envs.py 53.3 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, Optional
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
35
36
37
    VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai"
    VLLM_NO_USAGE_STATS: bool = False
    VLLM_DO_NOT_TRACK: bool = False
    VLLM_USAGE_SOURCE: str = ""
    VLLM_CONFIGURE_LOGGING: int = 1
38
    VLLM_LOGGING_LEVEL: str = "INFO"
39
    VLLM_LOGGING_PREFIX: str = ""
40
    VLLM_LOGGING_CONFIG_PATH: Optional[str] = None
41
    VLLM_LOGITS_PROCESSOR_THREADS: Optional[int] = None
42
    VLLM_LOG_STATS_INTERVAL: float = 10.
43
44
    VLLM_TRACE_FUNCTION: int = 0
    VLLM_ATTENTION_BACKEND: Optional[str] = None
45
    VLLM_USE_FLASHINFER_SAMPLER: Optional[bool] = None
46
    VLLM_PP_LAYER_PARTITION: Optional[str] = None
47
    VLLM_CPU_KVCACHE_SPACE: Optional[int] = 0
48
    VLLM_CPU_OMP_THREADS_BIND: str = ""
49
    VLLM_CPU_NUM_OF_RESERVED_CPU: Optional[int] = None
50
    VLLM_CPU_MOE_PREPACK: bool = True
51
    VLLM_CPU_SGL_KERNEL: bool = False
52
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
53
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
54
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
55
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
56
    VLLM_USE_RAY_SPMD_WORKER: bool = False
57
    VLLM_USE_RAY_COMPILED_DAG: bool = False
58
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: str = "auto"
59
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
60
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
61
    VLLM_XLA_USE_SPMD: bool = False
62
    VLLM_WORKER_MULTIPROC_METHOD: str = "fork"
63
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
64
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
65
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
66
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
67
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
68
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
69
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
70
    VLLM_MM_INPUT_CACHE_GIB: int = 4
71
72
73
74
    VLLM_TARGET_DEVICE: str = "cuda"
    MAX_JOBS: Optional[str] = None
    NVCC_THREADS: Optional[str] = None
    VLLM_USE_PRECOMPILED: bool = False
75
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
76
    VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
77
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
78
79
    CMAKE_BUILD_TYPE: Optional[str] = None
    VERBOSE: bool = False
80
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
81
    VLLM_RPC_TIMEOUT: int = 10000  # ms
82
    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
83
    VLLM_PLUGINS: Optional[list[str]] = None
84
    VLLM_LORA_RESOLVER_CACHE_DIR: Optional[str] = None
85
    VLLM_TORCH_PROFILER_DIR: Optional[str] = None
86
87
88
89
    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
90
    VLLM_USE_TRITON_AWQ: bool = False
91
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
92
    VLLM_SKIP_P2P_CHECK: bool = False
93
    VLLM_DISABLED_KERNELS: list[str] = []
94
    VLLM_USE_V1: bool = True
95
    VLLM_ROCM_USE_AITER: bool = False
96
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
97
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
98
    VLLM_ROCM_USE_AITER_MOE: bool = True
99
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
100
    VLLM_ROCM_USE_AITER_MLA: bool = True
101
    VLLM_ROCM_USE_AITER_MHA: bool = True
102
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
103
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
104
    VLLM_ROCM_FP8_PADDING: bool = True
105
    VLLM_ROCM_MOE_PADDING: bool = True
106
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
107
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
108
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
109
    VLLM_DISABLE_COMPILE_CACHE: bool = False
110
    Q_SCALE_CONSTANT: int = 200
111
112
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
113
    VLLM_SERVER_DEV_MODE: bool = False
114
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
115
    VLLM_MLA_DISABLE: bool = False
116
117
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
118
    VLLM_CUDART_SO_PATH: Optional[str] = None
119
    VLLM_DP_RANK: int = 0
120
    VLLM_DP_RANK_LOCAL: int = -1
121
122
123
    VLLM_DP_SIZE: int = 1
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
124
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
125
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
126
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
127
    VLLM_MXFP4_USE_MARLIN: Optional[bool] = None
128
    VLLM_V0_USE_OUTLINES_CACHE: bool = False
129
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
130
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
131
    VLLM_TPU_MOST_MODEL_LEN: Optional[int] = None
132
    VLLM_TPU_USING_PATHWAYS: bool = False
133
    VLLM_USE_DEEP_GEMM: bool = False
134
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
135
    VLLM_USE_DEEP_GEMM_E8M0_HOPPER: bool = False
136
    VLLM_SKIP_DEEP_GEMM_WARMUP: bool = False
137
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
138
139
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
140
    VLLM_FLASHINFER_MOE_BACKEND: str = "throughput"
141
    VLLM_XGRAMMAR_CACHE_MB: int = 0
142
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
143
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
Robert Shaw's avatar
Robert Shaw committed
144
145
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5557
146
    VLLM_ALL2ALL_BACKEND: str = "naive"
147
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
148
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
149
    VLLM_SLEEP_WHEN_IDLE: bool = False
150
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
151
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
152
    VLLM_KV_CACHE_LAYOUT: Optional[str] = None
153
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
154
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
155
156
157
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: str = "NONE"
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: Optional[int] = None
158
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 120
159
    VLLM_USE_CUDNN_PREFILL: bool = False
160
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
161
    VLLM_LOOPBACK_IP: str = ""
162
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = False
163
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
164
    VLLM_USE_TRTLLM_ATTENTION: Optional[str] = None
165
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
166
167
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
168
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = False
169
    VLLM_TUNED_CONFIG_FOLDER: Optional[str] = None
170
    VLLM_DISABLE_PAD_FOR_CUDAGRAPH: bool = False
171

