serving_engine.py 49.7 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3
import asyncio
4
import json
5
import sys
6
import time
7
import traceback
8
from collections.abc import AsyncGenerator, Callable, Iterable, Mapping, Sequence
9
from concurrent.futures import ThreadPoolExecutor
10
from http import HTTPStatus
11
from typing import Any, ClassVar, Generic, TypeAlias, TypeVar
12

13
import torch
14
from fastapi import Request
15
from pydantic import BaseModel, ConfigDict, Field, TypeAdapter
16
from starlette.datastructures import Headers
17
18
from typing_extensions import TypeIs

19
20
21
22
23
if sys.version_info >= (3, 12):
    from typing import TypedDict
else:
    from typing_extensions import TypedDict

24
25
26
27
from openai.types.responses import (
    ToolChoiceFunction,
)

28
import vllm.envs as envs
29
from vllm.beam_search import BeamSearchSequence, create_sort_beams_key_function
30
from vllm.engine.protocol import EngineClient
31
32
33
34
35
36
37
38
39
from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
    ConversationMessage,
    apply_hf_chat_template,
    apply_mistral_chat_template,
    parse_chat_messages_futures,
    resolve_chat_template_content_format,
)
40
from vllm.entrypoints.context import ConversationContext
41
from vllm.entrypoints.logger import RequestLogger
42
from vllm.entrypoints.openai.protocol import (
43
    ChatCompletionNamedToolChoiceParam,
44
45
    ChatCompletionRequest,
    ChatCompletionResponse,
46
47
    ClassificationChatRequest,
    ClassificationCompletionRequest,
48
49
50
51
52
53
54
55
56
57
58
    ClassificationRequest,
    ClassificationResponse,
    CompletionRequest,
    CompletionResponse,
    DetokenizeRequest,
    EmbeddingChatRequest,
    EmbeddingCompletionRequest,
    EmbeddingRequest,
    EmbeddingResponse,
    ErrorInfo,
    ErrorResponse,
59
60
    FunctionCall,
    FunctionDefinition,
61
62
    GenerateRequest,
    GenerateResponse,
63
64
65
66
67
68
69
70
71
72
73
74
75
    IOProcessorRequest,
    PoolingResponse,
    RerankRequest,
    ResponsesRequest,
    ScoreRequest,
    ScoreResponse,
    TokenizeChatRequest,
    TokenizeCompletionRequest,
    TokenizeResponse,
    TranscriptionRequest,
    TranscriptionResponse,
    TranslationRequest,
)
76
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
77
from vllm.entrypoints.openai.tool_parsers import ToolParser, ToolParserManager
78
from vllm.entrypoints.renderer import BaseRenderer, CompletionRenderer, RenderConfig
79
from vllm.entrypoints.utils import _validate_truncation_size
80
from vllm.inputs.data import PromptType
81
from vllm.inputs.data import TokensPrompt as EngineTokensPrompt
82
83
84
85
86
from vllm.inputs.parse import (
    PromptComponents,
    get_prompt_components,
    is_explicit_encoder_decoder_prompt,
)
87
from vllm.logger import init_logger
88
from vllm.logprobs import Logprob, PromptLogprobs
89
from vllm.lora.request import LoRARequest
90
from vllm.multimodal import (  # noqa: F401 - Required to resolve Pydantic error in RequestProcessingMixin
91
92
93
    MultiModalDataDict,
    MultiModalUUIDDict,
)
94
from vllm.outputs import CompletionOutput, PoolingRequestOutput, RequestOutput
95
from vllm.pooling_params import PoolingParams
96
from vllm.reasoning import ReasoningParser, ReasoningParserManager
97
from vllm.sampling_params import BeamSearchParams, SamplingParams
98
99
100
101
102
from vllm.tracing import (
    contains_trace_headers,
    extract_trace_headers,
    log_tracing_disabled_warning,
)
103
from vllm.transformers_utils.tokenizer import AnyTokenizer, MistralTokenizer
104
from vllm.utils import random_uuid
105
from vllm.utils.async_utils import (
106
    AsyncMicrobatchTokenizer,
107
    collect_from_async_generator,
108
    make_async,
109
110
    merge_async_iterators,
)
111
from vllm.utils.collection_utils import is_list_of
112
from vllm.v1.engine import EngineCoreRequest
113
114
115

logger = init_logger(__name__)

116
117
118
119
120
CompletionLikeRequest: TypeAlias = (
    CompletionRequest
    | DetokenizeRequest
    | EmbeddingCompletionRequest
    | RerankRequest
121
    | ClassificationCompletionRequest
122
123
124
    | ScoreRequest
    | TokenizeCompletionRequest
)
125

126
ChatLikeRequest: TypeAlias = (
127
128
129
130
    ChatCompletionRequest
    | EmbeddingChatRequest
    | TokenizeChatRequest
    | ClassificationChatRequest
131
132
133
134
135
136
137
138
)
SpeechToTextRequest: TypeAlias = TranscriptionRequest | TranslationRequest
AnyRequest: TypeAlias = (
    CompletionLikeRequest
    | ChatLikeRequest
    | SpeechToTextRequest
    | ResponsesRequest
    | IOProcessorRequest
139
    | GenerateRequest
140
141
142
143
144
145
146
147
148
149
150
)

AnyResponse: TypeAlias = (
    CompletionResponse
    | ChatCompletionResponse
    | EmbeddingResponse
    | TranscriptionResponse
    | TokenizeResponse
    | PoolingResponse
    | ClassificationResponse
    | ScoreResponse
151
    | GenerateResponse
152
)
153

154
155
156

class TextTokensPrompt(TypedDict):
    prompt: str
157
    prompt_token_ids: list[int]
158
159


160
161
162
163
class EmbedsPrompt(TypedDict):
    prompt_embeds: torch.Tensor


164
RequestPrompt: TypeAlias = list[int] | str | TextTokensPrompt | EmbedsPrompt
165
166
167


def is_text_tokens_prompt(prompt: RequestPrompt) -> TypeIs[TextTokensPrompt]:
168
169
170
171
172
    return (
        isinstance(prompt, dict)
        and "prompt_token_ids" in prompt
        and "prompt_embeds" not in prompt
    )
173
174
175


def is_embeds_prompt(prompt: RequestPrompt) -> TypeIs[EmbedsPrompt]:
176
177
178
179
180
    return (
        isinstance(prompt, dict)
        and "prompt_token_ids" not in prompt
        and "prompt_embeds" in prompt
    )
181

