serving.py 69.8 KB
Newer Older
1
2
3
4
5
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

import asyncio
import time
6
import uuid
7
from collections import deque
8
from collections.abc import AsyncGenerator, AsyncIterator, Callable, Sequence
9
from contextlib import AsyncExitStack
10
from copy import copy
11
from http import HTTPStatus
12
from typing import Final
13
14

from fastapi import Request
15
from openai.types.responses import (
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
    ResponseContentPartAddedEvent,
    ResponseContentPartDoneEvent,
    ResponseFunctionToolCall,
    ResponseOutputItem,
    ResponseOutputItemAddedEvent,
    ResponseOutputItemDoneEvent,
    ResponseOutputMessage,
    ResponseOutputText,
    ResponseReasoningItem,
    ResponseReasoningTextDeltaEvent,
    ResponseReasoningTextDoneEvent,
    ResponseStatus,
    ResponseTextDeltaEvent,
    ResponseTextDoneEvent,
    response_text_delta_event,
)
from openai.types.responses.response_output_text import Logprob, LogprobTopLogprob
33
from openai.types.responses.response_reasoning_item import (
34
35
    Content as ResponseReasoningTextContent,
)
36
from openai.types.responses.tool import Mcp, Tool
37
from openai_harmony import Message as OpenAIHarmonyMessage
38
from pydantic import TypeAdapter
39

40
from vllm import envs
41
from vllm.config.utils import replace
42
from vllm.engine.protocol import EngineClient
43
44
45
46
from vllm.entrypoints.chat_utils import (
    ChatCompletionMessageParam,
    ChatTemplateContentFormatOption,
)
47
from vllm.entrypoints.logger import RequestLogger
48
from vllm.entrypoints.mcp.tool_server import ToolServer
49
from vllm.entrypoints.openai.engine.protocol import (
50
51
52
53
    DeltaMessage,
    ErrorResponse,
    RequestResponseMetadata,
)
54
from vllm.entrypoints.openai.engine.serving import (
55
56
57
    GenerationError,
    OpenAIServing,
)
58
from vllm.entrypoints.openai.models.serving import OpenAIServingModels
59
60
61
62
63
64
65
66
from vllm.entrypoints.openai.parser.harmony_utils import (
    get_developer_message,
    get_stop_tokens_for_assistant_actions,
    get_system_message,
    get_user_message,
    has_custom_tools,
    render_for_completion,
)
67
68
69
70
71
72
73
from vllm.entrypoints.openai.responses.context import (
    ConversationContext,
    HarmonyContext,
    ParsableContext,
    SimpleContext,
    StreamingHarmonyContext,
)
74
75
76
77
78
79
from vllm.entrypoints.openai.responses.harmony import (
    construct_harmony_previous_input_messages,
    harmony_to_response_output,
    parser_state_to_response_output,
    response_input_to_harmony,
)
80
81
82
83
84
85
86
from vllm.entrypoints.openai.responses.protocol import (
    InputTokensDetails,
    OutputTokensDetails,
    ResponseCompletedEvent,
    ResponseCreatedEvent,
    ResponseInProgressEvent,
    ResponseInputOutputMessage,
87
88
    ResponseReasoningPartAddedEvent,
    ResponseReasoningPartDoneEvent,
89
90
91
92
93
    ResponsesRequest,
    ResponsesResponse,
    ResponseUsage,
    StreamingResponsesResponse,
)
94
from vllm.entrypoints.openai.responses.streaming_events import (
95
    StreamingState,
96
97
98
99
    emit_content_delta_events,
    emit_previous_item_done_events,
    emit_tool_action_events,
)
100
from vllm.entrypoints.openai.responses.utils import (
101
    construct_input_messages,
102
    construct_tool_dicts,
103
104
    extract_tool_types,
)
105
from vllm.entrypoints.utils import get_max_tokens
106
from vllm.exceptions import VLLMValidationError
107
from vllm.inputs.data import ProcessorInputs, token_inputs
108
from vllm.logger import init_logger
109
110
from vllm.logprobs import Logprob as SampleLogprob
from vllm.logprobs import SampleLogprobs
111
from vllm.outputs import CompletionOutput
112
from vllm.parser import ParserManager
113
from vllm.sampling_params import SamplingParams, StructuredOutputsParams
114
from vllm.tokenizers import TokenizerLike
115
116
117
118
119
from vllm.utils import random_uuid

logger = init_logger(__name__)


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
157
158
def _extract_allowed_tools_from_mcp_requests(
    tools: list[Tool],
) -> dict[str, list[str] | None]:
    """
    Extract allowed_tools mapping from MCP tool requests.

    Returns a dictionary mapping server_label to allowed_tools list.
    Handles both list format and McpAllowedToolsMcpToolFilter object format.

    Special handling:
    - If allowed_tools is None, returns None (allows all tools)
    - If allowed_tools contains "*", returns None (allows all tools)
    - Otherwise, returns the list of specific tool names

    This function can be reused for both harmony and non-harmony MCP calls.
    """
    allowed_tools_map: dict[str, list[str] | None] = {}
    for tool in tools:
        if not isinstance(tool, Mcp):
            continue

        # allowed_tools can be a list or an object with tool_names
        # Extract the actual list of tool names
        allowed_tools_val = None
        if tool.allowed_tools is not None:
            if isinstance(tool.allowed_tools, list):
                allowed_tools_val = tool.allowed_tools
            elif hasattr(tool.allowed_tools, "tool_names"):
                # It's an McpAllowedToolsMcpToolFilter object
                allowed_tools_val = tool.allowed_tools.tool_names

        # Normalize "*" to None (both mean "allow all tools")
        if allowed_tools_val is not None and "*" in allowed_tools_val:
            allowed_tools_val = None

        allowed_tools_map[tool.server_label] = allowed_tools_val
    return allowed_tools_map


159
160
161
162
163
164
class OpenAIServingResponses(OpenAIServing):
    def __init__(
        self,
        engine_client: EngineClient,
        models: OpenAIServingModels,
        *,
165
166
        request_logger: RequestLogger | None,
        chat_template: str | None,
167
168
169
170
        chat_template_content_format: ChatTemplateContentFormatOption,
        return_tokens_as_token_ids: bool = False,
        reasoning_parser: str = "",
        enable_auto_tools: bool = False,
171
172
        tool_parser: str | None = None,
        tool_server: ToolServer | None = None,
173
174
        enable_prompt_tokens_details: bool = False,
        enable_force_include_usage: bool = False,
175
        enable_log_outputs: bool = False,
176
177
178
179
180
181
182
183
184
185
    ) -> None:
        super().__init__(
            engine_client=engine_client,
            models=models,
            request_logger=request_logger,
            return_tokens_as_token_ids=return_tokens_as_token_ids,
        )

        self.chat_template = chat_template
        self.chat_template_content_format: Final = chat_template_content_format
186
        self.enable_log_outputs = enable_log_outputs
187

188
189
190
191
192
193
194
        # Set up the unified parser - either a unified parser or fall back to
        # separate parsers accessed through the parser interface
        self.parser = ParserManager.get_parser(
            tool_parser_name=tool_parser,
            reasoning_parser_name=reasoning_parser,
            enable_auto_tools=enable_auto_tools,
            model_name=self.model_config.model,
195
        )
196
197
        self.enable_prompt_tokens_details = enable_prompt_tokens_details
        self.enable_force_include_usage = enable_force_include_usage
198

199
        self.default_sampling_params = self.model_config.get_diff_sampling_param()
200
201
202
203
204
205
        mc = self.model_config
        self.override_max_tokens = (
            self.default_sampling_params.get("max_tokens")
            if mc.generation_config not in ("auto", "vllm")
            else getattr(mc, "override_generation_config", {}).get("max_new_tokens")
        )
206

