llm_engine.py 63.4 KB
Newer Older
Antoni Baum's avatar
Antoni Baum committed
1
import time
2
from contextlib import contextmanager
3
4
from typing import (TYPE_CHECKING, Any, ClassVar, Dict, Iterable, List,
                    Mapping, Optional)
5
from typing import Sequence as GenericSequence
6
from typing import Set, Tuple, Type, TypeVar, Union
7

8
import vllm.envs as envs
9
10
11
from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
                         EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
12
                         PromptAdapterConfig, SchedulerConfig,
13
                         SpeculativeConfig)
14
15
from vllm.core.scheduler import (ScheduledSequenceGroup, Scheduler,
                                 SchedulerOutputs)
Woosuk Kwon's avatar
Woosuk Kwon committed
16
from vllm.engine.arg_utils import EngineArgs
17
18
from vllm.engine.metrics import (LoggingStatLogger, PrometheusStatLogger,
                                 StatLoggerBase, Stats)
19
20
21
22
from vllm.engine.output_processor.interfaces import (
    SequenceGroupOutputProcessor)
from vllm.engine.output_processor.stop_checker import StopChecker
from vllm.engine.output_processor.util import create_output_by_sequence_group
23
from vllm.executor.executor_base import ExecutorBase
24
from vllm.executor.ray_utils import initialize_ray_cluster
25
26
from vllm.inputs import (INPUT_REGISTRY, LLMInputs, PromptInputs,
                         get_prompt_type)
Woosuk Kwon's avatar
Woosuk Kwon committed
27
from vllm.logger import init_logger
28
from vllm.lora.request import LoRARequest
29
30
31
from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
                          RequestOutputFactory)
from vllm.pooling_params import PoolingParams
32
from vllm.prompt_adapter.request import PromptAdapterRequest
Woosuk Kwon's avatar
Woosuk Kwon committed
33
from vllm.sampling_params import SamplingParams
34
from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
35
36
                           PoolerOutput, SamplerOutput, Sequence,
                           SequenceGroup, SequenceGroupMetadata,
37
                           SequenceStatus)
38
39
from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
                          init_tracer)
40
from vllm.transformers_utils.config import try_get_generation_config
41
from vllm.transformers_utils.detokenizer import Detokenizer
42
43
from vllm.transformers_utils.tokenizer_group import (
    AnyTokenizer, BaseTokenizerGroup, init_tokenizer_from_configs)
yhu422's avatar
yhu422 committed
44
45
from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                  usage_message)
46
47
from vllm.utils import (Counter, is_embedding_model_config,
                        is_encoder_decoder_model_config)
48
from vllm.version import __version__ as VLLM_VERSION
49
50

logger = init_logger(__name__)
51
_LOCAL_LOGGING_INTERVAL_SEC = 5
Woosuk Kwon's avatar
Woosuk Kwon committed
52

53

54
55
56
57
58
59
60
61
def _load_generation_config_dict(model_config: ModelConfig) -> Dict[str, Any]:
    config = try_get_generation_config(
        model_config.model,
        trust_remote_code=model_config.trust_remote_code,
        revision=model_config.revision,
    )

    if config is None:
62
63
        return {}

64
65
    return config.to_diff_dict()

66

67
68
69
_O = TypeVar("_O", RequestOutput, EmbeddingRequestOutput)


70
class LLMEngine:
Zhuohan Li's avatar
Zhuohan Li committed
71
    """An LLM engine that receives requests and generates texts.
72

Woosuk Kwon's avatar
Woosuk Kwon committed
73
    This is the main class for the vLLM engine. It receives requests
74
75
76
77
78
79
    from clients and generates texts from the LLM. It includes a tokenizer, a
    language model (possibly distributed across multiple GPUs), and GPU memory
    space allocated for intermediate states (aka KV cache). This class utilizes
    iteration-level scheduling and efficient memory management to maximize the
    serving throughput.

80
81
    The :class:`~vllm.LLM` class wraps this class for offline batched inference
    and the :class:`AsyncLLMEngine` class wraps this class for online serving.
82

83
84
    The config arguments are derived from :class:`~vllm.EngineArgs`. (See
    :ref:`engine_args`)
85
86
87
88
89
90
91

    Args:
        model_config: The configuration related to the LLM model.
        cache_config: The configuration related to the KV cache memory
            management.
        parallel_config: The configuration related to distributed execution.
        scheduler_config: The configuration related to the request scheduler.
92
        device_config: The configuration related to the device.
93
        lora_config (Optional): The configuration related to serving multi-LoRA.
94
95
        multimodal_config (Optional): The configuration related to multimodal 
            models.
96
97
        speculative_config (Optional): The configuration related to speculative
            decoding.
98
99
        executor_class: The model executor class for managing distributed
            execution.
100
101
        prompt_adapter_config (Optional): The configuration related to serving 
            prompt adapters.
102
        log_stats: Whether to log statistics.
103
        usage_context: Specified entry point, used for usage info collection.
104
    """
105

106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
    DO_VALIDATE_OUTPUT: ClassVar[bool] = False
    """A flag to toggle whether to validate the type of request output."""

    @classmethod
    @contextmanager
    def enable_output_validation(cls):
        cls.DO_VALIDATE_OUTPUT = True

        yield

        cls.DO_VALIDATE_OUTPUT = False

    @classmethod
    def validate_output(
        cls,
        output: object,
        output_type: Type[_O],
    ) -> _O:
        do_validate = cls.DO_VALIDATE_OUTPUT

        if ((TYPE_CHECKING or do_validate)
                and not isinstance(output, output_type)):
            raise TypeError(f"Expected output of type {output_type}, "
                            f"but found type {type(output)}")

        return output

    @classmethod
    def validate_outputs(
        cls,
        outputs: GenericSequence[object],
        output_type: Type[_O],
    ) -> List[_O]:
        do_validate = cls.DO_VALIDATE_OUTPUT

        outputs_: List[_O]
        if TYPE_CHECKING or do_validate:
            outputs_ = []
            for output in outputs:
                if not isinstance(output, output_type):
                    raise TypeError(f"Expected output of type {output_type}, "
                                    f"but found type {type(output)}")

                outputs_.append(output)
        else:
            outputs_ = outputs

        return outputs_

    tokenizer: Optional[BaseTokenizerGroup]

157
158
159
160
161
162
    def __init__(
        self,
        model_config: ModelConfig,
        cache_config: CacheConfig,
        parallel_config: ParallelConfig,
        scheduler_config: SchedulerConfig,
163
        device_config: DeviceConfig,
164
        load_config: LoadConfig,
165
        lora_config: Optional[LoRAConfig],
166
        multimodal_config: Optional[MultiModalConfig],
167
        speculative_config: Optional[SpeculativeConfig],
168
        decoding_config: Optional[DecodingConfig],
169
        observability_config: Optional[ObservabilityConfig],
170
        prompt_adapter_config: Optional[PromptAdapterConfig],
171
        executor_class: Type[ExecutorBase],
172
        log_stats: bool,
yhu422's avatar
yhu422 committed
173
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
174
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
175
176
    ) -> None:
        logger.info(
177
178
179
            "Initializing an LLM engine (v%s) with config: "
            "model=%r, speculative_config=%r, tokenizer=%r, "
            "skip_tokenizer_init=%s, tokenizer_mode=%s, revision=%s, "
180
            "rope_scaling=%r, rope_theta=%r, tokenizer_revision=%s, "
181
182
            "trust_remote_code=%s, dtype=%s, max_seq_len=%d, "
            "download_dir=%r, load_format=%s, tensor_parallel_size=%d, "
183
            "pipeline_parallel_size=%d, "
184
185
            "disable_custom_all_reduce=%s, quantization=%s, "
            "enforce_eager=%s, kv_cache_dtype=%s, "
186
            "quantization_param_path=%s, device_config=%s, "
187
            "decoding_config=%r, observability_config=%r, "
188
189
            "seed=%d, served_model_name=%s, use_v2_block_manager=%s, "
            "enable_prefix_caching=%s)",
190
            VLLM_VERSION,
191
192
193
194
195
196
            model_config.model,
            speculative_config,
            model_config.tokenizer,
            model_config.skip_tokenizer_init,
            model_config.tokenizer_mode,
            model_config.revision,
197
            model_config.rope_scaling,
198
            model_config.rope_theta,
199
200
201
202
203
204
205
            model_config.tokenizer_revision,
            model_config.trust_remote_code,
            model_config.dtype,
            model_config.max_model_len,
            load_config.download_dir,
            load_config.load_format,
            parallel_config.tensor_parallel_size,
206
            parallel_config.pipeline_parallel_size,
207
208
209
210
211
212
213
            parallel_config.disable_custom_all_reduce,
            model_config.quantization,
            model_config.enforce_eager,
            cache_config.cache_dtype,
            model_config.quantization_param_path,
            device_config.device,
            decoding_config,
214
            observability_config,
215
            model_config.seed,
216
            model_config.served_model_name,
217
218
            scheduler_config.use_v2_block_manager,
            cache_config.enable_prefix_caching,
219
        )
220
221
222
223
        # TODO(woosuk): Print more configs in debug mode.

