arg_utils.py 84 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
from dataclasses import MISSING, dataclass, fields, is_dataclass
12
from itertools import permutations
13
14
15
from typing import (TYPE_CHECKING, Annotated, Any, Callable, Dict, List,
                    Literal, Optional, Type, TypeVar, Union, cast, get_args,
                    get_origin)
16

17
import huggingface_hub
18
import regex as re
19
import torch
20
from pydantic import TypeAdapter, ValidationError
21
from typing_extensions import TypeIs, deprecated
22

23
import vllm.envs as envs
24
from vllm.config import (BlockSize, CacheConfig, CacheDType, CompilationConfig,
25
26
27
                         ConfigType, ConvertOption, DecodingConfig,
                         DetailedTraceModules, Device, DeviceConfig,
                         DistributedExecutorBackend, EPLBConfig,
28
29
                         GuidedDecodingBackend, HfOverrides, KVEventsConfig,
                         KVTransferConfig, LoadConfig, LogprobsMode,
30
31
32
33
34
35
                         LoRAConfig, MambaDType, MMEncoderTPMode, ModelConfig,
                         ModelDType, ModelImpl, MultiModalConfig,
                         ObservabilityConfig, ParallelConfig, PoolerConfig,
                         PrefixCachingHashAlgo, RunnerOption, SchedulerConfig,
                         SchedulerPolicy, SpeculativeConfig, TaskOption,
                         TokenizerMode, VllmConfig, get_attr_docs, get_field)
36
from vllm.logger import init_logger
37
from vllm.platforms import CpuArchEnum, current_platform
38
from vllm.plugins import load_general_plugins
39
from vllm.ray.lazy_utils import is_ray_initialized
40
from vllm.reasoning import ReasoningParserManager
41
from vllm.test_utils import MODEL_WEIGHTS_S3_BUCKET, MODELS_ON_S3
42
from vllm.transformers_utils.config import get_model_path, is_interleaved
43
from vllm.transformers_utils.utils import check_gguf_file
44
from vllm.utils import (STR_DUAL_CHUNK_FLASH_ATTN_VAL, FlexibleArgumentParser,
Rui Qiao's avatar
Rui Qiao committed
45
                        GiB_bytes, get_ip, is_in_ray_actor)
46
from vllm.v1.sample.logits_processor import LogitsProcessor
47
48

# yapf: enable
49

50
51
52
if TYPE_CHECKING:
    from vllm.executor.executor_base import ExecutorBase
    from vllm.model_executor.layers.quantization import QuantizationMethods
53
    from vllm.model_executor.model_loader import LoadFormats
54
55
56
57
    from vllm.usage.usage_lib import UsageContext
else:
    ExecutorBase = Any
    QuantizationMethods = Any
58
    LoadFormats = Any
59
60
    UsageContext = Any

61
62
logger = init_logger(__name__)

63
64
65
66
67
# object is used to allow for special typing forms
T = TypeVar("T")
TypeHint = Union[type[Any], object]
TypeHintT = Union[type[T], object]

68

69
def parse_type(return_type: Callable[[str], T]) -> Callable[[str], T]:
70

71
    def _parse_type(val: str) -> T:
72
73
74
75
76
        try:
            return return_type(val)
        except ValueError as e:
            raise argparse.ArgumentTypeError(
                f"Value {val} cannot be converted to {return_type}.") from e
77

78
79
80
81
82
83
84
85
86
87
88
    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)

89
    return _optional_type
90
91


92
def union_dict_and_str(val: str) -> Optional[Union[str, dict[str, str]]]:
93
    if not re.match(r"(?s)^\s*{.*}\s*$", val):
94
        return str(val)
95
    return optional_type(json.loads)(val)
96
97


98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
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)


113
def literal_to_kwargs(type_hints: set[TypeHint]) -> dict[str, Any]:
114
115
116
117
    """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`.
    """
118
    type_hint = get_type(type_hints, Literal)
119
120
121
    options = get_args(type_hint)
    option_type = type(options[0])
    if not all(isinstance(option, option_type) for option in options):
122
        raise ValueError(
123
124
125
126
            "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)}
127
128


129
130
131
132
133
def is_not_builtin(type_hint: TypeHint) -> bool:
    """Check if the class is not a built-in type."""
    return type_hint.__module__ != "builtins"


134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
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


151
152
153
154
def is_online_quantization(quantization: Any) -> bool:
    return quantization in ["inc"]


155
156
157
158
159
160
161
NEEDS_HELP = (
    "--help" in (argv := sys.argv)  # vllm SUBCOMMAND --help
    or (argv0 := argv[0]).endswith("mkdocs")  # mkdocs SUBCOMMAND
    or argv0.endswith("mkdocs/__main__.py")  # python -m mkdocs SUBCOMMAND
)


162
163
@functools.lru_cache(maxsize=30)
def _compute_kwargs(cls: ConfigType) -> dict[str, Any]:
164
165
    # Save time only getting attr docs if we're generating help text
    cls_docs = get_attr_docs(cls) if NEEDS_HELP else {}
166
167
    kwargs = {}
    for field in fields(cls):
168
        # Get the set of possible types for the field
169
        type_hints: set[TypeHint] = get_type_hints(field.type)
170
171
172
173
174

        # 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)

175
        # Get the default value of the field
176
177
178
        if field.default is not MISSING:
            default = field.default
        elif field.default_factory is not MISSING:
179
            default = field.default_factory()
180
181
182

        # Get the help text for the field
        name = field.name
183
        help = cls_docs.get(name, "").strip()
184
185
186
187
188
189
190
        # 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
191
192
        json_tip = ("Should either be a valid JSON string or JSON keys passed "
                    "individually.")
193
        if dataclass_cls is not None:
194
195
196
197
198
199
200
201

            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
202
            kwargs[name]["help"] += f"\n\n{json_tip}"
203
        elif contains_type(type_hints, bool):
204
205
206
            # Creates --no-<name> and --<name> flags
            kwargs[name]["action"] = argparse.BooleanOptionalAction
        elif contains_type(type_hints, Literal):
207
            kwargs[name].update(literal_to_kwargs(type_hints))
208
209
210
211
212
213
214
215
216
217
218
219
        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)
220
221
222
223
224
225
            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
226
227
228
            kwargs[name]["nargs"] = "+"
        elif contains_type(type_hints, int):
            kwargs[name]["type"] = int
229
            # Special case for large integers
230
            if name in {"max_model_len", "max_num_batched_tokens"}:
231
                kwargs[name]["type"] = human_readable_int
232
233
        elif contains_type(type_hints, float):
            kwargs[name]["type"] = float
234
235
236
        elif (contains_type(type_hints, dict)
              and (contains_type(type_hints, str)
                   or any(is_not_builtin(th) for th in type_hints))):
237
            kwargs[name]["type"] = union_dict_and_str
238
        elif contains_type(type_hints, dict):
239
            kwargs[name]["type"] = parse_type(json.loads)
240
            kwargs[name]["help"] += f"\n\n{json_tip}"
241
242
243
244
245
246
247
        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}.")