207
208
209
210
211
        # If False (default), the "store" option is (silently) ignored and the
        # response is not stored. If True, the response is stored in memory.
        # NOTE(woosuk): This may not be intuitive for users, as the default
        # behavior in OpenAI's Responses API is to store the response, but
        # vLLM's default behavior is not.
212
        self.enable_store = envs.VLLM_ENABLE_RESPONSES_API_STORE
213
214
215
216
        if self.enable_store:
            logger.warning_once(
                "`VLLM_ENABLE_RESPONSES_API_STORE` is enabled. This may "
                "cause a memory leak since we never remove responses from "
217
218
                "the store."
            )
219

220
        self.use_harmony = self.model_config.hf_config.model_type == "gpt_oss"
221
        if self.use_harmony:
222
223
224
225
            logger.warning(
                "For gpt-oss, we ignore --enable-auto-tool-choice "
                "and always enable tool use."
            )
226
227
228
229
230
            # OpenAI models have two EOS-like tokens: <|return|> and <|call|>.
            # We need to add them to the stop token ids.
            if "stop_token_ids" not in self.default_sampling_params:
                self.default_sampling_params["stop_token_ids"] = []
            self.default_sampling_params["stop_token_ids"].extend(
231
232
                get_stop_tokens_for_assistant_actions()
            )
233
234
235
236
237
238
239
240
241
242
243

        # Handle tool call ID type for Kimi K2 (supporting test mocking via overrides)
        hf_overrides = getattr(self.model_config, "hf_overrides", None)
        if self.model_config.hf_text_config.model_type == "kimi_k2" or (
            isinstance(hf_overrides, dict)
            and hf_overrides.get("model_type") == "kimi_k2"
        ):
            self.tool_call_id_type = "kimi_k2"
        else:
            self.tool_call_id_type = "random"

244
        self.enable_auto_tools = enable_auto_tools
245
        # HACK(woosuk): This is a hack. We should use a better store.
246
247
        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove responses from the store.
248
249
250
251
        self.response_store: dict[str, ResponsesResponse] = {}
        self.response_store_lock = asyncio.Lock()

        # HACK(woosuk): This is a hack. We should use a better store.
252
253
        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove messages from the store.
254
255
        self.msg_store: dict[str, list[ChatCompletionMessageParam]] = {}

256
257
258
        # HACK(wuhang): This is a hack. We should use a better store.
        # FIXME: If enable_store=True, this may cause a memory leak since we
        # never remove events from the store.
259
260
261
        self.event_store: dict[
            str, tuple[deque[StreamingResponsesResponse], asyncio.Event]
        ] = {}
262

263
264
        self.background_tasks: dict[str, asyncio.Task] = {}

265
266
        self.tool_server = tool_server

267
    def _validate_generator_input(
268
        self,
269
        engine_prompt: ProcessorInputs,
270
    ) -> ErrorResponse | None:
271
        """Add validations to the input to the generator here."""
272
        prompt_len = self._extract_prompt_len(engine_prompt)
273
274
275
        max_model_len = self.model_config.max_model_len

        if prompt_len >= max_model_len:
276
            error_message = (
277
                f"The engine prompt length {prompt_len} "
278
                f"exceeds the max_model_len {max_model_len}. "
279
280
                "Please reduce prompt."
            )
281
282
283
284
            return self.create_error_response(
                err_type="invalid_request_error",
                message=error_message,
                status_code=HTTPStatus.BAD_REQUEST,
285
                param="input",
286
            )
287

288
289
        return None

290
291
292
293
294
295
296
297
    def _validate_create_responses_input(
        self, request: ResponsesRequest
    ) -> ErrorResponse | None:
        if self.use_harmony and request.is_include_output_logprobs():
            return self.create_error_response(
                err_type="invalid_request_error",
                message="logprobs are not supported with gpt-oss models",
                status_code=HTTPStatus.BAD_REQUEST,
298
                param="logprobs",
299
300
301
302
303
304
305
306
307
308
309
310
            )
        if request.store and not self.enable_store and request.background:
            return self.create_error_response(
                err_type="invalid_request_error",
                message=(
                    "This vLLM engine does not support `store=True` and "
                    "therefore does not support the background mode. To "
                    "enable these features, set the environment variable "
                    "`VLLM_ENABLE_RESPONSES_API_STORE=1` when launching "
                    "the vLLM server."
                ),
                status_code=HTTPStatus.BAD_REQUEST,
311
                param="background",
312
            )
313
314
315
316
317
318
        if request.previous_input_messages and request.previous_response_id:
            return self.create_error_response(
                err_type="invalid_request_error",
                message="Only one of `previous_input_messages` and "
                "`previous_response_id` can be set.",
                status_code=HTTPStatus.BAD_REQUEST,
319
                param="previous_response_id",
320
            )
321
322
        return None

323
324
325
    async def create_responses(
        self,
        request: ResponsesRequest,
326
327
328
329
330
331
        raw_request: Request | None = None,
    ) -> (
        AsyncGenerator[StreamingResponsesResponse, None]
        | ResponsesResponse
        | ErrorResponse
    ):
332
333
334
335
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            logger.error("Error with model %s", error_check_ret)
            return error_check_ret
336
337
338
        maybe_validation_error = self._validate_create_responses_input(request)
        if maybe_validation_error is not None:
            return maybe_validation_error
339
340
341
342
343
344
345

        # 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

346
        if request.store and not self.enable_store:
347
348
349
350
351
352
353
            # Disable the store option.
            # NOTE(woosuk): Although returning an error is possible, we opted
            # to implicitly disable store and process the request anyway, as
            # we assume most users do not intend to actually store the response
            # (i.e., their request's `store=True` just because it's the default
            # value).
            request.store = False
354

355
356
357
358
359
360
361
362
363
364
        # Handle the previous response ID.
        prev_response_id = request.previous_response_id
        if prev_response_id is not None:
            async with self.response_store_lock:
                prev_response = self.response_store.get(prev_response_id)
            if prev_response is None:
                return self._make_not_found_error(prev_response_id)
        else:
            prev_response = None

365
366
        lora_request = self._maybe_get_adapters(request)
        model_name = self.models.model_name(lora_request)
367

368
369
370
371
372
373
        if self.use_harmony:
            messages, engine_prompts = self._make_request_with_harmony(
                request, prev_response
            )
        else:
            messages, engine_prompts = await self._make_request(request, prev_response)
374

375
        request_metadata = RequestResponseMetadata(request_id=request.request_id)
376
377
378
379
        if raw_request:
            raw_request.state.request_metadata = request_metadata

        # Schedule the request and get the result generator.
380
        max_model_len = self.model_config.max_model_len
381
        generators: list[AsyncGenerator[ConversationContext, None]] = []
382

383
384
385
386
387
        # Only include builtin tools that the request actually asked for.
        # Without this filter, tools registered on the server (e.g. via
        # --tool-server demo) would be available for execution even when
        # the request didn't enable them.
        requested_tool_types = extract_tool_types(request.tools)
388
        builtin_tool_list: list[str] = []
389
        if self.tool_server is not None:
390
391
392
393
            if (
                self.tool_server.has_tool("browser")
                and "web_search_preview" in requested_tool_types
            ):
394
                builtin_tool_list.append("browser")
395
396
397
398
            if (
                self.tool_server.has_tool("python")
                and "code_interpreter" in requested_tool_types
            ):
399
                builtin_tool_list.append("python")
400
401
402
403
            if (
                self.tool_server.has_tool("container")
                and "container" in requested_tool_types
            ):
404
                builtin_tool_list.append("container")
405