182

183
184
185
186
187
RequestT = TypeVar("RequestT", bound=AnyRequest)


class RequestProcessingMixin(BaseModel):
    """
188
    Mixin for request processing,
189
190
    handling prompt preparation and engine input.
    """
191

192
193
    request_prompts: Sequence[RequestPrompt] | None = []
    engine_prompts: list[EngineTokensPrompt] | None = []
194
195
196
197
198
199

    model_config = ConfigDict(arbitrary_types_allowed=True)


class ResponseGenerationMixin(BaseModel):
    """
200
    Mixin for response generation,
201
202
    managing result generators and final batch results.
    """
203

204
205
206
207
    result_generator: (
        AsyncGenerator[tuple[int, RequestOutput | PoolingRequestOutput], None] | None
    ) = None
    final_res_batch: list[RequestOutput | PoolingRequestOutput] = Field(
208
209
        default_factory=list
    )
210
211
212
213

    model_config = ConfigDict(arbitrary_types_allowed=True)


214
class ServeContext(
215
216
217
218
    RequestProcessingMixin,
    ResponseGenerationMixin,
    BaseModel,
    Generic[RequestT],
219
):
220
221
    # Shared across all requests
    request: RequestT
222
    raw_request: Request | None = None
223
224
225
    model_name: str
    request_id: str
    created_time: int = Field(default_factory=lambda: int(time.time()))
226
    lora_request: LoRARequest | None = None
227
228

    # Shared across most requests
229
    tokenizer: AnyTokenizer | None = None
230
231
232
233
234
235
236
237
238
239
240
241
242

    # `protected_namespaces` resolves Pydantic v2's warning
    # on conflict with protected namespace "model_"
    model_config = ConfigDict(
        protected_namespaces=(),
        arbitrary_types_allowed=True,
    )


ClassificationServeContext = ServeContext[ClassificationRequest]


class EmbeddingServeContext(ServeContext[EmbeddingRequest]):
243
    chat_template: str | None = None
244
245
246
247
248
249
250
251
252
253
    chat_template_content_format: ChatTemplateContentFormatOption


# Used to resolve the Pydantic error related to
# forward reference of MultiModalDataDict in TokensPrompt
RequestProcessingMixin.model_rebuild()
ServeContext.model_rebuild()
ClassificationServeContext.model_rebuild()
EmbeddingServeContext.model_rebuild()

254

255
class OpenAIServing:
256
257
258
259
    request_id_prefix: ClassVar[str] = """
    A short string prepended to every request’s ID (e.g. "embd", "classify")
    so you can easily tell “this ID came from Embedding vs Classification.”
    """
260

261
262
    def __init__(
        self,
263
        engine_client: EngineClient,
264
        models: OpenAIServingModels,
265
        *,
266
        request_logger: RequestLogger | None,
267
        return_tokens_as_token_ids: bool = False,
268
        log_error_stack: bool = False,
269
    ):
270
271
        super().__init__()

272
        self.engine_client = engine_client
273

274
        self.models = models
275

276
        self.request_logger = request_logger
277
        self.return_tokens_as_token_ids = return_tokens_as_token_ids
278
        self._tokenizer_executor = ThreadPoolExecutor(max_workers=1)
279
        self._apply_mistral_chat_template_async = make_async(
280
281
            apply_mistral_chat_template, executor=self._tokenizer_executor
        )
282

283
        self._async_tokenizer_pool: dict[AnyTokenizer, AsyncMicrobatchTokenizer] = {}
284
        self.log_error_stack = log_error_stack
285

286
287
288
289
290
        self.processor = self.models.processor
        self.io_processor = self.models.io_processor
        self.model_config = self.models.model_config
        self.max_model_len = self.model_config.max_model_len

291
    def _get_tool_parser(
292
293
        self, tool_parser_name: str | None = None, enable_auto_tools: bool = False
    ) -> Callable[[AnyTokenizer], ToolParser] | None:
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
        """Get the tool parser based on the name."""
        parser = None
        if not enable_auto_tools or tool_parser_name is None:
            return parser
        logger.info(
            '"auto" tool choice has been enabled please note that while'
            " the parallel_tool_calls client option is preset for "
            "compatibility reasons, it will be ignored."
        )

        try:
            if tool_parser_name == "pythonic" and self.model_config.model.startswith(
                "meta-llama/Llama-3.2"
            ):
                logger.warning(
                    "Llama3.2 models may struggle to emit valid pythonic tool calls"
                )
            parser = ToolParserManager.get_tool_parser(tool_parser_name)
        except Exception as e:
            raise TypeError(
                "Error: --enable-auto-tool-choice requires "
                f"tool_parser:'{tool_parser_name}' which has not "
                "been registered"
            ) from e
        return parser

    def _get_reasoning_parser(
        self,
        reasoning_parser_name: str,
323
    ) -> Callable[[AnyTokenizer], ReasoningParser] | None:
324
325
326
327
328
329
330
331
332
333
334
        """Get the reasoning parser based on the name."""
        parser = None
        if not reasoning_parser_name:
            return None
        try:
            parser = ReasoningParserManager.get_reasoning_parser(reasoning_parser_name)
            assert parser is not None
        except Exception as e:
            raise TypeError(f"{reasoning_parser_name=} has not been registered") from e
        return parser

335
336
337
338
    async def reset_mm_cache(self) -> None:
        self.processor.clear_mm_cache()
        await self.engine_client.reset_mm_cache()

339
340
341
342
343
    async def beam_search(
        self,
        prompt: PromptType,
        request_id: str,
        params: BeamSearchParams,
344
        lora_request: LoRARequest | None = None,
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
    ) -> AsyncGenerator[RequestOutput, None]:
        beam_width = params.beam_width
        max_tokens = params.max_tokens
        ignore_eos = params.ignore_eos
        temperature = params.temperature
        length_penalty = params.length_penalty
        include_stop_str_in_output = params.include_stop_str_in_output

        processor = self.processor
        tokenizer = processor.tokenizer
        if tokenizer is None:
            raise ValueError(
                "You cannot use beam search when `skip_tokenizer_init` is True"
            )

        eos_token_id: int = tokenizer.eos_token_id  # type: ignore

        if is_explicit_encoder_decoder_prompt(prompt):
            raise NotImplementedError

