arg_utils.py 84.8 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
# yapf: disable
5
import argparse
6
import copy
7
import dataclasses
8
import functools
9
import json
10
import sys
11
import threading
12
from dataclasses import MISSING, dataclass, fields, is_dataclass
13
from itertools import permutations
14
15
16
from typing import (TYPE_CHECKING, Annotated, Any, Callable, Dict, List,
                    Literal, Optional, Type, TypeVar, Union, cast, get_args,
                    get_origin)
17

18
import huggingface_hub
19
import regex as re
20
import torch
21
from pydantic import TypeAdapter, ValidationError
22
from typing_extensions import TypeIs, deprecated
23

24
import vllm.envs as envs
25
from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
26
27
                         ConfigFormat, ConfigType, ConvertOption,
                         DecodingConfig, DetailedTraceModules, Device,
28
                         DeviceConfig, DistributedExecutorBackend, EPLBConfig,
29
30
                         GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LogprobsMode,
31
32
33
34
35
36
                         LoRAConfig, MambaDType, MMEncoderTPMode, ModelConfig,
                         ModelDType, ModelImpl, MultiModalConfig,
                         ObservabilityConfig, ParallelConfig, PoolerConfig,
                         PrefixCachingHashAlgo, RunnerOption, SchedulerConfig,
                         SchedulerPolicy, SpeculativeConfig, TaskOption,
                         TokenizerMode, VllmConfig, get_attr_docs, get_field)
37
from vllm.logger import init_logger
38
from vllm.platforms import CpuArchEnum, current_platform
39
from vllm.plugins import load_general_plugins
40
from vllm.ray.lazy_utils import is_ray_initialized
41
from vllm.reasoning import ReasoningParserManager
42
from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
43
from vllm.transformers_utils.config import get_model_path, is_interleaved
44
from vllm.transformers_utils.utils import check_gguf_file
45
from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
Rui Qiao's avatar
Rui Qiao committed
46
                        GiB_bytes, get_ip, is_in_ray_actor)
47
from vllm.v1.sample.logits_processor import LogitsProcessor
48
49

# yapf: enable
50

51
52
53
if TYPE_CHECKING:
    from vllm.executor.executor_base import ExecutorBase
    from vllm.model_executor.layers.quantization import QuantizationMethods
54
    from vllm.model_executor.model_loader import LoadFormats
55
56
57
58
    from vllm.usage.usage_lib import UsageContext
else:
    ExecutorBase = Any
    QuantizationMethods = Any
59
    LoadFormats = Any
60
61
    UsageContext = Any

62
63
logger = init_logger(__name__)

64
65
66
67
68
# object is used to allow for special typing forms
T = TypeVar("T")
TypeHint = Union[type[Any], object]
TypeHintT = Union[type[T], object]

69

70
def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
71

72
    def _parse_type(val: str) -> T:
73
74
75
76
77
        try:
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
78

79
80
81
82
83
84
85
86
87
88
89
    return _parse_type


def optional_type(
        return_type: Callable[[str], T]) -> Callable[[str], Optional[T]]:

    def _optional_type(val: str) -> Optional[T]:
        if val == "" or val == "None":
            return None
        return parse_type(return_type)(val)

90
    return _optional_type
91
92


93
def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
94
    if not re.match(r"(?s)^\s*{.*}\s*$", val):
95
        return str(val)
96
    return optional_type(json.loads)(val)
97
98


99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
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)


114
def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
115
116
117
118
    """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`.
    """
119
    type_hint = get_type(type_hints, Literal)
120
121
122
    options = get_args(type_hint)
    option_type = type(options[0])
    if not all(isinstance(option, option_type) for option in options):
123
        raise ValueError(
124
125
126
127
            "All options must be of the same type. "
            f"Got {options} with types {[type(c) for c in options]}")
    kwarg = "metavar" if contains_type(type_hints, str) else "choices"
    return {"type": option_type, kwarg: sorted(options)}
128
129


130
131
132
133
134
def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
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]))
    elif origin is Union:
        for arg in args:
            type_hints.update(get_type_hints(arg))
    else:
        type_hints.add(type_hint)

    return type_hints


152
153
154
155
def is_online_quantization(quantization: Any) -> bool:
    return quantization in ["inc"]


156
157
@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
158
159
160
    cls_docs = get_attr_docs(cls)
    kwargs = {}
    for field in fields(cls):
161
        # Get the set of possible types for the field
162
        type_hints: set[TypeHint] = get_type_hints(field.type)
163
164
165
166
167

        # 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)

168
        # Get the default value of the field
169
170
171
        if field.default is not MISSING:
            default = field.default
        elif field.default_factory is not MISSING:
172
            default = field.default_factory()
173
174
175

        # Get the help text for the field
        name = field.name
176
        help = cls_docs[name].strip()
177
178
179
180
181
182
183
        # 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
184
185
        json_tip = ("Should either be a valid JSON string or JSON keys passed "
                    "individually.")
186
        if dataclass_cls is not None:
187
188
189
190
191
192
193
194

            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
195
            kwargs[name]["help"] += f"\n\n{json_tip}"
196
        elif contains_type(type_hints, bool):
197
198
199
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
200
            kwargs[name].update(literal_to_kwargs(type_hints))
201
202
203
204
205
206
207
208
209
210
211
212
        elif contains_type(type_hints, tuple):
            type_hint = get_type(type_hints, tuple)
            types = get_args(type_hint)
            tuple_type = types[0]
            assert all(t is tuple_type for t in types if t is not Ellipsis), (
                "All non-Ellipsis tuple elements must be of the same "
                f"type. Got {types}.")
            kwargs[name]["type"] = tuple_type
            kwargs[name]["nargs"] = "+" if Ellipsis in types else len(types)
        elif contains_type(type_hints, list):
            type_hint = get_type(type_hints, list)
            types = get_args(type_hint)
213
214
215
216
217
218
            list_type = types[0]
            if get_origin(list_type) is Union:
                msg = "List type must contain str if it is a Union."
                assert str in get_args(list_type), msg
                list_type = str
            kwargs[name]["type"] = list_type
219
220
221
            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
222
            # Special case for large integers
223
            if name in {"max_model_len", "max_num_batched_tokens"}:
224
                kwargs[name]["type"] = human_readable_int
225
226
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
227
228
229
        elif (contains_type(type_hints, dict)
              and (contains_type(type_hints, str)
                   or any(is_not_builtin(th) for th in type_hints))):
230
            kwargs[name]["type"] = union_dict_and_str
231
        elif contains_type(type_hints, dict):
232
            kwargs[name]["type"] = parse_type(json.loads)
233
            kwargs[name]["help"] += f"\n\n{json_tip}"
234
235
236
237
238
239
240
        elif (contains_type(type_hints, str)
              or any(is_not_builtin(th) for th in type_hints)):
            kwargs[name]["type"] = str
        else:
            raise ValueError(
                f"Unsupported type {type_hints} for argument {name}.")

241
242
243
244
245
        # 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"]}))

246
247
248
249
250
251
252
        # 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
253
254


255
256
257
258
259
260
261
262
263
264
def get_kwargs(cls: ConfigType) -> dict[str, Any]:
    """Return argparse kwargs for the given Config dataclass.