406
407
408
409
410
        if self.tool_server is not None:
            available_tools = builtin_tool_list
        else:
            assert len(builtin_tool_list) == 0
            available_tools = []
411
412
413
414
415
416
417
418
419
420
421
422
423
424
        tokenizer = self.renderer.get_tokenizer()

        for engine_prompt in engine_prompts:
            maybe_error = self._validate_generator_input(engine_prompt)
            if maybe_error is not None:
                return maybe_error

            default_max_tokens = get_max_tokens(
                max_model_len,
                request.max_output_tokens,
                self._extract_prompt_len(engine_prompt),
                self.default_sampling_params,
                self.override_max_tokens,
            )
425

426
427
428
            sampling_params = request.to_sampling_params(
                default_max_tokens, self.default_sampling_params
            )
429

430
431
432
433
434
            trace_headers = (
                None
                if raw_request is None
                else await self._get_trace_headers(raw_request.headers)
            )
435

436
437
438
439
            context: ConversationContext
            if self.use_harmony:
                if request.stream:
                    context = StreamingHarmonyContext(messages, available_tools)
440
                else:
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
                    context = HarmonyContext(messages, available_tools)
            else:
                if envs.VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT:
                    # This is a feature in development for parsing
                    # tokens during generation instead of at the end
                    context = ParsableContext(
                        response_messages=messages,
                        tokenizer=tokenizer,
                        reasoning_parser_cls=self.parser.reasoning_parser_cls
                        if self.parser
                        else None,
                        request=request,
                        tool_parser_cls=self.parser.tool_parser_cls
                        if self.parser
                        else None,
                        available_tools=available_tools,
                        chat_template=self.chat_template,
                        chat_template_content_format=self.chat_template_content_format,
                    )
                else:
                    context = SimpleContext()

            if self.parser and self.parser.reasoning_parser_cls is not None:
                reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
                if (
                    isinstance(
                        struct_out := sampling_params.structured_outputs,
                        StructuredOutputsParams,
                    )
                    and struct_out.all_non_structural_tag_constraints_none()
                ):
                    sampling_params.structured_outputs = replace(
                        struct_out,
                        structural_tag=reasoning_parser.prepare_structured_tag(
                            struct_out.structural_tag, self.tool_server
                        ),
                    )
            generator = self._generate_with_builtin_tools(
                request_id=request.request_id,
                engine_prompt=engine_prompt,
                sampling_params=sampling_params,
                context=context,
                lora_request=lora_request,
                priority=request.priority,
                trace_headers=trace_headers,
            )
            generators.append(generator)
488

489
        assert len(generators) == 1
490
        (result_generator,) = generators
491
492
493
494

        # Store the input messages.
        if request.store:
            self.msg_store[request.request_id] = messages
495

496
497
498
499
500
501
502
503
504
505
506
507
508
        if request.background:
            created_time = int(time.time())
            response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="queued",
                usage=None,
            )
            async with self.response_store_lock:
                self.response_store[response.id] = response
509

510
            # Run the request in the background.
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
            if request.stream:
                task = asyncio.create_task(
                    self._run_background_request_stream(
                        request,
                        sampling_params,
                        result_generator,
                        context,
                        model_name,
                        tokenizer,
                        request_metadata,
                        created_time,
                    ),
                    name=f"create_{request.request_id}",
                )
            else:
                task = asyncio.create_task(
                    self._run_background_request(
                        request,
                        sampling_params,
                        result_generator,
                        context,
                        model_name,
                        tokenizer,
                        request_metadata,
                        created_time,
                    ),
                    name=f"create_{response.id}",
                )
539

540
541
542
543
            # For cleanup.
            response_id = response.id
            self.background_tasks[response_id] = task
            task.add_done_callback(
544
545
                lambda _: self.background_tasks.pop(response_id, None)
            )
546
547

            if request.stream:
548
                return self.responses_background_stream_generator(request.request_id)
549
550
551
552
553
554
555
556
557
558
559
560
561
            return response

        if request.stream:
            return self.responses_stream_generator(
                request,
                sampling_params,
                result_generator,
                context,
                model_name,
                tokenizer,
                request_metadata,
            )

562
563
564
565
566
567
568
569
570
        return await self.responses_full_generator(
            request,
            sampling_params,
            result_generator,
            context,
            model_name,
            tokenizer,
            request_metadata,
        )
571

572
573
574
    async def _make_request(
        self,
        request: ResponsesRequest,
575
        prev_response: ResponsesResponse | None,
576
    ):
577
        tool_dicts = construct_tool_dicts(request.tools, request.tool_choice)
578
        # Construct the input messages.
579
580
581
582
583
584
        messages = construct_input_messages(
            request_instructions=request.instructions,
            request_input=request.input,
            prev_msg=self.msg_store.get(prev_response.id) if prev_response else None,
            prev_response_output=prev_response.output if prev_response else None,
        )
585

586
        _, engine_prompts = await self._preprocess_chat(
587
588
            request,
            messages,
589
590
591
            default_template=self.chat_template,
            default_template_content_format=self.chat_template_content_format,
            default_template_kwargs=None,
592
            tool_dicts=tool_dicts,
593
            tool_parser=self.parser.tool_parser_cls if self.parser else None,
594
        )
595
        return messages, engine_prompts
596
597
598
599

    def _make_request_with_harmony(
        self,
        request: ResponsesRequest,
600
        prev_response: ResponsesResponse | None,
601
602
603
    ):
        if request.tool_choice != "auto":
            raise NotImplementedError(
604
605
                "Only 'auto' tool_choice is supported in response API with Harmony"
            )
606

607
        messages = self._construct_input_messages_with_harmony(request, prev_response)
608
        prompt_token_ids = render_for_completion(messages)
609
        engine_prompt = token_inputs(prompt_token_ids)
610
611
612
613
614

        # Add cache_salt if provided in the request
        if request.cache_salt is not None:
            engine_prompt["cache_salt"] = request.cache_salt

615
        return messages, [engine_prompt]
616

617
618
619
620
621
622
    async def _initialize_tool_sessions(
        self,
        request: ResponsesRequest,
        context: ConversationContext,
        exit_stack: AsyncExitStack,
    ):
623
624
625
626
        # we should only initialize the tool session if the request needs tools
        if len(request.tools) == 0:
            return
        mcp_tools = {
627
            tool.server_label: tool for tool in request.tools if tool.type == "mcp"
628
        }
629
630
631
        await context.init_tool_sessions(
            self.tool_server, exit_stack, request.request_id, mcp_tools
        )
632

633
634
635
636
    async def responses_full_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
637
        result_generator: AsyncIterator[ConversationContext],
638
        context: ConversationContext,
639
        model_name: str,
640
        tokenizer: TokenizerLike,
641
        request_metadata: RequestResponseMetadata,
642
643
        created_time: int | None = None,
    ) -> ErrorResponse | ResponsesResponse:
644
645
646
        if created_time is None:
            created_time = int(time.time())

647
648
        async with AsyncExitStack() as exit_stack:
            try:
649
                await self._initialize_tool_sessions(request, context, exit_stack)
650
651
652
653
                async for _ in result_generator:
                    pass
            except asyncio.CancelledError:
                return self.create_error_response("Client disconnected")
654

655
        # NOTE: Implementation of status is still WIP, but for now
656
657
658
659
        # we guarantee that if the status is not "completed", it is accurate.
        # "completed" is implemented as the "catch-all" for now.
        status: ResponseStatus = "completed"

