arg_utils.py 59.5 KB
Newer Older
1
import argparse
2
import dataclasses
3
import json
4
from dataclasses import dataclass
5
from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Mapping, Optional,
6
                    Tuple, Type, Union, cast, get_args)
7

8
9
import torch

10
import vllm.envs as envs
11
from vllm.config import (CacheConfig, CompilationConfig, ConfigFormat,
12
13
14
15
                         DecodingConfig, DeviceConfig, HfOverrides,
                         KVTransferConfig, LoadConfig, LoadFormat, LoRAConfig,
                         ModelConfig, ObservabilityConfig, ParallelConfig,
                         PoolerConfig, PromptAdapterConfig, SchedulerConfig,
16
17
                         SpeculativeConfig, TaskOption, TokenizerPoolConfig,
                         VllmConfig)
18
from vllm.executor.executor_base import ExecutorBase
19
from vllm.logger import init_logger
20
from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
21
from vllm.platforms import current_platform
22
from vllm.transformers_utils.utils import check_gguf_file
23
from vllm.usage.usage_lib import UsageContext
24
from vllm.utils import FlexibleArgumentParser, StoreBoolean
25

26
if TYPE_CHECKING:
27
    from vllm.transformers_utils.tokenizer_group import BaseTokenizerGroup
28

29
30
logger = init_logger(__name__)

31
32
ALLOWED_DETAILED_TRACE_MODULES = ["model", "worker", "all"]

33
34
35
36
37
38
39
40
DEVICE_OPTIONS = [
    "auto",
    "cuda",
    "neuron",
    "cpu",
    "openvino",
    "tpu",
    "xpu",
41
    "hpu",
42
43
]

44

45
46
47
48
49
50
def nullable_str(val: str):
    if not val or val == "None":
        return None
    return val


51
def nullable_kvs(val: str) -> Optional[Mapping[str, int]]:
52
53
54
55
56
57
58
59
60
    """Parses a string containing comma separate key [str] to value [int]
    pairs into a dictionary.

    Args:
        val: String value to be parsed.

    Returns:
        Dictionary with parsed values.
    """
61
62
63
64
65
    if len(val) == 0:
        return None

    out_dict: Dict[str, int] = {}
    for item in val.split(","):
66
67
68
69
70
        kv_parts = [part.lower().strip() for part in item.split("=")]
        if len(kv_parts) != 2:
            raise argparse.ArgumentTypeError(
                "Each item should be in the form KEY=VALUE")
        key, value = kv_parts
71
72

        try:
73
            parsed_value = int(value)
74
75
        except ValueError as exc:
            msg = f"Failed to parse value of item {key}={value}"
76
77
78
79
80
81
            raise argparse.ArgumentTypeError(msg) from exc

        if key in out_dict and out_dict[key] != parsed_value:
            raise argparse.ArgumentTypeError(
                f"Conflicting values specified for key: {key}")
        out_dict[key] = parsed_value
82
83
84
85

    return out_dict


86
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
87
class EngineArgs:
Woosuk Kwon's avatar
Woosuk Kwon committed
88
    """Arguments for vLLM engine."""
89
    model: str = 'facebook/opt-125m'
90
    served_model_name: Optional[Union[str, List[str]]] = None
91
    tokenizer: Optional[str] = None
92
    task: TaskOption = "auto"
93
    skip_tokenizer_init: bool = False
94
    tokenizer_mode: str = 'auto'
95
    trust_remote_code: bool = False
96
    allowed_local_media_path: str = ""
97
    download_dir: Optional[str] = None
98
    load_format: str = 'auto'
99
    config_format: ConfigFormat = ConfigFormat.AUTO
100
    dtype: str = 'auto'
101
    kv_cache_dtype: str = 'auto'
102
    quantization_param_path: Optional[str] = None
103
    seed: int = 0
104
    max_model_len: Optional[int] = None
105
    worker_use_ray: bool = False
106
107
108
109
110
    # Note: Specifying a custom executor backend by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    distributed_executor_backend: Optional[Union[str,
                                                 Type[ExecutorBase]]] = None
111
    # number of P/D disaggregation (or other disaggregation) workers
112
113
    pipeline_parallel_size: int = 1
    tensor_parallel_size: int = 1
114
    max_parallel_loading_workers: Optional[int] = None
115
    block_size: Optional[int] = None
116
    enable_prefix_caching: Optional[bool] = None
117
    disable_sliding_window: bool = False
118
    use_v2_block_manager: bool = True
119
120
    swap_space: float = 4  # GiB
    cpu_offload_gb: float = 0  # GiB
121
    gpu_memory_utilization: float = 0.90
122
    max_num_batched_tokens: Optional[int] = None
123
    max_num_seqs: Optional[int] = None
124
    max_logprobs: int = 20  # Default value for OpenAI Chat Completions API
125
    disable_log_stats: bool = False
Jasmond L's avatar
Jasmond L committed
126
    revision: Optional[str] = None
127
    code_revision: Optional[str] = None
128
    rope_scaling: Optional[Dict[str, Any]] = None
129
    rope_theta: Optional[float] = None
130
    hf_overrides: Optional[HfOverrides] = None
131
    tokenizer_revision: Optional[str] = None
132
    quantization: Optional[str] = None
133
    enforce_eager: Optional[bool] = None
134
    max_seq_len_to_capture: int = 8192
135
    disable_custom_all_reduce: bool = False
136
    tokenizer_pool_size: int = 0
137
138
139
140
    # Note: Specifying a tokenizer pool by passing a class
    # is intended for expert use only. The API may change without
    # notice.
    tokenizer_pool_type: Union[str, Type["BaseTokenizerGroup"]] = "ray"
141
    tokenizer_pool_extra_config: Optional[Dict[str, Any]] = None
142
    limit_mm_per_prompt: Optional[Mapping[str, int]] = None
143
    mm_processor_kwargs: Optional[Dict[str, Any]] = None
144
    mm_cache_preprocessor: bool = False
145
    enable_lora: bool = False
146
    enable_lora_bias: bool = False
147
148
    max_loras: int = 1
    max_lora_rank: int = 16
149
150
151
    enable_prompt_adapter: bool = False
    max_prompt_adapters: int = 1
    max_prompt_adapter_token: int = 0
152
    fully_sharded_loras: bool = False
153
    lora_extra_vocab_size: int = 256
154
    long_lora_scaling_factors: Optional[Tuple[float]] = None
155
    lora_dtype: Optional[Union[str, torch.dtype]] = 'auto'
156
    max_cpu_loras: Optional[int] = None
157
    device: str = 'auto'
158
    num_scheduler_steps: int = 1
159
    multi_step_stream_outputs: bool = True
160
    ray_workers_use_nsight: bool = False
161
    num_gpu_blocks_override: Optional[int] = None
162
    num_lookahead_slots: int = 0
163
    model_loader_extra_config: Optional[dict] = None
164
    ignore_patterns: Optional[Union[str, List[str]]] = None
165
    preemption_mode: Optional[str] = None
166

167
    scheduler_delay_factor: float = 0.0
168
    enable_chunked_prefill: Optional[bool] = None
169