    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))


265
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
266
class EngineArgs:
Woosuk Kwon's avatar
Woosuk Kwon committed
267
    """Arguments for vLLM engine."""
268
269
270
271
272
    model: str = ModelConfig.model
    served_model_name: Optional[Union[
        str, List[str]]] = ModelConfig.served_model_name
    tokenizer: Optional[str] = ModelConfig.tokenizer
    hf_config_path: Optional[str] = ModelConfig.hf_config_path
273
274
275
    runner: RunnerOption = ModelConfig.runner
    convert: ConvertOption = ModelConfig.convert
    task: Optional[TaskOption] = ModelConfig.task
276
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
277
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
278
279
280
    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
281
    download_dir: Optional[str] = LoadConfig.download_dir
282
    load_format: Union[str, LoadFormats] = LoadConfig.load_format
283
284
    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
285
    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
286
287
    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
288
289
    cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
                                            "cuda_graph_sizes")
290
291
292
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
293
    distributed_executor_backend: Optional[Union[
294
295
        DistributedExecutorBackend,
        Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
296
    # number of P/D disaggregation (or other disaggregation) workers
297
298
299
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
    data_parallel_size: int = ParallelConfig.data_parallel_size
300
    data_parallel_rank: Optional[int] = None
301
    data_parallel_start_rank: Optional[int] = None
302
303
304
    data_parallel_size_local: Optional[int] = None
    data_parallel_address: Optional[str] = None
    data_parallel_rpc_port: Optional[int] = None
305
    data_parallel_hybrid_lb: bool = False
Rui Qiao's avatar
Rui Qiao committed
306
    data_parallel_backend: str = ParallelConfig.data_parallel_backend
307
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
308
    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
309
    enable_eplb: bool = ParallelConfig.enable_eplb
310
311
312
313
    num_redundant_experts: int = EPLBConfig.num_redundant_experts
    eplb_window_size: int = EPLBConfig.window_size
    eplb_step_interval: int = EPLBConfig.step_interval
    eplb_log_balancedness: bool = EPLBConfig.log_balancedness
314
315
    max_parallel_loading_workers: Optional[
        int] = ParallelConfig.max_parallel_loading_workers
316
317
318
319
    block_size: Optional[BlockSize] = CacheConfig.block_size
    enable_prefix_caching: Optional[bool] = CacheConfig.enable_prefix_caching
    prefix_caching_hash_algo: PrefixCachingHashAlgo = \
        CacheConfig.prefix_caching_hash_algo
320
321
    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
322
323
324
    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
325
326
327
328
329
330
331
    max_num_batched_tokens: Optional[
        int] = SchedulerConfig.max_num_batched_tokens
    max_num_partial_prefills: int = SchedulerConfig.max_num_partial_prefills
    max_long_partial_prefills: int = SchedulerConfig.max_long_partial_prefills
    long_prefill_token_threshold: int = \
        SchedulerConfig.long_prefill_token_threshold
    max_num_seqs: Optional[int] = SchedulerConfig.max_num_seqs
332
    max_logprobs: int = ModelConfig.max_logprobs
333
    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
334
    disable_log_stats: bool = False
335
336
337
338
339
    revision: Optional[str] = ModelConfig.revision
    code_revision: Optional[str] = ModelConfig.code_revision
    rope_scaling: dict[str, Any] = get_field(ModelConfig, "rope_scaling")
    rope_theta: Optional[float] = ModelConfig.rope_theta
    hf_token: Optional[Union[bool, str]] = ModelConfig.hf_token
340
    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
341
342
343
344
    tokenizer_revision: Optional[str] = ModelConfig.tokenizer_revision
    quantization: Optional[QuantizationMethods] = ModelConfig.quantization
    enforce_eager: bool = ModelConfig.enforce_eager
    max_seq_len_to_capture: int = ModelConfig.max_seq_len_to_capture
345
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
346
    limit_mm_per_prompt: dict[str, int] = \
347
        get_field(MultiModalConfig, "limit_per_prompt")
348
    interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings
349
350
351
    media_io_kwargs: dict[str, dict[str,
                                    Any]] = get_field(MultiModalConfig,
                                                      "media_io_kwargs")
352
353
    mm_processor_kwargs: Optional[Dict[str, Any]] = \
        MultiModalConfig.mm_processor_kwargs
354
355
    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
    mm_processor_cache_gb: int = MultiModalConfig.mm_processor_cache_gb
356
    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
357
    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
358
    # LoRA fields
359
    enable_lora: bool = False
360
361
362
    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
363
364
    default_mm_loras: Optional[Dict[str, str]] = \
        LoRAConfig.default_mm_loras
365
366
367
368
369
    fully_sharded_loras: bool = LoRAConfig.fully_sharded_loras
    max_cpu_loras: Optional[int] = LoRAConfig.max_cpu_loras
    lora_dtype: Optional[Union[str, torch.dtype]] = LoRAConfig.lora_dtype
    lora_extra_vocab_size: int = LoRAConfig.lora_extra_vocab_size

370
    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
371
372
    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
373
    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
374
375
    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
376
377
    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
378
    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
379

380
381
382
383
    scheduler_delay_factor: float = SchedulerConfig.delay_factor
    enable_chunked_prefill: Optional[
        bool] = SchedulerConfig.enable_chunked_prefill
    disable_chunked_mm_input: bool = SchedulerConfig.disable_chunked_mm_input
384

385
386
387
    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager)

388
389
390
391
392
393
    guided_decoding_backend: GuidedDecodingBackend = DecodingConfig.backend
    guided_decoding_disable_fallback: bool = DecodingConfig.disable_fallback
    guided_decoding_disable_any_whitespace: bool = \
        DecodingConfig.disable_any_whitespace
    guided_decoding_disable_additional_properties: bool = \
        DecodingConfig.disable_additional_properties
394
395
    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern
396

397
    speculative_config: Optional[Dict[str, Any]] = None
398

399
400
401
402
403
404
    show_hidden_metrics_for_version: Optional[str] = \
        ObservabilityConfig.show_hidden_metrics_for_version
    otlp_traces_endpoint: Optional[str] = \
        ObservabilityConfig.otlp_traces_endpoint
    collect_detailed_traces: Optional[list[DetailedTraceModules]] = \
        ObservabilityConfig.collect_detailed_traces
405
    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
406
407
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
408

409
410
411
412
    override_neuron_config: dict[str, Any] = \
        get_field(ModelConfig, "override_neuron_config")
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
413
414
    compilation_config: CompilationConfig = \
        get_field(VllmConfig, "compilation_config")
415
416
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
417

418
    kv_transfer_config: Optional[KVTransferConfig] = None
419
    kv_events_config: Optional[KVEventsConfig] = None
420

421
422
423
424
425
    generation_config: str = ModelConfig.generation_config
    enable_sleep_mode: bool = ModelConfig.enable_sleep_mode
    override_generation_config: dict[str, Any] = \
        get_field(ModelConfig, "override_generation_config")
    model_impl: str = ModelConfig.model_impl
426
    override_attention_dtype: str = ModelConfig.override_attention_dtype
427

428
    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
429
430
    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
431

432
433
    additional_config: dict[str, Any] = \
        get_field(VllmConfig, "additional_config")
434
435
    reasoning_parser: str = DecodingConfig.reasoning_backend

436
    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
437
    pt_load_map_location: str = LoadConfig.pt_load_map_location
438

439
440
    # DEPRECATED
    enable_multimodal_encoder_data_parallel: bool = False
441

442
443
444
445
    logits_processors: Optional[list[Union[
        str, type[LogitsProcessor]]]] = ModelConfig.logits_processors
    """Custom logitproc types"""

446
447
    async_scheduling: bool = SchedulerConfig.async_scheduling

448
449
450
    kv_sharing_fast_prefill: bool = \
        CacheConfig.kv_sharing_fast_prefill

451
    def __post_init__(self):
452
453
454
        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
455
456
457
        if isinstance(self.compilation_config, dict):
            self.compilation_config = CompilationConfig(
                **self.compilation_config)
458
459
460
        if isinstance(self.eplb_config, dict):
            self.eplb_config = EPLBConfig.from_cli(json.dumps(
                self.eplb_config))
461
        # Setup plugins
462
463
        from vllm.plugins import load_general_plugins
        load_general_plugins()
464
465
466
467
468
469
470
        # when use hf offline,replace model id to local model path
        if huggingface_hub.constants.HF_HUB_OFFLINE:
            model_id = self.model
            self.model = get_model_path(self.model, self.revision)
            logger.info(
                "HF_HUB_OFFLINE is True, replace model_id [%s] " \
                "to model_path [%s]",model_id, self.model)
471
472

    @staticmethod
473
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
Woosuk Kwon's avatar
Woosuk Kwon committed
474
        """Shared CLI arguments for vLLM engine."""
475

