"tests/quantization/untest_fp8.py" did not exist on "2ff1c360c628777beb2b2a4450eb5ab96d323d8c"
llm_engine.py 50.6 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, Type, TypeVar, Union
7

8
from transformers import PreTrainedTokenizer
9

10
import vllm.envs as envs
11
12
13
from vllm.config import (CacheConfig, DecodingConfig, DeviceConfig,
                         EngineConfig, LoadConfig, LoRAConfig, ModelConfig,
                         MultiModalConfig, ObservabilityConfig, ParallelConfig,
14
                         PromptAdapterConfig, SchedulerConfig,
15
                         SpeculativeConfig)
16
17
from vllm.core.scheduler import (ScheduledSequenceGroup, Scheduler,
                                 SchedulerOutputs)
Woosuk Kwon's avatar
Woosuk Kwon committed
18
from vllm.engine.arg_utils import EngineArgs
19
20
from vllm.engine.metrics import (LoggingStatLogger, PrometheusStatLogger,
                                 StatLoggerBase, Stats)
21
22
23
24
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
25
from vllm.executor.executor_base import ExecutorBase
26
from vllm.executor.ray_utils import initialize_ray_cluster
27
from vllm.inputs import INPUT_REGISTRY, LLMInputs, PromptInputs
Woosuk Kwon's avatar
Woosuk Kwon committed
28
from vllm.logger import init_logger
29
from vllm.lora.request import LoRARequest
30
31
32
from vllm.outputs import (EmbeddingRequestOutput, RequestOutput,
                          RequestOutputFactory)
from vllm.pooling_params import PoolingParams
33
from vllm.prompt_adapter.request import PromptAdapterRequest
Woosuk Kwon's avatar
Woosuk Kwon committed
34
from vllm.sampling_params import SamplingParams
35
from vllm.sequence import (EmbeddingSequenceGroupOutput, ExecuteModelRequest,
36
37
                           PoolerOutput, SamplerOutput, Sequence,
                           SequenceGroup, SequenceGroupMetadata,
38
                           SequenceStatus)
39
40
from vllm.tracing import (SpanAttributes, SpanKind, extract_trace_context,
                          init_tracer)
41
from vllm.transformers_utils.config import try_get_generation_config
42
from vllm.transformers_utils.detokenizer import Detokenizer
43
44
from vllm.transformers_utils.tokenizer_group import (BaseTokenizerGroup,
                                                     get_tokenizer_group)
yhu422's avatar
yhu422 committed
45
46
from vllm.usage.usage_lib import (UsageContext, is_usage_stats_enabled,
                                  usage_message)
47
from vllm.utils import Counter
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
480
481
482
483
    def get_tokenizer(
            self,
            lora_request: Optional[LoRARequest] = None
    ) -> "PreTrainedTokenizer":
        return self.get_tokenizer_group().get_lora_tokenizer(lora_request)
484
485
486

    def get_tokenizer_for_seq(self,
                              sequence: Sequence) -> "PreTrainedTokenizer":
487
488
        return self.get_tokenizer_group().get_lora_tokenizer(
            sequence.lora_request)
489

490
    def _init_tokenizer(self, **tokenizer_init_kwargs) -> BaseTokenizerGroup:
491
        init_kwargs = dict(
492
            tokenizer_id=self.model_config.tokenizer,
493
494
495
496
497
498
499
            enable_lora=bool(self.lora_config),
            max_num_seqs=self.scheduler_config.max_num_seqs,
            max_input_length=None,
            tokenizer_mode=self.model_config.tokenizer_mode,
            trust_remote_code=self.model_config.trust_remote_code,
            revision=self.model_config.tokenizer_revision)
        init_kwargs.update(tokenizer_init_kwargs)
500
501
502

        return get_tokenizer_group(self.parallel_config.tokenizer_pool_config,
                                   **init_kwargs)
503

504
505
    def _verify_args(self) -> None:
        self.model_config.verify_with_parallel_config(self.parallel_config)
506
        self.cache_config.verify_with_parallel_config(self.parallel_config)
507
508
509
510
        if self.lora_config:
            self.lora_config.verify_with_model_config(self.model_config)
            self.lora_config.verify_with_scheduler_config(
                self.scheduler_config)
511
512
513
        if self.prompt_adapter_config:
            self.prompt_adapter_config.verify_with_model_config(
                self.model_config)
514

515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
    def _get_eos_token_id(
            self, lora_request: Optional[LoRARequest]) -> Optional[int]:
        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

    def _add_processed_request(
        self,
        request_id: str,
        processed_inputs: LLMInputs,
        params: Union[SamplingParams, PoolingParams],
        arrival_time: float,
        lora_request: Optional[LoRARequest],
531
        prompt_adapter_request: Optional[PromptAdapterRequest],
532
        trace_headers: Optional[Mapping[str, str]] = None,
533
534
535
536
537
538
539
    ) -> 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,
540
                       lora_request, prompt_adapter_request)
541
542
543
544
545
546
547
548
549

        # 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,
550
                trace_headers=trace_headers,
551
                prompt_adapter_request=prompt_adapter_request)
552
553
554
555
556
557
558
        elif isinstance(params, PoolingParams):
            seq_group = self._create_sequence_group_with_pooling(
                request_id,
                seq,
                params,
                arrival_time=arrival_time,
                lora_request=lora_request,
559
                prompt_adapter_request=prompt_adapter_request)
560
561
562
563
        else:
            raise ValueError(
                "Either SamplingParams or PoolingParams must be provided.")