170
    guided_decoding_backend: str = 'xgrammar'
171
    logits_processor_pattern: Optional[str] = None
172
173
    # Speculative decoding configuration.
    speculative_model: Optional[str] = None
174
    speculative_model_quantization: Optional[str] = None
175
    speculative_draft_tensor_parallel_size: Optional[int] = None
176
    num_speculative_tokens: Optional[int] = None
177
    speculative_disable_mqa_scorer: Optional[bool] = False
178
    speculative_max_model_len: Optional[int] = None
179
    speculative_disable_by_batch_size: Optional[int] = None
180
181
    ngram_prompt_lookup_max: Optional[int] = None
    ngram_prompt_lookup_min: Optional[int] = None
182
183
184
    spec_decoding_acceptance_method: str = 'rejection_sampler'
    typical_acceptance_sampler_posterior_threshold: Optional[float] = None
    typical_acceptance_sampler_posterior_alpha: Optional[float] = None
185
    qlora_adapter_name_or_path: Optional[str] = None
186
    disable_logprobs_during_spec_decoding: Optional[bool] = None
187

188
    otlp_traces_endpoint: Optional[str] = None
189
    collect_detailed_traces: Optional[str] = None
190
    disable_async_output_proc: bool = False
191
    scheduling_policy: Literal["fcfs", "priority"] = "fcfs"
192

193
194
    override_neuron_config: Optional[Dict[str, Any]] = None
    override_pooler_config: Optional[PoolerConfig] = None
195
    compilation_config: Optional[CompilationConfig] = None
196
    worker_cls: str = "auto"
197

198
199
    kv_transfer_config: Optional[KVTransferConfig] = None

200
    def __post_init__(self):
201
        if not self.tokenizer:
202
            self.tokenizer = self.model
203

204
205
206
207
        # Override the default value of enable_prefix_caching if it's not set
        # by user.
        if self.enable_prefix_caching is None:
            self.enable_prefix_caching = bool(envs.VLLM_USE_V1)
208

209
210
211
        # Override max_num_seqs if it's not set by user.
        if self.max_num_seqs is None:
            self.max_num_seqs = 256 if not envs.VLLM_USE_V1 else 1024
212

213
214
215
        # support `EngineArgs(compilation_config={...})`
        # without having to manually construct a
        # CompilationConfig object
216
        if isinstance(self.compilation_config, (int, dict)):
217
218
            self.compilation_config = CompilationConfig.from_cli(
                str(self.compilation_config))
219

220
        # Setup plugins
221
222
        from vllm.plugins import load_general_plugins
        load_general_plugins()
223
224

    @staticmethod
225
    def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
Woosuk Kwon's avatar
Woosuk Kwon committed
226
        """Shared CLI arguments for vLLM engine."""
227

228
        # Model arguments
229
230
231
        parser.add_argument(
            '--model',
            type=str,
232
            default=EngineArgs.model,
233
            help='Name or path of the huggingface model to use.')
234
235
236
237
238
239
240
241
242
        parser.add_argument(
            '--task',
            default=EngineArgs.task,
            choices=get_args(TaskOption),
            help='The task to use the model for. Each vLLM instance only '
            'supports one task, even if the same model can be used for '
            'multiple tasks. When the model only supports one task, "auto" '
            'can be used to select it; otherwise, you must specify explicitly '
            'which task to use.')
243
244
        parser.add_argument(
            '--tokenizer',
245
            type=nullable_str,
246
            default=EngineArgs.tokenizer,
247
248
            help='Name or path of the huggingface tokenizer to use. '
            'If unspecified, model name or path will be used.')
249
250
251
252
        parser.add_argument(
            '--skip-tokenizer-init',
            action='store_true',
            help='Skip initialization of tokenizer and detokenizer')
Jasmond L's avatar
Jasmond L committed
253
254
        parser.add_argument(
            '--revision',
255
            type=nullable_str,
Jasmond L's avatar
Jasmond L committed
256
            default=None,
257
            help='The specific model version to use. It can be a branch '
Jasmond L's avatar
Jasmond L committed
258
259
            'name, a tag name, or a commit id. If unspecified, will use '
            'the default version.')
260
261
        parser.add_argument(
            '--code-revision',
262
            type=nullable_str,
263
            default=None,
264
            help='The specific revision to use for the model code on '
265
266
            'Hugging Face Hub. It can be a branch name, a tag name, or a '
            'commit id. If unspecified, will use the default version.')
267
268
        parser.add_argument(
            '--tokenizer-revision',
269
            type=nullable_str,
270
            default=None,
271
272
273
            help='Revision of the huggingface tokenizer to use. '
            'It can be a branch name, a tag name, or a commit id. '
            'If unspecified, will use the default version.')
274
275
276
277
        parser.add_argument(
            '--tokenizer-mode',
            type=str,
            default=EngineArgs.tokenizer_mode,
278
            choices=['auto', 'slow', 'mistral'],
279
280
            help='The tokenizer mode.\n\n* "auto" will use the '
            'fast tokenizer if available.\n* "slow" will '
281
282
            'always use the slow tokenizer. \n* '
            '"mistral" will always use the `mistral_common` tokenizer.')
283
284
        parser.add_argument('--trust-remote-code',
                            action='store_true',
285
                            help='Trust remote code from huggingface.')
286
287
288
        parser.add_argument(
            '--allowed-local-media-path',
            type=str,
289
290
291
292
            help="Allowing API requests to read local images or videos "
            "from directories specified by the server file system. "
            "This is a security risk. "
            "Should only be enabled in trusted environments.")
293
        parser.add_argument('--download-dir',
294
                            type=nullable_str,
Zhuohan Li's avatar
Zhuohan Li committed
295
                            default=EngineArgs.download_dir,
296
                            help='Directory to download and load the weights, '
297
                            'default to the default cache dir of '
298
                            'huggingface.')
299
300
301
302
        parser.add_argument(
            '--load-format',
            type=str,
            default=EngineArgs.load_format,
303
            choices=[f.value for f in LoadFormat],
304
305
            help='The format of the model weights to load.\n\n'
            '* "auto" will try to load the weights in the safetensors format '
306
            'and fall back to the pytorch bin format if safetensors format '
307
308
309
310
311
312
313
314
            'is not available.\n'
            '* "pt" will load the weights in the pytorch bin format.\n'
            '* "safetensors" will load the weights in the safetensors format.\n'
            '* "npcache" will load the weights in pytorch format and store '
            'a numpy cache to speed up the loading.\n'
            '* "dummy" will initialize the weights with random values, '
            'which is mainly for profiling.\n'
            '* "tensorizer" will load the weights using tensorizer from '
315
            'CoreWeave. See the Tensorize vLLM Model script in the Examples '
316
317
318
            'section for more information.\n'
            '* "bitsandbytes" will load the weights using bitsandbytes '
            'quantization.\n')
319
320
321
322
323
324
325
        parser.add_argument(
            '--config-format',
            default=EngineArgs.config_format,
            choices=[f.value for f in ConfigFormat],
            help='The format of the model config to load.\n\n'
            '* "auto" will try to load the config in hf format '
            'if available else it will try to load in mistral format ')
326
327
328
329
        parser.add_argument(
            '--dtype',
            type=str,
            default=EngineArgs.dtype,
Woosuk Kwon's avatar
Woosuk Kwon committed
330
331
332
            choices=[
                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
            ],
333
334
335
336
337
338
339
340
            help='Data type for model weights and activations.\n\n'
            '* "auto" will use FP16 precision for FP32 and FP16 models, and '
            'BF16 precision for BF16 models.\n'
            '* "half" for FP16. Recommended for AWQ quantization.\n'
            '* "float16" is the same as "half".\n'
            '* "bfloat16" for a balance between precision and range.\n'
            '* "float" is shorthand for FP32 precision.\n'
            '* "float32" for FP32 precision.')
341
342
343
        parser.add_argument(
            '--kv-cache-dtype',
            type=str,
344
            choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
345
            default=EngineArgs.kv_cache_dtype,
346
            help='Data type for kv cache storage. If "auto", will use model '
347
348
            'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
            'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
349
350
        parser.add_argument(
            '--quantization-param-path',
351
            type=nullable_str,
352
353
354
355
356
            default=None,
            help='Path to the JSON file containing the KV cache '
            'scaling factors. This should generally be supplied, when '
            'KV cache dtype is FP8. Otherwise, KV cache scaling factors '
            'default to 1.0, which may cause accuracy issues. '
357
            'FP8_E5M2 (without scaling) is only supported on cuda version '
358
            'greater than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead '
359
            'supported for common inference criteria.')
360
361
        parser.add_argument('--max-model-len',
                            type=int,
362
                            default=EngineArgs.max_model_len,
363
364
                            help='Model context length. If unspecified, will '
                            'be automatically derived from the model config.')
365
366
367
        parser.add_argument(
            '--guided-decoding-backend',
            type=str,
368
369
            default='xgrammar',
            choices=['outlines', 'lm-format-enforcer', 'xgrammar'],
370
            help='Which engine will be used for guided decoding'
371
            ' (JSON schema / regex etc) by default. Currently support '
372
373
            'https://github.com/outlines-dev/outlines,'
            'https://github.com/mlc-ai/xgrammar, and '
374
375
376
            'https://github.com/noamgat/lm-format-enforcer.'
            ' Can be overridden per request via guided_decoding_backend'
            ' parameter.')
377
378
379
380
381
382
383
384
        parser.add_argument(
            '--logits-processor-pattern',
            type=nullable_str,
            default=None,
            help='Optional regex pattern specifying valid logits processor '
            'qualified names that can be passed with the `logits_processors` '
            'extra completion argument. Defaults to None, which allows no '
            'processors.')
385
        # Parallel arguments
386
387
388
389
        parser.add_argument(
            '--distributed-executor-backend',
            choices=['ray', 'mp'],
            default=EngineArgs.distributed_executor_backend,
390
391
392
393
394
395
396
397
            help='Backend to use for distributed model '
            'workers, either "ray" or "mp" (multiprocessing). If the product '
            'of pipeline_parallel_size and tensor_parallel_size is less than '
            'or equal to the number of GPUs available, "mp" will be used to '
            'keep processing on a single host. Otherwise, this will default '
            'to "ray" if Ray is installed and fail otherwise. Note that tpu '
            'and hpu only support Ray for distributed inference.')

