serving_completion.py 28.6 KB
Newer Older
1
# SPDX-License-Identifier: Apache-2.0
2
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
3

4
import asyncio
5
import time
6
7
8
from collections.abc import AsyncGenerator, AsyncIterator
from collections.abc import Sequence as GenericSequence
from typing import Optional, Union, cast
9

10
import jinja2
11
from fastapi import Request
12
from typing_extensions import assert_never
13

14
from vllm.config import ModelConfig
15
from vllm.engine.protocol import EngineClient
16
from vllm.entrypoints.logger import RequestLogger
17
# yapf conflicts with isort for this block
18
19
20
# yapf: disable
from vllm.entrypoints.openai.protocol import (CompletionLogProbs,
                                              CompletionRequest,
21
22
23
24
                                              CompletionResponse,
                                              CompletionResponseChoice,
                                              CompletionResponseStreamChoice,
                                              CompletionStreamResponse,
25
                                              ErrorResponse,
26
                                              PromptTokenUsageInfo,
27
28
                                              RequestResponseMetadata,
                                              UsageInfo)
29
30
from vllm.entrypoints.openai.serving_engine import (
    EmbedsPrompt as ServingEngineEmbedsPrompt)
31
from vllm.entrypoints.openai.serving_engine import (OpenAIServing,
32
                                                    TextTokensPrompt,
33
34
                                                    clamp_prompt_logprobs,
                                                    is_text_tokens_prompt)
35
# yapf: enable
36
from vllm.entrypoints.openai.serving_models import OpenAIServingModels
37
from vllm.entrypoints.utils import get_max_tokens
38
39
from vllm.inputs.data import (EmbedsPrompt, TokensPrompt, is_embeds_prompt,
                              is_tokens_prompt)
40
41
from vllm.logger import init_logger
from vllm.outputs import RequestOutput
42
from vllm.sampling_params import BeamSearchParams, SamplingParams
43
from vllm.sequence import Logprob
44
from vllm.transformers_utils.tokenizer import AnyTokenizer
45
from vllm.utils import as_list, merge_async_iterators
46
47
48
49
50
51

logger = init_logger(__name__)


class OpenAIServingCompletion(OpenAIServing):

52
53
    def __init__(
        self,
54
        engine_client: EngineClient,
55
        model_config: ModelConfig,
56
        models: OpenAIServingModels,
57
58
        *,
        request_logger: Optional[RequestLogger],
59
        return_tokens_as_token_ids: bool = False,
60
        enable_prompt_tokens_details: bool = False,
61
        enable_force_include_usage: bool = False,
62
        log_error_stack: bool = False,
63
    ):
64
65
66
67
68
69
70
        super().__init__(
            engine_client=engine_client,
            model_config=model_config,
            models=models,
            request_logger=request_logger,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
            enable_force_include_usage=enable_force_include_usage,
71
            log_error_stack=log_error_stack,
72
        )
73
        self.enable_prompt_tokens_details = enable_prompt_tokens_details
74
75
76
        self.default_sampling_params = (
            self.model_config.get_diff_sampling_param())
        if self.default_sampling_params:
77
78
            source = self.model_config.generation_config
            source = "model" if source == "auto" else source
79
80
81
82
83
            logger.info(
                "Using default completion sampling params from %s: %s",
                source,
                self.default_sampling_params,
            )
84

85
86
87
    async def create_completion(
        self,
        request: CompletionRequest,
88
        raw_request: Optional[Request] = None,
89
    ) -> Union[AsyncGenerator[str, None], CompletionResponse, ErrorResponse]:
90
91
92
93
94
        """Completion API similar to OpenAI's API.

        See https://platform.openai.com/docs/api-reference/completions/create
        for the API specification. This API mimics the OpenAI Completion API.

95
        NOTE: Currently we do not support the following feature:
96
97
98
99
100
101
102
            - suffix (the language models we currently support do not support
            suffix)
        """
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            return error_check_ret

103
104
105
106
107
108
        # If the engine is dead, raise the engine's DEAD_ERROR.
        # This is required for the streaming case, where we return a
        # success status before we actually start generating text :).
        if self.engine_client.errored:
            raise self.engine_client.dead_error

109
        # Return error for unsupported features.
110
111
112
113
        if request.suffix is not None:
            return self.create_error_response(
                "suffix is not currently supported")

114
115
116
117
        if request.echo and request.prompt_embeds is not None:
            return self.create_error_response(
                "Echo is unsupported with prompt embeds.")

