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vllm-omni/vllm_omni/entrypoints/openai/serving_chat.py
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import asyncio
import base64
import json
import time
import uuid
from collections.abc import AsyncGenerator, AsyncIterator, Callable
from datetime import datetime, timedelta, timezone
from io import BytesIO
from typing import TYPE_CHECKING, Any, Final, Optional, cast
import jinja2
import torch
from fastapi import Request
from PIL import Image
from pydantic import TypeAdapter
from vllm_omni.entrypoints.async_omni import AsyncOmni
from vllm_omni.entrypoints.openai.protocol.chat_completion import OmniChatCompletionResponse
from vllm_omni.inputs.data import OmniDiffusionSamplingParams, OmniTextPrompt
try:
import soundfile
except ImportError:
soundfile = None
from openai.types.chat.chat_completion_audio import ChatCompletionAudio as OpenAIChatCompletionAudio
from vllm.entrypoints.chat_utils import (
ChatCompletionMessageParam,
ChatTemplateContentFormatOption,
ConversationMessage,
get_history_tool_calls_cnt,
make_tool_call_id,
)
from vllm.entrypoints.openai.chat_completion.protocol import (
ChatCompletionNamedToolChoiceParam,
ChatCompletionRequest,
ChatCompletionResponse,
ChatCompletionResponseChoice,
ChatCompletionResponseStreamChoice,
ChatMessage,
)
from vllm.entrypoints.openai.chat_completion.serving import OpenAIServingChat
from vllm.entrypoints.openai.engine.protocol import (
DeltaFunctionCall,
DeltaMessage,
DeltaToolCall,
ErrorInfo,
ErrorResponse,
FunctionCall,
FunctionDefinition,
PromptTokenUsageInfo,
RequestResponseMetadata,
ToolCall,
UsageInfo,
)
from vllm.entrypoints.openai.engine.serving import ChatLikeRequest, clamp_prompt_logprobs
from vllm.entrypoints.openai.parser.harmony_utils import (
get_streamable_parser_for_assistant,
parse_chat_output,
)
from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
from vllm.entrypoints.openai.utils import maybe_filter_parallel_tool_calls
from vllm.entrypoints.utils import should_include_usage
from vllm.inputs.data import PromptType
from vllm.logger import init_logger
from vllm.outputs import RequestOutput
from vllm.reasoning import ReasoningParser
from vllm.renderers import BaseRenderer, merge_kwargs
from vllm.renderers.inputs import TokPrompt
from vllm.sampling_params import SamplingParams
from vllm.tokenizers import TokenizerLike
from vllm.tokenizers import TokenizerLike as AnyTokenizer
from vllm.tokenizers.mistral import (
MistralTokenizer,
maybe_serialize_tool_calls,
truncate_tool_call_ids,
validate_request_params,
)
from vllm.tool_parsers import ToolParser
from vllm.tool_parsers.mistral_tool_parser import MistralToolCall
from vllm.utils.collection_utils import as_list
from vllm_omni.entrypoints.openai.audio_utils_mixin import AudioMixin
from vllm_omni.entrypoints.openai.protocol import OmniChatCompletionStreamResponse
from vllm_omni.entrypoints.openai.protocol.audio import AudioResponse, CreateAudio
from vllm_omni.entrypoints.openai.utils import parse_lora_request
from vllm_omni.lora.request import LoRARequest
from vllm_omni.outputs import OmniRequestOutput
if TYPE_CHECKING:
from vllm_omni.entrypoints.async_omni_diffusion import AsyncOmniDiffusion
logger = init_logger(__name__)
class OmniOpenAIServingChat(OpenAIServingChat, AudioMixin):
"""OpenAI-compatible chat serving for both LLM and Diffusion models.
This class extends OpenAIServingChat to support:
- Standard LLM chat completions
- Diffusion model image generation via chat interface
For diffusion mode, use the `for_diffusion` class method to create an instance.
"""
# Diffusion mode attributes
_diffusion_mode: bool = False
_diffusion_engine: Optional["AsyncOmniDiffusion"] = None
_diffusion_model_name: str = ""
@classmethod
def for_diffusion(
cls,
diffusion_engine: "AsyncOmniDiffusion",
model_name: str,
) -> "OmniOpenAIServingChat":
"""Create a chat serving instance for diffusion models.
Args:
diffusion_engine: The async diffusion engine
model_name: Name of the model being served
Returns:
OmniOpenAIServingChat instance configured for diffusion mode
Note:
Request-level parameters (num_inference_steps, guidance_scale, seed,
height, width, num_frames, fps, etc.) are passed per-request via the API.
"""
instance = cls.__new__(cls)
instance._diffusion_mode = True
instance._diffusion_engine = diffusion_engine
instance._diffusion_model_name = model_name
return instance
async def create_chat_completion(
self,
request: ChatCompletionRequest,
raw_request: Request | None = None,
) -> AsyncGenerator[str, None] | ChatCompletionResponse | ErrorResponse:
"""
Chat Completion API similar to OpenAI's API.
See https://platform.openai.com/docs/api-reference/chat/create
for the API specification. This API mimics the OpenAI
Chat Completion API.
For diffusion models, this generates images and returns them
in a chat completion response format.
"""
# Handle diffusion mode
if self._diffusion_mode:
return await self._create_diffusion_chat_completion(request, raw_request)
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
# 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
try:
lora_request = self._maybe_get_adapters(request, supports_default_mm_loras=True)
model_name = self.models.model_name(lora_request)
renderer = self.renderer
tokenizer = renderer.get_tokenizer()
if tokenizer is None:
tokenizer = await self.engine_client.get_tokenizer()
reasoning_parser: ReasoningParser | None = None
if self.reasoning_parser_cls:
chat_template_kwargs = self._prepare_extra_chat_template_kwargs(
request.chat_template_kwargs,
self.default_chat_template_kwargs,
)
reasoning_parser = self.reasoning_parser_cls(
tokenizer,
chat_template_kwargs=chat_template_kwargs, # type: ignore[call-arg]
)
tool_parser = self.tool_parser
if isinstance(tokenizer, MistralTokenizer):
# because of issues with pydantic we need to potentially
# re-serialize the tool_calls field of the request
# for more info: see comment in `maybe_serialize_tool_calls`
maybe_serialize_tool_calls(request)
truncate_tool_call_ids(request)
validate_request_params(request)
# Check if tool parsing is unavailable (common condition)
tool_parsing_unavailable = (
tool_parser is None and not isinstance(tokenizer, MistralTokenizer) and not self.use_harmony
)
# Validate tool_choice when tool parsing is required but unavailable
if tool_parsing_unavailable and request.tool_choice not in (
None,
"none",
):
if request.tool_choice == "auto" and not self.enable_auto_tools:
# for hf tokenizers, "auto" tools requires
# --enable-auto-tool-choice and --tool-call-parser
return self.create_error_response(
'"auto" tool choice requires --enable-auto-tool-choice and --tool-call-parser to be set'
)
elif request.tool_choice != "auto":
# "required" or named tool requires tool parser
return self.create_error_response(
f'tool_choice="{request.tool_choice}" requires --tool-call-parser to be set'
)
if request.tools is None or (request.tool_choice == "none" and self.exclude_tools_when_tool_choice_none):
tool_dicts = None
else:
tool_dicts = [tool.model_dump() for tool in request.tools]
if not self.use_harmony:
error_check_ret = self._validate_chat_template(
request_chat_template=request.chat_template,
chat_template_kwargs=request.chat_template_kwargs,
trust_request_chat_template=self.trust_request_chat_template,
)
if error_check_ret is not None:
return error_check_ret
chat_template_kwargs = request.chat_template_kwargs or {}
chat_template_kwargs.update(reasoning_effort=request.reasoning_effort)
# Merge chat_template_kwargs with defaults
merged_template_kwargs = self._prepare_extra_chat_template_kwargs(
chat_template_kwargs,
self.default_chat_template_kwargs,
)
conversation, engine_prompts = await self._preprocess_chat(
request,
request.messages,
default_template=request.chat_template or self.chat_template,
default_template_content_format=self.chat_template_content_format,
default_template_kwargs=merged_template_kwargs,
tool_dicts=tool_dicts,
tool_parser=tool_parser,
# OMNI: Additional parameters
renderer=renderer,
add_generation_prompt=request.add_generation_prompt,
continue_final_message=request.continue_final_message,
documents=getattr(request, "documents", None),
add_special_tokens=request.add_special_tokens,
)
else:
should_include_tools = tool_dicts is not None
conversation, engine_prompts = self.openai_serving_render._make_request_with_harmony(
request, should_include_tools
)
except (ValueError, TypeError, RuntimeError, jinja2.TemplateError) as e:
logger.exception("Error in preprocessing prompt inputs")
return self.create_error_response(f"{e} {e.__cause__}")
request_id = f"chatcmpl-{self._base_request_id(raw_request, request.request_id)}"
request_metadata = RequestResponseMetadata(request_id=request_id)
if raw_request:
raw_request.state.request_metadata = request_metadata
output_modalities = getattr(request, "modalities", self.engine_client.output_modalities)
request.modalities = (
output_modalities if output_modalities is not None else self.engine_client.output_modalities
)