172
173
174
175
176
177
178
179
180
181
182
183
184
185
186

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"),
    )


187
188
189
190
191
192
def maybe_convert_int(value: Optional[str]) -> Optional[int]:
    if value is None:
        return None
    return int(value)


193
194
195
196
197
198
def maybe_convert_bool(value: Optional[str]) -> Optional[bool]:
    if value is None:
        return None
    return bool(int(value))


199
200
def get_vllm_port() -> Optional[int]:
    """Get the port from VLLM_PORT environment variable.
201

202
203
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
204

205
206
207
208
209
210
211
212
213
214
215
216
    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
217
218
219
220
221
222
223
        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
224
225
226
227
        raise ValueError(
            f"VLLM_PORT '{port}' must be a valid integer") from err


228
229
230
# The begin-* and end* here are used by the documentation generator
# to extract the used env vars.

231
# --8<-- [start:env-vars-definition]
232

233
environment_variables: dict[str, Callable[[], Any]] = {
234
235
236

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

237
    # Target device of vLLM, supporting [cuda (by default),
238
    # rocm, neuron, cpu]
239
    "VLLM_TARGET_DEVICE":
240
    lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
241
242
243
244
245
246
247
248
249
250
251
252
253
254

    # 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":
255
256
257
258
259
260
261
262
    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"),
263

264
265
266
267
268
269
    # 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"))
                 ),

270
271
272
273
274
275
276
277
278
279
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
    "CMAKE_BUILD_TYPE":
    lambda: os.getenv("CMAKE_BUILD_TYPE"),

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

280
    # Root directory for vLLM configuration files
281
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
282
283
284
285
    # 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":
286
287
288
289
290
    lambda: os.path.expanduser(
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
        )),
291
292
293

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

294
    # Root directory for vLLM cache files
295
296
297
298
299
300
301
302
    # 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"),
        )),

303
304
305
306
    # 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.
307
    'VLLM_HOST_IP':
308
    lambda: os.getenv('VLLM_HOST_IP', ""),
309

310
    # used in distributed environment to manually set the communication port
311
312
313
    # 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.
314
    'VLLM_PORT':
315
    get_vllm_port,
316

