arg_utils.py 94.1 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import argparse
5
import copy
6
import dataclasses
7
import functools
8
import json
9
import sys
10
from collections.abc import Callable
11
from dataclasses import MISSING, dataclass, fields, is_dataclass
12
from itertools import permutations
13
from types import UnionType
14
15
16
17
18
from typing import (
    TYPE_CHECKING,
    Annotated,
    Any,
    Literal,
19
    TypeAlias,
20
21
22
23
24
25
    TypeVar,
    Union,
    cast,
    get_args,
    get_origin,
)
26

27
import huggingface_hub
28
import regex as re
29
import torch
30
from pydantic import TypeAdapter, ValidationError
31
from pydantic.fields import FieldInfo
32
from typing_extensions import TypeIs
33

34
import vllm.envs as envs
35
from vllm.config import (
36
    AttentionConfig,
37
38
39
40
    CacheConfig,
    CompilationConfig,
    ConfigType,
    DeviceConfig,
41
    ECTransferConfig,
42
    EPLBConfig,
43
    KernelConfig,
44
45
46
47
48
    KVEventsConfig,
    KVTransferConfig,
    LoadConfig,
    LoRAConfig,
    ModelConfig,
49
    MultiModalConfig,
50
    ObservabilityConfig,
51
    OffloadConfig,
52
53
    ParallelConfig,
    PoolerConfig,
54
    PrefetchOffloadConfig,
55
    ProfilerConfig,
56
57
58
    SchedulerConfig,
    SpeculativeConfig,
    StructuredOutputsConfig,
59
    UVAOffloadConfig,
60
    VllmConfig,
61
    WeightTransferConfig,
62
63
    get_attr_docs,
)
64
65
66
from vllm.config.cache import (
    CacheDType,
    KVOffloadingBackend,
67
    MambaCacheMode,
68
69
70
    MambaDType,
    PrefixCachingHashAlgo,
)
71
from vllm.config.device import Device
72
from vllm.config.kernel import MoEBackend
73
from vllm.config.lora import MaxLoRARanks
74
75
76
77
78
79
from vllm.config.model import (
    ConvertOption,
    HfOverrides,
    LogprobsMode,
    ModelDType,
    RunnerOption,
80
    TokenizerMode,
81
82
83
)
from vllm.config.multimodal import MMCacheType, MMEncoderTPMode
from vllm.config.observability import DetailedTraceModules
84
85
86
from vllm.config.parallel import (
    All2AllBackend,
    DataParallelBackend,
87
    DCPCommBackend,
88
89
90
    DistributedExecutorBackend,
    ExpertPlacementStrategy,
)
91
from vllm.config.scheduler import SchedulerPolicy
92
from vllm.config.utils import get_field
93
from vllm.config.vllm import OptimizationLevel, PerformanceMode
94
from vllm.logger import init_logger, suppress_logging
95
from vllm.platforms import CpuArchEnum, current_platform
96
from vllm.plugins import load_general_plugins
97
from vllm.ray.lazy_utils import is_in_ray_actor, is_ray_initialized
98
99
100
101
from vllm.transformers_utils.config import (
    is_interleaved,
    maybe_override_with_speculators,
)
102
from vllm.transformers_utils.gguf_utils import is_gguf
103
from vllm.transformers_utils.repo_utils import get_model_path
104
from vllm.transformers_utils.utils import is_cloud_storage
105
from vllm.utils.argparse_utils import FlexibleArgumentParser
106
from vllm.utils.mem_constants import GiB_bytes
107
from vllm.utils.network_utils import get_ip
108
from vllm.utils.torch_utils import resolve_kv_cache_dtype_string
109
from vllm.v1.attention.backends.registry import AttentionBackendEnum
110
from vllm.v1.sample.logits_processor import LogitsProcessor
111

112
113
if TYPE_CHECKING:
    from vllm.model_executor.layers.quantization import QuantizationMethods
114
    from vllm.model_executor.model_loader import LoadFormats
115
    from vllm.usage.usage_lib import UsageContext
116
    from vllm.v1.executor import Executor
117
else:
118
    Executor = Any
119
    QuantizationMethods = Any
120
    LoadFormats = Any
121
122
    UsageContext = Any

123

124
125
logger = init_logger(__name__)

126
127
# object is used to allow for special typing forms
T = TypeVar("T")
128
129
TypeHint: TypeAlias = type[Any] | object
TypeHintT: TypeAlias = type[T] | object
130

131

132
133
def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
    def _parse_type(val: str) -> T:
134
135
136
137
        try:
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
138
139
                f"Value {val} cannot be converted to {return_type}."
            ) from e
140

141
142
143
    return _parse_type


144
145
def optional_type(return_type: Callable[[str], T]) -> Callable[[str], T | None]:
    def _optional_type(val: str) -> T | None:
146
147
148
149
        if val == "" or val == "None":
            return None
        return parse_type(return_type)(val)

150
    return _optional_type
151
152


153
def union_dict_and_str(val: str) -> str | dict[str, str] | None:
154
    if not re.match(r"(?s)^\s*{.*}\s*$", val):
155
        return str(val)
156
    return optional_type(json.loads)(val)
157
158


159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
def is_type(type_hint: TypeHint, type: TypeHintT) -> TypeIs[TypeHintT]:
    """Check if the type hint is a specific type."""
    return type_hint is type or get_origin(type_hint) is type


def contains_type(type_hints: set[TypeHint], type: TypeHintT) -> bool:
    """Check if the type hints contain a specific type."""
    return any(is_type(type_hint, type) for type_hint in type_hints)


def get_type(type_hints: set[TypeHint], type: TypeHintT) -> TypeHintT:
    """Get the specific type from the type hints."""
    return next((th for th in type_hints if is_type(th, type)), None)


174
def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
175
176
177
178
    """Get the `type` and `choices` from a `Literal` type hint in `type_hints`.

    If `type_hints` also contains `str`, we use `metavar` instead of `choices`.
    """
179
    type_hint = get_type(type_hints, Literal)
180
181
182
    options = get_args(type_hint)
    option_type = type(options[0])
    if not all(isinstance(option, option_type) for option in options):
183
        raise ValueError(
184
            "All options must be of the same type. "
185
186
            f"Got {options} with types {[type(c) for c in options]}"
        )
187
188
    kwarg = "metavar" if contains_type(type_hints, str) else "choices"
    return {"type": option_type, kwarg: sorted(options)}
189
190


191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
def collection_to_kwargs(type_hints: set[TypeHint], type: TypeHint) -> dict[str, Any]:
    type_hint = get_type(type_hints, type)
    types = get_args(type_hint)
    elem_type = types[0]

    # Handle Ellipsis
    assert all(t is elem_type for t in types if t is not Ellipsis), (
        f"All non-Ellipsis elements must be of the same type. Got {types}."
    )

    # Handle Union types
    if get_origin(elem_type) in {Union, UnionType}:
        # Union for Union[X, Y] and UnionType for X | Y
        assert str in get_args(elem_type), (
            "If element can have multiple types, one must be 'str' "
            f"(i.e. 'list[int | str]'). Got {elem_type}."
        )
        elem_type = str

    return {
        "type": elem_type,
        "nargs": "+" if type is not tuple or Ellipsis in types else len(types),
    }


216
217
218
219
220
def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


221
222
223
224
225
226
227
228
def get_type_hints(type_hint: TypeHint) -> set[TypeHint]:
    """Extract type hints from Annotated or Union type hints."""
    type_hints: set[TypeHint] = set()
    origin = get_origin(type_hint)
    args = get_args(type_hint)

    if origin is Annotated:
        type_hints.update(get_type_hints(args[0]))
229
230
    elif origin in {Union, UnionType}:
        # Union for Union[X, Y] and UnionType for X | Y
231
232
233
234
235
236
237
238
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints


239
NEEDS_HELP = (
240
241
    any("--help" in arg for arg in sys.argv)  # vllm SUBCOMMAND --help
    or (argv0 := sys.argv[0]).endswith("mkdocs")  # mkdocs SUBCOMMAND
242
243
244
245
    or argv0.endswith("mkdocs/__main__.py")  # python -m mkdocs SUBCOMMAND
)


246
@functools.lru_cache(maxsize=30)
247
def _compute_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
248
249
    # Save time only getting attr docs if we're generating help text
    cls_docs = get_attr_docs(cls) if NEEDS_HELP else {}
250
251
    kwargs = {}
    for field in fields(cls):
252
        # Get the set of possible types for the field
253
        type_hints: set[TypeHint] = get_type_hints(field.type)
254
255
256
257
258

        # If the field is a dataclass, we can use the model_validate_json
        generator = (th for th in type_hints if is_dataclass(th))
        dataclass_cls = next(generator, None)

259
        # Get the default value of the field
260
261
        if field.default is not MISSING:
            default = field.default
262
263
            # Handle pydantic.Field defaults
            if isinstance(default, FieldInfo):
264
265
266
267
268
269
                if default.default_factory is None:
                    default = default.default
                else:
                    # VllmConfig's Fields have default_factory set to config classes.
                    # These could emit logs on init, which would be confusing.
                    with suppress_logging():
270
                        default = default.default_factory()  # type: ignore[call-arg]
271
        elif field.default_factory is not MISSING:
272
            default = field.default_factory()
273
274
275

        # Get the help text for the field
        name = field.name
276
        help = cls_docs.get(name, "").strip()
277
278
279
280
281
282
283
        # Escape % for argparse
        help = help.replace("%", "%%")

        # Initialise the kwargs dictionary for the field
        kwargs[name] = {"default": default, "help": help}

        # Set other kwargs based on the type hints
284
285
286
        json_tip = (
            "Should either be a valid JSON string or JSON keys passed individually."
        )
287
        if dataclass_cls is not None:
288
289
290
291
292
293
294
295

            def parse_dataclass(val: str, cls=dataclass_cls) -> Any:
                try:
                    return TypeAdapter(cls).validate_json(val)
                except ValidationError as e:
                    raise argparse.ArgumentTypeError(repr(e)) from e

            kwargs[name]["type"] = parse_dataclass
296
            kwargs[name]["help"] += f"\n\n{json_tip}"
297
        elif contains_type(type_hints, bool):
298
299
300
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
301
            kwargs[name].update(literal_to_kwargs(type_hints))
302
        elif contains_type(type_hints, tuple):
303
            kwargs[name].update(collection_to_kwargs(type_hints, tuple))
304
        elif contains_type(type_hints, list):
305
306
307
            kwargs[name].update(collection_to_kwargs(type_hints, list))
        elif contains_type(type_hints, set):
            kwargs[name].update(collection_to_kwargs(type_hints, set))
308
        elif contains_type(type_hints, int):
309
310
311
312
            if name == "max_model_len":
                kwargs[name]["type"] = human_readable_int_or_auto
                kwargs[name]["help"] += f"\n\n{human_readable_int_or_auto.__doc__}"
            elif name in ("max_num_batched_tokens", "kv_cache_memory_bytes"):
313
                kwargs[name]["type"] = human_readable_int
314
                kwargs[name]["help"] += f"\n\n{human_readable_int.__doc__}"
315
316
            else:
                kwargs[name]["type"] = int
317
318
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
319
320
321
322
        elif contains_type(type_hints, dict) and (
            contains_type(type_hints, str)
            or any(is_not_builtin(th) for th in type_hints)
        ):
323
            kwargs[name]["type"] = union_dict_and_str
324
        elif contains_type(type_hints, dict):
325
            kwargs[name]["type"] = parse_type(json.loads)
326
            kwargs[name]["help"] += f"\n\n{json_tip}"
327
328
329
        elif contains_type(type_hints, str) or any(
            is_not_builtin(th) for th in type_hints
        ):
330
331
            kwargs[name]["type"] = str
        else:
332
            raise ValueError(f"Unsupported type {type_hints} for argument {name}.")
333

334
335
336
337
338
        # If the type hint was a sequence of literals, use the helper function
        # to update the type and choices
        if get_origin(kwargs[name].get("type")) is Literal:
            kwargs[name].update(literal_to_kwargs({kwargs[name]["type"]}))

339
340
341
342
343
344
345
        # If None is in type_hints, make the argument optional.
        # But not if it's a bool, argparse will handle this better.
        if type(None) in type_hints and not contains_type(type_hints, bool):
            kwargs[name]["type"] = optional_type(kwargs[name]["type"])
            if kwargs[name].get("choices"):
                kwargs[name]["choices"].append("None")
    return kwargs
346
347


348
def get_kwargs(cls: ConfigType) -> dict[str, dict[str, Any]]:
349
350
    """Return argparse kwargs for the given Config dataclass.