        self.model_config = model_config
        self.cache_config = cache_config
224
        self.lora_config = lora_config
225
        self.multimodal_config = multimodal_config
226
227
        self.parallel_config = parallel_config
        self.scheduler_config = scheduler_config
228
        self.device_config = device_config
229
        self.speculative_config = speculative_config
230
        self.load_config = load_config
231
        self.decoding_config = decoding_config or DecodingConfig()
232
        self.prompt_adapter_config = prompt_adapter_config
233
234
        self.observability_config = observability_config or ObservabilityConfig(
        )
235
236
        self.log_stats = log_stats

237
        if not self.model_config.skip_tokenizer_init:
238
            self.tokenizer = self._init_tokenizer()
239
240
241
            self.detokenizer = Detokenizer(self.tokenizer)
        else:
            self.tokenizer = None
242
            self.detokenizer = None
243

244
        self.seq_counter = Counter()
245
246
        self.generation_config_fields = _load_generation_config_dict(
            model_config)
247

248
249
250
        self.input_processor = INPUT_REGISTRY.create_input_processor(
            self.model_config)

251
252
253
254
255
256
257
        self.model_executor = executor_class(
            model_config=model_config,
            cache_config=cache_config,
            parallel_config=parallel_config,
            scheduler_config=scheduler_config,
            device_config=device_config,
            lora_config=lora_config,
258
            multimodal_config=multimodal_config,
259
            speculative_config=speculative_config,
260
            load_config=load_config,
261
            prompt_adapter_config=prompt_adapter_config,
262
        )
263

264
265
        if not self.model_config.embedding_mode:
            self._initialize_kv_caches()
266

yhu422's avatar
yhu422 committed
267
268
        # If usage stat is enabled, collect relevant info.
        if is_usage_stats_enabled():
269
270
            from vllm.model_executor.model_loader import (
                get_architecture_class_name)
yhu422's avatar
yhu422 committed
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
            usage_message.report_usage(
                get_architecture_class_name(model_config),
                usage_context,
                extra_kvs={
                    # Common configuration
                    "dtype":
                    str(model_config.dtype),
                    "tensor_parallel_size":
                    parallel_config.tensor_parallel_size,
                    "block_size":
                    cache_config.block_size,
                    "gpu_memory_utilization":
                    cache_config.gpu_memory_utilization,

                    # Quantization
                    "quantization":
                    model_config.quantization,
                    "kv_cache_dtype":
289
                    str(cache_config.cache_dtype),
yhu422's avatar
yhu422 committed
290
291
292
293

                    # Feature flags
                    "enable_lora":
                    bool(lora_config),
294
295
                    "enable_prompt_adapter":
                    bool(prompt_adapter_config),
yhu422's avatar
yhu422 committed
296
297
298
299
300
301
302
303
                    "enable_prefix_caching":
                    cache_config.enable_prefix_caching,
                    "enforce_eager":
                    model_config.enforce_eager,
                    "disable_custom_all_reduce":
                    parallel_config.disable_custom_all_reduce,
                })

304
305
306
307
        if self.tokenizer:
            # Ping the tokenizer to ensure liveness if it runs in a
            # different process.
            self.tokenizer.ping()
308

309
        # Create the scheduler.
310
311
        # NOTE: the cache_config here have been updated with the numbers of
        # GPU and CPU blocks, which are profiled in the distributed executor.
312
313
314
315
316
        self.scheduler = [
            Scheduler(scheduler_config, cache_config, lora_config,
                      parallel_config.pipeline_parallel_size)
            for _ in range(parallel_config.pipeline_parallel_size)
        ]
Woosuk Kwon's avatar
Woosuk Kwon committed
317

318
319
        # Metric Logging.
        if self.log_stats:
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
            if stat_loggers is not None:
                self.stat_loggers = stat_loggers
            else:
                self.stat_loggers = {
                    "logging":
                    LoggingStatLogger(
                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC),
                    "prometheus":
                    PrometheusStatLogger(
                        local_interval=_LOCAL_LOGGING_INTERVAL_SEC,
                        labels=dict(model_name=model_config.served_model_name),
                        max_model_len=self.model_config.max_model_len),
                }
                self.stat_loggers["prometheus"].info("cache_config",
                                                     self.cache_config)
335

336
337
338
339
340
341
        self.tracer = None
        if self.observability_config.otlp_traces_endpoint:
            self.tracer = init_tracer(
                "vllm.llm_engine",
                self.observability_config.otlp_traces_endpoint)

342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
        # Create sequence output processor, e.g. for beam search or
        # speculative decoding.
        self.output_processor = (
            SequenceGroupOutputProcessor.create_output_processor(
                self.scheduler_config,
                self.detokenizer,
                self.scheduler,
                self.seq_counter,
                self.get_tokenizer_for_seq,
                stop_checker=StopChecker(
                    self.scheduler_config.max_model_len,
                    self.get_tokenizer_for_seq,
                ),
            ))

357
358
359
360
361
362
363
364
365
366
367
    def _initialize_kv_caches(self) -> None:
        """Initialize the KV cache in the worker(s).

        The workers will determine the number of blocks in both the GPU cache
        and the swap CPU cache.
        """
        num_gpu_blocks, num_cpu_blocks = (
            self.model_executor.determine_num_available_blocks())

        if self.cache_config.num_gpu_blocks_override is not None:
            num_gpu_blocks_override = self.cache_config.num_gpu_blocks_override
368
369
370
371
            logger.info(
                "Overriding num_gpu_blocks=%d with "
                "num_gpu_blocks_override=%d", num_gpu_blocks,
                num_gpu_blocks_override)
372
373
374
375
376
377
378
            num_gpu_blocks = num_gpu_blocks_override

        self.cache_config.num_gpu_blocks = num_gpu_blocks
        self.cache_config.num_cpu_blocks = num_cpu_blocks

        self.model_executor.initialize_cache(num_gpu_blocks, num_cpu_blocks)