476
        # Model arguments
477
478
479
480
481
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
Reid's avatar
Reid committed
482
        if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
483
            model_group.add_argument("--model", **model_kwargs["model"])
484
485
486
487
488
        model_group.add_argument("--runner", **model_kwargs["runner"])
        model_group.add_argument("--convert", **model_kwargs["convert"])
        model_group.add_argument("--task",
                                 **model_kwargs["task"],
                                 deprecated=True)
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
        model_group.add_argument("--tokenizer", **model_kwargs["tokenizer"])
        model_group.add_argument("--tokenizer-mode",
                                 **model_kwargs["tokenizer_mode"])
        model_group.add_argument("--trust-remote-code",
                                 **model_kwargs["trust_remote_code"])
        model_group.add_argument("--dtype", **model_kwargs["dtype"])
        model_group.add_argument("--seed", **model_kwargs["seed"])
        model_group.add_argument("--hf-config-path",
                                 **model_kwargs["hf_config_path"])
        model_group.add_argument("--allowed-local-media-path",
                                 **model_kwargs["allowed_local_media_path"])
        model_group.add_argument("--revision", **model_kwargs["revision"])
        model_group.add_argument("--code-revision",
                                 **model_kwargs["code_revision"])
        model_group.add_argument("--rope-scaling",
                                 **model_kwargs["rope_scaling"])
        model_group.add_argument("--rope-theta", **model_kwargs["rope_theta"])
        model_group.add_argument("--tokenizer-revision",
                                 **model_kwargs["tokenizer_revision"])
        model_group.add_argument("--max-model-len",
                                 **model_kwargs["max_model_len"])
        model_group.add_argument("--quantization", "-q",
                                 **model_kwargs["quantization"])
        model_group.add_argument("--enforce-eager",
                                 **model_kwargs["enforce_eager"])
        model_group.add_argument("--max-seq-len-to-capture",
                                 **model_kwargs["max_seq_len_to_capture"])
        model_group.add_argument("--max-logprobs",
                                 **model_kwargs["max_logprobs"])
518
        model_group.add_argument("--logprobs-mode",
519
                                 choices=[f.value for f in LogprobsMode],
520
                                 **model_kwargs["logprobs_mode"])
521
522
523
524
525
526
        model_group.add_argument("--disable-sliding-window",
                                 **model_kwargs["disable_sliding_window"])
        model_group.add_argument("--disable-cascade-attn",
                                 **model_kwargs["disable_cascade_attn"])
        model_group.add_argument("--skip-tokenizer-init",
                                 **model_kwargs["skip_tokenizer_init"])
527
528
        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
        model_group.add_argument("--served-model-name",
                                 **model_kwargs["served_model_name"])
        # This one is a special case because it is the
        # opposite of ModelConfig.use_async_output_proc
        model_group.add_argument(
            "--disable-async-output-proc",
            action="store_true",
            default=EngineArgs.disable_async_output_proc,
            help="Disable async output processing. This may result in "
            "lower performance.")
        model_group.add_argument("--config-format",
                                 choices=[f.value for f in ConfigFormat],
                                 **model_kwargs["config_format"])
        # This one is a special case because it can bool
        # or str. TODO: Handle this in get_kwargs
        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("--override-neuron-config",
                                 **model_kwargs["override_neuron_config"])
        model_group.add_argument("--override-pooler-config",
                                 **model_kwargs["override_pooler_config"])
        model_group.add_argument("--logits-processor-pattern",
                                 **model_kwargs["logits_processor_pattern"])
        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"])
        model_group.add_argument("--model-impl",
                                 choices=[f.value for f in ModelImpl],
                                 **model_kwargs["model_impl"])
567
568
        model_group.add_argument("--override-attention-dtype",
                                 **model_kwargs["override_attention_dtype"])
569
570
        model_group.add_argument("--logits-processors",
                                 **model_kwargs["logits_processors"])
571

572
573
574
575
576
577
        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
578
        load_group.add_argument("--load-format", **load_kwargs["load_format"])
579
        load_group.add_argument("--download-dir",
580
                                **load_kwargs["download_dir"])
581
        load_group.add_argument("--model-loader-extra-config",
582
                                **load_kwargs["model_loader_extra_config"])
583
584
585
        load_group.add_argument("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
586
                                **load_kwargs["use_tqdm_on_load"])
587
588
        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])
589

590
591
592
593
594
595
        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
596
597
        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
598
        guided_decoding_group.add_argument(
599
600
601
602
603
604
605
606
            "--guided-decoding-disable-fallback",
            **guided_decoding_kwargs["disable_fallback"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-any-whitespace",
            **guided_decoding_kwargs["disable_any_whitespace"])
        guided_decoding_group.add_argument(
            "--guided-decoding-disable-additional-properties",
            **guided_decoding_kwargs["disable_additional_properties"])
607
608
609
610
611
612
        guided_decoding_group.add_argument(
            "--reasoning-parser",
            # This choices is a special case because it's not static
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

613
        # Parallel arguments
614
615
616
617
618
619
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
620
            "--distributed-executor-backend",
621
622
            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
623
            "--pipeline-parallel-size", "-pp",
624
            **parallel_kwargs["pipeline_parallel_size"])
625
        parallel_group.add_argument("--tensor-parallel-size", "-tp",
626
                                    **parallel_kwargs["tensor_parallel_size"])
627
        parallel_group.add_argument("--data-parallel-size", "-dp",
628
                                    **parallel_kwargs["data_parallel_size"])
629
630
631
632
633
634
        parallel_group.add_argument(
            '--data-parallel-rank',
            '-dpn',
            type=int,
            help='Data parallel rank of this instance. '
            'When set, enables external load balancer mode.')
635
636
637
638
639
        parallel_group.add_argument('--data-parallel-start-rank',
                                    '-dpr',
                                    type=int,
                                    help='Starting data parallel rank '
                                    'for secondary nodes.')
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
        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.')
Rui Qiao's avatar
Rui Qiao committed
655
656
657
658
659
660
        parallel_group.add_argument('--data-parallel-backend',
                                    '-dpb',
                                    type=str,
                                    default='mp',
                                    help='Backend for data parallel, either '
                                    '"mp" or "ray".')
661
662
663
        parallel_group.add_argument(
            "--data-parallel-hybrid-lb",
            **parallel_kwargs["data_parallel_hybrid_lb"])
664
        parallel_group.add_argument(
665
            "--enable-expert-parallel",
666
            **parallel_kwargs["enable_expert_parallel"])
667
668
        parallel_group.add_argument("--enable-eplb",
                                    **parallel_kwargs["enable_eplb"])
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
        parallel_group.add_argument("--eplb-config",
                                    **parallel_kwargs["eplb_config"])
        parallel_group.add_argument(
            "--num-redundant-experts",
            type=int,
            help=
            "[DEPRECATED] --num-redundant-experts will be removed in v0.12.0.",
            deprecated=True)
        parallel_group.add_argument(
            "--eplb-window-size",
            type=int,
            help="[DEPRECATED] --eplb-window-size will be removed in v0.12.0.",
            deprecated=True)
        parallel_group.add_argument(
            "--eplb-step-interval",
            type=int,
            help=
            "[DEPRECATED] --eplb-step-interval will be removed in v0.12.0.",
            deprecated=True)
        parallel_group.add_argument(
            "--eplb-log-balancedness",
            action=argparse.BooleanOptionalAction,
            help=
            "[DEPRECATED] --eplb-log-balancedness will be removed in v0.12.0.",
            deprecated=True)

695
        parallel_group.add_argument(
696
            "--max-parallel-loading-workers",
697
698
            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
699
            "--ray-workers-use-nsight",
700
701
            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
702
            "--disable-custom-all-reduce",
703
            **parallel_kwargs["disable_custom_all_reduce"])
704
705
706
707
        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
708
709
        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
710
711
            action="store_true",
            deprecated=True)
712

