envs.py 91.2 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import functools
5
import json
6
import logging
7
import os
8
import sys
9
import tempfile
10
import uuid
11
12
from collections.abc import Callable
from typing import TYPE_CHECKING, Any, Literal
13
14
15

if TYPE_CHECKING:
    VLLM_HOST_IP: str = ""
16
    VLLM_PORT: int | None = None
17
    VLLM_RPC_BASE_PATH: str = tempfile.gettempdir()
18
    VLLM_USE_MODELSCOPE: bool = False
19
    VLLM_RINGBUFFER_WARNING_INTERVAL: int = 60
20
21
    VLLM_NCCL_SO_PATH: str | None = None
    LD_LIBRARY_PATH: str | None = None
22
    VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE: int = 256
23
    LOCAL_RANK: int = 0
24
    CUDA_VISIBLE_DEVICES: str | None = None
25
    VLLM_ENGINE_ITERATION_TIMEOUT_S: int = 60
26
    VLLM_ENGINE_READY_TIMEOUT_S: int = 600
27
    VLLM_API_KEY: str | None = None
28
    VLLM_DEBUG_LOG_API_SERVER_RESPONSE: bool = False
29
30
31
32
    S3_ACCESS_KEY_ID: str | None = None
    S3_SECRET_ACCESS_KEY: str | None = None
    S3_ENDPOINT_URL: str | None = None
    VLLM_MODEL_REDIRECT_PATH: str | None = None
33
34
    VLLM_CACHE_ROOT: str = os.path.expanduser("~/.cache/vllm")
    VLLM_CONFIG_ROOT: str = os.path.expanduser("~/.config/vllm")
35
36
37
38
    VLLM_USAGE_STATS_SERVER: str = "https://stats.vllm.ai"
    VLLM_NO_USAGE_STATS: bool = False
    VLLM_DO_NOT_TRACK: bool = False
    VLLM_USAGE_SOURCE: str = ""
39
    VLLM_CONFIGURE_LOGGING: bool = True
40
    VLLM_LOGGING_LEVEL: str = "INFO"
41
    VLLM_LOGGING_PREFIX: str = ""
42
    VLLM_LOGGING_STREAM: str = "ext://sys.stdout"
43
    VLLM_LOGGING_CONFIG_PATH: str | None = None
Nick Hill's avatar
Nick Hill committed
44
45
    VLLM_LOGGING_COLOR: str = "auto"
    NO_COLOR: bool = False
46
    VLLM_LOG_STATS_INTERVAL: float = 10.0
47
    VLLM_TRACE_FUNCTION: int = 0
48
49
50
    VLLM_USE_FLASHINFER_SAMPLER: bool | None = None
    VLLM_PP_LAYER_PARTITION: str | None = None
    VLLM_CPU_KVCACHE_SPACE: int | None = 0
51
    VLLM_CPU_OMP_THREADS_BIND: str = ""
52
    VLLM_CPU_NUM_OF_RESERVED_CPU: int | None = None
53
    VLLM_CPU_SGL_KERNEL: bool = False
54
    VLLM_XLA_CACHE_PATH: str = os.path.join(VLLM_CACHE_ROOT, "xla_cache")
55
    VLLM_XLA_CHECK_RECOMPILATION: bool = False
56
    VLLM_FUSED_MOE_CHUNK_SIZE: int = 16 * 1024
57
    VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING: bool = True
58
    VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE: Literal["auto", "nccl", "shm"] = "auto"
59
    VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM: bool = False
60
    VLLM_USE_RAY_WRAPPED_PP_COMM: bool = True
61
    VLLM_XLA_USE_SPMD: bool = False
62
    VLLM_WORKER_MULTIPROC_METHOD: Literal["fork", "spawn"] = "spawn"
63
    VLLM_ASSETS_CACHE: str = os.path.join(VLLM_CACHE_ROOT, "assets")
64
    VLLM_ASSETS_CACHE_MODEL_CLEAN: bool = False
65
    VLLM_IMAGE_FETCH_TIMEOUT: int = 5
66
    VLLM_VIDEO_FETCH_TIMEOUT: int = 30
67
    VLLM_AUDIO_FETCH_TIMEOUT: int = 10
68
    VLLM_MEDIA_URL_ALLOW_REDIRECTS: bool = True
69
    VLLM_MEDIA_LOADING_THREAD_COUNT: int = 8
70
    VLLM_MAX_AUDIO_CLIP_FILESIZE_MB: int = 25
71
    VLLM_VIDEO_LOADER_BACKEND: str = "opencv"
72
    VLLM_MEDIA_CONNECTOR: str = "http"
73
    VLLM_MM_HASHER_ALGORITHM: str = "blake3"
74
    VLLM_TARGET_DEVICE: str = "cuda"
75
    VLLM_MAIN_CUDA_VERSION: str = "12.9"
76
    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
77
78
    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
79
    VLLM_USE_PRECOMPILED: bool = False
80
    VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX: bool = False
81
    VLLM_DOCKER_BUILD_CONTEXT: bool = False
82
    VLLM_KEEP_ALIVE_ON_ENGINE_DEATH: bool = False
83
    CMAKE_BUILD_TYPE: Literal["Debug", "Release", "RelWithDebInfo"] | None = None
84
    VERBOSE: bool = False
85
    VLLM_ALLOW_LONG_MAX_MODEL_LEN: bool = False
86
    VLLM_RPC_TIMEOUT: int = 10000  # ms
87
    VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
88
89
    VLLM_PLUGINS: list[str] | None = None
    VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
90
91
92
    # Deprecated env variables for profiling, kept for backward compatibility
    # See also vllm/config/profiler.py and `--profiler-config` argument
    VLLM_TORCH_CUDA_PROFILE: str | None = None
93
    VLLM_TORCH_PROFILER_DIR: str | None = None
94
95
96
97
98
99
100
101
102
103
    VLLM_TORCH_PROFILER_RECORD_SHAPES: str | None = None
    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: str | None = None
    VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: str | None = None
    VLLM_TORCH_PROFILER_WITH_STACK: str | None = None
    VLLM_TORCH_PROFILER_WITH_FLOPS: str | None = None
    VLLM_TORCH_PROFILER_USE_GZIP: str | None = None
    VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: str | None = None
    VLLM_PROFILER_DELAY_ITERS: str | None = None
    VLLM_PROFILER_MAX_ITERS: str | None = None
    # End of deprecated env variables for profiling
104
    VLLM_USE_AOT_COMPILE: bool = False
105
    VLLM_USE_BYTECODE_HOOK: bool = False
106
    VLLM_FORCE_AOT_LOAD: bool = False
107
    VLLM_USE_MEGA_AOT_ARTIFACT: bool = False
108
    VLLM_USE_TRITON_AWQ: bool = False
109
    VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
110
    VLLM_SKIP_P2P_CHECK: bool = False
111
    VLLM_DISABLED_KERNELS: list[str] = []
112
    VLLM_DISABLE_PYNCCL: bool = False
113
    VLLM_ROCM_USE_AITER: bool = False
114
    VLLM_ROCM_USE_AITER_PAGED_ATTN: bool = False
115
    VLLM_ROCM_USE_AITER_LINEAR: bool = True
116
    VLLM_ROCM_USE_AITER_MOE: bool = True
117
    VLLM_ROCM_USE_AITER_RMSNORM: bool = True
118
    VLLM_ROCM_USE_AITER_MLA: bool = True
119
    VLLM_ROCM_USE_AITER_MHA: bool = True
120
    VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
121
    VLLM_ROCM_USE_AITER_TRITON_ROPE: bool = False
122
    VLLM_ROCM_USE_AITER_FP8BMM: bool = True
123
    VLLM_ROCM_USE_AITER_FP4BMM: bool = True
124
    VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
125
    VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = False
126
    VLLM_ROCM_USE_AITER_TRITON_GEMM: bool = True
127
    VLLM_ROCM_USE_SKINNY_GEMM: bool = True
128
    VLLM_ROCM_FP8_PADDING: bool = True
129
    VLLM_ROCM_MOE_PADDING: bool = True
130
    VLLM_ROCM_CUSTOM_PAGED_ATTN: bool = True
131
    VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT: bool = False
132
    VLLM_ENABLE_V1_MULTIPROCESSING: bool = True
133
    VLLM_LOG_BATCHSIZE_INTERVAL: float = -1
134
    VLLM_DISABLE_COMPILE_CACHE: bool = False
135
    Q_SCALE_CONSTANT: int = 200
136
137
    K_SCALE_CONSTANT: int = 200
    V_SCALE_CONSTANT: int = 100
138
    VLLM_SERVER_DEV_MODE: bool = False
139
    VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
140
    VLLM_MLA_DISABLE: bool = False
141
142
    VLLM_RAY_PER_WORKER_GPUS: float = 1.0
    VLLM_RAY_BUNDLE_INDICES: str = ""
143
    VLLM_CUDART_SO_PATH: str | None = None
144
    VLLM_DP_RANK: int = 0
145
    VLLM_DP_RANK_LOCAL: int = -1
146
    VLLM_DP_SIZE: int = 1
147
    VLLM_USE_STANDALONE_COMPILE: bool = True
148
149
    VLLM_DP_MASTER_IP: str = ""
    VLLM_DP_MASTER_PORT: int = 0
150
    VLLM_MOE_DP_CHUNK_SIZE: int = 256
151
    VLLM_ENABLE_MOE_DP_CHUNK: bool = True
152
    VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
153
    VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
154
    VLLM_MARLIN_USE_ATOMIC_ADD: bool = False
155
    VLLM_MARLIN_INPUT_DTYPE: Literal["int8", "fp8"] | None = None
156
    VLLM_MXFP4_USE_MARLIN: bool | None = None
157
    VLLM_DEEPEPLL_NVFP4_DISPATCH: bool = False
158
    VLLM_V1_USE_OUTLINES_CACHE: bool = False
159
    VLLM_TPU_BUCKET_PADDING_GAP: int = 0
160
    VLLM_TPU_MOST_MODEL_LEN: int | None = None
161
    VLLM_TPU_USING_PATHWAYS: bool = False
162
    VLLM_USE_DEEP_GEMM: bool = True
163
    VLLM_MOE_USE_DEEP_GEMM: bool = True
164
    VLLM_USE_DEEP_GEMM_E8M0: bool = True
165
    VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES: bool = True
166
167
168
169
170
    VLLM_DEEP_GEMM_WARMUP: Literal[
        "skip",
        "full",
        "relax",
    ] = "relax"
171
    VLLM_USE_FUSED_MOE_GROUPED_TOPK: bool = True
172
    VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER: bool = False
173
    VLLM_USE_FLASHINFER_MOE_FP16: bool = False
174
175
    VLLM_USE_FLASHINFER_MOE_FP8: bool = False
    VLLM_USE_FLASHINFER_MOE_FP4: bool = False
176
177
178
    VLLM_FLASHINFER_MOE_BACKEND: Literal["throughput", "latency", "masked_gemm"] = (
        "latency"
    )
179
    VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE: int = 394 * 1024 * 1024
180
    VLLM_XGRAMMAR_CACHE_MB: int = 0
181
    VLLM_MSGPACK_ZERO_COPY_THRESHOLD: int = 256
182
    VLLM_ALLOW_INSECURE_SERIALIZATION: bool = False
Robert Shaw's avatar
Robert Shaw committed
183
    VLLM_NIXL_SIDE_CHANNEL_HOST: str = "localhost"
184
    VLLM_NIXL_SIDE_CHANNEL_PORT: int = 5600
185
    VLLM_MOONCAKE_BOOTSTRAP_PORT: int = 8998
186
187
188
189
190
    VLLM_ALL2ALL_BACKEND: Literal[
        "naive",
        "pplx",
        "deepep_high_throughput",
        "deepep_low_latency",
191
        "mori",
192
193
194
        "allgather_reducescatter",
        "flashinfer_all2allv",
    ] = "allgather_reducescatter"
195
    VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE: int = 163840
196
    VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS: int = 1
197
    VLLM_SLEEP_WHEN_IDLE: bool = False
198
    VLLM_MQ_MAX_CHUNK_BYTES_MB: int = 16
199
    VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS: int = 300
200
    VLLM_KV_CACHE_LAYOUT: Literal["NHD", "HND"] | None = None
201
    VLLM_COMPUTE_NANS_IN_LOGITS: bool = False
202
    VLLM_USE_NVFP4_CT_EMULATIONS: bool = False
203
204
205
    VLLM_ROCM_QUICK_REDUCE_QUANTIZATION: Literal[
        "FP", "INT8", "INT6", "INT4", "NONE"
    ] = "NONE"
206
    VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16: bool = True
207
    VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB: int | None = None
208
    VLLM_NIXL_ABORT_REQUEST_TIMEOUT: int = 480
209
210
211
212
    VLLM_MORIIO_CONNECTOR_READ_MODE: bool = False
    VLLM_MORIIO_QP_PER_TRANSFER: int = 1
    VLLM_MORIIO_POST_BATCH_SIZE: int = -1
    VLLM_MORIIO_NUM_WORKERS: int = 1
213
    VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT: int = 480
214
    VLLM_ENABLE_CUDAGRAPH_GC: bool = False
215
    VLLM_LOOPBACK_IP: str = ""
216
    VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE: bool = True
217
    VLLM_ENABLE_RESPONSES_API_STORE: bool = False
218
    VLLM_NVFP4_GEMM_BACKEND: str | None = None
219
    VLLM_HAS_FLASHINFER_CUBIN: bool = False
220
221
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8: bool = False
    VLLM_USE_FLASHINFER_MOE_MXFP4_BF16: bool = False
xiao-llm's avatar
xiao-llm committed
222
    VLLM_ROCM_FP8_MFMA_PAGE_ATTN: bool = False
223
    VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS: bool = False
224
    VLLM_ALLREDUCE_USE_SYMM_MEM: bool = True
225
    VLLM_TUNED_CONFIG_FOLDER: str | None = None
226
    VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS: set[str] = set()
227
    VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT: bool = False
228
    VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS: bool = False
229
    VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY: bool = False
230
    VLLM_CUSTOM_SCOPES_FOR_PROFILING: bool = False
231
    VLLM_NVTX_SCOPES_FOR_PROFILING: bool = False
232
    VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES: bool = True
233
    VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME: str = "VLLM_OBJECT_STORAGE_SHM_BUFFER"
234
    VLLM_DEEPEP_BUFFER_SIZE_MB: int = 1024
235
236
    VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE: bool = False
    VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL: bool = False
237
    VLLM_DBO_COMM_SMS: int = 20
238
239
    VLLM_PATTERN_MATCH_DEBUG: str | None = None
    VLLM_DEBUG_DUMP_PATH: str | None = None
240
241
    VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE: bool = True
    VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING: bool = True
242
    VLLM_USE_NCCL_SYMM_MEM: bool = False
243
    VLLM_NCCL_INCLUDE_PATH: str | None = None
244
    VLLM_USE_FBGEMM: bool = False
245
    VLLM_GC_DEBUG: str = ""
246
    VLLM_DEBUG_WORKSPACE: bool = False
247
    VLLM_DISABLE_SHARED_EXPERTS_STREAM: bool = False
248
    VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD: int = 256
249
    VLLM_COMPILE_CACHE_SAVE_FORMAT: Literal["binary", "unpacked"] = "binary"
Woosuk Kwon's avatar
Woosuk Kwon committed
250
    VLLM_USE_V2_MODEL_RUNNER: bool = False
251
    VLLM_LOG_MODEL_INSPECTION: bool = False
252
    VLLM_DEBUG_MFU_METRICS: bool = False
253
    VLLM_DISABLE_LOG_LOGO: bool = False
254
    VLLM_LORA_DISABLE_PDL: bool = False
255
    