379
    @classmethod
380
381
    def _get_executor_cls(cls,
                          engine_config: EngineConfig) -> Type[ExecutorBase]:
382
383
        distributed_executor_backend = (
            engine_config.parallel_config.distributed_executor_backend)
384
        # Initialize the cluster and specify the executor class.
385
386
387
388
389
390
391
392
393
        if isinstance(distributed_executor_backend, type):
            if not issubclass(distributed_executor_backend, ExecutorBase):
                raise TypeError(
                    "distributed_executor_backend must be a subclass of "
                    f"ExecutorBase. Got {distributed_executor_backend}.")
            if distributed_executor_backend.uses_ray:  # type: ignore
                initialize_ray_cluster(engine_config.parallel_config)
            executor_class = distributed_executor_backend
        elif engine_config.device_config.device_type == "neuron":
394
395
            from vllm.executor.neuron_executor import NeuronExecutor
            executor_class = NeuronExecutor
396
        elif engine_config.device_config.device_type == "tpu":
397
398
399
400
401
402
403
404
            if distributed_executor_backend == "ray":
                initialize_ray_cluster(engine_config.parallel_config)
                from vllm.executor.ray_tpu_executor import RayTPUExecutor
                executor_class = RayTPUExecutor
            else:
                assert distributed_executor_backend is None
                from vllm.executor.tpu_executor import TPUExecutor
                executor_class = TPUExecutor
405
        elif engine_config.device_config.device_type == "cpu":
406
407
            from vllm.executor.cpu_executor import CPUExecutor
            executor_class = CPUExecutor
408
409
410
        elif engine_config.device_config.device_type == "openvino":
            from vllm.executor.openvino_executor import OpenVINOExecutor
            executor_class = OpenVINOExecutor
411
412
413
414
415
416
417
418
        elif engine_config.device_config.device_type == "xpu":
            if distributed_executor_backend == "ray":
                initialize_ray_cluster(engine_config.parallel_config)
                from vllm.executor.ray_xpu_executor import RayXPUExecutor
                executor_class = RayXPUExecutor
            else:
                from vllm.executor.xpu_executor import XPUExecutor
                executor_class = XPUExecutor
419
        elif distributed_executor_backend == "ray":
420
            initialize_ray_cluster(engine_config.parallel_config)
421
422
            from vllm.executor.ray_gpu_executor import RayGPUExecutor
            executor_class = RayGPUExecutor
423
424
425
        elif distributed_executor_backend == "mp":
            from vllm.executor.multiproc_gpu_executor import (
                MultiprocessingGPUExecutor)
426
427
428
            assert not envs.VLLM_USE_RAY_SPMD_WORKER, (
                "multiprocessing distributed executor backend does not "
                "support VLLM_USE_RAY_SPMD_WORKER=1")
429
            executor_class = MultiprocessingGPUExecutor
430
431
432
        else:
            from vllm.executor.gpu_executor import GPUExecutor
            executor_class = GPUExecutor
433
434
435
436
437
438
439
440
441
442
443
444
445
        return executor_class

    @classmethod
    def from_engine_args(
        cls,
        engine_args: EngineArgs,
        usage_context: UsageContext = UsageContext.ENGINE_CONTEXT,
        stat_loggers: Optional[Dict[str, StatLoggerBase]] = None,
    ) -> "LLMEngine":
        """Creates an LLM engine from the engine arguments."""
        # Create the engine configs.
        engine_config = engine_args.create_engine_config()
        executor_class = cls._get_executor_cls(engine_config)
446
        # Create the LLM engine.
yhu422's avatar
yhu422 committed
447
        engine = cls(
448
            **engine_config.to_dict(),
yhu422's avatar
yhu422 committed
449
450
451
            executor_class=executor_class,
            log_stats=not engine_args.disable_log_stats,
            usage_context=usage_context,
452
            stat_loggers=stat_loggers,
yhu422's avatar
yhu422 committed
453
        )
454

455
        return engine
456

457
458
459
460
461
    def __reduce__(self):
        # This is to ensure that the LLMEngine is not referenced in
        # the closure used to initialize Ray worker actors
        raise RuntimeError("LLMEngine should not be pickled!")

462
463
464
465
466
467
    def __del__(self):
        # Shutdown model executor when engine is garbage collected
        # Use getattr since __init__ can fail before the field is set
        if model_executor := getattr(self, "model_executor", None):
            model_executor.shutdown()

468
469
470
471
472
473
474
475
476
477
478
    MISSING_TOKENIZER_GROUP_MSG = ("Unable to get tokenizer because "
                                   "skip_tokenizer_init is True")

    def get_tokenizer_group(
            self,
            fail_msg: str = MISSING_TOKENIZER_GROUP_MSG) -> BaseTokenizerGroup:
        if self.tokenizer is None:
            raise ValueError(fail_msg)

        return self.tokenizer

479
    def get_tokenizer(
480
481
482
        self,
        lora_request: Optional[LoRARequest] = None,
    ) -> AnyTokenizer:
483
        return self.get_tokenizer_group().get_lora_tokenizer(lora_request)
484

485
    def get_tokenizer_for_seq(self, sequence: Sequence) -> AnyTokenizer:
486
487
        return self.get_tokenizer_group().get_lora_tokenizer(
            sequence.lora_request)
488

489
490
491
492
493
494
    def _init_tokenizer(self) -> BaseTokenizerGroup:
        return init_tokenizer_from_configs(
            model_config=self.model_config,
            scheduler_config=self.scheduler_config,
            parallel_config=self.parallel_config,
            enable_lora=bool(self.lora_config))
495

496
497
    def _verify_args(self) -> None:
        self.model_config.verify_with_parallel_config(self.parallel_config)
498
        self.cache_config.verify_with_parallel_config(self.parallel_config)
499
500
501
502
        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
503
504
505
        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)
506

507
508
509
510
511
512
513
514
515
516
517
518
519
    def _get_bos_token_id(self,
                          lora_request: Optional[LoRARequest] = None
                          ) -> Optional[int]:
        if self.tokenizer is None:
            logger.warning("Using None for BOS token id because tokenizer "
                           "is not initialized")
            return None

        return self.tokenizer.get_lora_tokenizer(lora_request).bos_token_id

    def _get_eos_token_id(self,
                          lora_request: Optional[LoRARequest] = None
                          ) -> Optional[int]:
520
521
522
523
524
525
526
        if self.tokenizer is None:
            logger.warning("Using None for EOS token id because tokenizer "
                           "is not initialized")
            return None

        return self.tokenizer.get_lora_tokenizer(lora_request).eos_token_id

527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
    def _get_decoder_start_token_id(self, ) -> Optional[int]:
        '''
        Obtain the decoder start token id employed by an encoder/decoder
        model. Returns None for non-encoder/decoder models or if the
        model config is unavailable.
        '''

        if not self.is_encoder_decoder_model():
            logger.warning("Using None for decoder start token id because "
                           "this is not an encoder/decoder model.")
            return None

        if (self.model_config is None or self.model_config.hf_config is None):
            logger.warning("Using None for decoder start token id because "
                           "model config is not available.")
            return None

        dec_start_token_id = getattr(self.model_config.hf_config,
                                     'decoder_start_token_id', None)
        if dec_start_token_id is None:
            logger.warning("Falling back on <BOS> for decoder start token id "
                           "because decoder start token id is not available.")
            dec_start_token_id = self._get_bos_token_id()

        return dec_start_token_id

553
554
555
556
557
558
559
    def _add_processed_request(
        self,
        request_id: str,
        processed_inputs: LLMInputs,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: float,
        lora_request: Optional[LoRARequest],
560
        prompt_adapter_request: Optional[PromptAdapterRequest],
561
        trace_headers: Optional[Mapping[str, str]] = None,
562
563
564
565
566
567
568
    ) -> None:
        # Create the sequences.
        block_size = self.cache_config.block_size
        seq_id = next(self.seq_counter)
        eos_token_id = self._get_eos_token_id(lora_request)

        seq = Sequence(seq_id, processed_inputs, block_size, eos_token_id,
569
                       lora_request, prompt_adapter_request)
570

571
572
573
574
575
576
577
578
579
580
        encoder_seq = None
        if 'encoder_prompt_token_ids' in processed_inputs:
            encoder_seq = Sequence(seq_id,
                                   processed_inputs,
                                   block_size,
                                   eos_token_id,
                                   lora_request,
                                   prompt_adapter_request,
                                   from_decoder_prompt=False)

581
582
583
584
585
586
587
588
        # Create a SequenceGroup based on SamplingParams or PoolingParams
        if isinstance(params, SamplingParams):
            seq_group = self._create_sequence_group_with_sampling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
589
                trace_headers=trace_headers,
590
591
                prompt_adapter_request=prompt_adapter_request,
                encoder_seq=encoder_seq)
592
593
594
595
596
597
598
        elif isinstance(params, PoolingParams):
            seq_group = self._create_sequence_group_with_pooling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
599
600
                prompt_adapter_request=prompt_adapter_request,
                encoder_seq=encoder_seq)
601
602
603
604
        else:
            raise ValueError(
                "Either SamplingParams or PoolingParams must be provided.")