248
249
250
251
252
        # 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"]}))

253
254
255
256
257
258
259
        # 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
260
261


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

265
266
267
    If `--help` or `mkdocs` are not present in the command line command, the
    attribute documentation will not be included in the help output.

268
269
270
271
272
273
274
    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))


275
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
276
class EngineArgs:
Woosuk Kwon's avatar
Woosuk Kwon committed
277
    """Arguments for vLLM engine."""
278
279
280
281
282
    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
283
284
285
    runner: RunnerOption = ModelConfig.runner
    convert: ConvertOption = ModelConfig.convert
    task: Optional[TaskOption] = ModelConfig.task
286
    skip_tokenizer_init: bool = ModelConfig.skip_tokenizer_init
287
    enable_prompt_embeds: bool = ModelConfig.enable_prompt_embeds
288
289
290
    tokenizer_mode: TokenizerMode = ModelConfig.tokenizer_mode
    trust_remote_code: bool = ModelConfig.trust_remote_code
    allowed_local_media_path: str = ModelConfig.allowed_local_media_path
291
    download_dir: Optional[str] = LoadConfig.download_dir
292
293
    safetensors_load_strategy: Optional[
        str] = LoadConfig.safetensors_load_strategy
294
    load_format: Union[str, LoadFormats] = LoadConfig.load_format
295
296
    config_format: str = ModelConfig.config_format
    dtype: ModelDType = ModelConfig.dtype
297
    kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
298
299
    seed: Optional[int] = ModelConfig.seed
    max_model_len: Optional[int] = ModelConfig.max_model_len
300
301
    cuda_graph_sizes: list[int] = get_field(SchedulerConfig,
                                            "cuda_graph_sizes")
302
303
304
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
305
    distributed_executor_backend: Optional[Union[
306
        str, DistributedExecutorBackend,
307
        Type[ExecutorBase]]] = ParallelConfig.distributed_executor_backend
308
    # number of P/D disaggregation (or other disaggregation) workers
309
310
    pipeline_parallel_size: int = ParallelConfig.pipeline_parallel_size
    tensor_parallel_size: int = ParallelConfig.tensor_parallel_size
311
312
    decode_context_parallel_size: int = \
        ParallelConfig.decode_context_parallel_size
313
    data_parallel_size: int = ParallelConfig.data_parallel_size
314
    data_parallel_rank: Optional[int] = None
315
    data_parallel_start_rank: Optional[int] = None
316
317
318
    data_parallel_size_local: Optional[int] = None
    data_parallel_address: Optional[str] = None
    data_parallel_rpc_port: Optional[int] = None
319
    data_parallel_hybrid_lb: bool = False
Rui Qiao's avatar
Rui Qiao committed
320
    data_parallel_backend: str = ParallelConfig.data_parallel_backend
321
    enable_expert_parallel: bool = ParallelConfig.enable_expert_parallel
322
    eplb_config: EPLBConfig = get_field(ParallelConfig, "eplb_config")
323
    enable_eplb: bool = ParallelConfig.enable_eplb
324
325
326
327
    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
328
329
    max_parallel_loading_workers: Optional[
        int] = ParallelConfig.max_parallel_loading_workers
330
331
332
333
    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
334
335
    disable_sliding_window: bool = ModelConfig.disable_sliding_window
    disable_cascade_attn: bool = ModelConfig.disable_cascade_attn
336
337
338
    swap_space: float = CacheConfig.swap_space
    cpu_offload_gb: float = CacheConfig.cpu_offload_gb
    gpu_memory_utilization: float = CacheConfig.gpu_memory_utilization
339
340
341
342
343
344
345
    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
346
    max_logprobs: int = ModelConfig.max_logprobs
347
    logprobs_mode: LogprobsMode = ModelConfig.logprobs_mode
348
    disable_log_stats: bool = False
349
350
351
352
353
    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
354
    hf_overrides: HfOverrides = get_field(ModelConfig, "hf_overrides")
355
356
357
358
    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
359
    disable_custom_all_reduce: bool = ParallelConfig.disable_custom_all_reduce
360
    limit_mm_per_prompt: dict[str, int] = \
361
        get_field(MultiModalConfig, "limit_per_prompt")
362
    interleave_mm_strings: bool = MultiModalConfig.interleave_mm_strings
363
364
365
    media_io_kwargs: dict[str, dict[str,
                                    Any]] = get_field(MultiModalConfig,
                                                      "media_io_kwargs")
366
367
    mm_processor_kwargs: Optional[Dict[str, Any]] = \
        MultiModalConfig.mm_processor_kwargs
368
    disable_mm_preprocessor_cache: bool = False  # DEPRECATED
369
    mm_processor_cache_gb: float = MultiModalConfig.mm_processor_cache_gb
370
    mm_encoder_tp_mode: MMEncoderTPMode = MultiModalConfig.mm_encoder_tp_mode
371
    io_processor_plugin: Optional[str] = None
372
    skip_mm_profiling: bool = MultiModalConfig.skip_mm_profiling
373
    # LoRA fields
374
    enable_lora: bool = False
375
376
377
    enable_lora_bias: bool = LoRAConfig.bias_enabled
    max_loras: int = LoRAConfig.max_loras
    max_lora_rank: int = LoRAConfig.max_lora_rank
378
379
    default_mm_loras: Optional[Dict[str, str]] = \
        LoRAConfig.default_mm_loras
380
381
382
383
384
    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

