dynamo/examples/multimodal/utils/chat_processor.py

# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import time
from typing import AsyncIterator, List, Optional, Protocol, Union, runtime_checkable

from vllm.config import ModelConfig
from vllm.engine.arg_utils import AsyncEngineArgs
from vllm.entrypoints.chat_utils import ConversationMessage
from vllm.entrypoints.openai.protocol import (
    ChatCompletionRequest,
    CompletionRequest,
    RequestResponseMetadata,
)
from vllm.entrypoints.openai.serving_chat import OpenAIServingChat
from vllm.entrypoints.openai.serving_completion import OpenAIServingCompletion
from vllm.entrypoints.openai.serving_models import BaseModelPath, OpenAIServingModels
from vllm.inputs.data import TokensPrompt
from vllm.sampling_params import SamplingParams
from vllm.tokenizers import TokenizerLike as AnyTokenizer


class StubEngineClient:
    """
    Stub EngineClient for preprocessing-only use of OpenAIServingChat/Completion.
    Provides the minimal attributes required by OpenAIServingModels.
    """

    def __init__(self, model_config: ModelConfig):
        self.model_config = model_config
        self.input_processor = None
        self.io_processor = None


@runtime_checkable
class ProcessMixInRequired(Protocol):
    engine_args: AsyncEngineArgs
    chat_processor: "ChatProcessor | None"
    completions_processor: "CompletionsProcessor | None"
    model_config: ModelConfig
    default_sampling_params: SamplingParams


class ProcessMixIn(ProcessMixInRequired):
    """
    Mixin for pre and post processing for vLLM
    """

    engine_args: AsyncEngineArgs
    chat_processor: "ChatProcessor | None"
    completions_processor: "CompletionsProcessor | None"
    model_config: ModelConfig
    default_sampling_params: SamplingParams

    def __init__(self):
        pass

    def _get_processor(
        self, raw_request: Union[CompletionRequest, ChatCompletionRequest]
    ):
        # Determine the processor type based on the request structure
        return (
            self.chat_processor
            if isinstance(raw_request, ChatCompletionRequest)
            else self.completions_processor
        )

    async def _parse_raw_request(
        self, raw_request: Union[CompletionRequest, ChatCompletionRequest]
    ):
        processor = self._get_processor(raw_request)
        if processor is None:
            raise RuntimeError("Processor has not been initialized")
        request = processor.parse_raw_request(raw_request)
        preprocess_result = await processor.preprocess(raw_request)

        default_max_tokens = self.model_config.max_model_len - len(
            preprocess_result.engine_prompt["prompt_token_ids"]
        )

        sampling_params = request.to_sampling_params(
            default_max_tokens,
            self.model_config.logits_processor_pattern,
            self.default_sampling_params,
        )
        return (
            request,
            preprocess_result.conversation,
            preprocess_result.engine_prompt,
            sampling_params,
        )

    async def _stream_response(self, request, generator, request_id, conversation):
        processor = self._get_processor(request)
        if processor is None:
            raise RuntimeError("processor has not been initialized")
        return processor.stream_response(
            request,
            generator,
            request_id,
            conversation,
        )


class PreprocessResult:
    def __init__(
        self,
        conversation: Optional[ConversationMessage],
        engine_prompt: TokensPrompt,
    ):
        self.conversation = conversation
        self.engine_prompt = engine_prompt


class ChatProcessor:
    def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
        self.tokenizer = tokenizer
        self.model_config = model_config
        # Create stub engine client and models for preprocessing-only usage
        stub_engine = StubEngineClient(model_config)
        serving_models = OpenAIServingModels(
            engine_client=stub_engine,
            base_model_paths=[
                BaseModelPath(name=model_config.model, model_path=model_config.model)
            ],
        )
        self.openai_serving = OpenAIServingChat(
            engine_client=stub_engine,
            models=serving_models,
            response_role="assistant",
            request_logger=None,
            chat_template=None,
            chat_template_content_format="auto",
        )

    def parse_raw_request(
        self, raw_request: ChatCompletionRequest
    ) -> ChatCompletionRequest:
        return ChatCompletionRequest.parse_obj(raw_request)

    async def preprocess(self, raw_request: ChatCompletionRequest) -> PreprocessResult:
        request = self.parse_raw_request(raw_request)

        # TODO: Revisit this later when adding multi-modal support for the frontend.
        # If no chat template is provided and tokenizer doesn't have one,
        # use a simple format that just concatenates messages
        if not request.chat_template and not self.tokenizer.chat_template:
            chat_template = "{% for message in messages %}{% if message['role'] == 'user' %}User: {{ message['content'] }}\n{% elif message['role'] == 'assistant' %}Assistant: {{ message['content'] }}\n{% endif %}{% endfor %}Assistant:"
        else:
            chat_template = request.chat_template or self.tokenizer.chat_template

        (
            conversation,
            engine_prompts,
        ) = await self.openai_serving._preprocess_chat(
            request,
            self.tokenizer,
            request.messages,
            chat_template=chat_template,
            chat_template_content_format=self.openai_serving.chat_template_content_format,
            add_generation_prompt=request.add_generation_prompt,
            continue_final_message=request.continue_final_message,
            tool_dicts=None,
            documents=request.documents,
            chat_template_kwargs=request.chat_template_kwargs,
            tool_parser=self.openai_serving.tool_parser,
            add_special_tokens=request.add_special_tokens,
        )