398
399
400
401
        parser.add_argument(
            '--worker-use-ray',
            action='store_true',
            help='Deprecated, use --distributed-executor-backend=ray.')
402
403
404
        parser.add_argument('--pipeline-parallel-size',
                            '-pp',
                            type=int,
Zhuohan Li's avatar
Zhuohan Li committed
405
                            default=EngineArgs.pipeline_parallel_size,
406
                            help='Number of pipeline stages.')
407
408
409
        parser.add_argument('--tensor-parallel-size',
                            '-tp',
                            type=int,
Zhuohan Li's avatar
Zhuohan Li committed
410
                            default=EngineArgs.tensor_parallel_size,
411
                            help='Number of tensor parallel replicas.')
412
413
414
        parser.add_argument(
            '--max-parallel-loading-workers',
            type=int,
415
            default=EngineArgs.max_parallel_loading_workers,
416
            help='Load model sequentially in multiple batches, '
417
            'to avoid RAM OOM when using tensor '
418
            'parallel and large models.')
419
420
421
        parser.add_argument(
            '--ray-workers-use-nsight',
            action='store_true',
422
            help='If specified, use nsight to profile Ray workers.')
423
        # KV cache arguments
424
425
        parser.add_argument('--block-size',
                            type=int,
Zhuohan Li's avatar
Zhuohan Li committed
426
                            default=EngineArgs.block_size,
427
                            choices=[8, 16, 32],
428
                            help='Token block size for contiguous chunks of '
429
430
                            'tokens. This is ignored on neuron devices and '
                            'set to max-model-len')
431

432
433
434
435
436
437
438
        parser.add_argument(
            "--enable-prefix-caching",
            action=argparse.BooleanOptionalAction,
            default=EngineArgs.enable_prefix_caching,
            help="Enables automatic prefix caching. "
            "Use --no-enable-prefix-caching to disable explicitly.",
        )
439
440
441
442
        parser.add_argument('--disable-sliding-window',
                            action='store_true',
                            help='Disables sliding window, '
                            'capping to sliding window size')
443
444
        parser.add_argument('--use-v2-block-manager',
                            action='store_true',
445
                            default=True,
446
447
448
449
450
                            help='[DEPRECATED] block manager v1 has been '
                            'removed and SelfAttnBlockSpaceManager (i.e. '
                            'block manager v2) is now the default. '
                            'Setting this flag to True or False'
                            ' has no effect on vLLM behavior.')
451
452
453
454
455
456
457
458
        parser.add_argument(
            '--num-lookahead-slots',
            type=int,
            default=EngineArgs.num_lookahead_slots,
            help='Experimental scheduling config necessary for '
            'speculative decoding. This will be replaced by '
            'speculative config in the future; it is present '
            'to enable correctness tests until then.')
459