# Omni multistage image generation: Stage-0 (AR) should receive a clean
# text prompt (and optional conditioning image/size) so the model's own
# processor can construct the correct inputs.
# If we pass pre-tokenized chat-template ids, GLM-Image can become
# effectively unconditioned and produce nonsense images.
if request.modalities and ("image" in request.modalities):
try:
messages_as_dicts: list[dict[str, Any]] = []
for msg in request.messages:
if hasattr(msg, "model_dump"):
messages_as_dicts.append(msg.model_dump())
elif isinstance(msg, dict):
messages_as_dicts.append(msg)
else:
messages_as_dicts.append(
{
"role": getattr(msg, "role", "user"),
"content": getattr(msg, "content", ""),
}
)
extracted_prompt, reference_images = self._extract_diffusion_prompt_and_images(messages_as_dicts)
if not extracted_prompt:
return self.create_error_response("No text prompt found in messages")
# [NOTE] When sending request via openai client Python library,
# `extra_body` is flattented and merged into the payload's root.
# These extra fields are accessible via `model_extra` property (from Pydantic base class).
# When sending raw request with curl, no flattening happens. Directly read the `extra_body` dict.
extra_body = getattr(request, "extra_body", None)
if not extra_body:
extra_body = request.model_extra or {}
height = extra_body.get("height")
width = extra_body.get("width")
if "size" in extra_body:
try:
size_str = extra_body["size"]
if isinstance(size_str, str) and "x" in size_str.lower():
w, h = size_str.lower().split("x")
width, height = int(w), int(h)
except Exception:
pass
negative_prompt = extra_body.get("negative_prompt")
engine_prompt_image: dict[str, Any] | None = None
is_img2img = False
if reference_images:
# Best-effort decode first reference image for i2i.
try:
img_bytes = base64.b64decode(reference_images[0])
img = Image.open(BytesIO(img_bytes))
engine_prompt_image = {"img2img": img}
is_img2img = True
except Exception:
engine_prompt_image = None
# Override the prompts produced by chat-template preprocessing.
tprompt: OmniTextPrompt = {"prompt": extracted_prompt}
if is_img2img:
tprompt["modalities"] = ["img2img"]
else:
tprompt["modalities"] = ["image"]
if negative_prompt is not None:
tprompt["negative_prompt"] = negative_prompt
# GLM-Image's _call_hf_processor expects target_h/target_w in mm_processor_kwargs
mm_processor_kwargs: dict[str, Any] = {}
if height is not None:
mm_processor_kwargs["target_h"] = height
if width is not None:
mm_processor_kwargs["target_w"] = width
if mm_processor_kwargs:
tprompt["mm_processor_kwargs"] = mm_processor_kwargs
if engine_prompt_image is not None:
tprompt["multi_modal_data"] = engine_prompt_image
engine_prompts = [tprompt]
# Store height/width for applying to diffusion stage sampling params later
_image_gen_height = height
_image_gen_width = width
except Exception as e:
logger.warning("Failed to build image-generation prompt for omni multistage: %s", e)
_image_gen_height = None
_image_gen_width = None
else:
_image_gen_height = None
_image_gen_width = None
# Schedule the request and get the result generator.
generators: list[AsyncGenerator[RequestOutput, None]] = []
try:
for i, engine_prompt in enumerate(engine_prompts):
if hasattr(request, "sampling_params_list"):
sampling_params_list = self._to_sampling_params_list(request.sampling_params_list)
else:
# Use standard OpenAI API parameters for comprehension stage
sampling_params_list = self._build_sampling_params_list_from_request(request)
# Apply user-specified height/width to diffusion stage(s) for image generation
if _image_gen_height is not None or _image_gen_width is not None:
for idx, sp in enumerate(sampling_params_list):
# Diffusion stages typically have height/width attributes
if hasattr(sp, "height") and _image_gen_height is not None:
sp.height = _image_gen_height
if hasattr(sp, "width") and _image_gen_width is not None:
sp.width = _image_gen_width
self._log_inputs(
request_id,
engine_prompt,
params_list=sampling_params_list,
lora_request=lora_request,
)
generator = self.engine_client.generate(
prompt=engine_prompt,
request_id=request_id,
sampling_params_list=sampling_params_list,
output_modalities=output_modalities,
)
generators.append(generator)
except ValueError as e:
return self.create_error_response(e)
assert len(generators) == 1
(result_generator,) = generators
# Streaming response
if request.stream:
return self.chat_completion_stream_generator(
request,
result_generator,
request_id,
model_name,
conversation,
tokenizer,
request_metadata,
reasoning_parser,
)
try:
return await self.chat_completion_full_generator(
request,
result_generator,
request_id,
model_name,
conversation,
tokenizer,
request_metadata,
reasoning_parser,
)
except ValueError as e:
return self.create_error_response(e)
async def _preprocess_chat(
self,
request: ChatLikeRequest | ResponsesRequest,
messages: list[ChatCompletionMessageParam],
default_template: str | None,
default_template_content_format: ChatTemplateContentFormatOption,
default_template_kwargs: dict[str, Any] | None = None,
tool_dicts: list[dict[str, Any]] | None = None,
tool_parser: Callable[[TokenizerLike], ToolParser] | None = None,
# OMNI: Additional parameters for backward compatibility
renderer: BaseRenderer | None = None,
add_generation_prompt: bool = True,
continue_final_message: bool = False,
documents: list[dict[str, str]] | None = None,
add_special_tokens: bool = False,
) -> tuple[list[ConversationMessage], list[TokPrompt]]:
if renderer is None:
renderer = self.renderer
# Keep OMNI compatibility args wired while delegating rendering
# to the upstream async renderer pipeline.
default_template_kwargs = merge_kwargs(
default_template_kwargs,
dict(
tools=tool_dicts,
documents=documents,
add_generation_prompt=add_generation_prompt,
continue_final_message=continue_final_message,
add_special_tokens=add_special_tokens,
tokenize=isinstance(renderer.tokenizer, MistralTokenizer),
),
)
tok_params = request.build_tok_params(self.model_config)
chat_params = request.build_chat_params(
default_template,
default_template_content_format,
).with_defaults(default_template_kwargs)
# OMNI: When use_audio_in_video=True, the qwen2_5_omni_thinker mm
# processor asserts that audio items are present alongside video items
# (inside render_chat_async). We must inject audio_url items into the
# messages BEFORE calling render_chat_async so the mm processor can
# count them correctly during tokenisation.
mm_proc_kw = getattr(request, "mm_processor_kwargs", None) or {}
if mm_proc_kw.get("use_audio_in_video", False):
messages = await self._inject_audio_from_video_urls(messages)
(conversation,), (engine_prompt,) = await renderer.render_chat_async(
[messages],
chat_params,
tok_params,
prompt_extras={
k: v for k in ("mm_processor_kwargs", "cache_salt") if (v := getattr(request, k, None)) is not None
},
)
tokenizer = renderer.get_tokenizer()
# tool parsing is done only if a tool_parser has been set and if
# tool_choice is not "none" (if tool_choice is "none" but a tool_parser
# is set, we want to prevent parsing a tool_call hallucinated by the LLM
should_parse_tools = tool_parser is not None and (
hasattr(request, "tool_choice") and request.tool_choice != "none"
)
if should_parse_tools:
if not isinstance(request, ChatCompletionRequest):
msg = "Tool usage is only supported for Chat Completions API"
raise NotImplementedError(msg)
request = tool_parser(tokenizer).adjust_request( # type: ignore
request=request
)
# Preserve a clean text prompt for downstream stages (e.g., GLM-Image diffusion).
# For image generation, we want the raw user caption instead of a rendered template.
# But for multimodal comprehension (img2text), we MUST keep the rendered prompt
# containing image tokens.
req_modalities = getattr(request, "modalities", [])
if req_modalities and ("image" in req_modalities):
messages_as_dicts: list[dict[str, Any]] = []
for msg in messages:
if hasattr(msg, "model_dump"):
messages_as_dicts.append(msg.model_dump())
elif isinstance(msg, dict):
messages_as_dicts.append(msg)
else:
messages_as_dicts.append(
{
"role": getattr(msg, "role", "user"),
"content": getattr(msg, "content", ""),
}
)
extracted_prompt, _ = self._extract_diffusion_prompt_and_images(messages_as_dicts)
if extracted_prompt:
engine_prompt["prompt"] = extracted_prompt
mm_processor_kwargs = getattr(request, "mm_processor_kwargs", None)
if mm_processor_kwargs is not None:
engine_prompt["mm_processor_kwargs"] = mm_processor_kwargs
if hasattr(request, "cache_salt") and request.cache_salt is not None:
engine_prompt["cache_salt"] = request.cache_salt
return conversation, [engine_prompt]
async def _inject_audio_from_video_urls(
self,
messages: list[ChatCompletionMessageParam],
) -> list[ChatCompletionMessageParam]:
"""Pre-extract audio from video URLs and inject as audio_url content items.