365
        prompt_text: str | None
366
        prompt_token_ids: list[int]
367
        multi_modal_data: MultiModalDataDict | None
368
369
370
371
372
373
374
375
376
        if isinstance(prompt, str):
            prompt_text = prompt
            prompt_token_ids = []
            multi_modal_data = None
        else:
            prompt_text = prompt.get("prompt")  # type: ignore
            prompt_token_ids = prompt.get("prompt_token_ids", [])  # type: ignore
            multi_modal_data = prompt.get("multi_modal_data")  # type: ignore

377
378
379
380
381
382
383
384
385
386
        mm_processor_kwargs: dict[str, Any] | None = None

        # This is a workaround to fix multimodal beam search; this is a
        # bandaid fix for 2 small problems:
        # 1. Multi_modal_data on the processed_inputs currently resolves to
        #    `None`.
        # 2. preprocessing above expands the multimodal placeholders. However,
        #    this happens again in generation, so the double expansion causes
        #    a mismatch.
        # TODO - would be ideal to handle this more gracefully.
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513

        tokenized_length = len(prompt_token_ids)

        sort_beams_key = create_sort_beams_key_function(eos_token_id, length_penalty)

        beam_search_params = SamplingParams(
            logprobs=2 * beam_width,
            max_tokens=1,
            temperature=temperature,
        )
        all_beams = [
            BeamSearchSequence(
                tokens=prompt_token_ids,
                cum_logprob=0,
                logprobs=[],
                multi_modal_data=multi_modal_data,
                mm_processor_kwargs=mm_processor_kwargs,
                lora_request=lora_request,
            )
        ]
        completed = []

        for _ in range(max_tokens):
            prompts_batch, lora_req_batch = zip(
                *[
                    (
                        EngineTokensPrompt(
                            prompt_token_ids=beam.tokens,
                            multi_modal_data=beam.multi_modal_data,
                            mm_processor_kwargs=beam.mm_processor_kwargs,
                        ),
                        beam.lora_request,
                    )
                    for beam in all_beams
                ]
            )

            tasks = []
            request_id_batch = f"{request_id}-{random_uuid()}"

            for i, (individual_prompt, lora_req) in enumerate(
                zip(prompts_batch, lora_req_batch)
            ):
                request_id_item = f"{request_id_batch}-beam-{i}"
                task = asyncio.create_task(
                    collect_from_async_generator(
                        self.engine_client.generate(
                            individual_prompt,
                            beam_search_params,
                            request_id_item,
                            lora_request=lora_req,
                        )
                    )
                )
                tasks.append(task)

            output = [x[0] for x in await asyncio.gather(*tasks)]

            new_beams = []
            for i, current_beam in enumerate(all_beams):
                result = output[i]

                if result.outputs[0].logprobs is not None:
                    logprobs = result.outputs[0].logprobs[0]
                    for token_id, logprob_obj in logprobs.items():
                        if token_id == eos_token_id and not ignore_eos:
                            completed.append(
                                BeamSearchSequence(
                                    tokens=current_beam.tokens + [token_id]
                                    if include_stop_str_in_output
                                    else current_beam.tokens,
                                    logprobs=current_beam.logprobs + [logprobs],
                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                    finish_reason="stop",
                                    stop_reason=eos_token_id,
                                )
                            )
                        else:
                            new_beams.append(
                                BeamSearchSequence(
                                    tokens=current_beam.tokens + [token_id],
                                    logprobs=current_beam.logprobs + [logprobs],
                                    lora_request=current_beam.lora_request,
                                    cum_logprob=current_beam.cum_logprob
                                    + logprob_obj.logprob,
                                    multi_modal_data=current_beam.multi_modal_data,
                                    mm_processor_kwargs=current_beam.mm_processor_kwargs,
                                )
                            )

            sorted_beams = sorted(new_beams, key=sort_beams_key, reverse=True)
            all_beams = sorted_beams[:beam_width]

        completed.extend(all_beams)
        sorted_completed = sorted(completed, key=sort_beams_key, reverse=True)
        best_beams = sorted_completed[:beam_width]

        for beam in best_beams:
            if beam.tokens[-1] == eos_token_id and not ignore_eos:
                # Skip the eos token in the text.
                tokens = beam.tokens[tokenized_length:-1]
            else:
                tokens = beam.tokens[tokenized_length:]
            beam.text = tokenizer.decode(tokens)

        yield RequestOutput(
            request_id=request_id,
            prompt=prompt_text,
            outputs=[
                CompletionOutput(
                    text=beam.text,  # type: ignore
                    cumulative_logprob=beam.cum_logprob,
                    token_ids=beam.tokens[tokenized_length:],
                    index=i,
                    logprobs=beam.logprobs,
                    finish_reason=beam.finish_reason
                    if beam.finish_reason is not None
                    else "length",
                    stop_reason=beam.stop_reason,
                )
                for (i, beam) in enumerate(best_beams)
            ],
            finished=True,
            prompt_token_ids=prompt_token_ids,
            prompt_logprobs=None,
        )
514

515
    def _get_renderer(self, tokenizer: AnyTokenizer | None) -> BaseRenderer:
516
517
518
519
520
521
522
        """
        Get a Renderer instance with the provided tokenizer.
        Uses shared async tokenizer pool for efficiency.
        """
        return CompletionRenderer(
            model_config=self.model_config,
            tokenizer=tokenizer,
523
524
            async_tokenizer_pool=self._async_tokenizer_pool,
        )
525

526
527
528
529
530
531
532
533
534
535
536
537
538
    def _build_render_config(
        self,
        request: Any,
    ) -> RenderConfig:
        """
        Build and return a `RenderConfig` for an endpoint.