460
461
462
        parser.add_argument('--seed',
                            type=int,
                            default=EngineArgs.seed,
463
                            help='Random seed for operations.')
464
        parser.add_argument('--swap-space',
465
                            type=float,
Zhuohan Li's avatar
Zhuohan Li committed
466
                            default=EngineArgs.swap_space,
467
                            help='CPU swap space size (GiB) per GPU.')
468
469
470
471
472
473
474
475
476
        parser.add_argument(
            '--cpu-offload-gb',
            type=float,
            default=0,
            help='The space in GiB to offload to CPU, per GPU. '
            'Default is 0, which means no offloading. Intuitively, '
            'this argument can be seen as a virtual way to increase '
            'the GPU memory size. For example, if you have one 24 GB '
            'GPU and set this to 10, virtually you can think of it as '
477
            'a 34 GB GPU. Then you can load a 13B model with BF16 weight, '
478
            'which requires at least 26GB GPU memory. Note that this '
479
            'requires fast CPU-GPU interconnect, as part of the model is '
480
481
            'loaded from CPU memory to GPU memory on the fly in each '
            'model forward pass.')
482
483
484
485
        parser.add_argument(
            '--gpu-memory-utilization',
            type=float,
            default=EngineArgs.gpu_memory_utilization,
486
487
488
            help='The fraction of GPU memory to be used for the model '
            'executor, which can range from 0 to 1. For example, a value of '
            '0.5 would imply 50%% GPU memory utilization. If unspecified, '
489
490
491
492
493
494
            'will use the default value of 0.9. This is a per-instance '
            'limit, and only applies to the current vLLM instance.'
            'It does not matter if you have another vLLM instance running '
            'on the same GPU. For example, if you have two vLLM instances '
            'running on the same GPU, you can set the GPU memory utilization '
            'to 0.5 for each instance.')
495
        parser.add_argument(
496
            '--num-gpu-blocks-override',
497
498
499
            type=int,
            default=None,
            help='If specified, ignore GPU profiling result and use this number'
500
            ' of GPU blocks. Used for testing preemption.')
501
502
        parser.add_argument('--max-num-batched-tokens',
                            type=int,
Zhuohan Li's avatar
Zhuohan Li committed
503
                            default=EngineArgs.max_num_batched_tokens,
504
505
                            help='Maximum number of batched tokens per '
                            'iteration.')
506
507
        parser.add_argument('--max-num-seqs',
                            type=int,
Zhuohan Li's avatar
Zhuohan Li committed
508
                            default=EngineArgs.max_num_seqs,
509
                            help='Maximum number of sequences per iteration.')
510
511
512
513
        parser.add_argument(
            '--max-logprobs',
            type=int,
            default=EngineArgs.max_logprobs,
514
515
            help=('Max number of log probs to return logprobs is specified in'
                  ' SamplingParams.'))
516
517
        parser.add_argument('--disable-log-stats',
                            action='store_true',
518
                            help='Disable logging statistics.')
519
520
521
        # Quantization settings.
        parser.add_argument('--quantization',
                            '-q',
522
                            type=nullable_str,
523
                            choices=[*QUANTIZATION_METHODS, None],
524
                            default=EngineArgs.quantization,
525
526
527
528
529
530
                            help='Method used to quantize the weights. If '
                            'None, we first check the `quantization_config` '
                            'attribute in the model config file. If that is '
                            'None, we assume the model weights are not '
                            'quantized and use `dtype` to determine the data '
                            'type of the weights.')
531
532
533
534
535
536
        parser.add_argument(
            '--rope-scaling',
            default=None,
            type=json.loads,
            help='RoPE scaling configuration in JSON format. '
            'For example, {"rope_type":"dynamic","factor":2.0}')
537
538
539
540
541
542
        parser.add_argument('--rope-theta',
                            default=None,
                            type=float,
                            help='RoPE theta. Use with `rope_scaling`. In '
                            'some cases, changing the RoPE theta improves the '
                            'performance of the scaled model.')
543
544
545
        parser.add_argument('--hf-overrides',
                            type=json.loads,
                            default=EngineArgs.hf_overrides,
546
                            help='Extra arguments for the HuggingFace config. '
547
548
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
549
550
551
552
553
        parser.add_argument('--enforce-eager',
                            action='store_true',
                            help='Always use eager-mode PyTorch. If False, '
                            'will use eager mode and CUDA graph in hybrid '
                            'for maximal performance and flexibility.')
554
        parser.add_argument('--max-seq-len-to-capture',
555
556
557
558
                            type=int,
                            default=EngineArgs.max_seq_len_to_capture,
                            help='Maximum sequence length covered by CUDA '
                            'graphs. When a sequence has context length '
559
560
561
562
                            'larger than this, we fall back to eager mode. '
                            'Additionally for encoder-decoder models, if the '
                            'sequence length of the encoder input is larger '
                            'than this, we fall back to the eager mode.')
563
564
565
        parser.add_argument('--disable-custom-all-reduce',
                            action='store_true',
                            default=EngineArgs.disable_custom_all_reduce,
566
                            help='See ParallelConfig.')
567
568
569
570
571
572
573
574
575
576
577
578
579
        parser.add_argument('--tokenizer-pool-size',
                            type=int,
                            default=EngineArgs.tokenizer_pool_size,
                            help='Size of tokenizer pool to use for '
                            'asynchronous tokenization. If 0, will '
                            'use synchronous tokenization.')
        parser.add_argument('--tokenizer-pool-type',
                            type=str,
                            default=EngineArgs.tokenizer_pool_type,
                            help='Type of tokenizer pool to use for '
                            'asynchronous tokenization. Ignored '
                            'if tokenizer_pool_size is 0.')
        parser.add_argument('--tokenizer-pool-extra-config',
580
                            type=nullable_str,
581
582
583
584
585
                            default=EngineArgs.tokenizer_pool_extra_config,
                            help='Extra config for tokenizer pool. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary. Ignored if '
                            'tokenizer_pool_size is 0.')
586
587
588
589
590
591
592
593
594
595
596
597
598
599

        # Multimodal related configs
        parser.add_argument(
            '--limit-mm-per-prompt',
            type=nullable_kvs,
            default=EngineArgs.limit_mm_per_prompt,
            # The default value is given in
            # MultiModalRegistry.init_mm_limits_per_prompt
            help=('For each multimodal plugin, limit how many '
                  'input instances to allow for each prompt. '
                  'Expects a comma-separated list of items, '
                  'e.g.: `image=16,video=2` allows a maximum of 16 '
                  'images and 2 videos per prompt. Defaults to 1 for '
                  'each modality.'))
600
601
602
603
        parser.add_argument(
            '--mm-processor-kwargs',
            default=None,
            type=json.loads,
604
            help=('Overrides for the multimodal input mapping/processing, '
605
                  'e.g., image processor. For example: {"num_crops": 4}.'))
606
607
608
609
610
611
        parser.add_argument(
            '--mm-cache-preprocessor',
            action='store_true',
            help='If true, then enables caching of the multi-modal '
            'preprocessor/mapper. Otherwise, the mapper executes each time'
            ', and for better performance consider enabling frontend process.')
612