When use_audio_in_video=True, the qwen2_5_omni_thinker multimodal
processor requires that the number of audio items equals the number of
video items (it subtracts mm_counts["video"] from mm_counts["audio"]).
The client only sends video_url items; this method adds the matching
audio_url items on the server side before the renderer processes them.
"""
import io
from vllm_omni.entrypoints.chat_utils import extract_audio_from_video_async
new_messages: list[ChatCompletionMessageParam] = []
for msg in messages:
content = msg.get("content") if isinstance(msg, dict) else getattr(msg, "content", None)
if not isinstance(content, list):
new_messages.append(msg)
continue
video_urls = [
part.get("video_url", {}).get("url")
for part in content
if isinstance(part, dict) and part.get("type") == "video_url" and part.get("video_url", {}).get("url")
]
if not video_urls:
new_messages.append(msg)
continue
audios = await asyncio.gather(*(extract_audio_from_video_async(u) for u in video_urls))
audio_items: list[dict] = []
for audio_array, sample_rate in audios:
buf = io.BytesIO()
if soundfile is not None:
soundfile.write(buf, audio_array, samplerate=int(sample_rate), format="WAV")
else:
import struct
import numpy as np
audio_np = np.asarray(audio_array, dtype=np.float32)
sr = int(sample_rate)
num_channels = 1
bits_per_sample = 32
num_frames = len(audio_np)
data_size = num_frames * num_channels * (bits_per_sample // 8)
# Write minimal RIFF/WAV header
buf.write(b"RIFF")
buf.write(struct.pack("<I", 36 + data_size))
buf.write(b"WAVE")
buf.write(b"fmt ")
buf.write(
struct.pack(
"<IHHIIHH",
16,
3,
num_channels,
sr,
sr * num_channels * (bits_per_sample // 8),
num_channels * (bits_per_sample // 8),
bits_per_sample,
)
)
buf.write(b"data")
buf.write(struct.pack("<I", data_size))
buf.write(audio_np.tobytes())
audio_b64 = base64.b64encode(buf.getvalue()).decode()
audio_items.append(
{
"type": "audio_url",
"audio_url": {"url": f"data:audio/wav;base64,{audio_b64}"},
}
)
new_content = list(content) + audio_items
if isinstance(msg, dict):
new_msg = {**msg, "content": new_content}
else:
new_msg = msg.model_copy(update={"content": new_content})
new_messages.append(new_msg)
return new_messages
def _to_sampling_params_list(self, sampling_params_list: list[dict]) -> list[SamplingParams]:
final_sampling_params_list = []
for sampling_params in sampling_params_list:
if isinstance(sampling_params, dict):
final_sampling_params_list.append(SamplingParams(**sampling_params))
elif isinstance(sampling_params, SamplingParams):
final_sampling_params_list.append(sampling_params)
else:
raise ValueError(f"Invalid sampling params: {sampling_params}")
return final_sampling_params_list
def _get_comprehension_stage_index(self) -> int:
for idx, stage in enumerate(self.engine_client.stage_configs):
if stage.is_comprehension:
return idx
raise ValueError("No comprehension stage (is_comprehension=True) found in stage configs")
# OpenAI API standard sampling parameters that can be safely overridden.
# These are the most commonly used parameters with compatible types
# between ChatCompletionRequest and SamplingParams.
# Users who need more control can use sampling_params_list in extra_body.
_OPENAI_SAMPLING_FIELDS: set[str] = {
"temperature",
"top_p",
"top_k",
"max_tokens",
"min_tokens",
"seed",
"ignore_eos",
"stop",
"stop_token_ids",
"frequency_penalty",
"presence_penalty",
}
def _apply_request_overrides(
self,
default_params: SamplingParams,
request: ChatCompletionRequest,
) -> SamplingParams:
"""Clone default params and override with user-provided request values.
Starts with YAML defaults and only overrides fields that the user
explicitly provided (non-None values) in the request.
Args:
default_params: Default SamplingParams from stage config YAML.
request: The chat completion request containing user-provided values.
Returns:
New SamplingParams with YAML defaults overridden by request values.
"""
params = default_params.clone()
for field_name in self._OPENAI_SAMPLING_FIELDS:
value = getattr(request, field_name, None)
if value is not None:
setattr(params, field_name, value)
return params
def _build_sampling_params_list_from_request(
self,
request: ChatCompletionRequest,
) -> list[SamplingParams]:
"""Build sampling_params_list using standard OpenAI API parameters.
For the comprehension stage, starts with YAML defaults and overrides with
user-provided request values. For other stages, uses cloned YAML defaults.
This approach ensures all YAML defaults (including seed, detokenize, etc.)
are preserved while allowing users to override specific parameters.
Args:
request: The chat completion request containing OpenAI API parameters.
Returns:
List of SamplingParams, one for each stage.
"""
default_params_list = self.engine_client.default_sampling_params_list
comprehension_idx = self._get_comprehension_stage_index()
sampling_params_list = []
for idx, default_params in enumerate(default_params_list):
if isinstance(default_params, dict):
default_params = SamplingParams(**default_params)
if idx == comprehension_idx:
params = self._apply_request_overrides(default_params, request)
sampling_params_list.append(params)
else:
# For other stages, clone default params
sampling_params_list.append(default_params.clone())
return sampling_params_list
def _log_inputs(
self,
request_id: str,
inputs: PromptType | TokPrompt,
params_list: list[SamplingParams] | None,
lora_request: LoRARequest | None,
) -> None:
if self.request_logger is None:
return
components = self._extract_prompt_components(inputs)
self.request_logger.log_inputs(
request_id,
components.text,
components.token_ids,
components.embeds,
params=params_list,
lora_request=lora_request,
)
async def chat_completion_stream_generator(
self,
request: ChatCompletionRequest,
result_generator: AsyncIterator[RequestOutput],
request_id: str,
model_name: str,
conversation: list[ConversationMessage],
tokenizer: AnyTokenizer,
request_metadata: RequestResponseMetadata,
reasoning_parser: ReasoningParser | None = None,
):
created_time = int(time.time())
chunk_object_type: Final = "chat.completion.chunk"
first_iteration_dict = {}
assert hasattr(request, "modalities") and request.modalities is not None, (
"Streaming request must specify output modalities"
)
for modality in request.modalities:
first_iteration_dict[modality] = True
# Send response for each token for each request.n (index)
num_choices = 1 if request.n is None else request.n
previous_num_tokens = [0] * num_choices
finish_reason_sent = [False] * num_choices
num_prompt_tokens = 0
num_cached_tokens = None
if self.use_harmony:
harmony_parsers = [get_streamable_parser_for_assistant() for _ in range(num_choices)]
harmony_tools_streamed = [False] * num_choices
tools_streamed = [False] * num_choices
if isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam):
tool_choice_function_name = request.tool_choice.function.name
else:
tool_choice_function_name = None
# Determine whether tools are in use with "auto" tool choice
tool_choice_auto = not tool_choice_function_name and self._should_stream_with_auto_tool_parsing(request)
all_previous_token_ids: list[list[int]] | None
function_name_returned = [False] * num_choices
if self.tool_call_id_type == "kimi_k2":
history_tool_call_cnt = get_history_tool_calls_cnt(conversation)
else:
history_tool_call_cnt = 0
# Always track previous_texts for comprehensive output logging
previous_texts = [""] * num_choices
# Only one of these will be used, thus previous_texts and
# all_previous_token_ids will not be used twice in the same iteration.
if tool_choice_auto or reasoning_parser:
# These are only required in "auto" tool choice case
all_previous_token_ids = [[]] * num_choices
# For reasoning parser and tool call all enabled
added_content_delta_arr = [False] * num_choices
reasoning_end_arr = [False] * num_choices
else:
all_previous_token_ids = None
# Prepare the tool parser if it's needed
try:
if tool_choice_auto and self.tool_parser:
tool_parsers: list[ToolParser | None] = [self.tool_parser(tokenizer)] * num_choices
else:
tool_parsers = [None] * num_choices
except Exception as e:
logger.exception("Error in tool parser creation.")