605
606
607
608
609
610
611
612
613
614
        # Add the sequence group to the scheduler with least unfinished seqs.
        costs = [
            scheduler.get_num_unfinished_seq_groups()
            for scheduler in self.scheduler
        ]
        min_cost_scheduler = self.scheduler[costs.index(min(costs))]
        min_cost_scheduler.add_seq_group(seq_group)

    def stop_remote_worker_execution_loop(self) -> None:
        self.model_executor.stop_remote_worker_execution_loop()
615

616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
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
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
    _LLMInputComponentsType = Tuple[str, List[int], ]

    def _prepare_decoder_input_ids_for_generation(
        self,
        decoder_input_ids: Optional[List[int]] = None,
    ) -> List[int]:
        """
        Prepares `decoder_input_ids` for generation with encoder-decoder models.

        Based on

        https://github.com/huggingface/transformers/blob/
        4037a2b5b1278736e566aec12e169100275545ea/
        src/transformers/generation/utils.py

        specifically GenerationMixin._prepare_decoder_input_ids_for_generation()

        Arguments:

        * decoder_input_ids: input token ids to preprocess

        Returns:

        * Processed token list
        """

        decoder_start_token_id: Optional[int] = (
            self._get_decoder_start_token_id())
        assert decoder_start_token_id is not None

        if decoder_input_ids is None:
            # no decoder prompt input ->
            # use decoder_start_token_id as decoder_input_ids
            (decoder_input_ids) = self._get_default_enc_dec_decoder_prompt()

        if (len(decoder_input_ids) == 0
                or decoder_input_ids[0] != decoder_start_token_id):
            decoder_input_ids = [decoder_start_token_id] + decoder_input_ids

        return decoder_input_ids

    def _tokenize_prompt(
        self,
        prompt: str,
        request_id: Optional[str] = None,
        lora_request: Optional[str] = None,
    ) -> List[int]:
        '''
        Wrapper around application of the model's
        tokenizer.

        Arguments:

        * prompt
        * request_id
        * lora_request

        Returns:

        * prompt token ids
        '''

        tokenizer = self.get_tokenizer_group("prompts must be None if "
                                             "skip_tokenizer_init is True")

        prompt_token_ids = tokenizer.encode(request_id=request_id,
                                            prompt=prompt,
                                            lora_request=lora_request)

        return prompt_token_ids

    def _extract_single_prompt_for_enc_dec_input(
        self,
        inputs: Optional[PromptInputs],
        request_id: Optional[str] = None,
        ptype: Optional[str] = None,
        is_encoder_prompt: bool = False,
    ) -> Tuple[Optional[str], List[int]]:
        '''
        Only for encoder/decoder models:
        Extract prompt & prompt_token_ids from any single
        encoder or decoder input prompt. For encoder input prompts
        in particular, also extract multi-modal data.

        This function handles the following scenarios:
        1. The user supplied a singleton encoder prompt
          & the prompt/prompt-token-ids must be extracted.
        2. The user supplied an explicit encoder/decoder
          prompt & the prompt/prompt-token-ids must be
          extracted from either the encoder and decoder prompts.

        For decoder prompts in particular (scenario 2), special
        processing is applied to the returned decoder token ids.

        Arguments:

        * request_id
        * ptype: str representation of the input prompt type.
                 If `ptype` is `None`, assume that the prompt
                 type is unknown and must be inferred. This is the
                 case for ExplicitEncoderDecoder sub-prompts.
        * inputs: single encoder or decoder input prompt
        * is_encoder_prompt: True if encoder input prompt.
                             If False, decoder prompt tokens
                             are preprocessed.

        Returns:

        * prompt
        * prompt_token_ids
        '''
        prompt_token_ids = None
        ptype = (get_prompt_type(inputs) if ptype is None else ptype)

        if inputs is None:
            prompt = None
        elif ptype == 'str':
            prompt = inputs
            prompt_token_ids = self._tokenize_prompt(
                prompt,
                request_id=request_id,
            )
        elif ptype == 'TokensPrompt':
            prompt = None
            prompt_token_ids = inputs['prompt_token_ids']
        else:
            prompt = inputs['prompt']
            prompt_token_ids = self._tokenize_prompt(
                prompt,
                request_id=request_id,
            )

        if not is_encoder_prompt:
            # Apply special pre-processing to
            # decoder prompts
            prompt_token_ids = (self._prepare_decoder_input_ids_for_generation(
                prompt_token_ids, ))

        assert prompt_token_ids is not None

        return (
            prompt,
            prompt_token_ids,
        )

    def _get_default_enc_dec_decoder_prompt(self, ) -> List[int]:
        '''
        Specifically for encoder/decoder models:
        generate a default decoder prompt for when
        the user specifies only the encoder prompt.

        Encoder/decoder models utilize the decoder
        prompt in different ways; as new models are
        added, it is intended that this function
        will be extended to produce differing
        default decoder prompts, depending on the
        model variety.

        Absent a special case, the default behavior
        of this method is to mirror the behavior of
        the HuggingFace (HF) GenerationMixin for a None
        decoder prompt, which is to employ a logit processor
        setting to force the first decoded token to be <BOS>.
        Here, this behavior is approximated by having the
        "default" decoder prompt be <BOS>.

        However, it is possible that in the future
        other models may have different or more 
        complex logic for the default decoder prompt.
        This motivates having a special helper method
        for default decoder prompts.

        Returns:

        * prompt_token_ids
        '''

        bos_token_id = self._get_bos_token_id()
        assert bos_token_id is not None
        prompt_token_ids: List[int] = [bos_token_id]
        return prompt_token_ids

    def _process_encoder_decoder_prompt(
        self,
        inputs: PromptInputs,
        request_id: Optional[str] = None,
    ) -> LLMInputs:
        '''
        For encoder/decoder models only:
        Process an input prompt
        into an `LLMInputs` instance.

        There are two types of input prompts:
        singleton prompts which carry only the
        encoder prompt, and explicit encoder/decoder
        prompts which carry both the encoder and the
        decoder prompts as member variables.

        This function handles the following scenarios:
        * Singleton encoder prompt: extract encoder prompt
          token ids & infer default decoder prompt token ids
        * Explicit encoder/decoder prompt: extract encoder
          and decoder prompt token ids

        Note that for Explicit encoder/decoder prompts,
        each sub-prompt (encoder or decoder prompt) can
        have any possible singleton type; thus this
        method relies on helper functions to obtain
        token ids for the sub-prompts.
        
