envs.py 32.4 KB
Newer Older
1
2
# SPDX-License-Identifier: Apache-2.0

3
import hashlib
4
import os
5
import sys
6
import tempfile
7
from typing import TYPE_CHECKING, Any, Callable, Optional
8
9
10

if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
11
    VLLM_PORT: Optional[int] = None
12
    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
13
    VLLM_USE_MODELSCOPE: bool = False
14
    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
15
16
17
    VLLM_NCCL_SO_PATH: Optional[str] = None
    LD_LIBRARY_PATH: Optional[str] = None
    VLLM_USE_TRITON_FLASH_ATTN: bool = False
18
    VLLM_FLASH_ATTN_VERSION: Optional[int] = None
19
20
21
22
23
24
25
    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
26
    VLLM_MODEL_REDIRECT_PATH: Optional[str] = None
27
28
    VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm")
    VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm")
29
30
31
32
33
    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
34
    VLLM_LOGGING_LEVEL: str = "INFO"
35
    VLLM_LOGGING_PREFIX: str = ""
36
    VLLM_LOGGING_CONFIG_PATH: Optional[str] = None
37
    VLLM_LOGITS_PROCESSOR_THREADS: Optional[int] = None
38
39
    VLLM_TRACE_FUNCTION: int = 0
    VLLM_ATTENTION_BACKEND: Optional[str] = None
40
    VLLM_USE_FLASHINFER_SAMPLER: Optional[bool] = None
41
    VLLM_FLASHINFER_FORCE_TENSOR_CORES: bool = False
42
    VLLM_PP_LAYER_PARTITION: Optional[str] = None
43
    VLLM_CPU_KVCACHE_SPACE: int = 0
44
    VLLM_CPU_OMP_THREADS_BIND: str = ""
45
    VLLM_CPU_MOE_PREPACK: bool = True
46
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
47
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
48
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 64 * 1024
49
    VLLM_USE_RAY_SPMD_WORKER: bool = False
50
    VLLM_USE_RAY_COMPILED_DAG: bool = False
51
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: str = "auto"
52
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
53
    VLLM_WORKER_MULTIPROC_METHOD: str = "fork"
54
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
55
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
56
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
57
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
58
    VLLM_MM_INPUT_CACHE_GIB: int = 8
59
60
61
62
    VLLM_TARGET_DEVICE: str = "cuda"
    MAX_JOBS: Optional[str] = None
    NVCC_THREADS: Optional[str] = None
    VLLM_USE_PRECOMPILED: bool = False
63
    VLLM_TEST_USE_PRECOMPILED_NIGHTLY_WHEEL: bool = False
64
    VLLM_NO_DEPRECATION_WARNING: bool = False
65
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
66
67
    CMAKE_BUILD_TYPE: Optional[str] = None
    VERBOSE: bool = False
68
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
69
    VLLM_RPC_TIMEOUT: int = 10000  # ms
70
    VLLM_PLUGINS: Optional[list[str]] = None
71
    VLLM_TORCH_PROFILER_DIR: Optional[str] = None
72
    VLLM_USE_TRITON_AWQ: bool = False
73
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
74
    VLLM_SKIP_P2P_CHECK: bool = False
75
    VLLM_DISABLED_KERNELS: list[str] = []
76
    VLLM_USE_V1: bool = True
77
    VLLM_ROCM_USE_AITER: bool = False
78
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
79
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
80
    VLLM_ROCM_USE_AITER_MOE: bool = True
81
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
82
    VLLM_ROCM_USE_AITER_MLA: bool = True
83
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
84
    VLLM_ROCM_FP8_PADDING: bool = True
85
    VLLM_ROCM_MOE_PADDING: bool = True
86
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
87
    VLLM_QUARK_EMU_MEM_OPT: bool = False
88
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
89
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
90
    VLLM_DISABLE_COMPILE_CACHE: bool = False
91
    Q_SCALE_CONSTANT: int = 200
92
93
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
94
    VLLM_SERVER_DEV_MODE: bool = False
95
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
96
    VLLM_MLA_DISABLE: bool = False
97
    VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON: bool = False
98
99
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
100
    VLLM_CUDART_SO_PATH: Optional[str] = None
101
    VLLM_USE_HPU_CONTIGUOUS_CACHE_FETCH: bool = True
102
    VLLM_HPU_USE_DELAYED_SAMPLING: bool = False
103
    VLLM_DP_RANK: int = 0
104
    VLLM_DP_RANK_LOCAL: int = -1
105
106
107
    VLLM_DP_SIZE: int = 1
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
108
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
109
    VLLM_V0_USE_OUTLINES_CACHE: bool = False
110
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
111
    VLLM_USE_DEEP_GEMM: bool = False
112
    VLLM_XGRAMMAR_CACHE_MB: int = 0
113
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
114
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
115

116
117
118
119
120
121
122
123
124
125
126
127
128
129
130

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"),
    )


131
132
133
134
135
136
def maybe_convert_int(value: Optional[str]) -> Optional[int]:
    if value is None:
        return None
    return int(value)


137
138
139
140
141
# The begin-* and end* here are used by the documentation generator
# to extract the used env vars.