118
119
120
        request_id = (
            f"cmpl-"
            f"{self._base_request_id(raw_request, request.request_id)}")
121
        created_time = int(time.time())
122

123
124
125
126
        request_metadata = RequestResponseMetadata(request_id=request_id)
        if raw_request:
            raw_request.state.request_metadata = request_metadata

127
        try:
128
            lora_request = self._maybe_get_adapters(request)
129

130
131
132
133
134
            if self.model_config.skip_tokenizer_init:
                tokenizer = None
            else:
                tokenizer = await self.engine_client.get_tokenizer(lora_request
                                                                   )
135

136
            request_prompts, engine_prompts = await self._preprocess_completion(
137
138
139
140
141
142
143
144
145
                request,
                tokenizer,
                request.prompt,
                truncate_prompt_tokens=request.truncate_prompt_tokens,
                add_special_tokens=request.add_special_tokens,
            )
        except ValueError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
146
147
148
149
150
151
152
153
154
        except TypeError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
        except RuntimeError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
        except jinja2.TemplateError as e:
            logger.exception("Error in preprocessing prompt inputs")
            return self.create_error_response(str(e))
155

156
        # Schedule the request and get the result generator.
157
        generators: list[AsyncGenerator[RequestOutput, None]] = []
158
159
        try:
            for i, engine_prompt in enumerate(engine_prompts):
160
                sampling_params: Union[SamplingParams, BeamSearchParams]
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
                # Mypy does not infer that engine_prompt will have only one of
                # "prompt_token_ids" or "prompt_embeds" defined, and both of
                # these as Union[object, the expected type], where it infers
                # object if engine_prompt is a subclass of one of the
                # typeddicts that defines both keys. Worse, because of
                # https://github.com/python/mypy/issues/8586, mypy does not
                # infer the type of engine_prompt correctly because of the
                # enumerate. So we need an unnecessary cast here.
                engine_prompt = cast(Union[EmbedsPrompt, TokensPrompt],
                                     engine_prompt)
                if is_embeds_prompt(engine_prompt):
                    input_length = len(engine_prompt["prompt_embeds"])
                elif is_tokens_prompt(engine_prompt):
                    input_length = len(engine_prompt["prompt_token_ids"])
                else:
                    assert_never(engine_prompt)
177
178
179
180
181
182
183
184

                if self.default_sampling_params is None:
                    self.default_sampling_params = {}

                max_tokens = get_max_tokens(
                    max_model_len=self.max_model_len,
                    request=request,
                    input_length=input_length,
185
186
                    default_sampling_params=self.default_sampling_params,
                )
187

188
189
                if request.use_beam_search:
                    sampling_params = request.to_beam_search_params(
190
                        max_tokens, self.default_sampling_params)
191
192
                else:
                    sampling_params = request.to_sampling_params(
193
194
195
196
                        max_tokens,
                        self.model_config.logits_processor_pattern,
                        self.default_sampling_params,
                    )
197

198
199
                request_id_item = f"{request_id}-{i}"

200
201
202
203
204
205
                self._log_inputs(
                    request_id_item,
                    request_prompts[i],
                    params=sampling_params,
                    lora_request=lora_request,
                )
206

207
                trace_headers = (None if raw_request is None else await
208
                                 self._get_trace_headers(raw_request.headers))
209

210
211
212
213
214
                # Mypy inconsistently requires this second cast in different
                # environments. It shouldn't be necessary (redundant from above)
                # but pre-commit in CI fails without it.
                engine_prompt = cast(Union[EmbedsPrompt, TokensPrompt],
                                     engine_prompt)
215
216
                if isinstance(sampling_params, BeamSearchParams):
                    generator = self.engine_client.beam_search(
217
                        prompt=engine_prompt,
218
219
                        request_id=request_id,
                        params=sampling_params,
220
                        lora_request=lora_request,
221
                    )
222
223
                else:
                    generator = self.engine_client.generate(
224
                        engine_prompt,
225
226
227
228
229
230
                        sampling_params,
                        request_id_item,
                        lora_request=lora_request,
                        trace_headers=trace_headers,
                        priority=request.priority,
                    )
231
232

                generators.append(generator)
233
        except ValueError as e:
234
            # TODO: Use a vllm-specific Validation Error
235
            return self.create_error_response(str(e))
236