564
565
566
567
568
569
570
571
572
573
        # 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()
574
575

    def process_model_inputs(
576
        self,
577
578
        request_id: str,
        inputs: PromptInputs,
579
        lora_request: Optional[LoRARequest] = None,
580
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
581
582
583
584
585
586
587
588
589
590
591
592
593
594
    ) -> LLMInputs:
        if isinstance(inputs, str):
            inputs = {"prompt": inputs}

        if "prompt_token_ids" not in inputs:
            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=inputs["prompt"],
                                                lora_request=lora_request)
        else:
            prompt_token_ids = inputs["prompt_token_ids"]

595
596
597
598
599
        if prompt_adapter_request:
            prompt_token_ids = \
                [0] * prompt_adapter_request.prompt_adapter_num_virtual_tokens\
                         + prompt_token_ids

600
601
602
603
604
        llm_inputs = LLMInputs(prompt_token_ids=prompt_token_ids,
                               prompt=inputs.get("prompt"),
                               multi_modal_data=inputs.get("multi_modal_data"))

        return self.input_processor(llm_inputs)
605

606
607
608
    def add_request(
        self,
        request_id: str,
609
        inputs: PromptInputs,
610
        params: Union[SamplingParams, PoolingParams],
611
        arrival_time: Optional[float] = None,
612
        lora_request: Optional[LoRARequest] = None,
613
        trace_headers: Optional[Mapping[str, str]] = None,
614
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
615
    ) -> None:
Zhuohan Li's avatar
Zhuohan Li committed
616
        """Add a request to the engine's request pool.
617
618

        The request is added to the request pool and will be processed by the
Zhuohan Li's avatar
Zhuohan Li committed
619
        scheduler as `engine.step()` is called. The exact scheduling policy is
620
621
622
623
        determined by the scheduler.

        Args:
            request_id: The unique ID of the request.
624
625
626
627
628
629
            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.
630
            arrival_time: The arrival time of the request. If None, we use
631
                the current monotonic time.
632
            trace_headers: OpenTelemetry trace headers.
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656

        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
            >>> ...
657
        """
658
659
660
        if lora_request is not None and not self.lora_config:
            raise ValueError(f"Got lora_request {lora_request} but LoRA is "
                             "not enabled!")
661
        if arrival_time is None:
662
            arrival_time = time.time()
663

664
665
666
667
668
        processed_inputs = self.process_model_inputs(
            request_id=request_id,
            inputs=inputs,
            lora_request=lora_request,
            prompt_adapter_request=prompt_adapter_request)
669

670
671
672
673
674
675
        self._add_processed_request(
            request_id=request_id,
            processed_inputs=processed_inputs,
            params=params,
            arrival_time=arrival_time,
            lora_request=lora_request,
676
            prompt_adapter_request=prompt_adapter_request,
677
            trace_headers=trace_headers,
678
        )
679
680
681
682
683
684

    def _create_sequence_group_with_sampling(
        self,
        request_id: str,
        seq: Sequence,
        sampling_params: SamplingParams,
685
686
        arrival_time: float,
        lora_request: Optional[LoRARequest],
687
        trace_headers: Optional[Mapping[str, str]] = None,
688
        prompt_adapter_request: Optional[PromptAdapterRequest] = None,
689
690
691
692
693
694
695
696
697
698
    ) -> 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.")

699
700
701
        # Defensive copy of SamplingParams, which are used by the sampler,
        # this doesn't deep-copy LogitsProcessor objects
        sampling_params = sampling_params.clone()
702

703
        sampling_params.update_from_generation_config(
704
            self.generation_config_fields, seq.eos_token_id)
705

706
        # Create the sequence group.
707
708
709
710
711
712
713
        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,
714
            prompt_adapter_request=prompt_adapter_request)
715

716
717
718
719
720
721
722
        return seq_group

    def _create_sequence_group_with_pooling(
        self,
        request_id: str,
        seq: Sequence,
        pooling_params: PoolingParams,
723
724
        arrival_time: float,
        lora_request: Optional[LoRARequest],
725
        prompt_adapter_request: Optional[PromptAdapterRequest],
726
727
728
729
730
    ) -> 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.
731
732
733
734
735
736
737
        seq_group = SequenceGroup(
            request_id=request_id,
            seqs=[seq],
            arrival_time=arrival_time,
            lora_request=lora_request,
            pooling_params=pooling_params,
            prompt_adapter_request=prompt_adapter_request)
738
        return seq_group
739