317
318
319
320
    # 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()),
321

322
323
324
325
326
    # 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",

327
328
329
330
    # 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")),

331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
    # 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")),

351
352
353
354
355
356
357
    # 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")),

358
359
360
361
362
363
    # 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")),

364
365
366
367
368
    # 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)),

369
370
371
372
    # Internal flag to enable Dynamo fullgraph capture
    "VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE":
    lambda: bool(
        os.environ.get("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1") != "0"),
373

374
375
376
377
378
    # Feature flag to enable/disable Inductor standalone compile.
    # In torch <= 2.7 we ignore this flag; in torch >= 2.8 this is
    # enabled by default.
    "VLLM_USE_STANDALONE_COMPILE":
    lambda: os.environ.get("VLLM_USE_STANDALONE_COMPILE", "1") == "1",
379

380
381
382
383
384
385
386
387
388
389
390
391
392
    # 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")),

393
    # API key for vLLM API server
394
395
396
    "VLLM_API_KEY":
    lambda: os.environ.get("VLLM_API_KEY", None),

397
398
    # Whether to log responses from API Server for debugging
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
399
400
    lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
                           ).lower() == "true",
401

402
403
    # S3 access information, used for tensorizer to load model from S3
    "S3_ACCESS_KEY_ID":
404
    lambda: os.environ.get("S3_ACCESS_KEY_ID", None),
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
    "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",
    "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"),

430
431
    # this is used for configuring the default logging level
    "VLLM_LOGGING_LEVEL":
432
    lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
433

434
435
436
437
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
    "VLLM_LOGGING_PREFIX":
    lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),

438
439
440
441
442
443
444
445
    # 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,

446
447
448
449
450
451
    # 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.,

452
453
454
455
456
457
458
459
460
461
462
463
    # 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
    # Available options:
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
    # - "FLASH_ATTN": use FlashAttention
    # - "XFORMERS": use XFormers
    # - "ROCM_FLASH": use ROCmFlashAttention
464
    # - "FLASHINFER": use flashinfer
465
    # - "FLASHMLA": use FlashMLA
466
467
468
    "VLLM_ATTENTION_BACKEND":
    lambda: os.getenv("VLLM_ATTENTION_BACKEND", None),

469
470
    # If set, vllm will use flashinfer sampler
    "VLLM_USE_FLASHINFER_SAMPLER":
471
472
    lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
473

474
475
476
477
    # Pipeline stage partition strategy
    "VLLM_PP_LAYER_PARTITION":
    lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),

478
    # (CPU backend only) CPU key-value cache space.
479
    # default is None and will be set as 4 GB
480
    "VLLM_CPU_KVCACHE_SPACE":
481
482
    lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ else None,
483

484
485
486
    # (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":
487
488
489
490
491
    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":
492
493
    lambda: int(os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0"))
    if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ else None,
494

495
496
497
498
499
500
    # (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"))),

501
502
503
504
    # (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"))),

505
506
507
508
509
    # 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":
510
    lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
511

512
513
514
    # If the env var is set, it uses the Ray's Compiled Graph
    # (previously known as ADAG) API which optimizes the
    # control plane overhead.
515
    # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
516
517
    # Note that this variable is set to 1 in V1 by default
    # when ray distributed executor is used.
518
    "VLLM_USE_RAY_COMPILED_DAG":
519
520
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),

521
522
523
524
525
526
527
528
529
530
    # 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":
    lambda: os.getenv("VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto"),
531

532
    # If the env var is set, it enables GPU communication overlap
533
    # (experimental feature) in Ray's Compiled Graph. This flag is ignored if
534
535
    # VLLM_USE_RAY_COMPILED_DAG is not set.
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
536
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
537
538
                 ),

539
540
541
542
543
544
545
    # 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"))),

546
547
548
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
    "VLLM_WORKER_MULTIPROC_METHOD":
549
    lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "fork"),
550

551
552
553
554
555
556
557
558
    # 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"),
        )),

