
410
    # 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**.
    "VLLM_CONFIG_ROOT":
411
412
413
414
415
    lambda: os.path.expanduser(
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
        )),
416
417
418

    # ================== Runtime Env Vars ==================

419
    # Root directory for vLLM cache files
420
421
422
423
424
425
426
427
    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
    "VLLM_CACHE_ROOT":
    lambda: os.path.expanduser(
        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
        )),

428
429
430
431
    # 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.
432
    'VLLM_HOST_IP':
433
    lambda: os.getenv('VLLM_HOST_IP', ""),
434

435
    # used in distributed environment to manually set the communication port
436
437
438
    # 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.
439
    'VLLM_PORT':
440
    get_vllm_port,
441

442
443
444
445
    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
    'VLLM_RPC_BASE_PATH':
    lambda: os.getenv('VLLM_RPC_BASE_PATH', tempfile.gettempdir()),
446

447
448
449
450
451
    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
    "VLLM_USE_MODELSCOPE":
    lambda: os.environ.get("VLLM_USE_MODELSCOPE", "False").lower() == "true",

452
453
454
455
    # Interval in seconds to log a warning message when the ring buffer is full
    "VLLM_RINGBUFFER_WARNING_INTERVAL":
    lambda: int(os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")),

456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
    "CUDA_HOME":
    lambda: os.environ.get("CUDA_HOME", None),

    # 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
    "VLLM_NCCL_SO_PATH":
    lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),

    # 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`
    "LD_LIBRARY_PATH":
    lambda: os.environ.get("LD_LIBRARY_PATH", None),

    # flag to control if vllm should use triton flash attention
    "VLLM_USE_TRITON_FLASH_ATTN":
    lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "True").lower() in
             ("true", "1")),

476
477
478
479
480
481
482
    # Use separate prefill and decode kernels for V1 attention instead of
    # the unified triton kernel.
    "VLLM_V1_USE_PREFILL_DECODE_ATTENTION":
    lambda:
    (os.getenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "False").lower() in
     ("true", "1")),

483
484
485
486
487
488
    # Use AITER triton unified attention for V1 attention
    "VLLM_USE_AITER_UNIFIED_ATTENTION":
    lambda:
    (os.getenv("VLLM_USE_AITER_UNIFIED_ATTENTION", "False").lower() in
     ("true", "1")),

489
490
491
492
493
    # Force vllm to use a specific flash-attention version (2 or 3), only valid
    # when using the flash-attention backend.
    "VLLM_FLASH_ATTN_VERSION":
    lambda: maybe_convert_int(os.environ.get("VLLM_FLASH_ATTN_VERSION", None)),

494
495
    # Feature flag to enable/disable Inductor standalone compile.
    # In torch <= 2.7 we ignore this flag; in torch >= 2.8 this is
496
    # disabled by default.
497
    "VLLM_USE_STANDALONE_COMPILE":
498
    lambda: os.environ.get("VLLM_USE_STANDALONE_COMPILE", "0") == "1",
499

500
501
502
503
504
    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
    "VLLM_PATTERN_MATCH_DEBUG":
    lambda: os.environ.get("VLLM_PATTERN_MATCH_DEBUG", None),

505
506
507
508
509
510
511
512
513
514
515
516
517
    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
    "LOCAL_RANK":
    lambda: int(os.environ.get("LOCAL_RANK", "0")),

    # used to control the visible devices in the distributed setting
    "CUDA_VISIBLE_DEVICES":
    lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),

    # timeout for each iteration in the engine
    "VLLM_ENGINE_ITERATION_TIMEOUT_S":
    lambda: int(os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "60")),

518
    # API key for vLLM API server
519
520
521
    "VLLM_API_KEY":
    lambda: os.environ.get("VLLM_API_KEY", None),

522
523
    # Whether to log responses from API Server for debugging
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
524
525
    lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
                           ).lower() == "true",
526

527
528
    # S3 access information, used for tensorizer to load model from S3
    "S3_ACCESS_KEY_ID":
529
    lambda: os.environ.get("S3_ACCESS_KEY_ID", None),
530
531
532
533
534
535
536
537
538
539
    "S3_SECRET_ACCESS_KEY":
    lambda: os.environ.get("S3_SECRET_ACCESS_KEY", None),
    "S3_ENDPOINT_URL":
    lambda: os.environ.get("S3_ENDPOINT_URL", None),

    # Usage stats collection
    "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",
540
541
    "VLLM_DISABLE_FLASHINFER_PREFILL":
    lambda: os.environ.get("VLLM_DISABLE_FLASHINFER_PREFILL", "0") == "1",
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
    "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"),