Antoni Baum's avatar
Antoni Baum committed
740
741
    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
        """Aborts a request(s) with the given ID.
742
743

        Args:
Antoni Baum's avatar
Antoni Baum committed
744
            request_id: The ID(s) of the request to abort.
745
746
747
748
749
750
751
752
753
754
755

        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)
756
        """
757
758
        for scheduler in self.scheduler:
            scheduler.abort_seq_group(request_id)
759

760
761
762
763
    def get_model_config(self) -> ModelConfig:
        """Gets the model configuration."""
        return self.model_config

764
765
766
767
    def get_decoding_config(self) -> DecodingConfig:
        """Gets the decoding configuration."""
        return self.decoding_config

768
    def get_num_unfinished_requests(self) -> int:
769
        """Gets the number of unfinished requests."""
770
771
        return sum(scheduler.get_num_unfinished_seq_groups()
                   for scheduler in self.scheduler)
772

773
    def has_unfinished_requests(self) -> bool:
774
        """Returns True if there are unfinished requests."""
775
776
777
778
779
780
781
782
783
        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()
784

785
786
787
788
789
790
791
792
793
794
795
796
    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

797
    def _process_model_outputs(
798
        self,
799
        output: GenericSequence[Union[SamplerOutput, PoolerOutput]],
800
        scheduled_seq_groups: List[ScheduledSequenceGroup],
801
802
        ignored_seq_groups: List[SequenceGroup],
        seq_group_metadata_list: List[SequenceGroupMetadata],
803
    ) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
804
        """Apply the model output to the sequences in the scheduled seq groups.
805

806
807
808
        Returns RequestOutputs that can be returned to the client.
        """

809
        now = time.time()
810
811
812
813

        # Organize outputs by [sequence group][step] instead of
        # [step][sequence group].
        output_by_sequence_group = create_output_by_sequence_group(
814
            output, num_seq_groups=len(scheduled_seq_groups))
815

816
        # Update the scheduled sequence groups with the model outputs.
817
818
819
        for scheduled_seq_group, outputs, seq_group_meta in zip(
                scheduled_seq_groups, output_by_sequence_group,
                seq_group_metadata_list):
820
            seq_group = scheduled_seq_group.seq_group
821
822
            seq_group.update_num_computed_tokens(
                scheduled_seq_group.token_chunk_size)
823
824
825
            if self.model_config.embedding_mode:
                self._process_sequence_group_outputs(seq_group, outputs)
                continue
826

827
828
            self.output_processor.process_prompt_logprob(seq_group, outputs)
            if seq_group_meta.do_sample:
829
                self.output_processor.process_outputs(seq_group, outputs)
830
831

        # Free the finished sequence groups.
832
833
        for scheduler in self.scheduler:
            scheduler.free_finished_seq_groups()
834
835

        # Create the outputs.
836
837
        request_outputs: List[Union[RequestOutput,
                                    EmbeddingRequestOutput]] = []
838
839
        for scheduled_seq_group in scheduled_seq_groups:
            seq_group = scheduled_seq_group.seq_group
840
            seq_group.maybe_set_first_token_time(now)
841
            request_output = RequestOutputFactory.create(seq_group)
842
            request_outputs.append(request_output)
843
        for seq_group in ignored_seq_groups:
844
            request_output = RequestOutputFactory.create(seq_group)
845
846
847
            request_outputs.append(request_output)
        return request_outputs

848
    def step(self) -> List[Union[RequestOutput, EmbeddingRequestOutput]]:
Antoni Baum's avatar
Antoni Baum committed
849
850
        """Performs one decoding iteration and returns newly generated results.

851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
        .. 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.

866
            - Step 2: Calls the distributed executor to execute the model.
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
            - 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)
888
            >>>         engine.add_request(str(req_id),prompt,sampling_params)
889
890
891
892
893
894
895
896
897
            >>>
            >>>     # 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
898
        """
899
900
901
902
903
904
        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
905

906
        if not scheduler_outputs.is_empty():
907
908
            finished_requests_ids = self.scheduler[
                0].get_and_reset_finished_requests_ids()
909
            execute_model_req = ExecuteModelRequest(
910
911
912
913
                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,
914
915
                num_lookahead_slots=scheduler_outputs.num_lookahead_slots,
                running_queue_size=scheduler_outputs.running_queue_size,
Mor Zusman's avatar
Mor Zusman committed
916
                finished_requests_ids=finished_requests_ids)
917
918
            output = self.model_executor.execute_model(
                execute_model_req=execute_model_req)
919
920
        else:
            output = []
Antoni Baum's avatar
Antoni Baum committed
921