559
560
561
562
    # 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")),
563

564
    # Timeout for fetching videos when serving multimodal models
565
    # Default is 30 seconds
566
    "VLLM_VIDEO_FETCH_TIMEOUT":
567
    lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")),
568

569
    # Timeout for fetching audio when serving multimodal models
570
    # Default is 10 seconds
571
    "VLLM_AUDIO_FETCH_TIMEOUT":
572
    lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
573

574
575
576
577
578
579
    # 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")),

580
581
582
583
584
585
    # 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")),

586
587
588
589
590
591
592
593
594
595
    # 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"),

596
    # [DEPRECATED] Cache size (in GiB per process) for multimodal input cache
597
    # Default is 4 GiB per API process + 4 GiB per engine core process
598
    "VLLM_MM_INPUT_CACHE_GIB":
599
    lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
600

601
602
603
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
    "VLLM_XLA_CACHE_PATH":
604
605
    lambda: os.path.expanduser(
        os.getenv(
606
            "VLLM_XLA_CACHE_PATH",
607
608
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
        )),
609
610
611
612

    # If set, assert on XLA recompilation after each execution step.
    "VLLM_XLA_CHECK_RECOMPILATION":
    lambda: bool(int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))),
613
614
615
616

    # Enable SPMD mode for TPU backend.
    "VLLM_XLA_USE_SPMD":
    lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
617
    "VLLM_FUSED_MOE_CHUNK_SIZE":
618
    lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
619
620
621
622
623
624
    # 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"))),
625

626
627
628
629
630
    # 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)),

631
632
633
634
635
636
637
638
    # 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")),
639
640
641
642
643
644
645

    # 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")),
646
647
    "VLLM_TEST_FORCE_LOAD_FORMAT":
    lambda: os.getenv("VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"),
648

649
650
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
651
652
    "VLLM_RPC_TIMEOUT":
    lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
653

654
655
656
657
    # 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")),

658
659
660
661
662
663
    # 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(","),
664

665
666
667
668
669
670
    # 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),

671
672
673
674
    # 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.
675
676
    "VLLM_TORCH_PROFILER_DIR":
    lambda: (None if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None else os
677
678
             .path.abspath(os.path.expanduser(os.getenv(
        "VLLM_TORCH_PROFILER_DIR", ".")))),
679

680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
    # 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"),

705
706
707
    # If set, vLLM will use Triton implementations of AWQ.
    "VLLM_USE_TRITON_AWQ":
    lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
708
709
710
711
712
713

    # 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")),
714

715
716
717
718
719
720
    # 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
721
    "VLLM_SKIP_P2P_CHECK":
722
    lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
723

724
725
726
727
728
729
730
    # 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(","),
731
732
733

    # If set, use the V1 code path.
    "VLLM_USE_V1":
734
    lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),
735

736
737
738
739
740
741
    # 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")),

742
743
744
745
746
747
    # 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")),

748
749
750
751
752
753
754
    # 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")),

755
756
757
758
759
760
    # 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")),

761
762
763
764
765
    # 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")),

766
767
768
769
770
    # 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")),
771
772
773
774
775
776
777

    # 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")),

778
779
780
781
782
783
    # 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")),

784
785
786
787
788
    # use rocm skinny gemms
    "VLLM_ROCM_USE_SKINNY_GEMM":
    lambda: (os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in
             ("true", "1")),

789
790
791
    # Pad the fp8 weights to 256 bytes for ROCm
    "VLLM_ROCM_FP8_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
792

793
794
795
796
    # Pad the weights for the moe kernel
    "VLLM_ROCM_MOE_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),

797
798
799
800
801
    # 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")),

802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
    # 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":
    lambda: os.getenv("VLLM_ROCM_QUICK_REDUCE_QUANTIZATION", "NONE").upper(),

    # 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)),

827
828
829
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
    "Q_SCALE_CONSTANT":
    lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
830
831
832
833
834
835
    # 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")),
836