351
352
353
    If `--help` or `mkdocs` are not present in the command line command, the
    attribute documentation will not be included in the help output.

354
355
356
357
358
359
360
    The heavy computation is cached via functools.lru_cache, and a deep copy
    is returned so callers can mutate the dictionary without affecting the
    cached version.
    """
    return copy.deepcopy(_compute_kwargs(cls))


361
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
362
class EngineArgs:
Woosuk Kwon's avatar
Woosuk Kwon committed
363
    """Arguments for vLLM engine."""
364

365
    model: str = ModelConfig.model
366
    enable_return_routed_experts: bool = ModelConfig.enable_return_routed_experts
367
    model_weights: str = ModelConfig.model_weights
368
    served_model_name: str | list[str] | None = ModelConfig.served_model_name
369
    tokenizer: str | None = ModelConfig.tokenizer
370
    hf_config_path: str | None = ModelConfig.hf_config_path
371
372
    runner: RunnerOption = ModelConfig.runner
    convert: ConvertOption = ModelConfig.convert
373
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
374
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
375
    tokenizer_mode: TokenizerMode | str = ModelConfig.tokenizer_mode
376
    trust_remote_code: bool = ModelConfig.trust_remote_code
377
378
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
    allowed_media_domains: list[str] | None = ModelConfig.allowed_media_domains
379
    download_dir: str | None = LoadConfig.download_dir
380
    safetensors_load_strategy: str = LoadConfig.safetensors_load_strategy
381
    load_format: str | LoadFormats = LoadConfig.load_format
382
383
    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
384
    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
385
    seed: int = ModelConfig.seed
386
    max_model_len: int = ModelConfig.max_model_len
387
388
389
390
391
392
    cudagraph_capture_sizes: list[int] | None = (
        CompilationConfig.cudagraph_capture_sizes
    )
    max_cudagraph_capture_size: int | None = get_field(
        CompilationConfig, "max_cudagraph_capture_size"
    )
393
394
395
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
396
    distributed_executor_backend: (
397
        str | DistributedExecutorBackend | type[Executor] | None
398
    ) = ParallelConfig.distributed_executor_backend
399
    # number of P/D disaggregation (or other disaggregation) workers
400
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
401
402
403
404
    master_addr: str = ParallelConfig.master_addr
    master_port: int = ParallelConfig.master_port
    nnodes: int = ParallelConfig.nnodes
    node_rank: int = ParallelConfig.node_rank
405
    distributed_timeout_seconds: int | None = ParallelConfig.distributed_timeout_seconds
406
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
407
    prefill_context_parallel_size: int = ParallelConfig.prefill_context_parallel_size
408
    decode_context_parallel_size: int = ParallelConfig.decode_context_parallel_size
409
    dcp_comm_backend: DCPCommBackend = ParallelConfig.dcp_comm_backend
410
    dcp_kv_cache_interleave_size: int = ParallelConfig.dcp_kv_cache_interleave_size
411
    cp_kv_cache_interleave_size: int = ParallelConfig.cp_kv_cache_interleave_size
412
    data_parallel_size: int = ParallelConfig.data_parallel_size
413
414
415
416
417
    data_parallel_rank: int | None = None
    data_parallel_start_rank: int | None = None
    data_parallel_size_local: int | None = None
    data_parallel_address: str | None = None
    data_parallel_rpc_port: int | None = None
418
    data_parallel_hybrid_lb: bool = False
419
    data_parallel_external_lb: bool = False
420
    data_parallel_backend: DataParallelBackend = ParallelConfig.data_parallel_backend
421
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
422
    enable_ep_weight_filter: bool = ParallelConfig.enable_ep_weight_filter
423
    moe_backend: MoEBackend = KernelConfig.moe_backend
424
    all2all_backend: All2AllBackend = ParallelConfig.all2all_backend
425
    enable_elastic_ep: bool = ParallelConfig.enable_elastic_ep
426
    enable_dbo: bool = ParallelConfig.enable_dbo
427
    ubatch_size: int = ParallelConfig.ubatch_size
428
429
    dbo_decode_token_threshold: int = ParallelConfig.dbo_decode_token_threshold
    dbo_prefill_token_threshold: int = ParallelConfig.dbo_prefill_token_threshold
430
    disable_nccl_for_dp_synchronization: bool | None = (
431
432
        ParallelConfig.disable_nccl_for_dp_synchronization
    )
433
    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
434
    enable_eplb: bool = ParallelConfig.enable_eplb
435
    expert_placement_strategy: ExpertPlacementStrategy = (
436
        ParallelConfig.expert_placement_strategy
437
    )
438
439
    _api_process_count: int = ParallelConfig._api_process_count
    _api_process_rank: int = ParallelConfig._api_process_rank
440
    max_parallel_loading_workers: int | None = (
441
442
        ParallelConfig.max_parallel_loading_workers
    )
443
    block_size: int | None = None
444
    enable_prefix_caching: bool | None = None
445
    prefix_caching_hash_algo: PrefixCachingHashAlgo = (
446
        CacheConfig.prefix_caching_hash_algo
447
    )
448
449
    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
450
451
452
453
454
455
456
    offload_backend: str = OffloadConfig.offload_backend
    cpu_offload_gb: float = UVAOffloadConfig.cpu_offload_gb
    cpu_offload_params: set[str] = get_field(UVAOffloadConfig, "cpu_offload_params")
    offload_group_size: int = PrefetchOffloadConfig.offload_group_size
    offload_num_in_group: int = PrefetchOffloadConfig.offload_num_in_group
    offload_prefetch_step: int = PrefetchOffloadConfig.offload_prefetch_step
    offload_params: set[str] = get_field(PrefetchOffloadConfig, "offload_params")
457
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
458
    kv_cache_memory_bytes: int | None = CacheConfig.kv_cache_memory_bytes
459
    max_num_batched_tokens: int | None = None
460
461
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
462
    long_prefill_token_threshold: int = SchedulerConfig.long_prefill_token_threshold
463
    max_num_seqs: int | None = None
464
    max_logprobs: int = ModelConfig.max_logprobs
465
    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
466
    disable_log_stats: bool = False
467
    aggregate_engine_logging: bool = False
468
469
470
    revision: str | None = ModelConfig.revision
    code_revision: str | None = ModelConfig.code_revision
    hf_token: bool | str | None = ModelConfig.hf_token
471
    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
472
    tokenizer_revision: str | None = ModelConfig.tokenizer_revision
473
    quantization: QuantizationMethods | str | None = ModelConfig.quantization
474
    allow_deprecated_quantization: bool = ModelConfig.allow_deprecated_quantization
475
    enforce_eager: bool = ModelConfig.enforce_eager
476
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
477
    language_model_only: bool = MultiModalConfig.language_model_only
478
    limit_mm_per_prompt: dict[str, int | dict[str, int]] = get_field(
479
480
        MultiModalConfig, "limit_per_prompt"
    )
481
    enable_mm_embeds: bool = MultiModalConfig.enable_mm_embeds
482
    interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings
483
484
485
    media_io_kwargs: dict[str, dict[str, Any]] = get_field(
        MultiModalConfig, "media_io_kwargs"
    )
486
    mm_processor_kwargs: dict[str, Any] | None = MultiModalConfig.mm_processor_kwargs
487
    mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
488
    mm_processor_cache_type: MMCacheType | None = (
489
        MultiModalConfig.mm_processor_cache_type
490
491
    )
    mm_shm_cache_max_object_size_mb: int = (
492
        MultiModalConfig.mm_shm_cache_max_object_size_mb
493
    )
494
    mm_encoder_only: bool = MultiModalConfig.mm_encoder_only
495
    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
496
    mm_encoder_attn_backend: AttentionBackendEnum | str | None = (
497
498
        MultiModalConfig.mm_encoder_attn_backend
    )
499
    io_processor_plugin: str | None = None
500
    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
501
    video_pruning_rate: float | None = MultiModalConfig.video_pruning_rate
502
    # LoRA fields
503
    enable_lora: bool = False
504
    max_loras: int = LoRAConfig.max_loras
505
    max_lora_rank: MaxLoRARanks = LoRAConfig.max_lora_rank
506
    default_mm_loras: dict[str, str] | None = LoRAConfig.default_mm_loras
507
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
508
509
    max_cpu_loras: int | None = LoRAConfig.max_cpu_loras
    lora_dtype: str | torch.dtype | None = LoRAConfig.lora_dtype
510
    lora_target_modules: list[str] | None = LoRAConfig.target_modules
511
    enable_tower_connector_lora: bool = LoRAConfig.enable_tower_connector_lora
512
    specialize_active_lora: bool = LoRAConfig.specialize_active_lora
513

514
    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
515
    num_gpu_blocks_override: int | None = CacheConfig.num_gpu_blocks_override
516
    model_loader_extra_config: dict = get_field(LoadConfig, "model_loader_extra_config")
517
    ignore_patterns: str | list[str] = get_field(LoadConfig, "ignore_patterns")
518

519
    enable_chunked_prefill: bool | None = None
520
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
521

522
    disable_hybrid_kv_cache_manager: bool | None = (
523
524
        SchedulerConfig.disable_hybrid_kv_cache_manager
    )
525

526
    structured_outputs_config: StructuredOutputsConfig = get_field(
527
528
        VllmConfig, "structured_outputs_config"
    )
529
    reasoning_parser: str = StructuredOutputsConfig.reasoning_parser
530
    reasoning_parser_plugin: str | None = None
531

532
    speculative_config: dict[str, Any] | None = None
533

534
    show_hidden_metrics_for_version: str | None = (
535
        ObservabilityConfig.show_hidden_metrics_for_version
536
    )
537
538
    otlp_traces_endpoint: str | None = ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: list[DetailedTraceModules] | None = (
539
        ObservabilityConfig.collect_detailed_traces
540
    )
541
542
543
544
    kv_cache_metrics: bool = ObservabilityConfig.kv_cache_metrics
    kv_cache_metrics_sample: float = get_field(
        ObservabilityConfig, "kv_cache_metrics_sample"
    )
545
    cudagraph_metrics: bool = ObservabilityConfig.cudagraph_metrics
546
547
548
    enable_layerwise_nvtx_tracing: bool = (
        ObservabilityConfig.enable_layerwise_nvtx_tracing
    )
549
    enable_mfu_metrics: bool = ObservabilityConfig.enable_mfu_metrics
550
551
552
    enable_logging_iteration_details: bool = (
        ObservabilityConfig.enable_logging_iteration_details
    )
553
    enable_mm_processor_stats: bool = ObservabilityConfig.enable_mm_processor_stats
554
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
555
    scheduler_cls: str | type[object] | None = SchedulerConfig.scheduler_cls
556