        # In newer vLLM, _preprocess_chat returns (conversation, engine_prompts) - 2 values
        if not conversation or not engine_prompts:
            raise ValueError(
                "Preprocessing returned empty conversation or engine_prompts"
            )
        return PreprocessResult(conversation[0], engine_prompts[0])

    async def stream_response(
        self,
        request: ChatCompletionRequest,
        result_generator: AsyncIterator,
        request_id: str,
        conversation: List,
    ):
        request_metadata = RequestResponseMetadata(request_id=request_id)
        if request.stream:
            # Handle streaming response
            num_output_text_so_far = 0
            async for raw_response in self.openai_serving.chat_completion_stream_generator(
                request,
                result_generator,
                request_id,
                request.model,
                conversation,
                self.tokenizer,
                request_metadata,
            ):
                if raw_response.startswith("data: [DONE]"):
                    yield raw_response
                    break

                # Parse the response
                response = json.loads(raw_response.lstrip("data: "))

                # Process delta content to extract only new text
                if "choices" in response and len(response["choices"]) > 0:
                    if "delta" in response["choices"][0]:
                        content = response["choices"][0]["delta"].get("content", "")
                        if content:
                            # Extract only the new part from the full content
                            new_content = content[num_output_text_so_far:]
                            response["choices"][0]["delta"]["content"] = new_content
                            num_output_text_so_far = len(content)

                # Yield the processed response
                yield f"data: {json.dumps(response)}\n\n"
        else:
            # Handle non-streaming response
            # Collect all chunks into a single response
            full_response = None
            num_output_text_so_far = 0
            async for raw_response in self.openai_serving.chat_completion_stream_generator(
                request,
                result_generator,
                request_id,
                request.model,
                conversation,
                self.tokenizer,
                request_metadata,
            ):
                if raw_response.startswith("data: [DONE]"):
                    break
                response = json.loads(raw_response.lstrip("data: "))
                if full_response is None:
                    # Initialize the full response structure
                    full_response = {
                        "id": response.get("id", ""),
                        "object": "chat.completion",
                        "created": int(time.time()),
                        "model": request.model,
                        "choices": [
                            {
                                "index": response.get("index", 0),
                                "message": {"role": "assistant", "content": ""},
                                "finish_reason": None,
                            }
                        ],
                    }

                # Concatenate content if it exists. Each delta contains the full text so far.
                if "choices" in response and len(response["choices"]) > 0:
                    if "delta" in response["choices"][0]:
                        content = response["choices"][0]["delta"].get("content", "")
                        if content:
                            # Extract only the new part from the full content
                            new_content = content[num_output_text_so_far:]
                            full_response["choices"][0]["message"][
                                "content"
                            ] += new_content
                            num_output_text_so_far = len(content)

                    # Update finish reason if present
                    if "finish_reason" in response["choices"][0]:
                        full_response["choices"][0]["finish_reason"] = response[
                            "choices"
                        ][0]["finish_reason"]

            if full_response is not None:
                yield json.dumps(full_response)


class CompletionsProcessor:
    def __init__(self, tokenizer: AnyTokenizer, model_config: ModelConfig):
        self.tokenizer = tokenizer
        self.model_config = model_config
        # Create stub engine client and models for preprocessing-only usage
        stub_engine = StubEngineClient(model_config)
        serving_models = OpenAIServingModels(
            engine_client=stub_engine,
            base_model_paths=[
                BaseModelPath(name=model_config.model, model_path=model_config.model)
            ],
        )
        self.openai_serving = OpenAIServingCompletion(
            engine_client=stub_engine,
            models=serving_models,
            request_logger=None,
        )

    def parse_raw_request(self, raw_request: CompletionRequest) -> CompletionRequest:
        return CompletionRequest.parse_obj(raw_request)

    async def preprocess(self, raw_request: CompletionRequest) -> PreprocessResult:
        request = self.parse_raw_request(raw_request)

        # In newer vLLM, _preprocess_completion was removed
        # Use the renderer approach instead
        renderer = self.openai_serving._get_renderer(self.tokenizer)
        config = self.openai_serving._build_render_config(request)
        engine_prompts = await renderer.render_prompt_and_embeds(
            prompt_or_prompts=request.prompt,
            prompt_embeds=getattr(request, "prompt_embeds", None),
            config=config,
        )

        # engine_prompts is now a list of TokensPrompt
        if not engine_prompts:
            raise ValueError("Renderer returned empty engine_prompts")
        return PreprocessResult(None, engine_prompts[0])

    async def stream_response(
        self,
        request: CompletionRequest,
        result_generator: AsyncIterator,
        request_id: str,
        conversation: Optional[List[ConversationMessage]] = None,
    ):
        request_metadata = RequestResponseMetadata(request_id=request_id)
        if not request.stream:
            raise ValueError("Only streaming responses are supported")
        async for raw_response in self.openai_serving.completion_stream_generator(
            request,
            result_generator,
            request_id,
            int(time.time()),  # created_time
            request.model,
            1,  # num_prompts
            self.tokenizer,
            request_metadata,
        ):
            if raw_response.startswith("data: [DONE]"):
                break
            response = json.loads(raw_response.lstrip("data: "))

            yield response