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
yield "data: [DONE]\n\n"
return
stream_options = request.stream_options
include_usage, include_continuous_usage = should_include_usage(stream_options, self.enable_force_include_usage)
last_metrics: dict[str, Any] | None = None
try:
async for omni_res in result_generator:
final_output_type = omni_res.final_output_type
res = omni_res.request_output
if final_output_type not in first_iteration_dict:
logger.warning(f"final output type: {final_output_type} is not needed by the request")
continue
if omni_res.metrics:
last_metrics = omni_res.metrics
if res.prompt_token_ids is not None:
num_prompt_tokens = len(res.prompt_token_ids)
if res.encoder_prompt_token_ids is not None:
num_prompt_tokens += len(res.encoder_prompt_token_ids)
# Initialize role before conditional blocks to avoid UnboundLocalError
# when handling audio/image responses
role = self.get_chat_request_role(request)
# We need to do it here, because if there are exceptions in
# the result_generator, it needs to be sent as the FIRST
# response (by the try...catch).
if first_iteration_dict[final_output_type] and final_output_type == "text":
num_cached_tokens = res.num_cached_tokens
# Send first response for each choice with role
# NOTE: num_choices defaults to 1 so this usually executes once per request
for i in range(num_choices):
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(
role=role,
content="",
),
logprobs=None,
finish_reason=None,
)
# return prompt_token_ids at the first chunk ever
chunk = OmniChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name,
prompt_token_ids=(res.prompt_token_ids if request.return_token_ids else None),
modality=final_output_type,
)
# if continuous usage stats are requested, add it
if include_continuous_usage:
chunk.usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=0,
total_tokens=num_prompt_tokens,
)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
# Send response to echo the input portion of the
# last message
if request.echo:
last_msg_content: str | list[dict[str, str]] = ""
if conversation and "content" in conversation[-1] and conversation[-1].get("role") == role:
last_msg_content = conversation[-1]["content"] or ""
if last_msg_content:
for i in range(num_choices):
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=DeltaMessage(content=last_msg_content),
logprobs=None,
finish_reason=None,
)
chunk = OmniChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name,
modality=final_output_type,
)
if include_continuous_usage:
chunk.usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=0,
total_tokens=num_prompt_tokens,
)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
first_iteration_dict[final_output_type] = False
if final_output_type == "text":
for output in res.outputs:
i = output.index
tool_parser = tool_parsers[i]
if finish_reason_sent[i]:
continue
if request.logprobs and request.top_logprobs is not None:
assert output.logprobs is not None, "Did not output logprobs"
logprobs = self._create_chat_logprobs(
token_ids=output.token_ids,
top_logprobs=output.logprobs,
tokenizer=tokenizer,
num_output_top_logprobs=request.top_logprobs,
return_as_token_id=request.return_tokens_as_token_ids,
)
else:
logprobs = None
if self.use_harmony:
harmony_parser = harmony_parsers[i]
prev_recipient = harmony_parser.current_recipient
delta_text = ""
for token_id in output.token_ids:
harmony_parser.process(token_id)
delta_text += harmony_parser.last_content_delta or ""
cur_channel = harmony_parser.current_channel
cur_recipient = harmony_parser.current_recipient
else:
# output.text is cumulative, extract only the delta portion
previous_text = previous_texts[i] if previous_texts else ""
if output.text is not None:
delta_text = output.text[len(previous_text) :]
else:
delta_text = ""
if not delta_text and not output.token_ids and not previous_num_tokens[i]:
# Chunked prefill case, don't return empty chunks
continue
delta_message: DeltaMessage | None
# just update previous_texts and previous_token_ids
if tool_choice_auto or reasoning_parser:
assert previous_texts is not None
assert all_previous_token_ids is not None
previous_text = previous_texts[i]
previous_token_ids = all_previous_token_ids[i]
current_text = previous_text + delta_text
# avoid the None + list error.
if previous_token_ids:
current_token_ids = previous_token_ids + as_list(output.token_ids)
else:
current_token_ids = as_list(output.token_ids)
if self.use_harmony:
if cur_channel == "final":
delta_message = DeltaMessage(content=delta_text)
elif cur_channel == "analysis":
if request.include_reasoning:
delta_message = DeltaMessage(reasoning=delta_text)
else:
delta_message = None
elif (
cur_channel == "commentary" and cur_recipient and cur_recipient.startswith("functions.")
):
# Count completed tool calls to determine index
base_index = 0
for msg in harmony_parser.messages:
if (
msg.channel == "commentary"
and msg.recipient
and msg.recipient.startswith("functions.")
):
base_index += 1
if prev_recipient != cur_recipient:
tool_name = cur_recipient.split("functions.", 1)[1]
delta_message = DeltaMessage(
tool_calls=[
DeltaToolCall(
id=make_tool_call_id(),
type="function",
function=DeltaFunctionCall(
name=tool_name,
arguments="",
),
index=base_index,
)
]
)
elif delta_text:
delta_message = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=base_index,
function=DeltaFunctionCall(arguments=delta_text),
)
]
)
else:
delta_message = None
if delta_message is not None:
harmony_tools_streamed[i] = True
else:
delta_message = None
# handle streaming deltas for tools with named tool_choice
elif tool_choice_function_name:
if (
reasoning_parser
and not reasoning_end_arr[i]
and not reasoning_parser.is_reasoning_end(previous_token_ids)
):
assert reasoning_parser is not None
delta_message = reasoning_parser.extract_reasoning_streaming(
previous_text,
current_text,
delta_text,
previous_token_ids,
current_token_ids,
output.token_ids,
)
# When encountering think end id in delta_token_ids
# or think end id in prompt_token_ids
# i.e {"enable_thinking": False},
# set reasoning status to end.
# Only keep 'content', remove 'reasoning'.
if reasoning_parser.is_reasoning_end(as_list(output.token_ids)) or (
res.prompt_token_ids and reasoning_parser.is_reasoning_end(res.prompt_token_ids)
):
reasoning_end_arr[i] = True
if delta_message and delta_message.content:
# This need to be added to next `delta_text`
current_text = delta_message.content
delta_message.content = None
else:
current_text = ""
else:
# Just to add remaining `content`
if reasoning_parser:
delta_text = previous_text + delta_text
current_text = ""
if function_name_returned[i]:
delta_tool_call = DeltaToolCall(
function=DeltaFunctionCall(arguments=delta_text),
index=i,
)
else:
delta_tool_call = DeltaToolCall(
id=make_tool_call_id(),
type="function",
function=DeltaFunctionCall(
name=tool_choice_function_name,
arguments=delta_text,
),
index=i,
)
function_name_returned[i] = True
delta_message = DeltaMessage(
tool_calls=[
delta_tool_call,
]
)
tools_streamed[i] = True
elif request.tool_choice == "required":
assert previous_texts is not None
previous_text = previous_texts[i]
current_text = previous_text + delta_text
fn_name_returned = function_name_returned[i]
output_token_ids = as_list(output.token_ids)
if (
reasoning_parser is not None
and not reasoning_end_arr[i]
and res.prompt_token_ids
and reasoning_parser.is_reasoning_end(res.prompt_token_ids)
):
reasoning_end_arr[i] = True
if reasoning_parser and not reasoning_end_arr[i]:
delta_message = reasoning_parser.extract_reasoning_streaming(
previous_text,
current_text,
delta_text,
previous_token_ids,
current_token_ids,
output_token_ids,
)
if reasoning_parser.is_reasoning_end(output_token_ids):
reasoning_end_arr[i] = True
if delta_message and delta_message.content:
current_text = delta_message.content
delta_message.content = None
else:
# reasoning ended
current_text = ""
else:
# either finished reasoning or no reasoning at all
content = current_text
delta_message, function_name_returned[i] = self.extract_tool_call_required_streaming(
previous_text=previous_text,
current_text=content,
delta_text=delta_text,
function_name_returned=fn_name_returned,
tool_call_idx=history_tool_call_cnt,
)
if (
delta_message
and delta_message.tool_calls
and delta_message.tool_calls[0].id is not None
):
history_tool_call_cnt += 1
tools_streamed[i] = True
# handle streaming deltas for tools with "auto" tool choice
# and reasoning parser
elif tool_choice_auto and reasoning_parser:
assert tool_parser is not None
assert reasoning_parser is not None
assert added_content_delta_arr is not None
assert reasoning_end_arr is not None
output_token_ids = as_list(output.token_ids)
if not reasoning_end_arr[i]:
delta_message = reasoning_parser.extract_reasoning_streaming(
previous_text,
current_text,
delta_text,
previous_token_ids,
current_token_ids,
output_token_ids,
)
# When encountering think end id in prompt_token_ids
# i.e {"enable_thinking": False},
# set reasoning status to end.
# Remove the text and token ids related
# to 'reasoning'.
if res.prompt_token_ids and reasoning_parser.is_reasoning_end(res.prompt_token_ids):
reasoning_end_arr[i] = True
current_token_ids = output_token_ids
if delta_message and delta_message.content:
current_text = delta_message.content
delta_message.content = None
else:
current_text = ""