557
    pooler_config: PoolerConfig | None = ModelConfig.pooler_config
558
    compilation_config: CompilationConfig = get_field(VllmConfig, "compilation_config")
559
    attention_config: AttentionConfig = get_field(VllmConfig, "attention_config")
560
561
562
563
    kernel_config: KernelConfig = get_field(VllmConfig, "kernel_config")
    enable_flashinfer_autotune: bool = get_field(
        KernelConfig, "enable_flashinfer_autotune"
    )
564
565
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
566

567
568
    profiler_config: ProfilerConfig = get_field(VllmConfig, "profiler_config")

569
570
    kv_transfer_config: KVTransferConfig | None = None
    kv_events_config: KVEventsConfig | None = None
571

572
573
    ec_transfer_config: ECTransferConfig | None = None

574
575
    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
576
577
578
    override_generation_config: dict[str, Any] = get_field(
        ModelConfig, "override_generation_config"
    )
579
    model_impl: str = ModelConfig.model_impl
580
    override_attention_dtype: str | None = ModelConfig.override_attention_dtype
581
    attention_backend: AttentionBackendEnum | None = AttentionConfig.backend
582

583
    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
584
585
    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
586
    mamba_block_size: int | None = get_field(CacheConfig, "mamba_block_size")
587
    mamba_cache_mode: MambaCacheMode = CacheConfig.mamba_cache_mode
588

589
    additional_config: dict[str, Any] = get_field(VllmConfig, "additional_config")
590

591
    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
592
    pt_load_map_location: str | dict[str, str] = LoadConfig.pt_load_map_location
593

594
    logits_processors: list[str | type[LogitsProcessor]] | None = (
595
596
        ModelConfig.logits_processors
    )
597
598
    """Custom logitproc types"""

599
    async_scheduling: bool | None = SchedulerConfig.async_scheduling
600

601
602
    stream_interval: int = SchedulerConfig.stream_interval

603
    kv_sharing_fast_prefill: bool = CacheConfig.kv_sharing_fast_prefill
604
    optimization_level: OptimizationLevel = VllmConfig.optimization_level
605
    performance_mode: PerformanceMode = VllmConfig.performance_mode
606

607
    kv_offloading_size: float | None = CacheConfig.kv_offloading_size
608
    kv_offloading_backend: KVOffloadingBackend = CacheConfig.kv_offloading_backend
609
    tokens_only: bool = False
610

611
612
    shutdown_timeout: int = 0

613
614
615
616
    weight_transfer_config: WeightTransferConfig | None = get_field(
        VllmConfig,
        "weight_transfer_config",
    )
617

618
    fail_on_environ_validation: bool = False
619
    gdn_prefill_backend: Literal["flashinfer", "triton"] | None = None
620

621
    def __post_init__(self):
622
623
624
        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
625
        if isinstance(self.compilation_config, dict):
626
            self.compilation_config = CompilationConfig(**self.compilation_config)
627
628
        if isinstance(self.attention_config, dict):
            self.attention_config = AttentionConfig(**self.attention_config)
629
630
        if isinstance(self.kernel_config, dict):
            self.kernel_config = KernelConfig(**self.kernel_config)
631
        if isinstance(self.eplb_config, dict):
632
            self.eplb_config = EPLBConfig(**self.eplb_config)
633
634
635
636
        if isinstance(self.weight_transfer_config, dict):
            self.weight_transfer_config = WeightTransferConfig(
                **self.weight_transfer_config
            )
637
        # Setup plugins
638
        from vllm.plugins import load_general_plugins
639

640
        load_general_plugins()
641
        # when use hf offline,replace model and tokenizer id to local model path
642
643
644
        if huggingface_hub.constants.HF_HUB_OFFLINE:
            model_id = self.model
            self.model = get_model_path(self.model, self.revision)
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
            if model_id is not self.model:
                logger.info(
                    "HF_HUB_OFFLINE is True, replace model_id [%s] to model_path [%s]",
                    model_id,
                    self.model,
                )
            if self.tokenizer is not None:
                tokenizer_id = self.tokenizer
                self.tokenizer = get_model_path(self.tokenizer, self.tokenizer_revision)
                if tokenizer_id is not self.tokenizer:
                    logger.info(
                        "HF_HUB_OFFLINE is True, replace tokenizer_id [%s] "
                        "to tokenizer_path [%s]",
                        tokenizer_id,
                        self.tokenizer,
                    )
661
662

    @staticmethod
663
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
Woosuk Kwon's avatar
Woosuk Kwon committed
664
        """Shared CLI arguments for vLLM engine."""
665

666
        # Model arguments
667
668
669
670
671
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
672
        if not ("serve" in sys.argv[1:] and "--help" in sys.argv[1:]):
673
            model_group.add_argument("--model", **model_kwargs["model"])
674
675
        model_group.add_argument("--runner", **model_kwargs["runner"])
        model_group.add_argument("--convert", **model_kwargs["convert"])
676
677
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode", **model_kwargs["tokenizer_mode"])
678
679
680
        model_group.add_argument(
            "--trust-remote-code", **model_kwargs["trust_remote_code"]
        )
681
682
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
683
        model_group.add_argument("--hf-config-path", **model_kwargs["hf_config_path"])
684
685
686
687
688
689
        model_group.add_argument(
            "--allowed-local-media-path", **model_kwargs["allowed_local_media_path"]
        )
        model_group.add_argument(
            "--allowed-media-domains", **model_kwargs["allowed_media_domains"]
        )
690
        model_group.add_argument("--revision", **model_kwargs["revision"])
691
        model_group.add_argument("--code-revision", **model_kwargs["code_revision"])
692
693
694
        model_group.add_argument(
            "--tokenizer-revision", **model_kwargs["tokenizer_revision"]
        )
695
696
        model_group.add_argument("--max-model-len", **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q", **model_kwargs["quantization"])
697
698
699
700
        model_group.add_argument(
            "--allow-deprecated-quantization",
            **model_kwargs["allow_deprecated_quantization"],
        )
701
        model_group.add_argument("--enforce-eager", **model_kwargs["enforce_eager"])
702
703
704
705
        model_group.add_argument(
            "--enable-return-routed-experts",
            **model_kwargs["enable_return_routed_experts"],
        )
706
707
708
709
710
711
712
713
        model_group.add_argument("--max-logprobs", **model_kwargs["max_logprobs"])
        model_group.add_argument("--logprobs-mode", **model_kwargs["logprobs_mode"])
        model_group.add_argument(
            "--disable-sliding-window", **model_kwargs["disable_sliding_window"]
        )
        model_group.add_argument(
            "--disable-cascade-attn", **model_kwargs["disable_cascade_attn"]
        )
714
715
716
        model_group.add_argument(
            "--skip-tokenizer-init", **model_kwargs["skip_tokenizer_init"]
        )
717
718
719
720
721
722
723
        model_group.add_argument(
            "--enable-prompt-embeds", **model_kwargs["enable_prompt_embeds"]
        )
        model_group.add_argument(
            "--served-model-name", **model_kwargs["served_model_name"]
        )
        model_group.add_argument("--config-format", **model_kwargs["config_format"])
724
725
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
        model_group.add_argument(
            "--hf-token",
            type=str,
            nargs="?",
            const=True,
            default=model_kwargs["hf_token"]["default"],
            help=model_kwargs["hf_token"]["help"],
        )
        model_group.add_argument("--hf-overrides", **model_kwargs["hf_overrides"])
        model_group.add_argument("--pooler-config", **model_kwargs["pooler_config"])
        model_group.add_argument(
            "--generation-config", **model_kwargs["generation_config"]
        )
        model_group.add_argument(
            "--override-generation-config", **model_kwargs["override_generation_config"]
        )
        model_group.add_argument(
            "--enable-sleep-mode", **model_kwargs["enable_sleep_mode"]
        )
745
        model_group.add_argument("--model-impl", **model_kwargs["model_impl"])
746
747
748
749
750
751
        model_group.add_argument(
            "--override-attention-dtype", **model_kwargs["override_attention_dtype"]
        )
        model_group.add_argument(
            "--logits-processors", **model_kwargs["logits_processors"]
        )
752
753
        model_group.add_argument(
            "--io-processor-plugin", **model_kwargs["io_processor_plugin"]
754
        )
755

756
757
758
759
760
761
        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
762
        load_group.add_argument("--load-format", **load_kwargs["load_format"])
763
764
765
766
767
768
769
770
771
772
773
774
        load_group.add_argument("--download-dir", **load_kwargs["download_dir"])
        load_group.add_argument(
            "--safetensors-load-strategy", **load_kwargs["safetensors_load_strategy"]
        )
        load_group.add_argument(
            "--model-loader-extra-config", **load_kwargs["model_loader_extra_config"]
        )
        load_group.add_argument("--ignore-patterns", **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load", **load_kwargs["use_tqdm_on_load"])
        load_group.add_argument(
            "--pt-load-map-location", **load_kwargs["pt_load_map_location"]
        )
775

776
777
778
779
780
781
782
783
784
785
        # Attention arguments
        attention_kwargs = get_kwargs(AttentionConfig)
        attention_group = parser.add_argument_group(
            title="AttentionConfig",
            description=AttentionConfig.__doc__,
        )
        attention_group.add_argument(
            "--attention-backend", **attention_kwargs["backend"]
        )

786
787
788
789
790
        # Structured outputs arguments
        structured_outputs_kwargs = get_kwargs(StructuredOutputsConfig)
        structured_outputs_group = parser.add_argument_group(
            title="StructuredOutputsConfig",
            description=StructuredOutputsConfig.__doc__,
791
        )
792
        structured_outputs_group.add_argument(
793
            "--reasoning-parser",
794
            # Choices need to be validated after parsing to include plugins
795
796
            **structured_outputs_kwargs["reasoning_parser"],
        )
797
798
799
800
        structured_outputs_group.add_argument(
            "--reasoning-parser-plugin",
            **structured_outputs_kwargs["reasoning_parser_plugin"],
        )
801