613
614
615
616
        # LoRA related configs
        parser.add_argument('--enable-lora',
                            action='store_true',
                            help='If True, enable handling of LoRA adapters.')
617
618
619
        parser.add_argument('--enable-lora-bias',
                            action='store_true',
                            help='If True, enable bias for LoRA adapters.')
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
        parser.add_argument('--max-loras',
                            type=int,
                            default=EngineArgs.max_loras,
                            help='Max number of LoRAs in a single batch.')
        parser.add_argument('--max-lora-rank',
                            type=int,
                            default=EngineArgs.max_lora_rank,
                            help='Max LoRA rank.')
        parser.add_argument(
            '--lora-extra-vocab-size',
            type=int,
            default=EngineArgs.lora_extra_vocab_size,
            help=('Maximum size of extra vocabulary that can be '
                  'present in a LoRA adapter (added to the base '
                  'model vocabulary).'))
        parser.add_argument(
            '--lora-dtype',
            type=str,
            default=EngineArgs.lora_dtype,
639
            choices=['auto', 'float16', 'bfloat16'],
640
641
            help=('Data type for LoRA. If auto, will default to '
                  'base model dtype.'))
642
643
644
645
646
647
648
649
650
651
652
        parser.add_argument(
            '--long-lora-scaling-factors',
            type=nullable_str,
            default=EngineArgs.long_lora_scaling_factors,
            help=('Specify multiple scaling factors (which can '
                  'be different from base model scaling factor '
                  '- see eg. Long LoRA) to allow for multiple '
                  'LoRA adapters trained with those scaling '
                  'factors to be used at the same time. If not '
                  'specified, only adapters trained with the '
                  'base model scaling factor are allowed.'))
653
654
655
656
657
        parser.add_argument(
            '--max-cpu-loras',
            type=int,
            default=EngineArgs.max_cpu_loras,
            help=('Maximum number of LoRAs to store in CPU memory. '
658
659
                  'Must be >= than max_loras. '
                  'Defaults to max_loras.'))
660
661
662
663
664
665
666
667
        parser.add_argument(
            '--fully-sharded-loras',
            action='store_true',
            help=('By default, only half of the LoRA computation is '
                  'sharded with tensor parallelism. '
                  'Enabling this will use the fully sharded layers. '
                  'At high sequence length, max rank or '
                  'tensor parallel size, this is likely faster.'))
668
669
670
671
672
673
674
675
676
677
678
        parser.add_argument('--enable-prompt-adapter',
                            action='store_true',
                            help='If True, enable handling of PromptAdapters.')
        parser.add_argument('--max-prompt-adapters',
                            type=int,
                            default=EngineArgs.max_prompt_adapters,
                            help='Max number of PromptAdapters in a batch.')
        parser.add_argument('--max-prompt-adapter-token',
                            type=int,
                            default=EngineArgs.max_prompt_adapter_token,
                            help='Max number of PromptAdapters tokens')
679
680
681
        parser.add_argument("--device",
                            type=str,
                            default=EngineArgs.device,
682
                            choices=DEVICE_OPTIONS,
683
                            help='Device type for vLLM execution.')
684
685
686
687
688
        parser.add_argument('--num-scheduler-steps',
                            type=int,
                            default=1,
                            help=('Maximum number of forward steps per '
                                  'scheduler call.'))
689

690
691
        parser.add_argument(
            '--multi-step-stream-outputs',
692
693
694
695
696
697
            action=StoreBoolean,
            default=EngineArgs.multi_step_stream_outputs,
            nargs="?",
            const="True",
            help='If False, then multi-step will stream outputs at the end '
            'of all steps')
698
699
700
701
        parser.add_argument(
            '--scheduler-delay-factor',
            type=float,
            default=EngineArgs.scheduler_delay_factor,
702
            help='Apply a delay (of delay factor multiplied by previous '
703
            'prompt latency) before scheduling next prompt.')
704
705
        parser.add_argument(
            '--enable-chunked-prefill',
706
707
708
709
            action=StoreBoolean,
            default=EngineArgs.enable_chunked_prefill,
            nargs="?",
            const="True",
710
            help='If set, the prefill requests can be chunked based on the '
711
            'max_num_batched_tokens.')
712
713
714

        parser.add_argument(
            '--speculative-model',
715
            type=nullable_str,
716
            default=EngineArgs.speculative_model,
717
718
            help=
            'The name of the draft model to be used in speculative decoding.')
719
720
721
722
723
724
        # Quantization settings for speculative model.
        parser.add_argument(
            '--speculative-model-quantization',
            type=nullable_str,
            choices=[*QUANTIZATION_METHODS, None],
            default=EngineArgs.speculative_model_quantization,
725
            help='Method used to quantize the weights of speculative model. '
726
727
728
729
730
            'If None, we first check the `quantization_config` '
            'attribute in the model config file. If that is '
            'None, we assume the model weights are not '
            'quantized and use `dtype` to determine the data '
            'type of the weights.')
731
732
733
        parser.add_argument(
            '--num-speculative-tokens',
            type=int,
734
            default=EngineArgs.num_speculative_tokens,
735
            help='The number of speculative tokens to sample from '
736
            'the draft model in speculative decoding.')
737
738
739
740
741
742
        parser.add_argument(
            '--speculative-disable-mqa-scorer',
            action='store_true',
            help=
            'If set to True, the MQA scorer will be disabled in speculative '
            ' and fall back to batch expansion')
743
744
745
746
747
748
749
        parser.add_argument(
            '--speculative-draft-tensor-parallel-size',
            '-spec-draft-tp',
            type=int,
            default=EngineArgs.speculative_draft_tensor_parallel_size,
            help='Number of tensor parallel replicas for '
            'the draft model in speculative decoding.')
750

751
752
        parser.add_argument(
            '--speculative-max-model-len',
753
            type=int,
754
755
756
757
758
            default=EngineArgs.speculative_max_model_len,
            help='The maximum sequence length supported by the '
            'draft model. Sequences over this length will skip '
            'speculation.')

759
760
761
762
763
764
765
        parser.add_argument(
            '--speculative-disable-by-batch-size',
            type=int,
            default=EngineArgs.speculative_disable_by_batch_size,
            help='Disable speculative decoding for new incoming requests '
            'if the number of enqueue requests is larger than this value.')

766
767
768
769
770
771
772
773
774
775
776
777
778
779
        parser.add_argument(
            '--ngram-prompt-lookup-max',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_max,
            help='Max size of window for ngram prompt lookup in speculative '
            'decoding.')

        parser.add_argument(
            '--ngram-prompt-lookup-min',
            type=int,
            default=EngineArgs.ngram_prompt_lookup_min,
            help='Min size of window for ngram prompt lookup in speculative '
            'decoding.')

780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
        parser.add_argument(
            '--spec-decoding-acceptance-method',
            type=str,
            default=EngineArgs.spec_decoding_acceptance_method,
            choices=['rejection_sampler', 'typical_acceptance_sampler'],
            help='Specify the acceptance method to use during draft token '
            'verification in speculative decoding. Two types of acceptance '
            'routines are supported: '
            '1) RejectionSampler which does not allow changing the '
            'acceptance rate of draft tokens, '
            '2) TypicalAcceptanceSampler which is configurable, allowing for '
            'a higher acceptance rate at the cost of lower quality, '
            'and vice versa.')

        parser.add_argument(
            '--typical-acceptance-sampler-posterior-threshold',
            type=float,
            default=EngineArgs.typical_acceptance_sampler_posterior_threshold,
            help='Set the lower bound threshold for the posterior '
            'probability of a token to be accepted. This threshold is '
            'used by the TypicalAcceptanceSampler to make sampling decisions '
            'during speculative decoding. Defaults to 0.09')

        parser.add_argument(
            '--typical-acceptance-sampler-posterior-alpha',
            type=float,
            default=EngineArgs.typical_acceptance_sampler_posterior_alpha,
            help='A scaling factor for the entropy-based threshold for token '
            'acceptance in the TypicalAcceptanceSampler. Typically defaults '
            'to sqrt of --typical-acceptance-sampler-posterior-threshold '
            'i.e. 0.3')

812
813
        parser.add_argument(
            '--disable-logprobs-during-spec-decoding',
814
            action=StoreBoolean,
815
            default=EngineArgs.disable_logprobs_during_spec_decoding,
816
817
            nargs="?",
            const="True",
818
819
820
821
822
823
824
825
            help='If set to True, token log probabilities are not returned '
            'during speculative decoding. If set to False, log probabilities '
            'are returned according to the settings in SamplingParams. If '
            'not specified, it defaults to True. Disabling log probabilities '
            'during speculative decoding reduces latency by skipping logprob '
            'calculation in proposal sampling, target sampling, and after '
            'accepted tokens are determined.')