237
        result_generator = merge_async_iterators(*generators)
238

239
        model_name = self._get_model_name(request.model, lora_request)
240
241
        num_prompts = len(engine_prompts)

242
243
244
245
246
247
        # Similar to the OpenAI API, when n != best_of, we do not stream the
        # results. Noting that best_of is only supported in V0. In addition,
        # we do not stream the results when use beam search.
        stream = (request.stream
                  and (request.best_of is None or request.n == request.best_of)
                  and not request.use_beam_search)
248
249
250

        # Streaming response
        if stream:
251
252
            return self.completion_stream_generator(
                request,
253
                request_prompts,
254
255
256
257
                result_generator,
                request_id,
                created_time,
                model_name,
258
                num_prompts=num_prompts,
259
                tokenizer=tokenizer,
260
                request_metadata=request_metadata,
261
262
                enable_force_include_usage=self.enable_force_include_usage,
            )
263
264

        # Non-streaming response
265
        final_res_batch: list[Optional[RequestOutput]] = [None] * num_prompts
266
267
268
        try:
            async for i, res in result_generator:
                final_res_batch[i] = res
269
270
271
272
273
274
275
276

            for i, final_res in enumerate(final_res_batch):
                assert final_res is not None

                # The output should contain the input text
                # We did not pass it into vLLM engine to avoid being redundant
                # with the inputs token IDs
                if final_res.prompt is None:
277
278
279
280
281
                    request_prompt = request_prompts[i]
                    if is_text_tokens_prompt(request_prompt):
                        final_res.prompt = request_prompt["prompt"]
                    else:
                        final_res.prompt = None
282

283
            final_res_batch_checked = cast(list[RequestOutput],
284
285
                                           final_res_batch)

286
            response = self.request_output_to_completion_response(
287
288
289
290
291
292
                final_res_batch_checked,
                request,
                request_id,
                created_time,
                model_name,
                tokenizer,
293
                request_metadata,
294
            )
295
296
        except asyncio.CancelledError:
            return self.create_error_response("Client disconnected")
297
298
299
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))
300

301
302
        # When user requests streaming but we don't stream, we still need to
        # return a streaming response with a single event.
303
        if request.stream:
304
            response_json = response.model_dump_json()
305
306
307
308
309
310
311
312

            async def fake_stream_generator() -> AsyncGenerator[str, None]:
                yield f"data: {response_json}\n\n"
                yield "data: [DONE]\n\n"

            return fake_stream_generator()

        return response
313
314
315
316

    async def completion_stream_generator(
        self,
        request: CompletionRequest,
317
318
        request_prompts: list[Union[TextTokensPrompt,
                                    ServingEngineEmbedsPrompt]],
319
        result_generator: AsyncIterator[tuple[int, RequestOutput]],
320
321
322
323
        request_id: str,
        created_time: int,
        model_name: str,
        num_prompts: int,
324
        tokenizer: AnyTokenizer,
325
        request_metadata: RequestResponseMetadata,
326
        enable_force_include_usage: bool,
327
    ) -> AsyncGenerator[str, None]:
328
        num_choices = 1 if request.n is None else request.n
329
        previous_text_lens = [0] * num_choices * num_prompts
330
331
        previous_num_tokens = [0] * num_choices * num_prompts
        has_echoed = [False] * num_choices * num_prompts
332
        num_prompt_tokens = [0] * num_prompts
333
334
        num_cached_tokens = None
        first_iteration = True
335

336
337
        stream_options = request.stream_options
        if stream_options:
338
339
340
341
            include_usage = (stream_options.include_usage
                             or enable_force_include_usage)
            include_continuous_usage = (include_usage and
                                        stream_options.continuous_usage_stats)
342
343
344
        else:
            include_usage, include_continuous_usage = False, False

345
346
        try:
            async for prompt_idx, res in result_generator:
347
348
                prompt_token_ids = res.prompt_token_ids
                prompt_logprobs = res.prompt_logprobs
349