802
        # Parallel arguments
803
804
805
806
807
808
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
809
            "--distributed-executor-backend",
810
811
            **parallel_kwargs["distributed_executor_backend"],
        )
812
        parallel_group.add_argument(
813
814
815
816
            "--pipeline-parallel-size",
            "-pp",
            **parallel_kwargs["pipeline_parallel_size"],
        )
817
818
819
820
        parallel_group.add_argument("--master-addr", **parallel_kwargs["master_addr"])
        parallel_group.add_argument("--master-port", **parallel_kwargs["master_port"])
        parallel_group.add_argument("--nnodes", "-n", **parallel_kwargs["nnodes"])
        parallel_group.add_argument("--node-rank", "-r", **parallel_kwargs["node_rank"])
821
822
823
824
        parallel_group.add_argument(
            "--distributed-timeout-seconds",
            **parallel_kwargs["distributed_timeout_seconds"],
        )
825
        parallel_group.add_argument(
826
827
            "--tensor-parallel-size", "-tp", **parallel_kwargs["tensor_parallel_size"]
        )
828
        parallel_group.add_argument(
829
830
831
832
            "--decode-context-parallel-size",
            "-dcp",
            **parallel_kwargs["decode_context_parallel_size"],
        )
833
834
835
836
        parallel_group.add_argument(
            "--dcp-comm-backend",
            **parallel_kwargs["dcp_comm_backend"],
        )
837
838
839
840
        parallel_group.add_argument(
            "--dcp-kv-cache-interleave-size",
            **parallel_kwargs["dcp_kv_cache_interleave_size"],
        )
841
842
843
844
845
846
847
848
849
        parallel_group.add_argument(
            "--cp-kv-cache-interleave-size",
            **parallel_kwargs["cp_kv_cache_interleave_size"],
        )
        parallel_group.add_argument(
            "--prefill-context-parallel-size",
            "-pcp",
            **parallel_kwargs["prefill_context_parallel_size"],
        )
850
851
852
853
854
855
        parallel_group.add_argument(
            "--data-parallel-size", "-dp", **parallel_kwargs["data_parallel_size"]
        )
        parallel_group.add_argument(
            "--data-parallel-rank",
            "-dpn",
856
            type=int,
857
858
859
            help="Data parallel rank of this instance. "
            "When set, enables external load balancer mode.",
        )
860
        parallel_group.add_argument(
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
            "--data-parallel-start-rank",
            "-dpr",
            type=int,
            help="Starting data parallel rank for secondary nodes.",
        )
        parallel_group.add_argument(
            "--data-parallel-size-local",
            "-dpl",
            type=int,
            help="Number of data parallel replicas to run on this node.",
        )
        parallel_group.add_argument(
            "--data-parallel-address",
            "-dpa",
            type=str,
            help="Address of data parallel cluster head-node.",
        )
        parallel_group.add_argument(
            "--data-parallel-rpc-port",
            "-dpp",
            type=int,
            help="Port for data parallel RPC communication.",
        )
        parallel_group.add_argument(
            "--data-parallel-backend",
            "-dpb",
            type=str,
            default="mp",
            help='Backend for data parallel, either "mp" or "ray".',
        )
891
        parallel_group.add_argument(
892
893
894
895
896
897
898
899
            "--data-parallel-hybrid-lb",
            "-dph",
            **parallel_kwargs["data_parallel_hybrid_lb"],
        )
        parallel_group.add_argument(
            "--data-parallel-external-lb",
            "-dpe",
            **parallel_kwargs["data_parallel_external_lb"],
900
901
        )
        parallel_group.add_argument(
902
903
904
            "--enable-expert-parallel",
            "-ep",
            **parallel_kwargs["enable_expert_parallel"],
905
        )
906
907
908
909
        parallel_group.add_argument(
            "--enable-ep-weight-filter",
            **parallel_kwargs["enable_ep_weight_filter"],
        )
910
911
912
        parallel_group.add_argument(
            "--all2all-backend", **parallel_kwargs["all2all_backend"]
        )
913
        parallel_group.add_argument("--enable-dbo", **parallel_kwargs["enable_dbo"])
914
915
916
917
        parallel_group.add_argument(
            "--ubatch-size",
            **parallel_kwargs["ubatch_size"],
        )
918
919
920
        parallel_group.add_argument(
            "--enable-elastic-ep", **parallel_kwargs["enable_elastic_ep"]
        )
921
922
        parallel_group.add_argument(
            "--dbo-decode-token-threshold",
923
924
            **parallel_kwargs["dbo_decode_token_threshold"],
        )
925
926
        parallel_group.add_argument(
            "--dbo-prefill-token-threshold",
927
928
            **parallel_kwargs["dbo_prefill_token_threshold"],
        )
929
930
931
932
        parallel_group.add_argument(
            "--disable-nccl-for-dp-synchronization",
            **parallel_kwargs["disable_nccl_for_dp_synchronization"],
        )
933
934
        parallel_group.add_argument("--enable-eplb", **parallel_kwargs["enable_eplb"])
        parallel_group.add_argument("--eplb-config", **parallel_kwargs["eplb_config"])
935
936
        parallel_group.add_argument(
            "--expert-placement-strategy",
937
938
            **parallel_kwargs["expert_placement_strategy"],
        )
939

940
        parallel_group.add_argument(
941
            "--max-parallel-loading-workers",
942
943
            **parallel_kwargs["max_parallel_loading_workers"],
        )
944
        parallel_group.add_argument(
945
946
            "--ray-workers-use-nsight", **parallel_kwargs["ray_workers_use_nsight"]
        )
947
        parallel_group.add_argument(
948
            "--disable-custom-all-reduce",
949
950
951
952
953
954
            **parallel_kwargs["disable_custom_all_reduce"],
        )
        parallel_group.add_argument("--worker-cls", **parallel_kwargs["worker_cls"])
        parallel_group.add_argument(
            "--worker-extension-cls", **parallel_kwargs["worker_extension_cls"]
        )
955

956
957
958
959
960
        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
961
        )
962
        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
963
964
965
966
967
968
969
970
971
972
973
        cache_group.add_argument(
            "--gpu-memory-utilization", **cache_kwargs["gpu_memory_utilization"]
        )
        cache_group.add_argument(
            "--kv-cache-memory-bytes", **cache_kwargs["kv_cache_memory_bytes"]
        )
        cache_group.add_argument("--kv-cache-dtype", **cache_kwargs["cache_dtype"])
        cache_group.add_argument(
            "--num-gpu-blocks-override", **cache_kwargs["num_gpu_blocks_override"]
        )
        cache_group.add_argument(
974
975
976
977
978
            "--enable-prefix-caching",
            **{
                **cache_kwargs["enable_prefix_caching"],
                "default": None,
            },
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
        )
        cache_group.add_argument(
            "--prefix-caching-hash-algo", **cache_kwargs["prefix_caching_hash_algo"]
        )
        cache_group.add_argument(
            "--calculate-kv-scales", **cache_kwargs["calculate_kv_scales"]
        )
        cache_group.add_argument(
            "--kv-sharing-fast-prefill", **cache_kwargs["kv_sharing_fast_prefill"]
        )
        cache_group.add_argument(
            "--mamba-cache-dtype", **cache_kwargs["mamba_cache_dtype"]
        )
        cache_group.add_argument(
            "--mamba-ssm-cache-dtype", **cache_kwargs["mamba_ssm_cache_dtype"]
        )
995
996
997
        cache_group.add_argument(
            "--mamba-block-size", **cache_kwargs["mamba_block_size"]
        )
998
999
1000
        cache_group.add_argument(
            "--mamba-cache-mode", **cache_kwargs["mamba_cache_mode"]
        )
1001
1002
1003
1004
1005
1006
        cache_group.add_argument(
            "--kv-offloading-size", **cache_kwargs["kv_offloading_size"]
        )
        cache_group.add_argument(
            "--kv-offloading-backend", **cache_kwargs["kv_offloading_backend"]
        )
1007

1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
1035
1036
1037
1038
        # Model weight offload related configs
        offload_kwargs = get_kwargs(OffloadConfig)
        uva_kwargs = get_kwargs(UVAOffloadConfig)
        prefetch_kwargs = get_kwargs(PrefetchOffloadConfig)
        offload_group = parser.add_argument_group(
            title="OffloadConfig",
            description=OffloadConfig.__doc__,
        )
        offload_group.add_argument(
            "--offload-backend", **offload_kwargs["offload_backend"]
        )
        offload_group.add_argument("--cpu-offload-gb", **uva_kwargs["cpu_offload_gb"])
        offload_group.add_argument(
            "--cpu-offload-params", **uva_kwargs["cpu_offload_params"]
        )
        offload_group.add_argument(
            "--offload-group-size",
            **prefetch_kwargs["offload_group_size"],
        )
        offload_group.add_argument(
            "--offload-num-in-group",
            **prefetch_kwargs["offload_num_in_group"],
        )
        offload_group.add_argument(
            "--offload-prefetch-step",
            **prefetch_kwargs["offload_prefetch_step"],
        )
        offload_group.add_argument(
            "--offload-params", **prefetch_kwargs["offload_params"]
        )

1039
        # Multimodal related configs
1040
1041
1042
1043
1044
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
1045
1046
1047
        multimodal_group.add_argument(
            "--language-model-only", **multimodal_kwargs["language_model_only"]
        )
1048
        multimodal_group.add_argument(
1049
1050
            "--limit-mm-per-prompt", **multimodal_kwargs["limit_per_prompt"]
        )
1051
1052
1053
        multimodal_group.add_argument(
            "--enable-mm-embeds", **multimodal_kwargs["enable_mm_embeds"]
        )
1054
1055
1056
        multimodal_group.add_argument(
            "--media-io-kwargs", **multimodal_kwargs["media_io_kwargs"]
        )
1057
1058
1059
1060
1061
1062
        multimodal_group.add_argument(
            "--mm-processor-kwargs", **multimodal_kwargs["mm_processor_kwargs"]
        )
        multimodal_group.add_argument(
            "--mm-processor-cache-gb", **multimodal_kwargs["mm_processor_cache_gb"]
        )
1063
        multimodal_group.add_argument(
1064
1065
            "--mm-processor-cache-type", **multimodal_kwargs["mm_processor_cache_type"]
        )
1066
1067
        multimodal_group.add_argument(
            "--mm-shm-cache-max-object-size-mb",
1068
1069
            **multimodal_kwargs["mm_shm_cache_max_object_size_mb"],
        )
1070
1071
1072
        multimodal_group.add_argument(
            "--mm-encoder-only", **multimodal_kwargs["mm_encoder_only"]
        )
1073
        multimodal_group.add_argument(
1074
1075
            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"]
        )
1076
1077
1078
1079
        multimodal_group.add_argument(
            "--mm-encoder-attn-backend",
            **multimodal_kwargs["mm_encoder_attn_backend"],
        )
1080
1081
1082
        multimodal_group.add_argument(
            "--interleave-mm-strings", **multimodal_kwargs["interleave_mm_strings"]
        )
1083
        multimodal_group.add_argument(
1084
1085
            "--skip-mm-profiling", **multimodal_kwargs["skip_mm_profiling"]
        )
1086

1087
        multimodal_group.add_argument(
1088
1089
            "--video-pruning-rate", **multimodal_kwargs["video_pruning_rate"]
        )
1090

1091
        # LoRA related configs
1092
1093
1094
1095
1096
1097
        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
1098
            "--enable-lora",
1099
            action=argparse.BooleanOptionalAction,
1100
1101
            help="If True, enable handling of LoRA adapters.",
        )
1102
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
1103
        lora_group.add_argument("--max-lora-rank", **lora_kwargs["max_lora_rank"])
1104
        lora_group.add_argument(
1105
            "--lora-dtype",
1106
1107
            **lora_kwargs["lora_dtype"],
        )
1108
1109
1110
1111
        lora_group.add_argument(
            "--enable-tower-connector-lora",
            **lora_kwargs["enable_tower_connector_lora"],
        )
1112
1113
1114
1115
        lora_group.add_argument("--max-cpu-loras", **lora_kwargs["max_cpu_loras"])
        lora_group.add_argument(
            "--fully-sharded-loras", **lora_kwargs["fully_sharded_loras"]
        )
1116
1117
1118
        lora_group.add_argument(
            "--lora-target-modules", **lora_kwargs["target_modules"]
        )
1119
        lora_group.add_argument("--default-mm-loras", **lora_kwargs["default_mm_loras"])
1120
1121
1122
        lora_group.add_argument(
            "--specialize-active-lora", **lora_kwargs["specialize_active_lora"]
        )
1123