# begin-env-vars-definition

142
environment_variables: dict[str, Callable[[], Any]] = {
143
144
145

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

146
    # Target device of vLLM, supporting [cuda (by default),
147
    # rocm, neuron, cpu]
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
    "VLLM_TARGET_DEVICE":
    lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda"),

    # 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":
164
165
    lambda: bool(os.environ.get("VLLM_USE_PRECOMPILED")) or bool(
        os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
166

167
168
169
170
171
172
    # 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"))
                 ),

173
174
175
176
177
178
179
180
181
182
    # 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'))),

183
    # Root directory for vLLM configuration files
184
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
185
186
187
188
    # 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":
189
190
191
192
193
    lambda: os.path.expanduser(
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
        )),
194
195
196

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

197
    # Root directory for vLLM cache files
198
199
200
201
202
203
204
205
    # 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"),
        )),

206
207
208
209
    # 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.
210
    'VLLM_HOST_IP':
211
    lambda: os.getenv('VLLM_HOST_IP', ""),
212

213
    # used in distributed environment to manually set the communication port
214
215
216
    # 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.
217
218
219
220
221
    # '0' is used to make mypy happy
    'VLLM_PORT':
    lambda: int(os.getenv('VLLM_PORT', '0'))
    if 'VLLM_PORT' in os.environ else None,

222
223
224
225
    # 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()),
226

227
228
229
230
231
    # 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",

232
233
234
235
    # 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")),

236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
    # 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")),

256
257
258
259
260
    # 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)),

261
262
263
264
    # Internal flag to enable Dynamo fullgraph capture
    "VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE":
    lambda: bool(
        os.environ.get("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1") != "0"),
265

266
267
268
269
    # Internal flag to enable/disable Inductor standalone compile
    "VLLM_TEST_STANDALONE_COMPILE":
    lambda: os.environ.get("VLLM_TEST_STANDALONE_COMPILE", "0") != "0",

270
271
272
273
274
275
276
277
278
279
280
281
282
    # 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")),

283
    # API key for vLLM API server
284
285
286
    "VLLM_API_KEY":
    lambda: os.environ.get("VLLM_API_KEY", None),

287
288
289
290
291
    # Whether to log responses from API Server for debugging
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE":
    lambda: os.environ.get("VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False").
    lower() == "true",

292
293
    # S3 access information, used for tensorizer to load model from S3
    "S3_ACCESS_KEY_ID":
294
    lambda: os.environ.get("S3_ACCESS_KEY_ID", None),
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
    "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"),

320
321
    # this is used for configuring the default logging level
    "VLLM_LOGGING_LEVEL":
322
    lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
323

324
325
326
327
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
    "VLLM_LOGGING_PREFIX":
    lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),

328
329
330
331
332
333
334
335
    # 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,

336
337
338
339
340
341
342
343
344
345
346
347
    # 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
348
    # - "FLASHINFER": use flashinfer
349
    # - "FLASHMLA": use FlashMLA
350
351
352
    "VLLM_ATTENTION_BACKEND":
    lambda: os.getenv("VLLM_ATTENTION_BACKEND", None),

353
354
    # If set, vllm will use flashinfer sampler
    "VLLM_USE_FLASHINFER_SAMPLER":
355
356
    lambda: bool(int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"]))
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ else None,
357

358
359
360
361
362
    # If set, vllm will force flashinfer to use tensor cores;
    # otherwise will use heuristic based on model architecture.
    "VLLM_FLASHINFER_FORCE_TENSOR_CORES":
    lambda: bool(int(os.getenv("VLLM_FLASHINFER_FORCE_TENSOR_CORES", "0"))),

363
364
365
366
    # Pipeline stage partition strategy
    "VLLM_PP_LAYER_PARTITION":
    lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),

367
    # (CPU backend only) CPU key-value cache space.
368
    # default is 4 GiB
369
370
371
    "VLLM_CPU_KVCACHE_SPACE":
    lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0")),

372
373
374
375
376
    # (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":
    lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "all"),

377
378
379
380
381
382
    # (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"))),

383
384
385
386
387
    # 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":
388
    lambda: bool(int(os.getenv("VLLM_USE_RAY_SPMD_WORKER", "0"))),
389