660
661
        input_messages: ResponseInputOutputMessage | None = None
        output_messages: ResponseInputOutputMessage | None = None
662
663
664
        if self.use_harmony:
            assert isinstance(context, HarmonyContext)
            output = self._make_response_output_items_with_harmony(context)
665
            if request.enable_response_messages:
666
667
                input_messages = context.messages[: context.num_init_messages]
                output_messages = context.messages[context.num_init_messages :]
668
            num_tool_output_tokens = context.num_tool_output_tokens
669
670
671
672
673
            if len(output) > 0:
                if context.finish_reason == "length":
                    status = "incomplete"
                elif context.finish_reason == "abort":
                    status = "cancelled"
674
675
                else:
                    self._raise_if_error(context.finish_reason, request.request_id)
676
677
            else:
                status = "incomplete"
678
        elif isinstance(context, ParsableContext):
679
            output = context.parser.make_response_output_items_from_parsable_context()
680
681

            if request.enable_response_messages:
682
683
                input_messages = context.input_messages
                output_messages = context.output_messages
684
685
686
687

            # TODO: Calculate usage.
            # assert final_res.prompt_token_ids is not None
            num_tool_output_tokens = 0
688
689
690
691

            # Check finish reason from the parser
            if context.parser.finish_reason == "length":
                status = "incomplete"
692
        else:
693
            assert isinstance(context, SimpleContext)
694
695
            # Use final_output which has accumulated text/token_ids/logprobs
            final_res = context.final_output
696
697
698
699
            assert final_res is not None
            assert len(final_res.outputs) == 1
            final_output = final_res.outputs[0]

700
701
702
            # finish_reason='error' indicates retryable internal error
            self._raise_if_error(final_output.finish_reason, request.request_id)

703
704
705
706
            # Check if generation was stopped due to max_tokens
            if final_output.finish_reason == "length":
                status = "incomplete"

707
            output = self._make_response_output_items(request, final_output, tokenizer)
708

709
            if request.enable_response_messages:
710
711
712
                input_messages = context.input_messages
                output_messages = context.output_messages

713
714
            # Calculate usage.
            assert final_res.prompt_token_ids is not None
715
716
            num_tool_output_tokens = 0

717
        assert isinstance(context, (SimpleContext, HarmonyContext, ParsableContext))
718
719
720
721
        num_prompt_tokens = context.num_prompt_tokens
        num_generated_tokens = context.num_output_tokens
        num_cached_tokens = context.num_cached_tokens
        num_reasoning_tokens = context.num_reasoning_tokens
722
723
724
725
726
727
728
729
730
731
732
733
734
        # For text-based reasoning parsers (e.g., <think>...</think>),
        # HarmonyContext already counts reasoning tokens via channels.
        # For Simple/Parsable contexts, derive reasoning_tokens from
        # accumulated output token IDs using the parser if not already set.
        if (
            num_reasoning_tokens == 0
            and self.parser is not None
            and self.parser.reasoning_parser_cls is not None
            and isinstance(context, (SimpleContext, ParsableContext))
        ):
            reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
            accumulated = getattr(context, "_accumulated_token_ids", []) or []
            num_reasoning_tokens = reasoning_parser.count_reasoning_tokens(accumulated)
735
736
737
738

        usage = ResponseUsage(
            input_tokens=num_prompt_tokens,
            output_tokens=num_generated_tokens,
739
            total_tokens=num_prompt_tokens + num_generated_tokens,
740
741
742
743
744
745
746
747
748
            input_tokens_details=InputTokensDetails(
                cached_tokens=num_cached_tokens,
                input_tokens_per_turn=[
                    turn.input_tokens for turn in context.all_turn_metrics
                ],
                cached_tokens_per_turn=[
                    turn.cached_input_tokens for turn in context.all_turn_metrics
                ],
            ),
749
            output_tokens_details=OutputTokensDetails(
750
                reasoning_tokens=num_reasoning_tokens,
751
                tool_output_tokens=num_tool_output_tokens,
752
753
754
755
756
757
                output_tokens_per_turn=[
                    turn.output_tokens for turn in context.all_turn_metrics
                ],
                tool_output_tokens_per_turn=[
                    turn.tool_output_tokens for turn in context.all_turn_metrics
                ],
758
            ),
759
760
761
762
        )
        response = ResponsesResponse.from_request(
            request,
            sampling_params,
763
764
            input_messages=input_messages,
            output_messages=output_messages,
765
766
767
            model_name=model_name,
            created_time=created_time,
            output=output,
768
            status=status,
769
770
771
772
773
774
775
            usage=usage,
        )

        if request.store:
            async with self.response_store_lock:
                stored_response = self.response_store.get(response.id)
                # If the response is already cancelled, don't update it.
776
                if stored_response is None or stored_response.status != "cancelled":
777
778
779
                    self.response_store[response.id] = response
        return response

780
781
782
783
    def _topk_logprobs(
        self,
        logprobs: dict[int, SampleLogprob],
        top_logprobs: int,
784
        tokenizer: TokenizerLike,
785
    ) -> list[LogprobTopLogprob]:
786
787
788
789
790
        """Returns the top-k logprobs from the logprobs dictionary."""
        out = []
        for i, (token_id, _logprob) in enumerate(logprobs.items()):
            if i >= top_logprobs:
                break
791
792
793
794
795
            text = self._get_decoded_token(
                logprob=_logprob,
                token_id=token_id,
                tokenizer=tokenizer,
                return_as_token_id=self.return_tokens_as_token_ids,
796
            )
797
798
799
800
801
            out.append(
                LogprobTopLogprob(
                    token=text,
                    logprob=max(_logprob.logprob, -9999.0),
                    bytes=list(text.encode("utf-8", errors="replace")),
802
803
                )
            )
804
805
806
        return out

    def _create_response_logprobs(
807
808
        self,
        token_ids: Sequence[int],
809
        logprobs: SampleLogprobs | None,
810
        tokenizer: TokenizerLike,
811
        top_logprobs: int | None = None,
812
    ) -> list[Logprob]:
813
814
        assert logprobs is not None, "logprobs must be provided"
        assert len(token_ids) == len(logprobs), (
815
816
            "token_ids and logprobs.token_ids must have the same length"
        )
817
818
819
820
        out = []
        for i, token_id in enumerate(token_ids):
            logprob = logprobs[i]
            token_logprob = logprob[token_id]
821
822
823
824
825
            text = self._get_decoded_token(
                logprob=token_logprob,
                token_id=token_id,
                tokenizer=tokenizer,
                return_as_token_id=self.return_tokens_as_token_ids,
826
            )
827
828
829
830
831
            out.append(
                Logprob(
                    token=text,
                    logprob=max(token_logprob.logprob, -9999.0),
                    bytes=list(text.encode("utf-8", errors="replace")),
832
833
834
835
836
837
838
                    top_logprobs=(
                        self._topk_logprobs(
                            logprob, top_logprobs=top_logprobs, tokenizer=tokenizer
                        )
                        if top_logprobs
                        else []
                    ),
839
840
                )
            )
841
842
        return out

843
844
845
    def _create_stream_response_logprobs(
        self,
        token_ids: Sequence[int],
846
        logprobs: SampleLogprobs | None,
847
        tokenizer: TokenizerLike,
848
        top_logprobs: int | None = None,
849
    ) -> list[response_text_delta_event.Logprob]:
850
851
852
853
854
855
        lgs = self._create_response_logprobs(
            token_ids=token_ids,
            logprobs=logprobs,
            tokenizer=tokenizer,
            top_logprobs=top_logprobs,
        )
856
857
858
859
860
861
        return [
            response_text_delta_event.Logprob(
                token=lg.token,
                logprob=lg.logprob,
                top_logprobs=[
                    response_text_delta_event.LogprobTopLogprob(
862
863
                        token=tl.token, logprob=tl.logprob
                    )
864
                    for tl in lg.top_logprobs
865
866
867
                ],
            )
            for lg in lgs
868
869
        ]