385
    ray_workers_use_nsight: bool = ParallelConfig.ray_workers_use_nsight
386
387
    num_gpu_blocks_override: Optional[
        int] = CacheConfig.num_gpu_blocks_override
388
    num_lookahead_slots: int = SchedulerConfig.num_lookahead_slots
389
390
    model_loader_extra_config: dict = \
        get_field(LoadConfig, "model_loader_extra_config")
391
392
    ignore_patterns: Optional[Union[str,
                                    List[str]]] = LoadConfig.ignore_patterns
393
    preemption_mode: Optional[str] = SchedulerConfig.preemption_mode
394

395
396
397
398
    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
399

400
401
402
    disable_hybrid_kv_cache_manager: bool = (
        SchedulerConfig.disable_hybrid_kv_cache_manager)

403
404
405
406
407
408
    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
409
410
    logits_processor_pattern: Optional[
        str] = ModelConfig.logits_processor_pattern
411

412
    speculative_config: Optional[Dict[str, Any]] = None
413

414
415
416
417
418
419
    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
420
    disable_async_output_proc: bool = not ModelConfig.use_async_output_proc
421
422
    scheduling_policy: SchedulerPolicy = SchedulerConfig.policy
    scheduler_cls: Union[str, Type[object]] = SchedulerConfig.scheduler_cls
423

424
425
    override_pooler_config: Optional[Union[dict, PoolerConfig]] = \
        ModelConfig.override_pooler_config
426
427
    compilation_config: CompilationConfig = \
        get_field(VllmConfig, "compilation_config")
428
429
    worker_cls: str = ParallelConfig.worker_cls
    worker_extension_cls: str = ParallelConfig.worker_extension_cls
430

431
    kv_transfer_config: Optional[KVTransferConfig] = None
432
    kv_events_config: Optional[KVEventsConfig] = None
433

434
435
436
437
438
    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
439
    override_attention_dtype: str = ModelConfig.override_attention_dtype
440

441
    calculate_kv_scales: bool = CacheConfig.calculate_kv_scales
442
443
    mamba_cache_dtype: MambaDType = CacheConfig.mamba_cache_dtype
    mamba_ssm_cache_dtype: MambaDType = CacheConfig.mamba_ssm_cache_dtype
444

445
446
    additional_config: dict[str, Any] = \
        get_field(VllmConfig, "additional_config")
447
448
    reasoning_parser: str = DecodingConfig.reasoning_backend

449
    use_tqdm_on_load: bool = LoadConfig.use_tqdm_on_load
450
    pt_load_map_location: str = LoadConfig.pt_load_map_location
451

452
453
    # DEPRECATED
    enable_multimodal_encoder_data_parallel: bool = False
454

455
456
457
458
    logits_processors: Optional[list[Union[
        str, type[LogitsProcessor]]]] = ModelConfig.logits_processors
    """Custom logitproc types"""

459
460
    async_scheduling: bool = SchedulerConfig.async_scheduling

461
462
463
    kv_sharing_fast_prefill: bool = \
        CacheConfig.kv_sharing_fast_prefill

464
    def __post_init__(self):
465
466
467
        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
468
469
470
        if isinstance(self.compilation_config, dict):
            self.compilation_config = CompilationConfig(
                **self.compilation_config)
471
        if isinstance(self.eplb_config, dict):
472
            self.eplb_config = EPLBConfig(**self.eplb_config)
473
        # Setup plugins
474
475
        from vllm.plugins import load_general_plugins
        load_general_plugins()
476
477
478
479
480
481
482
        # 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)
483
484

    @staticmethod
485
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
Woosuk Kwon's avatar
Woosuk Kwon committed
486
        """Shared CLI arguments for vLLM engine."""
487

488
        # Model arguments
489
490
491
492
493
        model_kwargs = get_kwargs(ModelConfig)
        model_group = parser.add_argument_group(
            title="ModelConfig",
            description=ModelConfig.__doc__,
        )
Reid's avatar
Reid committed
494
        if not ('serve' in sys.argv[1:] and '--help' in sys.argv[1:]):
495
            model_group.add_argument("--model", **model_kwargs["model"])
496
497
498
499
500
        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)
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
        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"])
530
        model_group.add_argument("--logprobs-mode",
531
                                 choices=[f.value for f in LogprobsMode],
532
                                 **model_kwargs["logprobs_mode"])
533
534
535
536
537
538
        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"])
539
540
        model_group.add_argument("--enable-prompt-embeds",
                                 **model_kwargs["enable_prompt_embeds"])
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
567
568
569
570
571
572
573
574
575
        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",
                                 **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-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"])
576
577
        model_group.add_argument("--override-attention-dtype",
                                 **model_kwargs["override_attention_dtype"])
578
579
        model_group.add_argument("--logits-processors",
                                 **model_kwargs["logits_processors"])
580
581
        model_group.add_argument("--io-processor-plugin",
                                 **model_kwargs["io_processor_plugin"])
582

583
584
585
586
587
588
        # Model loading arguments
        load_kwargs = get_kwargs(LoadConfig)
        load_group = parser.add_argument_group(
            title="LoadConfig",
            description=LoadConfig.__doc__,
        )
589
        load_group.add_argument("--load-format", **load_kwargs["load_format"])
590
        load_group.add_argument("--download-dir",
591
                                **load_kwargs["download_dir"])
592
593
        load_group.add_argument("--safetensors-load-strategy",
                                **load_kwargs["safetensors_load_strategy"])
594
        load_group.add_argument("--model-loader-extra-config",
595
                                **load_kwargs["model_loader_extra_config"])
596
597
598
        load_group.add_argument("--ignore-patterns",
                                **load_kwargs["ignore_patterns"])
        load_group.add_argument("--use-tqdm-on-load",
599
                                **load_kwargs["use_tqdm_on_load"])
600
601
        load_group.add_argument('--pt-load-map-location',
                                **load_kwargs["pt_load_map_location"])
602