1124
1125
1126
1127
1128
1129
1130
1131
        # Observability arguments
        observability_kwargs = get_kwargs(ObservabilityConfig)
        observability_group = parser.add_argument_group(
            title="ObservabilityConfig",
            description=ObservabilityConfig.__doc__,
        )
        observability_group.add_argument(
            "--show-hidden-metrics-for-version",
1132
1133
            **observability_kwargs["show_hidden_metrics_for_version"],
        )
1134
        observability_group.add_argument(
1135
1136
            "--otlp-traces-endpoint", **observability_kwargs["otlp_traces_endpoint"]
        )
1137
1138
1139
1140
1141
        # TODO: generalise this special case
        choices = observability_kwargs["collect_detailed_traces"]["choices"]
        metavar = f"{{{','.join(choices)}}}"
        observability_kwargs["collect_detailed_traces"]["metavar"] = metavar
        observability_kwargs["collect_detailed_traces"]["choices"] += [
1142
            ",".join(p) for p in permutations(get_args(DetailedTraceModules), r=2)
1143
1144
1145
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
1146
1147
            **observability_kwargs["collect_detailed_traces"],
        )
1148
1149
1150
1151
1152
1153
1154
        observability_group.add_argument(
            "--kv-cache-metrics", **observability_kwargs["kv_cache_metrics"]
        )
        observability_group.add_argument(
            "--kv-cache-metrics-sample",
            **observability_kwargs["kv_cache_metrics_sample"],
        )
1155
1156
1157
1158
        observability_group.add_argument(
            "--cudagraph-metrics",
            **observability_kwargs["cudagraph_metrics"],
        )
1159
1160
1161
1162
        observability_group.add_argument(
            "--enable-layerwise-nvtx-tracing",
            **observability_kwargs["enable_layerwise_nvtx_tracing"],
        )
1163
1164
1165
1166
        observability_group.add_argument(
            "--enable-mfu-metrics",
            **observability_kwargs["enable_mfu_metrics"],
        )
1167
1168
1169
1170
        observability_group.add_argument(
            "--enable-logging-iteration-details",
            **observability_kwargs["enable_logging_iteration_details"],
        )
1171

1172
1173
1174
1175
1176
1177
1178
        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
1179
1180
1181
1182
1183
            "--max-num-batched-tokens",
            **{
                **scheduler_kwargs["max_num_batched_tokens"],
                "default": None,
            },
1184
        )
1185
        scheduler_group.add_argument(
1186
1187
1188
1189
1190
            "--max-num-seqs",
            **{
                **scheduler_kwargs["max_num_seqs"],
                "default": None,
            },
1191
1192
1193
1194
        )
        scheduler_group.add_argument(
            "--max-num-partial-prefills", **scheduler_kwargs["max_num_partial_prefills"]
        )
1195
1196
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
1197
1198
            **scheduler_kwargs["max_long_partial_prefills"],
        )
1199
1200
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
1201
1202
            **scheduler_kwargs["long_prefill_token_threshold"],
        )
1203
1204
        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
1205
        scheduler_group.add_argument(
1206
1207
            "--scheduling-policy", **scheduler_kwargs["policy"]
        )
1208
        scheduler_group.add_argument(
1209
1210
1211
1212
1213
            "--enable-chunked-prefill",
            **{
                **scheduler_kwargs["enable_chunked_prefill"],
                "default": None,
            },
1214
1215
1216
1217
1218
1219
1220
        )
        scheduler_group.add_argument(
            "--disable-chunked-mm-input", **scheduler_kwargs["disable_chunked_mm_input"]
        )
        scheduler_group.add_argument(
            "--scheduler-cls", **scheduler_kwargs["scheduler_cls"]
        )
1221
1222
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
1223
1224
1225
1226
1227
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"],
        )
        scheduler_group.add_argument(
            "--async-scheduling", **scheduler_kwargs["async_scheduling"]
        )
1228
1229
1230
        scheduler_group.add_argument(
            "--stream-interval", **scheduler_kwargs["stream_interval"]
        )
1231

1232
1233
1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
        # Compilation arguments
        compilation_kwargs = get_kwargs(CompilationConfig)
        compilation_group = parser.add_argument_group(
            title="CompilationConfig",
            description=CompilationConfig.__doc__,
        )
        compilation_group.add_argument(
            "--cudagraph-capture-sizes", **compilation_kwargs["cudagraph_capture_sizes"]
        )
        compilation_group.add_argument(
            "--max-cudagraph-capture-size",
            **compilation_kwargs["max_cudagraph_capture_size"],
        )

1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
        # Kernel arguments
        kernel_kwargs = get_kwargs(KernelConfig)
        kernel_group = parser.add_argument_group(
            title="KernelConfig",
            description=KernelConfig.__doc__,
        )
        kernel_group.add_argument(
            "--enable-flashinfer-autotune",
            **kernel_kwargs["enable_flashinfer_autotune"],
        )
1256
1257
1258
        moe_backend_kwargs = kernel_kwargs["moe_backend"]
        moe_backend_kwargs["type"] = lambda s: s.lower().replace("-", "_")
        kernel_group.add_argument("--moe-backend", **moe_backend_kwargs)
1259

1260
        # vLLM arguments
1261
        vllm_kwargs = get_kwargs(VllmConfig)
1262
1263
1264
1265
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
1266
1267
1268
1269
        # We construct SpeculativeConfig using fields from other configs in
        # create_engine_config. So we set the type to a JSON string here to
        # delay the Pydantic validation that comes with SpeculativeConfig.
        vllm_kwargs["speculative_config"]["type"] = optional_type(json.loads)
1270
1271
1272
1273
1274
1275
1276
        vllm_group.add_argument(
            "--speculative-config", **vllm_kwargs["speculative_config"]
        )
        vllm_group.add_argument(
            "--kv-transfer-config", **vllm_kwargs["kv_transfer_config"]
        )
        vllm_group.add_argument("--kv-events-config", **vllm_kwargs["kv_events_config"])
1277
1278
1279
        vllm_group.add_argument(
            "--ec-transfer-config", **vllm_kwargs["ec_transfer_config"]
        )
1280
        vllm_group.add_argument(
1281
            "--compilation-config", "-cc", **vllm_kwargs["compilation_config"]
1282
        )
1283
1284
1285
        vllm_group.add_argument(
            "--attention-config", "-ac", **vllm_kwargs["attention_config"]
        )
1286
        vllm_group.add_argument("--kernel-config", **vllm_kwargs["kernel_config"])
1287
1288
1289
1290
1291
1292
        vllm_group.add_argument(
            "--additional-config", **vllm_kwargs["additional_config"]
        )
        vllm_group.add_argument(
            "--structured-outputs-config", **vllm_kwargs["structured_outputs_config"]
        )
1293
        vllm_group.add_argument("--profiler-config", **vllm_kwargs["profiler_config"])
1294
1295
1296
        vllm_group.add_argument(
            "--optimization-level", **vllm_kwargs["optimization_level"]
        )
1297
        vllm_group.add_argument("--performance-mode", **vllm_kwargs["performance_mode"])
1298
1299
1300
        vllm_group.add_argument(
            "--weight-transfer-config", **vllm_kwargs["weight_transfer_config"]
        )
1301

1302
        # Other arguments
1303
1304
1305
1306
1307
        parser.add_argument(
            "--disable-log-stats",
            action="store_true",
            help="Disable logging statistics.",
        )
1308

1309
1310
1311
1312
1313
1314
        parser.add_argument(
            "--aggregate-engine-logging",
            action="store_true",
            help="Log aggregate rather than per-engine statistics "
            "when using data parallelism.",
        )
1315
1316
1317
1318
1319
1320
1321
1322

        parser.add_argument(
            "--fail-on-environ-validation",
            help="If set, the engine will raise an error if "
            "environment validation fails.",
            default=False,
            action=argparse.BooleanOptionalAction,
        )
1323
1324
1325
1326
1327
1328
1329
1330

        parser.add_argument(
            "--shutdown-timeout",
            type=int,
            default=0,
            help="Shutdown timeout in seconds. 0 = abort, >0 = wait.",
        )

1331
1332
1333
1334
1335
1336
1337
        parser.add_argument(
            "--gdn-prefill-backend",
            dest="gdn_prefill_backend",
            choices=["flashinfer", "triton"],
            default=None,
            help="Select GDN prefill backend.",
        )
1338
        return parser
1339
1340

    @classmethod
1341
    def from_cli_args(cls, args: argparse.Namespace):
1342
1343
1344
        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
1345
1346
1347
        engine_args = cls(
            **{attr: getattr(args, attr) for attr in attrs if hasattr(args, attr)}
        )
Zhuohan Li's avatar
Zhuohan Li committed
1348
        return engine_args
1349

1350
    def create_model_config(self) -> ModelConfig:
1351
1352
        # gguf file needs a specific model loader
        if is_gguf(self.model):
1353
1354
            self.quantization = self.load_format = "gguf"

1355
1356
1357
1358
1359
1360
1361
        if not envs.VLLM_ENABLE_V1_MULTIPROCESSING:
            logger.warning(
                "The global random seed is set to %d. Since "
                "VLLM_ENABLE_V1_MULTIPROCESSING is set to False, this may "
                "affect the random state of the Python process that "
                "launched vLLM.",
                self.seed,
1362
1363
            )

1364
        return ModelConfig(
1365
            model=self.model,
1366
            model_weights=self.model_weights,
1367
            hf_config_path=self.hf_config_path,
1368
1369
            runner=self.runner,
            convert=self.convert,
1370
            tokenizer=self.tokenizer,  # type: ignore[arg-type]
1371
            tokenizer_mode=self.tokenizer_mode,
1372
            trust_remote_code=self.trust_remote_code,
1373
1374
            allowed_local_media_path=self.allowed_local_media_path,
            allowed_media_domains=self.allowed_media_domains,
1375
1376
1377
1378
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
1379
            hf_token=self.hf_token,
1380
            hf_overrides=self.hf_overrides,
1381
            tokenizer_revision=self.tokenizer_revision,
1382
1383
            max_model_len=self.max_model_len,
            quantization=self.quantization,
1384
            allow_deprecated_quantization=self.allow_deprecated_quantization,
1385
            enforce_eager=self.enforce_eager,
1386
            enable_return_routed_experts=self.enable_return_routed_experts,
1387
            max_logprobs=self.max_logprobs,
1388
            logprobs_mode=self.logprobs_mode,
1389
            disable_sliding_window=self.disable_sliding_window,
1390
            disable_cascade_attn=self.disable_cascade_attn,
1391
            skip_tokenizer_init=self.skip_tokenizer_init,
1392
            enable_prompt_embeds=self.enable_prompt_embeds,
1393
            served_model_name=self.served_model_name,
1394
            language_model_only=self.language_model_only,
1395
            limit_mm_per_prompt=self.limit_mm_per_prompt,
1396
            enable_mm_embeds=self.enable_mm_embeds,
1397
            interleave_mm_strings=self.interleave_mm_strings,
1398
            media_io_kwargs=self.media_io_kwargs,
1399
            skip_mm_profiling=self.skip_mm_profiling,
1400
            config_format=self.config_format,
1401
            mm_processor_kwargs=self.mm_processor_kwargs,
1402
            mm_processor_cache_gb=self.mm_processor_cache_gb,
1403
            mm_processor_cache_type=self.mm_processor_cache_type,
1404
            mm_shm_cache_max_object_size_mb=self.mm_shm_cache_max_object_size_mb,
1405
            mm_encoder_only=self.mm_encoder_only,
1406
            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
1407
            mm_encoder_attn_backend=self.mm_encoder_attn_backend,
1408
            pooler_config=self.pooler_config,
1409
            generation_config=self.generation_config,
1410
            override_generation_config=self.override_generation_config,
1411
            enable_sleep_mode=self.enable_sleep_mode,
1412
            model_impl=self.model_impl,
1413
            override_attention_dtype=self.override_attention_dtype,
1414
            logits_processors=self.logits_processors,
1415
            video_pruning_rate=self.video_pruning_rate,
1416
            io_processor_plugin=self.io_processor_plugin,
1417
        )
1418

1419
    def validate_tensorizer_args(self):
1420
1421
        from vllm.model_executor.model_loader.tensorizer import TensorizerConfig

1422
1423
        for key in self.model_loader_extra_config:
            if key in TensorizerConfig._fields:
1424
1425
1426
                self.model_loader_extra_config["tensorizer_config"][key] = (
                    self.model_loader_extra_config[key]
                )
1427

1428
    def create_load_config(self) -> LoadConfig:
1429
1430
        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
1431

1432
1433
1434
        if self.load_format == "tensorizer":
            if hasattr(self.model_loader_extra_config, "to_serializable"):
                self.model_loader_extra_config = (
1435
1436
                    self.model_loader_extra_config.to_serializable()
                )
1437
            self.model_loader_extra_config["tensorizer_config"] = {}
1438
1439
1440
            self.model_loader_extra_config["tensorizer_config"]["tensorizer_dir"] = (
                self.model
            )
1441
            self.validate_tensorizer_args()
1442

1443
1444
1445
        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
1446
            safetensors_load_strategy=self.safetensors_load_strategy,
1447
1448
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
1449
            use_tqdm_on_load=self.use_tqdm_on_load,
1450
            pt_load_map_location=self.pt_load_map_location,
1451
        )
1452

1453
1454
1455
1456
    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
1457
    ) -> SpeculativeConfig | None:
1458
1459
1460
1461
1462
1463
        """Initializes and returns a SpeculativeConfig object based on
        `speculative_config`.