837
838
    # If set, enable multiprocessing in LLM for the V1 code path.
    "VLLM_ENABLE_V1_MULTIPROCESSING":
839
    lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))),
840
841
    "VLLM_LOG_BATCHSIZE_INTERVAL":
    lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")),
842
843
    "VLLM_DISABLE_COMPILE_CACHE":
    lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))),
844
845
846
847
848
849

    # 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"))),
850
851
852
853
854
855
856
857
858
859

    # 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")),
860
861
862
863
864

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

865
866
867
868
869
870
871
872
873
874
875
876
    # 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", ""),

877
878
879
880
    # 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),
881

882
883
884
885
    # Rank of the process in the data parallel setting
    "VLLM_DP_RANK":
    lambda: int(os.getenv("VLLM_DP_RANK", "0")),

886
887
888
889
890
891
    # 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)),

892
893
894
895
896
897
898
899
900
901
902
    # 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")),
903

904
905
906
907
908
909
910
911
    # 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")),

912
913
914
915
    # 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",

916
917
918
    # 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",
919

920
    # Use model_redirect to redirect the model name to a local folder.
921
922
923
924
925
    # `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
926
927
928
    "VLLM_MODEL_REDIRECT_PATH":
    lambda: os.environ.get("VLLM_MODEL_REDIRECT_PATH", None),

929
930
931
    # 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",
932

933
934
935
936
    # 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)),

937
938
939
940
941
    # 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",
942

943
944
945
946
947
948
    # 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",

949
950
951
952
    # 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"])
953
    if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ else 0,
954
955
    "VLLM_TPU_MOST_MODEL_LEN":
    lambda: maybe_convert_int(os.environ.get("VLLM_TPU_MOST_MODEL_LEN", None)),
956

957
958
959
960
    # Whether using Pathways
    "VLLM_TPU_USING_PATHWAYS":
    lambda: bool("proxy" in os.getenv("JAX_PLATFORMS", "").lower()),

961
962
963
    # Allow use of DeepGemm kernels for fused moe ops.
    "VLLM_USE_DEEP_GEMM":
    lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "0"))),
964

965
966
967
    # 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"))),
968
969
970
971
    # 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"))),
972
973
974
975
976
977
978
979
    # 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"))),

980
981
982
983
    # 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"))),

984
985
986
987
    # 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"))),

988
    # Allow use of FlashInfer CUTLASS kernels for fused moe ops.
989
990
    "VLLM_USE_FLASHINFER_MOE_FP4":
    lambda: bool(int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))),
991

992
993
994
995
996
997
998
999
1000
1001
    # 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"))),

    # 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"))),

1002
1003
1004
1005
1006
    # 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")),
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016

    # 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")),
1017
1018
1019
1020
1021
1022

    # 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
1023
1024
1025
1026
1027
1028
1029
1030

    # 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")),
1031
1032

    # all2all backend for vllm's expert parallel communication
1033
1034
1035
    # Available options:
    # - "naive": naive all2all implementation using all-reduce
    # - "pplx": use pplx kernels
1036
1037
    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
1038
1039
    "VLLM_ALL2ALL_BACKEND":
    lambda: os.getenv("VLLM_ALL2ALL_BACKEND", "naive"),
1040

1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support. Both
    # require compute capability 10.0 or above.
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
    # To set this backend, define the environment variable:
    #     export VLLM_FLASHINFER_MOE_BACKEND=latency.
    # If not set, defaults to "throughput".
    "VLLM_FLASHINFER_MOE_BACKEND": lambda: os.getenv(
    "VLLM_FLASHINFER_MOE_BACKEND", "throughput"
    ),

1055
1056
1057
1058
1059
1060
    # 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")),
1061

1062
1063
1064
1065
1066
1067
1068
1069
1070
1071
    # 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> }
    # Unspecified world sizes will fallback to
    #     { 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", "{}")),

1072
1073
1074
1075
1076
1077
1078
1079
1080
    # 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(),

1081
1082
1083
    # 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")),
1084
1085
1086
1087
1088