603
604
605
606
607
608
        # Guided decoding arguments
        guided_decoding_kwargs = get_kwargs(DecodingConfig)
        guided_decoding_group = parser.add_argument_group(
            title="DecodingConfig",
            description=DecodingConfig.__doc__,
        )
609
610
        guided_decoding_group.add_argument("--guided-decoding-backend",
                                           **guided_decoding_kwargs["backend"])
611
        guided_decoding_group.add_argument(
612
613
614
615
616
617
618
619
            "--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"])
620
621
        guided_decoding_group.add_argument(
            "--reasoning-parser",
622
            # This choice is a special case because it's not static
623
624
625
            choices=list(ReasoningParserManager.reasoning_parsers),
            **guided_decoding_kwargs["reasoning_backend"])

626
        # Parallel arguments
627
628
629
630
631
632
        parallel_kwargs = get_kwargs(ParallelConfig)
        parallel_group = parser.add_argument_group(
            title="ParallelConfig",
            description=ParallelConfig.__doc__,
        )
        parallel_group.add_argument(
633
            "--distributed-executor-backend",
634
635
            **parallel_kwargs["distributed_executor_backend"])
        parallel_group.add_argument(
636
            "--pipeline-parallel-size", "-pp",
637
            **parallel_kwargs["pipeline_parallel_size"])
638
        parallel_group.add_argument("--tensor-parallel-size", "-tp",
639
                                    **parallel_kwargs["tensor_parallel_size"])
640
641
642
        parallel_group.add_argument(
            "--decode-context-parallel-size", "-dcp",
            **parallel_kwargs["decode_context_parallel_size"])
643
        parallel_group.add_argument("--data-parallel-size", "-dp",
644
                                    **parallel_kwargs["data_parallel_size"])
645
646
647
648
649
650
        parallel_group.add_argument(
            '--data-parallel-rank',
            '-dpn',
            type=int,
            help='Data parallel rank of this instance. '
            'When set, enables external load balancer mode.')
651
652
653
654
655
        parallel_group.add_argument('--data-parallel-start-rank',
                                    '-dpr',
                                    type=int,
                                    help='Starting data parallel rank '
                                    'for secondary nodes.')
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
        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
671
672
673
674
675
676
        parallel_group.add_argument('--data-parallel-backend',
                                    '-dpb',
                                    type=str,
                                    default='mp',
                                    help='Backend for data parallel, either '
                                    '"mp" or "ray".')
677
678
679
        parallel_group.add_argument(
            "--data-parallel-hybrid-lb",
            **parallel_kwargs["data_parallel_hybrid_lb"])
680
        parallel_group.add_argument(
681
            "--enable-expert-parallel",
682
            **parallel_kwargs["enable_expert_parallel"])
683
684
        parallel_group.add_argument("--enable-eplb",
                                    **parallel_kwargs["enable_eplb"])
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
        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)

711
        parallel_group.add_argument(
712
            "--max-parallel-loading-workers",
713
714
            **parallel_kwargs["max_parallel_loading_workers"])
        parallel_group.add_argument(
715
            "--ray-workers-use-nsight",
716
717
            **parallel_kwargs["ray_workers_use_nsight"])
        parallel_group.add_argument(
718
            "--disable-custom-all-reduce",
719
            **parallel_kwargs["disable_custom_all_reduce"])
720
721
722
723
        parallel_group.add_argument("--worker-cls",
                                    **parallel_kwargs["worker_cls"])
        parallel_group.add_argument("--worker-extension-cls",
                                    **parallel_kwargs["worker_extension_cls"])
724
725
        parallel_group.add_argument(
            "--enable-multimodal-encoder-data-parallel",
726
727
            action="store_true",
            deprecated=True)
728

729
730
731
732
733
        # KV cache arguments
        cache_kwargs = get_kwargs(CacheConfig)
        cache_group = parser.add_argument_group(
            title="CacheConfig",
            description=CacheConfig.__doc__,
734
        )
735
736
        cache_group.add_argument("--block-size", **cache_kwargs["block_size"])
        cache_group.add_argument("--gpu-memory-utilization",
737
                                 **cache_kwargs["gpu_memory_utilization"])
738
739
        cache_group.add_argument("--swap-space", **cache_kwargs["swap_space"])
        cache_group.add_argument("--kv-cache-dtype",
740
                                 **cache_kwargs["cache_dtype"])
741
        cache_group.add_argument("--num-gpu-blocks-override",
742
743
744
745
746
                                 **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"])
747
        cache_group.add_argument("--cpu-offload-gb",
748
                                 **cache_kwargs["cpu_offload_gb"])
749
        cache_group.add_argument("--calculate-kv-scales",
750
                                 **cache_kwargs["calculate_kv_scales"])
751
752
        cache_group.add_argument("--kv-sharing-fast-prefill",
                                 **cache_kwargs["kv_sharing_fast_prefill"])
753
754
755
756
        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"])
757

758
        # Multimodal related configs
759
760
761
762
763
        multimodal_kwargs = get_kwargs(MultiModalConfig)
        multimodal_group = parser.add_argument_group(
            title="MultiModalConfig",
            description=MultiModalConfig.__doc__,
        )
764
        multimodal_group.add_argument("--limit-mm-per-prompt",
765
                                      **multimodal_kwargs["limit_per_prompt"])
766
767
        multimodal_group.add_argument("--media-io-kwargs",
                                      **multimodal_kwargs["media_io_kwargs"])
768
        multimodal_group.add_argument(
769
            "--mm-processor-kwargs",
770
771
            **multimodal_kwargs["mm_processor_kwargs"])
        multimodal_group.add_argument(
772
773
774
            "--mm-processor-cache-gb",
            **multimodal_kwargs["mm_processor_cache_gb"])
        multimodal_group.add_argument("--disable-mm-preprocessor-cache",
775
                                      action="store_true",
776
                                      deprecated=True)
777
778
        multimodal_group.add_argument(
            "--mm-encoder-tp-mode", **multimodal_kwargs["mm_encoder_tp_mode"])
779
780
781
        multimodal_group.add_argument(
            "--interleave-mm-strings",
            **multimodal_kwargs["interleave_mm_strings"])
782
783
        multimodal_group.add_argument("--skip-mm-profiling",
                                      **multimodal_kwargs["skip_mm_profiling"])
784