256
    # add envs
zhuwenwen's avatar
zhuwenwen committed
257
    VLLM_OPTEST_URLS_PORT: int | None = None
258
259
    VLLM_OPTEST_MODELS_PATH: str = ""
    VLLM_USE_TRITON_PREFIX_FLASH_ATTN: bool = False
260
    VLLM_USE_FLASH_ATTN_FP8: bool = False
261
    VLLM_USE_QUERY_QUANT: bool = False
262
263
264
265
266
267
    VLLM_USE_FLASH_MLA: bool = False
    VLLM_USE_OPT_OP: bool = False
    VLLM_USE_TC_PAGED_ATTN: bool = False
    VLLM_USE_PA_PRINT_PARAM: bool = False 
    VLLM_SPEC_DECODE_EAGER: bool = False
    VLLM_PCIE_USE_CUSTOM_ALLREDUCE: bool = False
268
    VLLM_CUSTOM_CACHE: bool = False
zhuwenwen's avatar
zhuwenwen committed
269
    VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX: int = 16
zhuwenwen's avatar
zhuwenwen committed
270
    VLLM_ENFORCE_EAGER_BS_THRESHOLD: int | None  = None
271
    VLLM_HAS_CONTEXT_DEFAULT: bool = False
272
    VLLM_USE_NN: bool = False
273
    VLLM_ENABLE_TBO: bool = False
274
    VLLM_ENABLE_MOE_FUSED_GATE: bool = False
275
    VLLM_USE_FLASH_ATTN_PA: bool = False
zhuwenwen's avatar
zhuwenwen committed
276
    VLLM_USE_APEX_RN: bool = False
277
    VLLM_USE_GLOBAL_CACHE13: bool = False
278
279
    VLLM_USE_LIGHTOP: bool = False
    VLLM_USE_OPT_CAT: bool = False
zhuwenwen's avatar
zhuwenwen committed
280
281
    VLLM_USE_LIGHTOP_MOE_SUM: bool = False
    VLLM_USE_LIGHTOP_MOE_ALIGN: bool = False
282
    VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
王敏's avatar
王敏 committed
283
    USE_FUSED_RMS_QUANT: bool = False
zhuwenwen's avatar
zhuwenwen committed
284
    VLLM_USE_PD_SPLIT: bool = False
zhuwenwen's avatar
zhuwenwen committed
285
    VLLM_USE_PP_SYNC: bool = False
286
    VLLM_USE_PIECEWISE: bool = False
287
    VLLM_USE_V32_ENCODE: bool = False
288
289
290
    VLLM_USE_FUSE_SILU_AND_MUL: bool = False
    VLLM_USE_OPT_RESHAPE_AND_CACHE: bool = False
    VLLM_USE_TOPK_RENORM: bool = False
291
    VLLM_USE_FUSED_RMS_ROPE: bool = False
292
    VLLM_USE_FUSED_FILL_RMS_CAT: bool = False
293
    VLLM_W8A8_BACKEND: int = 3
jujl1's avatar
jujl1 committed
294
    VLLM_USE_PP_BALANCE = True
295
296
297
298
299
300
    VLLM_MOE_ROUTER_CAPTURE: bool = False
    VLLM_MOE_ROUTER_CAPTURE_DIR: str = "/tmp"
    VLLM_MOE_ROUTER_CAPTURE_RANK: int = -1
    VLLM_MOE_ROUTER_CAPTURE_MAX_LAYERS: int = 0
    VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_GT: int = -1
    VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_LT: int = -1
王敏's avatar
王敏 committed
301
    VLLM_REJECT_SAMPLE_OPT: bool = False
302
    VLLM_USE_MOE_W16A16_TRITON: bool = False
303
    VLLM_V1_FAST_TOKEN_ID_COPY: bool = False
304
    VLLM_V1_USE_REDUCED_TOPK_TOPP_SAMPLER: bool = False
305
    VLLM_V1_USE_FUSED_QKV_SPLIT_RMS_ROPE_KVSTORE: bool = False
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321


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"),
    )


322
def maybe_convert_int(value: str | None) -> int | None:
323
324
325
326
327
    if value is None:
        return None
    return int(value)


328
def maybe_convert_bool(value: str | None) -> bool | None:
329
330
331
332
333
    if value is None:
        return None
    return bool(int(value))


334
335
336
337
def disable_compile_cache() -> bool:
    return bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0")))


338
def use_aot_compile() -> bool:
339
340
341
    from vllm.model_executor.layers.batch_invariant import (
        vllm_is_batch_invariant,
    )
zhuwenwen's avatar
zhuwenwen committed
342
    from vllm.platforms import current_platform
343
    from vllm.utils.torch_utils import is_torch_equal_or_newer
344

345
346
    default_value = (
        "1"
zhuwenwen's avatar
zhuwenwen committed
347
348
349
350
351
        if is_torch_equal_or_newer("2.10.0.dev")
        and not disable_compile_cache()
        # Disabling AOT_COMPILE for CPU
        # See: https://github.com/vllm-project/vllm/issues/32033
        and not current_platform.is_cpu()
352
353
354
        else "0"
    )

355
356
357
358
    return (
        not vllm_is_batch_invariant()
        and os.environ.get("VLLM_USE_AOT_COMPILE", default_value) == "1"
    )
359
360


361
def env_with_choices(
362
    env_name: str,
363
364
    default: str | None,
    choices: list[str] | Callable[[], list[str]],
365
    case_sensitive: bool = True,
366
) -> Callable[[], str | None]:
367
368
    """
    Create a lambda that validates environment variable against allowed choices
369

370
371
372
373
374
    Args:
        env_name: Name of the environment variable
        default: Default value if not set (can be None)
        choices: List of valid string options or callable that returns list
        case_sensitive: Whether validation should be case sensitive
375

376
377
378
379
    Returns:
        Lambda function for environment_variables dict
    """

380
    def _get_validated_env() -> str | None:
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
        value = os.getenv(env_name)
        if value is None:
            return default

        # Resolve choices if it's a callable (for lazy loading)
        actual_choices = choices() if callable(choices) else choices

        if not case_sensitive:
            check_value = value.lower()
            check_choices = [choice.lower() for choice in actual_choices]
        else:
            check_value = value
            check_choices = actual_choices

        if check_value not in check_choices:
396
397
398
399
            raise ValueError(
                f"Invalid value '{value}' for {env_name}. "
                f"Valid options: {actual_choices}."
            )
400
401
402
403
404
405

        return value

    return _get_validated_env


406
def env_list_with_choices(
407
408
    env_name: str,
    default: list[str],
409
    choices: list[str] | Callable[[], list[str]],
410
411
    case_sensitive: bool = True,
) -> Callable[[], list[str]]:
412
    """
413
    Create a lambda that validates environment variable
414
    containing comma-separated values against allowed choices
415

416
417
418
419
420
    Args:
        env_name: Name of the environment variable
        default: Default list of values if not set
        choices: List of valid string options or callable that returns list
        case_sensitive: Whether validation should be case sensitive
421

422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
    Returns:
        Lambda function for environment_variables
        dict that returns list of strings
    """

    def _get_validated_env_list() -> list[str]:
        value = os.getenv(env_name)
        if value is None:
            return default

        # Split comma-separated values and strip whitespace
        values = [v.strip() for v in value.split(",") if v.strip()]

        if not values:
            return default

        # Resolve choices if it's a callable (for lazy loading)
        actual_choices = choices() if callable(choices) else choices

        # Validate each value
        for val in values:
            if not case_sensitive:
                check_value = val.lower()
                check_choices = [choice.lower() for choice in actual_choices]
            else:
                check_value = val
                check_choices = actual_choices

            if check_value not in check_choices:
451
452
453
454
                raise ValueError(
                    f"Invalid value '{val}' in {env_name}. "
                    f"Valid options: {actual_choices}."
                )
455
456
457
458
459
460

        return values

    return _get_validated_env_list


461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
def env_set_with_choices(
    env_name: str,
    default: list[str],
    choices: list[str] | Callable[[], list[str]],
    case_sensitive: bool = True,
) -> Callable[[], set[str]]:
    """
    Creates a lambda which that validates environment variable
    containing comma-separated values against allowed choices which
    returns choices as a set.
    """

    def _get_validated_env_set() -> set[str]:
        return set(env_list_with_choices(env_name, default, choices, case_sensitive)())

    return _get_validated_env_set


479
def get_vllm_port() -> int | None:
480
    """Get the port from VLLM_PORT environment variable.
481

482
483
    Returns:
        The port number as an integer if VLLM_PORT is set, None otherwise.
484

485
486
487
    Raises:
        ValueError: If VLLM_PORT is a URI, suggest k8s service discovery issue.
    """
488
    if "VLLM_PORT" not in os.environ:
489
490
        return None

491
    port = os.getenv("VLLM_PORT", "0")
492
493
494
495

    try:
        return int(port)
    except ValueError as err:
496
        from urllib3.util import parse_url
497

498
        parsed = parse_url(port)
499
500
501
502
503
504
        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
505
        raise ValueError(f"VLLM_PORT '{port}' must be a valid integer") from err
506
507


508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
def get_env_or_set_default(
    env_name: str,
    default_factory: Callable[[], str],
) -> Callable[[], str]:
    """
    Create a lambda that returns an environment variable value if set,
    or generates and sets a default value using the provided factory function.
    """

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

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

    return _get_or_set_default


Ning Xie's avatar
Ning Xie committed
529
# The start-* and end* here are used by the documentation generator
530
531
# to extract the used env vars.