    # 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
    "VLLM_CONFIGURE_LOGGING":
    lambda: int(os.getenv("VLLM_CONFIGURE_LOGGING", "1")),
    "VLLM_LOGGING_CONFIG_PATH":
    lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),

557
558
    # this is used for configuring the default logging level
    "VLLM_LOGGING_LEVEL":
559
    lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
560

561
562
563
564
    # this is used for configuring the default logging stream
    "VLLM_LOGGING_STREAM":
    lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),

565
566
567
568
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
    "VLLM_LOGGING_PREFIX":
    lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),

569
570
571
572
573
574
575
576
    # if set, vllm will call logits processors in a thread pool with this many
    # threads. This is useful when using custom logits processors that either
    # (a) launch additional CUDA kernels or (b) do significant CPU-bound work
    # while not holding the python GIL, or both.
    "VLLM_LOGITS_PROCESSOR_THREADS":
    lambda: int(os.getenv("VLLM_LOGITS_PROCESSOR_THREADS", "0"))
    if "VLLM_LOGITS_PROCESSOR_THREADS" in os.environ else None,

577
578
579
580
581
582
    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
    "VLLM_LOG_STATS_INTERVAL":
    lambda: val if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10.")))
        > 0. else 10.,

583
584
585
586
587
588
589
    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
    "VLLM_TRACE_FUNCTION":
    lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),

    # Backend for attention computation
590
    # Example options:
591
592
593
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
    # - "XFORMERS": use XFormers
594
    # - "FLASHINFER": use flashinfer
595
    # - "FLASHMLA": use FlashMLA
596
    # - "FLASH_ATTN_MLA": use FlashAttention for MLA
597
598
    # - "FLASHINFER_MLA": use FlashInfer for MLA
    # - "CUTLASS_MLA": use CUTLASS for MLA
599
    # All possible options loaded dynamically from _Backend enum
600
    "VLLM_ATTENTION_BACKEND":
601
602
603
    env_with_choices("VLLM_ATTENTION_BACKEND", None,
                     lambda: list(__import__('vllm.platforms.interface', \
                        fromlist=['_Backend'])._Backend.__members__.keys())),
604

605
606
    # If set, vllm will use flashinfer sampler
    "VLLM_USE_FLASHINFER_SAMPLER":
607
608
    lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
609

610
611
612
613
    # Pipeline stage partition strategy
    "VLLM_PP_LAYER_PARTITION":
    lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),

614
    # (CPU backend only) CPU key-value cache space.
615
    # default is None and will be set as 4 GB
616
    "VLLM_CPU_KVCACHE_SPACE":
617
618
    lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ else None,
619

620
621
622
    # (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 '|'.
    "VLLM_CPU_OMP_THREADS_BIND":
623
624
625
626
627
    lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),

    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
    "VLLM_CPU_NUM_OF_RESERVED_CPU":
628
629
    lambda: int(os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0"))
    if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ else None,
630

631
632
633
634
635
636
    # (CPU backend only) whether to use prepack for MoE layer. This will be
    # passed to ipex.llm.modules.GatedMLPMOE. On unsupported CPUs, you might
    # need to set this to "0" (False).
    "VLLM_CPU_MOE_PREPACK":
    lambda: bool(int(os.getenv("VLLM_CPU_MOE_PREPACK", "1"))),

637
638
639
640
    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
    "VLLM_CPU_SGL_KERNEL":
    lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),

641
642
643
644
645
    # If the env var is set, then all workers will execute as separate
    # processes from the engine, and we use the same mechanism to trigger
    # execution on all workers.
    # Run vLLM with VLLM_USE_RAY_SPMD_WORKER=1 to enable it.
    "VLLM_USE_RAY_SPMD_WORKER":
646
    lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
647

648
649
650
    # If the env var is set, it uses the Ray's Compiled Graph
    # (previously known as ADAG) API which optimizes the
    # control plane overhead.
651
    # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
652
653
    # Note that this variable is set to 1 in V1 by default
    # when ray distributed executor is used.
654
    "VLLM_USE_RAY_COMPILED_DAG":
655
656
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),

657
658
659
660
661
662
663
664
665
    # 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
    # This flag is ignored if VLLM_USE_RAY_COMPILED_DAG is not set.
    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE":
666
667
    env_with_choices("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto",
        ["auto", "nccl", "shm"]),
668

669
    # If the env var is set, it enables GPU communication overlap
670
    # (experimental feature) in Ray's Compiled Graph. This flag is ignored if
671
672
    # VLLM_USE_RAY_COMPILED_DAG is not set.
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
673
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
674
675
                 ),