785
        # LoRA related configs
786
787
788
789
790
791
        lora_kwargs = get_kwargs(LoRAConfig)
        lora_group = parser.add_argument_group(
            title="LoRAConfig",
            description=LoRAConfig.__doc__,
        )
        lora_group.add_argument(
792
            "--enable-lora",
793
            action=argparse.BooleanOptionalAction,
794
795
            help="If True, enable handling of LoRA adapters.")
        lora_group.add_argument("--enable-lora-bias",
796
                                **lora_kwargs["bias_enabled"])
797
798
        lora_group.add_argument("--max-loras", **lora_kwargs["max_loras"])
        lora_group.add_argument("--max-lora-rank",
799
                                **lora_kwargs["max_lora_rank"])
800
        lora_group.add_argument("--lora-extra-vocab-size",
801
802
                                **lora_kwargs["lora_extra_vocab_size"])
        lora_group.add_argument(
803
            "--lora-dtype",
804
805
            **lora_kwargs["lora_dtype"],
        )
806
        lora_group.add_argument("--max-cpu-loras",
807
                                **lora_kwargs["max_cpu_loras"])
808
        lora_group.add_argument("--fully-sharded-loras",
809
                                **lora_kwargs["fully_sharded_loras"])
810
811
        lora_group.add_argument("--default-mm-loras",
                                **lora_kwargs["default_mm_loras"])
812

813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
        # 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"])
836

837
838
839
840
841
842
843
        # Scheduler arguments
        scheduler_kwargs = get_kwargs(SchedulerConfig)
        scheduler_group = parser.add_argument_group(
            title="SchedulerConfig",
            description=SchedulerConfig.__doc__,
        )
        scheduler_group.add_argument(
844
            "--max-num-batched-tokens",
845
            **scheduler_kwargs["max_num_batched_tokens"])
846
        scheduler_group.add_argument("--max-num-seqs",
847
848
849
850
851
852
853
                                     **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"])
854
855
        scheduler_group.add_argument('--cuda-graph-sizes',
                                     **scheduler_kwargs["cuda_graph_sizes"])
856
857
858
        scheduler_group.add_argument(
            "--long-prefill-token-threshold",
            **scheduler_kwargs["long_prefill_token_threshold"])
859
        scheduler_group.add_argument("--num-lookahead-slots",
860
                                     **scheduler_kwargs["num_lookahead_slots"])
861
        scheduler_group.add_argument("--scheduler-delay-factor",
862
                                     **scheduler_kwargs["delay_factor"])
863
        scheduler_group.add_argument("--preemption-mode",
864
                                     **scheduler_kwargs["preemption_mode"])
865
866
        # multi-step scheduling has been removed; corresponding arguments
        # are no longer supported.
867
        scheduler_group.add_argument("--scheduling-policy",
868
                                     **scheduler_kwargs["policy"])
869
        scheduler_group.add_argument(
870
            "--enable-chunked-prefill",
871
            **scheduler_kwargs["enable_chunked_prefill"])
872
873
874
        scheduler_group.add_argument(
            "--disable-chunked-mm-input",
            **scheduler_kwargs["disable_chunked_mm_input"])
875
876
        scheduler_group.add_argument("--scheduler-cls",
                                     **scheduler_kwargs["scheduler_cls"])
877
878
879
        scheduler_group.add_argument(
            "--disable-hybrid-kv-cache-manager",
            **scheduler_kwargs["disable_hybrid_kv_cache_manager"])
880
881
        scheduler_group.add_argument("--async-scheduling",
                                     **scheduler_kwargs["async_scheduling"])
882
883

        # vLLM arguments
884
        vllm_kwargs = get_kwargs(VllmConfig)
885
886
887
888
        vllm_group = parser.add_argument_group(
            title="VllmConfig",
            description=VllmConfig.__doc__,
        )
889
890
891
892
        # 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)
893
894
        vllm_group.add_argument("--speculative-config",
                                **vllm_kwargs["speculative_config"])
895
896
897
898
899
900
901
902
        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"])
903

904
905
906
907
        # Other arguments
        parser.add_argument('--disable-log-stats',
                            action='store_true',
                            help='Disable logging statistics.')
908

909
        return parser
910
911

    @classmethod
912
    def from_cli_args(cls, args: argparse.Namespace):
913
914
915
        # 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
916
917
        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args
918

919
    def create_model_config(self) -> ModelConfig:
920
921
922
923
924
925
        # 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
926
                and self.model in MODELS_ON_S3 and self.load_format == "auto"):
927
            self.model = f"{MODEL_WEIGHTS_S3_BUCKET}/{self.model}"
928
            self.load_format = "runai_streamer"
929

930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
        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

947
948
949
950
951
952
953
954
        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"

955
        return ModelConfig(
956
            model=self.model,
957
            hf_config_path=self.hf_config_path,
958
959
            runner=self.runner,
            convert=self.convert,
960
            task=self.task,
961
            tokenizer=self.tokenizer,
962
963
            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
964
            allowed_local_media_path=self.allowed_local_media_path,
965
966
967
968
969
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
970
            rope_theta=self.rope_theta,
971
            hf_token=self.hf_token,
972
            hf_overrides=self.hf_overrides,
973
974
975
976
977
978
            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,
979
            logprobs_mode=self.logprobs_mode,
980
            disable_sliding_window=self.disable_sliding_window,
981
            disable_cascade_attn=self.disable_cascade_attn,
982
            skip_tokenizer_init=self.skip_tokenizer_init,
983
            enable_prompt_embeds=self.enable_prompt_embeds,
984
            served_model_name=self.served_model_name,
985
            limit_mm_per_prompt=self.limit_mm_per_prompt,
986
            interleave_mm_strings=self.interleave_mm_strings,
987
            media_io_kwargs=self.media_io_kwargs,
988
            skip_mm_profiling=self.skip_mm_profiling,
989
            use_async_output_proc=not self.disable_async_output_proc,
990
            config_format=self.config_format,
991
            mm_processor_kwargs=self.mm_processor_kwargs,
992
            mm_processor_cache_gb=self.mm_processor_cache_gb,
993
            mm_encoder_tp_mode=self.mm_encoder_tp_mode,
994
            override_pooler_config=self.override_pooler_config,
995
            logits_processor_pattern=self.logits_processor_pattern,
996
            generation_config=self.generation_config,
997
            override_generation_config=self.override_generation_config,
998
            enable_sleep_mode=self.enable_sleep_mode,
999
            model_impl=self.model_impl,
1000
            override_attention_dtype=self.override_attention_dtype,
1001
            logits_processors=self.logits_processors,
1002
            io_processor_plugin=self.io_processor_plugin,
1003
        )
1004