713
714
715
716
717
        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
718
        )
719
720
        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
        cache_group.add_argument("--gpu-memory-utilization",
721
                                 **cache_kwargs["gpu_memory_utilization"])
722
723
        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
724
                                 **cache_kwargs["cache_dtype"])
725
        cache_group.add_argument("--num-gpu-blocks-override",
726
727
728
729
730
                                 **cache_kwargs["num_gpu_blocks_override"])
        cache_group.add_argument("--enable-prefix-caching",
                                 **cache_kwargs["enable_prefix_caching"])
        cache_group.add_argument("--prefix-caching-hash-algo",
                                 **cache_kwargs["prefix_caching_hash_algo"])
731
        cache_group.add_argument("--cpu-offload-gb",
732
                                 **cache_kwargs["cpu_offload_gb"])
733
        cache_group.add_argument("--calculate-kv-scales",
734
                                 **cache_kwargs["calculate_kv_scales"])
735
736
        cache_group.add_argument("--kv-sharing-fast-prefill",
                                 **cache_kwargs["kv_sharing_fast_prefill"])
737
738
739
740
        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"])
741

742
        # Multimodal related configs
743
744
745
746
747
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
748
        multimodal_group.add_argument("--limit-mm-per-prompt",
749
                                      **multimodal_kwargs["limit_per_prompt"])
750
751
        multimodal_group.add_argument("--media-io-kwargs",
                                      **multimodal_kwargs["media_io_kwargs"])
752
        multimodal_group.add_argument(
753
            "--mm-processor-kwargs",
754
755
            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
756
757
758
            "--mm-processor-cache-gb",
            **multimodal_kwargs["mm_processor_cache_gb"])
        multimodal_group.add_argument("--disable-mm-preprocessor-cache",
759
                                      action="store_true",
760
                                      deprecated=True)
761
762
        multimodal_group.add_argument(
            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"])
763
764
765
        multimodal_group.add_argument(
            "--interleave-mm-strings",
            **multimodal_kwargs["interleave_mm_strings"])
766
767
        multimodal_group.add_argument("--skip-mm-profiling",
                                      **multimodal_kwargs["skip_mm_profiling"])
768

769
        # LoRA related configs
770
771
772
773
774
775
        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
776
            "--enable-lora",
777
            action=argparse.BooleanOptionalAction,
778
779
            help="If True, enable handling of LoRA adapters.")
        lora_group.add_argument("--enable-lora-bias",
780
                                **lora_kwargs["bias_enabled"])
781
782
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
783
                                **lora_kwargs["max_lora_rank"])
784
        lora_group.add_argument("--lora-extra-vocab-size",
785
786
                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
787
            "--lora-dtype",
788
789
            **lora_kwargs["lora_dtype"],
        )
790
        lora_group.add_argument("--max-cpu-loras",
791
                                **lora_kwargs["max_cpu_loras"])
792
        lora_group.add_argument("--fully-sharded-loras",
793
                                **lora_kwargs["fully_sharded_loras"])
794
795
        lora_group.add_argument("--default-mm-loras",
                                **lora_kwargs["default_mm_loras"])
796

797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
        # 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",
            **observability_kwargs["show_hidden_metrics_for_version"])
        observability_group.add_argument(
            "--otlp-traces-endpoint",
            **observability_kwargs["otlp_traces_endpoint"])
        # 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"] += [
            ",".join(p)
            for p in permutations(get_args(DetailedTraceModules), r=2)
        ]
        observability_group.add_argument(
            "--collect-detailed-traces",
            **observability_kwargs["collect_detailed_traces"])
820

821
822
823
824
825
826
827
        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
828
            "--max-num-batched-tokens",
829
            **scheduler_kwargs["max_num_batched_tokens"])
830
        scheduler_group.add_argument("--max-num-seqs",
831
832
833
834
835
836
837
                                     **scheduler_kwargs["max_num_seqs"])
        scheduler_group.add_argument(
            "--max-num-partial-prefills",
            **scheduler_kwargs["max_num_partial_prefills"])
        scheduler_group.add_argument(
            "--max-long-partial-prefills",
            **scheduler_kwargs["max_long_partial_prefills"])
838
839
        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
840
841
842
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
843
        scheduler_group.add_argument("--num-lookahead-slots",
844
                                     **scheduler_kwargs["num_lookahead_slots"])
845
        scheduler_group.add_argument("--scheduler-delay-factor",
846
                                     **scheduler_kwargs["delay_factor"])
847
        scheduler_group.add_argument("--preemption-mode",
848
                                     **scheduler_kwargs["preemption_mode"])
849
850
        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
851
        scheduler_group.add_argument("--scheduling-policy",
852
                                     **scheduler_kwargs["policy"])
853
        scheduler_group.add_argument(
854
            "--enable-chunked-prefill",
855
            **scheduler_kwargs["enable_chunked_prefill"])
856
857
858
        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
859
860
        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])
861
862
863
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"])
864
865
        scheduler_group.add_argument("--async-scheduling",
                                     **scheduler_kwargs["async_scheduling"])
866
867

        # vLLM arguments
868
        vllm_kwargs = get_kwargs(VllmConfig)
869
870
871
872
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
873
874
875
876
        # 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)
877
878
        vllm_group.add_argument("--speculative-config",
                                **vllm_kwargs["speculative_config"])
879
880
881
882
883
884
885
886
        vllm_group.add_argument("--kv-transfer-config",
                                **vllm_kwargs["kv_transfer_config"])
        vllm_group.add_argument('--kv-events-config',
                                **vllm_kwargs["kv_events_config"])
        vllm_group.add_argument("--compilation-config", "-O",
                                **vllm_kwargs["compilation_config"])
        vllm_group.add_argument("--additional-config",
                                **vllm_kwargs["additional_config"])
887

888
889
890
891
        # Other arguments
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
892

893
        return parser
894
895

    @classmethod
896
    def from_cli_args(cls, args: argparse.Namespace):
897
898
899
        # Get the list of attributes of this dataclass.
        attrs = [attr.name for attr in dataclasses.fields(cls)]
        # Set the attributes from the parsed arguments.
Zhuohan Li's avatar
Zhuohan Li committed
900
901
        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args
902

903
    def create_model_config(self) -> ModelConfig:
904
905
906
907
908
909
        # gguf file needs a specific model loader and doesn't use hf_repo
        if check_gguf_file(self.model):
            self.quantization = self.load_format = "gguf"

        # NOTE: This is to allow model loading from S3 in CI
        if (not isinstance(self, AsyncEngineArgs) and envs.VLLM_CI_USE_S3
910
                and self.model in MODELS_ON_S3 and self.load_format == "auto"):
911
            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
912
            self.load_format = "runai_streamer"
913

914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
        if self.disable_mm_preprocessor_cache:
            logger.warning(
                "`--disable-mm-preprocessor-cache` is deprecated "
                "and will be removed in v0.13. "
                "Please use `--mm-processor-cache-gb 0` instead.", )

            self.mm_processor_cache_gb = 0
        elif envs.VLLM_MM_INPUT_CACHE_GIB != 4:
            logger.warning(
                "VLLM_MM_INPUT_CACHE_GIB` is deprecated "
                "and will be removed in v0.13. "
                "Please use `--mm-processor-cache-gb %d` instead.",
                envs.VLLM_MM_INPUT_CACHE_GIB,
            )

            self.mm_processor_cache_gb = envs.VLLM_MM_INPUT_CACHE_GIB

931
932
933
934
935
936
937
938
        if self.enable_multimodal_encoder_data_parallel:
            logger.warning(
                "--enable-multimodal-encoder-data-parallel` is deprecated "
                "and will be removed in v0.13. "
                "Please use `--mm-encoder-tp-mode data` instead.")

            self.mm_encoder_tp_mode = "data"