676
677
678
679
680
681
682
    # 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.
    # This flag is ignored if VLLM_USE_RAY_COMPILED_DAG is not set.
    "VLLM_USE_RAY_WRAPPED_PP_COMM":
    lambda: bool(int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))),

683
684
685
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
    "VLLM_WORKER_MULTIPROC_METHOD":
686
687
    env_with_choices("VLLM_WORKER_MULTIPROC_METHOD", "fork",
       ["spawn", "fork"]),
688

689
690
691
692
693
694
695
696
    # Path to the cache for storing downloaded assets
    "VLLM_ASSETS_CACHE":
    lambda: os.path.expanduser(
        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
        )),

697
698
699
700
    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
    "VLLM_IMAGE_FETCH_TIMEOUT":
    lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
701

702
    # Timeout for fetching videos when serving multimodal models
703
    # Default is 30 seconds
704
    "VLLM_VIDEO_FETCH_TIMEOUT":
705
    lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")),
706

707
    # Timeout for fetching audio when serving multimodal models
708
    # Default is 10 seconds
709
    "VLLM_AUDIO_FETCH_TIMEOUT":
710
    lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
711

712
713
714
715
716
717
    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
    "VLLM_MEDIA_LOADING_THREAD_COUNT":
    lambda: int(os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")),

718
719
720
721
722
723
    # 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
    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB":
    lambda: int(os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")),

724
725
726
727
728
729
730
731
732
733
    # 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.
    "VLLM_VIDEO_LOADER_BACKEND":
    lambda: os.getenv("VLLM_VIDEO_LOADER_BACKEND", "opencv"),

734
    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
735
    # Default is 4 GiB per API process + 4 GiB per engine core process
736
    "VLLM_MM_INPUT_CACHE_GIB":
737
    lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
738

739
740
741
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
    "VLLM_XLA_CACHE_PATH":
742
743
    lambda: os.path.expanduser(
        os.getenv(
744
            "VLLM_XLA_CACHE_PATH",
745
746
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
        )),
747
748
749
750

    # If set, assert on XLA recompilation after each execution step.
    "VLLM_XLA_CHECK_RECOMPILATION":
    lambda: bool(int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))),
751
752
753
754

    # Enable SPMD mode for TPU backend.
    "VLLM_XLA_USE_SPMD":
    lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
755
    "VLLM_FUSED_MOE_CHUNK_SIZE":
756
    lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
757
758
759
760
761
762
    # 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.
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING":
    lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))),
763

764
765
766
767
768
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH":
    lambda: bool(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", 0)),

769
770
771
772
773
774
775
776
    # 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.
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN":
    lambda:
    (os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower() in
     ("1", "true")),
777
778
779
780
781
782
783

    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
    "VLLM_TEST_FORCE_FP8_MARLIN":
    lambda:
    (os.environ.get("VLLM_TEST_FORCE_FP8_MARLIN", "0").strip().lower() in
     ("1", "true")),
784
785
    "VLLM_TEST_FORCE_LOAD_FORMAT":
    lambda: os.getenv("VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"),
786

787
788
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
789
790
    "VLLM_RPC_TIMEOUT":
    lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
791

792
793
794
795
    # Timeout in seconds for keeping HTTP connections alive in API server
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE":
    lambda: int(os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")),

796
797
798
799
800
801
    # 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
    "VLLM_PLUGINS":
    lambda: None if "VLLM_PLUGINS" not in os.environ else os.environ[
        "VLLM_PLUGINS"].split(","),
802

803
804
805
806
807
808
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
    "VLLM_LORA_RESOLVER_CACHE_DIR":
    lambda: os.getenv("VLLM_LORA_RESOLVER_CACHE_DIR", None),

809
810
811
812
    # 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.
813
814
    "VLLM_TORCH_PROFILER_DIR":
    lambda: (None if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None else os
815
816
             .path.abspath(os.path.expanduser(os.getenv(
        "VLLM_TORCH_PROFILER_DIR", ".")))),
817

818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
    # Enable torch profiler to record shapes if set
    # VLLM_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
    # not record shapes.
    "VLLM_TORCH_PROFILER_RECORD_SHAPES":
    lambda: bool(os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"),

    # Enable torch profiler to profile memory if set
    # VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
    # will not profile memory.
    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY":
    lambda: bool(
        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"),

    # Enable torch profiler to profile stack if set
    # VLLM_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
    # profile stack by default.
    "VLLM_TORCH_PROFILER_WITH_STACK":
    lambda: bool(os.getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"),