1005
1006
1007
1008
1009
1010
1011
    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]
1012

1013
1014
    def create_load_config(self) -> LoadConfig:

1015
1016
        if self.quantization == "bitsandbytes":
            self.load_format = "bitsandbytes"
1017

1018
1019
1020
1021
1022
1023
1024
1025
        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()
1026

1027
1028
1029
        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
1030
            safetensors_load_strategy=self.safetensors_load_strategy,
1031
1032
            device="cpu"
            if is_online_quantization(self.quantization) else None,
1033
1034
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
1035
            use_tqdm_on_load=self.use_tqdm_on_load,
1036
            pt_load_map_location=self.pt_load_map_location,
1037
        )
1038

1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
    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
1052
        dictionary from the engine.
1053
        """
1054
1055
1056
1057
1058

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

1059
        if self.speculative_config is None:
1060
1061
1062
1063
            hf_config = get_config(
                self.hf_config_path or target_model_config.model,
                self.trust_remote_code, self.revision, self.code_revision,
                self.config_format)
1064

1065
            # if loading a SpeculatorsConfig, load the speculative_config
1066
1067
1068
            # details from the config directly
            # no user input required / expected
            if isinstance(hf_config, SpeculatorsConfig):
1069
                # We create one since we don't create one
1070
1071
1072
                self.speculative_config = {}
                self.speculative_config[
                    "num_speculative_tokens"] = hf_config.num_lookahead_tokens
1073
                self.speculative_config["model"] = target_model_config.model
1074
1075
1076
                self.speculative_config["method"] = hf_config.method
            else:
                return None
1077

1078
1079
1080
1081
1082
1083
1084
1085
1086
        # 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,
        })
1087
        return SpeculativeConfig(**self.speculative_config)
1088

1089
1090
1091
    def create_engine_config(
        self,
        usage_context: Optional[UsageContext] = None,
1092
        headless: bool = False,
1093
1094
1095
1096
1097
1098
1099
    ) -> 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
1100

1101
1102
1103
1104
1105
1106
        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.
        """
1107
        current_platform.pre_register_and_update()
1108

1109
1110
        device_config = DeviceConfig(
            device=cast(Device, current_platform.device_type))
1111
1112
        model_config = self.create_model_config()

1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127
1128
1129
1130
1131
        # * 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:
1132
            self._set_default_args_v1(usage_context, model_config)
1133
            # Disable chunked prefill for POWER (ppc64le)/ARM/s390x CPUs in V1
1134
1135
            if current_platform.is_cpu(
            ) and current_platform.get_cpu_architecture() in (
1136
                    CpuArchEnum.POWERPC, CpuArchEnum.S390X, CpuArchEnum.ARM):
1137
                logger.info(
1138
1139
                    "Chunked prefill is not supported for ARM and POWER "
                    "and S390X CPUs; "
1140
1141
                    "disabling it for V1 backend.")
                self.enable_chunked_prefill = False
1142
1143
        else:
            self._set_default_args_v0(model_config)
1144
1145
        assert self.enable_chunked_prefill is not None

1146
1147
1148
1149
1150
1151
1152
1153
1154
1155
1156
        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'")

1157
1158
1159
1160
1161
1162
1163
        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()

1164
1165
1166
        # Note(hc): In the current implementation of decode context
        # parallel(DCP), tp_size needs to be divisible by dcp_size,
        # because the world size does not change by dcp, it simply
1167
        # reuses the GPUs of TP group, and split one TP group into
1168
1169
1170
1171
1172
1173
1174
        # tp_size//dcp_size DCP groups.
        assert self.tensor_parallel_size % self.decode_context_parallel_size \
            == 0, (
            f"tp_size={self.tensor_parallel_size} must be divisible by"
            f"dcp_size={self.decode_context_parallel_size}."
        )

1175
        cache_config = CacheConfig(
1176
            block_size=self.block_size,
1177
1178
1179
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
1180
            is_attention_free=model_config.is_attention_free,
1181
            num_gpu_blocks_override=self.num_gpu_blocks_override,
1182
            sliding_window=sliding_window,
1183
            enable_prefix_caching=self.enable_prefix_caching,
1184
            prefix_caching_hash_algo=self.prefix_caching_hash_algo,
1185
            cpu_offload_gb=self.cpu_offload_gb,
1186
            calculate_kv_scales=self.calculate_kv_scales,
1187
            kv_sharing_fast_prefill=self.kv_sharing_fast_prefill,
1188
1189
            mamba_cache_dtype=self.mamba_cache_dtype,
            mamba_ssm_cache_dtype=self.mamba_ssm_cache_dtype,
1190
        )
1191

1192
1193
1194
1195
1196
1197
1198
1199
1200
        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)

1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
        # 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()

1212
1213
1214
1215
        assert not headless or not self.data_parallel_hybrid_lb, (
            "data_parallel_hybrid_lb is not applicable in "
            "headless mode")

1216
        data_parallel_external_lb = self.data_parallel_rank is not None
1217
        # Local DP rank = 1, use pure-external LB.
1218
1219
1220
1221
1222
        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
1223
1224
            # Use full external lb if we have local_size of 1.
            self.data_parallel_hybrid_lb = False
1225
1226
        elif self.data_parallel_size_local is not None:
            data_parallel_size_local = self.data_parallel_size_local
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
1240
1241

            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
1242
        else:
1243
1244
1245
1246
            assert not self.data_parallel_hybrid_lb, (
                "data_parallel_size_local must be set to use "
                "data_parallel_hybrid_lb.")

1247
1248
            # Local DP size defaults to global DP size if not set.
            data_parallel_size_local = self.data_parallel_size
1249
1250
1251

        # DP address, used in multi-node case for torch distributed group
        # and ZMQ sockets.
Rui Qiao's avatar
Rui Qiao committed
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
        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
1266
1267
1268
1269
1270
1271
1272

        # 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

1273
1274
1275
1276
1277
1278
1279
1280
1281
1282
1283
1284
1285
1286
1287
1288
1289
1290
1291
1292
        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.")