939
        return ModelConfig(
940
            model=self.model,
941
            hf_config_path=self.hf_config_path,
942
943
            runner=self.runner,
            convert=self.convert,
944
            task=self.task,
945
            tokenizer=self.tokenizer,
946
947
            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
948
            allowed_local_media_path=self.allowed_local_media_path,
949
950
951
952
953
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
954
            rope_theta=self.rope_theta,
955
            hf_token=self.hf_token,
956
            hf_overrides=self.hf_overrides,
957
958
959
960
961
962
            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
963
            logprobs_mode=self.logprobs_mode,
964
            disable_sliding_window=self.disable_sliding_window,
965
            disable_cascade_attn=self.disable_cascade_attn,
966
            skip_tokenizer_init=self.skip_tokenizer_init,
967
            enable_prompt_embeds=self.enable_prompt_embeds,
968
            served_model_name=self.served_model_name,
969
            limit_mm_per_prompt=self.limit_mm_per_prompt,
970
            interleave_mm_strings=self.interleave_mm_strings,
971
            media_io_kwargs=self.media_io_kwargs,
972
            skip_mm_profiling=self.skip_mm_profiling,
973
            use_async_output_proc=not self.disable_async_output_proc,
974
            config_format=self.config_format,
975
            mm_processor_kwargs=self.mm_processor_kwargs,
976
            mm_processor_cache_gb=self.mm_processor_cache_gb,
977
            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
978
979
            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
980
            logits_processor_pattern=self.logits_processor_pattern,
981
            generation_config=self.generation_config,
982
            override_generation_config=self.override_generation_config,
983
            enable_sleep_mode=self.enable_sleep_mode,
984
            model_impl=self.model_impl,
985
            override_attention_dtype=self.override_attention_dtype,
986
            logits_processors=self.logits_processors,
987
        )
988

989
990
991
992
993
994
995
    def validate_tensorizer_args(self):
        from vllm.model_executor.model_loader.tensorizer import (
            TensorizerConfig)
        for key in self.model_loader_extra_config:
            if key in TensorizerConfig._fields:
                self.model_loader_extra_config["tensorizer_config"][
                    key] = self.model_loader_extra_config[key]
996

997
998
    def create_load_config(self) -> LoadConfig:

999
1000
        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
1001

1002
1003
1004
1005
1006
1007
1008
1009
        if self.load_format == "tensorizer":
            if hasattr(self.model_loader_extra_config, "to_serializable"):
                self.model_loader_extra_config = (
                    self.model_loader_extra_config.to_serializable())
            self.model_loader_extra_config["tensorizer_config"] = {}
            self.model_loader_extra_config["tensorizer_config"][
                "tensorizer_dir"] = self.model
            self.validate_tensorizer_args()
1010

1011
1012
1013
        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
1014
1015
            device="cpu"
            if is_online_quantization(self.quantization) else None,
1016
1017
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
1018
            use_tqdm_on_load=self.use_tqdm_on_load,
1019
            pt_load_map_location=self.pt_load_map_location,
1020
        )
1021

1022
1023
1024
1025
1026
1027
1028
1029
1030
1031
1032
1033
1034
    def create_speculative_config(
        self,
        target_model_config: ModelConfig,
        target_parallel_config: ParallelConfig,
        enable_chunked_prefill: bool,
        disable_log_stats: bool,
    ) -> Optional["SpeculativeConfig"]:
        """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
1035
        dictionary from the engine.
1036
        """
1037
1038
1039
1040
1041

        from vllm.transformers_utils.config import get_config
        from vllm.transformers_utils.configs.speculators.base import (
            SpeculatorsConfig)

1042
        if self.speculative_config is None:
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
            hf_config = get_config(self.hf_config_path or self.model,
                                   self.trust_remote_code, self.revision,
                                   self.code_revision, self.config_format)

            # if loading a SpeculatorsConfig, load the specualtive_config
            # details from the config directly
            # no user input required / expected
            if isinstance(hf_config, SpeculatorsConfig):
                # We create one since we dont create one
                self.speculative_config = {}
                self.speculative_config[
                    "num_speculative_tokens"] = hf_config.num_lookahead_tokens
                self.speculative_config["model"] = self.model
                self.speculative_config["method"] = hf_config.method
            else:
                return None
1059

1060
1061
1062
1063
1064
1065
1066
1067
1068
        # Note(Shangming): These parameters are not obtained from the cli arg
        # '--speculative-config' and must be passed in when creating the engine
        # config.
        self.speculative_config.update({
            "target_model_config": target_model_config,
            "target_parallel_config": target_parallel_config,
            "enable_chunked_prefill": enable_chunked_prefill,
            "disable_log_stats": disable_log_stats,
        })
1069
        return SpeculativeConfig(**self.speculative_config)
1070

1071
1072
1073
    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
1074
        headless: bool = False,
1075
1076
1077
1078
1079
1080
1081
    ) -> VllmConfig:
        """
        Create the VllmConfig.

        NOTE: for autoselection of V0 vs V1 engine, we need to
        create the ModelConfig first, since ModelConfig's attrs
        (e.g. the model arch) are needed to make the decision.
Simon Mo's avatar
Simon Mo committed
1082

1083
1084
1085
1086
1087
1088
        This function set VLLM_USE_V1=X if VLLM_USE_V1 is
        unspecified by the user.

        If VLLM_USE_V1 is specified by the user but the VllmConfig
        is incompatible, we raise an error.
        """
1089
        current_platform.pre_register_and_update()
1090

1091
1092
        device_config = DeviceConfig(
            device=cast(Device, current_platform.device_type))
1093
1094
        model_config = self.create_model_config()

1095
1096
1097
1098
1099
1100
1101
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
        # * If VLLM_USE_V1 is unset, we enable V1 for "supported features"
        #   and fall back to V0 for experimental or unsupported features.
        # * If VLLM_USE_V1=1, we enable V1 for supported + experimental
        #   features and raise error for unsupported features.
        # * If VLLM_USE_V1=0, we disable V1.
        use_v1 = False
        try_v1 = envs.VLLM_USE_V1 or not envs.is_set("VLLM_USE_V1")
        if try_v1 and self._is_v1_supported_oracle(model_config):
            use_v1 = True

        # If user explicitly set VLLM_USE_V1, sanity check we respect it.
        if envs.is_set("VLLM_USE_V1"):
            assert use_v1 == envs.VLLM_USE_V1
        # Otherwise, set the VLLM_USE_V1 variable globally.
        else:
            envs.set_vllm_use_v1(use_v1)

        # Set default arguments for V0 or V1 Engine.
        if use_v1:
1114
            self._set_default_args_v1(usage_context, model_config)
1115
            # Disable chunked prefill for POWER (ppc64le)/ARM/s390x CPUs in V1
1116
1117
            if current_platform.is_cpu(
            ) and current_platform.get_cpu_architecture() in (
1118
                    CpuArchEnum.POWERPC, CpuArchEnum.S390X, CpuArchEnum.ARM):
1119
                logger.info(
1120
1121
                    "Chunked prefill is not supported for ARM and POWER "
                    "and S390X CPUs; "
1122
1123
                    "disabling it for V1 backend.")
                self.enable_chunked_prefill = False
1124
1125
        else:
            self._set_default_args_v0(model_config)
1126
1127
        assert self.enable_chunked_prefill is not None

1128
1129
1130
1131
1132
1133
1134
1135
1136
1137
1138
        if envs.VLLM_ATTENTION_BACKEND in [STR_DUAL_CHUNK_FLASH_ATTN_VAL]:
            assert self.enforce_eager, (
                "Cuda graph is not supported with DualChunkFlashAttention. "
                "To run the model in eager mode, set 'enforce_eager=True' "
                "or use '--enforce-eager' in the CLI.")
            assert current_platform.is_cuda(), (
                "DualChunkFlashAttention is only supported on CUDA platform.")
            assert not use_v1, (
                "DualChunkFlashAttention is not supported on V1 engine. "
                "To run the model in V0 engine, try set 'VLLM_USE_V1=0'")

1139
1140
1141
1142
1143
1144
1145
        sliding_window: Optional[int] = None
        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()