870
871
872
873
    def _make_response_output_items(
        self,
        request: ResponsesRequest,
        final_output: CompletionOutput,
874
        tokenizer: TokenizerLike,
875
    ) -> list[ResponseOutputItem]:
876
877
        # Log complete response if output logging is enabled
        if self.enable_log_outputs and self.request_logger:
878
879
880
881
882
883
884
885
            self.request_logger.log_outputs(
                request_id=request.request_id,
                outputs=final_output.text,
                output_token_ids=final_output.token_ids,
                finish_reason=final_output.finish_reason,
                is_streaming=False,
                delta=False,
            )
886

887
888
889
890
891
892
893
894
        # Compute logprobs if requested
        logprobs = None
        if request.is_include_output_logprobs() and final_output.logprobs:
            logprobs = self._create_response_logprobs(
                token_ids=final_output.token_ids,
                logprobs=final_output.logprobs,
                tokenizer=tokenizer,
                top_logprobs=request.top_logprobs,
895
            )
896

897
898
899
900
901
902
903
904
905
906
907
908
909
910
        # Use parser to extract and create response output items
        if self.parser:
            parser = self.parser(tokenizer)
            return parser.extract_response_outputs(
                model_output=final_output.text,
                request=request,
                enable_auto_tools=self.enable_auto_tools,
                tool_call_id_type=self.tool_call_id_type,
                logprobs=logprobs,
            )

        # Fallback when no parser is configured
        return [
            ResponseOutputMessage(
911
                id=f"msg_{random_uuid()}",
912
913
914
915
916
917
918
919
920
921
                content=[
                    ResponseOutputText(
                        text=final_output.text,
                        annotations=[],
                        type="output_text",
                        logprobs=logprobs,
                    )
                ]
                if final_output.text
                else [],
922
923
924
925
                role="assistant",
                status="completed",
                type="message",
            )
926
        ]
927
928
929
930
931

    def _make_response_output_items_with_harmony(
        self,
        context: HarmonyContext,
    ) -> list[ResponseOutputItem]:
932
        output_items: list[ResponseOutputItem] = []
933
934
        num_init_messages = context.num_init_messages
        for msg in context.messages[num_init_messages:]:
935
            output_items.extend(harmony_to_response_output(msg))
936
        # Handle the generation stopped in the middle (if any).
937
        last_items = parser_state_to_response_output(context.parser)
938
939
940
941
        if last_items:
            output_items.extend(last_items)
        return output_items

942
943
944
    def _extract_system_message_from_request(
        self, request: ResponsesRequest
    ) -> str | None:
945
946
947
948
949
950
951
        system_msg = None
        if not isinstance(request.input, str):
            for response_msg in request.input:
                if (
                    isinstance(response_msg, dict)
                    and response_msg.get("role") == "system"
                ):
952
953
954
955
956
957
958
959
960
961
962
                    content = response_msg.get("content")
                    if isinstance(content, str):
                        system_msg = content
                    elif isinstance(content, list):
                        for param in content:
                            if (
                                isinstance(param, dict)
                                and param.get("type") == "input_text"
                            ):
                                system_msg = param.get("text")
                                break
963
964
965
                    break
        return system_msg

966
    def _construct_harmony_system_input_message(
967
        self, request: ResponsesRequest, with_custom_tools: bool, tool_types: set[str]
968
    ) -> OpenAIHarmonyMessage:
969
970
        model_identity = self._extract_system_message_from_request(request)

971
        reasoning_effort = request.reasoning.effort if request.reasoning else None
972
973
974
975
976
977
978
979
980
981
982

        # Extract allowed_tools from MCP tool requests
        allowed_tools_map = _extract_allowed_tools_from_mcp_requests(request.tools)

        # Get filtered tool descriptions first.
        # If get_tool_description returns None (due to filtering), the tool is disabled.
        browser_description = (
            self.tool_server.get_tool_description(
                "browser", allowed_tools_map.get("web_search_preview")
            )
            if "web_search_preview" in tool_types
983
984
            and self.tool_server is not None
            and self.tool_server.has_tool("browser")
985
            else None
986
        )
987
988
989
990
991
        python_description = (
            self.tool_server.get_tool_description(
                "python", allowed_tools_map.get("code_interpreter")
            )
            if "code_interpreter" in tool_types
992
993
            and self.tool_server is not None
            and self.tool_server.has_tool("python")
994
            else None
995
        )
996
997
998
999
1000
        container_description = (
            self.tool_server.get_tool_description(
                "container", allowed_tools_map.get("container")
            )
            if "container" in tool_types
1001
1002
            and self.tool_server is not None
            and self.tool_server.has_tool("container")
1003
            else None
1004
        )
1005

1006
        sys_msg = get_system_message(
1007
            model_identity=model_identity,
1008
            reasoning_effort=reasoning_effort,
1009
1010
1011
            browser_description=browser_description,
            python_description=python_description,
            container_description=container_description,
1012
1013
1014
1015
1016
            instructions=request.instructions,
            with_custom_tools=with_custom_tools,
        )
        return sys_msg

1017
1018
1019
    def _construct_input_messages_with_harmony(
        self,
        request: ResponsesRequest,
1020
        prev_response: ResponsesResponse | None,
1021
1022
1023
1024
    ) -> list[OpenAIHarmonyMessage]:
        messages: list[OpenAIHarmonyMessage] = []
        if prev_response is None:
            # New conversation.
1025
            tool_types = extract_tool_types(request.tools)
1026
            with_custom_tools = has_custom_tools(tool_types)
1027
1028
1029

            sys_msg = self._construct_harmony_system_input_message(
                request, with_custom_tools, tool_types
1030
1031
            )
            messages.append(sys_msg)
1032
1033
            if with_custom_tools:
                dev_msg = get_developer_message(
1034
1035
                    instructions=request.instructions, tools=request.tools
                )
1036
                messages.append(dev_msg)
1037
1038
            messages += construct_harmony_previous_input_messages(request)

1039
1040
1041
1042
1043
        else:
            # Continue the previous conversation.
            # FIXME(woosuk): Currently, request params like reasoning and
            # instructions are ignored.
            prev_msgs = self.msg_store[prev_response.id]
1044
1045
1046
1047
1048
1049
1050
1051
1052

            # FIXME(woosuk): The slice-delete-reappend cycle below is
            # currently a no-op --- it removes messages then puts them all
            # back unfiltered. It may be intentionally deferred (see FIXME
            # above) or redundant if the Harmony encoder already strips
            # analysis messages at render time. If analysis messages need
            # to be dropped here, add a channel != "analysis" filter when
            # re-appending, similar to auto_drop_analysis_messages in
            # harmony_utils.py.
1053
1054
1055
1056
1057
1058
1059
1060
1061
1062
1063
            if len(prev_msgs) > 0:
                last_msg = prev_msgs[-1]
                assert isinstance(last_msg, OpenAIHarmonyMessage)
                if last_msg.channel == "final":
                    prev_final_msg_idx = -1
                    for i in range(len(prev_msgs) - 2, -1, -1):
                        prev_msg_i = prev_msgs[i]
                        assert isinstance(prev_msg_i, OpenAIHarmonyMessage)
                        if prev_msg_i.channel == "final":
                            prev_final_msg_idx = i
                            break
1064
1065
                    recent_turn_msgs = prev_msgs[prev_final_msg_idx + 1 :]
                    del prev_msgs[prev_final_msg_idx + 1 :]
1066
1067
                    for msg in recent_turn_msgs:
                        assert isinstance(msg, OpenAIHarmonyMessage)
1068
                        prev_msgs.append(msg)
1069
1070
            messages.extend(prev_msgs)
        # Append the new input.
co63oc's avatar
co63oc committed
1071
        # Responses API supports simple text inputs without chat format.
1072
        if isinstance(request.input, str):
1073
1074
1075
1076
1077
            # Skip empty string input when previous_input_messages supplies
            # the full conversation history --- an empty trailing user message
            # confuses the model into thinking nothing was sent.
            if request.input or not request.previous_input_messages:
                messages.append(get_user_message(request.input))
1078
1079
1080
1081
1082
1083
        else:
            if prev_response is not None:
                prev_outputs = copy(prev_response.output)
            else:
                prev_outputs = []
            for response_msg in request.input:
1084
                new_msg = response_input_to_harmony(response_msg, prev_outputs)
1085
1086
1087
                if new_msg.author.role != "system":
                    messages.append(new_msg)