        Arguments:

        * inputs: an input prompt
        * request_id

        Returns:

        * `LLMInputs` instance
        '''

        ptype = get_prompt_type(inputs)

        # Obtain encoder and decoder prompt tokens. Note
        # that, no matter what, the decoder
        # prompt type is unknown.
        if ptype == "ExplicitEncoderDecoder":
            # If input is explicit encoder/decoder prompt,
            # then it remains to be determined what type
            # of encoder prompt we have
            extracted_encoder_prompt = inputs.get('encoder_prompt')
            encoder_ptype = None
            # Extract decoder prompt from explicit
            # encoder/decoder prompt
            extracted_decoder_prompt = inputs.get('decoder_prompt')
        else:
            # If input is singleton encoder prompt, then
            # we know the encoder prompt type
            extracted_encoder_prompt = inputs
            encoder_ptype = ptype
            # Decoder prompt is always unknown if
            # encoder/decoder prompt is not explicit
            extracted_decoder_prompt = None

        # Invoke helper function to obtain encoder
        # prompt and prompt token ids, either from
        # singleton encoder prompt or from the
        # encoder sub-prompt of an explicit
        # encoder/decode scenario 2), special
        # processing is applied to the returned decoder token ids
        (
            encoder_prompt,
            encoder_prompt_token_ids,
        ) = self._extract_single_prompt_for_enc_dec_input(
            extracted_encoder_prompt,
            request_id=request_id,
            ptype=encoder_ptype,
            is_encoder_prompt=True,
        )

        # Invoke helper method to obtain
        # decoder prompt and prompt token ids.
        #
        # The helper method will detect the decoder
        # prompt type.
        #
        # Helper method will also apply special
        # preprocessing unique to decoder prompts.
        (
            decoder_prompt,
            decoder_prompt_token_ids,
        ) = self._extract_single_prompt_for_enc_dec_input(
            extracted_decoder_prompt,
            request_id=request_id,
            ptype=None,
            is_encoder_prompt=False,
        )

        return LLMInputs(
            prompt_token_ids=decoder_prompt_token_ids,
            prompt=decoder_prompt,
            encoder_prompt_token_ids=encoder_prompt_token_ids,
            encoder_prompt=encoder_prompt,
        )

    def _process_decoder_only_prompt(
901
        self,
902
        inputs: PromptInputs,
903
        lora_request: Optional[LoRARequest] = None,
904
        request_id: Optional[str] = None,
905
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
906
    ) -> LLMInputs:
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
        '''
        For decoder-only models:
        Process an input prompt
        into an `LLMInputs` instance.

        Arguments:

        * inputs: input prompt
        * lora_request
        * request_id
        * prompt_adapter_request

        Returns:

        * `LLMInputs` instance
        '''

924
925
        if isinstance(inputs, str):
            inputs = {"prompt": inputs}
926
        prompt = inputs.get("prompt")
927
928

        if "prompt_token_ids" not in inputs:
929
930
931
932
933
            prompt_token_ids = self._tokenize_prompt(
                prompt,
                request_id=request_id,
                lora_request=lora_request,
            )
934
935
936
        else:
            prompt_token_ids = inputs["prompt_token_ids"]

937
        if prompt_adapter_request:
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
            prompt_token_ids = (
                [0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens
                + prompt_token_ids)

        return LLMInputs(prompt_token_ids=prompt_token_ids,
                         prompt=prompt,
                         multi_modal_data=inputs.get("multi_modal_data"))

    def process_model_inputs(
        self,
        request_id: str,
        inputs: PromptInputs,
        lora_request: Optional[LoRARequest] = None,
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
    ) -> LLMInputs:
953

954
955
956
        if self.is_encoder_decoder_model():
            # Encoder-decoder model requires special mapping of
            # input prompts to encoder & decoder
957

958
959
960
961
962
963
964
965
966
967
968
969
970
971
            model_inputs = self._process_encoder_decoder_prompt(
                inputs,
                request_id=request_id,
            )
        else:
            # Decoder-only operation
            model_inputs = self._process_decoder_only_prompt(
                inputs,
                request_id=request_id,
                lora_request=lora_request,
                prompt_adapter_request=prompt_adapter_request,
            )

        return self.input_processor(model_inputs)
972

973
974
975
    def add_request(
        self,
        request_id: str,
976
        inputs: PromptInputs,
977
        params: Union[SamplingParams, PoolingParams],
978
        arrival_time: Optional[float] = None,
979
        lora_request: Optional[LoRARequest] = None,
980
        trace_headers: Optional[Mapping[str, str]] = None,
981
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
982
    ) -> None:
Zhuohan Li's avatar
Zhuohan Li committed
983
        """Add a request to the engine's request pool.
984
985

        The request is added to the request pool and will be processed by the
Zhuohan Li's avatar
Zhuohan Li committed
986
        scheduler as `engine.step()` is called. The exact scheduling policy is
987
988
989
990
        determined by the scheduler.

        Args:
            request_id: The unique ID of the request.
991
992
993
994
995
996
            inputs: The inputs to the LLM. See
                :class:`~vllm.inputs.PromptInputs`
                for more details about the format of each input.
            params: Parameters for sampling or pooling.
                :class:`~vllm.SamplingParams` for text generation.
                :class:`~vllm.PoolingParams` for pooling.
997
            arrival_time: The arrival time of the request. If None, we use
998
                the current monotonic time.
999
            trace_headers: OpenTelemetry trace headers.
1000
1001
1002
1003
1004
1005
1006
1007
1008
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021
1022
1023

        Details:
            - Set arrival_time to the current time if it is None.
            - Set prompt_token_ids to the encoded prompt if it is None.
            - Create `best_of` number of :class:`~vllm.Sequence` objects.
            - Create a :class:`~vllm.SequenceGroup` object
              from the list of :class:`~vllm.Sequence`.
            - Add the :class:`~vllm.SequenceGroup` object to the scheduler.

        Example:
            >>> # initialize engine
            >>> engine = LLMEngine.from_engine_args(engine_args)
            >>> # set request arguments
            >>> example_prompt = "Who is the president of the United States?"
            >>> sampling_params = SamplingParams(temperature=0.0)
            >>> request_id = 0
            >>>
            >>> # add the request to the engine
            >>> engine.add_request(
            >>>    str(request_id),
            >>>    example_prompt,
            >>>    SamplingParams(temperature=0.0))
            >>> # continue the request processing
            >>> ...
1024
        """
1025
1026
1027
        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")
1028
        if arrival_time is None:
1029
            arrival_time = time.time()
1030

1031
1032
1033
1034
1035
        processed_inputs = self.process_model_inputs(
            request_id=request_id,
            inputs=inputs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request)
1036

1037
1038
1039
1040
1041
1042
        self._add_processed_request(
            request_id=request_id,
            processed_inputs=processed_inputs,
            params=params,
            arrival_time=arrival_time,
            lora_request=lora_request,
1043
            prompt_adapter_request=prompt_adapter_request,
1044
            trace_headers=trace_headers,
1045
        )
1046
1047
1048
1049
1050
1051

    def _create_sequence_group_with_sampling(
        self,
        request_id: str,
        seq: Sequence,
        sampling_params: SamplingParams,
1052
1053
        arrival_time: float,
        lora_request: Optional[LoRARequest],
1054
        trace_headers: Optional[Mapping[str, str]] = None,
1055
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
1056
        encoder_seq: Optional[Sequence] = None,
1057
1058
1059
1060
1061
1062
1063
1064
1065
1066
    ) -> SequenceGroup:
        """Creates a SequenceGroup with SamplingParams."""
        max_logprobs = self.get_model_config().max_logprobs
        if (sampling_params.logprobs
                and sampling_params.logprobs > max_logprobs) or (
                    sampling_params.prompt_logprobs
                    and sampling_params.prompt_logprobs > max_logprobs):
            raise ValueError(f"Cannot request more than "
                             f"{max_logprobs} logprobs.")