1146
        cache_config = CacheConfig(
1147
            block_size=self.block_size,
1148
1149
1150
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
1151
            is_attention_free=model_config.is_attention_free,
1152
            num_gpu_blocks_override=self.num_gpu_blocks_override,
1153
            sliding_window=sliding_window,
1154
            enable_prefix_caching=self.enable_prefix_caching,
1155
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
1156
            cpu_offload_gb=self.cpu_offload_gb,
1157
            calculate_kv_scales=self.calculate_kv_scales,
1158
            kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
1159
1160
            mamba_cache_dtype=self.mamba_cache_dtype,
            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
1161
        )
1162

1163
1164
1165
1166
1167
1168
1169
1170
1171
        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
            ray_runtime_env = ray.get_runtime_context().runtime_env
            logger.info("Using ray runtime env: %s", ray_runtime_env)

1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
        # 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()

1183
1184
1185
1186
        assert not headless or not self.data_parallel_hybrid_lb, (
            "data_parallel_hybrid_lb is not applicable in "
            "headless mode")

1187
        data_parallel_external_lb = self.data_parallel_rank is not None
1188
        # Local DP rank = 1, use pure-external LB.
1189
1190
1191
1192
1193
        if data_parallel_external_lb:
            assert self.data_parallel_size_local in (1, None), (
                "data_parallel_size_local must be 1 when data_parallel_rank "
                "is set")
            data_parallel_size_local = 1
1194
1195
            # Use full external lb if we have local_size of 1.
            self.data_parallel_hybrid_lb = False
1196
1197
        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212

            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.
                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

            self.data_parallel_rank = self.data_parallel_start_rank or 0
1213
        else:
1214
1215
1216
1217
            assert not self.data_parallel_hybrid_lb, (
                "data_parallel_size_local must be set to use "
                "data_parallel_hybrid_lb.")

1218
1219
            # Local DP size defaults to global DP size if not set.
            data_parallel_size_local = self.data_parallel_size
1220
1221
1222

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
Rui Qiao's avatar
Rui Qiao committed
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
        if self.data_parallel_address is None:
            if self.data_parallel_backend == "ray":
                host_ip = get_ip()
                logger.info(
                    "Using host IP %s as ray-based data parallel address",
                    host_ip)
                data_parallel_address = host_ip
            else:
                assert self.data_parallel_backend == "mp", (
                    "data_parallel_backend can only be ray or mp, got %s",
                    self.data_parallel_backend)
                data_parallel_address = ParallelConfig.data_parallel_master_ip
        else:
            data_parallel_address = self.data_parallel_address
1237
1238
1239
1240
1241
1242
1243

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

1244
1245
1246
1247
1248
1249
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
        if self.async_scheduling:
            # Async scheduling does not work with the uniprocess backend.
            if self.distributed_executor_backend is None:
                self.distributed_executor_backend = "mp"
                logger.info("Using mp-based distributed executor backend "
                            "for async scheduling.")
            if self.distributed_executor_backend == "uni":
                raise ValueError("Async scheduling is not supported with "
                                 "uni-process backend.")
            if self.pipeline_parallel_size > 1:
                raise ValueError("Async scheduling is not supported with "
                                 "pipeline-parallel-size > 1.")

            # Currently, async scheduling does not support speculative decoding.
            # TODO(woosuk): Support it.
            if self.speculative_config is not None:
                raise ValueError(
                    "Currently, speculative decoding is not supported with "
                    "async scheduling.")

1264
1265
1266
1267
1268
1269
1270
1271
1272
1273
        # Forward the deprecated CLI args to the EPLB config.
        if self.num_redundant_experts is not None:
            self.eplb_config.num_redundant_experts = self.num_redundant_experts
        if self.eplb_window_size is not None:
            self.eplb_config.window_size = self.eplb_window_size
        if self.eplb_step_interval is not None:
            self.eplb_config.step_interval = self.eplb_step_interval
        if self.eplb_log_balancedness is not None:
            self.eplb_config.log_balancedness = self.eplb_log_balancedness

1274
        parallel_config = ParallelConfig(
1275
1276
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
1277
            data_parallel_size=self.data_parallel_size,
1278
1279
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
1280
1281
1282
            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
1283
            data_parallel_backend=self.data_parallel_backend,
1284
            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
1285
            enable_expert_parallel=self.enable_expert_parallel,
1286
            enable_eplb=self.enable_eplb,
1287
            eplb_config=self.eplb_config,
1288
1289
1290
            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,
1291
            ray_runtime_env=ray_runtime_env,
1292
            placement_group=placement_group,
1293
1294
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1295
            worker_extension_cls=self.worker_extension_cls,
1296
        )
1297

1298
1299
1300
1301
        if model_config.is_multimodal_model:
            dp_supports_mm_processor_cache = (self.data_parallel_size == 1
                                              or data_parallel_external_lb)
            if (not dp_supports_mm_processor_cache
1302
                    and model_config.mm_processor_cache_gb > 0):
1303
1304
1305
1306
1307
                logger.warning(
                    "Multi-modal processor cache is disabled because "
                    "it is not compatible with data parallelism when "
                    "there does not exist a one-to-one correspondance "
                    "between API and engine core processes.")
1308
                model_config.set_mm_processor_cache_gb(0)
1309

1310
        speculative_config = self.create_speculative_config(
1311
1312
            target_model_config=model_config,
            target_parallel_config=parallel_config,
1313
            enable_chunked_prefill=self.enable_chunked_prefill,
1314
            disable_log_stats=self.disable_log_stats,
1315
1316
        )

1317
1318
1319
1320
1321
        # make sure num_lookahead_slots is set appropriately depending on
        # whether speculative decoding is enabled
        num_lookahead_slots = self.num_lookahead_slots
        if speculative_config is not None:
            num_lookahead_slots = speculative_config.num_lookahead_slots
1322

1323
        scheduler_config = SchedulerConfig(
1324
            runner_type=model_config.runner_type,
1325
1326
1327
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1328
            cuda_graph_sizes=self.cuda_graph_sizes,
1329
            num_lookahead_slots=num_lookahead_slots,
1330
1331
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1332
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1333
            is_multimodal_model=model_config.is_multimodal_model,
1334
            preemption_mode=self.preemption_mode,
1335
1336
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1337
            policy=self.scheduling_policy,
1338
            scheduler_cls=self.scheduler_cls,
1339
1340
1341
            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,
1342
1343
            disable_hybrid_kv_cache_manager=self.
            disable_hybrid_kv_cache_manager,
1344
            async_scheduling=self.async_scheduling,
1345
        )
1346

1347
1348
1349
1350
1351
        if not model_config.is_multimodal_model and self.default_mm_loras:
            raise ValueError(
                "Default modality-specific LoRA(s) were provided for a "
                "non multimodal model")

1352
        lora_config = LoRAConfig(
1353
            bias_enabled=self.enable_lora_bias,
1354
1355
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1356
            default_mm_loras=self.default_mm_loras,
1357
            fully_sharded_loras=self.fully_sharded_loras,
1358
1359
1360
1361
            lora_extra_vocab_size=self.lora_extra_vocab_size,
            lora_dtype=self.lora_dtype,
            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
1362

1363
1364
1365
1366
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1367
        load_config = self.create_load_config()
1368

1369
        decoding_config = DecodingConfig(
1370
1371
1372
1373
1374
            backend=self.guided_decoding_backend,
            disable_fallback=self.guided_decoding_disable_fallback,
            disable_any_whitespace=self.guided_decoding_disable_any_whitespace,
            disable_additional_properties=\
                self.guided_decoding_disable_additional_properties,
1375
1376
            reasoning_backend=self.reasoning_parser
        )
1377

1378
        observability_config = ObservabilityConfig(
1379
1380
            show_hidden_metrics_for_version=(
                self.show_hidden_metrics_for_version),
1381
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1382
            collect_detailed_traces=self.collect_detailed_traces,
1383
        )
1384

1385
        config = VllmConfig(
1386
1387
1388
1389
1390
1391
1392
1393
1394
1395
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
            speculative_config=speculative_config,
            load_config=load_config,
            decoding_config=decoding_config,
            observability_config=observability_config,
1396
            compilation_config=self.compilation_config,
1397
            kv_transfer_config=self.kv_transfer_config,
1398
            kv_events_config=self.kv_events_config,
1399
            additional_config=self.additional_config,
1400
        )
1401

1402
1403
        return config

1404
1405
1406
1407
1408
1409
    def _is_v1_supported_oracle(self, model_config: ModelConfig) -> bool:
        """Oracle for whether to use V0 or V1 Engine by default."""