1293
1294
1295
1296
1297
1298
1299
1300
1301
1302
        # 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

1303
        parallel_config = ParallelConfig(
1304
1305
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
1306
            data_parallel_size=self.data_parallel_size,
1307
1308
            data_parallel_rank=self.data_parallel_rank or 0,
            data_parallel_external_lb=data_parallel_external_lb,
1309
1310
1311
            data_parallel_size_local=data_parallel_size_local,
            data_parallel_master_ip=data_parallel_address,
            data_parallel_rpc_port=data_parallel_rpc_port,
1312
            data_parallel_backend=self.data_parallel_backend,
1313
            data_parallel_hybrid_lb=self.data_parallel_hybrid_lb,
1314
            enable_expert_parallel=self.enable_expert_parallel,
1315
            enable_eplb=self.enable_eplb,
1316
            eplb_config=self.eplb_config,
1317
1318
1319
            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,
1320
            ray_runtime_env=ray_runtime_env,
1321
            placement_group=placement_group,
1322
1323
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
1324
            worker_extension_cls=self.worker_extension_cls,
1325
            decode_context_parallel_size=self.decode_context_parallel_size,
1326
        )
1327

1328
        speculative_config = self.create_speculative_config(
1329
1330
            target_model_config=model_config,
            target_parallel_config=parallel_config,
1331
            enable_chunked_prefill=self.enable_chunked_prefill,
1332
            disable_log_stats=self.disable_log_stats,
1333
1334
        )

1335
1336
1337
1338
1339
        # 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
1340

1341
        scheduler_config = SchedulerConfig(
1342
            runner_type=model_config.runner_type,
1343
1344
1345
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1346
            cuda_graph_sizes=self.cuda_graph_sizes,
1347
            num_lookahead_slots=num_lookahead_slots,
1348
1349
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1350
            disable_chunked_mm_input=self.disable_chunked_mm_input,
1351
            is_multimodal_model=model_config.is_multimodal_model,
1352
            preemption_mode=self.preemption_mode,
1353
1354
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1355
            policy=self.scheduling_policy,
1356
            scheduler_cls=self.scheduler_cls,
1357
1358
1359
            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,
1360
1361
            disable_hybrid_kv_cache_manager=self.
            disable_hybrid_kv_cache_manager,
1362
            async_scheduling=self.async_scheduling,
1363
        )
1364

1365
1366
1367
1368
1369
        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")

1370
        lora_config = LoRAConfig(
1371
            bias_enabled=self.enable_lora_bias,
1372
1373
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1374
            default_mm_loras=self.default_mm_loras,
1375
            fully_sharded_loras=self.fully_sharded_loras,
1376
1377
1378
1379
            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
1380

1381
1382
1383
1384
        # bitsandbytes pre-quantized model need a specific model loader
        if model_config.quantization == "bitsandbytes":
            self.quantization = self.load_format = "bitsandbytes"

1385
        load_config = self.create_load_config()
1386

1387
        decoding_config = DecodingConfig(
1388
1389
1390
1391
1392
            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,
1393
1394
            reasoning_backend=self.reasoning_parser
        )
1395

1396
        observability_config = ObservabilityConfig(
1397
1398
            show_hidden_metrics_for_version=(
                self.show_hidden_metrics_for_version),
1399
            otlp_traces_endpoint=self.otlp_traces_endpoint,
1400
            collect_detailed_traces=self.collect_detailed_traces,
1401
        )
1402

1403
        config = VllmConfig(
1404
1405
1406
1407
1408
1409
1410
1411
1412
1413
            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,
1414
            compilation_config=self.compilation_config,
1415
            kv_transfer_config=self.kv_transfer_config,
1416
            kv_events_config=self.kv_events_config,
1417
            additional_config=self.additional_config,
1418
        )
1419

1420
1421
        return config

1422
1423
1424
1425
1426
1427
    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.

1428
        if self.load_format == "sharded_state":
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
            _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

1440
        if self.preemption_mode != SchedulerConfig.preemption_mode:
1441
1442
1443
1444
1445
1446
1447
1448
1449
1450
            _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

1451
        if self.scheduler_delay_factor != SchedulerConfig.delay_factor:
1452
1453
1454
1455
1456
            _raise_or_fallback(feature_name="--scheduler-delay-factor",
                               recommend_to_remove=True)
            return False

        if self.kv_cache_dtype != "auto":
1457
            supported = current_platform.is_kv_cache_dtype_supported(
1458
                self.kv_cache_dtype, model_config)
1459
1460
1461
1462
            if not supported:
                _raise_or_fallback(feature_name="--kv-cache-dtype",
                                   recommend_to_remove=False)
                return False
1463

1464
1465
1466
1467
1468
1469
        # 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

1470
        # No Mamba or Encoder-Decoder so far.
1471
1472
1473
1474
1475
1476
1477
        if not model_config.is_v1_compatible:
            _raise_or_fallback(feature_name=model_config.architectures,
                               recommend_to_remove=False)
            return False

        # No Concurrent Partial Prefills so far.
        if (self.max_num_partial_prefills
1478
                != SchedulerConfig.max_num_partial_prefills
1479
                or self.max_long_partial_prefills
1480
                != SchedulerConfig.max_long_partial_prefills):
1481
1482
1483
1484
1485
1486
1487
1488
1489
1490
            _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

1491
        # V1 supports N-gram, Medusa, and Eagle speculative decoding.
1492
1493
1494
1495
1496
1497
        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.")
1498
1499

        V1_BACKENDS = [
1500
1501
1502
1503
1504
1505
            "FLASH_ATTN_VLLM_V1",
            "FLASH_ATTN",
            "PALLAS",
            "PALLAS_VLLM_V1",
            "TRITON_ATTN_VLLM_V1",
            "TRITON_MLA",
1506
            "CUTLASS_MLA",
1507
            "FLASHMLA",
1508
1509
            "FLASHMLA_VLLM_V1",
            "FLASH_ATTN_MLA",
1510
1511
            "FLASHINFER",
            "FLASHINFER_VLLM_V1",
1512
            "FLASHINFER_MLA",
1513
            "ROCM_AITER_MLA",
1514
            "TORCH_SDPA_VLLM_V1",
1515
            "FLEX_ATTENTION",
1516
            "TREE_ATTN",
1517
            "XFORMERS_VLLM_V1",
1518
1519
1520
1521
1522
1523
1524
        ]
        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

1525
1526
        # Platforms must decide if they can support v1 for this model
        if not current_platform.supports_v1(model_config=model_config):
1527
1528
1529
1530
            _raise_or_fallback(
                feature_name=f"device type={current_platform.device_type}",
                recommend_to_remove=False)
            return False
1531
1532
1533
        #############################################################
        # Experimental Features - allow users to opt in.