    # Enable torch profiler to profile flops if set
    # VLLM_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
    # not profile flops.
    "VLLM_TORCH_PROFILER_WITH_FLOPS":
    lambda: bool(os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"),

843
844
845
    # If set, vLLM will use Triton implementations of AWQ.
    "VLLM_USE_TRITON_AWQ":
    lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
846
847
848
849
850
851

    # If set, allow loading or unloading lora adapters in runtime,
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING":
    lambda:
    (os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower() in
     ("1", "true")),
852

853
854
855
856
857
858
    # 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
859
    "VLLM_SKIP_P2P_CHECK":
860
    lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
861

862
863
864
865
866
867
868
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
    "VLLM_DISABLED_KERNELS":
    lambda: [] if "VLLM_DISABLED_KERNELS" not in os.environ else os.environ[
        "VLLM_DISABLED_KERNELS"].split(","),
869

870
871
872
873
874
875
876
    # Swaps the all reduce backend that we use to coordinate the DP padding
    # information from NCCL to gloo.
    "VLLM_DISABLE_NCCL_FOR_DP_SYNCHRONIZATION":
    lambda:
    (os.getenv("VLLM_DISABLE_NCCL_FOR_DP_SYNCHRONIZATION", "False").lower() in
             ("true", "1")),

877
878
    # If set, use the V1 code path.
    "VLLM_USE_V1":
879
    lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),
880

881
882
883
884
885
886
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
    "VLLM_ROCM_USE_AITER":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in
             ("true", "1")),

887
888
889
890
891
892
    # Whether to use aiter paged attention.
    # By default is disabled.
    "VLLM_ROCM_USE_AITER_PAGED_ATTN":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in
             ("true", "1")),

893
894
895
896
897
898
899
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
    "VLLM_ROCM_USE_AITER_LINEAR":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "True").lower() in
             ("true", "1")),

900
901
902
903
904
905
    # Whether to use aiter moe ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_MOE":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_MOE", "True").lower() in
             ("true", "1")),

906
907
908
909
910
    # use aiter rms norm op if aiter ops are enabled.
    "VLLM_ROCM_USE_AITER_RMSNORM":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "True").lower() in
             ("true", "1")),

911
912
913
914
915
    # Whether to use aiter mla ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_MLA":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_MLA", "True").lower() in
             ("true", "1")),
916
917
918
919
920
921
922

    # Whether to use aiter mha ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_MHA":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_MHA", "True").lower() in
             ("true", "1")),

923
924
925
926
927
928
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_FP8BMM":
    lambda: (os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in
             ("true", "1")),

929
930
931
932
933
    # use rocm skinny gemms
    "VLLM_ROCM_USE_SKINNY_GEMM":
    lambda: (os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in
             ("true", "1")),

934
935
936
    # Pad the fp8 weights to 256 bytes for ROCm
    "VLLM_ROCM_FP8_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
937

938
939
940
941
    # Pad the weights for the moe kernel
    "VLLM_ROCM_MOE_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),

942
943
944
945
946
    # custom paged attention kernel for MI3* cards
    "VLLM_ROCM_CUSTOM_PAGED_ATTN":
    lambda: (os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in
             ("true", "1")),

947
948
949
950
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION":
951
952
    env_with_choices("VLLM_ROCM_QUICK_REDUCE_QUANTIZATION", "NONE",
                            ["FP", "INT8", "INT6", "INT4", "NONE"]),
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972

    # 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
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16":
    lambda:
    (os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower() in
     ("true", "1")),

    # 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.
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB":
    lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)),

973
974
975
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
    "Q_SCALE_CONSTANT":
    lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
976
977
978
979
980
981
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
    "K_SCALE_CONSTANT":
    lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
    "V_SCALE_CONSTANT":
    lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
982

983
984
    # If set, enable multiprocessing in LLM for the V1 code path.
    "VLLM_ENABLE_V1_MULTIPROCESSING":
985
    lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))),
986
987
    "VLLM_LOG_BATCHSIZE_INTERVAL":
    lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")),
988
989
    "VLLM_DISABLE_COMPILE_CACHE":
    lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))),
990
991
992
993
994
995

    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
    "VLLM_SERVER_DEV_MODE":
    lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
996
997
998
999
1000
1001
1002
1003
1004
1005

    # 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.
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE":
    lambda: int(os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")),
1006
1007
1008
1009
1010

    # If set, vLLM will disable the MLA attention optimizations.
    "VLLM_MLA_DISABLE":
    lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),