826
        parser.add_argument('--model-loader-extra-config',
827
                            type=nullable_str,
828
829
830
831
832
833
                            default=EngineArgs.model_loader_extra_config,
                            help='Extra config for model loader. '
                            'This will be passed to the model loader '
                            'corresponding to the chosen load_format. '
                            'This should be a JSON string that will be '
                            'parsed into a dictionary.')
834
835
836
837
838
839
        parser.add_argument(
            '--ignore-patterns',
            action="append",
            type=str,
            default=[],
            help="The pattern(s) to ignore when loading the model."
840
            "Default to `original/**/*` to avoid repeated loading of llama's "
841
            "checkpoints.")
842
        parser.add_argument(
843
            '--preemption-mode',
844
845
            type=str,
            default=None,
846
847
848
            help='If \'recompute\', the engine performs preemption by '
            'recomputing; If \'swap\', the engine performs preemption by '
            'block swapping.')
849

850
851
852
853
854
855
856
857
858
859
        parser.add_argument(
            "--served-model-name",
            nargs="+",
            type=str,
            default=None,
            help="The model name(s) used in the API. If multiple "
            "names are provided, the server will respond to any "
            "of the provided names. The model name in the model "
            "field of a response will be the first name in this "
            "list. If not specified, the model name will be the "
860
            "same as the `--model` argument. Noted that this name(s) "
861
            "will also be used in `model_name` tag content of "
862
            "prometheus metrics, if multiple names provided, metrics "
863
            "tag will take the first one.")
864
865
866
867
        parser.add_argument('--qlora-adapter-name-or-path',
                            type=str,
                            default=None,
                            help='Name or path of the QLoRA adapter.')
868
869
870
871
872
873

        parser.add_argument(
            '--otlp-traces-endpoint',
            type=str,
            default=None,
            help='Target URL to which OpenTelemetry traces will be sent.')
874
875
876
877
878
879
880
881
882
883
        parser.add_argument(
            '--collect-detailed-traces',
            type=str,
            default=None,
            help="Valid choices are " +
            ",".join(ALLOWED_DETAILED_TRACE_MODULES) +
            ". It makes sense to set this only if --otlp-traces-endpoint is"
            " set. If set, it will collect detailed traces for the specified "
            "modules. This involves use of possibly costly and or blocking "
            "operations and hence might have a performance impact.")
884

885
886
887
888
889
890
        parser.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.")
891

892
893
894
895
896
897
898
899
900
901
        parser.add_argument(
            '--scheduling-policy',
            choices=['fcfs', 'priority'],
            default="fcfs",
            help='The scheduling policy to use. "fcfs" (first come first served'
            ', i.e. requests are handled in order of arrival; default) '
            'or "priority" (requests are handled based on given '
            'priority (lower value means earlier handling) and time of '
            'arrival deciding any ties).')

902
        parser.add_argument(
903
904
            '--override-neuron-config',
            type=json.loads,
905
            default=None,
906
907
            help="Override or set neuron device configuration. "
            "e.g. {\"cast_logits_dtype\": \"bloat16\"}.'")
908
        parser.add_argument(
909
910
            '--override-pooler-config',
            type=PoolerConfig.from_json,
911
            default=None,
912
            help="Override or set the pooling method for pooling models. "
913
            "e.g. {\"pooling_type\": \"mean\", \"normalize\": false}.'")
914

915
916
917
918
919
920
921
922
923
924
925
926
        parser.add_argument('--compilation-config',
                            '-O',
                            type=CompilationConfig.from_cli,
                            default=None,
                            help='torch.compile configuration for the model.'
                            'When it is a number (0, 1, 2, 3), it will be '
                            'interpreted as the optimization level.\n'
                            'NOTE: level 0 is the default level without '
                            'any optimization. level 1 and 2 are for internal '
                            'testing only. level 3 is the recommended level '
                            'for production.\n'
                            'To specify the full compilation config, '
927
928
929
930
                            'use a JSON string.\n'
                            'Following the convention of traditional '
                            'compilers, using -O without space is also '
                            'supported. -O3 is equivalent to -O 3.')
931

932
933
934
935
936
937
        parser.add_argument('--kv-transfer-config',
                            type=KVTransferConfig.from_cli,
                            default=None,
                            help='The configurations for distributed KV cache '
                            'transfer. Should be a JSON string.')

938
939
940
941
942
943
        parser.add_argument(
            '--worker-cls',
            type=str,
            default="auto",
            help='The worker class to use for distributed execution.')

944
        return parser
945
946

    @classmethod
947
    def from_cli_args(cls, args: argparse.Namespace):
948
949
950
        # 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
951
952
        engine_args = cls(**{attr: getattr(args, attr) for attr in attrs})
        return engine_args
953

954
955
    def create_model_config(self) -> ModelConfig:
        return ModelConfig(
956
            model=self.model,
957
            task=self.task,
958
959
            # We know this is not None because we set it in __post_init__
            tokenizer=cast(str, self.tokenizer),
960
961
            tokenizer_mode=self.tokenizer_mode,
            trust_remote_code=self.trust_remote_code,
962
            allowed_local_media_path=self.allowed_local_media_path,
963
964
965
966
967
            dtype=self.dtype,
            seed=self.seed,
            revision=self.revision,
            code_revision=self.code_revision,
            rope_scaling=self.rope_scaling,
968
            rope_theta=self.rope_theta,
969
            hf_overrides=self.hf_overrides,
970
971
972
973
974
975
976
977
978
            tokenizer_revision=self.tokenizer_revision,
            max_model_len=self.max_model_len,
            quantization=self.quantization,
            quantization_param_path=self.quantization_param_path,
            enforce_eager=self.enforce_eager,
            max_seq_len_to_capture=self.max_seq_len_to_capture,
            max_logprobs=self.max_logprobs,
            disable_sliding_window=self.disable_sliding_window,
            skip_tokenizer_init=self.skip_tokenizer_init,
979
            served_model_name=self.served_model_name,
980
            limit_mm_per_prompt=self.limit_mm_per_prompt,
981
            use_async_output_proc=not self.disable_async_output_proc,
982
            config_format=self.config_format,
983
            mm_processor_kwargs=self.mm_processor_kwargs,
984
            mm_cache_preprocessor=self.mm_cache_preprocessor,
985
986
            override_neuron_config=self.override_neuron_config,
            override_pooler_config=self.override_pooler_config,
987
            logits_processor_pattern=self.logits_processor_pattern)
988