390
391
392
    # If the env var is set, it uses the Ray's Compiled Graph
    # (previously known as ADAG) API which optimizes the
    # control plane overhead.
393
    # Run vLLM with VLLM_USE_RAY_COMPILED_DAG=1 to enable it.
394
395
    # Note that this variable is set to 1 in V1 by default
    # when ray distributed executor is used.
396
    "VLLM_USE_RAY_COMPILED_DAG":
397
398
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG", "0"))),

399
400
401
402
403
404
405
406
407
408
    # 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"),
409

410
    # If the env var is set, it enables GPU communication overlap
411
    # (experimental feature) in Ray's Compiled Graph. This flag is ignored if
412
413
    # VLLM_USE_RAY_COMPILED_DAG is not set.
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM":
414
    lambda: bool(int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
415
416
                 ),

417
418
419
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
    "VLLM_WORKER_MULTIPROC_METHOD":
420
    lambda: os.getenv("VLLM_WORKER_MULTIPROC_METHOD", "fork"),
421

422
423
424
425
426
427
428
429
    # 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"),
        )),

430
431
432
433
    # 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")),
434

435
    # Timeout for fetching videos when serving multimodal models
436
    # Default is 30 seconds
437
    "VLLM_VIDEO_FETCH_TIMEOUT":
438
    lambda: int(os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")),
439

440
    # Timeout for fetching audio when serving multimodal models
441
    # Default is 10 seconds
442
    "VLLM_AUDIO_FETCH_TIMEOUT":
443
    lambda: int(os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")),
444

445
    # Cache size (in GiB) for multimodal input cache
446
    # Default is 4 GiB
447
    "VLLM_MM_INPUT_CACHE_GIB":
448
    lambda: int(os.getenv("VLLM_MM_INPUT_CACHE_GIB", "4")),
449

450
451
452
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
    "VLLM_XLA_CACHE_PATH":
453
454
    lambda: os.path.expanduser(
        os.getenv(
455
            "VLLM_XLA_CACHE_PATH",
456
457
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
        )),
458
459
460
461

    # If set, assert on XLA recompilation after each execution step.
    "VLLM_XLA_CHECK_RECOMPILATION":
    lambda: bool(int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))),
462
    "VLLM_FUSED_MOE_CHUNK_SIZE":
463
    lambda: int(os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", "32768")),
464
465
466
467

    # If set, vllm will skip the deprecation warnings.
    "VLLM_NO_DEPRECATION_WARNING":
    lambda: bool(int(os.getenv("VLLM_NO_DEPRECATION_WARNING", "0"))),
468

469
470
471
472
473
    # 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)),

474
475
476
477
478
479
480
481
    # 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")),
482
483
484
485
486
487
488

    # 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")),
489
490
    "VLLM_TEST_FORCE_LOAD_FORMAT":
    lambda: os.getenv("VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"),
491

492
493
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
494
495
    "VLLM_RPC_TIMEOUT":
    lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
496

497
498
499
500
501
502
    # 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(","),
503
504
505
506
507
508

    # Enables torch profiler if set. Path to the directory where torch profiler
    # traces are saved. Note that it must be an absolute path.
    "VLLM_TORCH_PROFILER_DIR":
    lambda: (None if os.getenv("VLLM_TORCH_PROFILER_DIR", None) is None else os
             .path.expanduser(os.getenv("VLLM_TORCH_PROFILER_DIR", "."))),
509
510
511
512

    # If set, vLLM will use Triton implementations of AWQ.
    "VLLM_USE_TRITON_AWQ":
    lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
513
514
515
516
517
518

    # 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")),
519
520
521
522
523
524
525

    # By default, vLLM will check the peer-to-peer capability itself,
    # in case of broken drivers. See https://github.com/vllm-project/vllm/blob/a9b15c606fea67a072416ea0ea115261a2756058/vllm/distributed/device_communicators/custom_all_reduce_utils.py#L101-L108 for details. # noqa
    # If this env var is set to 1, vLLM will skip the peer-to-peer check,
    # and trust the driver's peer-to-peer capability report.
    "VLLM_SKIP_P2P_CHECK":
    lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "0") == "1",
526

527
528
529
530
531
532
533
    # 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(","),
534
535
536

    # If set, use the V1 code path.
    "VLLM_USE_V1":
537
    lambda: bool(int(os.getenv("VLLM_USE_V1", "1"))),
538

539
540
541
542
543
544
    # 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")),

545
546
547
548
549
550
    # 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")),

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

558
559
560
561
562
563
    # 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")),

564
565
566
567
568
    # 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")),