1011
1012
1013
1014
1015
1016
    # If set, vLLM will pick up the provided Flash Attention MLA
    # max number splits for cuda graph decode
    "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH":
    lambda: int(os.getenv("VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH",
                          "16")),

1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
    # 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.
    "VLLM_RAY_PER_WORKER_GPUS":
    lambda: float(os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")),

    # 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"
    "VLLM_RAY_BUNDLE_INDICES":
    lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),

1029
1030
1031
1032
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
    "VLLM_CUDART_SO_PATH":
    lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1033

1034
1035
1036
1037
    # Rank of the process in the data parallel setting
    "VLLM_DP_RANK":
    lambda: int(os.getenv("VLLM_DP_RANK", "0")),

1038
1039
1040
1041
1042
1043
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
    "VLLM_DP_RANK_LOCAL":
    lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)),

1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
    # World size of the data parallel setting
    "VLLM_DP_SIZE":
    lambda: int(os.getenv("VLLM_DP_SIZE", "1")),

    # IP address of the master node in the data parallel setting
    "VLLM_DP_MASTER_IP":
    lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),

    # Port of the master node in the data parallel setting
    "VLLM_DP_MASTER_PORT":
    lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1055

1056
1057
1058
1059
1060
1061
1062
1063
    # 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.
    "VLLM_MOE_DP_CHUNK_SIZE":
    lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),

1064
1065
1066
1067
    # Randomize inputs during dummy runs when using Data Parallel
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS":
    lambda: os.environ.get("VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0") == "1",

1068
1069
1070
    # Whether to use S3 path for model loading in CI via RunAI Streamer
    "VLLM_CI_USE_S3":
    lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1071

1072
    # Use model_redirect to redirect the model name to a local folder.
1073
1074
1075
1076
1077
    # `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
1078
1079
1080
    "VLLM_MODEL_REDIRECT_PATH":
    lambda: os.environ.get("VLLM_MODEL_REDIRECT_PATH", None),

1081
1082
1083
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
    "VLLM_MARLIN_USE_ATOMIC_ADD":
    lambda: os.environ.get("VLLM_MARLIN_USE_ATOMIC_ADD", "0") == "1",
1084

1085
1086
1087
1088
    # Whether to use marlin kernel in mxfp4 quantization method
    "VLLM_MXFP4_USE_MARLIN":
    lambda: maybe_convert_bool(os.environ.get("VLLM_MXFP4_USE_MARLIN", None)),

1089
1090
1091
1092
1093
    # Whether to turn on the outlines cache for V0
    # This cache is unbounded and on disk, so it's not safe to use in
    # an environment with potentially malicious users.
    "VLLM_V0_USE_OUTLINES_CACHE":
    lambda: os.environ.get("VLLM_V0_USE_OUTLINES_CACHE", "0") == "1",
1094

1095
1096
1097
1098
1099
1100
    # 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.
    "VLLM_V1_USE_OUTLINES_CACHE":
    lambda: os.environ.get("VLLM_V1_USE_OUTLINES_CACHE", "0") == "1",

1101
1102
1103
1104
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
    "VLLM_TPU_BUCKET_PADDING_GAP":
    lambda: int(os.environ["VLLM_TPU_BUCKET_PADDING_GAP"])
1105
    if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ else 0,
1106
1107
    "VLLM_TPU_MOST_MODEL_LEN":
    lambda: maybe_convert_int(os.environ.get("VLLM_TPU_MOST_MODEL_LEN", None)),
1108

1109
1110
1111
1112
    # Whether using Pathways
    "VLLM_TPU_USING_PATHWAYS":
    lambda: bool("proxy" in os.getenv("JAX_PLATFORMS", "").lower()),

1113
1114
    # Allow use of DeepGemm kernels for fused moe ops.
    "VLLM_USE_DEEP_GEMM":
1115
    lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1116

1117
1118
1119
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
    "VLLM_USE_DEEP_GEMM_E8M0":
    lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))),
1120
1121
1122
1123
    # TODO(wentao): unify the two E8M0 flags after verifying the correctness.
    # Whether to use E8M0 scaling when DeepGEMM is used on Hopper GPUs.
    "VLLM_USE_DEEP_GEMM_E8M0_HOPPER":
    lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0_HOPPER", "0"))),
1124
1125
1126
1127
1128
1129
1130
1131
    # 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.
    # Set `VLLM_SKIP_DEEP_GEMM_WARMUP` to disable the warmup.
    "VLLM_SKIP_DEEP_GEMM_WARMUP":
    lambda: bool(int(os.getenv("VLLM_SKIP_DEEP_GEMM_WARMUP", "0"))),