1067
1068
1069
        # Defensive copy of SamplingParams, which are used by the sampler,
        # this doesn't deep-copy LogitsProcessor objects
        sampling_params = sampling_params.clone()
1070

1071
        sampling_params.update_from_generation_config(
1072
            self.generation_config_fields, seq.eos_token_id)
1073

1074
        # Create the sequence group.
1075
1076
1077
1078
1079
1080
1081
        seq_group = SequenceGroup(
            request_id=request_id,
            seqs=[seq],
            arrival_time=arrival_time,
            sampling_params=sampling_params,
            lora_request=lora_request,
            trace_headers=trace_headers,
1082
1083
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq)
1084

1085
1086
1087
1088
1089
1090
1091
        return seq_group

    def _create_sequence_group_with_pooling(
        self,
        request_id: str,
        seq: Sequence,
        pooling_params: PoolingParams,
1092
1093
        arrival_time: float,
        lora_request: Optional[LoRARequest],
1094
        prompt_adapter_request: Optional[PromptAdapterRequest],
1095
        encoder_seq: Optional[Sequence] = None,
1096
1097
1098
1099
1100
    ) -> SequenceGroup:
        """Creates a SequenceGroup with PoolingParams."""
        # Defensive copy of PoolingParams, which are used by the pooler
        pooling_params = pooling_params.clone()
        # Create the sequence group.
1101
1102
1103
1104
1105
1106
        seq_group = SequenceGroup(
            request_id=request_id,
            seqs=[seq],
            arrival_time=arrival_time,
            lora_request=lora_request,
            pooling_params=pooling_params,
1107
1108
            prompt_adapter_request=prompt_adapter_request,
            encoder_seq=encoder_seq)
1109
        return seq_group
1110

Antoni Baum's avatar
Antoni Baum committed
1111
1112
    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
        """Aborts a request(s) with the given ID.
1113
1114

        Args:
Antoni Baum's avatar
Antoni Baum committed
1115
            request_id: The ID(s) of the request to abort.
1116
1117
1118
1119
1120
1121
1122
1123
1124
1125
1126

        Details:
            - Refer to the
              :meth:`~vllm.core.scheduler.Scheduler.abort_seq_group`
              from class :class:`~vllm.core.scheduler.Scheduler`.

        Example:
            >>> # initialize engine and add a request with request_id
            >>> request_id = str(0)
            >>> # abort the request
            >>> engine.abort_request(request_id)
1127
        """
1128
1129
        for scheduler in self.scheduler:
            scheduler.abort_seq_group(request_id)
1130

1131
1132
1133
1134
    def get_model_config(self) -> ModelConfig:
        """Gets the model configuration."""
        return self.model_config

1135
1136
1137
1138
    def get_parallel_config(self) -> ParallelConfig:
        """Gets the parallel configuration."""
        return self.parallel_config

1139
1140
1141
1142
    def get_decoding_config(self) -> DecodingConfig:
        """Gets the decoding configuration."""
        return self.decoding_config

1143
1144
1145
1146
1147
1148
1149
1150
    def get_scheduler_config(self) -> SchedulerConfig:
        """Gets the scheduler configuration."""
        return self.scheduler_config

    def get_lora_config(self) -> LoRAConfig:
        """Gets the LoRA configuration."""
        return self.lora_config

1151
    def get_num_unfinished_requests(self) -> int:
1152
        """Gets the number of unfinished requests."""
1153
1154
        return sum(scheduler.get_num_unfinished_seq_groups()
                   for scheduler in self.scheduler)
1155

1156
    def has_unfinished_requests(self) -> bool:
1157
        """Returns True if there are unfinished requests."""
1158
1159
1160
1161
1162
1163
1164
1165
1166
        return any(scheduler.has_unfinished_seqs()
                   for scheduler in self.scheduler)

    def has_unfinished_requests_for_virtual_engine(
            self, virtual_engine: int) -> bool:
        """
        Returns True if there are unfinished requests for the virtual engine.
        """
        return self.scheduler[virtual_engine].has_unfinished_seqs()
1167

1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
    def _process_sequence_group_outputs(
        self,
        seq_group: SequenceGroup,
        outputs: List[EmbeddingSequenceGroupOutput],
    ) -> None:
        seq_group.embeddings = outputs[0].embeddings

        for seq in seq_group.get_seqs():
            seq.status = SequenceStatus.FINISHED_STOPPED

        return

1180
    def _process_model_outputs(
1181
        self,
1182
        output: GenericSequence[Union[SamplerOutput, PoolerOutput]],
1183
        scheduled_seq_groups: List[ScheduledSequenceGroup],
1184
1185
        ignored_seq_groups: List[SequenceGroup],
        seq_group_metadata_list: List[SequenceGroupMetadata],
1186
    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
1187
        """Apply the model output to the sequences in the scheduled seq groups.
1188

1189
1190
1191
        Returns RequestOutputs that can be returned to the client.
        """

1192
        now = time.time()
1193
1194
1195
1196

        # Organize outputs by [sequence group][step] instead of
        # [step][sequence group].
        output_by_sequence_group = create_output_by_sequence_group(
1197
            output, num_seq_groups=len(scheduled_seq_groups))
1198

1199
        # Update the scheduled sequence groups with the model outputs.
1200
1201
1202
        for scheduled_seq_group, outputs, seq_group_meta in zip(
                scheduled_seq_groups, output_by_sequence_group,
                seq_group_metadata_list):
1203
            seq_group = scheduled_seq_group.seq_group
1204
1205
            seq_group.update_num_computed_tokens(
                scheduled_seq_group.token_chunk_size)
1206
1207
1208
            if self.model_config.embedding_mode:
                self._process_sequence_group_outputs(seq_group, outputs)
                continue
1209

1210
1211
            self.output_processor.process_prompt_logprob(seq_group, outputs)
            if seq_group_meta.do_sample:
1212
                self.output_processor.process_outputs(seq_group, outputs)
1213
1214

        # Free the finished sequence groups.
1215
1216
        for scheduler in self.scheduler:
            scheduler.free_finished_seq_groups()
1217
1218

        # Create the outputs.
1219
1220
        request_outputs: List[Union[RequestOutput,
                                    EmbeddingRequestOutput]] = []
1221
1222
        for scheduled_seq_group in scheduled_seq_groups:
            seq_group = scheduled_seq_group.seq_group
1223
            seq_group.maybe_set_first_token_time(now)
1224
            request_output = RequestOutputFactory.create(seq_group)
1225
            request_outputs.append(request_output)
1226
        for seq_group in ignored_seq_groups:
1227
            request_output = RequestOutputFactory.create(seq_group)
1228
1229
1230
            request_outputs.append(request_output)
        return request_outputs

1231
    def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
Antoni Baum's avatar
Antoni Baum committed
1232
1233
        """Performs one decoding iteration and returns newly generated results.

1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
1245
1246
1247
1248
        .. figure:: https://i.imgur.com/sv2HssD.png
            :alt: Overview of the step function
            :align: center

            Overview of the step function.

        Details:
            - Step 1: Schedules the sequences to be executed in the next
              iteration and the token blocks to be swapped in/out/copy.

                - Depending on the scheduling policy,
                  sequences may be `preempted/reordered`.
                - A Sequence Group (SG) refer to a group of sequences
                  that are generated from the same prompt.

1249
            - Step 2: Calls the distributed executor to execute the model.
1250
1251
1252
1253
1254
1255
1256
1257
1258
1259
1260
1261
1262
1263
1264
1265
1266
1267
1268
1269
1270
            - Step 3: Processes the model output. This mainly includes:

                - Decodes the relevant outputs.
                - Updates the scheduled sequence groups with model outputs
                  based on its `sampling parameters` (`use_beam_search` or not).
                - Frees the finished sequence groups.

            - Finally, it creates and returns the newly generated results.