989
990
991
992
993
994
995
996
    def create_load_config(self) -> LoadConfig:
        return LoadConfig(
            load_format=self.load_format,
            download_dir=self.download_dir,
            model_loader_extra_config=self.model_loader_extra_config,
            ignore_patterns=self.ignore_patterns,
        )

997
998
999
1000
1001
1002
    def create_engine_config(self,
                             usage_context: Optional[UsageContext] = None
                             ) -> VllmConfig:
        if envs.VLLM_USE_V1:
            self._override_v1_engine_args(usage_context)

1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023
1024
1025
1026
1027
1028
1029
        # 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"

        # bitsandbytes quantization needs a specific model loader
        # so we make sure the quant method and the load format are consistent
        if (self.quantization == "bitsandbytes" or
           self.qlora_adapter_name_or_path is not None) and \
           self.load_format != "bitsandbytes":
            raise ValueError(
                "BitsAndBytes quantization and QLoRA adapter only support "
                f"'bitsandbytes' load format, but got {self.load_format}")

        if (self.load_format == "bitsandbytes" or
            self.qlora_adapter_name_or_path is not None) and \
            self.quantization != "bitsandbytes":
            raise ValueError(
                "BitsAndBytes load format and QLoRA adapter only support "
                f"'bitsandbytes' quantization, but got {self.quantization}")

        assert self.cpu_offload_gb >= 0, (
            "CPU offload space must be non-negative"
            f", but got {self.cpu_offload_gb}")

        device_config = DeviceConfig(device=self.device)
        model_config = self.create_model_config()

1030
1031
1032
1033
1034
        if (model_config.is_multimodal_model and not envs.VLLM_USE_V1
                and self.enable_prefix_caching):
            logger.warning("--enable-prefix-caching is currently not "
                           "supported for multimodal models in v0 and "
                           "has been disabled.")
1035
1036
            self.enable_prefix_caching = False

1037
        cache_config = CacheConfig(
1038
            block_size=self.block_size,
1039
1040
1041
            gpu_memory_utilization=self.gpu_memory_utilization,
            swap_space=self.swap_space,
            cache_dtype=self.kv_cache_dtype,
1042
            is_attention_free=model_config.is_attention_free,
1043
1044
            num_gpu_blocks_override=self.num_gpu_blocks_override,
            sliding_window=model_config.get_sliding_window(),
1045
1046
1047
            enable_prefix_caching=self.enable_prefix_caching,
            cpu_offload_gb=self.cpu_offload_gb,
        )
1048
        parallel_config = ParallelConfig(
1049
1050
1051
1052
1053
1054
            pipeline_parallel_size=self.pipeline_parallel_size,
            tensor_parallel_size=self.tensor_parallel_size,
            worker_use_ray=self.worker_use_ray,
            max_parallel_loading_workers=self.max_parallel_loading_workers,
            disable_custom_all_reduce=self.disable_custom_all_reduce,
            tokenizer_pool_config=TokenizerPoolConfig.create_config(
1055
1056
1057
                self.tokenizer_pool_size,
                self.tokenizer_pool_type,
                self.tokenizer_pool_extra_config,
1058
            ),
1059
            ray_workers_use_nsight=self.ray_workers_use_nsight,
1060
1061
1062
            distributed_executor_backend=self.distributed_executor_backend,
            worker_cls=self.worker_cls,
        )
1063

1064
1065
1066
1067
1068
1069
        max_model_len = model_config.max_model_len
        use_long_context = max_model_len > 32768
        if self.enable_chunked_prefill is None:
            # If not explicitly set, enable chunked prefill by default for
            # long context (> 32K) models. This is to avoid OOM errors in the
            # initial memory profiling phase.
1070

1071
1072
1073
1074
1075
1076
            # For multimodal models, chunked prefill is disabled by default in
            # V0, but enabled by design in V1
            if model_config.is_multimodal_model:
                self.enable_chunked_prefill = bool(envs.VLLM_USE_V1)

            elif use_long_context:
1077
1078
1079
1080
1081
1082
                is_gpu = device_config.device_type == "cuda"
                use_sliding_window = (model_config.get_sliding_window()
                                      is not None)
                use_spec_decode = self.speculative_model is not None
                if (is_gpu and not use_sliding_window and not use_spec_decode
                        and not self.enable_lora
1083
                        and not self.enable_prompt_adapter
1084
1085
                        and model_config.runner_type != "pooling"
                        and not current_platform.is_rocm()):
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
1101
                    self.enable_chunked_prefill = True
                    logger.warning(
                        "Chunked prefill is enabled by default for models with "
                        "max_model_len > 32K. Currently, chunked prefill might "
                        "not work with some features or models. If you "
                        "encounter any issues, please disable chunked prefill "
                        "by setting --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 "
                "errors during the initial memory profiling phase, or result "
                "in low performance due to small KV cache space. Consider "
                "setting --max-model-len to a smaller value.", max_model_len)
1102
1103
        elif (self.enable_chunked_prefill
              and model_config.runner_type == "pooling"):
1104
            msg = "Chunked prefill is not supported for pooling models"
1105
            raise ValueError(msg)
1106

1107

1108
1109
1110
1111
1112
        speculative_config = SpeculativeConfig.maybe_create_spec_config(
            target_model_config=model_config,
            target_parallel_config=parallel_config,
            target_dtype=self.dtype,
            speculative_model=self.speculative_model,
1113
1114
            speculative_model_quantization = \
                self.speculative_model_quantization,
1115
1116
            speculative_draft_tensor_parallel_size = \
                self.speculative_draft_tensor_parallel_size,
1117
            num_speculative_tokens=self.num_speculative_tokens,
1118
            speculative_disable_mqa_scorer=self.speculative_disable_mqa_scorer,
1119
1120
            speculative_disable_by_batch_size=self.
            speculative_disable_by_batch_size,
1121
1122
            speculative_max_model_len=self.speculative_max_model_len,
            enable_chunked_prefill=self.enable_chunked_prefill,
1123
            disable_log_stats=self.disable_log_stats,
1124
1125
            ngram_prompt_lookup_max=self.ngram_prompt_lookup_max,
            ngram_prompt_lookup_min=self.ngram_prompt_lookup_min,
1126
1127
1128
1129
1130
1131
            draft_token_acceptance_method=\
                self.spec_decoding_acceptance_method,
            typical_acceptance_sampler_posterior_threshold=self.
            typical_acceptance_sampler_posterior_threshold,
            typical_acceptance_sampler_posterior_alpha=self.
            typical_acceptance_sampler_posterior_alpha,
1132
            disable_logprobs=self.disable_logprobs_during_spec_decoding,
1133
1134
        )