        Used by the renderer to control how prompts are prepared
        (e.g., tokenization and length handling). Endpoints should
        implement this with logic appropriate to their request type.
        """
        raise NotImplementedError

539
540
    def _get_async_tokenizer(self, tokenizer) -> AsyncMicrobatchTokenizer:
        """
541
        Return (and cache) an `AsyncMicrobatchTokenizer` bound to the
542
543
544
545
546
547
548
        given tokenizer.
        """
        async_tokenizer = self._async_tokenizer_pool.get(tokenizer)
        if async_tokenizer is None:
            async_tokenizer = AsyncMicrobatchTokenizer(tokenizer)
            self._async_tokenizer_pool[tokenizer] = async_tokenizer
        return async_tokenizer
549

550
551
552
    async def _preprocess(
        self,
        ctx: ServeContext,
553
    ) -> ErrorResponse | None:
554
555
556
557
558
559
560
561
562
        """
        Default preprocessing hook. Subclasses may override
        to prepare `ctx` (classification, embedding, etc.).
        """
        return None

    def _build_response(
        self,
        ctx: ServeContext,
563
    ) -> AnyResponse | ErrorResponse:
564
565
566
567
568
569
570
571
572
        """
        Default response builder. Subclass may override this method
        to return the appropriate response object.
        """
        return self.create_error_response("unimplemented endpoint")

    async def handle(
        self,
        ctx: ServeContext,
573
574
    ) -> AnyResponse | ErrorResponse:
        generation: AsyncGenerator[AnyResponse | ErrorResponse, None]
575
576
577
578
579
580
581
582
583
584
        generation = self._pipeline(ctx)

        async for response in generation:
            return response

        return self.create_error_response("No response yielded from pipeline")

    async def _pipeline(
        self,
        ctx: ServeContext,
585
    ) -> AsyncGenerator[AnyResponse | ErrorResponse, None]:
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
        """Execute the request processing pipeline yielding responses."""
        if error := await self._check_model(ctx.request):
            yield error
        if error := self._validate_request(ctx):
            yield error

        preprocess_ret = await self._preprocess(ctx)
        if isinstance(preprocess_ret, ErrorResponse):
            yield preprocess_ret

        generators_ret = await self._prepare_generators(ctx)
        if isinstance(generators_ret, ErrorResponse):
            yield generators_ret

        collect_ret = await self._collect_batch(ctx)
        if isinstance(collect_ret, ErrorResponse):
            yield collect_ret

        yield self._build_response(ctx)

606
    def _validate_request(self, ctx: ServeContext) -> ErrorResponse | None:
607
        truncate_prompt_tokens = getattr(ctx.request, "truncate_prompt_tokens", None)
608

609
610
611
612
        if (
            truncate_prompt_tokens is not None
            and truncate_prompt_tokens > self.max_model_len
        ):
613
614
615
            return self.create_error_response(
                "truncate_prompt_tokens value is "
                "greater than max_model_len."
616
617
                " Please, select a smaller truncation size."
            )
618
619
        return None

620
621
622
    def _create_pooling_params(
        self,
        ctx: ServeContext,
623
    ) -> PoolingParams | ErrorResponse:
624
625
        if not hasattr(ctx.request, "to_pooling_params"):
            return self.create_error_response(
626
627
                "Request type does not support pooling parameters"
            )
628
629
630

        return ctx.request.to_pooling_params()

631
632
633
    async def _prepare_generators(
        self,
        ctx: ServeContext,
634
    ) -> ErrorResponse | None:
635
        """Schedule the request and get the result generator."""
636
        generators: list[
637
            AsyncGenerator[RequestOutput | PoolingRequestOutput, None]
638
        ] = []
639
640

        try:
641
642
643
644
645
            trace_headers = (
                None
                if ctx.raw_request is None
                else await self._get_trace_headers(ctx.raw_request.headers)
            )
646

647
648
649
            pooling_params = self._create_pooling_params(ctx)
            if isinstance(pooling_params, ErrorResponse):
                return pooling_params
650
651

            if ctx.engine_prompts is None:
652
                return self.create_error_response("Engine prompts not available")
653
654
655
656

            for i, engine_prompt in enumerate(ctx.engine_prompts):
                request_id_item = f"{ctx.request_id}-{i}"

657
658
                self._log_inputs(
                    request_id_item,
659
                    engine_prompt,
660
661
662
                    params=pooling_params,
                    lora_request=ctx.lora_request,
                )
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685

                generator = self.engine_client.encode(
                    engine_prompt,
                    pooling_params,
                    request_id_item,
                    lora_request=ctx.lora_request,
                    trace_headers=trace_headers,
                    priority=getattr(ctx.request, "priority", 0),
                )

                generators.append(generator)

            ctx.result_generator = merge_async_iterators(*generators)

            return None

        except Exception as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

    async def _collect_batch(
        self,
        ctx: ServeContext,
686
    ) -> ErrorResponse | None:
687
688
689
        """Collect batch results from the result generator."""
        try:
            if ctx.engine_prompts is None:
690
                return self.create_error_response("Engine prompts not available")
691
692

            num_prompts = len(ctx.engine_prompts)
693
            final_res_batch: list[RequestOutput | PoolingRequestOutput | None]
694
695
696
            final_res_batch = [None] * num_prompts

            if ctx.result_generator is None:
697
                return self.create_error_response("Result generator not available")
698
699
700
701
702
703

            async for i, res in ctx.result_generator:
                final_res_batch[i] = res

            if None in final_res_batch:
                return self.create_error_response(
704
705
                    "Failed to generate results for all prompts"
                )
706

707
            ctx.final_res_batch = [res for res in final_res_batch if res is not None]
708
709
710
711
712
713

            return None

        except Exception as e:
            return self.create_error_response(str(e))

714
    def create_error_response(
715
716
717
718
719
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> ErrorResponse:
720
721
722
723
724
725
        if self.log_error_stack:
            exc_type, _, _ = sys.exc_info()
            if exc_type is not None:
                traceback.print_exc()
            else:
                traceback.print_stack()
726
727
728
        return ErrorResponse(
            error=ErrorInfo(message=message, type=err_type, code=status_code.value)
        )
729

730
    def create_streaming_error_response(
731
732
733
734
735
        self,
        message: str,
        err_type: str = "BadRequestError",
        status_code: HTTPStatus = HTTPStatus.BAD_REQUEST,
    ) -> str:
736
        json_str = json.dumps(
737
738
739
740
            self.create_error_response(
                message=message, err_type=err_type, status_code=status_code
            ).model_dump()
        )
741
742
        return json_str

743
    async def _check_model(
744
745
        self,
        request: AnyRequest,
746
    ) -> ErrorResponse | None:
747
748
        error_response = None