1088
                # User passes in a tool call request and its output. We need
1089
1090
                # to add the tool call request to prev_outputs so that
                # response_input_to_harmony can find the tool call request when
1091
1092
1093
1094
1095
                # parsing the tool call output.
                if isinstance(response_msg, ResponseFunctionToolCall):
                    prev_outputs.append(response_msg)
        return messages

1096
1097
1098
1099
1100
1101
    async def _run_background_request_stream(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
1102
        event_deque: deque[StreamingResponsesResponse] = deque()
1103
1104
1105
        new_event_signal = asyncio.Event()
        self.event_store[request.request_id] = (event_deque, new_event_signal)
        response = None
1106
        generator = self.responses_stream_generator(request, *args, **kwargs)
1107
1108
1109
1110
1111
1112
1113
1114
1115
1116
1117
1118
1119
1120
1121
1122
        try:
            async for event in generator:
                event_deque.append(event)
                new_event_signal.set()  # Signal new event available
        finally:
            new_event_signal.set()

        if response is not None and isinstance(response, ErrorResponse):
            # If the request has failed, update the status to "failed".
            response_id = request.request_id
            async with self.response_store_lock:
                stored_response = self.response_store.get(response_id)
                assert stored_response is not None
                if stored_response.status not in ("completed", "cancelled"):
                    stored_response.status = "failed"

1123
1124
1125
1126
1127
1128
    async def _run_background_request(
        self,
        request: ResponsesRequest,
        *args,
        **kwargs,
    ):
1129
        response = await self.responses_full_generator(request, *args, **kwargs)
1130
1131
1132
1133
1134
1135
1136
1137
1138
1139

        if isinstance(response, ErrorResponse):
            # If the request has failed, update the status to "failed".
            response_id = request.request_id
            async with self.response_store_lock:
                stored_response = self.response_store.get(response_id)
                assert stored_response is not None
                if stored_response.status not in ("completed", "cancelled"):
                    stored_response.status = "failed"

1140
1141
1142
    async def responses_background_stream_generator(
        self,
        response_id: str,
1143
        starting_after: int | None = None,
1144
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1145
        if response_id not in self.event_store:
1146
1147
1148
1149
1150
            raise VLLMValidationError(
                f"Unknown response_id: {response_id}",
                parameter="response_id",
                value=response_id,
            )
1151
1152
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162

        event_deque, new_event_signal = self.event_store[response_id]
        start_index = 0 if starting_after is None else starting_after + 1
        current_index = start_index

        while True:
            new_event_signal.clear()

            # Yield existing events from start_index
            while current_index < len(event_deque):
                event = event_deque[current_index]
                yield event
1163
                if getattr(event, "type", "unknown") == "response.completed":
1164
                    return
1165
1166
1167
1168
                current_index += 1

            await new_event_signal.wait()

1169
1170
1171
    async def retrieve_responses(
        self,
        response_id: str,
1172
1173
1174
1175
1176
1177
1178
        starting_after: int | None,
        stream: bool | None,
    ) -> (
        ErrorResponse
        | ResponsesResponse
        | AsyncGenerator[StreamingResponsesResponse, None]
    ):
1179
1180
1181
1182
1183
        async with self.response_store_lock:
            response = self.response_store.get(response_id)

        if response is None:
            return self._make_not_found_error(response_id)
1184
1185
1186
1187
1188
1189

        if stream:
            return self.responses_background_stream_generator(
                response_id,
                starting_after,
            )
1190
1191
1192
1193
1194
        return response

    async def cancel_responses(
        self,
        response_id: str,
1195
    ) -> ErrorResponse | ResponsesResponse:
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
        async with self.response_store_lock:
            response = self.response_store.get(response_id)
            if response is None:
                return self._make_not_found_error(response_id)

            prev_status = response.status
            if prev_status not in ("queued", "in_progress"):
                return self.create_error_response(
                    err_type="invalid_request_error",
                    message="Cannot cancel a synchronous response.",
1206
                    param="response_id",
1207
1208
1209
1210
1211
1212
                )

            # Update the status to "cancelled".
            response.status = "cancelled"

        # Abort the request.
1213
        if task := self.background_tasks.get(response_id):
1214
1215
1216
1217
            task.cancel()
            try:
                await task
            except asyncio.CancelledError:
1218
                logger.exception("Background task for %s was cancelled", response_id)
1219
1220
1221
1222
1223
1224
1225
        return response

    def _make_not_found_error(self, response_id: str) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
            message=f"Response with id '{response_id}' not found.",
            status_code=HTTPStatus.NOT_FOUND,
1226
            param="response_id",
1227
        )
1228
1229
1230
1231

    def _make_store_not_supported_error(self) -> ErrorResponse:
        return self.create_error_response(
            err_type="invalid_request_error",
1232
1233
1234
1235
1236
1237
            message=(
                "`store=True` (default) is not supported. Please set "
                "`store=False` in Responses API or set "
                "`VLLM_ENABLE_RESPONSES_API_STORE=1` in the env var when "
                "starting the vLLM server."
            ),
1238
            status_code=HTTPStatus.BAD_REQUEST,
1239
            param="store",
1240
        )
1241