350
351
352
353
                if first_iteration:
                    num_cached_tokens = res.num_cached_tokens
                    first_iteration = False

354
355
356
357
358
359
360
361
                if res.prompt is not None:
                    prompt_text = res.prompt
                else:
                    request_prompt = request_prompts[prompt_idx]
                    if is_text_tokens_prompt(request_prompt):
                        prompt_text = request_prompt["prompt"]
                    else:
                        prompt_text = None
362

363
                # Prompt details are excluded from later streamed outputs
364
365
                if prompt_token_ids is not None:
                    num_prompt_tokens[prompt_idx] = len(prompt_token_ids)
366

367
                delta_token_ids: GenericSequence[int]
368
                out_logprobs: Optional[GenericSequence[Optional[dict[
369
                    int, Logprob]]]]
370
371

                for output in res.outputs:
372
                    i = output.index + prompt_idx * num_choices
373

374
375
376
377
378
                    # Useful when request.return_token_ids is True
                    # Returning prompt token IDs shares the same logic
                    # with the echo implementation.
                    prompt_token_ids_to_return: Optional[list[int]] = None

379
                    assert request.max_tokens is not None
380
                    if request.echo and not has_echoed[i]:
381
                        assert prompt_token_ids is not None
382
                        assert prompt_text is not None
383
384
385
386
387
388
389
390
391
                        if request.max_tokens == 0:
                            # only return the prompt
                            delta_text = prompt_text
                            delta_token_ids = prompt_token_ids
                            out_logprobs = prompt_logprobs
                        else:
                            # echo the prompt and first token
                            delta_text = prompt_text + output.text
                            delta_token_ids = [
392
393
                                *prompt_token_ids,
                                *output.token_ids,
394
395
                            ]
                            out_logprobs = [
396
                                *(prompt_logprobs or []),
397
398
                                *(output.logprobs or []),
                            ]
399
                        prompt_token_ids_to_return = prompt_token_ids
400
401
402
                        has_echoed[i] = True
                    else:
                        # return just the delta
403
404
405
                        delta_text = output.text
                        delta_token_ids = output.token_ids
                        out_logprobs = output.logprobs
406

407
408
409
410
411
412
                        # has_echoed[i] is reused here to indicate whether
                        # we have already returned the prompt token IDs.
                        if not has_echoed[i]:
                            prompt_token_ids_to_return = prompt_token_ids
                            has_echoed[i] = True

413
414
                        if (not delta_text and not delta_token_ids
                                and not previous_num_tokens[i]):
415
416
417
                            # Chunked prefill case, don't return empty chunks
                            continue

418
                    if request.logprobs is not None:
419
420
                        assert out_logprobs is not None, (
                            "Did not output logprobs")
421
                        logprobs = self._create_completion_logprobs(
422
                            token_ids=delta_token_ids,
423
                            top_logprobs=out_logprobs,
424
                            num_output_top_logprobs=request.logprobs,
425
                            tokenizer=tokenizer,
426
                            initial_text_offset=previous_text_lens[i],
427
428
                            return_as_token_id=request.
                            return_tokens_as_token_ids,
429
430
431
432
                        )
                    else:
                        logprobs = None

433
434
                    previous_text_lens[i] += len(output.text)
                    previous_num_tokens[i] += len(output.token_ids)
435
                    finish_reason = output.finish_reason
436
                    stop_reason = output.stop_reason
437
438

                    chunk = CompletionStreamResponse(
439
440
441
442
443
444
445
446
447
                        id=request_id,
                        created=created_time,
                        model=model_name,
                        choices=[
                            CompletionResponseStreamChoice(
                                index=i,
                                text=delta_text,
                                logprobs=logprobs,
                                finish_reason=finish_reason,
448
                                stop_reason=stop_reason,
449
450
451
                                prompt_token_ids=prompt_token_ids_to_return,
                                token_ids=(as_list(output.token_ids) if
                                           request.return_token_ids else None),
452
                            )
453
454
                        ],
                    )
455
456
457
458
459
460
461
462
                    if include_continuous_usage:
                        prompt_tokens = num_prompt_tokens[prompt_idx]
                        completion_tokens = previous_num_tokens[i]
                        chunk.usage = UsageInfo(
                            prompt_tokens=prompt_tokens,
                            completion_tokens=completion_tokens,
                            total_tokens=prompt_tokens + completion_tokens,
                        )
463