        This function utilizes `speculative_config` to create a
        SpeculativeConfig object. The `speculative_config` can either be
        provided as a JSON string input via CLI arguments or directly as a
1464
        dictionary from the engine.
1465
1466
        """
        if self.speculative_config is None:
1467
            return None
1468

1469
1470
1471
        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
1472
1473
1474
1475
1476
1477
        self.speculative_config.update(
            {
                "target_model_config": target_model_config,
                "target_parallel_config": target_parallel_config,
            }
        )
1478
        return SpeculativeConfig(**self.speculative_config)
1479

1480
1481
    def create_engine_config(
        self,
1482
        usage_context: UsageContext | None = None,
1483
        headless: bool = False,
1484
1485
1486
1487
    ) -> VllmConfig:
        """
        Create the VllmConfig.

1488
        NOTE: If VllmConfig is incompatible, we raise an error.
1489
        """
1490
        current_platform.pre_register_and_update()
1491

1492
        device_config = DeviceConfig(device=cast(Device, current_platform.device_type))
1493

1494
1495
        envs.validate_environ(self.fail_on_environ_validation)

1496
1497
        # Check if the model is a speculator and override model/tokenizer/config
        # BEFORE creating ModelConfig, so the config is created with the target model
1498
1499
1500
1501
        # Skip speculator detection for cloud storage models (eg: S3, GCS) since
        # HuggingFace cannot load configs directly from S3 URLs. S3 models can still
        # use speculators with explicit --speculative-config.
        if not is_cloud_storage(self.model):
1502
1503
1504
1505
1506
1507
1508
1509
1510
1511
            (self.model, self.tokenizer, self.speculative_config) = (
                maybe_override_with_speculators(
                    model=self.model,
                    tokenizer=self.tokenizer,
                    revision=self.revision,
                    trust_remote_code=self.trust_remote_code,
                    vllm_speculative_config=self.speculative_config,
                )
            )

1512
        model_config = self.create_model_config()
1513
        self.model = model_config.model
1514
        self.model_weights = model_config.model_weights
1515
1516
        self.tokenizer = model_config.tokenizer

1517
        self._check_feature_supported()
1518
1519
1520
1521
        self._set_default_chunked_prefill_and_prefix_caching_args(model_config)
        self._set_default_max_num_seqs_and_batched_tokens_args(
            usage_context, model_config
        )
1522

1523
        sliding_window: int | None = None
1524
1525
1526
1527
1528
1529
        if not is_interleaved(model_config.hf_text_config):
            # Only set CacheConfig.sliding_window if the model is all sliding
            # window. Otherwise CacheConfig.sliding_window will override the
            # global layers in interleaved sliding window models.
            sliding_window = model_config.get_sliding_window()

1530
1531
1532
1533
1534
        # Resolve "auto" kv_cache_dtype to actual value from model config
        resolved_cache_dtype = resolve_kv_cache_dtype_string(
            self.kv_cache_dtype, model_config
        )

1535
1536
1537
1538
        assert self.enable_prefix_caching is not None, (
            "enable_prefix_caching must be set by this point"
        )

1539
        cache_config = CacheConfig(
1540
            block_size=self.block_size,  # type: ignore[arg-type]
1541
            gpu_memory_utilization=self.gpu_memory_utilization,
1542
            kv_cache_memory_bytes=self.kv_cache_memory_bytes,
1543
            cache_dtype=resolved_cache_dtype,  # type: ignore[arg-type]
1544
            is_attention_free=model_config.is_attention_free,
1545
            num_gpu_blocks_override=self.num_gpu_blocks_override,
1546
            sliding_window=sliding_window,
1547
            enable_prefix_caching=self.enable_prefix_caching,
1548
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
1549
            calculate_kv_scales=self.calculate_kv_scales,
1550
            kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
1551
1552
            mamba_cache_dtype=self.mamba_cache_dtype,
            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
1553
            mamba_block_size=self.mamba_block_size,
1554
            mamba_cache_mode=self.mamba_cache_mode,
1555
1556
            kv_offloading_size=self.kv_offloading_size,
            kv_offloading_backend=self.kv_offloading_backend,
1557
        )
1558

1559
1560
1561
1562
1563
1564
        ray_runtime_env = None
        if is_ray_initialized():
            # Ray Serve LLM calls `create_engine_config` in the context
            # of a Ray task, therefore we check is_ray_initialized()
            # as opposed to is_in_ray_actor().
            import ray
1565

1566
            ray_runtime_env = ray.get_runtime_context().runtime_env
1567
1568
1569
1570
1571
1572
1573
            # Avoid logging sensitive environment variables
            sanitized_env = ray_runtime_env.to_dict() if ray_runtime_env else {}
            if "env_vars" in sanitized_env:
                sanitized_env["env_vars"] = {
                    k: "***" for k in sanitized_env["env_vars"]
                }
            logger.info("Using ray runtime env (env vars redacted): %s", sanitized_env)
1574

1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
        # Get the current placement group if Ray is initialized and
        # we are in a Ray actor. If so, then the placement group will be
        # passed to spawned processes.
        placement_group = None
        if is_in_ray_actor():
            import ray

            # This call initializes Ray automatically if it is not initialized,
            # but we should not do this here.
            placement_group = ray.util.get_current_placement_group()

1586
        assert not headless or not self.data_parallel_hybrid_lb, (
1587
1588
            "data_parallel_hybrid_lb is not applicable in headless mode"
        )
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606
1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
1626
1627
1628
1629
        assert not (self.data_parallel_hybrid_lb and self.data_parallel_external_lb), (
            "data_parallel_hybrid_lb and data_parallel_external_lb cannot both be True."
        )
        assert self.data_parallel_backend == "mp" or self.nnodes == 1, (
            "nnodes > 1 is only supported with data_parallel_backend=mp"
        )
        inferred_data_parallel_rank = 0
        if self.nnodes > 1:
            world_size = (
                self.data_parallel_size
                * self.pipeline_parallel_size
                * self.tensor_parallel_size
            )
            world_size_within_dp = (
                self.pipeline_parallel_size * self.tensor_parallel_size
            )
            local_world_size = world_size // self.nnodes
            assert world_size % self.nnodes == 0, (
                f"world_size={world_size} must be divisible by nnodes={self.nnodes}."
            )
            assert self.node_rank < self.nnodes, (
                f"node_rank={self.node_rank} must be less than nnodes={self.nnodes}."
            )
            inferred_data_parallel_rank = (
                self.node_rank * local_world_size
            ) // world_size_within_dp
            if self.data_parallel_size > 1 and self.data_parallel_external_lb:
                self.data_parallel_rank = inferred_data_parallel_rank
                logger.info(
                    "Inferred data_parallel_rank %d from node_rank %d for external lb",
                    self.data_parallel_rank,
                    self.node_rank,
                )
            elif self.data_parallel_size_local is None:
                # Infer data parallel size local for internal dplb:
                self.data_parallel_size_local = max(
                    local_world_size // world_size_within_dp, 1
                )
        data_parallel_external_lb = (
            self.data_parallel_external_lb or self.data_parallel_rank is not None
        )
1630
        # Local DP rank = 1, use pure-external LB.
1631
        if data_parallel_external_lb:
1632
            assert self.data_parallel_rank is not None, (
1633
                "data_parallel_rank or node_rank must be specified if "
1634
1635
                "data_parallel_external_lb is enable."
            )
1636
            assert self.data_parallel_size_local in (1, None), (
1637
1638
                "data_parallel_size_local must be 1 or None when data_parallel_rank "
                "is set"
1639
            )
1640
            data_parallel_size_local = 1
1641
1642
            # Use full external lb if we have local_size of 1.
            self.data_parallel_hybrid_lb = False
1643
1644
        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local
1645
1646
1647
1648
1649
1650
1651

            if self.data_parallel_start_rank and not headless:
                # Infer hybrid LB mode.
                self.data_parallel_hybrid_lb = True

            if self.data_parallel_hybrid_lb and data_parallel_size_local == 1:
                # Use full external lb if we have local_size of 1.
1652
1653
1654
1655
1656
                logger.warning(
                    "data_parallel_hybrid_lb is not eligible when "
                    "data_parallel_size_local = 1, autoswitch to "
                    "data_parallel_external_lb."
                )
1657
1658
1659
1660
1661
1662
1663
                data_parallel_external_lb = True
                self.data_parallel_hybrid_lb = False

            if data_parallel_size_local == self.data_parallel_size:
                # Disable hybrid LB mode if set for a single node
                self.data_parallel_hybrid_lb = False

1664
1665
1666
1667
1668
1669
1670
1671
1672
            self.data_parallel_rank = (
                self.data_parallel_start_rank or inferred_data_parallel_rank
            )
            if self.nnodes > 1:
                logger.info(
                    "Inferred data_parallel_rank %d from node_rank %d",
                    self.data_parallel_rank,
                    self.node_rank,
                )
1673
        else:
1674
            assert not self.data_parallel_hybrid_lb, (
1675
1676
                "data_parallel_size_local must be set to use data_parallel_hybrid_lb."
            )
1677

1678
1679
1680
1681
1682
1683
1684
1685
1686
            if self.data_parallel_backend == "ray" and (
                envs.VLLM_RAY_DP_PACK_STRATEGY == "span"
            ):
                # Data parallel size defaults to 1 if DP ranks are spanning
                # multiple nodes
                data_parallel_size_local = 1
            else:
                # Otherwise local DP size defaults to global DP size if not set
                data_parallel_size_local = self.data_parallel_size
1687
1688
1689

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
Rui Qiao's avatar
Rui Qiao committed
1690
1691
1692
1693
        if self.data_parallel_address is None:
            if self.data_parallel_backend == "ray":
                host_ip = get_ip()
                logger.info(
1694
1695
                    "Using host IP %s as ray-based data parallel address", host_ip
                )
Rui Qiao's avatar
Rui Qiao committed
1696
1697
1698
1699
                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
1700
1701
                    self.data_parallel_backend,
                )
1702
1703
1704
                data_parallel_address = (
                    self.master_addr or ParallelConfig.data_parallel_master_ip
                )
Rui Qiao's avatar
Rui Qiao committed
1705
1706
        else:
            data_parallel_address = self.data_parallel_address
1707
1708
1709

        # This port is only used when there are remote data parallel engines,
        # otherwise the local IPC transport is used.
1710
        data_parallel_rpc_port = (
1711
            self.data_parallel_rpc_port
1712
1713
1714
            if (self.data_parallel_rpc_port is not None)
            else ParallelConfig.data_parallel_rpc_port
        )
1715

1716
1717
1718
1719
        if self.tokens_only and not model_config.skip_tokenizer_init:
            model_config.skip_tokenizer_init = True
            logger.info("Skipping tokenizer initialization for tokens-only mode.")