# When encountering think end id in delta_token_ids,
# set reasoning status to end.
# Remove the text and token ids related
# to 'reasoning'.
if reasoning_parser.is_reasoning_end(output_token_ids):
reasoning_end_arr[i] = True
current_token_ids = reasoning_parser.extract_content_ids(output_token_ids)
if delta_message and delta_message.content:
current_text = delta_message.content
delta_message.content = None
else:
current_text = ""
# handle tool calls only after reasoning is done,
else:
delta_token_ids = output_token_ids
# First time to tool call,
# add the remaining text and token ids
# to delta from previous
if not added_content_delta_arr[i]:
added_content_delta_arr[i] = True
previous_text = ""
previous_token_ids = []
delta_text = current_text
delta_token_ids = current_token_ids
delta_message = tool_parser.extract_tool_calls_streaming(
previous_text=previous_text,
current_text=current_text,
delta_text=delta_text,
previous_token_ids=previous_token_ids,
current_token_ids=current_token_ids,
delta_token_ids=delta_token_ids,
request=request,
)
if delta_message and delta_message.tool_calls:
tools_streamed[i] = True
# when only tool calls
elif tool_choice_auto:
assert tool_parser is not None
delta_message = tool_parser.extract_tool_calls_streaming(
previous_text=previous_text,
current_text=current_text,
delta_text=delta_text,
previous_token_ids=previous_token_ids,
current_token_ids=current_token_ids,
delta_token_ids=output.token_ids,
request=request,
)
if delta_message and delta_message.tool_calls:
tools_streamed[i] = True
# when only reasoning
elif reasoning_parser:
delta_message = reasoning_parser.extract_reasoning_streaming(
previous_text,
current_text,
delta_text,
previous_token_ids,
current_token_ids,
output.token_ids,
)
# handle streaming just a content delta
else:
delta_message = DeltaMessage(content=delta_text)
# update the previous values for the next iteration
if (tool_choice_auto or reasoning_parser) and not self.use_harmony:
assert previous_texts is not None
assert all_previous_token_ids is not None
previous_texts[i] = current_text
all_previous_token_ids[i] = current_token_ids
else:
# Update for comprehensive logging even in simple case
assert previous_texts is not None
previous_texts[i] += delta_text
# set the previous values for the next iteration
previous_num_tokens[i] += len(output.token_ids)
# if the message delta is None (e.g. because it was a
# "control token" for tool calls or the parser otherwise
# wasn't ready to send a token, then
# get the next token without streaming a chunk
if delta_message is None:
if output.finish_reason is None and not request.return_token_ids:
continue
delta_message = DeltaMessage()
# Log streaming delta if output logging is enabled
if self.enable_log_outputs and self.request_logger:
delta_content = ""
if delta_message.content:
delta_content = delta_message.content
elif delta_message.tool_calls:
delta_content = "".join(
tc.function.arguments
for tc in delta_message.tool_calls
if tc.function and tc.function.arguments
)
if delta_content:
self.request_logger.log_outputs(
request_id=request_id,
outputs=delta_content,
output_token_ids=as_list(output.token_ids),
finish_reason=output.finish_reason,
is_streaming=True,
delta=True,
)
if output.finish_reason is None:
# Send token-by-token response for each request.n
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=delta_message,
logprobs=logprobs,
finish_reason=None,
token_ids=(as_list(output.token_ids) if request.return_token_ids else None),
)
# if the model is finished generating
else:
# check to make sure we haven't "forgotten" to stream
# any tokens that were generated but previously
# matched by partial json parsing
# only happens if we are NOT using structured outputs
auto_tools_called = False
if tool_parser:
auto_tools_called = len(tool_parser.prev_tool_call_arr) > 0
index = len(tool_parser.prev_tool_call_arr) - 1 if auto_tools_called else 0
else:
index = 0
if self._should_check_for_unstreamed_tool_arg_tokens(delta_message, output) and tool_parser:
latest_delta_len = 0
if (
isinstance(
delta_message.tool_calls[0].function,
DeltaFunctionCall,
)
) and isinstance(delta_message.tool_calls[0].function.arguments, str):
latest_delta_len = len(delta_message.tool_calls[0].function.arguments)
# get the expected call based on partial JSON
# parsing which "autocompletes" the JSON.
# Tool parsers (e.g. Qwen3Coder) store
# arguments as a JSON string in
# prev_tool_call_arr. Calling json.dumps()
# on an already-serialized string would
# double-serialize it (e.g. '{"k":1}' becomes
# '"{\\"k\\":1}"'), which then causes the
# replace() below to fail and append the
# entire double-serialized string as a
# spurious final delta.
args = tool_parser.prev_tool_call_arr[index].get("arguments", {})
if isinstance(args, str):
expected_call = args
else:
expected_call = json.dumps(args, ensure_ascii=False)
# get what we've streamed so far for arguments
# for the current tool
actual_call = tool_parser.streamed_args_for_tool[index]
if latest_delta_len > 0:
actual_call = actual_call[:-latest_delta_len]
# check to see if there's anything left to stream
remaining_call = expected_call.replace(actual_call, "", 1)
# set that as a delta message
delta_message = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=index,
function=DeltaFunctionCall(arguments=remaining_call).model_dump(
exclude_none=True
),
)
]
)
# Send the finish response for each request.n only once
# In OpenAI's API, when a tool is called, the
# finish_reason is:
# "tool_calls" for "auto" or "required" tool calls,
# and "stop" for named tool calls.
if (
auto_tools_called
or (tools_streamed[i] and not tool_choice_function_name)
or (self.use_harmony and harmony_tools_streamed[i])
):
finish_reason_ = "tool_calls"
else:
finish_reason_ = output.finish_reason if output.finish_reason else "stop"
choice_data = ChatCompletionResponseStreamChoice(
index=i,
delta=delta_message,
logprobs=logprobs,
finish_reason=finish_reason_,
stop_reason=output.stop_reason,
token_ids=(as_list(output.token_ids) if request.return_token_ids else None),
)
finish_reason_sent[i] = True
choice_data = maybe_filter_parallel_tool_calls(choice_data, request)
chunk = OmniChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[choice_data],
model=model_name,
modality=final_output_type,
metrics=omni_res.metrics,
)
# handle usage stats if requested & if continuous
if include_continuous_usage:
completion_tokens = previous_num_tokens[i]
chunk.usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=num_prompt_tokens + completion_tokens,
)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
elif final_output_type == "audio":
role = self.get_chat_request_role(request)
choices_data = self._create_audio_choice(omni_res, role, request, stream=True)
chunk = OmniChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=choices_data,
model=model_name,
modality=final_output_type,
)
chunk.usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=0,
total_tokens=num_prompt_tokens,
)
data = chunk.model_dump_json(exclude_unset=True)
yield f"data: {data}\n\n"
else:
logger.warning(f"Unsupported streaming final output type: {final_output_type}")
continue
# once the final token is handled, if stream_options.include_usage
# is sent, send the usage
if include_usage:
completion_tokens = sum(previous_num_tokens)
final_usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=completion_tokens,
total_tokens=num_prompt_tokens + completion_tokens,
)
if self.enable_prompt_tokens_details and num_cached_tokens:
final_usage.prompt_tokens_details = PromptTokenUsageInfo(cached_tokens=num_cached_tokens)
final_usage_chunk = OmniChatCompletionStreamResponse(
id=request_id,
object=chunk_object_type,
created=created_time,
choices=[],
model=model_name,
usage=final_usage,
metrics=last_metrics,
)
final_usage_data = final_usage_chunk.model_dump_json(exclude_unset=True, exclude_none=True)
yield f"data: {final_usage_data}\n\n"
# report to FastAPI middleware aggregate usage across all choices
num_completion_tokens = sum(previous_num_tokens)
request_metadata.final_usage_info = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_completion_tokens,
total_tokens=num_prompt_tokens + num_completion_tokens,
)
# Log complete streaming response if output logging is enabled
if self.enable_log_outputs and self.request_logger:
# Log the complete response for each choice
for i in range(num_choices):
full_text = (
previous_texts[i]
if previous_texts and i < len(previous_texts)
else f"<streaming_complete: {previous_num_tokens[i]} tokens>"
)
self.request_logger.log_outputs(
request_id=request_id,
outputs=full_text,
output_token_ids=None, # Consider also logging all token IDs
finish_reason="streaming_complete",
is_streaming=True,
delta=False,
)
except Exception as e:
logger.exception("Error in chat completion stream generator.")