1132
1133
1134
1135
    # Whether to use fused grouped_topk used for MoE expert selection.
    "VLLM_USE_FUSED_MOE_GROUPED_TOPK":
    lambda: bool(int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))),

1136
1137
1138
1139
    # Allow use of FlashInfer MoE kernels for fused moe ops.
    "VLLM_USE_FLASHINFER_MOE_FP8":
    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))),

1140
    # Allow use of FlashInfer CUTLASS kernels for fused moe ops.
1141
1142
    "VLLM_USE_FLASHINFER_MOE_FP4":
    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))),
1143

1144
1145
1146
1147
1148
    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8":
    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))),

1149
1150
1151
1152
1153
1154
1155
1156
1157
    # 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.
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS":
    lambda: bool(int(
        os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0")
        )),

1158
1159
1160
1161
1162
    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16":
    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))),

1163
1164
1165
1166
1167
    # 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.
    "VLLM_XGRAMMAR_CACHE_MB":
    lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177

    # 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.
    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD":
    lambda: int(os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")),
1178
1179
1180
1181
1182
1183

    # 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.
    "VLLM_ALLOW_INSECURE_SERIALIZATION":
    lambda: bool(int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))),
Robert Shaw's avatar
Robert Shaw committed
1184
1185
1186
1187
1188
1189
1190
1191

    # IP address used for NIXL handshake between remote agents.
    "VLLM_NIXL_SIDE_CHANNEL_HOST":
    lambda: os.getenv("VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"),

    # Port used for NIXL handshake between remote agents.
    "VLLM_NIXL_SIDE_CHANNEL_PORT":
    lambda: int(os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5557")),
1192
1193

    # all2all backend for vllm's expert parallel communication
1194
    # Available options:
1195
1196
1197
    # - "naive": naive all2all implementation using broadcasts
    # - "allgather_reducescatter": all2all implementation based on allgather and
    #  reducescatter
1198
    # - "pplx": use pplx kernels
1199
1200
    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
1201
    "VLLM_ALL2ALL_BACKEND":
1202
    env_with_choices("VLLM_ALL2ALL_BACKEND", "allgather_reducescatter",
1203
                     ["naive", "pplx",
1204
1205
1206
                     "deepep_high_throughput",
                     "deepep_low_latency",
                     "allgather_reducescatter"]),
1207

1208
1209
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
1210
1211
1212
1213
1214
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
1215
1216
1217
    "VLLM_FLASHINFER_MOE_BACKEND":
    env_with_choices("VLLM_FLASHINFER_MOE_BACKEND", "throughput",
    ["throughput", "latency"]),
1218

1219
1220
1221
1222
1223
1224
    # 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.
    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE":
    lambda: int(os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")),
1225

1226
1227
1228
1229
    # 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> }
1230
    # Unspecified world sizes will fall back to
1231
1232
1233
1234
1235
    #     { 2: 64, 4: 1, <everything else>: 0.5 }
    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB":
    lambda: json.loads(os.getenv(
        "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")),

1236
1237
1238
1239
1240
1241
1242
1243
1244
    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
    # Cutstom routing strategies can be registered by
    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY":
    lambda: os.environ.get("VLLM_MOE_ROUTING_SIMULATION_STRATEGY", "").lower(),

1245
1246
1247
    # Regex timeout for use by the vLLM tool parsing plugins.
    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS":
    lambda: int(os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")),
1248
1249
1250
1251
1252

    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
    "VLLM_SLEEP_WHEN_IDLE":
    lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
1253
1254
1255
1256
1257
1258

    # 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.
    "VLLM_MQ_MAX_CHUNK_BYTES_MB":
    lambda: int(os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")),
1259

1260
1261
1262
1263
1264
    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS":
    lambda: int(os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")),

1265
1266
1267
1268
1269
1270
1271
1272
    # 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.
    "VLLM_KV_CACHE_LAYOUT":
1273
    env_with_choices("VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]),
1274
1275
1276
1277
1278
1279

    # 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.
    "VLLM_COMPUTE_NANS_IN_LOGITS":
    lambda: bool(int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))),
1280
1281
1282
1283
1284

    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
    "VLLM_USE_NVFP4_CT_EMULATIONS":
1285
1286
1287
1288
1289
1290
1291
    lambda: bool(int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))),

    # 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.
    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT":
1292
1293
    lambda: int(os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "120")),

1294
1295
1296
1297
    # Controls whether or not to use cudnn prefill
    "VLLM_USE_CUDNN_PREFILL":
    lambda: bool(int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))),