1720
        parallel_config = ParallelConfig(
1721
1722
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
1723
            prefill_context_parallel_size=self.prefill_context_parallel_size,
1724
            data_parallel_size=self.data_parallel_size,
1725
1726
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
1727
            data_parallel_size_local=data_parallel_size_local,
1728
1729
1730
1731
            master_addr=self.master_addr,
            master_port=self.master_port,
            nnodes=self.nnodes,
            node_rank=self.node_rank,
1732
            distributed_timeout_seconds=self.distributed_timeout_seconds,
1733
1734
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
1735
            data_parallel_backend=self.data_parallel_backend,
1736
            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
1737
            is_moe_model=model_config.is_moe,
1738
            enable_expert_parallel=self.enable_expert_parallel,
1739
            enable_ep_weight_filter=self.enable_ep_weight_filter,
1740
            all2all_backend=self.all2all_backend,
1741
            enable_elastic_ep=self.enable_elastic_ep,
1742
            enable_dbo=self.enable_dbo,
1743
            ubatch_size=self.ubatch_size,
1744
            dbo_decode_token_threshold=self.dbo_decode_token_threshold,
1745
            dbo_prefill_token_threshold=self.dbo_prefill_token_threshold,
1746
            disable_nccl_for_dp_synchronization=self.disable_nccl_for_dp_synchronization,
1747
            enable_eplb=self.enable_eplb,
1748
            eplb_config=self.eplb_config,
1749
            expert_placement_strategy=self.expert_placement_strategy,
1750
1751
1752
            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            ray_workers_use_nsight=self.ray_workers_use_nsight,
1753
            ray_runtime_env=ray_runtime_env,
1754
            placement_group=placement_group,
1755
1756
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1757
            worker_extension_cls=self.worker_extension_cls,
1758
            decode_context_parallel_size=self.decode_context_parallel_size,
1759
            dcp_comm_backend=self.dcp_comm_backend,
1760
            dcp_kv_cache_interleave_size=self.dcp_kv_cache_interleave_size,
1761
            cp_kv_cache_interleave_size=self.cp_kv_cache_interleave_size,
1762
1763
            _api_process_count=self._api_process_count,
            _api_process_rank=self._api_process_rank,
1764
        )
1765

1766
        speculative_config = self.create_speculative_config(
1767
1768
1769
1770
            target_model_config=model_config,
            target_parallel_config=parallel_config,
        )

1771
1772
1773
1774
1775
1776
1777
1778
1779
1780
        assert self.max_num_batched_tokens is not None, (
            "max_num_batched_tokens must be set by this point"
        )
        assert self.max_num_seqs is not None, "max_num_seqs must be set by this point"
        assert self.enable_chunked_prefill is not None, (
            "enable_chunked_prefill must be set by this point"
        )
        assert model_config.max_model_len is not None, (
            "max_model_len must be set by this point"
        )
1781
        scheduler_config = SchedulerConfig(
1782
            runner_type=model_config.runner_type,
1783
1784
1785
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1786
            enable_chunked_prefill=self.enable_chunked_prefill,
1787
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1788
            is_multimodal_model=model_config.is_multimodal_model,
1789
            is_encoder_decoder=model_config.is_encoder_decoder,
1790
            policy=self.scheduling_policy,
1791
            scheduler_cls=self.scheduler_cls,
1792
1793
1794
            max_num_partial_prefills=self.max_num_partial_prefills,
            max_long_partial_prefills=self.max_long_partial_prefills,
            long_prefill_token_threshold=self.long_prefill_token_threshold,
1795
            disable_hybrid_kv_cache_manager=self.disable_hybrid_kv_cache_manager,
1796
            async_scheduling=self.async_scheduling,
1797
            stream_interval=self.stream_interval,
1798
        )
1799

1800
1801
1802
        if not model_config.is_multimodal_model and self.default_mm_loras:
            raise ValueError(
                "Default modality-specific LoRA(s) were provided for a "
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
                "non multimodal model"
            )

        lora_config = (
            LoRAConfig(
                max_lora_rank=self.max_lora_rank,
                max_loras=self.max_loras,
                default_mm_loras=self.default_mm_loras,
                fully_sharded_loras=self.fully_sharded_loras,
                lora_dtype=self.lora_dtype,
1813
                target_modules=self.lora_target_modules,
1814
                enable_tower_connector_lora=self.enable_tower_connector_lora,
1815
                specialize_active_lora=self.specialize_active_lora,
1816
1817
1818
1819
1820
1821
1822
                max_cpu_loras=self.max_cpu_loras
                if self.max_cpu_loras and self.max_cpu_loras > 0
                else None,
            )
            if self.enable_lora
            else None
        )
1823

1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
        if (
            lora_config is not None
            and speculative_config is not None
            and scheduler_config.max_num_batched_tokens
            < (
                scheduler_config.max_num_seqs
                * (speculative_config.num_speculative_tokens + 1)
            )
        ):
            raise ValueError(
                "Consider increasing max_num_batched_tokens or "
                "decreasing num_speculative_tokens"
            )

1838
1839
1840
1841
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1842
1843
1844
1845
1846
1847
1848
1849
        # Attention config overrides
        attention_config = copy.deepcopy(self.attention_config)
        if self.attention_backend is not None:
            if attention_config.backend is not None:
                raise ValueError(
                    "attention_backend and attention_config.backend "
                    "are mutually exclusive"
                )
1850
1851
1852
1853
            # Reuse the validator to handle "auto" and string-to-enum conversion
            attention_config.backend = AttentionConfig.validate_backend_before(
                self.attention_backend
            )
1854

1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
        # Kernel config overrides
        kernel_config = copy.deepcopy(self.kernel_config)
        if self.enable_flashinfer_autotune is not None:
            if kernel_config.enable_flashinfer_autotune is not None:
                raise ValueError(
                    "enable_flashinfer_autotune and "
                    "kernel_config.enable_flashinfer_autotune "
                    "are mutually exclusive"
                )
            kernel_config.enable_flashinfer_autotune = self.enable_flashinfer_autotune
1865
1866
        if self.moe_backend != "auto":
            kernel_config.moe_backend = self.moe_backend
1867

1868
        load_config = self.create_load_config()
1869

1870
1871
        # Pass reasoning_parser into StructuredOutputsConfig
        if self.reasoning_parser:
1872
            self.structured_outputs_config.reasoning_parser = self.reasoning_parser
1873

1874
1875
1876
1877
1878
        if self.reasoning_parser_plugin:
            self.structured_outputs_config.reasoning_parser_plugin = (
                self.reasoning_parser_plugin
            )

1879
        observability_config = ObservabilityConfig(
1880
            show_hidden_metrics_for_version=self.show_hidden_metrics_for_version,
1881
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1882
            collect_detailed_traces=self.collect_detailed_traces,
1883
1884
            kv_cache_metrics=self.kv_cache_metrics,
            kv_cache_metrics_sample=self.kv_cache_metrics_sample,
1885
            cudagraph_metrics=self.cudagraph_metrics,
1886
            enable_layerwise_nvtx_tracing=self.enable_layerwise_nvtx_tracing,
1887
            enable_mfu_metrics=self.enable_mfu_metrics,
1888
            enable_mm_processor_stats=self.enable_mm_processor_stats,
1889
            enable_logging_iteration_details=self.enable_logging_iteration_details,
1890
        )
1891

1892
        # Compilation config overrides
1893
        compilation_config = copy.deepcopy(self.compilation_config)
1894
        if self.cudagraph_capture_sizes is not None:
1895
            if compilation_config.cudagraph_capture_sizes is not None:
1896
1897
1898
1899
                raise ValueError(
                    "cudagraph_capture_sizes and compilation_config."
                    "cudagraph_capture_sizes are mutually exclusive"
                )
1900
            compilation_config.cudagraph_capture_sizes = self.cudagraph_capture_sizes
1901
        if self.max_cudagraph_capture_size is not None:
1902
            if compilation_config.max_cudagraph_capture_size is not None:
1903
1904
1905
1906
                raise ValueError(
                    "max_cudagraph_capture_size and compilation_config."
                    "max_cudagraph_capture_size are mutually exclusive"
                )
1907
            compilation_config.max_cudagraph_capture_size = (
1908
1909
                self.max_cudagraph_capture_size
            )
1910
1911
1912
1913
1914
1915
1916
1917
1918
1919
1920
1921
1922
1923
1924

        offload_config = OffloadConfig(
            offload_backend=self.offload_backend,
            uva=UVAOffloadConfig(
                cpu_offload_gb=self.cpu_offload_gb,
                cpu_offload_params=self.cpu_offload_params,
            ),
            prefetch=PrefetchOffloadConfig(
                offload_group_size=self.offload_group_size,
                offload_num_in_group=self.offload_num_in_group,
                offload_prefetch_step=self.offload_prefetch_step,
                offload_params=self.offload_params,
            ),
        )

1925
1926
1927
        if self.gdn_prefill_backend is not None:
            self.additional_config["gdn_prefill_backend"] = self.gdn_prefill_backend

1928
        config = VllmConfig(
1929
1930
1931
1932
1933
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
1934
            load_config=load_config,
1935
            offload_config=offload_config,
1936
            attention_config=attention_config,
1937
            kernel_config=kernel_config,
1938
1939
            lora_config=lora_config,
            speculative_config=speculative_config,
1940
            structured_outputs_config=self.structured_outputs_config,
1941
            observability_config=observability_config,
1942
            compilation_config=compilation_config,
1943
            kv_transfer_config=self.kv_transfer_config,
1944
            kv_events_config=self.kv_events_config,
1945
            ec_transfer_config=self.ec_transfer_config,
1946
            profiler_config=self.profiler_config,
1947
            additional_config=self.additional_config,
1948
            optimization_level=self.optimization_level,
1949
            performance_mode=self.performance_mode,
1950
            weight_transfer_config=self.weight_transfer_config,
1951
            shutdown_timeout=self.shutdown_timeout,
1952
        )
1953

1954
1955
        return config

1956
    def _check_feature_supported(self):
1957
        """Raise an error if the feature is not supported."""
1958
        # No Concurrent Partial Prefills so far.
1959
1960
1961
1962
1963
        if (
            self.max_num_partial_prefills != SchedulerConfig.max_num_partial_prefills
            or self.max_long_partial_prefills
            != SchedulerConfig.max_long_partial_prefills
        ):
1964
            _raise_unsupported_error(feature_name="Concurrent Partial Prefill")
1965