749
        if self._is_model_supported(request.model):
750
            return None
751
        if request.model in self.models.lora_requests:
752
            return None
753
754
755
756
757
        if (
            envs.VLLM_ALLOW_RUNTIME_LORA_UPDATING
            and request.model
            and (load_result := await self.models.resolve_lora(request.model))
        ):
758
759
            if isinstance(load_result, LoRARequest):
                return None
760
761
762
763
            if (
                isinstance(load_result, ErrorResponse)
                and load_result.error.code == HTTPStatus.BAD_REQUEST.value
            ):
764
765
766
                error_response = load_result

        return error_response or self.create_error_response(
767
768
            message=f"The model `{request.model}` does not exist.",
            err_type="NotFoundError",
769
770
            status_code=HTTPStatus.NOT_FOUND,
        )
771

772
    def _get_active_default_mm_loras(self, request: AnyRequest) -> LoRARequest | None:
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
        """Determine if there are any active default multimodal loras."""
        # TODO: Currently this is only enabled for chat completions
        # to be better aligned with only being enabled for .generate
        # when run offline. It would be nice to support additional
        # tasks types in the future.
        message_types = self._get_message_types(request)
        default_mm_loras = set()

        for lora in self.models.lora_requests.values():
            # Best effort match for default multimodal lora adapters;
            # There is probably a better way to do this, but currently
            # this matches against the set of 'types' in any content lists
            # up until '_', e.g., to match audio_url -> audio
            if lora.lora_name in message_types:
                default_mm_loras.add(lora)

        # Currently only support default modality specific loras if
        # we have exactly one lora matched on the request.
        if len(default_mm_loras) == 1:
            return default_mm_loras.pop()
        return None

795
    def _maybe_get_adapters(
796
797
798
        self,
        request: AnyRequest,
        supports_default_mm_loras: bool = False,
799
    ) -> LoRARequest | None:
800
        if request.model in self.models.lora_requests:
801
            return self.models.lora_requests[request.model]
802
803
804
805
806
807

        # Currently only support default modality specific loras
        # if we have exactly one lora matched on the request.
        if supports_default_mm_loras:
            default_mm_lora = self._get_active_default_mm_loras(request)
            if default_mm_lora is not None:
808
                return default_mm_lora
809
810

        if self._is_model_supported(request.model):
811
            return None
812

813
        # if _check_model has been called earlier, this will be unreachable
814
        raise ValueError(f"The model `{request.model}` does not exist.")
815

816
817
818
819
820
821
822
823
824
825
    def _get_message_types(self, request: AnyRequest) -> set[str]:
        """Retrieve the set of types from message content dicts up
        until `_`; we use this to match potential multimodal data
        with default per modality loras.
        """
        message_types: set[str] = set()

        if not hasattr(request, "messages"):
            return message_types

826
827
828
829
830
        messages = request.messages
        if messages is None or isinstance(messages, (str, bytes)):
            return message_types

        for message in messages:
831
832
833
834
835
            if (
                isinstance(message, dict)
                and "content" in message
                and isinstance(message["content"], list)
            ):
836
837
838
839
840
                for content_dict in message["content"]:
                    if "type" in content_dict:
                        message_types.add(content_dict["type"].split("_")[0])
        return message_types

841
    async def _normalize_prompt_text_to_input(
842
843
844
        self,
        request: AnyRequest,
        prompt: str,
845
        tokenizer: AnyTokenizer,
846
847
        add_special_tokens: bool,
    ) -> TextTokensPrompt:
848
849
        async_tokenizer = self._get_async_tokenizer(tokenizer)

850
851
852
853
        if (
            self.model_config.encoder_config is not None
            and self.model_config.encoder_config.get("do_lower_case", False)
        ):
854
855
            prompt = prompt.lower()

856
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
857

858
        if truncate_prompt_tokens is None:
859
            encoded = await async_tokenizer(
860
861
                prompt, add_special_tokens=add_special_tokens
            )
862
863
        elif truncate_prompt_tokens < 0:
            # Negative means we cap at the model's max length
864
865
866
867
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
868
869
                max_length=self.max_model_len,
            )
870
        else:
871
872
873
874
            encoded = await async_tokenizer(
                prompt,
                add_special_tokens=add_special_tokens,
                truncation=True,
875
876
                max_length=truncate_prompt_tokens,
            )
877
878
879
880
881
882

        input_ids = encoded.input_ids
        input_text = prompt

        return self._validate_input(request, input_ids, input_text)

883
    async def _normalize_prompt_tokens_to_input(
884
885
        self,
        request: AnyRequest,
886
        prompt_ids: list[int],
887
        tokenizer: AnyTokenizer | None,
888
    ) -> TextTokensPrompt:
889
        truncate_prompt_tokens = getattr(request, "truncate_prompt_tokens", None)
890

891
        if truncate_prompt_tokens is None:
892
            input_ids = prompt_ids
893
        elif truncate_prompt_tokens < 0:
894
            input_ids = prompt_ids[-self.max_model_len :]
895
896
897
        else:
            input_ids = prompt_ids[-truncate_prompt_tokens:]

898
899
900
901
902
        if tokenizer is None:
            input_text = ""
        else:
            async_tokenizer = self._get_async_tokenizer(tokenizer)
            input_text = await async_tokenizer.decode(input_ids)
903

904
905
906
907
908
        return self._validate_input(request, input_ids, input_text)

    def _validate_input(
        self,
        request: AnyRequest,
909
        input_ids: list[int],
910
911
        input_text: str,
    ) -> TextTokensPrompt:
912
913
        token_num = len(input_ids)

914
915
        # Note: EmbeddingRequest, ClassificationRequest,
        # and ScoreRequest doesn't have max_tokens
916
        if isinstance(
917
            request,
918
919
920
921
922
            (
                EmbeddingChatRequest,
                EmbeddingCompletionRequest,
                ScoreRequest,
                RerankRequest,
923
924
                ClassificationCompletionRequest,
                ClassificationChatRequest,
925
926
            ),
        ):
927
928
            # Note: input length can be up to the entire model context length
            # since these requests don't generate tokens.
929
            if token_num > self.max_model_len:
930
931
                operations: dict[type[AnyRequest], str] = {
                    ScoreRequest: "score",
932
933
                    ClassificationCompletionRequest: "classification",
                    ClassificationChatRequest: "classification",
934
                }
935
                operation = operations.get(type(request), "embedding generation")
936
937
938
                raise ValueError(
                    f"This model's maximum context length is "
                    f"{self.max_model_len} tokens. However, you requested "
939
                    f"{token_num} tokens in the input for {operation}. "
940
941
942
                    f"Please reduce the length of the input."
                )
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
943