464
                    response_json = chunk.model_dump_json(exclude_unset=False)
465
                    yield f"data: {response_json}\n\n"
466

467
468
469
470
471
            total_prompt_tokens = sum(num_prompt_tokens)
            total_completion_tokens = sum(previous_num_tokens)
            final_usage_info = UsageInfo(
                prompt_tokens=total_prompt_tokens,
                completion_tokens=total_completion_tokens,
472
473
                total_tokens=total_prompt_tokens + total_completion_tokens,
            )
474

475
476
477
478
            if self.enable_prompt_tokens_details and num_cached_tokens:
                final_usage_info.prompt_tokens_details = PromptTokenUsageInfo(
                    cached_tokens=num_cached_tokens)

479
            if include_usage:
480
481
482
483
484
                final_usage_chunk = CompletionStreamResponse(
                    id=request_id,
                    created=created_time,
                    model=model_name,
                    choices=[],
485
                    usage=final_usage_info,
486
                )
487
488
                final_usage_data = final_usage_chunk.model_dump_json(
                    exclude_unset=False, exclude_none=True)
489
490
                yield f"data: {final_usage_data}\n\n"

491
            # report to FastAPI middleware aggregate usage across all choices
492
            request_metadata.final_usage_info = final_usage_info
493

494
        except Exception as e:
495
496
497
498
499
500
501
            # TODO: Use a vllm-specific Validation Error
            data = self.create_streaming_error_response(str(e))
            yield f"data: {data}\n\n"
        yield "data: [DONE]\n\n"

    def request_output_to_completion_response(
        self,
502
        final_res_batch: list[RequestOutput],
503
504
505
506
        request: CompletionRequest,
        request_id: str,
        created_time: int,
        model_name: str,
507
        tokenizer: AnyTokenizer,
508
        request_metadata: RequestResponseMetadata,
509
    ) -> CompletionResponse:
510
        choices: list[CompletionResponseChoice] = []
511
512
        num_prompt_tokens = 0
        num_generated_tokens = 0
513
514
        kv_transfer_params = None
        last_final_res = None
515
        for final_res in final_res_batch:
516
            last_final_res = final_res
517
            prompt_token_ids = final_res.prompt_token_ids
518
            assert prompt_token_ids is not None
519
            prompt_logprobs = clamp_prompt_logprobs(final_res.prompt_logprobs)
520
521
            prompt_text = final_res.prompt

522
            token_ids: GenericSequence[int]
523
            out_logprobs: Optional[GenericSequence[Optional[dict[int,
524
525
                                                                 Logprob]]]]

526
            for output in final_res.outputs:
527
                assert request.max_tokens is not None
528
                if request.echo:
529
                    assert prompt_text is not None
530
531
532
533
                    if request.max_tokens == 0:
                        token_ids = prompt_token_ids
                        out_logprobs = prompt_logprobs
                        output_text = prompt_text
534
                    else:
535
536
537
538
539
540
541
542
543
544
545
546
547
                        token_ids = [*prompt_token_ids, *output.token_ids]

                        if request.logprobs is None:
                            out_logprobs = None
                        else:
                            assert prompt_logprobs is not None
                            assert output.logprobs is not None
                            out_logprobs = [
                                *prompt_logprobs,
                                *output.logprobs,
                            ]

                        output_text = prompt_text + output.text
548
549
                else:
                    token_ids = output.token_ids
550
                    out_logprobs = output.logprobs
551
552
553
                    output_text = output.text

                if request.logprobs is not None:
554
                    assert out_logprobs is not None, "Did not output logprobs"
555
                    logprobs = self._create_completion_logprobs(
556
                        token_ids=token_ids,
557
                        top_logprobs=out_logprobs,
558
                        tokenizer=tokenizer,
559
                        num_output_top_logprobs=request.logprobs,
560
                        return_as_token_id=request.return_tokens_as_token_ids,
561
562
563
564
565
566
567
568
569
                    )
                else:
                    logprobs = None

                choice_data = CompletionResponseChoice(
                    index=len(choices),
                    text=output_text,
                    logprobs=logprobs,
                    finish_reason=output.finish_reason,
570
                    stop_reason=output.stop_reason,
571
                    prompt_logprobs=final_res.prompt_logprobs,
572
573
574
575
                    prompt_token_ids=(prompt_token_ids
                                      if request.return_token_ids else None),
                    token_ids=(as_list(output.token_ids)
                               if request.return_token_ids else None),
576
577
578
                )
                choices.append(choice_data)