1242
    async def _process_simple_streaming_events(
1243
1244
1245
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1246
        result_generator: AsyncIterator[ConversationContext | None],
1247
1248
        context: ConversationContext,
        model_name: str,
1249
        tokenizer: TokenizerLike,
1250
        request_metadata: RequestResponseMetadata,
1251
        created_time: int,
1252
        _increment_sequence_number_and_return: Callable[
1253
1254
            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
1255
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1256
1257
1258
1259
        current_content_index = 0
        current_output_index = 0
        current_item_id = ""
        reasoning_parser = None
1260
1261
        if self.parser and self.parser.reasoning_parser_cls:
            reasoning_parser = self.parser.reasoning_parser_cls(tokenizer)
1262
1263
1264
1265
1266
1267
1268
1269
1270
1271
        previous_text = ""
        previous_token_ids: list[int] = []
        first_delta_sent = False
        previous_delta_messages: list[DeltaMessage] = []
        async for ctx in result_generator:
            assert isinstance(ctx, SimpleContext)
            if ctx.last_output is None:
                continue
            if ctx.last_output.outputs:
                output = ctx.last_output.outputs[0]
1272
1273
                # finish_reason='error' indicates a retryable error
                self._raise_if_error(output.finish_reason, request.request_id)
1274
                if reasoning_parser:
1275
1276
1277
1278
1279
1280
1281
                    delta_message = reasoning_parser.extract_reasoning_streaming(
                        previous_text=previous_text,
                        current_text=previous_text + output.text,
                        delta_text=output.text,
                        previous_token_ids=previous_token_ids,
                        current_token_ids=previous_token_ids + output.token_ids,
                        delta_token_ids=output.token_ids,
1282
1283
                    )
                else:
1284
1285
1286
                    delta_message = DeltaMessage(
                        content=output.text,
                    )
1287
1288
1289
1290
1291
1292
                previous_text += output.text
                previous_token_ids += output.token_ids
                if not delta_message:
                    continue
                if not first_delta_sent:
                    current_item_id = str(uuid.uuid4())
1293
                    if delta_message.reasoning:
1294
                        yield _increment_sequence_number_and_return(
1295
1296
1297
1298
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1299
                                item=ResponseReasoningItem(
1300
1301
1302
1303
1304
                                    type="reasoning",
                                    id=current_item_id,
                                    summary=[],
                                    status="in_progress",
                                ),
1305
1306
                            )
                        )
1307
1308
1309
1310
1311
1312
1313
1314
1315
1316
1317
1318
1319
                        yield _increment_sequence_number_and_return(
                            ResponseReasoningPartAddedEvent(
                                type="response.reasoning_part.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
                                content_index=current_content_index,
                                part=ResponseReasoningTextContent(
                                    text="",
                                    type="reasoning_text",
                                ),
                            )
                        )
1320
                    else:
1321
                        yield _increment_sequence_number_and_return(
1322
1323
1324
1325
                            ResponseOutputItemAddedEvent(
                                type="response.output_item.added",
                                sequence_number=-1,
                                output_index=current_output_index,
1326
                                item=ResponseOutputMessage(
1327
1328
1329
1330
1331
1332
                                    id=current_item_id,
                                    type="message",
                                    role="assistant",
                                    content=[],
                                    status="in_progress",
                                ),
1333
1334
                            )
                        )
1335
1336
1337
1338
1339
1340
1341
1342
1343
1344
1345
1346
1347
1348
                        yield _increment_sequence_number_and_return(
                            ResponseContentPartAddedEvent(
                                type="response.content_part.added",
                                sequence_number=-1,
                                output_index=current_output_index,
                                item_id=current_item_id,
                                content_index=current_content_index,
                                part=ResponseOutputText(
                                    type="output_text",
                                    text="",
                                    annotations=[],
                                    logprobs=[],
                                ),
                            )
1349
                        )
1350
1351
1352
1353
1354
                    first_delta_sent = True
                # todo(kebe7jun) tool call support

                # check delta message and previous delta message are
                # same as content or reasoning content
1355
1356
                if (
                    previous_delta_messages
1357
                    and previous_delta_messages[-1].reasoning is not None
1358
1359
                    and delta_message.content is not None
                ):
1360
1361
                    # from reasoning to normal content, send done
                    # event for reasoning
1362
                    reason_content = "".join(
1363
                        pm.reasoning
1364
                        for pm in previous_delta_messages
1365
                        if pm.reasoning is not None
1366
                    )
1367
1368
1369
1370
1371
1372
1373
1374
1375
1376
1377
1378
1379
1380
1381
1382
1383
1384
1385
1386

                    # delta message could have both reasoning and
                    # content. Include current delta's reasoning in the
                    # finalization since it may carry the tail end of
                    # reasoning text (e.g. when reasoning end and
                    # content start arrive in the same delta).
                    if delta_message.reasoning is not None:
                        yield _increment_sequence_number_and_return(
                            ResponseReasoningTextDeltaEvent(
                                type="response.reasoning_text.delta",
                                sequence_number=-1,
                                content_index=current_content_index,
                                output_index=current_output_index,
                                item_id=current_item_id,
                                delta=delta_message.reasoning,
                            )
                        )
                        reason_content += delta_message.reasoning
                        delta_message = DeltaMessage(content=delta_message.content)

1387
                    yield _increment_sequence_number_and_return(
1388
1389
1390
1391
1392
1393
1394
                        ResponseReasoningTextDoneEvent(
                            type="response.reasoning_text.done",
                            item_id=current_item_id,
                            sequence_number=-1,
                            output_index=current_output_index,
                            content_index=current_content_index,
                            text=reason_content,
1395
1396
                        )
                    )
1397
1398
1399
1400
1401
1402
1403
1404
1405
1406
1407
1408
1409
                    yield _increment_sequence_number_and_return(
                        ResponseReasoningPartDoneEvent(
                            type="response.reasoning_part.done",
                            sequence_number=-1,
                            item_id=current_item_id,
                            output_index=current_output_index,
                            content_index=current_content_index,
                            part=ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        )
                    )
1410
1411
1412
1413
1414
1415
1416
1417
1418
1419
1420
1421
1422
                    current_content_index = 0
                    reasoning_item = ResponseReasoningItem(
                        type="reasoning",
                        content=[
                            ResponseReasoningTextContent(
                                text=reason_content,
                                type="reasoning_text",
                            ),
                        ],
                        status="completed",
                        id=current_item_id,
                        summary=[],
                    )
1423
                    yield _increment_sequence_number_and_return(
1424
1425
1426
1427
1428
                        ResponseOutputItemDoneEvent(
                            type="response.output_item.done",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item=reasoning_item,
1429
1430
                        )
                    )
1431
1432
                    current_output_index += 1
                    current_item_id = str(uuid.uuid4())
1433
                    yield _increment_sequence_number_and_return(
1434
                        ResponseOutputItemAddedEvent(
1435
1436
1437
                            type="response.output_item.added",
                            sequence_number=-1,
                            output_index=current_output_index,
1438
                            item=ResponseOutputMessage(
1439
1440
1441
1442
1443
1444
                                id=current_item_id,
                                type="message",
                                role="assistant",
                                content=[],
                                status="in_progress",
                            ),
1445
1446
                        )
                    )
1447
                    yield _increment_sequence_number_and_return(
1448
                        ResponseContentPartAddedEvent(
1449
1450
1451
1452
1453
                            type="response.content_part.added",
                            sequence_number=-1,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            content_index=current_content_index,
1454
                            part=ResponseOutputText(
1455
1456
1457
1458
1459
                                type="output_text",
                                text="",
                                annotations=[],
                                logprobs=[],
                            ),
1460
1461
                        )
                    )
1462
1463
                    # reset previous delta messages
                    previous_delta_messages = []
1464

1465
                if delta_message.reasoning is not None:
1466
                    yield _increment_sequence_number_and_return(
1467
1468
1469
1470
1471
1472
                        ResponseReasoningTextDeltaEvent(
                            type="response.reasoning_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
1473
                            delta=delta_message.reasoning,
1474
1475
                        )
                    )
1476
                elif delta_message.content is not None:
1477
                    yield _increment_sequence_number_and_return(
1478
                        ResponseTextDeltaEvent(
1479
1480
1481
1482
1483
1484
                            type="response.output_text.delta",
                            sequence_number=-1,
                            content_index=current_content_index,
                            output_index=current_output_index,
                            item_id=current_item_id,
                            delta=delta_message.content,
1485
1486
1487
1488
1489
1490
1491
1492
1493
1494
                            logprobs=(
                                self._create_stream_response_logprobs(
                                    token_ids=output.token_ids,
                                    logprobs=output.logprobs,
                                    tokenizer=tokenizer,
                                    top_logprobs=request.top_logprobs,
                                )
                                if request.is_include_output_logprobs()
                                else []
                            ),
1495
1496
                        )
                    )
1497
1498
1499