532
# --8<-- [start:env-vars-definition]
533

534
logger = logging.getLogger(__name__)
535

536
environment_variables: dict[str, Callable[[], Any]] = {
537
    # ================== Installation Time Env Vars ==================
538
    # Target device of vLLM, supporting [cuda (by default),
539
    # rocm, cpu]
540
    "VLLM_TARGET_DEVICE": lambda: os.getenv("VLLM_TARGET_DEVICE", "cuda").lower(),
541
    # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
542
    "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
543
    or "12.9",
544
    # Controls PyTorch float32 matmul precision mode within vLLM workers.
545
    # Valid options mirror torch.set_float32_matmul_precision
546
547
    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
        "VLLM_FLOAT32_MATMUL_PRECISION",
548
549
        "highest",
        ["highest", "high", "medium"],
550
551
        case_sensitive=False,
    ),
552
553
    # Maximum number of compilation jobs to run in parallel.
    # By default this is the number of CPUs
554
    "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
555
556
557
    # Number of threads to use for nvcc
    # By default this is 1.
    # If set, `MAX_JOBS` will be reduced to avoid oversubscribing the CPU.
558
    "NVCC_THREADS": lambda: os.getenv("NVCC_THREADS", None),
559
    # If set, vllm will use precompiled binaries (*.so)
560
561
562
563
564
    "VLLM_USE_PRECOMPILED": lambda: os.environ.get("VLLM_USE_PRECOMPILED", "")
    .strip()
    .lower()
    in ("1", "true")
    or bool(os.environ.get("VLLM_PRECOMPILED_WHEEL_LOCATION")),
565
566
567
568
    # If set, skip adding +precompiled suffix to version string
    "VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX": lambda: bool(
        int(os.environ.get("VLLM_SKIP_PRECOMPILED_VERSION_SUFFIX", "0"))
    ),
569
570
    # Used to mark that setup.py is running in a Docker build context,
    # in order to force the use of precompiled binaries.
571
572
573
574
    "VLLM_DOCKER_BUILD_CONTEXT": lambda: os.environ.get("VLLM_DOCKER_BUILD_CONTEXT", "")
    .strip()
    .lower()
    in ("1", "true"),
575
576
577
    # CMake build type
    # If not set, defaults to "Debug" or "RelWithDebInfo"
    # Available options: "Debug", "Release", "RelWithDebInfo"
578
579
580
    "CMAKE_BUILD_TYPE": env_with_choices(
        "CMAKE_BUILD_TYPE", None, ["Debug", "Release", "RelWithDebInfo"]
    ),
581
    # If set, vllm will print verbose logs during installation
582
    "VERBOSE": lambda: bool(int(os.getenv("VERBOSE", "0"))),
583
    # Root directory for vLLM configuration files
584
    # Defaults to `~/.config/vllm` unless `XDG_CONFIG_HOME` is set
585
586
587
    # 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**.
588
    "VLLM_CONFIG_ROOT": lambda: os.path.expanduser(
589
590
591
        os.getenv(
            "VLLM_CONFIG_ROOT",
            os.path.join(get_default_config_root(), "vllm"),
592
593
        )
    ),
594
    # ================== Runtime Env Vars ==================
595
    # Root directory for vLLM cache files
596
    # Defaults to `~/.cache/vllm` unless `XDG_CACHE_HOME` is set
597
    "VLLM_CACHE_ROOT": lambda: os.path.expanduser(
598
599
600
        os.getenv(
            "VLLM_CACHE_ROOT",
            os.path.join(get_default_cache_root(), "vllm"),
601
602
        )
    ),
603
604
605
606
    # 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.
607
    "VLLM_HOST_IP": lambda: os.getenv("VLLM_HOST_IP", ""),
608
    # used in distributed environment to manually set the communication port
609
610
611
    # 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.
612
    "VLLM_PORT": get_vllm_port,
613
614
    # path used for ipc when the frontend api server is running in
    # multi-processing mode to communicate with the backend engine process.
615
616
617
    "VLLM_RPC_BASE_PATH": lambda: os.getenv(
        "VLLM_RPC_BASE_PATH", tempfile.gettempdir()
    ),
618
619
    # If true, will load models from ModelScope instead of Hugging Face Hub.
    # note that the value is true or false, not numbers
620
621
622
623
    "VLLM_USE_MODELSCOPE": lambda: os.environ.get(
        "VLLM_USE_MODELSCOPE", "False"
    ).lower()
    == "true",
624
    # Interval in seconds to log a warning message when the ring buffer is full
625
626
627
    "VLLM_RINGBUFFER_WARNING_INTERVAL": lambda: int(
        os.environ.get("VLLM_RINGBUFFER_WARNING_INTERVAL", "60")
    ),
628
629
    # path to cudatoolkit home directory, under which should be bin, include,
    # and lib directories.
630
    "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None),
631
632
    # 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
633
    "VLLM_NCCL_SO_PATH": lambda: os.environ.get("VLLM_NCCL_SO_PATH", None),
634
635
    # 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`
636
    "LD_LIBRARY_PATH": lambda: os.environ.get("LD_LIBRARY_PATH", None),
637
638
    # flag to control if vllm should use triton flash attention
    "VLLM_USE_TRITON_FLASH_ATTN":
639
    lambda: (os.environ.get("VLLM_USE_TRITON_FLASH_ATTN", "False").lower() in
640
             ("true", "1")),
641
642
643
644
    # flag to control the chunk size (in MB) for sleeping memory allocations under ROCm
    "VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE": lambda: int(
        os.environ.get("VLLM_ROCM_SLEEP_MEM_CHUNK_SIZE", "256")
    ),
645
    # Feature flag to enable/disable Inductor standalone compile.
646
647
    # In torch <= 2.7 we ignore this flag; in torch >= 2.9 this is
    # enabled by default.
648
    "VLLM_USE_STANDALONE_COMPILE": lambda: os.environ.get(
649
        "VLLM_USE_STANDALONE_COMPILE", "1"
650
651
    )
    == "1",
652
653
    # Debug pattern matching inside custom passes.
    # Should be set to the fx.Node name (e.g. 'getitem_34' or 'scaled_mm_3').
654
655
656
    "VLLM_PATTERN_MATCH_DEBUG": lambda: os.environ.get(
        "VLLM_PATTERN_MATCH_DEBUG", None
    ),
657
658
    # Dump fx graphs to the given directory.
    # It will override CompilationConfig.debug_dump_path if set.
659
    "VLLM_DEBUG_DUMP_PATH": lambda: os.environ.get("VLLM_DEBUG_DUMP_PATH", None),
660
661
662
663
    # Feature flag to enable/disable AOT compilation. This will ensure
    # compilation is done in warmup phase and the compilation will be
    # reused in subsequent calls.
    "VLLM_USE_AOT_COMPILE": use_aot_compile,
664
665
666
    # Feature flag to enable/disable bytecode in
    # TorchCompileWithNoGuardsWrapper.
    "VLLM_USE_BYTECODE_HOOK": lambda: bool(
667
        int(os.environ.get("VLLM_USE_BYTECODE_HOOK", "0"))
668
    ),
669
670
671
672
    # Force vllm to always load AOT compiled models from disk. Failure
    # to load will result in a hard error when this is enabled.
    # Will be ignored when VLLM_USE_AOT_COMPILE is disabled.
    "VLLM_FORCE_AOT_LOAD": lambda: os.environ.get("VLLM_FORCE_AOT_LOAD", "0") == "1",
673
674
675
676
677
678
679
    # Enable loading compiled models directly from cached standalone compile artifacts
    # without re-splitting graph modules. This reduces overhead during model
    # loading by using reconstruct_serializable_fn_from_mega_artifact.
    "VLLM_USE_MEGA_AOT_ARTIFACT": lambda: os.environ.get(
        "VLLM_USE_MEGA_AOT_ARTIFACT", "0"
    )
    == "1",
680
681
    # local rank of the process in the distributed setting, used to determine
    # the GPU device id
682
    "LOCAL_RANK": lambda: int(os.environ.get("LOCAL_RANK", "0")),
683
    # used to control the visible devices in the distributed setting
684
    "CUDA_VISIBLE_DEVICES": lambda: os.environ.get("CUDA_VISIBLE_DEVICES", None),
685
    # timeout for each iteration in the engine
686
    "VLLM_ENGINE_ITERATION_TIMEOUT_S": lambda: int(
687
        os.environ.get("VLLM_ENGINE_ITERATION_TIMEOUT_S", "120")
688
    ),
689
690
691
692
693
    # Timeout in seconds for waiting for engine cores to become ready
    # during startup. Default is 600 seconds (10 minutes).
    "VLLM_ENGINE_READY_TIMEOUT_S": lambda: int(
        os.environ.get("VLLM_ENGINE_READY_TIMEOUT_S", "600")
    ),
694
    # API key for vLLM API server
695
    "VLLM_API_KEY": lambda: os.environ.get("VLLM_API_KEY", None),
696
    # Whether to log responses from API Server for debugging
697
698
699
700
    "VLLM_DEBUG_LOG_API_SERVER_RESPONSE": lambda: os.environ.get(
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE", "False"
    ).lower()
    == "true",
701
    # S3 access information, used for tensorizer to load model from S3
702
703
704
    "S3_ACCESS_KEY_ID": lambda: os.environ.get("S3_ACCESS_KEY_ID", None),
    "S3_SECRET_ACCESS_KEY": lambda: os.environ.get("S3_SECRET_ACCESS_KEY", None),
    "S3_ENDPOINT_URL": lambda: os.environ.get("S3_ENDPOINT_URL", None),
705
    # Usage stats collection
706
707
708
709
710
711
712
713
714
715
716
    "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"),
717
718
719
720
    # 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
721
722
723
    "VLLM_CONFIGURE_LOGGING": lambda: bool(
        int(os.getenv("VLLM_CONFIGURE_LOGGING", "1"))
    ),
724
    "VLLM_LOGGING_CONFIG_PATH": lambda: os.getenv("VLLM_LOGGING_CONFIG_PATH"),
725
    # this is used for configuring the default logging level
726
    "VLLM_LOGGING_LEVEL": lambda: os.getenv("VLLM_LOGGING_LEVEL", "INFO").upper(),
727
    # this is used for configuring the default logging stream
728
    "VLLM_LOGGING_STREAM": lambda: os.getenv("VLLM_LOGGING_STREAM", "ext://sys.stdout"),
729
    # if set, VLLM_LOGGING_PREFIX will be prepended to all log messages
730
    "VLLM_LOGGING_PREFIX": lambda: os.getenv("VLLM_LOGGING_PREFIX", ""),
Nick Hill's avatar
Nick Hill committed
731
732
733
734
735
    # Controls colored logging output. Options: "auto" (default, colors when terminal),
    # "1" (always use colors), "0" (never use colors)
    "VLLM_LOGGING_COLOR": lambda: os.getenv("VLLM_LOGGING_COLOR", "auto"),
    # Standard unix flag for disabling ANSI color codes
    "NO_COLOR": lambda: os.getenv("NO_COLOR", "0") != "0",
736
737
    # If set, vllm will log stats at this interval in seconds
    # If not set, vllm will log stats every 10 seconds.
738
739
740
    "VLLM_LOG_STATS_INTERVAL": lambda: val
    if (val := float(os.getenv("VLLM_LOG_STATS_INTERVAL", "10."))) > 0.0
    else 10.0,
741
742
743
    # Trace function calls
    # If set to 1, vllm will trace function calls
    # Useful for debugging
744
    "VLLM_TRACE_FUNCTION": lambda: int(os.getenv("VLLM_TRACE_FUNCTION", "0")),
745
    # If set, vllm will use flashinfer sampler
746
747
748
749
750
    "VLLM_USE_FLASHINFER_SAMPLER": lambda: bool(
        int(os.environ["VLLM_USE_FLASHINFER_SAMPLER"])
    )
    if "VLLM_USE_FLASHINFER_SAMPLER" in os.environ
    else None,
751
    # Pipeline stage partition strategy
752
    "VLLM_PP_LAYER_PARTITION": lambda: os.getenv("VLLM_PP_LAYER_PARTITION", None),
753
    # (CPU backend only) CPU key-value cache space.
754
    # default is None and will be set as 4 GB
755
756
757
    "VLLM_CPU_KVCACHE_SPACE": lambda: int(os.getenv("VLLM_CPU_KVCACHE_SPACE", "0"))
    if "VLLM_CPU_KVCACHE_SPACE" in os.environ
    else None,
758
759
    # (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 '|'.
760
    "VLLM_CPU_OMP_THREADS_BIND": lambda: os.getenv("VLLM_CPU_OMP_THREADS_BIND", "auto"),
761
762
    # (CPU backend only) CPU cores not used by OMP threads .
    # Those CPU cores will not be used by OMP threads of a rank.
763
764
765
766
767
    "VLLM_CPU_NUM_OF_RESERVED_CPU": lambda: int(
        os.getenv("VLLM_CPU_NUM_OF_RESERVED_CPU", "0")
    )
    if "VLLM_CPU_NUM_OF_RESERVED_CPU" in os.environ
    else None,
768
    # (CPU backend only) whether to use SGL kernels, optimized for small batch.
769
    "VLLM_CPU_SGL_KERNEL": lambda: bool(int(os.getenv("VLLM_CPU_SGL_KERNEL", "0"))),
770
771
772
773
774
775
776
    # 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
777
778
779
    "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE": env_with_choices(
        "VLLM_USE_RAY_COMPILED_DAG_CHANNEL_TYPE", "auto", ["auto", "nccl", "shm"]
    ),
780
    # If the env var is set, it enables GPU communication overlap
781
    # (experimental feature) in Ray's Compiled Graph.
782
783
784
    "VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_COMPILED_DAG_OVERLAP_COMM", "0"))
    ),
785
786
787
    # 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.
788
789
790
    "VLLM_USE_RAY_WRAPPED_PP_COMM": lambda: bool(
        int(os.getenv("VLLM_USE_RAY_WRAPPED_PP_COMM", "1"))
    ),
791
792
    # Use dedicated multiprocess context for workers.
    # Both spawn and fork work
793
    "VLLM_WORKER_MULTIPROC_METHOD": env_with_choices(
794
        "VLLM_WORKER_MULTIPROC_METHOD", "spawn", ["spawn", "fork"]
795
    ),
796
    # Path to the cache for storing downloaded assets
797
    "VLLM_ASSETS_CACHE": lambda: os.path.expanduser(
798
799
800
        os.getenv(
            "VLLM_ASSETS_CACHE",
            os.path.join(get_default_cache_root(), "vllm", "assets"),
801
802
        )
    ),
803
804
    # If the env var is set, we will clean model file in
    # this path $VLLM_ASSETS_CACHE/model_streamer/$model_name
805
806
807
    "VLLM_ASSETS_CACHE_MODEL_CLEAN": lambda: bool(
        int(os.getenv("VLLM_ASSETS_CACHE_MODEL_CLEAN", "0"))
    ),
808
809
    # Timeout for fetching images when serving multimodal models
    # Default is 5 seconds
810
    "VLLM_IMAGE_FETCH_TIMEOUT": lambda: int(os.getenv("VLLM_IMAGE_FETCH_TIMEOUT", "5")),
811
    # Timeout for fetching videos when serving multimodal models
812
    # Default is 30 seconds
813
814
815
    "VLLM_VIDEO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_VIDEO_FETCH_TIMEOUT", "30")
    ),
816
    # Timeout for fetching audio when serving multimodal models
817