1298
1299
1300
    # 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.
1301
    "VLLM_USE_TRTLLM_ATTENTION":
1302
1303
    lambda: (None if "VLLM_USE_TRTLLM_ATTENTION" not in os.environ else
             os.environ["VLLM_USE_TRTLLM_ATTENTION"].lower() in ("1", "true")),
1304

1305
1306
1307
1308
    # If set to 1, when we use fp8 kv, we do not quantize Q to fp8
    "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION":
    lambda: bool(int(os.getenv("VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION", "0"))),

1309
1310
1311
1312
1313
    # If set, it means we pre-downloaded cubin files and flashinfer will
    # read the cubin files directly.
    "VLLM_HAS_FLASHINFER_CUBIN":
    lambda: os.getenv("VLLM_HAS_FLASHINFER_CUBIN", False),

1314
1315
1316
1317
1318
1319
    # If set to 1, force the use of TRTLLM FP4 GEMM backend in flashinfer.
    # Otherwise, uses the first available of: flashinfer cutlass GEMM,
    # vllm cutlass GEMM, marlin GEMM.
    "VLLM_USE_TRTLLM_FP4_GEMM":
    lambda: bool(int(os.getenv("VLLM_USE_TRTLLM_FP4_GEMM", "0"))),

1320
1321
1322
1323
1324
1325
    # 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.
    "VLLM_ENABLE_CUDAGRAPH_GC":
    lambda: bool(int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))),

1326
1327
1328
1329
1330
1331
    # Disable padding to CUDA graph capture batch sizes.
    # TODO(wentao): https://github.com/vllm-project/vllm/issues/23378
    # After the issue is fixed, we can remove this flag.
    "VLLM_DISABLE_PAD_FOR_CUDAGRAPH":
    lambda: bool(int(os.getenv("VLLM_DISABLE_PAD_FOR_CUDAGRAPH", "0"))),

1332
1333
1334
    # Used to force set up loopback IP
    "VLLM_LOOPBACK_IP":
    lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1335
1336
1337
1338
1339
1340

    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
    "VLLM_PROCESS_NAME_PREFIX":
    lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351

    # 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.
    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE":
    lambda: bool(int(os.getenv(\
            "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "0"))),
1352
1353
1354

    # 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
1355
1356
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1357
1358
1359
1360
1361
1362
1363
    # 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.
    "VLLM_ENABLE_RESPONSES_API_STORE":
    lambda: bool(int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))),
1364

xiao-llm's avatar
xiao-llm committed
1365
1366
1367
1368
    # If set, use the fp8 mfma in rocm paged attention.
    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN":
    lambda: bool(int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))),

1369
1370
    # Whether to use pytorch symmetric memory for allreduce
    "VLLM_ALLREDUCE_USE_SYMM_MEM":
1371
    lambda: bool(int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "0"))),
1372

1373
1374
1375
1376
    # Allows vllm to find tuned config under customized folder
    "VLLM_TUNED_CONFIG_FOLDER":
    lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),

1377
1378
1379
1380
1381
    # Allows harmony instructions to be injected on system messages
    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS":
    lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))),

1382
1383
1384
    # Add optional custom scopes for profiling, disable to avoid overheads
    "VLLM_CUSTOM_SCOPES_FOR_PROFILING":
    lambda: bool(int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))),
1385
1386
1387
1388
1389

    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES":
    lambda: bool(int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))),
1390
1391
1392
1393
1394
1395

    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME":
    lambda: os.getenv("VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
                      "VLLM_OBJECT_STORAGE_SHM_BUFFER"),
1396

1397
1398
1399
1400
1401
1402
1403
1404
1405
    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
    "VLLM_DEEPEP_BUFFER_SIZE_MB":
    lambda: int(os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")),

    # 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
    "VLLM_DBO_COMM_SMS":
    lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),

1406
1407
1408
1409
1410
1411
1412
    # Valid values are container,code_interpreter,web_search_preview
    # ex GPT_OSS_SYSTEM_TOOL_MCP_LABELS=container,code_interpreter
    "GPT_OSS_SYSTEM_TOOL_MCP_LABELS":
    env_list_with_choices("GPT_OSS_SYSTEM_TOOL_MCP_LABELS", [],
                            ["container",
                            "code_interpreter",
                            "web_search_preview"]),
1413
1414
}