                previous_delta_messages.append(delta_message)
        if previous_delta_messages:
1500
            if previous_delta_messages[-1].reasoning is not None:
1501
                reason_content = "".join(
1502
                    pm.reasoning
1503
                    for pm in previous_delta_messages
1504
                    if pm.reasoning is not None
1505
                )
1506
                yield _increment_sequence_number_and_return(
1507
1508
1509
1510
1511
1512
1513
                    ResponseReasoningTextDoneEvent(
                        type="response.reasoning_text.done",
                        item_id=current_item_id,
                        sequence_number=-1,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        text=reason_content,
1514
1515
                    )
                )
1516
1517
1518
1519
1520
1521
1522
1523
1524
1525
1526
1527
1528
                yield _increment_sequence_number_and_return(
                    ResponseReasoningPartDoneEvent(
                        type="response.reasoning_part.done",
                        sequence_number=-1,
                        item_id=current_item_id,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        part=ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    )
                )
1529
1530
1531
1532
1533
1534
1535
1536
1537
1538
1539
1540
                reasoning_item = ResponseReasoningItem(
                    type="reasoning",
                    content=[
                        ResponseReasoningTextContent(
                            text=reason_content,
                            type="reasoning_text",
                        ),
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
1541
                yield _increment_sequence_number_and_return(
1542
1543
1544
1545
1546
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=reasoning_item,
1547
1548
                    )
                )
1549
            elif previous_delta_messages[-1].content is not None:
1550
1551
1552
1553
1554
                final_content = "".join(
                    pm.content
                    for pm in previous_delta_messages
                    if pm.content is not None
                )
1555
                yield _increment_sequence_number_and_return(
1556
                    ResponseTextDoneEvent(
1557
1558
1559
1560
1561
1562
1563
                        type="response.output_text.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        text=final_content,
                        logprobs=[],
                        item_id=current_item_id,
1564
1565
                    )
                )
1566
1567
1568
1569
1570
                part = ResponseOutputText(
                    text=final_content,
                    type="output_text",
                    annotations=[],
                )
1571
                yield _increment_sequence_number_and_return(
1572
                    ResponseContentPartDoneEvent(
1573
1574
1575
1576
1577
1578
                        type="response.content_part.done",
                        sequence_number=-1,
                        item_id=current_item_id,
                        output_index=current_output_index,
                        content_index=current_content_index,
                        part=part,
1579
1580
                    )
                )
1581
1582
1583
1584
1585
1586
1587
1588
1589
1590
                item = ResponseOutputMessage(
                    type="message",
                    role="assistant",
                    content=[
                        part,
                    ],
                    status="completed",
                    id=current_item_id,
                    summary=[],
                )
1591
                yield _increment_sequence_number_and_return(
1592
1593
1594
1595
1596
                    ResponseOutputItemDoneEvent(
                        type="response.output_item.done",
                        sequence_number=-1,
                        output_index=current_output_index,
                        item=item,
1597
1598
                    )
                )
1599
1600
1601
1602
1603

    async def _process_harmony_streaming_events(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1604
        result_generator: AsyncIterator[ConversationContext | None],
1605
1606
        context: ConversationContext,
        model_name: str,
1607
        tokenizer: TokenizerLike,
1608
1609
        request_metadata: RequestResponseMetadata,
        created_time: int,
1610
        _increment_sequence_number_and_return: Callable[
1611
1612
            [StreamingResponsesResponse], StreamingResponsesResponse
        ],
1613
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1614
        state = StreamingState()
1615

1616
1617
1618
        async for ctx in result_generator:
            assert isinstance(ctx, StreamingHarmonyContext)

1619
1620
1621
            # finish_reason='error' indicates a retryable error
            self._raise_if_error(ctx.finish_reason, request.request_id)

1622
1623
1624
            if ctx.is_expecting_start():
                if len(ctx.parser.messages) > 0:
                    previous_item = ctx.parser.messages[-1]
1625
                    for event in emit_previous_item_done_events(previous_item, state):
1626
1627
1628
1629
                        yield _increment_sequence_number_and_return(event)
                state.reset_for_new_item()

            # Stream the output of a harmony message
1630
            for event in emit_content_delta_events(ctx, state):
1631
1632
1633
                yield _increment_sequence_number_and_return(event)

            # Stream tool call outputs
1634
            for event in emit_tool_action_events(ctx, state, self.tool_server):
1635
                yield _increment_sequence_number_and_return(event)
1636

1637
1638
1639
1640
    async def responses_stream_generator(
        self,
        request: ResponsesRequest,
        sampling_params: SamplingParams,
1641
        result_generator: AsyncIterator[ConversationContext | None],
1642
1643
        context: ConversationContext,
        model_name: str,
1644
        tokenizer: TokenizerLike,
1645
        request_metadata: RequestResponseMetadata,
1646
        created_time: int | None = None,
1647
    ) -> AsyncGenerator[StreamingResponsesResponse, None]:
1648
1649
1650
1651
1652
        # TODO:
        # 1. Handle disconnect

        created_time = created_time or int(time.time())

1653
1654
        sequence_number = 0

1655
        def _increment_sequence_number_and_return(
1656
            event: StreamingResponsesResponse,
1657
        ) -> StreamingResponsesResponse:
1658
1659
            nonlocal sequence_number
            # Set sequence_number if the event has this attribute
1660
            if hasattr(event, "sequence_number"):
1661
1662
                event.sequence_number = sequence_number
            sequence_number += 1
1663
            return event
1664

1665
        async with AsyncExitStack() as exit_stack:
1666
            if self.use_harmony:
1667
1668
                # TODO: in streaming, we noticed this bug:
                # https://github.com/vllm-project/vllm/issues/25697
1669
                await self._initialize_tool_sessions(request, context, exit_stack)
Jiayi Yan's avatar
Jiayi Yan committed
1670
                processor = self._process_harmony_streaming_events
1671
            else:
Jiayi Yan's avatar
Jiayi Yan committed
1672
                processor = self._process_simple_streaming_events
1673
            # TODO Hanchen make sampling params to include the structural tag
1674
1675
1676
1677
1678
1679
1680
1681
1682
1683

            initial_response = ResponsesResponse.from_request(
                request,
                sampling_params,
                model_name=model_name,
                created_time=created_time,
                output=[],
                status="in_progress",
                usage=None,
            ).model_dump()
1684
            yield _increment_sequence_number_and_return(
1685
1686
1687
1688
                ResponseCreatedEvent(
                    type="response.created",
                    sequence_number=-1,
                    response=initial_response,
1689
1690
                )
            )
1691
            yield _increment_sequence_number_and_return(
1692
1693
1694
1695
                ResponseInProgressEvent(
                    type="response.in_progress",
                    sequence_number=-1,
                    response=initial_response,
1696
1697
                )
            )
1698

1699
            try:
Jiayi Yan's avatar
Jiayi Yan committed
1700
                async for event_data in processor(
1701
1702
1703
1704
1705
1706
1707
1708
1709
1710
1711
1712
1713
1714
1715
1716
1717
                    request,
                    sampling_params,
                    result_generator,
                    context,
                    model_name,
                    tokenizer,
                    request_metadata,
                    created_time,
                    _increment_sequence_number_and_return,
                ):
                    yield event_data
            except GenerationError as e:
                error_json = self._convert_generation_error_to_streaming_response(e)
                yield _increment_sequence_number_and_return(
                    TypeAdapter(StreamingResponsesResponse).validate_json(error_json)
                )
                return
1718
1719
1720
1721
1722
1723
1724
1725
1726
1727
1728
1729
1730
1731
1732
1733
1734

            async def empty_async_generator():
                # A hack to trick Python to think this is a generator but
                # in fact it immediately returns.
                if False:
                    yield

            final_response = await self.responses_full_generator(
                request,
                sampling_params,
                empty_async_generator(),
                context,
                model_name,
                tokenizer,
                request_metadata,
                created_time=created_time,
            )
1735
            yield _increment_sequence_number_and_return(
1736
                ResponseCompletedEvent(
1737
1738
                    type="response.completed",
                    sequence_number=-1,
1739
                    response=final_response,
1740
1741
                )
            )