    # Default is 10 seconds
818
819
820
    "VLLM_AUDIO_FETCH_TIMEOUT": lambda: int(
        os.getenv("VLLM_AUDIO_FETCH_TIMEOUT", "10")
    ),
821
822
    # Whether to allow HTTP redirects when fetching from media URLs.
    # Default to True
823
824
825
    "VLLM_MEDIA_URL_ALLOW_REDIRECTS": lambda: bool(
        int(os.getenv("VLLM_MEDIA_URL_ALLOW_REDIRECTS", "1"))
    ),
826
827
828
    # Max number of workers for the thread pool handling
    # media bytes loading. Set to 1 to disable parallel processing.
    # Default is 8
829
830
831
    "VLLM_MEDIA_LOADING_THREAD_COUNT": lambda: int(
        os.getenv("VLLM_MEDIA_LOADING_THREAD_COUNT", "8")
    ),
832
833
834
    # 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
835
836
837
    "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB": lambda: int(
        os.getenv("VLLM_MAX_AUDIO_CLIP_FILESIZE_MB", "25")
    ),
838
839
    # Backend for Video IO
    # - "opencv": Default backend that uses OpenCV stream buffered backend.
Roger Wang's avatar
Roger Wang committed
840
    # - "identity": Returns raw video bytes for model processor to handle.
841
842
843
844
845
    #
    # 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.
846
847
848
    "VLLM_VIDEO_LOADER_BACKEND": lambda: os.getenv(
        "VLLM_VIDEO_LOADER_BACKEND", "opencv"
    ),
849
850
851
852
853
854
855
856
    # Media connector implementation.
    # - "http": Default connector that supports fetching media via HTTP.
    #
    # Custom implementations can be registered
    # via `@MEDIA_CONNECTOR_REGISTRY.register("my_custom_media_connector")` and
    # imported at runtime.
    # If a non-existing backend is used, an AssertionError will be thrown.
    "VLLM_MEDIA_CONNECTOR": lambda: os.getenv("VLLM_MEDIA_CONNECTOR", "http"),
857
858
859
860
861
862
863
864
865
866
867
    # Hash algorithm for multimodal content hashing.
    # - "blake3": Default, fast cryptographic hash (not FIPS 140-3 compliant)
    # - "sha256": FIPS 140-3 compliant, widely supported
    # - "sha512": FIPS 140-3 compliant, faster on 64-bit systems
    # Use sha256 or sha512 for FIPS compliance in government/enterprise deployments
    "VLLM_MM_HASHER_ALGORITHM": env_with_choices(
        "VLLM_MM_HASHER_ALGORITHM",
        "blake3",
        ["blake3", "sha256", "sha512"],
        case_sensitive=False,
    ),
868
869
    # Path to the XLA persistent cache directory.
    # Only used for XLA devices such as TPUs.
870
    "VLLM_XLA_CACHE_PATH": lambda: os.path.expanduser(
871
        os.getenv(
872
            "VLLM_XLA_CACHE_PATH",
873
            os.path.join(get_default_cache_root(), "vllm", "xla_cache"),
874
875
        )
    ),
876
    # If set, assert on XLA recompilation after each execution step.
877
878
879
    "VLLM_XLA_CHECK_RECOMPILATION": lambda: bool(
        int(os.getenv("VLLM_XLA_CHECK_RECOMPILATION", "0"))
    ),
880
    # Enable SPMD mode for TPU backend.
881
882
    "VLLM_XLA_USE_SPMD": lambda: bool(int(os.getenv("VLLM_XLA_USE_SPMD", "0"))),
    "VLLM_FUSED_MOE_CHUNK_SIZE": lambda: int(
883
        os.getenv("VLLM_FUSED_MOE_CHUNK_SIZE", str(16 * 1024))
884
    ),
885
886
887
    # 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.
888
889
890
    "VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_FUSED_MOE_ACTIVATION_CHUNKING", "1"))
    ),
891
892
    # If set, the OpenAI API server will stay alive even after the underlying
    # AsyncLLMEngine errors and stops serving requests
893
    "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH": lambda: bool(
894
        int(os.getenv("VLLM_KEEP_ALIVE_ON_ENGINE_DEATH", "0"))
895
    ),
896
897
898
899
    # 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.
900
901
902
903
    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": lambda: (
        os.environ.get("VLLM_ALLOW_LONG_MAX_MODEL_LEN", "0").strip().lower()
        in ("1", "true")
    ),
904
905
    # If set, forces FP8 Marlin to be used for FP8 quantization regardless
    # of the hardware support for FP8 compute.
906
907
908
909
910
911
912
    "VLLM_TEST_FORCE_FP8_MARLIN": lambda: (
        os.environ.get("VLLM_TEST_FORCE_FP8_MARLIN", "0").strip().lower()
        in ("1", "true")
    ),
    "VLLM_TEST_FORCE_LOAD_FORMAT": lambda: os.getenv(
        "VLLM_TEST_FORCE_LOAD_FORMAT", "dummy"
    ),
913
914
    # Time in ms for the zmq client to wait for a response from the backend
    # server for simple data operations
915
    "VLLM_RPC_TIMEOUT": lambda: int(os.getenv("VLLM_RPC_TIMEOUT", "10000")),
916
    # Timeout in seconds for keeping HTTP connections alive in API server
917
918
919
    "VLLM_HTTP_TIMEOUT_KEEP_ALIVE": lambda: int(
        os.environ.get("VLLM_HTTP_TIMEOUT_KEEP_ALIVE", "5")
    ),
920
921
922
    # 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
923
924
925
    "VLLM_PLUGINS": lambda: None
    if "VLLM_PLUGINS" not in os.environ
    else os.environ["VLLM_PLUGINS"].split(","),
926
927
928
    # a local directory to look in for unrecognized LoRA adapters.
    # only works if plugins are enabled and
    # VLLM_ALLOW_RUNTIME_LORA_UPDATING is enabled.
929
930
931
    "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
        "VLLM_LORA_RESOLVER_CACHE_DIR", None
    ),
932
933
934
    # Enables torch CUDA profiling if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_CUDA_PROFILE": lambda: os.getenv("VLLM_TORCH_CUDA_PROFILE"),
935
    # Enables torch profiler if set.
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DIR": lambda: os.getenv("VLLM_TORCH_PROFILER_DIR"),
    # Enable torch profiler to record shapes if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES")
    ),
    # Enable torch profiler to profile memory if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY")
    ),
    # Enable torch profiler to profile stack if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK")
    ),
    # Enable torch profiler to profile flops if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS")
    ),
    # Disable torch profiling of the AsyncLLMEngine process if set to 1.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM")
962
963
964
    ),
    # Delay number of iterations before starting profiling when using
    # the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
965
966
    # Deprecated, see profiler_config.
    "VLLM_PROFILER_DELAY_ITERS": lambda: (os.getenv("VLLM_PROFILER_DELAY_ITERS")),
967
968
    # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
    # If set to 0, will not limit the number of iterations.
969
    "VLLM_PROFILER_MAX_ITERS": lambda: os.getenv("VLLM_PROFILER_MAX_ITERS"),
970
    # Control whether torch profiler gzip-compresses profiling files.
971
972
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: os.getenv("VLLM_TORCH_PROFILER_USE_GZIP"),
973
    # Control whether torch profiler dumps the self_cuda_time_total table.
974
975
976
977
    # Set to 0 to disable dumping the table.
    # Deprecated, see profiler_config.
    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: (
        os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL")
978
    ),
979
    # If set, vLLM will use Triton implementations of AWQ.
980
    "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
981
    # If set, allow loading or unloading lora adapters in runtime,
982
983
984
985
    "VLLM_ALLOW_RUNTIME_LORA_UPDATING": lambda: (
        os.environ.get("VLLM_ALLOW_RUNTIME_LORA_UPDATING", "0").strip().lower()
        in ("1", "true")
    ),
986
987
988
989
990
991
    # 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
992
    "VLLM_SKIP_P2P_CHECK": lambda: os.getenv("VLLM_SKIP_P2P_CHECK", "1") == "1",
993
994
995
996
    # List of quantization kernels that should be disabled, used for testing
    # and performance comparisons. Currently only affects MPLinearKernel
    # selection
    # (kernels: MacheteLinearKernel, MarlinLinearKernel, ExllamaLinearKernel)
997
998
999
    "VLLM_DISABLED_KERNELS": lambda: []
    if "VLLM_DISABLED_KERNELS" not in os.environ
    else os.environ["VLLM_DISABLED_KERNELS"].split(","),
1000
    # Disable pynccl (using torch.distributed instead)
1001
1002
1003
    "VLLM_DISABLE_PYNCCL": lambda: (
        os.getenv("VLLM_DISABLE_PYNCCL", "False").lower() in ("true", "1")
    ),
1004
1005
    # Disable aiter ops unless specifically enabled.
    # Acts as a parent switch to enable the rest of the other operations.
1006
1007
1008
    "VLLM_ROCM_USE_AITER": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER", "False").lower() in ("true", "1")
    ),
1009
1010
    # Whether to use aiter paged attention.
    # By default is disabled.
1011
1012
1013
    "VLLM_ROCM_USE_AITER_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_PAGED_ATTN", "False").lower() in ("true", "1")
    ),
1014
1015
1016
    # use aiter linear op if aiter ops are enabled
    # The following list of related ops
    # - scaled_mm (per-tensor / rowwise)
1017
    "VLLM_ROCM_USE_AITER_LINEAR": lambda: (
1018
        os.getenv("VLLM_ROCM_USE_AITER_LINEAR", "False").lower() in ("true", "1")
1019
    ),
1020
1021
    # Whether to use aiter moe ops.
    # By default is enabled.
1022
    "VLLM_ROCM_USE_AITER_MOE": lambda: (
1023
        os.getenv("VLLM_ROCM_USE_AITER_MOE", "False").lower() in ("true", "1")
1024
    ),
1025
    # use aiter rms norm op if aiter ops are enabled.
1026
    "VLLM_ROCM_USE_AITER_RMSNORM": lambda: (
1027
        os.getenv("VLLM_ROCM_USE_AITER_RMSNORM", "False").lower() in ("true", "1")
1028
    ),
1029
1030
    # Whether to use aiter mla ops.
    # By default is enabled.
1031
    "VLLM_ROCM_USE_AITER_MLA": lambda: (
1032
        os.getenv("VLLM_ROCM_USE_AITER_MLA", "False").lower() in ("true", "1")
1033
    ),
1034
1035
    # Whether to use aiter mha ops.
    # By default is enabled.
1036
    "VLLM_ROCM_USE_AITER_MHA": lambda: (
1037
        os.getenv("VLLM_ROCM_USE_AITER_MHA", "False").lower() in ("true", "1")
1038
    ),
1039
1040
    # Whether to use aiter fp4 gemm asm.
    # By default is disabled.
1041
1042
1043
    "VLLM_ROCM_USE_AITER_FP4_ASM_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4_ASM_GEMM", "False").lower() in ("true", "1")
    ),
1044
1045
    # Whether to use aiter rope.
    # By default is disabled.
1046
1047
    "VLLM_ROCM_USE_AITER_TRITON_ROPE": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_ROPE", "False").lower() in ("true", "1")
1048
    ),
1049
1050
    # Whether to use aiter triton fp8 bmm kernel
    # By default is enabled.
1051
    "VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
1052
        os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "False").lower() in ("true", "1")
1053
    ),
1054
1055
1056
1057
1058
    # Whether to use aiter triton fp4 bmm kernel
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_FP4BMM": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_FP4BMM", "True").lower() in ("true", "1")
    ),
1059
1060
1061
1062
1063
    # Use AITER triton unified attention for V1 attention
    "VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION": lambda: (
        os.getenv("VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION", "False").lower()
        in ("true", "1")
    ),
1064
    # Whether to use aiter fusion shared experts ops.
1065
    # By default is disabled.
1066
    "VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS": lambda: (
1067
        os.getenv("VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS", "False").lower()
1068
1069
        in ("true", "1")
    ),
1070
1071
1072
    # Whether to use aiter triton kernels for gemm ops.
    # By default is enabled.
    "VLLM_ROCM_USE_AITER_TRITON_GEMM": lambda: (
1073
        os.getenv("VLLM_ROCM_USE_AITER_TRITON_GEMM", "False").lower() in ("true", "1")
1074
    ),
1075
    # use rocm skinny gemms
1076
1077
1078
    "VLLM_ROCM_USE_SKINNY_GEMM": lambda: (
        os.getenv("VLLM_ROCM_USE_SKINNY_GEMM", "True").lower() in ("true", "1")
    ),
1079
    # Pad the fp8 weights to 256 bytes for ROCm
1080
    "VLLM_ROCM_FP8_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_FP8_PADDING", "1"))),
1081
    # Pad the weights for the moe kernel
1082
    "VLLM_ROCM_MOE_PADDING": lambda: bool(int(os.getenv("VLLM_ROCM_MOE_PADDING", "0"))),
1083
    # custom paged attention kernel for MI3* cards
1084
1085
1086
    "VLLM_ROCM_CUSTOM_PAGED_ATTN": lambda: (
        os.getenv("VLLM_ROCM_CUSTOM_PAGED_ATTN", "True").lower() in ("true", "1")
    ),
1087
1088
1089
1090
    # Whether to use the shuffled kv cache layout
    "VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT": lambda: (
        os.getenv("VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT", "False").lower() in ("true", "1")
    ),
1091
1092
1093
    # Custom quick allreduce kernel for MI3* cards
    # Choice of quantization level: FP, INT8, INT6, INT4 or NONE
    # Recommended for large models to get allreduce
1094
1095
1096
1097
1098
    "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION": env_with_choices(
        "VLLM_ROCM_QUICK_REDUCE_QUANTIZATION",
        "NONE",
        ["FP", "INT8", "INT6", "INT4", "NONE"],
    ),
1099
1100
1101
1102
    # 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
1103
1104
1105
1106
    "VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16": lambda: (
        os.getenv("VLLM_ROCM_QUICK_REDUCE_CAST_BF16_TO_FP16", "True").lower()
        in ("true", "1")
    ),
1107
1108
1109
1110
1111
1112
    # 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.
1113
1114
1115
    "VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB": lambda: maybe_convert_int(
        os.environ.get("VLLM_ROCM_QUICK_REDUCE_MAX_SIZE_BYTES_MB", None)
    ),
1116
    # Divisor for dynamic query scale factor calculation for FP8 KV Cache