        #############################################################
        # Unsupported Feature Flags on V1.

1410
        if self.load_format == "sharded_state":
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
            _raise_or_fallback(
                feature_name=f"--load_format {self.load_format}",
                recommend_to_remove=False)
            return False

        if (self.logits_processor_pattern
                != EngineArgs.logits_processor_pattern):
            _raise_or_fallback(feature_name="--logits-processor-pattern",
                               recommend_to_remove=False)
            return False

1422
        if self.preemption_mode != SchedulerConfig.preemption_mode:
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
            _raise_or_fallback(feature_name="--preemption-mode",
                               recommend_to_remove=True)
            return False

        if (self.disable_async_output_proc
                != EngineArgs.disable_async_output_proc):
            _raise_or_fallback(feature_name="--disable-async-output-proc",
                               recommend_to_remove=True)
            return False

1433
        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
1434
1435
1436
1437
1438
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

        # Need at least Ampere for now (FA support required).
1439
1440
1441
        # Skip this check if we are running on a non-GPU platform,
        # or if the device capability is not available
        # (e.g. in a Ray actor without GPUs).
1442
        if (current_platform.is_cuda()
1443
                and current_platform.get_device_capability()
1444
1445
1446
1447
1448
1449
1450
                and current_platform.get_device_capability().major < 8):
            _raise_or_fallback(feature_name="Compute Capability < 8.0",
                               recommend_to_remove=False)
            return False

        # No Fp8 KV cache so far.
        if self.kv_cache_dtype != "auto":
1451
1452
            supported = current_platform.is_kv_cache_dtype_supported(
                self.kv_cache_dtype)
1453
1454
1455
1456
            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
1457

1458
1459
1460
1461
1462
1463
        # No text embedding inputs so far.
        if self.enable_prompt_embeds:
            _raise_or_fallback(feature_name="--enable-prompt-embeds",
                               recommend_to_remove=False)
            return False

1464
        # No Mamba or Encoder-Decoder so far.
1465
1466
1467
1468
1469
        if not model_config.is_v1_compatible:
            _raise_or_fallback(feature_name=model_config.architectures,
                               recommend_to_remove=False)
            return False

Chen Zhang's avatar
Chen Zhang committed
1470
1471
1472
1473
1474
        # V1 mamba models are unoptimized.
        if model_config.has_inner_state and _warn_or_fallback(
                feature_name="Mamba"):
            return False

1475
1476
        # No Concurrent Partial Prefills so far.
        if (self.max_num_partial_prefills
1477
                != SchedulerConfig.max_num_partial_prefills
1478
                or self.max_long_partial_prefills
1479
                != SchedulerConfig.max_long_partial_prefills):
1480
1481
1482
1483
1484
1485
1486
1487
1488
1489
            _raise_or_fallback(feature_name="Concurrent Partial Prefill",
                               recommend_to_remove=False)
            return False

        # No OTLP observability so far.
        if (self.otlp_traces_endpoint or self.collect_detailed_traces):
            _raise_or_fallback(feature_name="--otlp-traces-endpoint",
                               recommend_to_remove=False)
            return False

1490
        # V1 supports N-gram, Medusa, and Eagle speculative decoding.
1491
1492
1493
1494
1495
1496
        if (self.speculative_config is not None
                and self.speculative_config.get("method") == "draft_model"):
            raise NotImplementedError(
                "Speculative decoding with draft model is not supported yet. "
                "Please consider using other speculative decoding methods "
                "such as ngram, medusa, eagle, or deepseek_mtp.")
1497
1498

        V1_BACKENDS = [
1499
1500
1501
1502
1503
1504
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
1505
            "CUTLASS_MLA",
1506
1507
1508
            "FLASHMLA",
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
1509
            "ROCM_AITER_MLA",
1510
            "TORCH_SDPA_VLLM_V1",
1511
            "FLEX_ATTENTION",
1512
            "TREE_ATTN",
1513
            "XFORMERS_VLLM_V1",
1514
1515
1516
1517
1518
1519
1520
        ]
        if (envs.is_set("VLLM_ATTENTION_BACKEND")
                and envs.VLLM_ATTENTION_BACKEND not in V1_BACKENDS):
            name = f"VLLM_ATTENTION_BACKEND={envs.VLLM_ATTENTION_BACKEND}"
            _raise_or_fallback(feature_name=name, recommend_to_remove=True)
            return False

1521
1522
        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
1523
1524
1525
1526
            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
1527
1528
1529
        #############################################################
        # Experimental Features - allow users to opt in.

1530
1531
1532
1533
1534
        # Signal Handlers requires running in main thread.
        if (threading.current_thread() != threading.main_thread()
                and _warn_or_fallback("Engine in background thread")):
            return False

1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
1546
        if self.pipeline_parallel_size > 1:
            supports_pp = getattr(self.distributed_executor_backend,
                                  'supports_pp', False)
            if not supports_pp and self.distributed_executor_backend not in (
                    ParallelConfig.distributed_executor_backend, "ray", "mp",
                    "external_launcher"):
                name = "Pipeline Parallelism without Ray distributed " \
                        "executor or multiprocessing executor or external " \
                        "launcher"
                _raise_or_fallback(feature_name=name,
                                   recommend_to_remove=False)
                return False
1547

1548
1549
1550
1551
        # The platform may be supported on V1, but off by default for now.
        if not current_platform.default_v1(  # noqa: SIM103
                model_config=model_config) and _warn_or_fallback(
                    current_platform.device_name):
1552
            return False
1553
1554
1555
1556
1557
1558
1559

        if (current_platform.is_cpu()
                and model_config.get_sliding_window() is not None):
            _raise_or_fallback(feature_name="sliding window (CPU backend)",
                               recommend_to_remove=False)
            return False

1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
        #############################################################

        return True

    def _set_default_args_v0(self, model_config: ModelConfig) -> None:
        """Set Default Arguments for V0 Engine."""

        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # Chunked prefill not supported for Multimodal or MLA in V0.
            if model_config.is_multimodal_model or model_config.use_mla:
                self.enable_chunked_prefill = False

            # Enable chunked prefill by default for long context (> 32K)
            # models to avoid OOM errors in initial memory profiling phase.
            elif use_long_context:
                is_gpu = current_platform.is_cuda()
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
1580
                use_spec_decode = self.speculative_config is not None
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
1601
1602
1603
1604
1605
1606

                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
                        and model_config.runner_type != "pooling"):
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models "
                        "with max_model_len > 32K. Chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable by launching "
                        "with --enable-chunked-prefill=False.")