944
945
        # Note: TokenizeRequest and DetokenizeRequest doesn't have max_tokens
        # and does not require model context length validation
946
        if isinstance(
947
948
            request,
            (TokenizeCompletionRequest, TokenizeChatRequest, DetokenizeRequest),
949
        ):
950
            return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)
951

952
953
954
955
956
        # chat completion endpoint supports max_completion_tokens
        if isinstance(request, ChatCompletionRequest):
            # TODO(#9845): remove max_tokens when field dropped from OpenAI API
            max_tokens = request.max_completion_tokens or request.max_tokens
        else:
957
            max_tokens = getattr(request, "max_tokens", None)
958
959
960
961

        # Note: input length can be up to model context length - 1 for
        # completion-like requests.
        if token_num >= self.max_model_len:
962
            raise ValueError(
963
                f"This model's maximum context length is "
964
965
                f"{self.max_model_len} tokens. However, your request has "
                f"{token_num} input tokens. Please reduce the length of "
966
967
                "the input messages."
            )
968

969
        if max_tokens is not None and token_num + max_tokens > self.max_model_len:
970
971
972
973
974
            raise ValueError(
                "'max_tokens' or 'max_completion_tokens' is too large: "
                f"{max_tokens}. This model's maximum context length is "
                f"{self.max_model_len} tokens and your request has "
                f"{token_num} input tokens ({max_tokens} > {self.max_model_len}"
975
976
                f" - {token_num})."
            )
977
978
979

        return TextTokensPrompt(prompt=input_text, prompt_token_ids=input_ids)

980
    async def _tokenize_prompt_input_async(
981
982
983
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
984
        prompt_input: str | list[int],
985
986
987
        add_special_tokens: bool = True,
    ) -> TextTokensPrompt:
        """
988
        A simpler implementation that tokenizes a single prompt input.
989
        """
990
        async for result in self._tokenize_prompt_inputs_async(
991
992
            request,
            tokenizer,
993
            [prompt_input],
994
            add_special_tokens=add_special_tokens,
995
996
997
        ):
            return result
        raise ValueError("No results yielded from tokenization")
998

999
    async def _tokenize_prompt_inputs_async(
1000
1001
1002
        self,
        request: AnyRequest,
        tokenizer: AnyTokenizer,
1003
        prompt_inputs: Iterable[str | list[int]],
1004
        add_special_tokens: bool = True,
1005
    ) -> AsyncGenerator[TextTokensPrompt, None]:
1006
        """
1007
        A simpler implementation that tokenizes multiple prompt inputs.
1008
        """
1009
1010
        for prompt in prompt_inputs:
            if isinstance(prompt, str):
1011
                yield await self._normalize_prompt_text_to_input(
1012
                    request,
1013
1014
                    prompt=prompt,
                    tokenizer=tokenizer,
1015
1016
1017
                    add_special_tokens=add_special_tokens,
                )
            else:
1018
                yield await self._normalize_prompt_tokens_to_input(
1019
                    request,
1020
1021
                    prompt_ids=prompt,
                    tokenizer=tokenizer,
1022
1023
                )

1024
1025
    def _validate_chat_template(
        self,
1026
1027
        request_chat_template: str | None,
        chat_template_kwargs: dict[str, Any] | None,
1028
        trust_request_chat_template: bool,
1029
    ) -> ErrorResponse | None:
1030
        if not trust_request_chat_template and (
1031
1032
1033
1034
1035
1036
            request_chat_template is not None
            or (
                chat_template_kwargs
                and chat_template_kwargs.get("chat_template") is not None
            )
        ):
1037
1038
1039
            return self.create_error_response(
                "Chat template is passed with request, but "
                "--trust-request-chat-template is not set. "
1040
1041
                "Refused request with untrusted chat template."
            )
1042
1043
        return None

1044
1045
    async def _preprocess_chat(
        self,
1046
        request: ChatLikeRequest | ResponsesRequest,
1047
        tokenizer: AnyTokenizer,
1048
        messages: list[ChatCompletionMessageParam],
1049
        chat_template: str | None,
1050
        chat_template_content_format: ChatTemplateContentFormatOption,
1051
1052
        add_generation_prompt: bool = True,
        continue_final_message: bool = False,
1053
1054
1055
1056
        tool_dicts: list[dict[str, Any]] | None = None,
        documents: list[dict[str, str]] | None = None,
        chat_template_kwargs: dict[str, Any] | None = None,
        tool_parser: Callable[[AnyTokenizer], ToolParser] | None = None,
1057
        add_special_tokens: bool = False,
1058
    ) -> tuple[
1059
1060
1061
        list[ConversationMessage],
        Sequence[RequestPrompt],
        list[EngineTokensPrompt],
1062
    ]:
1063
1064
        model_config = self.model_config

1065
1066
        resolved_content_format = resolve_chat_template_content_format(
            chat_template,
1067
            tool_dicts,
1068
1069
            chat_template_content_format,
            tokenizer,
1070
            model_config=model_config,
1071
        )
1072
        conversation, mm_data_future, mm_uuids = parse_chat_messages_futures(
1073
            messages,
1074
            model_config,
1075
            tokenizer,
1076
            content_format=resolved_content_format,
1077
1078
        )

1079
        _chat_template_kwargs: dict[str, Any] = dict(
1080
1081
1082
1083
1084
1085
1086
1087
            chat_template=chat_template,
            add_generation_prompt=add_generation_prompt,
            continue_final_message=continue_final_message,
            tools=tool_dicts,
            documents=documents,
        )
        _chat_template_kwargs.update(chat_template_kwargs or {})