1117
    "Q_SCALE_CONSTANT": lambda: int(os.getenv("Q_SCALE_CONSTANT", "200")),
1118
    # Divisor for dynamic key scale factor calculation for FP8 KV Cache
1119
    "K_SCALE_CONSTANT": lambda: int(os.getenv("K_SCALE_CONSTANT", "200")),
1120
    # Divisor for dynamic value scale factor calculation for FP8 KV Cache
1121
    "V_SCALE_CONSTANT": lambda: int(os.getenv("V_SCALE_CONSTANT", "100")),
1122
    # If set, enable multiprocessing in LLM for the V1 code path.
1123
1124
1125
1126
1127
1128
    "VLLM_ENABLE_V1_MULTIPROCESSING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_V1_MULTIPROCESSING", "1"))
    ),
    "VLLM_LOG_BATCHSIZE_INTERVAL": lambda: float(
        os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")
    ),
1129
    "VLLM_DISABLE_COMPILE_CACHE": disable_compile_cache,
1130
1131
1132
    # If set, vllm will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
1133
    "VLLM_SERVER_DEV_MODE": lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
1134
1135
1136
1137
1138
1139
1140
    # 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.
1141
1142
1143
    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE": lambda: int(
        os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")
    ),
1144
    # If set, vLLM will disable the MLA attention optimizations.
1145
    "VLLM_MLA_DISABLE": lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
1146
    # If set, vLLM will pick up the provided Flash Attention MLA
1147
1148
1149
    # 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.
1150
1151
1152
    "VLLM_RAY_PER_WORKER_GPUS": lambda: float(
        os.getenv("VLLM_RAY_PER_WORKER_GPUS", "1.0")
    ),
1153
1154
1155
    # 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"
1156
    "VLLM_RAY_BUNDLE_INDICES": lambda: os.getenv("VLLM_RAY_BUNDLE_INDICES", ""),
1157
1158
    # In some system, find_loaded_library() may not work. So we allow users to
    # specify the path through environment variable VLLM_CUDART_SO_PATH.
1159
    "VLLM_CUDART_SO_PATH": lambda: os.getenv("VLLM_CUDART_SO_PATH", None),
1160
    # Rank of the process in the data parallel setting
1161
    "VLLM_DP_RANK": lambda: int(os.getenv("VLLM_DP_RANK", "0")),
1162
1163
    # Rank of the process in the data parallel setting.
    # Defaults to VLLM_DP_RANK when not set.
1164
1165
1166
    "VLLM_DP_RANK_LOCAL": lambda: int(
        os.getenv("VLLM_DP_RANK_LOCAL", sys.modules[__name__].VLLM_DP_RANK)
    ),
1167
    # World size of the data parallel setting
1168
    "VLLM_DP_SIZE": lambda: int(os.getenv("VLLM_DP_SIZE", "1")),
1169
    # IP address of the master node in the data parallel setting
1170
    "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
1171
    # Port of the master node in the data parallel setting
1172
    "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
1173
1174
1175
1176
1177
    # 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.
1178
    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
1179
1180
1181
    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
    ),
1182
    # Randomize inputs during dummy runs when using Data Parallel
1183
1184
1185
1186
    "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: os.environ.get(
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0"
    )
    == "1",
1187
1188
1189
1190
1191
1192
1193
    # Strategy to pack the data parallel ranks for Ray.
    # Available options:
    # - "fill":
    #   for DP master node, allocate exactly data-parallel-size-local DP ranks,
    #   for non-master nodes, allocate as many DP ranks as can fit;
    # - "strict":
    #   allocate exactly data-parallel-size-local DP ranks to each picked node;
1194
1195
1196
    # - "span":
    #   Should be used only when a single DP rank requires multiple nodes.
    #   allocate one DP rank over as many nodes as required for set world_size;
1197
1198
1199
1200
    # This environment variable is ignored if data-parallel-backend is not Ray.
    "VLLM_RAY_DP_PACK_STRATEGY": lambda: os.getenv(
        "VLLM_RAY_DP_PACK_STRATEGY", "strict"
    ),
1201
    # Whether to use S3 path for model loading in CI via RunAI Streamer
1202
    "VLLM_CI_USE_S3": lambda: os.environ.get("VLLM_CI_USE_S3", "0") == "1",
1203
    # Use model_redirect to redirect the model name to a local folder.
1204
1205
1206
1207
1208
    # `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
1209
1210
1211
    "VLLM_MODEL_REDIRECT_PATH": lambda: os.environ.get(
        "VLLM_MODEL_REDIRECT_PATH", None
    ),
1212
    # Whether to use atomicAdd reduce in gptq/awq marlin kernel.
1213
1214
1215
1216
    "VLLM_MARLIN_USE_ATOMIC_ADD": lambda: os.environ.get(
        "VLLM_MARLIN_USE_ATOMIC_ADD", "0"
    )
    == "1",
1217
    # Whether to use marlin kernel in mxfp4 quantization method
1218
1219
1220
    "VLLM_MXFP4_USE_MARLIN": lambda: maybe_convert_bool(
        os.environ.get("VLLM_MXFP4_USE_MARLIN", None)
    ),
1221
1222
1223
1224
    # The activation dtype for marlin kernel
    "VLLM_MARLIN_INPUT_DTYPE": env_with_choices(
        "VLLM_MARLIN_INPUT_DTYPE", None, ["int8", "fp8"]
    ),
1225
1226
1227
1228
1229
1230
    # Whether to use DeepEPLL kernels for NVFP4 quantization and dispatch method
    # only supported on Blackwell GPUs and with
    # https://github.com/deepseek-ai/DeepEP/pull/341
    "VLLM_DEEPEPLL_NVFP4_DISPATCH": lambda: bool(
        int(os.getenv("VLLM_DEEPEPLL_NVFP4_DISPATCH", "0"))
    ),
1231
1232
1233
    # 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.
1234
1235
1236
1237
    "VLLM_V1_USE_OUTLINES_CACHE": lambda: os.environ.get(
        "VLLM_V1_USE_OUTLINES_CACHE", "0"
    )
    == "1",
1238
1239
    # Gap between padding buckets for the forward pass. So we have
    # 8, we will run forward pass with [16, 24, 32, ...].
1240
1241
1242
1243
1244
1245
1246
1247
    "VLLM_TPU_BUCKET_PADDING_GAP": lambda: int(
        os.environ["VLLM_TPU_BUCKET_PADDING_GAP"]
    )
    if "VLLM_TPU_BUCKET_PADDING_GAP" in os.environ
    else 0,
    "VLLM_TPU_MOST_MODEL_LEN": lambda: maybe_convert_int(
        os.environ.get("VLLM_TPU_MOST_MODEL_LEN", None)
    ),
1248
    # Whether using Pathways
1249
1250
1251
    "VLLM_TPU_USING_PATHWAYS": lambda: bool(
        "proxy" in os.getenv("JAX_PLATFORMS", "").lower()
    ),
1252
    # Allow use of DeepGemm kernels for fused moe ops.
1253
    "VLLM_USE_DEEP_GEMM": lambda: bool(int(os.getenv("VLLM_USE_DEEP_GEMM", "1"))),
1254
1255
1256
1257
    # Allow use of DeepGemm specifically for MoE fused ops (overrides only MoE).
    "VLLM_MOE_USE_DEEP_GEMM": lambda: bool(
        int(os.getenv("VLLM_MOE_USE_DEEP_GEMM", "1"))
    ),
1258
    # Whether to use E8M0 scaling when DeepGEMM is used on Blackwell GPUs.
1259
1260
1261
    "VLLM_USE_DEEP_GEMM_E8M0": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_E8M0", "1"))
    ),
1262
1263
1264
1265
    # Whether to create TMA-aligned scale tensor when DeepGEMM is used.
    "VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES": lambda: bool(
        int(os.getenv("VLLM_USE_DEEP_GEMM_TMA_ALIGNED_SCALES", "1"))
    ),
1266
1267
1268
1269
    # 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.
1270
1271
1272
1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
    # Available options:
    #  - "skip"  : Skip warmup.
    #  - "full"  : Warmup deepgemm by running all possible gemm shapes the
    #   engine could encounter.
    #  - "relax" : Select gemm shapes to run based on some heuristics. The
    #   heuristic aims to have the same effect as running all possible gemm
    #   shapes, but provides no guarantees.
    "VLLM_DEEP_GEMM_WARMUP": env_with_choices(
        "VLLM_DEEP_GEMM_WARMUP",
        "relax",
        [
            "skip",
            "full",
            "relax",
        ],
1285
    ),
1286
    # Whether to use fused grouped_topk used for MoE expert selection.
1287
1288
1289
    "VLLM_USE_FUSED_MOE_GROUPED_TOPK": lambda: bool(
        int(os.getenv("VLLM_USE_FUSED_MOE_GROUPED_TOPK", "1"))
    ),
1290
1291
1292
1293
1294
    # Allow use of FlashInfer FP8 block-scale GEMM for linear layers.
    # This uses TensorRT-LLM kernels and requires SM90+ (Hopper).
    "VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER": lambda: bool(
        int(os.getenv("VLLM_BLOCKSCALE_FP8_GEMM_FLASHINFER", "0"))
    ),
1295
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1296
1297
1298
    "VLLM_USE_FLASHINFER_MOE_FP16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP16", "0"))
    ),
1299
    # Allow use of FlashInfer MoE kernels for fused moe ops.
1300
1301
1302
    "VLLM_USE_FLASHINFER_MOE_FP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP8", "0"))
    ),
1303
    # Allow use of FlashInfer CUTLASS kernels for fused moe ops.
1304
1305
1306
    "VLLM_USE_FLASHINFER_MOE_FP4": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_FP4", "0"))
    ),
1307
1308
    # If set to 1, use the FlashInfer
    # MXFP8 (activation) x MXFP4 (weight) MoE backend.
1309
1310
1311
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8", "0"))
    ),
1312
1313
1314
1315
    # If set to 1, use the FlashInfer CUTLASS backend for
    # MXFP8 (activation) x MXFP4 (weight) MoE.
    # This is separate from the TRTLLMGEN path controlled by
    # VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8.
1316
1317
1318
    "VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8_CUTLASS", "0"))
    ),
1319
1320
    # If set to 1, use the FlashInfer
    # BF16 (activation) x MXFP4 (weight) MoE backend.
1321
1322
1323
    "VLLM_USE_FLASHINFER_MOE_MXFP4_BF16": lambda: bool(
        int(os.getenv("VLLM_USE_FLASHINFER_MOE_MXFP4_BF16", "0"))
    ),
1324
1325
1326
    # 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.
1327
    "VLLM_XGRAMMAR_CACHE_MB": lambda: int(os.getenv("VLLM_XGRAMMAR_CACHE_MB", "512")),
1328
1329
1330
1331
1332
1333
1334
    # 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.
1335
1336
1337
    "VLLM_MSGPACK_ZERO_COPY_THRESHOLD": lambda: int(
        os.getenv("VLLM_MSGPACK_ZERO_COPY_THRESHOLD", "256")
    ),
1338
1339
1340
    # 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.
1341
1342
1343
    "VLLM_ALLOW_INSECURE_SERIALIZATION": lambda: bool(
        int(os.getenv("VLLM_ALLOW_INSECURE_SERIALIZATION", "0"))
    ),
Robert Shaw's avatar
Robert Shaw committed
1344
    # IP address used for NIXL handshake between remote agents.
1345
1346
1347
    "VLLM_NIXL_SIDE_CHANNEL_HOST": lambda: os.getenv(
        "VLLM_NIXL_SIDE_CHANNEL_HOST", "localhost"
    ),
Robert Shaw's avatar
Robert Shaw committed
1348
    # Port used for NIXL handshake between remote agents.
1349
1350
1351
    "VLLM_NIXL_SIDE_CHANNEL_PORT": lambda: int(
        os.getenv("VLLM_NIXL_SIDE_CHANNEL_PORT", "5600")
    ),
1352
1353
1354
1355
    # Port used for Mooncake handshake between remote agents.
    "VLLM_MOONCAKE_BOOTSTRAP_PORT": lambda: int(
        os.getenv("VLLM_MOONCAKE_BOOTSTRAP_PORT", "8998")
    ),
1356
1357
    # [DEPRECATED - will be removed in v0.15.0] all2all backend for vllm's
    # expert parallel communication. Use --all2all-backend CLI argument instead.
1358
    # Available options:
1359
1360
1361
    # - "naive": naive all2all implementation using broadcasts
    # - "allgather_reducescatter": all2all implementation based on allgather and
    #  reducescatter
1362
    # - "pplx": use pplx kernels
1363
1364
    # - "deepep_high_throughput", use deepep high-throughput kernels
    # - "deepep_low_latency", use deepep low-latency kernels
1365
    # - "mori", use MoRI kernels
1366
    # - "flashinfer_all2allv", use flashinfer alltoallv kernels for mnnvl
1367
1368
    "VLLM_ALL2ALL_BACKEND": env_with_choices(
        "VLLM_ALL2ALL_BACKEND",
1369
        None,
1370
1371
1372
1373
1374
        [
            "naive",
            "pplx",
            "deepep_high_throughput",
            "deepep_low_latency",
1375
            "mori",
1376
1377
1378
1379
            "allgather_reducescatter",
            "flashinfer_all2allv",
        ],
    ),
1380
1381
    # Flashinfer MoE backend for vLLM's fused Mixture-of-Experts support.
    # Both require compute capability 10.0 or above.
1382
1383
1384
1385
1386
    # Available options:
    # - "throughput":  [default]
    #     Uses CUTLASS kernels optimized for high-throughput batch inference.
    # - "latency":
    #     Uses TensorRT-LLM kernels optimized for low-latency inference.
1387
    "VLLM_FLASHINFER_MOE_BACKEND": env_with_choices(
1388
1389
1390
        "VLLM_FLASHINFER_MOE_BACKEND",
        "latency",
        ["throughput", "latency", "masked_gemm"],
1391
    ),
1392
1393
1394
1395
    # Control the workspace buffer size for the FlashInfer backend.
    "VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE": lambda: int(
        os.getenv("VLLM_FLASHINFER_WORKSPACE_BUFFER_SIZE", str(394 * 1024 * 1024))
    ),
1396
1397
1398
1399
    # 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.
1400
1401
1402
    "VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE": lambda: int(
        os.getenv("VLLM_MAX_TOKENS_PER_EXPERT_FP4_MOE", "163840")
    ),
1403
1404
1405
1406
    # 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> }
1407
    # Unspecified world sizes will fall back to
1408
    #     { 2: 64, 4: 1, <everything else>: 0.5 }
1409
1410
1411
    "VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB": lambda: json.loads(
        os.getenv("VLLM_FLASHINFER_ALLREDUCE_FUSION_THRESHOLDS_MB", "{}")
    ),
1412
1413
1414
    # MoE routing strategy selector.
    # See `RoutingSimulator.get_available_strategies()` # for available
    # strategies.
1415
    # Custom routing strategies can be registered by
1416
1417
    # RoutingSimulator.register_strategy()