    # 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"))),
1089
1090
1091
1092
1093
1094

    # 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")),
1095

1096
1097
1098
1099
1100
    # 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")),

1101
1102
1103
1104
1105
1106
1107
1108
    # 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":
1109
1110
1111
1112
1113
1114
1115
    lambda: os.getenv("VLLM_KV_CACHE_LAYOUT", None),

    # 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"))),
1116
1117
1118
1119
1120

    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
    "VLLM_USE_NVFP4_CT_EMULATIONS":
1121
1122
1123
1124
1125
1126
1127
    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":
1128
1129
    lambda: int(os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "120")),

1130
1131
1132
1133
    # Controls whether or not to use cudnn prefill
    "VLLM_USE_CUDNN_PREFILL":
    lambda: bool(int(os.getenv("VLLM_USE_CUDNN_PREFILL", "0"))),

1134
1135
1136
    # If set to 1, use the TRTLLM attention backend in flashinfer.
    "VLLM_USE_TRTLLM_ATTENTION":
    lambda: os.getenv("VLLM_USE_TRTLLM_ATTENTION", None),
1137

1138
1139
1140
1141
1142
    # 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),

1143
1144
1145
1146
1147
1148
    # 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"))),

1149
1150
1151
1152
1153
1154
    # 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"))),

1155
1156
1157
1158
1159
1160
    # 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"))),

1161
1162
1163
    # Used to force set up loopback IP
    "VLLM_LOOPBACK_IP":
    lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1164
1165
1166
1167
1168
1169

    # 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"),
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180

    # 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"))),
1181
1182
1183

    # 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
1184
1185
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1186
1187
1188
1189
1190
1191
1192
    # 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"))),
1193

1194
1195
1196
1197
    # Whether to use pytorch symmetric memory for allreduce
    "VLLM_ALLREDUCE_USE_SYMM_MEM":
    lambda: bool(int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "0"))),

1198
1199
1200
1201
    # Allows vllm to find tuned config under customized folder
    "VLLM_TUNED_CONFIG_FOLDER":
    lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),

1202
1203
}

1204
# --8<-- [end:env-vars-definition]
1205

1206

1207
def __getattr__(name: str):
1208
1209
1210
1211
1212
1213
1214
1215
    # 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())
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231


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"
1232
1233
1234
1235
1236
1237
1238
1239


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
1240
    graphs, so it is included in the factors list. The env vars that
1241
1242
1243
1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
    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",
        "VLLM_USE_TRITON_FLASH_ATTN",
        "VLLM_USE_TRITON_AWQ",
        "VLLM_DP_RANK",
        "VLLM_DP_SIZE",
1256
        "VLLM_USE_STANDALONE_COMPILE",
1257
        "VLLM_FUSED_MOE_CHUNK_SIZE",
1258
1259
1260
1261
1262
1263
1264
        "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",
1265
1266
        "VLLM_USE_DEEP_GEMM_E8M0",
        "VLLM_USE_DEEP_GEMM_E8M0_HOPPER",
1267
        "VLLM_USE_TRTLLM_FP4_GEMM",
1268
        "VLLM_USE_FUSED_MOE_GROUPED_TOPK",
1269
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
        "VLLM_USE_FLASHINFER_MOE_FP8",
        "VLLM_USE_FLASHINFER_MOE_FP4",
        "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8",
        "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16",
        "VLLM_USE_CUDNN_PREFILL",
        "VLLM_USE_TRTLLM_ATTENTION",
        "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",
1282
        "VLLM_ROCM_USE_AITER_FP8BMM",
1283
1284
1285
1286
1287
1288
1289
        "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",
1290
1291
    ]
    for key in environment_variables_to_hash:
1292
1293
1294
1295
1296
1297
1298
1299
        # 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
    ]
1300

1301
1302
    hash_str = hashlib.md5(str(factors).encode(),
                           usedforsecurity=False).hexdigest()
1303
1304

    return hash_str