data = self.create_streaming_error_response(e)
yield f"data: {data}\n\n"
# Send the final done message after all response.n are finished
yield "data: [DONE]\n\n"
async def chat_completion_full_generator(
self,
request: ChatCompletionRequest,
result_generator: AsyncIterator[RequestOutput],
request_id: str,
model_name: str,
conversation: list[ConversationMessage],
tokenizer: TokenizerLike,
request_metadata: RequestResponseMetadata,
reasoning_parser: ReasoningParser | None = None,
) -> ErrorResponse | OmniChatCompletionResponse:
created_time = int(time.time())
final_res: RequestOutput | None = None
final_outputs: list[OmniRequestOutput] = []
try:
async for res in result_generator:
final_outputs.append(res)
except asyncio.CancelledError:
return self.create_error_response("Client disconnected")
except ValueError as e:
return self.create_error_response(e)
assert final_outputs is not None
choices: list[ChatCompletionResponseChoice] = []
usage = UsageInfo(prompt_tokens=0, completion_tokens=0, total_tokens=0)
role = self.get_chat_request_role(request)
prompt_logprobs = None
prompt_token_ids = None
kv_transfer_params = None
response_metrics: dict[str, Any] | None = None
# Build requested modalities set for filtering
requested_modalities = (
set(request.modalities) if hasattr(request, "modalities") and request.modalities else None
)
for omni_outputs in final_outputs:
choices_data = []
if omni_outputs.request_output is not None and not getattr(omni_outputs.request_output, "finished", False):
continue
# Filter outputs based on requested modalites
if requested_modalities is not None and omni_outputs.final_output_type not in requested_modalities:
logger.warning(f"final output type: {omni_outputs.final_output_type} is not needed by the request")
continue
if omni_outputs.final_output_type == "text":
(
choices_data,
usage,
prompt_logprobs,
prompt_token_ids,
kv_transfer_params,
) = self._create_text_choice(
request,
omni_outputs,
tokenizer,
conversation,
role,
reasoning_parser,
)
elif omni_outputs.final_output_type == "audio":
choices_data = self._create_audio_choice(omni_outputs, role, request, stream=False)
elif omni_outputs.final_output_type == "image":
choices_data = self._create_image_choice(omni_outputs, role, request, stream=False)
else:
logger.warning(f"Unsupported final output type: {omni_outputs.final_output_type}")
continue
if omni_outputs.metrics:
response_metrics = omni_outputs.metrics
choices.extend(choices_data)
response = OmniChatCompletionResponse(
id=request_id,
created=created_time,
model=model_name,
choices=choices,
usage=usage,
prompt_logprobs=prompt_logprobs,
prompt_token_ids=prompt_token_ids,
kv_transfer_params=kv_transfer_params,
metrics=response_metrics,
)
# Log complete response if output logging is enabled
if self.enable_log_outputs and self.request_logger:
for choice in choices:
output_text = ""
if choice.message.content:
output_text = choice.message.content
elif choice.message.tool_calls:
# For tool calls, log the function name and arguments
tool_call_descriptions = []
for tc in choice.message.tool_calls:
if hasattr(tc.function, "name") and hasattr(tc.function, "arguments"):
tool_call_descriptions.append(f"{tc.function.name}({tc.function.arguments})")
tool_calls_str = ", ".join(tool_call_descriptions)
output_text = f"[tool_calls: {tool_calls_str}]"
if output_text:
# Get the corresponding output token IDs
output_token_ids = None
if choice.index < len(final_res.outputs):
output_token_ids = final_res.outputs[choice.index].token_ids
self.request_logger.log_outputs(
request_id=request_id,
outputs=output_text,
output_token_ids=output_token_ids,
finish_reason=choice.finish_reason,
is_streaming=False,
delta=False,
)
return response
def _create_text_choice(
self,
request: ChatCompletionRequest,
omni_outputs: OmniRequestOutput,
tokenizer: TokenizerLike,
conversation: list[ConversationMessage],
role: str,
reasoning_parser: ReasoningParser | None = None,
):
final_res = omni_outputs.request_output
if self.tool_call_id_type == "kimi_k2":
history_tool_call_cnt = get_history_tool_calls_cnt(conversation)
else:
history_tool_call_cnt = 0
choices: list[ChatCompletionResponseChoice] = []
for output in final_res.outputs:
token_ids = output.token_ids
out_logprobs = output.logprobs
tool_call_info = None
if request.logprobs and request.top_logprobs is not None:
assert out_logprobs is not None, "Did not output logprobs"
logprobs = self._create_chat_logprobs(
token_ids=token_ids,
top_logprobs=out_logprobs,
num_output_top_logprobs=request.top_logprobs,
tokenizer=tokenizer,
return_as_token_id=request.return_tokens_as_token_ids,
)
else:
logprobs = None
if self.use_harmony:
reasoning_content, content, _ = parse_chat_output(token_ids)
if not request.include_reasoning:
reasoning_content = None
if self.tool_parser is not None:
tool_parser = self.tool_parser(tokenizer)
# NOTE: We use token_ids for openai tool parser
tool_call_info = tool_parser.extract_tool_calls(
"",
request=request,
token_ids=token_ids, # type: ignore
)
content = tool_call_info.content
message = ChatMessage(
role=role,
reasoning_content=reasoning_content,
content=content,
tool_calls=tool_call_info.tool_calls,
)
else:
message = ChatMessage(
role=role,
reasoning_content=reasoning_content,
content=content,
)
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=message,
logprobs=logprobs,
finish_reason=(
"tool_calls"
if (tool_call_info is not None and tool_call_info.tools_called)
else (output.finish_reason if output.finish_reason else "stop")
),
stop_reason=output.stop_reason,
)
choices.append(choice_data)
continue
if reasoning_parser:
# If the reasoning parser is enabled,
# tool calls are extracted exclusively from the content.
reasoning_content, content = reasoning_parser.extract_reasoning(output.text, request=request)
if not request.include_reasoning:
reasoning_content = None
else:
reasoning_content = None
content = output.text
auto_tools_called = False
# if auto tools are not enabled, and a named tool choice using
# outlines is not being used
if (not self.enable_auto_tools or not self.tool_parser) and (
not isinstance(request.tool_choice, ChatCompletionNamedToolChoiceParam)
and request.tool_choice != "required"
):
message = ChatMessage(role=role, reasoning_content=reasoning_content, content=content)
# if the request uses tools and specified a tool choice
elif request.tool_choice and type(request.tool_choice) is ChatCompletionNamedToolChoiceParam:
tool_call_class = MistralToolCall if isinstance(tokenizer, MistralTokenizer) else ToolCall
message = ChatMessage(
role=role,
reasoning_content=reasoning_content,
content="",
tool_calls=[
tool_call_class(
function=FunctionCall(
name=request.tool_choice.function.name,
arguments=content,
)
)
],
)
elif request.tool_choice and request.tool_choice == "required":
tool_call_class = MistralToolCall if isinstance(tokenizer, MistralTokenizer) else ToolCall
# the fields of FunctionDefinition are a superset of the
# tool call outputs and can be used for parsing
assert content is not None
tool_calls = TypeAdapter(list[FunctionDefinition]).validate_json(content)
tool_call_ids = []
for tool_call in tool_calls:
tool_call_ids.append(
make_tool_call_id(
id_type=self.tool_call_id_type,
func_name=tool_call.name,
idx=history_tool_call_cnt,
)
)
history_tool_call_cnt += 1
message = ChatMessage(
role=role,
content="",
tool_calls=[
tool_call_class(
id=tool_call_ids[i],
function=FunctionCall(
name=tool_call.name,
arguments=json.dumps(tool_call.parameters, ensure_ascii=False),
),
)
for i, tool_call in enumerate(tool_calls)
],
reasoning_content=reasoning_content,
)
# if the request doesn't use tool choice
# OR specifies to not use a tool
elif not request.tool_choice or request.tool_choice == "none":
message = ChatMessage(role=role, reasoning_content=reasoning_content, content=content)
# handle when there are tools and tool choice is auto
elif (
request.tools
and (request.tool_choice == "auto" or request.tool_choice is None)
and self.enable_auto_tools
and self.tool_parser
):
try:
tool_parser = self.tool_parser(tokenizer)
except RuntimeError as e:
logger.exception("Error in tool parser creation.")
return self.create_error_response(e)
tool_call_info = tool_parser.extract_tool_calls(content if content is not None else "", request=request)
# In the OpenAI API the finish_reason is "tools_called"
# if the tool choice is auto and the model produced a tool
# call. The same is not true for named function calls
auto_tools_called = tool_call_info.tools_called
if tool_call_info.tools_called:
message = ChatMessage(
role=role,
reasoning_content=reasoning_content,
content=tool_call_info.content,
tool_calls=tool_call_info.tool_calls,
)
else:
# FOR NOW make it a chat message; we will have to detect
# the type to make it later.
ret_content = content
# try to use content return from tool parser first,
# tool parser may do some modify for the content.
if tool_call_info.content and len(tool_call_info.content) > 0:
ret_content = tool_call_info.content
message = ChatMessage(
role=role,
reasoning_content=reasoning_content,
content=ret_content,
)
# undetermined case that is still important to handle
else:
logger.error(
"Error in chat_completion_full_generator - cannot determine if tools should be extracted. "
"Returning a standard chat completion."