    # Note: custom strategies may not produce correct model outputs
1418
1419
1420
    "VLLM_MOE_ROUTING_SIMULATION_STRATEGY": lambda: os.environ.get(
        "VLLM_MOE_ROUTING_SIMULATION_STRATEGY", ""
    ).lower(),
1421
    # Regex timeout for use by the vLLM tool parsing plugins.
1422
1423
1424
    "VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_TOOL_PARSE_REGEX_TIMEOUT_SECONDS", "1")
    ),
1425
1426
    # Reduce CPU usage when vLLM is idle. Enabling this will incur small
    # latency penalty when a request eventually comes.
1427
    "VLLM_SLEEP_WHEN_IDLE": lambda: bool(int(os.getenv("VLLM_SLEEP_WHEN_IDLE", "0"))),
1428
1429
1430
    # 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.
1431
1432
1433
    "VLLM_MQ_MAX_CHUNK_BYTES_MB": lambda: int(
        os.getenv("VLLM_MQ_MAX_CHUNK_BYTES_MB", "16")
    ),
1434
1435
    # Timeout in seconds for execute_model RPC calls in multiprocessing
    # executor (only applies when TP > 1).
1436
1437
1438
    "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS": lambda: int(
        os.getenv("VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS", "300")
    ),
1439
1440
1441
1442
1443
1444
1445
    # 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.
1446
1447
1448
    "VLLM_KV_CACHE_LAYOUT": env_with_choices(
        "VLLM_KV_CACHE_LAYOUT", None, ["NHD", "HND"]
    ),
1449
1450
1451
    # 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.
1452
1453
1454
    "VLLM_COMPUTE_NANS_IN_LOGITS": lambda: bool(
        int(os.getenv("VLLM_COMPUTE_NANS_IN_LOGITS", "0"))
    ),
1455
1456
1457
    # Controls whether or not emulations are used for NVFP4
    # generations on machines < 100 for compressed-tensors
    # models
1458
1459
1460
    "VLLM_USE_NVFP4_CT_EMULATIONS": lambda: bool(
        int(os.getenv("VLLM_USE_NVFP4_CT_EMULATIONS", "0"))
    ),
1461
1462
1463
1464
    # 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.
1465
1466
1467
    "VLLM_NIXL_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_NIXL_ABORT_REQUEST_TIMEOUT", "480")
    ),
1468
1469
1470
1471
1472
1473
1474
1475
1476
1477
1478
1479
1480
1481
    # Controls the read mode for the Mori-IO connector
    "VLLM_MORIIO_CONNECTOR_READ_MODE": lambda: (
        os.getenv("VLLM_MORIIO_CONNECTOR_READ_MODE", "False").lower() in ("true", "1")
    ),
    # Controls the QP (Queue Pair) per transfer configuration for the Mori-IO connector
    "VLLM_MORIIO_QP_PER_TRANSFER": lambda: int(
        os.getenv("VLLM_MORIIO_QP_PER_TRANSFER", "1")
    ),
    # Controls the post-processing batch size for the Mori-IO connector
    "VLLM_MORIIO_POST_BATCH_SIZE": lambda: int(
        os.getenv("VLLM_MORIIO_POST_BATCH_SIZE", "-1")
    ),
    # Controls the number of workers for Mori operations for the Mori-IO connector
    "VLLM_MORIIO_NUM_WORKERS": lambda: int(os.getenv("VLLM_MORIIO_NUM_WORKERS", "1")),
1482
1483
1484
1485
    # Timeout (in seconds) for MooncakeConnector in PD disaggregated setup.
    "VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT": lambda: int(
        os.getenv("VLLM_MOONCAKE_ABORT_REQUEST_TIMEOUT", "480")
    ),
1486
1487
    # If set, it means we pre-downloaded cubin files and flashinfer will
    # read the cubin files directly.
1488
1489
1490
    "VLLM_HAS_FLASHINFER_CUBIN": lambda: bool(
        int(os.getenv("VLLM_HAS_FLASHINFER_CUBIN", "0"))
    ),
1491
1492
1493
1494
    # Supported options:
    # - "flashinfer-cudnn": use flashinfer cudnn GEMM backend
    # - "flashinfer-trtllm": use flashinfer trtllm GEMM backend
    # - "flashinfer-cutlass": use flashinfer cutlass GEMM backend
1495
    # - "marlin": use marlin GEMM backend (for GPUs without native FP4 support)
1496
1497
1498
1499
    # - <none>: automatically pick an available backend
    "VLLM_NVFP4_GEMM_BACKEND": env_with_choices(
        "VLLM_NVFP4_GEMM_BACKEND",
        None,
1500
1501
1502
1503
1504
        [
            "flashinfer-cudnn",
            "flashinfer-trtllm",
            "flashinfer-cutlass",
            "cutlass",
1505
            "marlin",
1506
        ],
1507
    ),
1508
1509
1510
    # 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.
1511
1512
1513
    "VLLM_ENABLE_CUDAGRAPH_GC": lambda: bool(
        int(os.getenv("VLLM_ENABLE_CUDAGRAPH_GC", "0"))
    ),
1514
    # Used to force set up loopback IP
1515
    "VLLM_LOOPBACK_IP": lambda: os.getenv("VLLM_LOOPBACK_IP", ""),
1516
1517
1518
    # Used to set the process name prefix for vLLM processes.
    # This is useful for debugging and monitoring purposes.
    # The default value is "VLLM".
1519
    "VLLM_PROCESS_NAME_PREFIX": lambda: os.getenv("VLLM_PROCESS_NAME_PREFIX", "VLLM"),
1520
1521
1522
1523
1524
1525
1526
    # 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.
1527
    "VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE": lambda: bool(
1528
        int(os.getenv("VLLM_ALLOW_CHUNKED_LOCAL_ATTN_WITH_HYBRID_KV_CACHE", "1"))
1529
    ),
1530
1531
    # 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
1532
1533
    # messages for those requests in memory. By default, this is disabled (0),
    # and the "store" option is ignored.
1534
1535
1536
1537
1538
    # 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.
1539
1540
1541
    "VLLM_ENABLE_RESPONSES_API_STORE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_RESPONSES_API_STORE", "0"))
    ),
xiao-llm's avatar
xiao-llm committed
1542
    # If set, use the fp8 mfma in rocm paged attention.
1543
1544
1545
    "VLLM_ROCM_FP8_MFMA_PAGE_ATTN": lambda: bool(
        int(os.getenv("VLLM_ROCM_FP8_MFMA_PAGE_ATTN", "0"))
    ),
1546
    # Whether to use pytorch symmetric memory for allreduce
1547
    "VLLM_ALLREDUCE_USE_SYMM_MEM": lambda: bool(
1548
        int(os.getenv("VLLM_ALLREDUCE_USE_SYMM_MEM", "1"))
1549
    ),
1550
1551
1552
1553
    # Experimental: use this to enable MCP tool calling for non harmony models
    "VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT": lambda: bool(
        int(os.getenv("VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT", "0"))
    ),
1554
    # Allows vllm to find tuned config under customized folder
1555
    "VLLM_TUNED_CONFIG_FOLDER": lambda: os.getenv("VLLM_TUNED_CONFIG_FOLDER", None),
1556
1557
1558
1559
1560
1561
1562
1563
1564
    # Valid values are container,code_interpreter,web_search_preview
    # ex VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS=container,code_interpreter
    # If the server_label of your mcp tool is not in this list it will
    # be completely ignored.
    "VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS": env_set_with_choices(
        "VLLM_GPT_OSS_SYSTEM_TOOL_MCP_LABELS",
        default=[],
        choices=["container", "code_interpreter", "web_search_preview"],
    ),
1565
    # Allows harmony instructions to be injected on system messages
1566
1567
1568
    "VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS": lambda: bool(
        int(os.getenv("VLLM_GPT_OSS_HARMONY_SYSTEM_INSTRUCTIONS", "0"))
    ),
1569
1570
1571
1572
1573
1574
    # Enable automatic retry when tool call JSON parsing fails
    # If enabled, returns an error message to the model to retry
    # If disabled (default), raises an exception and fails the request
    "VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY": lambda: bool(
        int(os.getenv("VLLM_TOOL_JSON_ERROR_AUTOMATIC_RETRY", "0"))
    ),
1575
    # Add optional custom scopes for profiling, disable to avoid overheads
1576
1577
1578
    "VLLM_CUSTOM_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_CUSTOM_SCOPES_FOR_PROFILING", "0"))
    ),
1579
    # Add optional nvtx scopes for profiling, disable to avoid overheads
1580
1581
1582
    "VLLM_NVTX_SCOPES_FOR_PROFILING": lambda: bool(
        int(os.getenv("VLLM_NVTX_SCOPES_FOR_PROFILING", "0"))
    ),
1583
1584
    # Represent block hashes in KV cache events as 64-bit integers instead of
    # raw bytes. Defaults to True for backward compatibility.
1585
1586
1587
    "VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES": lambda: bool(
        int(os.getenv("VLLM_KV_EVENTS_USE_INT_BLOCK_HASHES", "1"))
    ),
1588
1589
    # Name of the shared memory buffer used for object storage.
    # Only effective when mm_config.mm_processor_cache_type == "shm".
1590
1591
1592
1593
1594
    # Automatically generates a unique UUID-based name per process tree
    # if not explicitly set.
    "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME": get_env_or_set_default(
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
        lambda: f"VLLM_OBJECT_STORAGE_SHM_BUFFER_{uuid.uuid4().hex}",
1595
    ),
1596
    # The size in MB of the buffers (NVL and RDMA) used by DeepEP
1597
1598
1599
    "VLLM_DEEPEP_BUFFER_SIZE_MB": lambda: int(
        os.getenv("VLLM_DEEPEP_BUFFER_SIZE_MB", "1024")
    ),
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
    # Force DeepEP to use intranode kernel for inter-node communication in
    # high throughput mode. This is useful archive higher prefill throuhgput
    # on system supports multi-node nvlink (e.g GB200).
    "VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE": lambda: bool(
        int(os.getenv("VLLM_DEEPEP_HIGH_THROUGHPUT_FORCE_INTRA_NODE", "0"))
    ),
    # Allow DeepEP to use MNNVL (multi-node nvlink) for internode_ll kernel,
    # turn this for better latency on GB200 like system
    "VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL": lambda: bool(
        int(os.getenv("VLLM_DEEPEP_LOW_LATENCY_USE_MNNVL", "0"))
    ),
1611
1612
    # The number of SMs to allocate for communication kernels when running DBO
    # the rest of the SMs on the device will be allocated to compute
1613
    "VLLM_DBO_COMM_SMS": lambda: int(os.getenv("VLLM_DBO_COMM_SMS", "20")),
1614
1615
1616
    # Enable max_autotune & coordinate_descent_tuning in inductor_config
    # to compile static shapes passed from compile_sizes in compilation_config
    # If set to 1, enable max_autotune; By default, this is enabled (1)
1617
1618
1619
    "VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_MAX_AUTOTUNE", "1"))
    ),
1620
1621
    # If set to 1, enable coordinate_descent_tuning;
    # By default, this is enabled (1)
1622
1623
1624
    "VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING": lambda: bool(
        int(os.getenv("VLLM_ENABLE_INDUCTOR_COORDINATE_DESCENT_TUNING", "1"))
    ),
1625
    # Flag to enable NCCL symmetric memory allocation and registration
1626
1627
1628
    "VLLM_USE_NCCL_SYMM_MEM": lambda: bool(
        int(os.getenv("VLLM_USE_NCCL_SYMM_MEM", "0"))
    ),
1629
    # NCCL header path
1630
    "VLLM_NCCL_INCLUDE_PATH": lambda: os.environ.get("VLLM_NCCL_INCLUDE_PATH", None),
1631
1632
    # Flag to enable FBGemm kernels on model execution
    "VLLM_USE_FBGEMM": lambda: bool(int(os.getenv("VLLM_USE_FBGEMM", "0"))),
1633
1634
1635
1636
1637
1638
    # GC debug config
    # - VLLM_GC_DEBUG=0: disable GC debugger
    # - VLLM_GC_DEBUG=1: enable GC debugger with gc.collect elpased times
    # - VLLM_GC_DEBUG='{"top_objects":5}': enable GC debugger with
    #                                      top 5 collected objects
    "VLLM_GC_DEBUG": lambda: os.getenv("VLLM_GC_DEBUG", ""),
1639
1640
1641
    # Debug workspace allocations.
    # logging of workspace resize operations.
    "VLLM_DEBUG_WORKSPACE": lambda: bool(int(os.getenv("VLLM_DEBUG_WORKSPACE", "0"))),
1642
    # Disables parallel execution of shared_experts via separate cuda stream
1643
1644
    "VLLM_DISABLE_SHARED_EXPERTS_STREAM": lambda: bool(
        int(os.getenv("VLLM_DISABLE_SHARED_EXPERTS_STREAM", "0"))
1645
    ),
1646
1647
1648
1649
1650
1651
1652
    # Limits when we run shared_experts in a separate stream.
    # We found out that for large batch sizes, the separate stream
    # execution is not beneficial (most likely because of the input clone)
    # TODO(alexm-redhat): Tune to be more dynamic based on GPU type
    "VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD": lambda: int(
        int(os.getenv("VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD", 256))
    ),
1653
1654
1655
1656
1657
1658
1659
1660
1661
    # Format for saving torch.compile cache artifacts
    # - "binary": saves as binary file
    #     Safe for multiple vllm serve processes accessing the same torch compile cache.
    # - "unpacked": saves as directory structure (for inspection/debugging)
    #     NOT multiprocess safe - race conditions may occur with multiple processes.
    #     Allows viewing and setting breakpoints in Inductor's code output files.
    "VLLM_COMPILE_CACHE_SAVE_FORMAT": env_with_choices(
        "VLLM_COMPILE_CACHE_SAVE_FORMAT", "binary", ["binary", "unpacked"]
    ),
Woosuk Kwon's avatar
Woosuk Kwon committed
1662
1663
1664
1665
    # Flag to enable v2 model runner.
    "VLLM_USE_V2_MODEL_RUNNER": lambda: bool(
        int(os.getenv("VLLM_USE_V2_MODEL_RUNNER", "0"))
    ),
1666
1667
1668
1669
1670
1671
    # Log model inspection after loading.
    # If enabled, logs a transformers-style hierarchical view of the model
    # with quantization methods and attention backends.
    "VLLM_LOG_MODEL_INSPECTION": lambda: bool(
        int(os.getenv("VLLM_LOG_MODEL_INSPECTION", "0"))
    ),
1672
1673
1674
1675
    # Debug logging for --enable-mfu-metrics
    "VLLM_DEBUG_MFU_METRICS": lambda: bool(
        int(os.getenv("VLLM_DEBUG_MFU_METRICS", "0"))
    ),
1676
1677
    # Disable logging of vLLM logo at server startup time.
    "VLLM_DISABLE_LOG_LOGO": lambda: bool(int(os.getenv("VLLM_DISABLE_LOG_LOGO", "0"))),
1678
1679
1680
    # Disable PDL for LoRA, as enabling PDL with LoRA on SM100 causes
    # Triton compilation to fail.
    "VLLM_LORA_DISABLE_PDL": lambda: bool(int(os.getenv("VLLM_LORA_DISABLE_PDL", "0"))),
1681
    