569
570
571
572
573
    # 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")),
574
575
576
577
578
    # use rocm skinny gemms
    "VLLM_ROCM_USE_SKINNY_GEMM":
    lambda: (os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in
             ("true", "1")),

579
580
581
    # Pad the fp8 weights to 256 bytes for ROCm
    "VLLM_ROCM_FP8_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
582

583
584
585
586
    # Pad the weights for the moe kernel
    "VLLM_ROCM_MOE_PADDING":
    lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "1"))),

587
588
589
590
591
    # 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")),

592
593
594
595
596
597
598
599
    # If set, when running in Quark emulation mode, do not dequantize the
    # weights at load time. Instead, dequantize weights on-the-fly during
    # kernel execution.
    # This allows running larger models at the cost of slower inference.
    # This flag has no effect when not running in Quark emulation mode.
    "VLLM_QUARK_EMU_MEM_OPT":
    lambda: bool(int(os.getenv("VLLM_QUARK_EMU_MEM_OPT", "0"))),

600
601
602
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
    "Q_SCALE_CONSTANT":
    lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
603
604
605
606
607
608
    # 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")),
609

610
611
    # If set, enable multiprocessing in LLM for the V1 code path.
    "VLLM_ENABLE_V1_MULTIPROCESSING":
612
    lambda: bool(int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))),
613
614
    "VLLM_LOG_BATCHSIZE_INTERVAL":
    lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")),
615
616
    "VLLM_DISABLE_COMPILE_CACHE":
    lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))),
617
618
619
620
621
622

    # 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"))),
623
624
625
626
627
628
629
630
631
632

    # 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")),
633
634
635
636
637

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

638
639
640
641
642
    # If set, vLLM will use the Triton implementation of moe_align_block_size,
    # i.e. moe_align_block_size_triton in fused_moe.py.
    "VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON":
    lambda: bool(int(os.getenv("VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
                 ),
643

644
645
646
647
648
649
650
651
652
653
654
655
    # 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", ""),

656
657
658
659
    # 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),
660
661
662
663
664
665
666

    # Contiguous cache fetching to avoid using costly gather operation on
    # Gaudi3. This is only applicable to HPU contiguous cache. If set to true,
    # contiguous cache fetch will be used.
    "VLLM_USE_HPU_CONTIGUOUS_CACHE_FETCH":
    lambda: os.environ.get("VLLM_CONTIGUOUS_PA", "true").lower() in
    ("1", "true"),
667

668
669
670
671
672
673
    # Use delayed sampling for HPU to reduce host cpu overhead
    # between each step.
    "VLLM_HPU_USE_DELAYED_SAMPLING":
    lambda: os.environ.get("VLLM_DELAYED_SAMPLING", "false").lower() in
    ("1", "true"),

674
675
676
677
    # Rank of the process in the data parallel setting
    "VLLM_DP_RANK":
    lambda: int(os.getenv("VLLM_DP_RANK", "0")),

678
679
680
681
682
683
    # 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)),

684
685
686
687
688
689
690
691
692
693
694
    # 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")),
695
696
697
698

    # 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",
699

700
    # Use model_redirect to redirect the model name to a local folder.
701
702
703
704
705
    # `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
706
707
708
    "VLLM_MODEL_REDIRECT_PATH":
    lambda: os.environ.get("VLLM_MODEL_REDIRECT_PATH", None),

709
710
711
    # 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",
712
713
714
715
716
717

    # 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",
718

719
720
721
722
    # 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"])
723
    if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ else 0,
724
725
726
727

    # Allow use of DeepGemm kernels for fused moe ops.
    "VLLM_USE_DEEP_GEMM":
    lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "0"))),
728
729
730
731
732
733

    # 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")),
734
735
736
737
738
739
740
741
742
743

    # 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")),
744
745
746
747
748
749

    # 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"))),
750
751
}

752
753
# end-env-vars-definition

754

755
def __getattr__(name: str):
756
757
758
759
760
761
762
763
    # 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())
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779


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"
780
781
782
783
784
785
786
787


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
788
    graphs, so it is included in the factors list. The env vars that
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
    affect the choice of different kernels or attention backends should
    also be included in the factors list.
    """
    factors: list[Any] = []

    # summarize environment variables
    def factorize(name: str):
        if __getattr__(name):
            factors.append(__getattr__(name))
        else:
            factors.append("None")

    # 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",
812
        "VLLM_TEST_STANDALONE_COMPILE",
813
814
815
816
817
    ]
    for key in environment_variables_to_hash:
        if key in environment_variables:
            factorize(key)

818
819
    hash_str = hashlib.md5(str(factors).encode(),
                           usedforsecurity=False).hexdigest()
820
821

    return hash_str