        Example:
            >>> # Please see the example/ folder for more detailed examples.
            >>>
            >>> # initialize engine and request arguments
            >>> engine = LLMEngine.from_engine_args(engine_args)
            >>> example_inputs = [(0, "What is LLM?",
            >>>    SamplingParams(temperature=0.0))]
            >>>
            >>> # Start the engine with an event loop
            >>> while True:
            >>>     if example_inputs:
            >>>         req_id, prompt, sampling_params = example_inputs.pop(0)
1271
            >>>         engine.add_request(str(req_id),prompt,sampling_params)
1272
1273
1274
1275
1276
1277
1278
1279
1280
            >>>
            >>>     # continue the request processing
            >>>     request_outputs = engine.step()
            >>>     for request_output in request_outputs:
            >>>         if request_output.finished:
            >>>             # return or show the request output
            >>>
            >>>     if not (engine.has_unfinished_requests() or example_inputs):
            >>>         break
Antoni Baum's avatar
Antoni Baum committed
1281
        """
1282
1283
1284
1285
1286
1287
        if self.parallel_config.pipeline_parallel_size > 1:
            raise NotImplementedError(
                "Pipeline parallelism is only supported through AsyncLLMEngine "
                "as performance will be severely degraded otherwise.")
        seq_group_metadata_list, scheduler_outputs = self.scheduler[
            0].schedule()
Antoni Baum's avatar
Antoni Baum committed
1288

1289
        if not scheduler_outputs.is_empty():
1290
1291
            finished_requests_ids = self.scheduler[
                0].get_and_reset_finished_requests_ids()
1292
            execute_model_req = ExecuteModelRequest(
1293
1294
1295
1296
                seq_group_metadata_list=seq_group_metadata_list,
                blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
                blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
                blocks_to_copy=scheduler_outputs.blocks_to_copy,
1297
1298
                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                running_queue_size=scheduler_outputs.running_queue_size,
Mor Zusman's avatar
Mor Zusman committed
1299
                finished_requests_ids=finished_requests_ids)
1300
1301
            output = self.model_executor.execute_model(
                execute_model_req=execute_model_req)
1302
1303
        else:
            output = []
Antoni Baum's avatar
Antoni Baum committed
1304

1305
1306
        request_outputs = self._process_model_outputs(
            output, scheduler_outputs.scheduled_seq_groups,
1307
            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
1308
1309

        # Log stats.
1310
        self.do_log_stats(scheduler_outputs, output)
1311

1312
1313
1314
        # Tracing
        self.do_tracing(scheduler_outputs)

1315
        if not self.has_unfinished_requests():
1316
1317
1318
1319
1320
1321
1322
            # Stop the execute model loop in parallel workers until there are
            # more requests to process. This avoids waiting indefinitely in
            # torch.distributed ops which may otherwise timeout, and unblocks
            # the RPC thread in the workers so that they can process any other
            # queued control plane messages, such as add/remove lora adapters.
            self.model_executor.stop_remote_worker_execution_loop()

1323
        return request_outputs
Antoni Baum's avatar
Antoni Baum committed
1324

1325
1326
1327
1328
1329
1330
1331
1332
1333
1334
    def add_logger(self, logger_name: str, logger: StatLoggerBase) -> None:
        if logger_name in self.stat_loggers:
            raise KeyError(f"Logger with name {logger_name} already exists.")
        self.stat_loggers[logger_name] = logger

    def remove_logger(self, logger_name: str) -> None:
        if logger_name not in self.stat_loggers:
            raise KeyError(f"Logger with name {logger_name} does not exist.")
        del self.stat_loggers[logger_name]

1335
1336
1337
1338
    def do_log_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs] = None,
            model_output: Optional[List[SamplerOutput]] = None) -> None:
1339
1340
        """Forced log when no requests active."""
        if self.log_stats:
1341
            stats = self._get_stats(scheduler_outputs, model_output)
1342
            for logger in self.stat_loggers.values():
1343
                logger.log(stats)
1344

1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
    def _get_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs],
            model_output: Optional[List[SamplerOutput]] = None) -> Stats:
        """Get Stats to be Logged to Prometheus.

        Args:
            scheduler_outputs: Optional, used to populate metrics related to
                the scheduled batch,
            model_output: Optional, used to emit speculative decoding metrics
                which are created by the workers.
        """
1357
        now = time.time()
Woosuk Kwon's avatar
Woosuk Kwon committed
1358

1359
1360
        # System State
        #   Scheduler State
1361
1362
1363
1364
1365
1366
        num_running_sys = sum(
            len(scheduler.running) for scheduler in self.scheduler)
        num_swapped_sys = sum(
            len(scheduler.swapped) for scheduler in self.scheduler)
        num_waiting_sys = sum(
            len(scheduler.waiting) for scheduler in self.scheduler)
1367
1368

        # KV Cache Usage in %
1369
        num_total_gpu = self.cache_config.num_gpu_blocks
1370
1371
        gpu_cache_usage_sys = 0.
        if num_total_gpu is not None:
1372
1373
1374
            num_free_gpu = sum(
                scheduler.block_manager.get_num_free_gpu_blocks()
                for scheduler in self.scheduler)
1375
            gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)
Woosuk Kwon's avatar
Woosuk Kwon committed
1376

1377
        num_total_cpu = self.cache_config.num_cpu_blocks
1378
        cpu_cache_usage_sys = 0.
1379
        if num_total_cpu is not None and num_total_cpu > 0:
1380
1381
1382
            num_free_cpu = sum(
                scheduler.block_manager.get_num_free_cpu_blocks()
                for scheduler in self.scheduler)
1383
1384
1385
1386
1387
1388
1389
            cpu_cache_usage_sys = 1.0 - (num_free_cpu / num_total_cpu)

        # Iteration stats
        num_prompt_tokens_iter = 0
        num_generation_tokens_iter = 0
        time_to_first_tokens_iter: List[float] = []
        time_per_output_tokens_iter: List[float] = []
1390
1391
        num_preemption_iter = (0 if scheduler_outputs is None else
                               scheduler_outputs.preempted)
1392
1393
1394
1395
1396
1397
1398
1399
1400
1401
1402
1403
1404

        # Request stats
        #   Latency
        time_e2e_requests: List[float] = []
        #   Metadata
        num_prompt_tokens_requests: List[int] = []
        num_generation_tokens_requests: List[int] = []
        best_of_requests: List[int] = []
        n_requests: List[int] = []
        finished_reason_requests: List[str] = []

        # NOTE: This loop assumes prefill seq_groups are before
        # decode seq_groups in scheduled_seq_groups.
1405
        if scheduler_outputs is not None:
1406
            num_generation_tokens_from_prefill_groups = 0.
1407
1408
1409
1410
            # NOTE: if scheduler_outputs.num_prefill_groups > 0 and
            # the len of scheduler_outputs.scheduled_seq_groups is !=
            # scheduler_outputs.num_prefill_groups, this means that
            # chunked prefills have been detected.
1411
1412
1413
1414

            for idx, scheduled_seq_group in enumerate(
                    scheduler_outputs.scheduled_seq_groups):
                group_was_prefill = idx < scheduler_outputs.num_prefill_groups
1415
                seq_group = scheduled_seq_group.seq_group
1416
1417
1418
1419
1420
1421
1422
1423
1424
1425
1426
1427
1428
1429
1430
1431
1432
1433
1434
1435
1436
1437
1438
1439
1440
1441
1442
1443

                # NOTE: a seq_group that completed all of its prefill tokens
                # in the last iteration will have seq_group.is_prefill() = False
                # with group_was_prefill = True
                if group_was_prefill:
                    # Number of prompt tokens.
                    num_prompt_tokens_iter += (
                        scheduled_seq_group.token_chunk_size)

                    # If the seq_group just finished the prefill state
                    # get TTFT.
                    if not seq_group.is_prefill():
                        latency = seq_group.get_last_latency(now)
                        time_to_first_tokens_iter.append(latency)