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

        if not self.enable_chunked_prefill and use_long_context:
            logger.warning(
                "The model has a long context length (%s). This may cause"
                "OOM during the initial memory profiling phase, or result "
                "in low performance due to small KV cache size. Consider "
                "setting --max-model-len to a smaller value.", max_model_len)
        elif (self.enable_chunked_prefill
              and model_config.runner_type == "pooling"):
            msg = "Chunked prefill is not supported for pooling models"
            raise ValueError(msg)

1607
1608
1609
1610
1611
1612
1613
1614
1615
1616
        # if using prefix caching, we must set a hash algo
        if self.enable_prefix_caching:
            # Disable prefix caching for multimodal models for VLLM_V0.
            if model_config.is_multimodal_model:
                logger.warning(
                    "--enable-prefix-caching is not supported for multimodal "
                    "models in V0 and has been disabled.")
                self.enable_prefix_caching = False

            # VLLM_V0 only supports builtin hash algo for prefix caching.
1617
            if self.prefix_caching_hash_algo == "sha256":
1618
1619
1620
                raise ValueError(
                    "sha256 is not supported for prefix caching in V0 engine. "
                    "Please use 'builtin'.")
1621
1622
1623
1624
1625

        # Set max_num_seqs to 256 for VLLM_V0.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256

1626
1627
    def _set_default_args_v1(self, usage_context: UsageContext,
                             model_config: ModelConfig) -> None:
1628
        """Set Default Arguments for V1 Engine."""
1629

1630
1631
1632
1633
1634
1635
1636
1637
1638
1639
        # V1 always uses chunked prefills and prefix caching
        # for non-pooling tasks.
        # For pooling tasks the default is False
        if model_config.runner_type != "pooling":
            self.enable_chunked_prefill = True
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = True
        else:

            pooling_type = model_config.pooler_config.pooling_type
1640
1641
1642
1643
            is_causal = getattr(model_config.hf_config, "is_causal", True)
            incremental_prefill_supported = (pooling_type is not None
                                             and pooling_type.lower() == "last"
                                             and is_causal)
1644

1645
1646
1647
1648
1649
1650
1651
1652
1653
1654
            action = "Enabling" if \
                incremental_prefill_supported else "Disabling"

            if self.enable_chunked_prefill is None:
                self.enable_chunked_prefill = incremental_prefill_supported
                logger.info("(%s) chunked prefill by default", action)
            if self.enable_prefix_caching is None:
                self.enable_prefix_caching = incremental_prefill_supported
                logger.info("(%s) prefix caching by default", action)

1655
1656
1657
        # V1 should use the new scheduler by default.
        # Swap it only if this arg is set to the original V0 default
        if self.scheduler_cls == EngineArgs.scheduler_cls:
1658
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
1659

1660
1661
        # When no user override, set the default values based on the usage
        # context.
1662
        # Use different default values for different hardware.
1663
1664
1665
1666
1667
1668
1669

        # 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:
1670
            device_memory = current_platform.get_device_total_memory()
1671
            device_name = current_platform.get_device_name().lower()
1672
1673
        except Exception:
            # This is only used to set default_max_num_batched_tokens
1674
            device_memory = 0
1675

1676
1677
1678
        # 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.
1679
        from vllm.usage.usage_lib import UsageContext
1680
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
1681
            # For GPUs like H100 and MI300x, use larger default values.
1682
1683
1684
1685
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
1686
1687
1688
1689
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
1690
1691
1692
1693
1694
1695
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
1696
1697
1698
1699
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }
1700

1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
        # tpu specific default values.
        if current_platform.is_tpu():
            default_max_num_batched_tokens_tpu = {
                UsageContext.LLM_CLASS: {
                    'V6E': 2048,
                    'V5E': 1024,
                    'V5P': 512,
                },
                UsageContext.OPENAI_API_SERVER: {
                    'V6E': 1024,
                    'V5E': 512,
                    'V5P': 256,
                }
            }

1716
1717
        # cpu specific default values.
        if current_platform.is_cpu():
1718
            world_size = self.pipeline_parallel_size * self.tensor_parallel_size
1719
            default_max_num_batched_tokens = {
1720
1721
                UsageContext.LLM_CLASS: 4096 * world_size,
                UsageContext.OPENAI_API_SERVER: 2048 * world_size,
1722
1723
            }
            default_max_num_seqs = {
1724
1725
                UsageContext.LLM_CLASS: 256 * world_size,
                UsageContext.OPENAI_API_SERVER: 128 * world_size,
1726
1727
            }

1728
        use_context_value = usage_context.value if usage_context else None
1729
1730
        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
1731
1732
1733
1734
1735
1736
1737
1738
1739
1740
1741
            if current_platform.is_tpu():
                chip_name = current_platform.get_device_name()
                if chip_name in default_max_num_batched_tokens_tpu[
                        usage_context]:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens_tpu[
                            usage_context][chip_name]
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
            else:
1742
1743
1744
1745
1746
                if not self.enable_chunked_prefill:
                    self.max_num_batched_tokens = model_config.max_model_len
                else:
                    self.max_num_batched_tokens = \
                        default_max_num_batched_tokens[usage_context]
1747
            logger.debug(
1748
                "Setting max_num_batched_tokens to %d for %s usage context.",
1749
                self.max_num_batched_tokens, use_context_value)
1750

1751
1752
        if (self.max_num_seqs is None
                and usage_context in default_max_num_seqs):
1753
1754
            self.max_num_seqs = min(default_max_num_seqs[usage_context],
                                    self.max_num_batched_tokens or sys.maxsize)
1755
1756
1757

            logger.debug("Setting max_num_seqs to %d for %s usage context.",
                         self.max_num_seqs, use_context_value)
1758

1759

1760
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
1761
class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
1762
    """Arguments for asynchronous vLLM engine."""
1763
1764
1765
1766
1767
1768
1769
1770
1771
1772
1773
1774
1775
1776
1777
1778
1779
    enable_log_requests: bool = False

    @property
    @deprecated(
        "`disable_log_requests` is deprecated and has been replaced with "
        "`enable_log_requests`. This will be removed in v0.12.0. Please use "
        "`enable_log_requests` instead.")
    def disable_log_requests(self) -> bool:
        return not self.enable_log_requests

    @disable_log_requests.setter
    @deprecated(
        "`disable_log_requests` is deprecated and has been replaced with "
        "`enable_log_requests`. This will be removed in v0.12.0. Please use "
        "`enable_log_requests` instead.")
    def disable_log_requests(self, value: bool):
        self.enable_log_requests = not value
1780
1781

    @staticmethod
1782
1783
    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
1784
1785
1786
1787
        # Initialize plugin to update the parser, for example, The plugin may
        # adding a new kind of quantization method to --quantization argument or
        # a new device to --device argument.
        load_general_plugins()
1788
1789
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
1790
1791
1792
1793
        parser.add_argument('--enable-log-requests',
                            action=argparse.BooleanOptionalAction,
                            default=AsyncEngineArgs.enable_log_requests,
                            help='Enable logging requests.')
1794
        parser.add_argument('--disable-log-requests',
1795
1796
1797
1798
                            action=argparse.BooleanOptionalAction,
                            default=not AsyncEngineArgs.enable_log_requests,
                            help='[DEPRECATED] Disable logging requests.',
                            deprecated=True)
1799
        current_platform.pre_register_and_update(parser)
1800
        return parser
1801
1802


1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
1816
1817
1818
1819
1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
def _raise_or_fallback(feature_name: str, recommend_to_remove: bool):
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        raise NotImplementedError(
            f"VLLM_USE_V1=1 is not supported with {feature_name}.")
    msg = f"{feature_name} is not supported by the V1 Engine. "
    msg += "Falling back to V0. "
    if recommend_to_remove:
        msg += f"We recommend to remove {feature_name} from your config "
        msg += "in favor of the V1 Engine."
    logger.warning(msg)


def _warn_or_fallback(feature_name: str) -> bool:
    if envs.is_set("VLLM_USE_V1") and envs.VLLM_USE_V1:
        logger.warning(
            "Detected VLLM_USE_V1=1 with %s. Usage should "
            "be considered experimental. Please report any "
            "issues on Github.", feature_name)
        should_exit = False
    else:
        logger.info(
            "%s is experimental on VLLM_USE_V1=1. "
            "Falling back to V0 Engine.", feature_name)
        should_exit = True
    return should_exit


1830
1831
1832
def human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
1833

1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
1855
1856
1857
1858
1859
1860
1861
1862
1863
1864
1865
1866
1867
1868
    Examples:
    - '1k' -> 1,000
    - '1K' -> 1,024
    - '25.6k' -> 25,600
    """
    value = value.strip()
    match = re.fullmatch(r'(\d+(?:\.\d+)?)([kKmMgGtT])', value)
    if match:
        decimal_multiplier = {
            'k': 10**3,
            'm': 10**6,
            'g': 10**9,
        }
        binary_multiplier = {
            'K': 2**10,
            'M': 2**20,
            'G': 2**30,
        }

        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:
                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

    # Regular plain number.
    return int(value)