1415
# --8<-- [end:env-vars-definition]
1416

1417

1418
def __getattr__(name: str):
1419
1420
1421
1422
1423
1424
1425
1426
    # lazy evaluation of environment variables
    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


def __dir__():
    return list(environment_variables.keys())
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442


def is_set(name: str):
    """Check if an environment variable is explicitly set."""
    if name in environment_variables:
        return name in os.environ
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


def set_vllm_use_v1(use_v1: bool):
    if is_set("VLLM_USE_V1"):
        raise ValueError(
            "Should not call set_vllm_use_v1() if VLLM_USE_V1 is set "
            "explicitly by the user. Please raise this as a Github "
            "Issue and explicitly set VLLM_USE_V1=0 or 1.")
    os.environ["VLLM_USE_V1"] = "1" if use_v1 else "0"
1443
1444
1445
1446
1447
1448
1449
1450


def compute_hash() -> str:
    """
    WARNING: Whenever a new key is added to this environment
    variables, ensure that it is included in the factors list if
    it affects the computation graph. For example, different values
    of VLLM_PP_LAYER_PARTITION will generate different computation
1451
    graphs, so it is included in the factors list. The env vars that
1452
1453
1454
1455
1456
1457
1458
1459
1460
1461
1462
    affect the choice of different kernels or attention backends should
    also be included in the factors list.
    """

    # The values of envs may affects the computation graph.
    # TODO(DefTruth): hash all environment variables?
    # for key in environment_variables:
    #     factorize(key)
    environment_variables_to_hash = [
        "VLLM_PP_LAYER_PARTITION",
        "VLLM_MLA_DISABLE",
1463
        "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH",
1464
1465
1466
1467
        "VLLM_USE_TRITON_FLASH_ATTN",
        "VLLM_USE_TRITON_AWQ",
        "VLLM_DP_RANK",
        "VLLM_DP_SIZE",
1468
        "VLLM_USE_STANDALONE_COMPILE",
1469
        "VLLM_FUSED_MOE_CHUNK_SIZE",
1470
1471
1472
1473
1474
1475
1476
        "VLLM_FLASHINFER_MOE_BACKEND",
        "VLLM_V1_USE_PREFILL_DECODE_ATTENTION",
        "VLLM_USE_AITER_UNIFIED_ATTENTION",
        "VLLM_ATTENTION_BACKEND",
        "VLLM_USE_FLASHINFER_SAMPLER",
        "VLLM_DISABLED_KERNELS",
        "VLLM_USE_DEEP_GEMM",
1477
1478
        "VLLM_USE_DEEP_GEMM_E8M0",
        "VLLM_USE_DEEP_GEMM_E8M0_HOPPER",
1479
        "VLLM_USE_TRTLLM_FP4_GEMM",
1480
        "VLLM_USE_FUSED_MOE_GROUPED_TOPK",
1481
1482
1483
        "VLLM_USE_FLASHINFER_MOE_FP8",
        "VLLM_USE_FLASHINFER_MOE_FP4",
        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8",
1484
        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS",
1485
1486
1487
        "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16",
        "VLLM_USE_CUDNN_PREFILL",
        "VLLM_USE_TRTLLM_ATTENTION",
1488
        "VLLM_FLASHINFER_DISABLE_Q_QUANTIZATION",
1489
1490
1491
1492
1493
1494
1495
        "VLLM_ROCM_USE_AITER",
        "VLLM_ROCM_USE_AITER_PAGED_ATTN",
        "VLLM_ROCM_USE_AITER_LINEAR",
        "VLLM_ROCM_USE_AITER_MOE",
        "VLLM_ROCM_USE_AITER_RMSNORM",
        "VLLM_ROCM_USE_AITER_MLA",
        "VLLM_ROCM_USE_AITER_MHA",
1496
        "VLLM_ROCM_USE_AITER_FP8BMM",
1497
1498
1499
1500
1501
1502
1503
        "VLLM_ROCM_USE_SKINNY_GEMM",
        "VLLM_ROCM_FP8_PADDING",
        "VLLM_ROCM_MOE_PADDING",
        "VLLM_ROCM_CUSTOM_PAGED_ATTN",
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16",
        "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB",
xiao-llm's avatar
xiao-llm committed
1504
        "VLLM_ROCM_FP8_MFMA_PAGE_ATTN",
1505
1506
    ]
    for key in environment_variables_to_hash:
1507
1508
1509
1510
1511
1512
1513
1514
        # if this goes out of sync with environment_variables,
        # it's not a user error, it's a bug
        assert key in environment_variables, \
            "Please update environment_variables_to_hash in envs.py"

    factors = [
        environment_variables[key]() for key in environment_variables_to_hash
    ]
1515

1516
1517
    hash_str = hashlib.md5(str(factors).encode(),
                           usedforsecurity=False).hexdigest()
1518
1519

    return hash_str