1088
        request_prompt: str | list[int]
1089
1090
1091
1092

        if tokenizer is None:
            request_prompt = "placeholder"
        elif isinstance(tokenizer, MistralTokenizer):
1093
            request_prompt = await self._apply_mistral_chat_template_async(
1094
1095
                tokenizer,
                messages=messages,
1096
                **_chat_template_kwargs,
1097
1098
1099
            )
        else:
            request_prompt = apply_hf_chat_template(
1100
                tokenizer=tokenizer,
1101
                conversation=conversation,
1102
                model_config=model_config,
1103
                **_chat_template_kwargs,
1104
1105
1106
1107
            )

        mm_data = await mm_data_future

1108
1109
1110
        # tool parsing is done only if a tool_parser has been set and if
        # tool_choice is not "none" (if tool_choice is "none" but a tool_parser
        # is set, we want to prevent parsing a tool_call hallucinated by the LLM
1111
1112
1113
        should_parse_tools = tool_parser is not None and (
            hasattr(request, "tool_choice") and request.tool_choice != "none"
        )
1114
1115

        if should_parse_tools:
1116
1117
1118
1119
1120
            if not isinstance(request, ChatCompletionRequest | ResponsesRequest):
                msg = (
                    "Tool usage is only supported for Chat Completions API "
                    "or Responses API requests."
                )
1121
                raise NotImplementedError(msg)
1122
            request = tool_parser(tokenizer).adjust_request(request=request)  # type: ignore
1123

1124
1125
        if tokenizer is None:
            assert isinstance(request_prompt, str), (
1126
1127
                "Prompt has to be a string",
                "when the tokenizer is not initialised",
1128
            )
1129
1130
1131
            prompt_inputs = TextTokensPrompt(
                prompt=request_prompt, prompt_token_ids=[1]
            )
1132
        elif isinstance(request_prompt, str):
1133
            prompt_inputs = await self._tokenize_prompt_input_async(
1134
1135
1136
1137
1138
1139
1140
1141
                request,
                tokenizer,
                request_prompt,
                add_special_tokens=add_special_tokens,
            )
        else:
            # For MistralTokenizer
            assert is_list_of(request_prompt, int), (
1142
1143
                "Prompt has to be either a string or a list of token ids"
            )
1144
1145
            prompt_inputs = TextTokensPrompt(
                prompt=tokenizer.decode(request_prompt),
1146
1147
                prompt_token_ids=request_prompt,
            )
1148

1149
        engine_prompt = EngineTokensPrompt(
1150
1151
            prompt_token_ids=prompt_inputs["prompt_token_ids"]
        )
1152
1153
        if mm_data is not None:
            engine_prompt["multi_modal_data"] = mm_data
1154
1155
1156
1157

        if mm_uuids is not None:
            engine_prompt["multi_modal_uuids"] = mm_uuids

1158
1159
        if request.mm_processor_kwargs is not None:
            engine_prompt["mm_processor_kwargs"] = request.mm_processor_kwargs
1160

1161
1162
1163
        if hasattr(request, "cache_salt") and request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

1164
1165
        return conversation, [request_prompt], [engine_prompt]

1166
1167
1168
1169
    async def _process_inputs(
        self,
        request_id: str,
        engine_prompt: PromptType,
1170
        params: SamplingParams | PoolingParams,
1171
        *,
1172
1173
        lora_request: LoRARequest | None,
        trace_headers: Mapping[str, str] | None,
1174
1175
        priority: int,
    ) -> tuple[EngineCoreRequest, dict[str, Any]]:
1176
        """Use the Processor to process inputs for AsyncLLM."""
1177
        tokenization_kwargs: dict[str, Any] = {}
1178
1179
1180
        _validate_truncation_size(
            self.max_model_len, params.truncate_prompt_tokens, tokenization_kwargs
        )
1181

1182
        engine_request = self.processor.process_inputs(
1183
1184
            request_id,
            engine_prompt,
1185
            params,
1186
1187
1188
1189
1190
1191
1192
            lora_request=lora_request,
            tokenization_kwargs=tokenization_kwargs,
            trace_headers=trace_headers,
            priority=priority,
        )
        return engine_request, tokenization_kwargs

1193
1194
1195
1196
1197
1198
1199
    async def _generate_with_builtin_tools(
        self,
        request_id: str,
        request_prompt: RequestPrompt,
        engine_prompt: EngineTokensPrompt,
        sampling_params: SamplingParams,
        context: ConversationContext,
1200
        lora_request: LoRARequest | None = None,
1201
1202
1203
        priority: int = 0,
        **kwargs,
    ):
1204
        prompt_text, _, _ = self._get_prompt_components(request_prompt)
1205
1206
1207
1208
1209
1210
1211
1212
        orig_priority = priority
        while True:
            self._log_inputs(
                request_id,
                request_prompt,
                params=sampling_params,
                lora_request=lora_request,
            )
1213
            trace_headers = kwargs.get("trace_headers")
1214
            engine_request, tokenization_kwargs = await self._process_inputs(
1215
                request_id,
1216
1217
                engine_prompt,
                sampling_params,
1218
1219
1220
                lora_request=lora_request,
                trace_headers=trace_headers,
                priority=priority,
1221
            )
1222
1223
1224
1225

            generator = self.engine_client.generate(
                engine_request,
                sampling_params,
1226
1227
1228
                request_id,
                lora_request=lora_request,
                priority=priority,
1229
1230
                prompt_text=prompt_text,
                tokenization_kwargs=tokenization_kwargs,
1231
1232
                **kwargs,
            )
1233

1234
1235
1236
1237
1238
1239
1240
1241
1242
1243
1244
            async for res in generator:
                context.append_output(res)
                # NOTE(woosuk): The stop condition is handled by the engine.
                yield context

            if not context.need_builtin_tool_call():
                # The model did not ask for a tool call, so we're done.
                break

            # Call the tool and update the context with the result.
            tool_output = await context.call_tool()
1245
            context.append_tool_output(tool_output)
1246
1247
1248
1249
1250
1251
1252

            # TODO: uncomment this and enable tool output streaming
            # yield context

            # Create inputs for the next turn.
            # Render the next prompt token ids.
            prompt_token_ids = context.render_for_completion()
1253
            engine_prompt = EngineTokensPrompt(prompt_token_ids=prompt_token_ids)
1254
1255
            request_prompt = prompt_token_ids
            # Update the sampling params.
1256
            sampling_params.max_tokens = self.max_model_len - len(prompt_token_ids)
1257
1258
1259
            # OPTIMIZATION
            priority = orig_priority - 1