1534
1535
1536
1537
1538
1539
1540
1541
1542
1543
1544
1545
        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
1546

1547
1548
1549
1550
        # 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):
1551
            return False
1552
1553
1554
1555
1556
1557
1558

        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

1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
        #############################################################

        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)
1579
                use_spec_decode = self.speculative_config is not None
1580
1581

                if (is_gpu and not use_sliding_window and not use_spec_decode
1582
                        and not self.enable_lora):
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597
1598
1599
1600
                    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)

1601
1602
1603
1604
1605
1606
        # Disable prefix caching for multimodal models for VLLM_V0.
        if self.enable_prefix_caching and 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
1607
1608
1609
1610
1611

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

1612
1613
    def _set_default_args_v1(self, usage_context: UsageContext,
                             model_config: ModelConfig) -> None:
1614
        """Set Default Arguments for V1 Engine."""
1615

1616
1617
1618
1619
1620
1621
1622
1623
1624
1625
        # 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
1626
1627
1628
1629
            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)
1630

1631
1632
1633
1634
1635
1636
1637
1638
1639
1640
            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)

1641
1642
1643
        # 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:
1644
            self.scheduler_cls = "vllm.v1.core.sched.scheduler.Scheduler"
1645

1646
1647
        # When no user override, set the default values based on the usage
        # context.
1648
        # Use different default values for different hardware.
1649
1650
1651
1652
1653
1654
1655

        # 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:
1656
            device_memory = current_platform.get_device_total_memory()
1657
            device_name = current_platform.get_device_name().lower()
1658
1659
        except Exception:
            # This is only used to set default_max_num_batched_tokens
1660
            device_memory = 0
1661

1662
1663
1664
        # 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.
1665
        from vllm.usage.usage_lib import UsageContext
1666
        if device_memory >= 70 * GiB_bytes and "a100" not in device_name:
1667
            # For GPUs like H100 and MI300x, use larger default values.
1668
1669
1670
1671
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 16384,
                UsageContext.OPENAI_API_SERVER: 8192,
            }
1672
1673
1674
1675
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 1024,
                UsageContext.OPENAI_API_SERVER: 1024,
            }
1676
1677
1678
1679
1680
1681
        else:
            # TODO(woosuk): Tune the default values for other hardware.
            default_max_num_batched_tokens = {
                UsageContext.LLM_CLASS: 8192,
                UsageContext.OPENAI_API_SERVER: 2048,
            }
1682
1683
1684
1685
            default_max_num_seqs = {
                UsageContext.LLM_CLASS: 256,
                UsageContext.OPENAI_API_SERVER: 256,
            }
1686

1687
1688
1689
1690
1691
1692
1693
1694
1695
1696
1697
1698
1699
1700
1701
        # 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,
                }
            }

1702
1703
        # cpu specific default values.
        if current_platform.is_cpu():
1704
            world_size = self.pipeline_parallel_size * self.tensor_parallel_size
1705
            default_max_num_batched_tokens = {
1706
1707
                UsageContext.LLM_CLASS: 4096 * world_size,
                UsageContext.OPENAI_API_SERVER: 2048 * world_size,
1708
1709
            }
            default_max_num_seqs = {
1710
1711
                UsageContext.LLM_CLASS: 256 * world_size,
                UsageContext.OPENAI_API_SERVER: 128 * world_size,
1712
1713
            }

1714
        use_context_value = usage_context.value if usage_context else None
1715
1716
        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
1717
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
            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:
1728
1729
1730
1731
1732
                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]
1733
            logger.debug(
1734
                "Setting max_num_batched_tokens to %d for %s usage context.",
1735
                self.max_num_batched_tokens, use_context_value)
1736

1737
1738
        if (self.max_num_seqs is None
                and usage_context in default_max_num_seqs):
1739
1740
            self.max_num_seqs = min(default_max_num_seqs[usage_context],
                                    self.max_num_batched_tokens or sys.maxsize)
1741
1742
1743

            logger.debug("Setting max_num_seqs to %d for %s usage context.",
                         self.max_num_seqs, use_context_value)
1744

1745

1746
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
1747
class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
1748
    """Arguments for asynchronous vLLM engine."""
1749
1750
1751
1752
1753
1754
1755
1756
1757
1758
1759
1760
1761
1762
1763
1764
1765
    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
1766
1767

    @staticmethod
1768
1769
    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
1770
        # Initialize plugin to update the parser, for example, The plugin may
1771
        # add a new kind of quantization method to --quantization argument or
1772
1773
        # a new device to --device argument.
        load_general_plugins()
1774
1775
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
1776
1777
1778
1779
        parser.add_argument('--enable-log-requests',
                            action=argparse.BooleanOptionalAction,
                            default=AsyncEngineArgs.enable_log_requests,
                            help='Enable logging requests.')
1780
        parser.add_argument('--disable-log-requests',
1781
1782
1783
1784
                            action=argparse.BooleanOptionalAction,
                            default=not AsyncEngineArgs.enable_log_requests,
                            help='[DEPRECATED] Disable logging requests.',
                            deprecated=True)
1785
        current_platform.pre_register_and_update(parser)
1786
        return parser
1787
1788


1789
1790
1791
1792
1793
1794
1795
1796
1797
1798
1799
1800
1801
1802
1803
1804
1805
1806
1807
1808
1809
1810
1811
1812
1813
1814
1815
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


1816
1817
1818
def human_readable_int(value):
    """Parse human-readable integers like '1k', '2M', etc.
    Including decimal values with decimal multipliers.
1819

1820
1821
1822
1823
1824
1825
1826
1827
1828
1829
1830
1831
1832
1833
1834
1835
1836
1837
1838
1839
1840
1841
1842
1843
1844
1845
1846
1847
1848
1849
1850
1851
1852
1853
1854
    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)