1682
1683
1684
    # add envs
    
    # used in optest environment to manually set the https port
1685
1686
1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
    'VLLM_OPTEST_URLS_PORT':
    lambda: int(os.getenv('VLLM_OPTEST_URLS_PORT', '8000'))
    if 'VLLM_OPTEST_URLS_PORT' in os.environ else None,
    
    # Path to the optest models.
    # If set, will load models from local path instead of Hugging Face Hub.
    'VLLM_OPTEST_MODELS_PATH':
    lambda: os.getenv('VLLM_OPTEST_MODELS_PATH', "") or os.getenv("OPTEST_MODELS_PATH", ""),
    
    # flag to control if vllm should use triton prefix flash attention
    "VLLM_USE_TRITON_PREFIX_FLASH_ATTN":
    lambda: (os.environ.get("VLLM_USE_TRITON_PREFIX_FLASH_ATTN", "False").lower() in
             ("true", "1")),
    
1699
1700
1701
1702
    # If set, vLLM will use FLASH ATTN fp8 attention optimizations.
    "VLLM_USE_FLASH_ATTN_FP8":
    lambda: bool(int(os.getenv("VLLM_USE_FLASH_ATTN_FP8", "0"))),
    
1703
1704
1705
1706
1707
    # flag to control if vllm should use q quant
    "VLLM_USE_QUERY_QUANT":
    lambda: (os.environ.get("VLLM_USE_QUERY_QUANT", "False").lower() in
             ("true", "1")),
    
zhuwenwen's avatar
zhuwenwen committed
1708
1709
1710
1711
    # If set, vLLM will use FLASH MLA attention optimizations.
    "VLLM_USE_FLASH_MLA":
    lambda: bool(int(os.getenv("VLLM_USE_FLASH_MLA", "1"))),
    
1712
1713
1714
1715
1716
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
    # flag to control vllm to use optimized kernels
    "VLLM_USE_OPT_OP":
    lambda: (os.environ.get("VLLM_USE_OPT_OP", "True").lower() in
             ("true", "1")),
    
    # flag to control vllm to use optimized tc paged attn kernels
    "VLLM_USE_TC_PAGED_ATTN":
    lambda: (os.environ.get("VLLM_USE_TC_PAGED_ATTN", "True").lower() in
             ("true", "1")),
    
    # flag to control if vllm print pa parameters
    "VLLM_USE_PA_PRINT_PARAM":
    lambda: (os.environ.get("VLLM_USE_PA_PRINT_PARAM", "False").lower() in
             ("true", "1")),
    
    # If set, vLLM will disable the draft model in cudagraph mode.
    "VLLM_SPEC_DECODE_EAGER":
    lambda: bool(int(os.getenv("VLLM_SPEC_DECODE_EAGER", "0"))),
    
    # flag to control vllm to use optimized kernels
    "VLLM_PCIE_USE_CUSTOM_ALLREDUCE":
1733
    lambda: bool(int(os.environ.get("VLLM_PCIE_USE_CUSTOM_ALLREDUCE", "1"))),
1734
    
1735
1736
    # flag to control vllm to use optimized kernels
    "VLLM_CUSTOM_CACHE":
1737
    lambda: bool(int(os.environ.get("VLLM_CUSTOM_CACHE", "1"))),
1738
    
zhuwenwen's avatar
zhuwenwen committed
1739
1740
1741
1742
    # flag to control vllm to use optimized kernels
    "VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX":
    lambda: int(os.getenv("VLLM_CUSTOM_ALLREDUCE_SUPPORTED_WORLDSIZE_MAX", "16")),
    
1743
1744
1745
    # If set, vLLM will disable the draft model in cudagraph mode.
    "VLLM_ENFORCE_EAGER_BS_THRESHOLD":
    lambda: int(os.environ.get("VLLM_ENFORCE_EAGER_BS_THRESHOLD", "-1")),
1746

1747
1748
1749
1750
1751
1752
    # 'has_comtext' is a variable in common.py, which is calculated
    # by metadata by default. However, it may introduce synchronization 
    # and affect performance, so it is directly assigned as False. 
    # If there are any problems during use, use environment variables 
    # to restore the default usage.
    "VLLM_HAS_CONTEXT_DEFAULT":
zhuwenwen's avatar
zhuwenwen committed
1753
    lambda: bool(int(os.getenv("VLLM_HAS_CONTEXT_DEFAULT", "1"))),
1754
1755
1756
    
    # If set, vLLM will transpose weight to use nn layout
    "VLLM_USE_NN":
zhuwenwen's avatar
zhuwenwen committed
1757
    lambda: (os.environ.get("VLLM_USE_NN", "True").lower() in
1758
             ("true", "1")),
1759

1760
1761
1762
    # Enable two batch overlap.
    "VLLM_ENABLE_TBO":
    lambda: bool(int(os.getenv("VLLM_ENABLE_TBO", "0"))),
1763
1764
1765

    # If set, vLLM will enable the moe_fused_gate kernel.
    "VLLM_ENABLE_MOE_FUSED_GATE":
zhuwenwen's avatar
zhuwenwen committed
1766
    lambda: bool(int(os.getenv("VLLM_ENABLE_MOE_FUSED_GATE", "1"))),
zhuwenwen's avatar
zhuwenwen committed
1767
    
1768
1769
    # vLLM will use FlashAttention Backend for page attention computation on rocm
    "VLLM_USE_FLASH_ATTN_PA":
zhuwenwen's avatar
zhuwenwen committed
1770
    lambda: (os.environ.get("VLLM_USE_FLASH_ATTN_PA", "True").lower() in
zhuwenwen's avatar
zhuwenwen committed
1771
             ("true", "1")),
zhuwenwen's avatar
zhuwenwen committed
1772
1773
1774
1775
1776
    
    # vLLM will use apex for rmsnorm
    "VLLM_USE_APEX_RN":
    lambda: (os.environ.get("VLLM_USE_APEX_RN", "False").lower() in
             ("true", "1")),
1777
1778
1779
    
    # vLLM will use global cache for moe
    "VLLM_USE_GLOBAL_CACHE13":
1780
        lambda: (os.environ.get("VLLM_USE_GLOBAL_CACHE13", "False").lower() in
1781
                 ("true", "1")),
1782
        
1783
1784
1785
    # vLLM will use lightop for deepseek-v3
    "VLLM_USE_LIGHTOP":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP", "False").lower() in
1786
                 ("true", "1")),
1787
        