579
580
                num_generated_tokens += len(output.token_ids)

581
582
583
584
585
586
587
588
            num_prompt_tokens += len(prompt_token_ids)

        usage = UsageInfo(
            prompt_tokens=num_prompt_tokens,
            completion_tokens=num_generated_tokens,
            total_tokens=num_prompt_tokens + num_generated_tokens,
        )

589
590
        if (self.enable_prompt_tokens_details and last_final_res
                and last_final_res.num_cached_tokens):
591
            usage.prompt_tokens_details = PromptTokenUsageInfo(
592
                cached_tokens=last_final_res.num_cached_tokens)
593

594
        request_metadata.final_usage_info = usage
595
596
        if final_res_batch:
            kv_transfer_params = final_res_batch[0].kv_transfer_params
597
598
599
600
601
602
        return CompletionResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            choices=choices,
            usage=usage,
603
604
            kv_transfer_params=kv_transfer_params,
        )
605
606
607
608

    def _create_completion_logprobs(
        self,
        token_ids: GenericSequence[int],
609
        top_logprobs: GenericSequence[Optional[dict[int, Logprob]]],
610
        num_output_top_logprobs: int,
611
        tokenizer: AnyTokenizer,
612
        initial_text_offset: int = 0,
613
        return_as_token_id: Optional[bool] = None,
614
615
    ) -> CompletionLogProbs:
        """Create logprobs for OpenAI Completion API."""
616
617
618
619
        out_text_offset: list[int] = []
        out_token_logprobs: list[Optional[float]] = []
        out_tokens: list[str] = []
        out_top_logprobs: list[Optional[dict[str, float]]] = []
620
621
622

        last_token_len = 0

623
624
625
        should_return_as_token_id = (return_as_token_id
                                     if return_as_token_id is not None else
                                     self.return_tokens_as_token_ids)
626
627
628
        for i, token_id in enumerate(token_ids):
            step_top_logprobs = top_logprobs[i]
            if step_top_logprobs is None:
629
                token = tokenizer.decode(token_id)
630
                if should_return_as_token_id:
631
                    token = f"token_id:{token_id}"
632

633
634
635
636
                out_tokens.append(token)
                out_token_logprobs.append(None)
                out_top_logprobs.append(None)
            else:
637
638
                step_token = step_top_logprobs[token_id]

639
                token = self._get_decoded_token(
640
                    step_token,
641
642
                    token_id,
                    tokenizer,
643
                    return_as_token_id=should_return_as_token_id,
644
645
646
                )
                token_logprob = max(step_token.logprob, -9999.0)

647
648
649
650
651
652
653
654
655
656
                out_tokens.append(token)
                out_token_logprobs.append(token_logprob)

                # makes sure to add the top num_output_top_logprobs + 1
                # logprobs, as defined in the openai API
                # (cf. https://github.com/openai/openai-openapi/blob/
                # 893ba52242dbd5387a97b96444ee1c742cfce9bd/openapi.yaml#L7153)
                out_top_logprobs.append({
                    # Convert float("-inf") to the
                    # JSON-serializable float that OpenAI uses
657
658
659
660
661
662
                    self._get_decoded_token(
                        top_lp[1],
                        top_lp[0],
                        tokenizer,
                        return_as_token_id=should_return_as_token_id,
                    ):
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
                    max(top_lp[1].logprob, -9999.0)
                    for i, top_lp in enumerate(step_top_logprobs.items())
                    if num_output_top_logprobs >= i
                })

            if len(out_text_offset) == 0:
                out_text_offset.append(initial_text_offset)
            else:
                out_text_offset.append(out_text_offset[-1] + last_token_len)
            last_token_len = len(token)

        return CompletionLogProbs(
            text_offset=out_text_offset,
            token_logprobs=out_token_logprobs,
            tokens=out_tokens,
            top_logprobs=out_top_logprobs,
        )