                        # One generation token per finished prefill.
                        num_generation_tokens_from_prefill_groups += (
                            seq_group.num_seqs())
                else:
                    # TPOTs.
                    latency = seq_group.get_last_latency(now)
                    time_per_output_tokens_iter.append(latency)

                # Because of chunked prefill, we can have a single sequence
                # group that does multiple prompt_runs. To prevent logging
                # the same metadata more than once per request, we standardize
                # on logging request level information for finished requests,
                # which can only happen once.
1444
                if seq_group.is_finished():
1445
                    # Latency timings
1446
1447
                    time_e2e_requests.append(now -
                                             seq_group.metrics.arrival_time)
1448

1449
1450
1451
1452
1453
1454
1455
                    # Metadata
                    num_prompt_tokens_requests.append(
                        len(seq_group.prompt_token_ids))
                    num_generation_tokens_requests.extend([
                        seq.get_output_len()
                        for seq in seq_group.get_finished_seqs()
                    ])
1456
1457
1458
1459
                    if seq_group.sampling_params is not None:
                        best_of_requests.append(
                            seq_group.sampling_params.best_of)
                        n_requests.append(seq_group.sampling_params.n)
1460
1461
1462
1463
1464
1465
1466
1467
1468
1469
1470
1471
1472
1473
                    finished_reason_requests.extend([
                        SequenceStatus.get_finished_reason(seq.status)
                        for seq in seq_group.get_finished_seqs()
                    ])

            # Number of generation tokens.
            #   num_batched_tokens equals the number of prompt_tokens plus the
            #   number of decode_tokens in a single iteration. So,
            #   num_generation_tokens = num_batched_tokens - num_prompt_tokens
            #   + num_generation_tokens_from_prefill_groups (since we generate
            #   one token on prefills on iters where the prefill finishes).
            num_generation_tokens_iter = (
                scheduler_outputs.num_batched_tokens - num_prompt_tokens_iter +
                num_generation_tokens_from_prefill_groups)
1474

1475
1476
1477
1478
1479
1480
1481
1482
        # Spec decode, if enabled, emits specialized metrics from the worker in
        # sampler output.
        if model_output and (model_output[0].spec_decode_worker_metrics
                             is not None):
            spec_decode_metrics = model_output[0].spec_decode_worker_metrics
        else:
            spec_decode_metrics = None

1483
1484
        return Stats(
            now=now,
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
1495
1496
1497
1498
            # System stats
            #   Scheduler State
            num_running_sys=num_running_sys,
            num_swapped_sys=num_swapped_sys,
            num_waiting_sys=num_waiting_sys,
            #   KV Cache Usage in %
            gpu_cache_usage_sys=gpu_cache_usage_sys,
            cpu_cache_usage_sys=cpu_cache_usage_sys,

            # Iteration stats
            num_prompt_tokens_iter=num_prompt_tokens_iter,
            num_generation_tokens_iter=num_generation_tokens_iter,
            time_to_first_tokens_iter=time_to_first_tokens_iter,
            time_per_output_tokens_iter=time_per_output_tokens_iter,
1499
            spec_decode_metrics=spec_decode_metrics,
1500
            num_preemption_iter=num_preemption_iter,
1501
1502
1503
1504
1505
1506
1507
1508
1509
1510

            # Request stats
            #   Latency
            time_e2e_requests=time_e2e_requests,
            #   Metadata
            num_prompt_tokens_requests=num_prompt_tokens_requests,
            num_generation_tokens_requests=num_generation_tokens_requests,
            best_of_requests=best_of_requests,
            n_requests=n_requests,
            finished_reason_requests=finished_reason_requests,
1511
1512
        )

1513
    def add_lora(self, lora_request: LoRARequest) -> bool:
1514
        return self.model_executor.add_lora(lora_request)
1515
1516

    def remove_lora(self, lora_id: int) -> bool:
1517
        return self.model_executor.remove_lora(lora_id)
1518

1519
    def list_loras(self) -> Set[int]:
1520
        return self.model_executor.list_loras()
1521

1522
1523
1524
    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)

1525
1526
1527
1528
1529
1530
1531
1532
1533
1534
    def add_prompt_adapter(
            self, prompt_adapter_request: PromptAdapterRequest) -> bool:
        return self.model_executor.add_prompt_adapter(prompt_adapter_request)

    def remove_prompt_adapter(self, prompt_adapter_id: int) -> bool:
        return self.model_executor.remove_prompt_adapter(prompt_adapter_id)

    def list_prompt_adapters(self) -> List[int]:
        return self.model_executor.list_prompt_adapters()

1535
    def check_health(self) -> None:
1536
1537
        if self.tokenizer:
            self.tokenizer.check_health()
1538
        self.model_executor.check_health()
1539
1540
1541
1542
1543
1544
1545
1546
1547
1548
1549
1550
1551
1552
1553
1554
1555
1556
1557
1558
1559
1560
1561
1562
1563
1564
1565
1566
1567
1568
1569
1570
1571
1572
1573
1574
1575
1576
1577
1578
1579
1580
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
1591
1592
1593
1594
1595
1596
1597

    def is_tracing_enabled(self) -> bool:
        return self.tracer is not None

    def do_tracing(self, scheduler_outputs: SchedulerOutputs) -> None:
        if self.tracer is None:
            return

        for scheduled_seq_group in scheduler_outputs.scheduled_seq_groups:
            seq_group = scheduled_seq_group.seq_group
            if seq_group.is_finished():
                self.create_trace_span(seq_group)

    def create_trace_span(self, seq_group: SequenceGroup) -> None:
        if self.tracer is None or seq_group.sampling_params is None:
            return
        arrival_time_nano_seconds = int(seq_group.metrics.arrival_time * 1e9)

        trace_context = extract_trace_context(seq_group.trace_headers)

        with self.tracer.start_as_current_span(
                "llm_request",
                kind=SpanKind.SERVER,
                context=trace_context,
                start_time=arrival_time_nano_seconds) as seq_span:
            metrics = seq_group.metrics
            ttft = metrics.first_token_time - metrics.arrival_time
            e2e_time = metrics.finished_time - metrics.arrival_time
            # attribute names are based on
            # https://github.com/open-telemetry/semantic-conventions/blob/main/docs/gen-ai/llm-spans.md
            seq_span.set_attribute(SpanAttributes.LLM_RESPONSE_MODEL,
                                   self.model_config.model)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_ID,
                                   seq_group.request_id)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TEMPERATURE,
                                   seq_group.sampling_params.temperature)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_TOP_P,
                                   seq_group.sampling_params.top_p)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_MAX_TOKENS,
                                   seq_group.sampling_params.max_tokens)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_BEST_OF,
                                   seq_group.sampling_params.best_of)
            seq_span.set_attribute(SpanAttributes.LLM_REQUEST_N,
                                   seq_group.sampling_params.n)
            seq_span.set_attribute(SpanAttributes.LLM_USAGE_NUM_SEQUENCES,
                                   seq_group.num_seqs())
            seq_span.set_attribute(SpanAttributes.LLM_USAGE_PROMPT_TOKENS,
                                   len(seq_group.prompt_token_ids))
            seq_span.set_attribute(
                SpanAttributes.LLM_USAGE_COMPLETION_TOKENS,
                sum([
                    seq.get_output_len()
                    for seq in seq_group.get_finished_seqs()
                ]))
            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_TIME_IN_QUEUE,
                                   metrics.time_in_queue)
            seq_span.set_attribute(
                SpanAttributes.LLM_LATENCY_TIME_TO_FIRST_TOKEN, ttft)
            seq_span.set_attribute(SpanAttributes.LLM_LATENCY_E2E, e2e_time)
1598
1599
1600
1601
1602
1603

    def is_encoder_decoder_model(self):
        return is_encoder_decoder_model_config(self.model_config)

    def is_embedding_model(self):
        return is_embedding_model_config(self.model_config)