1788
1789
1790
    # vLLM will use opt cat for deepseek-v3
    "VLLM_USE_OPT_CAT":
        lambda: (os.environ.get("VLLM_USE_OPT_CAT", "True").lower() in
zhuwenwen's avatar
zhuwenwen committed
1791
                 ("true", "1")), 
zhuwenwen's avatar
zhuwenwen committed
1792
1793
1794
1795
1796
1797
1798
1799
    # vLLM will use lightop moe_sum 
    "VLLM_USE_LIGHTOP_MOE_SUM":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_SUM", "False").lower() in
                 ("true", "1")),  
    # vLLM will use lightop moe_align_block_size 
    "VLLM_USE_LIGHTOP_MOE_ALIGN":
        lambda: (os.environ.get("VLLM_USE_LIGHTOP_MOE_ALIGN", "False").lower() in
                 ("true", "1")),     
1800
1801
1802
1803
    # vLLM will use opt merge_aatn_states,not triton
    "VLLM_USE_MERGE_ATTN_STATES_OPT":
        lambda: (os.environ.get("VLLM_USE_MERGE_ATTN_STATES_OPT", "True").lower() in
                 ("true", "1")),  
jujl1's avatar
jujl1 committed
1804
1805
    # vllm will use rmsquant fused op
    "USE_FUSED_RMS_QUANT":
王敏's avatar
王敏 committed
1806
        lambda: bool(int(os.getenv("USE_FUSED_RMS_QUANT", "0"))),
zhuwenwen's avatar
zhuwenwen committed
1807
1808
    # vLLM will split prefill and decode, not mix up
    "VLLM_USE_PD_SPLIT":
1809
        lambda: (os.environ.get("VLLM_USE_PD_SPLIT", "True").lower() in
zhuwenwen's avatar
zhuwenwen committed
1810
                 ("true", "1")), 
zhuwenwen's avatar
zhuwenwen committed
1811
1812
1813
1814
    # vLLM will sync to avoid pp vmfault
    "VLLM_USE_PP_SYNC":
        lambda: (os.environ.get("VLLM_USE_PP_SYNC", "False").lower() in
                 ("true", "1")), 
1815
1816
1817
1818
    # vLLM will use piecewise
    "VLLM_USE_PIECEWISE":
        lambda: (os.environ.get("VLLM_USE_PIECEWISE", "True").lower() in
                 ("true", "1")), 
1819
1820
    # vllm will use encoding_dsv32.py for dpsk-v32
    "VLLM_USE_V32_ENCODE":
1821
        lambda: (os.environ.get('VLLM_USE_V32_ENCODE', 'False').lower() in
1822
                 ("true", "1")),  
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
    # vLLM will use fused silu+mul kernel (fp16 + qwen3-30b)
    "VLLM_USE_FUSE_SILU_AND_MUL":
        lambda: (os.environ.get("VLLM_USE_FUSE_SILU_AND_MUL", "False").lower() in
                 ("true", "1")),
    # vLLM will use optimized reshape_and_cache kernel when enabled (fp16 + qwen3-30b)
    "VLLM_USE_OPT_RESHAPE_AND_CACHE":
        lambda:
        (os.environ.get("VLLM_USE_OPT_RESHAPE_AND_CACHE", "False").lower() in
                ("true", "1")),
    # vLLM will use optimized topk_softmax + renormalize
    "VLLM_USE_TOPK_RENORM":
        lambda:
zhuwenwen's avatar
zhuwenwen committed
1835
        (os.environ.get("VLLM_USE_TOPK_RENORM", "False").lower() in
1836
                ("true", "1")),
1837
1838
    # vLLM will use fused RMS + RoPE kernel
    "VLLM_USE_FUSED_RMS_ROPE":
1839
        lambda: (os.environ.get("VLLM_USE_FUSED_RMS_ROPE", "True").lower() in
1840
                 ("true", "1")),
1841
1842
1843
1844
    # vLLM will use lightop for dpsk mtp fill + rms*2 + cat
    "VLLM_USE_FUSED_FILL_RMS_CAT":
        lambda: (os.environ.get("VLLM_USE_FUSED_FILL_RMS_CAT", "False").lower() in
                 ("true", "1")),
jujl1's avatar
jujl1 committed
1845
1846
1847
    "VLLM_USE_PP_BALANCE":
        lambda: (os.environ.get("VLLM_USE_PP_BALANCE", "True").lower() in
                 ("true", "1")),
1848
1849
1850
1851
1852
1853
    # W8A8 GEMM backend selection for vLLM quantized models.
    # lightop/triton: 1
    # cutlass: 2 (will remove in the future)
    # blaslt: 3 (default)
    # rocblas: others
    "VLLM_W8A8_BACKEND": lambda: int(os.getenv("VLLM_W8A8_BACKEND", "3")),
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
1869
1870
1871
1872
1873
1874
    # Capture MoE router logits for debugging/analysis.
    "VLLM_MOE_ROUTER_CAPTURE":
    lambda: (os.getenv("VLLM_MOE_ROUTER_CAPTURE", "0").lower() in ("true", "1")),
    # Output directory for MoE router capture dumps.
    "VLLM_MOE_ROUTER_CAPTURE_DIR":
    lambda: os.environ.get(
        "VLLM_MOE_ROUTER_CAPTURE_DIR",
        "/tmp",
    ),
    # Capture only the specified rank; set to -1 to capture all ranks.
    "VLLM_MOE_ROUTER_CAPTURE_RANK":
    lambda: int(os.environ.get("VLLM_MOE_ROUTER_CAPTURE_RANK", "-1")),
    # Max number of MoE layers to record per process (0 = unlimited).
    "VLLM_MOE_ROUTER_CAPTURE_MAX_LAYERS":
    lambda: int(os.environ.get("VLLM_MOE_ROUTER_CAPTURE_MAX_LAYERS", "0")),
    # Only capture when num_tokens > N (negative disables).
    "VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_GT":
    lambda: int(os.environ.get("VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_GT", "-1")),
    # Only capture when num_tokens < N (0 disables).
    "VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_LT":
    lambda: int(os.environ.get("VLLM_MOE_ROUTER_CAPTURE_NUM_TOKENS_LT", "-1")),
王敏's avatar
王敏 committed
1875
1876
1877
1878
1879

    # vllm will use optimized reject sample
    "VLLM_REJECT_SAMPLE_OPT":
        lambda: (os.getenv('VLLM_REJECT_SAMPLE_OPT', 'True').lower() in
                 ("true", "1")),
1880
1881
1882
1883
    # Force using Triton MoE path (disable Marlin W16A16 MoE).
    "VLLM_USE_MOE_W16A16_TRITON":
        lambda: (os.environ.get("VLLM_USE_MOE_W16A16_TRITON", "0").lower() in
                 ("true", "1")),
1884
1885
1886
1887
    #If set to 1/True, enable the V1 fast token-id copy path in InputBatch.
    "VLLM_V1_FAST_TOKEN_ID_COPY":
        lambda: (os.environ.get("VLLM_V1_FAST_TOKEN_ID_COPY", "False").lower() in
                    ("true", "1")),
1888
1889
1890
1891
1892
1893
1894
1895
1896
1897
1898
1899
1900
    # If set to 1/True, enable reduced top-k/top-p sampling fast path in the
    # V1 PyTorch-native sampler path.
    #
    # Recommended when both top_k is enabled and top_p < 1.0 (nucleus
    # sampling). Not recommended for top-k only (top_p == 1.0) due to
    # potential behavior differences when the k-th logit is tied.
    "VLLM_V1_USE_REDUCED_TOPK_TOPP_SAMPLER":
        lambda: (
            os.environ.get(
                "VLLM_V1_USE_REDUCED_TOPK_TOPP_SAMPLER", "False"
            ).lower()
            in ("true", "1")
        ),
1901
1902
1903
1904
1905

    #If set to 1/True, enable fuse split qkv+rmsnorm+rope+kv update just like glm4.7 moe attention.
    "VLLM_V1_USE_FUSED_QKV_SPLIT_RMS_ROPE_KVSTORE":
        lambda: (os.environ.get("VLLM_V1_USE_FUSED_QKV_SPLIT_RMS_ROPE_KVSTORE", "False").lower() in
                    ("true", "1")),
1906
1907
}

1908
# --8<-- [end:env-vars-definition]
1909

1910

1911
def __getattr__(name: str):
1912
1913
1914
1915
1916
1917
    """
    Gets environment variables lazily.

    NOTE: After enable_envs_cache() invocation (which triggered after service
    initialization), all environment variables will be cached.
    """
1918
1919
1920
1921
1922
    if name in environment_variables:
        return environment_variables[name]()
    raise AttributeError(f"module {__name__!r} has no attribute {name!r}")


1923
1924
1925
1926
1927
1928
def _is_envs_cache_enabled() -> bool:
    """Checked if __getattr__ is wrapped with functools.cache"""
    global __getattr__
    return hasattr(__getattr__, "cache_clear")


1929
1930
1931
1932
1933
1934
1935
1936
1937
1938
def enable_envs_cache() -> None:
    """
    Enables caching of environment variables. This is useful for performance
    reasons, as it avoids the need to re-evaluate environment variables on
    every call.

    NOTE: Currently, it's invoked after service initialization to reduce
    runtime overhead. This also means that environment variables should NOT
    be updated after the service is initialized.
    """
1939
1940
1941
    if _is_envs_cache_enabled():
        # Avoid wrapping functools.cache multiple times
        return
1942
1943
1944
1945
1946
1947
1948
1949
1950
    # Tag __getattr__ with functools.cache
    global __getattr__
    __getattr__ = functools.cache(__getattr__)

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


1951
1952
1953
1954
1955
1956
1957
1958
1959
1960
1961
def disable_envs_cache() -> None:
    """
    Resets the environment variables cache. It could be used to isolate environments
    between unit tests.
    """
    global __getattr__
    # If __getattr__ is wrapped by functions.cache, unwrap the caching layer.
    if _is_envs_cache_enabled():
        __getattr__ = __getattr__.__wrapped__


1962
1963
def __dir__():
    return list(environment_variables.keys())
1964
1965
1966
1967
1968
1969
1970
1971
1972


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}")


1973
1974
1975
1976
1977
1978
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
def compile_factors() -> dict[str, object]:
    """Return env vars used for torch.compile cache keys.

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

    ignored_factors: set[str] = {
        "MAX_JOBS",
        "VLLM_RPC_BASE_PATH",
        "VLLM_USE_MODELSCOPE",
        "VLLM_RINGBUFFER_WARNING_INTERVAL",
        "VLLM_DEBUG_DUMP_PATH",
        "VLLM_PORT",
        "VLLM_CACHE_ROOT",
        "LD_LIBRARY_PATH",
        "VLLM_SERVER_DEV_MODE",
        "VLLM_DP_MASTER_IP",
        "VLLM_DP_MASTER_PORT",
        "VLLM_RANDOMIZE_DP_DUMMY_INPUTS",
        "VLLM_CI_USE_S3",
        "VLLM_MODEL_REDIRECT_PATH",
        "VLLM_HOST_IP",
1995
        "VLLM_FORCE_AOT_LOAD",
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
        "S3_ACCESS_KEY_ID",
        "S3_SECRET_ACCESS_KEY",
        "S3_ENDPOINT_URL",
        "VLLM_USAGE_STATS_SERVER",
        "VLLM_NO_USAGE_STATS",
        "VLLM_DO_NOT_TRACK",
        "VLLM_LOGGING_LEVEL",
        "VLLM_LOGGING_PREFIX",
        "VLLM_LOGGING_STREAM",
        "VLLM_LOGGING_CONFIG_PATH",
Nick Hill's avatar
Nick Hill committed
2006
        "VLLM_LOGGING_COLOR",
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
        "VLLM_LOG_STATS_INTERVAL",
        "VLLM_DEBUG_LOG_API_SERVER_RESPONSE",
        "VLLM_TUNED_CONFIG_FOLDER",
        "VLLM_ENGINE_ITERATION_TIMEOUT_S",
        "VLLM_HTTP_TIMEOUT_KEEP_ALIVE",
        "VLLM_EXECUTE_MODEL_TIMEOUT_SECONDS",
        "VLLM_KEEP_ALIVE_ON_ENGINE_DEATH",
        "VLLM_SLEEP_WHEN_IDLE",
        "VLLM_IMAGE_FETCH_TIMEOUT",
        "VLLM_VIDEO_FETCH_TIMEOUT",
        "VLLM_AUDIO_FETCH_TIMEOUT",
        "VLLM_MEDIA_URL_ALLOW_REDIRECTS",
        "VLLM_MEDIA_LOADING_THREAD_COUNT",
        "VLLM_MAX_AUDIO_CLIP_FILESIZE_MB",
        "VLLM_VIDEO_LOADER_BACKEND",
        "VLLM_MEDIA_CONNECTOR",
2023
        "VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME",
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
        "VLLM_ASSETS_CACHE",
        "VLLM_ASSETS_CACHE_MODEL_CLEAN",
        "VLLM_WORKER_MULTIPROC_METHOD",
        "VLLM_ENABLE_V1_MULTIPROCESSING",
        "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE",
        "VLLM_CPU_KVCACHE_SPACE",
        "VLLM_CPU_OMP_THREADS_BIND",
        "VLLM_CPU_NUM_OF_RESERVED_CPU",
        "VLLM_CPU_MOE_PREPACK",
        "VLLM_CPU_SGL_KERNEL",
        "VLLM_TEST_FORCE_LOAD_FORMAT",
        "LOCAL_RANK",
        "CUDA_VISIBLE_DEVICES",
Nick Hill's avatar
Nick Hill committed
2037
        "NO_COLOR",
2038
        "VLLM_W8A8_BACKEND",
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
2053
2054
2055
2056
    }

    from vllm.config.utils import normalize_value

    factors: dict[str, object] = {}
    for factor, getter in environment_variables.items():
        if factor in ignored_factors:
            continue

        try:
            raw = getter()
        except Exception as exc:  # pragma: no cover - defensive logging
            logger.warning(
                "Skipping environment variable %s while hashing compile factors: %s",
                factor,
                exc,
            )
            continue
2057

2058
        factors[factor] = normalize_value(raw)
2059

2060
2061
2062
2063
2064
2065
2066
2067
2068
2069
2070
2071
2072
2073
2074
2075
2076
2077
2078
2079
    ray_noset_env_vars = [
        # Refer to
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/nvidia_gpu.py#L11
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/amd_gpu.py#L11
        # https://github.com/ray-project/ray/blob/b97d21dab233c2bd8ed7db749a82a1e594222b5c/python/ray/_private/accelerators/amd_gpu.py#L10
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/npu.py#L12
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/hpu.py#L12
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/neuron.py#L14
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/tpu.py#L38
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/intel_gpu.py#L10
        # https://github.com/ray-project/ray/blob/c584b1ea97b00793d1def71eaf81537d70efba42/python/ray/_private/accelerators/rbln.py#L10
        "RAY_EXPERIMENTAL_NOSET_CUDA_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HIP_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_ASCEND_RT_VISIBLE_DEVICES",
        "RAY_EXPERIMENTAL_NOSET_HABANA_VISIBLE_MODULES",
        "RAY_EXPERIMENTAL_NOSET_NEURON_RT_VISIBLE_CORES",
        "RAY_EXPERIMENTAL_NOSET_TPU_VISIBLE_CHIPS",
        "RAY_EXPERIMENTAL_NOSET_ONEAPI_DEVICE_SELECTOR",
        "RAY_EXPERIMENTAL_NOSET_RBLN_RT_VISIBLE_DEVICES",
2080
    ]
2081

2082
2083
    for var in ray_noset_env_vars:
        factors[var] = normalize_value(os.getenv(var))
2084

2085
    return factors