1135
        # Reminder: Please update docs/source/usage/compatibility_matrix.rst
1136
        # If the feature combo become valid
1137
1138
1139
1140
        if self.num_scheduler_steps > 1:
            if speculative_config is not None:
                raise ValueError("Speculative decoding is not supported with "
                                 "multi-step (--num-scheduler-steps > 1)")
1141
1142
1143
            if self.enable_chunked_prefill and self.pipeline_parallel_size > 1:
                raise ValueError("Multi-Step Chunked-Prefill is not supported "
                                 "for pipeline-parallel-size > 1")
1144
1145
1146
1147
1148
1149
1150
1151
1152

        # make sure num_lookahead_slots is set the higher value depending on
        # if we are using speculative decoding or multi-step
        num_lookahead_slots = max(self.num_lookahead_slots,
                                  self.num_scheduler_steps - 1)
        num_lookahead_slots = num_lookahead_slots \
            if speculative_config is None \
            else speculative_config.num_lookahead_slots

1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
        if not self.use_v2_block_manager:
            logger.warning(
                "[DEPRECATED] Block manager v1 has been removed, "
                "and setting --use-v2-block-manager to True or False has "
                "no effect on vLLM behavior. Please remove "
                "--use-v2-block-manager in your engine argument. "
                "If your use case is not supported by "
                "SelfAttnBlockSpaceManager (i.e. block manager v2),"
                " please file an issue with detailed information.")

1163
        scheduler_config = SchedulerConfig(
1164
            runner_type=model_config.runner_type,
1165
1166
1167
            max_num_batched_tokens=self.max_num_batched_tokens,
            max_num_seqs=self.max_num_seqs,
            max_model_len=model_config.max_model_len,
1168
            num_lookahead_slots=num_lookahead_slots,
1169
1170
            delay_factor=self.scheduler_delay_factor,
            enable_chunked_prefill=self.enable_chunked_prefill,
1171
            is_multimodal_model=model_config.is_multimodal_model,
1172
            preemption_mode=self.preemption_mode,
1173
            num_scheduler_steps=self.num_scheduler_steps,
1174
            multi_step_stream_outputs=self.multi_step_stream_outputs,
1175
1176
            send_delta_data=(envs.VLLM_USE_RAY_SPMD_WORKER
                             and parallel_config.use_ray),
1177
            policy=self.scheduling_policy)
1178
        lora_config = LoRAConfig(
1179
            bias_enabled=self.enable_lora_bias,
1180
1181
            max_lora_rank=self.max_lora_rank,
            max_loras=self.max_loras,
1182
            fully_sharded_loras=self.fully_sharded_loras,
1183
            lora_extra_vocab_size=self.lora_extra_vocab_size,
1184
            long_lora_scaling_factors=self.long_lora_scaling_factors,
1185
1186
1187
            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
1188

1189
1190
1191
1192
1193
1194
1195
        if self.qlora_adapter_name_or_path is not None and \
            self.qlora_adapter_name_or_path != "":
            if self.model_loader_extra_config is None:
                self.model_loader_extra_config = {}
            self.model_loader_extra_config[
                "qlora_adapter_name_or_path"] = self.qlora_adapter_name_or_path

1196
        load_config = self.create_load_config()
1197

1198
1199
1200
1201
1202
        prompt_adapter_config = PromptAdapterConfig(
            max_prompt_adapters=self.max_prompt_adapters,
            max_prompt_adapter_token=self.max_prompt_adapter_token) \
                                        if self.enable_prompt_adapter else None

1203
1204
1205
        decoding_config = DecodingConfig(
            guided_decoding_backend=self.guided_decoding_backend)

1206
1207
1208
1209
1210
1211
1212
1213
        detailed_trace_modules = []
        if self.collect_detailed_traces is not None:
            detailed_trace_modules = self.collect_detailed_traces.split(",")
        for m in detailed_trace_modules:
            if m not in ALLOWED_DETAILED_TRACE_MODULES:
                raise ValueError(
                    f"Invalid module {m} in collect_detailed_traces. "
                    f"Valid modules are {ALLOWED_DETAILED_TRACE_MODULES}")
1214
        observability_config = ObservabilityConfig(
1215
1216
1217
1218
1219
1220
            otlp_traces_endpoint=self.otlp_traces_endpoint,
            collect_model_forward_time="model" in detailed_trace_modules
            or "all" in detailed_trace_modules,
            collect_model_execute_time="worker" in detailed_trace_modules
            or "all" in detailed_trace_modules,
        )
1221

1222
        config = VllmConfig(
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
            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,
1233
            prompt_adapter_config=prompt_adapter_config,
1234
            compilation_config=self.compilation_config,
1235
            kv_transfer_config=self.kv_transfer_config,
1236
        )
1237

1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
        if envs.VLLM_USE_V1:
            self._override_v1_engine_config(config)
        return config

    def _override_v1_engine_args(self, usage_context: UsageContext) -> None:
        """
        Override the EngineArgs's args based on the usage context for V1.
        """
        assert envs.VLLM_USE_V1, "V1 is not enabled"

1248
1249
1250
1251
1252
        # V1 always uses chunked prefills.
        self.enable_chunked_prefill = True
        # When no user override, set the default values based on the usage
        # context.
        # TODO(woosuk): Tune the default values for different hardware.
1253
1254
1255
1256
1257
1258
1259
1260
        default_max_num_batched_tokens = {
            UsageContext.LLM_CLASS: 8192,
            UsageContext.OPENAI_API_SERVER: 2048,
        }
        if (self.max_num_batched_tokens is None
                and usage_context in default_max_num_batched_tokens):
            self.max_num_batched_tokens = default_max_num_batched_tokens[
                usage_context]
1261
1262
1263
            logger.warning(
                "Setting max_num_batched_tokens to %d for %s usage context.",
                self.max_num_batched_tokens, usage_context.value)
1264
1265
1266
1267
1268
1269
1270

    def _override_v1_engine_config(self, engine_config: VllmConfig) -> None:
        """
        Override the EngineConfig's configs based on the usage context for V1.
        """
        assert envs.VLLM_USE_V1, "V1 is not enabled"

1271

1272
@dataclass
Zhuohan Li's avatar
Zhuohan Li committed
1273
class AsyncEngineArgs(EngineArgs):
Woosuk Kwon's avatar
Woosuk Kwon committed
1274
    """Arguments for asynchronous vLLM engine."""
1275
    disable_log_requests: bool = False
1276
1277

    @staticmethod
1278
1279
    def add_cli_args(parser: FlexibleArgumentParser,
                     async_args_only: bool = False) -> FlexibleArgumentParser:
1280
1281
        if not async_args_only:
            parser = EngineArgs.add_cli_args(parser)
1282
1283
        parser.add_argument('--disable-log-requests',
                            action='store_true',
1284
                            help='Disable logging requests.')
1285
        return parser
1286
1287
1288
1289


# These functions are used by sphinx to build the documentation
def _engine_args_parser():
1290
    return EngineArgs.add_cli_args(FlexibleArgumentParser())
1291
1292
1293


def _async_engine_args_parser():
1294
    return AsyncEngineArgs.add_cli_args(FlexibleArgumentParser(),
1295
                                        async_args_only=True)