1966
        if self.pipeline_parallel_size > 1:
1967
1968
1969
            supports_pp = getattr(
                self.distributed_executor_backend, "supports_pp", False
            )
1970
            if not supports_pp and self.distributed_executor_backend not in (
1971
1972
1973
1974
1975
1976
1977
1978
1979
1980
                ParallelConfig.distributed_executor_backend,
                "ray",
                "mp",
                "external_launcher",
            ):
                name = (
                    "Pipeline Parallelism without Ray distributed "
                    "executor or multiprocessing executor or external "
                    "launcher"
                )
1981
                _raise_unsupported_error(feature_name=name)
1982

1983
1984
1985
1986
1987
1988
    @classmethod
    def get_batch_defaults(
        cls,
        world_size: int,
    ) -> tuple[dict[UsageContext | None, int], dict[UsageContext | None, int]]:
        from vllm.usage.usage_lib import UsageContext
1989

1990
1991
        default_max_num_batched_tokens: dict[UsageContext | None, int]
        default_max_num_seqs: dict[UsageContext | None, int]
1992

1993
1994
        # When no user override, set the default values based on the usage
        # context.
1995
        # Use different default values for different hardware.
1996
1997
1998
1999
2000
2001
2002

        # Try to query the device name on the current platform. If it fails,
        # it may be because the platform that imports vLLM is not the same
        # as the platform that vLLM is running on (e.g. the case of scaling
        # vLLM with Ray) and has no GPUs. In this case we use the default
        # values for non-H100/H200 GPUs.
        try:
2003
            device_memory = current_platform.get_device_total_memory()
2004
            device_name = current_platform.get_device_name().lower()
2005
2006
        except Exception:
            # This is only used to set default_max_num_batched_tokens
2007
            device_memory = 0
2008
            device_name = ""
2009

2010
2011
2012
2013
        # NOTE(Kuntai): Setting large `max_num_batched_tokens` for A100 reduces
        # throughput, see PR #17885 for more details.
        # So here we do an extra device name check to prevent such regression.
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
2014
            # For GPUs like H100 and MI300x, use larger default values.
2015
2016
2017
2018
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
2019
2020
2021
2022
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
2023
2024
2025
2026
2027
2028
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
2029
2030
2031
2032
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }
2033

2034
2035
        # tpu specific default values.
        if current_platform.is_tpu():
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
2048
2049
2050
2051
2052
            chip_name = current_platform.get_device_name()

            if chip_name == "V6E":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 2048,
                    UsageContext.OPENAI_API_SERVER: 1024,
                }
            elif chip_name == "V5E":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 1024,
                    UsageContext.OPENAI_API_SERVER: 512,
                }
            elif chip_name == "V5P":
                default_max_num_batched_tokens = {
                    UsageContext.LLM_CLASS: 512,
                    UsageContext.OPENAI_API_SERVER: 256,
                }
2053

2054
2055
2056
        # cpu specific default values.
        if current_platform.is_cpu():
            default_max_num_batched_tokens = {
2057
2058
                UsageContext.LLM_CLASS: 4096 * world_size,
                UsageContext.OPENAI_API_SERVER: 2048 * world_size,
2059
2060
            }
            default_max_num_seqs = {
2061
2062
                UsageContext.LLM_CLASS: 256 * world_size,
                UsageContext.OPENAI_API_SERVER: 128 * world_size,
2063
2064
            }

2065
2066
        return default_max_num_batched_tokens, default_max_num_seqs

2067
2068
    def _set_default_chunked_prefill_and_prefix_caching_args(
        self, model_config: ModelConfig
2069
    ) -> None:
2070
2071
        default_chunked_prefill = model_config.is_chunked_prefill_supported
        default_prefix_caching = model_config.is_prefix_caching_supported
2072
2073
2074
2075
2076
2077
2078
2079

        if self.enable_chunked_prefill is None:
            self.enable_chunked_prefill = default_chunked_prefill

            logger.debug(
                "%s chunked prefill by default",
                "Enabling" if default_chunked_prefill else "Disabling",
            )
2080
2081
2082
2083
2084
2085
2086
2087
2088
2089
2090
        elif (
            model_config.runner_type == "generate"
            and not self.enable_chunked_prefill
            and default_chunked_prefill
        ):
            logger.warning_once(
                "This model does not officially support disabling chunked prefill. "
                "Disabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
                scope="local",
            )
2091
2092
2093
2094
        elif (
            model_config.runner_type == "pooling"
            and self.enable_chunked_prefill
            and not default_chunked_prefill
2095
        ):
2096
            logger.warning_once(
2097
2098
2099
                "This model does not officially support chunked prefill. "
                "Enabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
2100
                scope="local",
2101
2102
2103
2104
2105
            )

        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = default_prefix_caching

2106
            logger.debug(
2107
2108
2109
2110
2111
2112
2113
2114
                "%s prefix caching by default",
                "Enabling" if default_prefix_caching else "Disabling",
            )
        elif (
            model_config.runner_type == "pooling"
            and self.enable_prefix_caching
            and not default_prefix_caching
        ):
2115
            logger.warning_once(
2116
2117
2118
                "This model does not officially support prefix caching. "
                "Enabling this manually may cause the engine to crash "
                "or produce incorrect outputs.",
2119
                scope="local",
2120
2121
            )

2122
        # Disable chunked prefill and prefix caching for:
2123
        # RISCV CPUs in V1
2124
2125
2126
2127
        if current_platform.is_cpu() and current_platform.get_cpu_architecture() in (
            CpuArchEnum.RISCV,
        ):
            logger.info(
2128
2129
                "Chunked prefill is not supported for"
                "RISC-V CPUs; "
2130
2131
2132
2133
                "disabling it for V1 backend."
            )
            self.enable_chunked_prefill = False
            logger.info(
2134
2135
                "Prefix caching is not supported for "
                "RISC-V CPUs; "
2136
2137
2138
2139
2140
                "disabling it for V1 backend."
            )
            self.enable_prefix_caching = False

    def _set_default_max_num_seqs_and_batched_tokens_args(
2141
2142
2143
        self,
        usage_context: UsageContext | None,
        model_config: ModelConfig,
2144
    ):
2145
2146
2147
2148
2149
2150
2151
2152
2153
2154
2155
2156
2157
2158
2159
2160
2161
2162
2163
2164
2165
        world_size = self.pipeline_parallel_size * self.tensor_parallel_size
        (
            default_max_num_batched_tokens,
            default_max_num_seqs,
        ) = self.get_batch_defaults(world_size)

        orig_max_num_batched_tokens = self.max_num_batched_tokens
        orig_max_num_seqs = self.max_num_seqs

        if self.max_num_batched_tokens is None:
            self.max_num_batched_tokens = default_max_num_batched_tokens.get(
                usage_context,
                SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS,
            )

        if self.max_num_seqs is None:
            self.max_num_seqs = default_max_num_seqs.get(
                usage_context,
                SchedulerConfig.DEFAULT_MAX_NUM_SEQS,
            )

2166
2167
2168
2169
2170
2171
2172
        # If throughput mode is set, double max_num_batched_tokens and max_num_seqs.
        if self.performance_mode == "throughput":
            if orig_max_num_batched_tokens is None:
                self.max_num_batched_tokens *= 2
            if orig_max_num_seqs is None:
                self.max_num_seqs *= 2

2173
        if orig_max_num_batched_tokens is None:
2174
2175
2176
            assert model_config.max_model_len is not None, (
                "max_model_len must be set by this point"
            )
2177
2178
2179
2180
2181
2182
2183
2184
2185
2186
2187
2188
            if not self.enable_chunked_prefill:
                # If max_model_len is too short, use the default for higher throughput.
                self.max_num_batched_tokens = max(
                    model_config.max_model_len,
                    self.max_num_batched_tokens,
                )

            # When using default settings,
            # Ensure max_num_batched_tokens does not exceed model limit.
            # Some models (e.g., Whisper) have embeddings tied to max length.
            self.max_num_batched_tokens = min(
                self.max_num_seqs * model_config.max_model_len,
2189
2190
                self.max_num_batched_tokens,
            )
2191

2192
2193
2194
2195
            logger.debug(
                "Defaulting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens,
                usage_context.value if usage_context else None,
2196
            )
2197

2198
2199
2200
2201
        if orig_max_num_seqs is None:
            assert self.max_num_batched_tokens is not None  # For type checking
            self.max_num_seqs = min(self.max_num_seqs, self.max_num_batched_tokens)

2202
            logger.debug(
2203
                "Defaulting max_num_seqs to %d for %s usage context.",
2204
                self.max_num_seqs,
2205
                usage_context.value if usage_context else None,
2206
            )
2207

2208

2209
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
2210
class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
2211
    """Arguments for asynchronous vLLM engine."""
2212

2213
2214
    enable_log_requests: bool = False

2215
    @staticmethod
2216
2217
2218
    def add_cli_args(
        parser: FlexibleArgumentParser, async_args_only: bool = False
    ) -> FlexibleArgumentParser:
2219
        # Initialize plugin to update the parser, for example, The plugin may
2220
        # add a new kind of quantization method to --quantization argument or
2221
2222
        # a new device to --device argument.
        load_general_plugins()
2223
2224
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
2225
2226
2227
2228
        parser.add_argument(
            "--enable-log-requests",
            action=argparse.BooleanOptionalAction,
            default=AsyncEngineArgs.enable_log_requests,
2229
            help="Enable logging request information, dependent on log level:\n"
2230
2231
2232
            "- INFO: Request ID, parameters and LoRA request.\n"
            "- DEBUG: Prompt inputs (e.g: text, token IDs).\n"
            "You can set the minimum log level via `VLLM_LOGGING_LEVEL`.",
2233
        )
2234
        current_platform.pre_register_and_update(parser)
2235
        return parser
2236
2237


2238
2239
2240
2241
2242
2243
def _raise_unsupported_error(feature_name: str):
    msg = (
        f"{feature_name} is not supported. We recommend to "
        f"remove {feature_name} from your config."
    )
    raise NotImplementedError(msg)
2244
2245


2246
def human_readable_int(value: str) -> int:
2247
2248
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
2249

2250
2251
2252
2253
2254
2255
    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
2256

2257
    match = re.fullmatch(r"(\d+(?:\.\d+)?)([kKmMgGtT])", value)
2258
2259
    if match:
        decimal_multiplier = {
2260
2261
2262
            "k": 10**3,
            "m": 10**6,
            "g": 10**9,
2263
            "t": 10**12,
2264
2265
        }
        binary_multiplier = {
2266
2267
2268
            "K": 2**10,
            "M": 2**20,
            "G": 2**30,
2269
            "T": 2**40,
2270
2271
2272
2273
2274
2275
2276
2277
2278
2279
2280
2281
        }

        number, suffix = match.groups()
        if suffix in decimal_multiplier:
            mult = decimal_multiplier[suffix]
            return int(float(number) * mult)
        elif suffix in binary_multiplier:
            mult = binary_multiplier[suffix]
            # Do not allow decimals with binary multipliers
            try:
                return int(number) * mult
            except ValueError as e:
2282
2283
2284
2285
2286
                raise argparse.ArgumentTypeError(
                    "Decimals are not allowed "
                    f"with binary suffixes like {suffix}. Did you mean to use "
                    f"{number}{suffix.lower()} instead?"
                ) from e
2287
2288
2289

    # Regular plain number.
    return int(value)
2290
2291
2292
2293
2294
2295
2296
2297
2298
2299
2300
2301
2302
2303
2304
2305
2306
2307
2308


def human_readable_int_or_auto(value: str) -> int:
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
    Also accepts -1 or 'auto' as a special value for auto-detection.

    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    - '-1' or 'auto' -> -1 (special value for auto-detection)
    """
    value = value.strip()

    if value == "-1" or value.lower() == "auto":
        return -1

    return human_readable_int(value)