922
923
        request_outputs = self._process_model_outputs(
            output, scheduler_outputs.scheduled_seq_groups,
924
            scheduler_outputs.ignored_seq_groups, seq_group_metadata_list)
925
926

        # Log stats.
927
        self.do_log_stats(scheduler_outputs, output)
928

929
930
931
        # Tracing
        self.do_tracing(scheduler_outputs)

932
        if not self.has_unfinished_requests():
933
934
935
936
937
938
939
            # 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()

940
        return request_outputs
Antoni Baum's avatar
Antoni Baum committed
941

942
943
944
945
946
947
948
949
950
951
    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]

952
953
954
955
    def do_log_stats(
            self,
            scheduler_outputs: Optional[SchedulerOutputs] = None,
            model_output: Optional[List[SamplerOutput]] = None) -> None:
956
957
        """Forced log when no requests active."""
        if self.log_stats:
958
            stats = self._get_stats(scheduler_outputs, model_output)
959
            for logger in self.stat_loggers.values():
960
                logger.log(stats)
961

962
963
964
965
966
967
968
969
970
971
972
973
    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.
        """
974
        now = time.time()
Woosuk Kwon's avatar
Woosuk Kwon committed
975

976
977
        # System State
        #   Scheduler State
978
979
980
981
982
983
        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)
984
985

        # KV Cache Usage in %
986
        num_total_gpu = self.cache_config.num_gpu_blocks
987
988
        gpu_cache_usage_sys = 0.
        if num_total_gpu is not None:
989
990
991
            num_free_gpu = sum(
                scheduler.block_manager.get_num_free_gpu_blocks()
                for scheduler in self.scheduler)
992
            gpu_cache_usage_sys = 1.0 - (num_free_gpu / num_total_gpu)
Woosuk Kwon's avatar
Woosuk Kwon committed
993

994
        num_total_cpu = self.cache_config.num_cpu_blocks
995
        cpu_cache_usage_sys = 0.
996
        if num_total_cpu is not None and num_total_cpu > 0:
997
998
999
            num_free_cpu = sum(
                scheduler.block_manager.get_num_free_cpu_blocks()
                for scheduler in self.scheduler)
1000
1001
1002
1003
1004
1005
1006
            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] = []
1007
1008
        num_preemption_iter = (0 if scheduler_outputs is None else
                               scheduler_outputs.preempted)
1009
1010
1011
1012
1013
1014
1015
1016
1017
1018
1019
1020
1021

        # 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.
1022
        if scheduler_outputs is not None:
1023
            num_generation_tokens_from_prefill_groups = 0.
1024
1025
1026
1027
            # 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.
1028
1029
1030
1031

            for idx, scheduled_seq_group in enumerate(
                    scheduler_outputs.scheduled_seq_groups):
                group_was_prefill = idx < scheduler_outputs.num_prefill_groups
1032
                seq_group = scheduled_seq_group.seq_group
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
1047
1048
1049
1050
1051
1052
1053
1054
1055
1056
1057
1058
1059
1060

                # 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.
1061
                if seq_group.is_finished():
1062
                    # Latency timings
1063
1064
                    time_e2e_requests.append(now -
                                             seq_group.metrics.arrival_time)
1065

1066
1067
1068
1069
1070
1071
1072
                    # 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()
                    ])
1073
1074
1075
1076
                    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)
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
                    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)
1091

1092
1093
1094
1095
1096
1097
1098
1099
        # 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

1100
1101
        return Stats(
            now=now,
1102
1103
1104
1105
1106
1107
1108
1109
1110
1111
1112
1113
1114
1115
            # 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,
1116
            spec_decode_metrics=spec_decode_metrics,
1117
            num_preemption_iter=num_preemption_iter,
1118
1119
1120
1121
1122
1123
1124
1125
1126
1127

            # 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,
1128
1129
        )

1130
    def add_lora(self, lora_request: LoRARequest) -> bool:
1131
        return self.model_executor.add_lora(lora_request)
1132
1133

    def remove_lora(self, lora_id: int) -> bool:
1134
        return self.model_executor.remove_lora(lora_id)
1135

1136
    def list_loras(self) -> Set[int]:
1137
        return self.model_executor.list_loras()
1138

1139
1140
1141
    def pin_lora(self, lora_id: int) -> bool:
        return self.model_executor.pin_lora(lora_id)

1142
1143
1144
1145
1146
1147
1148
1149
1150
1151
    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()

1152
    def check_health(self) -> None:
1153
1154
        if self.tokenizer:
            self.tokenizer.check_health()
1155
        self.model_executor.check_health()
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
1174
1175
1176
1177
1178
1179
1180
1181
1182
1183
1184
1185
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214

    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)