)
message = ChatMessage(role=role, reasoning_content=reasoning_content, content=content)
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=message,
logprobs=logprobs,
finish_reason=(
"tool_calls" if auto_tools_called else output.finish_reason if output.finish_reason else "stop"
),
stop_reason=output.stop_reason,
token_ids=(as_list(output.token_ids) if request.return_token_ids else None),
)
choices.append(choice_data)
if request.echo:
last_msg_content: str | list[dict[str, str]] = ""
if conversation and "content" in conversation[-1] and conversation[-1].get("role") == role:
last_msg_content = conversation[-1]["content"] or ""
if isinstance(last_msg_content, list):
last_msg_content = "\n".join(msg["text"] for msg in last_msg_content)
for choice in choices:
full_message = last_msg_content + (choice.message.content or "")
choice.message.content = full_message
assert final_res.prompt_token_ids is not None
num_prompt_tokens = len(final_res.prompt_token_ids)
if final_res.encoder_prompt_token_ids is not None:
num_prompt_tokens += len(final_res.encoder_prompt_token_ids)
num_generated_tokens = sum(len(output.token_ids) for output in final_res.outputs)
usage = UsageInfo(
prompt_tokens=num_prompt_tokens,
completion_tokens=num_generated_tokens,
total_tokens=num_prompt_tokens + num_generated_tokens,
)
if self.enable_prompt_tokens_details and final_res.num_cached_tokens:
usage.prompt_tokens_details = PromptTokenUsageInfo(cached_tokens=final_res.num_cached_tokens)
prompt_logprobs = clamp_prompt_logprobs(final_res.prompt_logprobs)
prompt_token_ids = final_res.prompt_token_ids if request.return_token_ids else None
kv_transfer_params = final_res.kv_transfer_params
return choices, usage, prompt_logprobs, prompt_token_ids, kv_transfer_params
def _create_audio_choice(
self, omni_outputs: OmniRequestOutput, role: str, request: ChatCompletionRequest, stream: bool = False
):
choices: list[ChatCompletionResponseChoice] = []
final_res = omni_outputs.request_output
# OMNI: Access multimodal_output from CompletionOutput (outputs[0]), not from RequestOutput
# Reference: examples/offline_inference/qwen3_omni/end2end.py line 421
audio_data = final_res.outputs[0].multimodal_output.get("audio")
if isinstance(audio_data, list):
if stream:
audio_tensor = audio_data[-1]
else:
audio_tensor = torch.cat(audio_data, dim=-1)
else:
audio_tensor = audio_data
audio_tensor = audio_tensor.float().detach().cpu().numpy()
# Ensure audio is 1D (flatten if needed)
if audio_tensor.ndim > 1:
audio_tensor = audio_tensor.flatten()
audio_obj = CreateAudio(
audio_tensor=audio_tensor,
sample_rate=24000,
response_format="wav",
speed=1.0,
stream_format="audio",
base64_encode=True,
)
audio_response: AudioResponse = self.create_audio(audio_obj)
audio_base64 = audio_response.audio_data
# Generate unique ID for the audio
audio_id = f"audio-{uuid.uuid4().hex[:16]}"
# Set expiration time (e.g., 24 hours from now) as Unix timestamp
expires_at = int((datetime.now(timezone.utc) + timedelta(hours=24)).timestamp())
# Create OpenAIChatCompletionAudio object with all required fields
audio_obj = OpenAIChatCompletionAudio(
id=audio_id,
data=audio_base64,
expires_at=expires_at,
transcript="", # Empty transcript if not available
)
for output in final_res.outputs:
if stream:
choice_data = ChatCompletionResponseStreamChoice(
index=output.index,
delta=DeltaMessage(role=role, content=audio_base64),
logprobs=None,
finish_reason="stop",
stop_reason=output.stop_reason,
token_ids=(as_list(output.token_ids) if request.return_token_ids else None),
)
else:
choice_data = ChatCompletionResponseChoice(
index=output.index,
message=ChatMessage(role=role, audio=audio_obj),
logprobs=None,
finish_reason="stop",
stop_reason=None,
)
choices.append(choice_data)
return choices
def _create_image_choice(
self, omni_outputs: OmniRequestOutput, role: str, request: ChatCompletionRequest, stream: bool = False
):
"""Create chat completion response choices for image output.
Converts image tensor or PIL Image output from diffusion models
into base64-encoded image data for API response.
Args:
omni_outputs: Output containing image data from diffusion stage
role: The role for the response message (e.g., "assistant")
Returns:
List of ChatCompletionResponseChoice with image content
"""
from PIL import Image
choices: list[ChatCompletionResponseChoice] = []
final_res = omni_outputs.request_output
# Handle profiling data
stage_durations = omni_outputs.stage_durations
# Handle different image output formats
images = []
# First check omni_outputs.images directly (for diffusion mode via from_diffusion)
if omni_outputs.images:
images = omni_outputs.images
# Fall back to request_output for pipeline mode
# OMNI: Access multimodal_output from CompletionOutput (outputs[0]), not from RequestOutput
elif final_res is not None and final_res.outputs:
completion_output = final_res.outputs[0]
if hasattr(completion_output, "multimodal_output") and completion_output.multimodal_output:
image_data = completion_output.multimodal_output.get("image")
if image_data is not None:
if isinstance(image_data, Image.Image):
images.append(image_data)
elif hasattr(image_data, "cpu"): # Tensor
import numpy as np
# Convert tensor to PIL Image
img_array = image_data.float().detach().cpu().numpy()
# Handle different tensor formats (CHW -> HWC)
if img_array.ndim == 3 and img_array.shape[0] in [1, 3, 4]:
img_array = np.transpose(img_array, (1, 2, 0))
# Normalize to 0-255
if img_array.max() <= 1.0:
img_array = (img_array * 255).astype(np.uint8)
else:
img_array = img_array.astype(np.uint8)
# Handle grayscale
if img_array.ndim == 2:
images.append(Image.fromarray(img_array, mode="L"))
elif img_array.shape[-1] == 1:
images.append(Image.fromarray(img_array.squeeze(-1), mode="L"))
elif img_array.shape[-1] == 3:
images.append(Image.fromarray(img_array, mode="RGB"))
elif img_array.shape[-1] == 4:
images.append(Image.fromarray(img_array, mode="RGBA"))
elif hasattr(final_res, "images") and final_res.images:
images = final_res.images
# Convert images to base64
image_contents = []
for img in images:
with BytesIO() as buffer:
img.save(buffer, format="PNG")
img_bytes = buffer.getvalue()
img_base64 = base64.b64encode(img_bytes).decode("utf-8")
image_contents.append(
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{img_base64}",
},
"stage_durations": stage_durations,
}
)
# Create message content
if len(image_contents) == 1:
content = image_contents
elif len(image_contents) > 1:
content = image_contents
else:
content = [{"type": "text", "text": "Image generation completed but no images were produced."}]
# Create response choice
# Use model_construct to bypass validation for multimodal content
# (ChatMessage.content only accepts str, but we need list for images)
# Then use object.__setattr__ to directly set the field, bypassing Pydantic's type checking
import warnings as warnings_module
with warnings_module.catch_warnings():
warnings_module.filterwarnings("ignore", category=UserWarning, module="pydantic")
message = ChatMessage.model_construct(role=role)
object.__setattr__(message, "content", content)
# Mark content as set in fields_set to ensure proper serialization
if hasattr(message, "__pydantic_fields_set__"):
message.__pydantic_fields_set__.add("content")
choice_data = ChatCompletionResponseChoice(
index=0,
message=message,
logprobs=None,
finish_reason="stop",
stop_reason=None,
)
choices.append(choice_data)
return choices
# ==================== Diffusion Mode Methods ====================
async def _create_diffusion_chat_completion(
self,
request: ChatCompletionRequest,
raw_request: Request | None = None,
) -> ChatCompletionResponse | ErrorResponse:
"""Generate images via chat completion interface for diffusion models.