1260
1261
    def _get_prompt_components(
        self,
1262
        prompt: RequestPrompt | PromptType,
1263
    ) -> PromptComponents:
1264
1265
        if isinstance(prompt, list):
            return PromptComponents(token_ids=prompt)
1266

1267
        return get_prompt_components(prompt)  # type: ignore[arg-type]
1268

1269
1270
1271
    def _log_inputs(
        self,
        request_id: str,
1272
1273
1274
        inputs: RequestPrompt | PromptType,
        params: SamplingParams | PoolingParams | BeamSearchParams | None,
        lora_request: LoRARequest | None,
1275
1276
1277
    ) -> None:
        if self.request_logger is None:
            return
1278

1279
        prompt, prompt_token_ids, prompt_embeds = self._get_prompt_components(inputs)
1280
1281
1282
1283
1284

        self.request_logger.log_inputs(
            request_id,
            prompt,
            prompt_token_ids,
1285
            prompt_embeds,
1286
1287
1288
            params=params,
            lora_request=lora_request,
        )
1289

1290
1291
1292
    async def _get_trace_headers(
        self,
        headers: Headers,
1293
    ) -> Mapping[str, str] | None:
1294
1295
1296
1297
1298
1299
1300
1301
1302
1303
        is_tracing_enabled = await self.engine_client.is_tracing_enabled()

        if is_tracing_enabled:
            return extract_trace_headers(headers)

        if contains_trace_headers(headers):
            log_tracing_disabled_warning()

        return None

1304
    @staticmethod
1305
    def _base_request_id(
1306
1307
        raw_request: Request | None, default: str | None = None
    ) -> str | None:
1308
1309
        """Pulls the request id to use from a header, if provided"""
        default = default or random_uuid()
1310
1311
1312
1313
        if raw_request is None:
            return default

        return raw_request.headers.get("X-Request-Id", default)
1314

1315
1316
1317
1318
1319
1320
1321
1322
1323
1324
1325
1326
1327
1328
1329
    @staticmethod
    def _get_data_parallel_rank(raw_request: Request | None) -> int | None:
        """Pulls the data parallel rank from a header, if provided"""
        if raw_request is None:
            return None

        rank_str = raw_request.headers.get("X-data-parallel-rank")
        if rank_str is None:
            return None

        try:
            return int(rank_str)
        except ValueError:
            return None

1330
1331
1332
1333
1334
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
1349
1350
1351
1352
1353
1354
1355
1356
1357
1358
1359
1360
1361
1362
1363
1364
1365
1366
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386
1387
1388
1389
1390
1391
1392
    @staticmethod
    def _parse_tool_calls_from_content(
        request: ResponsesRequest | ChatCompletionRequest,
        tokenizer: AnyTokenizer,
        enable_auto_tools: bool,
        tool_parser_cls: Callable[[AnyTokenizer], ToolParser] | None,
        content: str | None = None,
    ) -> tuple[list[FunctionCall] | None, str | None]:
        function_calls = list[FunctionCall]()
        if request.tool_choice and isinstance(request.tool_choice, ToolChoiceFunction):
            assert content is not None
            # Forced Function Call
            function_calls.append(
                FunctionCall(name=request.tool_choice.name, arguments=content)
            )
            content = None  # Clear content since tool is called.
        elif request.tool_choice and isinstance(
            request.tool_choice, ChatCompletionNamedToolChoiceParam
        ):
            assert content is not None
            # Forced Function Call
            function_calls.append(
                FunctionCall(name=request.tool_choice.function.name, arguments=content)
            )
            content = None  # Clear content since tool is called.
        elif request.tool_choice == "required":
            assert content is not None
            tool_calls = TypeAdapter(list[FunctionDefinition]).validate_json(content)
            function_calls.extend(
                [
                    FunctionCall(
                        name=tool_call.name,
                        arguments=json.dumps(tool_call.parameters, ensure_ascii=False),
                    )
                    for tool_call in tool_calls
                ]
            )
            content = None  # Clear content since tool is called.
        elif (
            tool_parser_cls
            and enable_auto_tools
            and (request.tool_choice == "auto" or request.tool_choice is None)
        ):
            # Automatic Tool Call Parsing
            try:
                tool_parser = tool_parser_cls(tokenizer)
            except RuntimeError as e:
                logger.exception("Error in tool parser creation.")
                raise e
            tool_call_info = tool_parser.extract_tool_calls(
                content if content is not None else "",
                request=request,  # type: ignore
            )
            if tool_call_info is not None and tool_call_info.tools_called:
                # extract_tool_calls() returns a list of tool calls.
                function_calls.extend(
                    FunctionCall(
                        name=tool_call.function.name,
                        arguments=tool_call.function.arguments,
                    )
                    for tool_call in tool_call_info.tool_calls
                )
                content = tool_call_info.content
1393
1394
                if content and content.strip() == "":
                    content = None
1395
1396
1397
1398
1399
1400
            else:
                # No tool calls.
                return None, content

        return function_calls, content

1401
    @staticmethod
1402
1403
1404
1405
1406
1407
    def _get_decoded_token(
        logprob: Logprob,
        token_id: int,
        tokenizer: AnyTokenizer,
        return_as_token_id: bool = False,
    ) -> str:
1408
1409
1410
        if return_as_token_id:
            return f"token_id:{token_id}"

1411
1412
        if logprob.decoded_token is not None:
            return logprob.decoded_token
1413
        return tokenizer.decode(token_id)
1414

1415
    def _is_model_supported(self, model_name: str | None) -> bool:
1416
1417
        if not model_name:
            return True
1418
        return self.models.is_base_model(model_name)
1419

1420
1421

def clamp_prompt_logprobs(
1422
1423
    prompt_logprobs: PromptLogprobs | None,
) -> PromptLogprobs | None:
1424
1425
1426
1427
1428
1429
1430
    if prompt_logprobs is None:
        return prompt_logprobs

    for logprob_dict in prompt_logprobs:
        if logprob_dict is None:
            continue
        for logprob_values in logprob_dict.values():
1431
            if logprob_values.logprob == float("-inf"):
1432
1433
                logprob_values.logprob = -9999.0
    return prompt_logprobs