Args:
request: Chat completion request
raw_request: Raw FastAPI request object
Returns:
ChatCompletionResponse with generated images or ErrorResponse
"""
try:
request_id = f"chatcmpl-{uuid.uuid4().hex[:16]}"
created_time = int(time.time())
# Convert messages to dict format
messages = []
for msg in request.messages:
if hasattr(msg, "model_dump"):
messages.append(msg.model_dump())
elif isinstance(msg, dict):
messages.append(msg)
else:
messages.append({"role": getattr(msg, "role", "user"), "content": getattr(msg, "content", "")})
# Extract prompt and images from messages
prompt, reference_images = self._extract_diffusion_prompt_and_images(messages)
if not prompt:
return self._create_error_response("No text prompt found in messages")
# Extract generation parameters from extra_body (preferred)
# Reference: text_to_image.py and text_to_video.py for supported parameters
# [NOTE] When sending request via openai client Python library,
# `extra_body` is flattented and merged into the payload's root.
# These extra fields are accessible via `model_extra` property (from Pydantic base class).
# When sending raw request with curl, no flattening happens. Directly read the `extra_body` dict.
extra_body = getattr(request, "extra_body", None)
if not extra_body:
extra_body = request.model_extra or {}
# Parse size if provided (supports "1024x1024" format)
height = extra_body.get("height")
width = extra_body.get("width")
if "size" in extra_body:
try:
size_str = extra_body["size"]
if isinstance(size_str, str) and "x" in size_str.lower():
w, h = size_str.lower().split("x")
width, height = int(w), int(h)
except ValueError:
logger.warning("Invalid size format: %s", extra_body.get("size"))
# Get request parameters from extra_body.
# Avoid hardcoded defaults here — let each pipeline's forward()
# method apply its own model-specific default when the user does
# not provide a value.
num_inference_steps = extra_body.get("num_inference_steps")
guidance_scale = extra_body.get("guidance_scale")
true_cfg_scale = extra_body.get("true_cfg_scale") or extra_body.get("cfg_scale")
seed = extra_body.get("seed")
negative_prompt = extra_body.get("negative_prompt")
num_outputs_per_prompt = extra_body.get("num_outputs_per_prompt", 1)
# Text-to-video parameters (ref: text_to_video.py)
num_frames = extra_body.get("num_frames")
guidance_scale_2 = extra_body.get("guidance_scale_2")
lora_body = extra_body.get("lora")
# Qwen-Image-Layered parameters
layers = extra_body.get("layers")
resolution = extra_body.get("resolution")
logger.info(
"Diffusion chat request %s: prompt=%r, ref_images=%d, params=%s",
request_id,
prompt[:50] + "..." if len(prompt) > 50 else prompt,
len(reference_images),
{k: v for k, v in extra_body.items() if v is not None},
)
# Decode reference images if provided
pil_images: list[Image.Image] = []
for img_b64 in reference_images:
try:
img_bytes = base64.b64decode(img_b64)
pil_images.append(Image.open(BytesIO(img_bytes)))
except Exception as e:
logger.warning("Failed to decode reference image: %s", e)
# Build generation kwargs
gen_prompt: OmniTextPrompt = {
"prompt": prompt,
"negative_prompt": negative_prompt,
}
gen_params = OmniDiffusionSamplingParams(
height=height,
width=width,
num_outputs_per_prompt=num_outputs_per_prompt,
seed=seed,
)
# Only override defaults when the user explicitly provides values
if num_inference_steps is not None:
gen_params.num_inference_steps = num_inference_steps
if guidance_scale is not None:
gen_params.guidance_scale = guidance_scale
if true_cfg_scale is not None:
gen_params.true_cfg_scale = true_cfg_scale
if num_frames is not None:
gen_params.num_frames = num_frames
if guidance_scale_2 is not None:
gen_params.guidance_scale_2 = guidance_scale_2
if layers is not None:
gen_params.layers = layers
if resolution is not None:
gen_params.resolution = resolution
# Parse per-request LoRA (works for both AsyncOmniDiffusion and AsyncOmni).
if lora_body and isinstance(lora_body, dict):
try:
lora_req, lora_scale = parse_lora_request(lora_body)
if lora_req is not None:
gen_params.lora_request = lora_req
if lora_scale is not None:
gen_params.lora_scale = lora_scale
except Exception as e: # pragma: no cover - safeguard
logger.warning("Failed to parse LoRA request: %s", e)
# Add reference image if provided
if pil_images:
if len(pil_images) == 1:
gen_prompt["multi_modal_data"] = {}
gen_prompt["multi_modal_data"]["image"] = pil_images[0]
else:
od_config = getattr(self._diffusion_engine, "od_config", None)
supports_multimodal_inputs = getattr(od_config, "supports_multimodal_inputs", False)
if od_config is None:
# TODO: entry is asyncOmni. We hack the od config here.
supports_multimodal_inputs = True
if supports_multimodal_inputs:
gen_prompt["multi_modal_data"] = {}
gen_prompt["multi_modal_data"]["image"] = pil_images
else:
return self._create_error_response(
"Multiple input images are not supported by the current diffusion model. "
"For multi-image editing, start the server with Qwen-Image-Edit-2509 "
"and send multiple images in the user message content.",
status_code=400,
)
# Generate image
# Handle both AsyncOmniDiffusion (returns OmniRequestOutput) and AsyncOmni (returns AsyncGenerator)
if isinstance(self._diffusion_engine, AsyncOmni):
diffusion_engine = cast(AsyncOmni, self._diffusion_engine)
result = None
async for output in diffusion_engine.generate(
prompt=gen_prompt,
sampling_params_list=[gen_params], # Pass as single-stage params
request_id=request_id,
):
result = output
if result is None:
return self._create_error_response("No output generated from AsyncOmni")
else:
# AsyncOmniDiffusion: direct call
diffusion_engine = cast(AsyncOmniDiffusion, self._diffusion_engine)
result = await diffusion_engine.generate(
prompt=gen_prompt,
sampling_params=gen_params,
request_id=request_id,
)
# Extract images from result
# Handle nested OmniRequestOutput structure where images might be in request_output
images = getattr(result.request_output, "images", [])
stage_durations = result.stage_durations
# Convert images to base64 content
image_contents: list[dict[str, Any]] = []
flat_images = []
for item in images:
if isinstance(item, list):
flat_images.extend(item)
else:
flat_images.append(item)
for img in flat_images:
with BytesIO() as buffer:
img.save(buffer, format="PNG")
img_base64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
image_contents.append(
{
"type": "image_url",
"image_url": {
"url": f"data:image/png;base64,{img_base64}",
},
"stage_durations": stage_durations,
}
)
# Build response
if not image_contents:
content = "Image generation completed but no images were produced."
else:
content = image_contents
# Use model_construct to bypass validation for multimodal content
# (ChatMessage.content only accepts str, but we need list for images)
# Then use object.__setattr__ to directly set the field, bypassing Pydantic's type checking
import warnings as warnings_module
with warnings_module.catch_warnings():
warnings_module.filterwarnings("ignore", category=UserWarning, module="pydantic")
message = ChatMessage.model_construct(role="assistant")
object.__setattr__(message, "content", content)
# Mark content as set in fields_set to ensure proper serialization
if hasattr(message, "__pydantic_fields_set__"):
message.__pydantic_fields_set__.add("content")
choice = ChatCompletionResponseChoice.model_construct(
index=0,
message=message,
finish_reason="stop",
logprobs=None,
stop_reason=None,
)
response = ChatCompletionResponse(
id=request_id,
created=created_time,
model=self._diffusion_model_name,
choices=[choice],
usage=UsageInfo(
prompt_tokens=len(prompt.split()),
completion_tokens=1,
total_tokens=len(prompt.split()) + 1,
),
)
logger.info(
"Diffusion chat completed for request %s: %d images",
request_id,
len(images),
)
return response
except Exception as e:
logger.exception("Diffusion chat completion failed: %s", e)
return self._create_error_response(
f"Image generation failed: {str(e)}",
status_code=500,
)
def _extract_diffusion_prompt_and_images(
self,
messages: list[dict[str, Any]],
) -> tuple[str, list[str]]:
"""Extract text prompt and base64 images from chat messages.
Args:
messages: List of chat messages
Returns:
Tuple of (prompt_text, list_of_base64_images)
"""
prompt_parts: list[str] = []
images: list[str] = []
for message in messages:
role = message.get("role", "")
if role != "user":
continue
content = message.get("content", "")
# String content
if isinstance(content, str):
prompt_parts.append(content)
continue
# List of content items
if isinstance(content, list):
for item in content:
if isinstance(item, str):
prompt_parts.append(item)
elif isinstance(item, dict):
# Handle {"type": "text", "text": "..."} format
if item.get("type") == "text":
prompt_parts.append(item.get("text", ""))
# Handle {"text": "..."} format
elif "text" in item and "type" not in item:
prompt_parts.append(item["text"])
# Handle {"type": "image_url", "image_url": {"url": "..."}}
elif item.get("type") == "image_url":
url = item.get("image_url", {}).get("url", "")
if url.startswith("data:image"):
try:
_, b64_data = url.split(",", 1)
images.append(b64_data)
except ValueError:
logger.warning("Invalid data URL format")
# Handle {"image": "base64..."} format
elif "image" in item:
images.append(item["image"])
prompt = " ".join(prompt_parts).strip()
return prompt, images
def _create_error_response(
self,
message: str,
err_type: str = "BadRequestError",
status_code: int = 400,
) -> ErrorResponse:
"""Create an error response following OpenAI error format."""
return ErrorResponse(
error=ErrorInfo(
message=message,
type=err_type,
code=status_code,
)
)
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